From 51bd7ab0fc010e1f07d3540a4450c6c3eab8cd63 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 11 Oct 2017 16:09:10 +0200 Subject: [PATCH 01/28] Adds initial poincare evaluation notebook --- docs/notebooks/Poincare Evaluation.ipynb | 429 +++++++++++++++++++++++ 1 file changed, 429 insertions(+) create mode 100644 docs/notebooks/Poincare Evaluation.ipynb diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb new file mode 100644 index 0000000000..d11d843231 --- /dev/null +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -0,0 +1,429 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Evaluation of Poincare Embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook demonstrates how well poincare embeddings trained using this [implementation](https://github.com/TatsuyaShirakawa/poincare-embedding) perform on the tasks detailed in the [original paper](https://arxiv.org/pdf/1705.08039.pdf).\n", + "\n", + "This is the list of tasks - \n", + "1. WordNet reconstruction\n", + "2. WordNet link prediction\n", + "3. Link prediction in collaboration networks\n", + "4. Lexical entailment on HyperLex\n", + "\n", + "A more detailed explanation of the tasks and the evaluation methodology is present in the individual evaluation subsections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Setup\n", + "\n", + "TODO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training\n", + "TODO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Loading the embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 C++ embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext line_profiler" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jayant/projects/gensim\n" + ] + } + ], + "source": [ + "% cd ../.." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "embeddings_dir = '/home/jayant/projects/poincare-embedding/work' # TODO: put model files into repo?" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "from gensim.models.keyedvectors import KeyedVectors\n", + "import numpy as np\n", + "from scipy.spatial.distance import euclidean, pdist\n", + "from smart_open import smart_open\n", + "\n", + "def transform_cpp_embedding_to_kv(input_file, output_file, encoding='utf8'):\n", + " \"\"\"Given a C++ embedding tsv filepath, converts it to a KeyedVector-supported file\"\"\"\n", + " with smart_open(input_file, 'rb') as f:\n", + " lines = [line.decode(encoding) for line in f]\n", + " if not len(lines):\n", + " raise ValueError(\"file is empty\")\n", + " first_line = lines[0]\n", + " parts = first_line.rstrip().split(\"\\t\")\n", + " model_size = len(parts) - 1\n", + " vocab_size = len(lines)\n", + " with open(output_file, 'w') as f:\n", + " f.write('%d %d\\n' % (vocab_size, model_size))\n", + " for line in lines:\n", + " f.write(line.replace('\\t', ' '))\n", + " \n", + " \n", + "class PoincareEmbedding(object):\n", + " \"\"\"Load and perform distance operations on poincare embeddings\"\"\"\n", + "\n", + " def __init__(self, keyed_vectors):\n", + " \"\"\"Initialize PoincareEmbeddings via a KeyedVectors instance\"\"\"\n", + " self.kv = keyed_vectors\n", + " \n", + " @staticmethod\n", + " def poincare_dist(vector_1, vector_2):\n", + " \"\"\"Return poincare distance between two vectors\"\"\"\n", + " norm_1 = np.linalg.norm(vector_1)\n", + " norm_2 = np.linalg.norm(vector_2)\n", + " euclidean_dist = euclidean(vector_1, vector_2)\n", + " return np.arccosh(\n", + " 1 + 2 * (\n", + " (euclidean_dist ** 2) / ((1 - norm_1 ** 2) * (1 - norm_2 ** 2))\n", + " )\n", + " )\n", + " \n", + " @classmethod\n", + " def load_poincare_cpp(cls, input_filename):\n", + " \"\"\"Load embeddings trained via C++ Poincare model\n", + "\n", + " Args:\n", + " filepath (str): Path to tsv file containing embeddings\n", + "\n", + " Returns:\n", + " PoincareEmbedding instance\n", + "\n", + " \"\"\"\n", + " keyed_vectors_filename = input_filename + '.kv'\n", + " transform_cpp_embedding_to_kv(input_filename, keyed_vectors_filename)\n", + " keyed_vectors = KeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", + " os.unlink(keyed_vectors_filename)\n", + " return cls(keyed_vectors)\n", + " \n", + " def get_distances(self, term_1, terms):\n", + " \"\"\"Returns distance between vector for term and vectors for given terms\n", + " Args:\n", + " term_1 (str)\n", + " terms (list/tuple/set): terms for which distance from vector for term_1 is to be returned\n", + "\n", + " Returns:\n", + " List of Poincare distances between term_1 and all terms in `terms` (list of floats)\n", + "\n", + " \"\"\"\n", + " term_1_vector = self.kv.word_vec(term_1)\n", + "\n", + " vocab = self.kv.vocab\n", + " terms_indices = [vocab[term].index for term in terms]\n", + " other_vectors = self.kv.syn0[terms_indices]\n", + " \n", + " euclidean_dists = np.linalg.norm(term_1_vector - other_vectors, axis=1)\n", + " norm_1 = np.linalg.norm(term_1_vector)\n", + " other_norms = np.linalg.norm(other_vectors, axis=1)\n", + "\n", + " return list(np.arccosh(\n", + " 1 + 2 * (\n", + " (euclidean_dists ** 2) / ((1 - norm_1 ** 2) * (1 - other_norms ** 2))\n", + " )\n", + " ))\n", + " \n", + " \n", + " def get_distance(self, term_1, term_2):\n", + " \"\"\"Returns distance between vectors for input terms\n", + "\n", + " Args:\n", + " term_1 (str)\n", + " term_2 (str)\n", + "\n", + " Returns:\n", + " Poincare distance between the two terms (float)\n", + " \n", + " Note:\n", + " Raises KeyError if either term_1 or term_2 is absent from vocabulary\n", + "\n", + " \"\"\"\n", + " vector_1, vector_2 = self.kv[term_1], self.kv[term_2]\n", + " return self.poincare_dist(vector_1, vector_2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "filenames = [\n", + "# 'wordnet_embeddings_2.tsv',\n", + "# 'wordnet_embeddings_5.tsv',\n", + " 'wordnet_embeddings_10.tsv',\n", + "# 'wordnet_embeddings_20.tsv',\n", + "# 'wordnet_embeddings_50.tsv',\n", + "# 'wordnet_embeddings_100.tsv',\n", + "]\n", + "embeddings = {fname: PoincareEmbedding.load_poincare_cpp(os.path.join(embeddings_dir, fname)) for fname in filenames}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 WordNet reconstruction" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "from collections import defaultdict\n", + "import itertools\n", + "\n", + "def get_rank(sorted_list, given_value):\n", + " \"\"\"Return rank of given value in sorted list (sorted in increasing order)\"\"\"\n", + " for i, value in enumerate(sorted_list, start=1):\n", + " if given_value < value:\n", + " return i\n", + " return len(sorted_list) + 1\n", + "\n", + "class ReconstructionEvaluation(object):\n", + " \"\"\"Evaluating reconstruction on given network for any embeddings\"\"\"\n", + " def __init__(self, filepath):\n", + " \"\"\"Initialize evaluation instance with tsv file containing relation pairs\n", + " \n", + " Args:\n", + " filepath (str): path to tsv file containing relation pairs\n", + " \n", + " Returns\n", + " ReconstructionEvaluation instance\n", + "\n", + " \"\"\"\n", + " items = set()\n", + " relation_pairs = defaultdict(set)\n", + " with smart_open(filepath, 'r') as f:\n", + " reader = csv.reader(f, delimiter='\\t')\n", + " for row in reader:\n", + " assert len(row) == 2, 'Hypernym pair has more than two items'\n", + " relation_pairs[row[0]].add(row[1])\n", + " items.update(row)\n", + " self.items = items\n", + " self.relation_pairs = relation_pairs\n", + " \n", + " def get_negative_instances(self, item, max_n=None):\n", + " \"\"\"Get all item that don't have a relation with given item\n", + " \n", + " Args:\n", + " item (str): item for which negative instances are to be returned\n", + " max_n (int or None): Return at most max_n negative instances. Return all if None\n", + " \n", + " Returns:\n", + " set of items which don't have a relation with input item\n", + "\n", + " \"\"\"\n", + " related_items = self.relation_pairs[item]\n", + " negative_items = {item_ for item_ in self.items if item_ not in related_items}\n", + " if max_n is not None:\n", + " return set(itertools.islice(negative_items, max_n))\n", + " else:\n", + " return negative_items\n", + "\n", + " def evaluate_reconstruction(self, embeddings):\n", + " \"\"\"Evaluate mean rank and MAP for reconstruction for given embeddings\n", + " \n", + " Args:\n", + " embeddings (PoincareEmbedding instance): embeddings for which evaluation is to be done\n", + " \n", + " Returns:\n", + " ??\n", + "\n", + " \"\"\"\n", + " ranks = []\n", + " for i, item in enumerate(self.items):\n", + " if not i % 1000:\n", + " print('Evaluating item %d %s' % (i, item))\n", + " if item not in self.relation_pairs:\n", + " continue\n", + " positive_items = self.relation_pairs[item]\n", + " negative_items = self.get_negative_instances(item)\n", + " negative_item_distances = embeddings.get_distances(item, negative_items)\n", + " negative_item_distances = sorted(negative_item_distances)\n", + " positive_item_distances = embeddings.get_distances(item, positive_items)\n", + " positive_item_ranks = [get_rank(negative_item_distances, positive_item_distance) for positive_item_distance in positive_item_distances]\n", + " ranks += positive_item_ranks\n", + " if i > 100:\n", + " break\n", + " return ranks\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "eval_instance = ReconstructionEvaluation(os.path.join(embeddings_dir, 'wordnet_noun_hypernyms.tsv'))" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating item 0 pica.n.01\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:82: RuntimeWarning: divide by zero encountered in true_divide\n" + ] + } + ], + "source": [ + "%lprun -f PoincareEmbedding.get_distances eval_instance.evaluate_reconstruction(test_embedding)" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating item 0 pica.n.01\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:82: RuntimeWarning: divide by zero encountered in true_divide\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "84.080472103\n", + "CPU times: user 12.6 s, sys: 0 ns, total: 12.6 s\n", + "Wall time: 12.6 s\n" + ] + } + ], + "source": [ + "%%time\n", + "print(np.mean(eval_instance.evaluate_reconstruction(test_embedding)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 WordNet link prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 HyperLex lexical entailment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Link Prediction for collaboration networks" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 9a511a79450acdba1d7c8368f0d26a6d286905ba Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 11 Oct 2017 20:32:22 +0200 Subject: [PATCH 02/28] More efficient computation of mean rank for graph reconstruction --- docs/notebooks/Poincare Evaluation.ipynb | 216 +++++++++++++---------- 1 file changed, 123 insertions(+), 93 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index d11d843231..6808cc5544 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -55,11 +55,21 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 93, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The line_profiler extension is already loaded. To reload it, use:\n", + " %reload_ext line_profiler\n" + ] + } + ], "source": [ - "%load_ext line_profiler" + "%load_ext line_profiler\n", + "%load_ext snakeviz" ] }, { @@ -90,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -152,33 +162,21 @@ " keyed_vectors = KeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", " os.unlink(keyed_vectors_filename)\n", " return cls(keyed_vectors)\n", - " \n", - " def get_distances(self, term_1, terms):\n", - " \"\"\"Returns distance between vector for term and vectors for given terms\n", - " Args:\n", - " term_1 (str)\n", - " terms (list/tuple/set): terms for which distance from vector for term_1 is to be returned\n", - "\n", - " Returns:\n", - " List of Poincare distances between term_1 and all terms in `terms` (list of floats)\n", - "\n", - " \"\"\"\n", - " term_1_vector = self.kv.word_vec(term_1)\n", - "\n", - " vocab = self.kv.vocab\n", - " terms_indices = [vocab[term].index for term in terms]\n", - " other_vectors = self.kv.syn0[terms_indices]\n", + " \n", + " def get_all_distances(self, term):\n", + " \"\"\"Return distances to all terms for given term, including itself\"\"\"\n", + " term_vector = self.kv.word_vec(term)\n", + " all_vectors = self.kv.syn0\n", " \n", - " euclidean_dists = np.linalg.norm(term_1_vector - other_vectors, axis=1)\n", - " norm_1 = np.linalg.norm(term_1_vector)\n", - " other_norms = np.linalg.norm(other_vectors, axis=1)\n", + " euclidean_dists = np.linalg.norm(term_vector - all_vectors, axis=1)\n", + " norm = np.linalg.norm(term_vector)\n", + " all_norms = np.linalg.norm(all_vectors, axis=1)\n", "\n", - " return list(np.arccosh(\n", + " return np.arccosh(\n", " 1 + 2 * (\n", - " (euclidean_dists ** 2) / ((1 - norm_1 ** 2) * (1 - other_norms ** 2))\n", + " (euclidean_dists ** 2) / ((1 - norm ** 2) * (1 - all_norms ** 2))\n", " )\n", - " ))\n", - " \n", + " )\n", " \n", " def get_distance(self, term_1, term_2):\n", " \"\"\"Returns distance between vectors for input terms\n", @@ -200,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -215,6 +213,45 @@ "embeddings = {fname: PoincareEmbedding.load_poincare_cpp(os.path.join(embeddings_dir, fname)) for fname in filenames}" ] }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "test_embedding = embeddings['wordnet_embeddings_10.tsv']" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:71: RuntimeWarning: divide by zero encountered in true_divide\n", + "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:71: RuntimeWarning: invalid value encountered in true_divide\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 39.6 s, sys: 0 ns, total: 39.6 s\n", + "Wall time: 39.6 s\n" + ] + } + ], + "source": [ + "%%time\n", + "for i, term in enumerate(test_embedding.kv.vocab.keys(), start=1):\n", + " if i > 10000:\n", + " break\n", + " dists = test_embedding.get_all_distances(term)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -231,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 150, "metadata": {}, "outputs": [], "source": [ @@ -239,16 +276,10 @@ "from collections import defaultdict\n", "import itertools\n", "\n", - "def get_rank(sorted_list, given_value):\n", - " \"\"\"Return rank of given value in sorted list (sorted in increasing order)\"\"\"\n", - " for i, value in enumerate(sorted_list, start=1):\n", - " if given_value < value:\n", - " return i\n", - " return len(sorted_list) + 1\n", "\n", "class ReconstructionEvaluation(object):\n", " \"\"\"Evaluating reconstruction on given network for any embeddings\"\"\"\n", - " def __init__(self, filepath):\n", + " def __init__(self, filepath, embedding):\n", " \"\"\"Initialize evaluation instance with tsv file containing relation pairs\n", " \n", " Args:\n", @@ -259,33 +290,38 @@ "\n", " \"\"\"\n", " items = set()\n", - " relation_pairs = defaultdict(set)\n", + " embedding_vocab = embedding.kv.vocab\n", + " positive_relations = defaultdict(set)\n", " with smart_open(filepath, 'r') as f:\n", " reader = csv.reader(f, delimiter='\\t')\n", " for row in reader:\n", " assert len(row) == 2, 'Hypernym pair has more than two items'\n", - " relation_pairs[row[0]].add(row[1])\n", - " items.update(row)\n", + " item_1_index = embedding_vocab[row[0]].index\n", + " item_2_index = embedding_vocab[row[1]].index\n", + " positive_relations[item_1_index].add(item_2_index)\n", + " items.update([item_1_index, item_2_index])\n", " self.items = items\n", - " self.relation_pairs = relation_pairs\n", + " self.positive_relations = positive_relations\n", + " self.embedding = embedding\n", " \n", - " def get_negative_instances(self, item, max_n=None):\n", - " \"\"\"Get all item that don't have a relation with given item\n", + " \n", + " @staticmethod\n", + " def get_positive_item_ranks(distances, positive_item_indices):\n", + " \"\"\"Given a numpy array of distances and indices of positive items, compute ranks of positive item distances\n", " \n", " Args:\n", - " item (str): item for which negative instances are to be returned\n", - " max_n (int or None): Return at most max_n negative instances. Return all if None\n", + " distances (numpy float array): np array of all distances for a specific item\n", + " positive_item_indices (list): list of indices of positive items\n", " \n", " Returns:\n", - " set of items which don't have a relation with input item\n", - "\n", + " list of ranks of positive items in the same order as `positive_indices`\n", " \"\"\"\n", - " related_items = self.relation_pairs[item]\n", - " negative_items = {item_ for item_ in self.items if item_ not in related_items}\n", - " if max_n is not None:\n", - " return set(itertools.islice(negative_items, max_n))\n", - " else:\n", - " return negative_items\n", + " positive_item_distances = distances[positive_item_indices]\n", + " negative_item_distances = np.ma.array(distances, mask=False)\n", + " negative_item_distances.mask[positive_item_indices] = True\n", + " # Compute how many negative item distances are less than each positive item distance, plus 1 for rank\n", + " ranks = (negative_item_distances < positive_item_distances[:, np.newaxis]).sum(axis=1) + 1\n", + " return list(ranks) \n", "\n", " def evaluate_reconstruction(self, embeddings):\n", " \"\"\"Evaluate mean rank and MAP for reconstruction for given embeddings\n", @@ -301,80 +337,74 @@ " for i, item in enumerate(self.items):\n", " if not i % 1000:\n", " print('Evaluating item %d %s' % (i, item))\n", - " if item not in self.relation_pairs:\n", + " if item not in self.positive_relations:\n", " continue\n", - " positive_items = self.relation_pairs[item]\n", - " negative_items = self.get_negative_instances(item)\n", - " negative_item_distances = embeddings.get_distances(item, negative_items)\n", - " negative_item_distances = sorted(negative_item_distances)\n", - " positive_item_distances = embeddings.get_distances(item, positive_items)\n", - " positive_item_ranks = [get_rank(negative_item_distances, positive_item_distance) for positive_item_distance in positive_item_distances]\n", + " positive_items = list(self.positive_relations[item])\n", + " item_term = self.embedding.kv.index2word[item]\n", + " item_distances = self.embedding.get_all_distances(item_term)\n", + " positive_item_ranks = self.get_positive_item_ranks(item_distances, positive_items)\n", " ranks += positive_item_ranks\n", - " if i > 100:\n", + " if i > 20000:\n", " break\n", - " return ranks\n", + " return np.mean(ranks)\n", " " ] }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 151, "metadata": {}, "outputs": [], "source": [ - "eval_instance = ReconstructionEvaluation(os.path.join(embeddings_dir, 'wordnet_noun_hypernyms.tsv'))" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Evaluating item 0 pica.n.01\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:82: RuntimeWarning: divide by zero encountered in true_divide\n" - ] - } - ], - "source": [ - "%lprun -f PoincareEmbedding.get_distances eval_instance.evaluate_reconstruction(test_embedding)" + "eval_instance = ReconstructionEvaluation(os.path.join(embeddings_dir, 'wordnet_noun_hypernyms.tsv'), test_embedding)" ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 152, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Evaluating item 0 pica.n.01\n" + "Evaluating item 0 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:82: RuntimeWarning: divide by zero encountered in true_divide\n" + "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:71: RuntimeWarning: divide by zero encountered in true_divide\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "84.080472103\n", - "CPU times: user 12.6 s, sys: 0 ns, total: 12.6 s\n", - "Wall time: 12.6 s\n" + "Evaluating item 1000 1000\n", + "Evaluating item 2000 2000\n", + "Evaluating item 3000 3000\n", + "Evaluating item 4000 4000\n", + "Evaluating item 5000 5000\n", + "Evaluating item 6000 6000\n", + "Evaluating item 7000 7000\n", + "Evaluating item 8000 8000\n", + "Evaluating item 9000 9000\n", + "Evaluating item 10000 10000\n", + "Evaluating item 11000 11000\n", + "Evaluating item 12000 12000\n", + "Evaluating item 13000 13000\n", + "Evaluating item 14000 14000\n", + "Evaluating item 15000 15000\n", + "Evaluating item 16000 16000\n", + "Evaluating item 17000 17000\n", + "Evaluating item 18000 18000\n", + "Evaluating item 19000 19000\n", + "Evaluating item 20000 20000\n", + "116.01562108\n", + "CPU times: user 1min 55s, sys: 4 ms, total: 1min 55s\n", + "Wall time: 1min 55s\n" ] } ], From 0a06fd5f5e1f5490c7c10c1eae35c73efe70bc20 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Thu, 12 Oct 2017 09:10:19 +0200 Subject: [PATCH 03/28] Adds initial evaluation for lexical entailment on HyperLex --- docs/notebooks/Poincare Evaluation.ipynb | 205 ++++++++++++++++------- 1 file changed, 144 insertions(+), 61 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 6808cc5544..39d1434cbf 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 222, "metadata": {}, "outputs": [], "source": [ @@ -163,6 +163,10 @@ " os.unlink(keyed_vectors_filename)\n", " return cls(keyed_vectors)\n", " \n", + " def get_vector(self, term):\n", + " \"\"\"Return vector for given term\"\"\"\n", + " return self.kv.word_vec(term)\n", + " \n", " def get_all_distances(self, term):\n", " \"\"\"Return distances to all terms for given term, including itself\"\"\"\n", " term_vector = self.kv.word_vec(term)\n", @@ -171,7 +175,8 @@ " euclidean_dists = np.linalg.norm(term_vector - all_vectors, axis=1)\n", " norm = np.linalg.norm(term_vector)\n", " all_norms = np.linalg.norm(all_vectors, axis=1)\n", - "\n", + "# import pdb\n", + "# pdb.set_trace()\n", " return np.arccosh(\n", " 1 + 2 * (\n", " (euclidean_dists ** 2) / ((1 - norm ** 2) * (1 - all_norms ** 2))\n", @@ -198,16 +203,18 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 223, "metadata": {}, "outputs": [], "source": [ "filenames = [\n", "# 'wordnet_embeddings_2.tsv',\n", "# 'wordnet_embeddings_5.tsv',\n", - " 'wordnet_embeddings_10.tsv',\n", + "# 'wordnet_embeddings_10.tsv',\n", "# 'wordnet_embeddings_20.tsv',\n", - "# 'wordnet_embeddings_50.tsv',\n", + "# 'wordnet_embeddings_20_ep50.tsv',\n", + " 'wordnet_embeddings_50.tsv',\n", + " 'wordnet_embeddings_50_ep100.tsv',\n", "# 'wordnet_embeddings_100.tsv',\n", "]\n", "embeddings = {fname: PoincareEmbedding.load_poincare_cpp(os.path.join(embeddings_dir, fname)) for fname in filenames}" @@ -215,39 +222,48 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 224, + "metadata": {}, + "outputs": [], + "source": [ + "test_embedding = embeddings['wordnet_embeddings_50_ep100.tsv']" + ] + }, + { + "cell_type": "code", + "execution_count": 202, "metadata": {}, "outputs": [], "source": [ - "test_embedding = embeddings['wordnet_embeddings_10.tsv']" + "test_embedding = embeddings['wordnet_embeddings_20_ep50.tsv']" ] }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 207, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:71: RuntimeWarning: divide by zero encountered in true_divide\n", - "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:71: RuntimeWarning: invalid value encountered in true_divide\n" + "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:72: RuntimeWarning: divide by zero encountered in true_divide\n", + "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:72: RuntimeWarning: invalid value encountered in arccosh\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 39.6 s, sys: 0 ns, total: 39.6 s\n", - "Wall time: 39.6 s\n" + "CPU times: user 9.5 s, sys: 2.2 s, total: 11.7 s\n", + "Wall time: 11.7 s\n" ] } ], "source": [ "%%time\n", "for i, term in enumerate(test_embedding.kv.vocab.keys(), start=1):\n", - " if i > 10000:\n", + " if i > 1000:\n", " break\n", " dists = test_embedding.get_all_distances(term)" ] @@ -268,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 216, "metadata": {}, "outputs": [], "source": [ @@ -280,10 +296,11 @@ "class ReconstructionEvaluation(object):\n", " \"\"\"Evaluating reconstruction on given network for any embeddings\"\"\"\n", " def __init__(self, filepath, embedding):\n", - " \"\"\"Initialize evaluation instance with tsv file containing relation pairs\n", + " \"\"\"Initialize evaluation instance with tsv file containing relation pairs and embedding to be evaluated\n", " \n", " Args:\n", " filepath (str): path to tsv file containing relation pairs\n", + " embedding (PoincareEmbedding instance): embedding to be evaluated\n", " \n", " Returns\n", " ReconstructionEvaluation instance\n", @@ -317,26 +334,29 @@ " list of ranks of positive items in the same order as `positive_indices`\n", " \"\"\"\n", " positive_item_distances = distances[positive_item_indices]\n", + " if np.any(positive_item_distances == np.inf):\n", + " import pdb\n", + " pdb.set_trace()\n", " negative_item_distances = np.ma.array(distances, mask=False)\n", " negative_item_distances.mask[positive_item_indices] = True\n", " # Compute how many negative item distances are less than each positive item distance, plus 1 for rank\n", " ranks = (negative_item_distances < positive_item_distances[:, np.newaxis]).sum(axis=1) + 1\n", " return list(ranks) \n", "\n", - " def evaluate_reconstruction(self, embeddings):\n", - " \"\"\"Evaluate mean rank and MAP for reconstruction for given embeddings\n", + " def evaluate_reconstruction(self, max_n=None):\n", + " \"\"\"Evaluate mean rank and MAP for reconstruction\n", " \n", " Args:\n", - " embeddings (PoincareEmbedding instance): embeddings for which evaluation is to be done\n", + " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", " \n", " Returns:\n", " ??\n", "\n", " \"\"\"\n", " ranks = []\n", - " for i, item in enumerate(self.items):\n", + " for i, item in enumerate(self.items, start=1):\n", " if not i % 1000:\n", - " print('Evaluating item %d %s' % (i, item))\n", + " print('Evaluating item number %d: %s' % (i, item))\n", " if item not in self.positive_relations:\n", " continue\n", " positive_items = list(self.positive_relations[item])\n", @@ -344,7 +364,7 @@ " item_distances = self.embedding.get_all_distances(item_term)\n", " positive_item_ranks = self.get_positive_item_ranks(item_distances, positive_items)\n", " ranks += positive_item_ranks\n", - " if i > 20000:\n", + " if max_n is not None and i > max_n:\n", " break\n", " return np.mean(ranks)\n", " " @@ -352,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 213, "metadata": {}, "outputs": [], "source": [ @@ -361,50 +381,18 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 214, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Evaluating item 0 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:71: RuntimeWarning: divide by zero encountered in true_divide\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Evaluating item 0 0\n", "Evaluating item 1000 1000\n", - "Evaluating item 2000 2000\n", - "Evaluating item 3000 3000\n", - "Evaluating item 4000 4000\n", - "Evaluating item 5000 5000\n", - "Evaluating item 6000 6000\n", - "Evaluating item 7000 7000\n", - "Evaluating item 8000 8000\n", - "Evaluating item 9000 9000\n", - "Evaluating item 10000 10000\n", - "Evaluating item 11000 11000\n", - "Evaluating item 12000 12000\n", - "Evaluating item 13000 13000\n", - "Evaluating item 14000 14000\n", - "Evaluating item 15000 15000\n", - "Evaluating item 16000 16000\n", - "Evaluating item 17000 17000\n", - "Evaluating item 18000 18000\n", - "Evaluating item 19000 19000\n", - "Evaluating item 20000 20000\n", - "116.01562108\n", - "CPU times: user 1min 55s, sys: 4 ms, total: 1min 55s\n", - "Wall time: 1min 55s\n" + "72.8014208014\n", + "CPU times: user 11.6 s, sys: 2.08 s, total: 13.7 s\n", + "Wall time: 13.7 s\n" ] } ], @@ -417,7 +405,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 4.2 WordNet link prediction" + "### 4.2 WordNet link prediction\n", + "TODO (tricky)" ] }, { @@ -427,11 +416,105 @@ "### 4.3 HyperLex lexical entailment" ] }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import spearmanr\n", + "\n", + "class LexicalEntailmentEvaluation(object):\n", + " \"\"\"Evaluating reconstruction on given network for any embeddings\"\"\"\n", + " def __init__(self, filepath, embedding):\n", + " \"\"\"Initialize evaluation instance with HyperLex text file containing relation pairs\n", + " \n", + " Args:\n", + " filepath (str): path to HyperLex text file\n", + " embedding (PoincareEmbedding instance): embedding to be evaluated\n", + " \n", + " Returns\n", + " LexicalEntailmentEvaluation instance\n", + "\n", + " \"\"\"\n", + " expected_scores = {}\n", + " with smart_open(filepath, 'r') as f:\n", + " reader = csv.DictReader(f, delimiter=' ')\n", + " for row in reader:\n", + " word_1, word_2 = row['WORD1'], row['WORD2']\n", + " expected_scores[(word_1, word_2)] = float(row['AVG_SCORE'])\n", + " self.scores = expected_scores\n", + " self.embedding = embedding\n", + " self.alpha = 1000\n", + " \n", + " def score_function(self, term_1, term_2):\n", + " \"\"\"Given two terms, return the predicted score for them (extent to which term_1 is a type of term_2)\"\"\"\n", + " distance = self.embedding.get_distance(term_1, term_2)\n", + " vector_1, vector_2 = self.embedding.get_vector(term_1), self.embedding.get_vector(term_2)\n", + " norm_1, norm_2 = np.linalg.norm(vector_1), np.linalg.norm(vector_2)\n", + " return -(1 + self.alpha(norm_2 - norm_1)) * distance\n", + " \n", + " def evaluate_spearman(self, embeddings):\n", + " \"\"\"Evaluate spearman scores for lexical entailment for given embeddings\n", + " \n", + " Args:\n", + " embeddings (PoincareEmbedding instance): embeddings for which evaluation is to be done\n", + " \n", + " Returns:\n", + " ??\n", + "\n", + " \"\"\"\n", + " predicted_scores = []\n", + " expected_scores = []\n", + " for (term_1, term_2), expected_score in self.scores.items():\n", + " predicted_scores.append(self.score_function(term_1, term_2))\n", + " expected_scores.append(expected_score)\n", + " spearman = spearmanr(expected_scores, predicted_scores).correlation\n", + " import pdb\n", + " pdb.set_trace()" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": {}, + "outputs": [], + "source": [ + "eval_instance = LexicalEntailmentEvaluation(os.path.join(embeddings_dir, 'nouns-verbs', 'hyperlex-nouns.txt'), test_embedding)" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"word 'asia' not in vocabulary\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0meval_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_spearman\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mevaluate_spearman\u001b[0;34m(self, embeddings)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mexpected_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mterm_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_score\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m 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term_2)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mscore_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\"\"\"Given two terms, return the predicted score for them (extent to which term_1 is a type of term_2)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mdistance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0mvector_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector_2\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;36mget_distance\u001b[0;34m(self, term_1, term_2)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \"\"\"\n\u001b[0;32m---> 94\u001b[0;31m \u001b[0mvector_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mterm_1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mterm_2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 95\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoincare_dist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvector_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/gensim/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, words)\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0;31m# allow calls like trained_model['office'], as a shorthand for trained_model[['office']]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 597\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 598\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/projects/gensim/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mword_vec\u001b[0;34m(self, word, use_norm)\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msyn0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"word '%s' not in vocabulary\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmost_similar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpositive\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrestrict_vocab\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: \"word 'asia' not in vocabulary\"" + ] + } + ], + "source": [ + "eval_instance.evaluate_spearman(test_embedding)" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4.1 Link Prediction for collaboration networks" + "### 4.1 Link Prediction for collaboration networks\n", + "TODO (tricky)" ] } ], From fd86c326dbbe8859fc853a1f87ca8abfaad83f06 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Thu, 12 Oct 2017 13:13:49 +0200 Subject: [PATCH 04/28] Adds complete optimized evaluation of lexical entailment to notebook --- docs/notebooks/Poincare Evaluation.ipynb | 178 +++++++++++++---------- 1 file changed, 98 insertions(+), 80 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 39d1434cbf..298e4b398d 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -55,18 +55,9 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The line_profiler extension is already loaded. To reload it, use:\n", - " %reload_ext line_profiler\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext line_profiler\n", "%load_ext snakeviz" @@ -74,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -91,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -100,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 222, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -108,6 +99,7 @@ "\n", "from gensim.models.keyedvectors import KeyedVectors\n", "import numpy as np\n", + "from pygtrie import Trie\n", "from scipy.spatial.distance import euclidean, pdist\n", "from smart_open import smart_open\n", "\n", @@ -133,6 +125,13 @@ " def __init__(self, keyed_vectors):\n", " \"\"\"Initialize PoincareEmbeddings via a KeyedVectors instance\"\"\"\n", " self.kv = keyed_vectors\n", + " self.init_key_trie()\n", + " \n", + " def init_key_trie(self):\n", + " \"\"\"Setup trie containing vocab keys for quick prefix lookups\"\"\"\n", + " self.key_trie = Trie()\n", + " for key in self.kv.vocab:\n", + " self.key_trie[key] = True\n", " \n", " @staticmethod\n", " def poincare_dist(vector_1, vector_2):\n", @@ -163,6 +162,12 @@ " os.unlink(keyed_vectors_filename)\n", " return cls(keyed_vectors)\n", " \n", + " def find_matching_keys(self, word):\n", + " \"\"\"Find all senses of given word in embedding vocabulary\"\"\"\n", + " matches = self.key_trie.items('%s.' % word)\n", + " matching_keys = [''.join(key_chars) for key_chars, value in matches]\n", + " return matching_keys\n", + "\n", " def get_vector(self, term):\n", " \"\"\"Return vector for given term\"\"\"\n", " return self.kv.word_vec(term)\n", @@ -175,8 +180,6 @@ " euclidean_dists = np.linalg.norm(term_vector - all_vectors, axis=1)\n", " norm = np.linalg.norm(term_vector)\n", " all_norms = np.linalg.norm(all_vectors, axis=1)\n", - "# import pdb\n", - "# pdb.set_trace()\n", " return np.arccosh(\n", " 1 + 2 * (\n", " (euclidean_dists ** 2) / ((1 - norm ** 2) * (1 - all_norms ** 2))\n", @@ -203,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 223, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -211,27 +214,18 @@ "# 'wordnet_embeddings_2.tsv',\n", "# 'wordnet_embeddings_5.tsv',\n", "# 'wordnet_embeddings_10.tsv',\n", - "# 'wordnet_embeddings_20.tsv',\n", - "# 'wordnet_embeddings_20_ep50.tsv',\n", + " 'wordnet_embeddings_20.tsv',\n", + " 'wordnet_embeddings_20_ep50.tsv',\n", " 'wordnet_embeddings_50.tsv',\n", - " 'wordnet_embeddings_50_ep100.tsv',\n", - "# 'wordnet_embeddings_100.tsv',\n", + "# 'wordnet_embeddings_50_ep100.tsv',\n", + " 'wordnet_embeddings_100.tsv',\n", "]\n", "embeddings = {fname: PoincareEmbedding.load_poincare_cpp(os.path.join(embeddings_dir, fname)) for fname in filenames}" ] }, { "cell_type": "code", - "execution_count": 224, - "metadata": {}, - "outputs": [], - "source": [ - "test_embedding = embeddings['wordnet_embeddings_50_ep100.tsv']" - ] - }, - { - "cell_type": "code", - "execution_count": 202, + "execution_count": 100, "metadata": {}, "outputs": [], "source": [ @@ -240,23 +234,15 @@ }, { "cell_type": "code", - "execution_count": 207, + "execution_count": 101, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:72: RuntimeWarning: divide by zero encountered in true_divide\n", - "/home/jayant/projects/py3/lib/python3.5/site-packages/ipykernel_launcher.py:72: RuntimeWarning: invalid value encountered in arccosh\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 9.5 s, sys: 2.2 s, total: 11.7 s\n", - "Wall time: 11.7 s\n" + "CPU times: user 7.11 s, sys: 0 ns, total: 7.11 s\n", + "Wall time: 7.11 s\n" ] } ], @@ -284,7 +270,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 102, "metadata": {}, "outputs": [], "source": [ @@ -334,9 +320,6 @@ " list of ranks of positive items in the same order as `positive_indices`\n", " \"\"\"\n", " positive_item_distances = distances[positive_item_indices]\n", - " if np.any(positive_item_distances == np.inf):\n", - " import pdb\n", - " pdb.set_trace()\n", " negative_item_distances = np.ma.array(distances, mask=False)\n", " negative_item_distances.mask[positive_item_indices] = True\n", " # Compute how many negative item distances are less than each positive item distance, plus 1 for rank\n", @@ -372,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -381,24 +364,23 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 104, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Evaluating item 0 0\n", - "Evaluating item 1000 1000\n", - "72.8014208014\n", - "CPU times: user 11.6 s, sys: 2.08 s, total: 13.7 s\n", - "Wall time: 13.7 s\n" + "Evaluating item number 1000: 999\n", + "74.4394798266\n", + "CPU times: user 9.34 s, sys: 0 ns, total: 9.34 s\n", + "Wall time: 9.34 s\n" ] } ], "source": [ "%%time\n", - "print(np.mean(eval_instance.evaluate_reconstruction(test_embedding)))" + "print(np.mean(eval_instance.evaluate_reconstruction(max_n=1000)))" ] }, { @@ -418,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -447,12 +429,29 @@ " self.embedding = embedding\n", " self.alpha = 1000\n", " \n", - " def score_function(self, term_1, term_2):\n", + " def score_function(self, word_1, word_2):\n", " \"\"\"Given two terms, return the predicted score for them (extent to which term_1 is a type of term_2)\"\"\"\n", - " distance = self.embedding.get_distance(term_1, term_2)\n", - " vector_1, vector_2 = self.embedding.get_vector(term_1), self.embedding.get_vector(term_2)\n", + " try:\n", + " word_1_terms = self.embedding.find_matching_keys(word_1)\n", + " word_2_terms = self.embedding.find_matching_keys(word_2)\n", + " except KeyError:\n", + " raise ValueError(\"No matching terms found for either %s or %s\" % (word_1, word_2))\n", + " min_distance = np.inf\n", + " min_term_1, min_term_2 = None, None\n", + " for term_1 in word_1_terms:\n", + " for term_2 in word_2_terms:\n", + " distance = self.embedding.get_distance(term_1, term_2)\n", + " if distance < min_distance:\n", + " min_term_1, min_term_2 = term_1, term_2\n", + " min_distance = distance\n", + " try:\n", + " assert min_term_1 is not None and min_term_2 is not None\n", + " except AssertionError:\n", + " import pdb\n", + " pdb.set_trace()\n", + " vector_1, vector_2 = self.embedding.get_vector(min_term_1), self.embedding.get_vector(min_term_2)\n", " norm_1, norm_2 = np.linalg.norm(vector_1), np.linalg.norm(vector_2)\n", - " return -(1 + self.alpha(norm_2 - norm_1)) * distance\n", + " return -1 * (1 + self.alpha * (norm_2 - norm_1)) * distance\n", " \n", " def evaluate_spearman(self, embeddings):\n", " \"\"\"Evaluate spearman scores for lexical entailment for given embeddings\n", @@ -466,17 +465,25 @@ " \"\"\"\n", " predicted_scores = []\n", " expected_scores = []\n", - " for (term_1, term_2), expected_score in self.scores.items():\n", - " predicted_scores.append(self.score_function(term_1, term_2))\n", + " skipped = 0\n", + " count = 0\n", + " for (word_1, word_2), expected_score in self.scores.items():\n", + " try:\n", + " predicted_score = self.score_function(word_1, word_2)\n", + " except ValueError:\n", + " skipped += 1\n", + " continue\n", + " count += 1\n", + " predicted_scores.append(predicted_score)\n", " expected_scores.append(expected_score)\n", - " spearman = spearmanr(expected_scores, predicted_scores).correlation\n", - " import pdb\n", - " pdb.set_trace()" + " print('Skipped pairs: %d out of %d' % (skipped, len(self.scores)))\n", + " spearman = spearmanr(expected_scores, predicted_scores)\n", + " return spearman\n" ] }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -485,30 +492,41 @@ }, { "cell_type": "code", - "execution_count": 230, - "metadata": {}, + "execution_count": 107, + "metadata": { + "scrolled": true + }, "outputs": [ { - "ename": "KeyError", - "evalue": "\"word 'asia' not in vocabulary\"", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0meval_instance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate_spearman\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_embedding\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mevaluate_spearman\u001b[0;34m(self, embeddings)\u001b[0m\n\u001b[1;32m 44\u001b[0m \u001b[0mexpected_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mterm_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_score\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0mpredicted_scores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m \u001b[0mexpected_scores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpected_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0mspearman\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspearmanr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpected_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpredicted_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorrelation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mscore_function\u001b[0;34m(self, term_1, term_2)\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mscore_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\"\"\"Given two terms, return the predicted score for them (extent to which term_1 is a type of term_2)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mdistance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mterm_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 29\u001b[0m \u001b[0mvector_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm_1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mterm_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0mnorm_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvector_1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvector_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36mget_distance\u001b[0;34m(self, term_1, term_2)\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 93\u001b[0m \"\"\"\n\u001b[0;32m---> 94\u001b[0;31m \u001b[0mvector_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mterm_1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mterm_2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 95\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoincare_dist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvector_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvector_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/projects/gensim/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, words)\u001b[0m\n\u001b[1;32m 595\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[0;31m# allow calls like trained_model['office'], as a shorthand for trained_model[['office']]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 597\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 598\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mvstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mwords\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/projects/gensim/gensim/models/keyedvectors.py\u001b[0m in \u001b[0;36mword_vec\u001b[0;34m(self, word, use_norm)\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msyn0\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"word '%s' not in vocabulary\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmost_similar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpositive\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtopn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrestrict_vocab\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: \"word 'asia' not in vocabulary\"" + "name": "stdout", + "output_type": "stream", + "text": [ + "Skipped pairs: 182 out of 2163\n" ] + }, + { + "data": { + "text/plain": [ + "SpearmanrResult(correlation=0.44053139956543591, pvalue=7.7820211828687839e-95)" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ "eval_instance.evaluate_spearman(test_embedding)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Link Prediction for collaboration networks\n", + "TODO (tricky)" + ] + }, { "cell_type": "markdown", "metadata": {}, From 69b4d61e9e1e90fa5ea85a9e4781d32f5859c1f8 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Tue, 17 Oct 2017 09:53:53 +0200 Subject: [PATCH 05/28] Adds setup and training steps to notebook, tabulated results --- docs/notebooks/Poincare Evaluation.ipynb | 564 ++++++++++++++++++----- 1 file changed, 442 insertions(+), 122 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 298e4b398d..4dd87810a9 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -22,13 +22,30 @@ "A more detailed explanation of the tasks and the evaluation methodology is present in the individual evaluation subsections." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tables" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Setup\n", "\n", - "TODO" + "Clone the [poincare-embedding](https://github.com/TatsuyaShirakawa/poincare-embedding) repository and follow the README to compile the sources into a binary. Set the variable below to the directory containing the `poincare-embedding` directory." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Set this to the path of the directory containing the poincare-embedding directory\n", + "parent_directory = '/home/jayant/projects/'" ] }, { @@ -36,66 +53,177 @@ "metadata": {}, "source": [ "## 2. Training\n", - "TODO" + "\n", + "### 2.1 Create the data" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 3, "metadata": {}, + "outputs": [], "source": [ - "## 3. Loading the embeddings" + "import os\n", + "\n", + "# These directories are auto created in the current directory for storing poincare datasets and models\n", + "data_directory = 'poincare_data'\n", + "models_directory = os.path.join(data_directory, 'models')\n", + "\n", + "# Create directories\n", + "! mkdir {data_directory}\n", + "! mkdir {models_directory}" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the WordNet data\n", + "wordnet_file = os.path.join(data_directory, 'wordnet_noun_hypernyms.tsv')\n", + "! python {parent_directory}/poincare-embedding/scripts/create_wordnet_noun_hierarchy.py {wordnet_data_file}" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "hyperlex_url = \"http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\"\n", + "! wget {hyperlex_url} -P {data_directory}\n", + "! unzip {data_directory}/hyperlex-data.zip -d {data_directory}\n", + "hyperlex_file = os.path.join(data_directory, 'nouns-verbs', 'hyperlex-nouns.txt')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.1 C++ embeddings" + "### 2.2 Traing C++ embeddings" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "%load_ext line_profiler\n", - "%load_ext snakeviz" + "from gensim.utils import check_output\n", + "\n", + "def train_cpp_model(binary_path, data_file, output_file, dim, epochs, neg, num_threads, seed=0):\n", + " \"\"\"Train a poincare embedding using the c++ implementation\n", + " \n", + " Args:\n", + " binary_path (str): Path to the compiled c++ implementation binary\n", + " \n", + " \"\"\"\n", + " args = {\n", + " 'dim': dim,\n", + " 'max_epoch': epochs,\n", + " 'neg_size': neg,\n", + " 'num_thread': num_threads,\n", + " 'learning_rate_init': 0.1,\n", + " 'learning_rate_final': 0.0001,\n", + " }\n", + " cmd = [binary_path, data_file, output_file]\n", + " for option, value in args.items():\n", + " cmd.append(\"--%s\" % option)\n", + " cmd.append(str(value))\n", + "\n", + " return check_output(args=cmd)" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jayant/projects/gensim\n" - ] - } - ], + "outputs": [], "source": [ - "% cd ../.." + "cpp_binary_path = os.path.join(parent_directory, 'poincare-embedding', 'work', 'poincare_embedding')" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "embeddings_dir = '/home/jayant/projects/poincare-embedding/work' # TODO: put model files into repo?" + "model_sizes = [5, 10, 20, 50, 100]\n", + "neg_sizes = [10, 20]\n", + "epochs = [50, 100]\n", + "threads = [8, 1]" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "model_files = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Possibly re-write with permutations instead of nested loops?\n", + "for epochs_ in epochs:\n", + " for threads_ in threads:\n", + " for neg_size in neg_sizes:\n", + " model_name = 'cpp_epochs_%d_threads_%d_neg_%d' % (epochs_, threads_, neg_size)\n", + " model_files[model_name] = {}\n", + " for model_size in model_sizes:\n", + " output_file_name = '%s_dim_%d' % (model_name, model_size)\n", + " output_file = os.path.join(models_directory, output_file_name)\n", + " print('Training model with size %d neg %d threads %d epochs %d, saving to %s' %\n", + " (model_size, neg_size, threads_, epochs_, output_file))\n", + " out = train_cpp_model(\n", + " cpp_binary_path, wordnet_data_file, output_file,\n", + " model_size, epochs_, neg_size, threads_, seed=0)\n", + " model_files[model_name][model_size] = output_file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Loading the embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "embeddings = {}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 C++ embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "import os\n", + "import pickle\n", + "import re\n", "\n", "from gensim.models.keyedvectors import KeyedVectors\n", "import numpy as np\n", @@ -117,13 +245,13 @@ " f.write('%d %d\\n' % (vocab_size, model_size))\n", " for line in lines:\n", " f.write(line.replace('\\t', ' '))\n", - " \n", + "\n", " \n", "class PoincareEmbedding(object):\n", - " \"\"\"Load and perform distance operations on poincare embeddings\"\"\"\n", + " \"\"\"Load and perform distance operations on poincare embedding\"\"\"\n", "\n", " def __init__(self, keyed_vectors):\n", - " \"\"\"Initialize PoincareEmbeddings via a KeyedVectors instance\"\"\"\n", + " \"\"\"Initialize PoincareEmbedding via a KeyedVectors instance\"\"\"\n", " self.kv = keyed_vectors\n", " self.init_key_trie()\n", " \n", @@ -147,10 +275,10 @@ " \n", " @classmethod\n", " def load_poincare_cpp(cls, input_filename):\n", - " \"\"\"Load embeddings trained via C++ Poincare model\n", + " \"\"\"Load embedding trained via C++ Poincare model\n", "\n", " Args:\n", - " filepath (str): Path to tsv file containing embeddings\n", + " filepath (str): Path to tsv file containing embedding\n", "\n", " Returns:\n", " PoincareEmbedding instance\n", @@ -161,6 +289,23 @@ " keyed_vectors = KeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", " os.unlink(keyed_vectors_filename)\n", " return cls(keyed_vectors)\n", + "\n", + " @classmethod\n", + " def load_poincare_numpy(cls, input_filename):\n", + " \"\"\"Load embedding trained via Python numpy Poincare model\n", + "\n", + " Args:\n", + " filepath (str): Path to pkl file containing embedding\n", + "\n", + " Returns:\n", + " PoincareEmbedding instance\n", + "\n", + " \"\"\"\n", + " keyed_vectors_filename = input_filename + '.kv'\n", + " transform_numpy_embedding_to_kv(input_filename, keyed_vectors_filename)\n", + " keyed_vectors = KeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", + " os.unlink(keyed_vectors_filename)\n", + " return cls(keyed_vectors)\n", " \n", " def find_matching_keys(self, word):\n", " \"\"\"Find all senses of given word in embedding vocabulary\"\"\"\n", @@ -206,59 +351,66 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "filenames = [\n", - "# 'wordnet_embeddings_2.tsv',\n", - "# 'wordnet_embeddings_5.tsv',\n", - "# 'wordnet_embeddings_10.tsv',\n", - " 'wordnet_embeddings_20.tsv',\n", - " 'wordnet_embeddings_20_ep50.tsv',\n", - " 'wordnet_embeddings_50.tsv',\n", - "# 'wordnet_embeddings_50_ep100.tsv',\n", - " 'wordnet_embeddings_100.tsv',\n", - "]\n", - "embeddings = {fname: PoincareEmbedding.load_poincare_cpp(os.path.join(embeddings_dir, fname)) for fname in filenames}" + "for model_name, models in model_files.items():\n", + " embeddings[model_name] = {}\n", + " for model_size, model_file in models.items():\n", + " embeddings[model_name][model_size] = PoincareEmbedding.load_poincare_cpp(model_file)" ] }, { - "cell_type": "code", - "execution_count": 100, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "test_embedding = embeddings['wordnet_embeddings_20_ep50.tsv']" + "### 3.2 Numpy embeddings\n", + "TODO" ] }, { - "cell_type": "code", - "execution_count": 101, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.11 s, sys: 0 ns, total: 7.11 s\n", - "Wall time: 7.11 s\n" - ] - } - ], "source": [ - "%%time\n", - "for i, term in enumerate(test_embedding.kv.vocab.keys(), start=1):\n", - " if i > 1000:\n", - " break\n", - " dists = test_embedding.get_all_distances(term)" + "## 4. Evaluation" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 64, "metadata": {}, + "outputs": [], "source": [ - "## 4. Evaluation" + "from prettytable import PrettyTable\n", + "\n", + "def display_results(task_name, results):\n", + " \"\"\"Display evaluation results of multiple embeddings on a single task in a tabular format\n", + " \n", + " Args:\n", + " task_name (str): name the task being evaluated\n", + " results (dict): mapping between embeddings and corresponding results\n", + " \n", + " \"\"\"\n", + " header = PrettyTable()\n", + " # TODO: infer widths from table rather than hard-coding\n", + " header.field_names = [\" \" * 42, \" \" * 7 + \"Model Dimensions\" + \" \" * 8]\n", + "\n", + " data = PrettyTable()\n", + " data.field_names = [\"Model Description\", \"Metric\"] + [str(dim) for dim in sorted(model_sizes)]\n", + " for model_name, model_results in results.items():\n", + " metrics = [metric for metric in model_results.keys()]\n", + " dims = sorted([dim for dim in model_results[metrics[0]].keys()])\n", + " row = [model_name, '\\n'.join(metrics)]\n", + " for dim in dims:\n", + " scores = ['%.2f' % model_results[metric][dim] for metric in metrics]\n", + " row.append('\\n'.join(scores))\n", + " data.add_row(row)\n", + " \n", + " header_lines = header.get_string(start=0, end=0).split(\"\\n\")[:2]\n", + " print('Results for %s task' % task_name)\n", + " print(\"\\n\".join(header_lines))\n", + " print(data) " ] }, { @@ -270,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -280,7 +432,7 @@ "\n", "\n", "class ReconstructionEvaluation(object):\n", - " \"\"\"Evaluating reconstruction on given network for any embeddings\"\"\"\n", + " \"\"\"Evaluating reconstruction on given network for given embedding\"\"\"\n", " def __init__(self, filepath, embedding):\n", " \"\"\"Initialize evaluation instance with tsv file containing relation pairs and embedding to be evaluated\n", " \n", @@ -325,21 +477,37 @@ " # Compute how many negative item distances are less than each positive item distance, plus 1 for rank\n", " ranks = (negative_item_distances < positive_item_distances[:, np.newaxis]).sum(axis=1) + 1\n", " return list(ranks) \n", + " \n", + " def evaluate_metric(self, metric, max_n=None):\n", + " \"\"\"Evaluate given metric for the reconstruction task\n", + " \n", + " Args:\n", + " metric (str): accepted values are 'mean_rank' and 'MAP'\n", + " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", + " \n", + " Returns:\n", + " Computed value of given metric (float)\n", + "\n", + " \"\"\"\n", + " if metric == 'mean_rank':\n", + " return self.evaluate_mean_rank(max_n)\n", + " elif metric == 'MAP':\n", + " return self.evaluate_map(max_n)\n", + " else:\n", + " raise ValueError('Invalid value for metric')\n", "\n", - " def evaluate_reconstruction(self, max_n=None):\n", + " def evaluate_mean_rank(self, max_n=None):\n", " \"\"\"Evaluate mean rank and MAP for reconstruction\n", " \n", " Args:\n", " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", " \n", " Returns:\n", - " ??\n", + " Computed value of mean rank (float)\n", "\n", " \"\"\"\n", " ranks = []\n", " for i, item in enumerate(self.items, start=1):\n", - " if not i % 1000:\n", - " print('Evaluating item number %d: %s' % (i, item))\n", " if item not in self.positive_relations:\n", " continue\n", " positive_items = list(self.positive_relations[item])\n", @@ -350,45 +518,153 @@ " if max_n is not None and i > max_n:\n", " break\n", " return np.mean(ranks)\n", - " " + " \n", + " def evaluate_map(self, max_n=None):\n", + " \"\"\"Evaluate MAP (Mean Average Precision) for reconstruction\n", + " \n", + " Args:\n", + " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", + " \n", + " Returns:\n", + " Computed value of MAP (float)\n", + "\n", + " \"\"\"\n", + " raise NotImplementedError" ] }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ - "eval_instance = ReconstructionEvaluation(os.path.join(embeddings_dir, 'wordnet_noun_hypernyms.tsv'), test_embedding)" + "reconstruction_results = {}\n", + "metrics = ['mean_rank']" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 20\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 10\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 100\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 50\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 5\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 20\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 10\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 100\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 50\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 5\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 20\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 10\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 100\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 50\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 5\n" + ] + } + ], + "source": [ + "for model_name, models in embeddings.items():\n", + " reconstruction_results[model_name] = {}\n", + " for metric in metrics:\n", + " reconstruction_results[model_name][metric] = {}\n", + " for model_size, embedding in models.items():\n", + " print('Evaluating model %s of size %d' % (model_name, model_size))\n", + " eval_instance = ReconstructionEvaluation(wordnet_file, embedding)\n", + " for metric in metrics:\n", + " reconstruction_results[model_name][metric][model_size] = eval_instance.evaluate_metric(metric, max_n=1000)" ] }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Evaluating item number 1000: 999\n", - "74.4394798266\n", - "CPU times: user 9.34 s, sys: 0 ns, total: 9.34 s\n", - "Wall time: 9.34 s\n" + "Results for WordNet Reconstruction task\n", + "+--------------------------------------------+---------------------------------+\n", + "| | Model Dimensions |\n", + "+--------------------------------+-----------+--------+--------+-------+-------+-------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 |\n", + "+--------------------------------+-----------+--------+--------+-------+-------+-------+\n", + "| cpp_epochs_50_threads_8_neg_10 | mean_rank | 268.44 | 129.66 | 86.78 | 76.58 | 71.15 |\n", + "| cpp_epochs_50_threads_8_neg_20 | mean_rank | 251.71 | 145.56 | 96.18 | 72.44 | 57.26 |\n", + "| cpp_epochs_50_threads_1_neg_10 | mean_rank | 325.21 | 107.59 | 71.23 | 61.48 | 60.01 |\n", + "+--------------------------------+-----------+--------+--------+-------+-------+-------+\n" ] } ], "source": [ - "%%time\n", - "print(np.mean(eval_instance.evaluate_reconstruction(max_n=1000)))" + "display_results('WordNet Reconstruction', reconstruction_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4.2 WordNet link prediction\n", - "TODO (tricky)" + "### 4.2 WordNet link prediction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import random\n", + "\n", + "def train_test_split(data_file, test_ratio=0.1):\n", + " \"\"\"Creates train and test files from given data file, returns train/test file names\n", + " \n", + " Args:\n", + " data_file (str): path to data file for which train/test split is to be created\n", + " test_ratio (float): fraction of lines to be used for test data\n", + " \n", + " Returns\n", + " (train_file, test_file): tuple of strings with train file and test file paths\n", + " \"\"\"\n", + " with open(data_file, 'rb') as f:\n", + " line_count = sum(1 for line in f)\n", + " num_test_lines = int(test_ratio * line_count)\n", + " test_line_indices = set(random.sample(range(line_count), num_test_lines))\n", + " train_line_indices = set(l for l in range(line_count) if l not in test_line_indices)\n", + " \n", + " train_filename = data_file + '.train'\n", + " test_filename = data_file + '.test'\n", + " \n", + " with open(data_file, 'rb') as f:\n", + " train_file = open(train_filename, 'wb')\n", + " test_file = open(test_filename, 'wb')\n", + " for i, line in enumerate(f):\n", + " if i in train_line_indices:\n", + " train_file.write(line)\n", + " elif i in test_line_indices:\n", + " test_file.write(line)\n", + " else:\n", + " raise AssertionError('Line %d not present in either train or test line indices' % i)\n", + " train_file.close()\n", + " test_file.close()\n", + "\n", + " return (train_filename, test_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "# On second thoughts, this might be trickier than anticipated - the test split cannot have root or leaf nodes\n", + "# Find root and leaf nodes and put them in train set first?" ] }, { @@ -400,20 +676,19 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import spearmanr\n", "\n", "class LexicalEntailmentEvaluation(object):\n", - " \"\"\"Evaluating reconstruction on given network for any embeddings\"\"\"\n", - " def __init__(self, filepath, embedding):\n", + " \"\"\"Evaluating reconstruction on given network for any embedding\"\"\"\n", + " def __init__(self, filepath):\n", " \"\"\"Initialize evaluation instance with HyperLex text file containing relation pairs\n", " \n", " Args:\n", " filepath (str): path to HyperLex text file\n", - " embedding (PoincareEmbedding instance): embedding to be evaluated\n", " \n", " Returns\n", " LexicalEntailmentEvaluation instance\n", @@ -426,38 +701,33 @@ " word_1, word_2 = row['WORD1'], row['WORD2']\n", " expected_scores[(word_1, word_2)] = float(row['AVG_SCORE'])\n", " self.scores = expected_scores\n", - " self.embedding = embedding\n", " self.alpha = 1000\n", " \n", - " def score_function(self, word_1, word_2):\n", - " \"\"\"Given two terms, return the predicted score for them (extent to which term_1 is a type of term_2)\"\"\"\n", + " def score_function(self, embedding, word_1, word_2):\n", + " \"\"\"Given an embedding and two terms, return the predicted score for them (extent to which term_1 is a type of term_2)\"\"\"\n", " try:\n", - " word_1_terms = self.embedding.find_matching_keys(word_1)\n", - " word_2_terms = self.embedding.find_matching_keys(word_2)\n", + " word_1_terms = embedding.find_matching_keys(word_1)\n", + " word_2_terms = embedding.find_matching_keys(word_2)\n", " except KeyError:\n", " raise ValueError(\"No matching terms found for either %s or %s\" % (word_1, word_2))\n", " min_distance = np.inf\n", " min_term_1, min_term_2 = None, None\n", " for term_1 in word_1_terms:\n", " for term_2 in word_2_terms:\n", - " distance = self.embedding.get_distance(term_1, term_2)\n", + " distance = embedding.get_distance(term_1, term_2)\n", " if distance < min_distance:\n", " min_term_1, min_term_2 = term_1, term_2\n", " min_distance = distance\n", - " try:\n", - " assert min_term_1 is not None and min_term_2 is not None\n", - " except AssertionError:\n", - " import pdb\n", - " pdb.set_trace()\n", - " vector_1, vector_2 = self.embedding.get_vector(min_term_1), self.embedding.get_vector(min_term_2)\n", + " assert min_term_1 is not None and min_term_2 is not None\n", + " vector_1, vector_2 = embedding.get_vector(min_term_1), embedding.get_vector(min_term_2)\n", " norm_1, norm_2 = np.linalg.norm(vector_1), np.linalg.norm(vector_2)\n", " return -1 * (1 + self.alpha * (norm_2 - norm_1)) * distance\n", " \n", - " def evaluate_spearman(self, embeddings):\n", - " \"\"\"Evaluate spearman scores for lexical entailment for given embeddings\n", + " def evaluate_spearman(self, embedding):\n", + " \"\"\"Evaluate spearman scores for lexical entailment for given embedding\n", " \n", " Args:\n", - " embeddings (PoincareEmbedding instance): embeddings for which evaluation is to be done\n", + " embedding (PoincareEmbedding instance): embedding for which evaluation is to be done\n", " \n", " Returns:\n", " ??\n", @@ -469,7 +739,7 @@ " count = 0\n", " for (word_1, word_2), expected_score in self.scores.items():\n", " try:\n", - " predicted_score = self.score_function(word_1, word_2)\n", + " predicted_score = self.score_function(embedding, word_1, word_2)\n", " except ValueError:\n", " skipped += 1\n", " continue\n", @@ -478,61 +748,111 @@ " expected_scores.append(expected_score)\n", " print('Skipped pairs: %d out of %d' % (skipped, len(self.scores)))\n", " spearman = spearmanr(expected_scores, predicted_scores)\n", - " return spearman\n" + " return spearman.correlation\n" ] }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ - "eval_instance = LexicalEntailmentEvaluation(os.path.join(embeddings_dir, 'nouns-verbs', 'hyperlex-nouns.txt'), test_embedding)" + "entailment_results = {}\n", + "eval_instance = LexicalEntailmentEvaluation(hyperlex_file)" ] }, { "cell_type": "code", - "execution_count": 107, - "metadata": { - "scrolled": true - }, + "execution_count": 62, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 20\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 10\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 100\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 50\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 5\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 20\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 10\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 100\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 50\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 5\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 20\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 10\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 100\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 50\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 5\n", "Skipped pairs: 182 out of 2163\n" ] - }, + } + ], + "source": [ + "for model_name, models in embeddings.items():\n", + " entailment_results[model_name] = {}\n", + " entailment_results[model_name]['spearman'] = {}\n", + " for model_size, embedding in models.items():\n", + " print('Evaluating model %s of size %d' % (model_name, model_size))\n", + " entailment_results[model_name]['spearman'][model_size] = eval_instance.evaluate_spearman(embedding)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ { - "data": { - "text/plain": [ - "SpearmanrResult(correlation=0.44053139956543591, pvalue=7.7820211828687839e-95)" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for Lexical Entailment (HyperLex) task\n", + "+--------------------------------------------+---------------------------------+\n", + "| | Model Dimensions |\n", + "+--------------------------------+----------+------+------+------+------+------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 |\n", + "+--------------------------------+----------+------+------+------+------+------+\n", + "| cpp_epochs_50_threads_8_neg_10 | spearman | 0.44 | 0.43 | 0.44 | 0.44 | 0.44 |\n", + "| cpp_epochs_50_threads_8_neg_20 | spearman | 0.47 | 0.45 | 0.46 | 0.46 | 0.46 |\n", + "| cpp_epochs_50_threads_1_neg_10 | spearman | 0.45 | 0.48 | 0.47 | 0.47 | 0.47 |\n", + "+--------------------------------+----------+------+------+------+------+------+\n" + ] } ], "source": [ - "eval_instance.evaluate_spearman(test_embedding)" + "display_results('Lexical Entailment (HyperLex)', entailment_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4.1 Link Prediction for collaboration networks\n", - "TODO (tricky)" + "### 4.4 Link Prediction for collaboration networks\n" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 68, "metadata": {}, + "outputs": [], "source": [ - "### 4.1 Link Prediction for collaboration networks\n", - "TODO (tricky)" + "# TODO - quite tricky, since the loss function used for training the model on this network is different\n", + "# Will require changes to how gradients are calculated in C++ code" ] } ], From c17846ffe38cad274b85f763b34410b821c36aff Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Tue, 17 Oct 2017 22:34:05 +0200 Subject: [PATCH 06/28] Implements link prediction task for poincare and adds results --- docs/notebooks/Poincare Evaluation.ipynb | 529 +++++++++++++++++++++-- 1 file changed, 491 insertions(+), 38 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 4dd87810a9..850185ddcd 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 8, "metadata": { "scrolled": true }, @@ -148,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -160,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -169,11 +169,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training model with size 5 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_5\n", + "Training model with size 10 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_10\n", + "Training model with size 20 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_20\n", + "Training model with size 50 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_50\n", + "Training model with size 100 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_100\n", + "Training model with size 5 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_5\n", + "Training model with size 10 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_10\n", + "Training model with size 20 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_20\n", + "Training model with size 50 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_50\n", + "Training model with size 100 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_100\n", + "Training model with size 5 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_5\n", + "Training model with size 10 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_10\n", + "Training model with size 20 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_20\n", + "Training model with size 50 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_50\n", + "Training model with size 100 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_100\n", + "Training model with size 5 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_5\n", + "Training model with size 10 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_10\n", + "Training model with size 20 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_20\n", + "Training model with size 50 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_50\n", + "Training model with size 100 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_100\n", + "Training model with size 5 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_5\n", + "Training model with size 10 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_10\n", + "Training model with size 20 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_20\n", + "Training model with size 50 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_50\n", + "Training model with size 100 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_100\n", + "Training model with size 5 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_5\n", + "Training model with size 10 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_10\n", + "Training model with size 20 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_20\n", + "Training model with size 50 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_50\n", + "Training model with size 100 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_100\n", + "Training model with size 5 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_5\n", + "Training model with size 10 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_10\n", + "Training model with size 20 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_20\n", + "Training model with size 50 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_50\n", + "Training model with size 100 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_100\n", + "Training model with size 5 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_5\n", + "Training model with size 10 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_10\n", + "Training model with size 20 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_20\n", + "Training model with size 50 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_50\n", + "Training model with size 100 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_100\n" + ] + } + ], "source": [ "# Possibly re-write with permutations instead of nested loops?\n", "for epochs_ in epochs:\n", @@ -201,7 +248,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -217,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -351,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -378,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -422,7 +469,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -446,36 +493,38 @@ " \"\"\"\n", " items = set()\n", " embedding_vocab = embedding.kv.vocab\n", - " positive_relations = defaultdict(set)\n", + " relations = defaultdict(set)\n", " with smart_open(filepath, 'r') as f:\n", " reader = csv.reader(f, delimiter='\\t')\n", " for row in reader:\n", " assert len(row) == 2, 'Hypernym pair has more than two items'\n", " item_1_index = embedding_vocab[row[0]].index\n", " item_2_index = embedding_vocab[row[1]].index\n", - " positive_relations[item_1_index].add(item_2_index)\n", + " relations[item_1_index].add(item_2_index)\n", " items.update([item_1_index, item_2_index])\n", " self.items = items\n", - " self.positive_relations = positive_relations\n", + " self.relations = relations\n", " self.embedding = embedding\n", " \n", " \n", " @staticmethod\n", - " def get_positive_item_ranks(distances, positive_item_indices):\n", - " \"\"\"Given a numpy array of distances and indices of positive items, compute ranks of positive item distances\n", + " def get_positive_relation_ranks(distances, positive_relations):\n", + " \"\"\"\n", + " Given a numpy array of all distances from an item and indices of its positive relations,\n", + " compute ranks of positive relations\n", " \n", " Args:\n", " distances (numpy float array): np array of all distances for a specific item\n", - " positive_item_indices (list): list of indices of positive items\n", + " positive_relations (list): list of indices of positive relations for the item\n", " \n", " Returns:\n", " list of ranks of positive items in the same order as `positive_indices`\n", " \"\"\"\n", - " positive_item_distances = distances[positive_item_indices]\n", - " negative_item_distances = np.ma.array(distances, mask=False)\n", - " negative_item_distances.mask[positive_item_indices] = True\n", - " # Compute how many negative item distances are less than each positive item distance, plus 1 for rank\n", - " ranks = (negative_item_distances < positive_item_distances[:, np.newaxis]).sum(axis=1) + 1\n", + " positive_relation_distances = distances[positive_relations]\n", + " negative_relation_distances = np.ma.array(distances, mask=False)\n", + " negative_relation_distances.mask[positive_relations] = True\n", + " # Compute how many negative relation distances are less than each positive relation distance, plus 1 for rank\n", + " ranks = (negative_relation_distances < positive_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n", " return list(ranks) \n", " \n", " def evaluate_metric(self, metric, max_n=None):\n", @@ -508,13 +557,13 @@ " \"\"\"\n", " ranks = []\n", " for i, item in enumerate(self.items, start=1):\n", - " if item not in self.positive_relations:\n", + " if item not in self.relations:\n", " continue\n", - " positive_items = list(self.positive_relations[item])\n", + " item_relations = list(self.relations[item])\n", " item_term = self.embedding.kv.index2word[item]\n", " item_distances = self.embedding.get_all_distances(item_term)\n", - " positive_item_ranks = self.get_positive_item_ranks(item_distances, positive_items)\n", - " ranks += positive_item_ranks\n", + " positive_relation_ranks = self.get_positive_relation_ranks(item_distances, item_relations)\n", + " ranks += positive_relation_ranks\n", " if max_n is not None and i > max_n:\n", " break\n", " return np.mean(ranks)\n", @@ -614,9 +663,16 @@ "### 4.2 WordNet link prediction" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.2.1 Preparing data" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -632,20 +688,39 @@ " Returns\n", " (train_file, test_file): tuple of strings with train file and test file paths\n", " \"\"\"\n", + " root_nodes, leaf_nodes = get_root_and_leaf_nodes(data_file)\n", + " test_line_candidates = []\n", + " line_count = 0\n", + " all_nodes = set()\n", " with open(data_file, 'rb') as f:\n", - " line_count = sum(1 for line in f)\n", + " for i, line in enumerate(f):\n", + " node_1, node_2 = line.split()\n", + " all_nodes.update([node_1, node_2])\n", + " if (\n", + " node_1 not in leaf_nodes\n", + " and node_2 not in leaf_nodes\n", + " and node_1 not in root_nodes\n", + " and node_2 not in root_nodes\n", + " ):\n", + " test_line_candidates.append(i)\n", + " line_count += 1\n", + "\n", " num_test_lines = int(test_ratio * line_count)\n", - " test_line_indices = set(random.sample(range(line_count), num_test_lines))\n", + " if num_test_lines > len(test_line_candidates):\n", + " raise ValueError('Not enough candidate relations for test set')\n", + " print('Choosing %d test lines from %d candidates' % (num_test_lines, len(test_line_candidates)))\n", + " test_line_indices = set(random.sample(test_line_candidates, num_test_lines))\n", " train_line_indices = set(l for l in range(line_count) if l not in test_line_indices)\n", " \n", " train_filename = data_file + '.train'\n", " test_filename = data_file + '.test'\n", - " \n", + " train_set_nodes = set()\n", " with open(data_file, 'rb') as f:\n", " train_file = open(train_filename, 'wb')\n", " test_file = open(test_filename, 'wb')\n", " for i, line in enumerate(f):\n", " if i in train_line_indices:\n", + " train_set_nodes.update(line.split())\n", " train_file.write(line)\n", " elif i in test_line_indices:\n", " test_file.write(line)\n", @@ -653,25 +728,403 @@ " raise AssertionError('Line %d not present in either train or test line indices' % i)\n", " train_file.close()\n", " test_file.close()\n", - "\n", + " assert len(train_set_nodes) == len(all_nodes), 'Not all nodes from dataset present in train set relations'\n", " return (train_filename, test_filename)" ] }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 69, + "metadata": {}, + "outputs": [], + "source": [ + "def get_root_and_leaf_nodes(data_file):\n", + " \"\"\"Return keys of root and leaf nodes from a file with transitive closure relations\n", + " \n", + " Args:\n", + " data_file(str): file path containing transitive closure relations\n", + " \n", + " Returns:\n", + " (root_nodes, leaf_nodes) - tuple containing keys of root and leaf nodes\n", + " \"\"\"\n", + " root_candidates = set()\n", + " leaf_candidates = set()\n", + " with open(data_file, 'rb') as f:\n", + " for line in f:\n", + " nodes = line.split()\n", + " root_candidates.update(nodes)\n", + " leaf_candidates.update(nodes)\n", + " \n", + " with open(data_file, 'rb') as f:\n", + " for line in f:\n", + " node_1, node_2 = line.split()\n", + " if node_1 == node_2:\n", + " continue\n", + " leaf_candidates.discard(node_1)\n", + " root_candidates.discard(node_2)\n", + " \n", + " return (leaf_candidates, root_candidates)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Choosing 74324 test lines from 126730 candidates\n" + ] + } + ], + "source": [ + "wordnet_train_file, wordnet_test_file = train_test_split(wordnet_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.2.2 Training and loading models" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "# Training models for link prediction\n", + "lp_model_files = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training model with size 5 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_5\n", + "Training model with size 10 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_10\n", + "Training model with size 20 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_20\n", + "Training model with size 50 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_50\n", + "Training model with size 100 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_100\n", + "Training model with size 5 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_5\n", + "Training model with size 10 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_10\n", + "Training model with size 20 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_20\n", + "Training model with size 50 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_50\n", + "Training model with size 100 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_100\n", + "Training model with size 5 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_5\n", + "Training model with size 10 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_10\n", + "Training model with size 20 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_20\n", + "Training model with size 50 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_50\n", + "Training model with size 100 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_100\n", + "Training model with size 5 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_5\n", + "Training model with size 10 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_10\n", + "Training model with size 20 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_20\n", + "Training model with size 50 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_50\n", + "Training model with size 100 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_100\n", + "Training model with size 5 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_5\n", + "Training model with size 10 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_10\n", + "Training model with size 20 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_20\n", + "Training model with size 50 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_50\n", + "Training model with size 100 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_100\n", + "Training model with size 5 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_5\n", + "Training model with size 10 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_10\n", + "Training model with size 20 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_20\n", + "Training model with size 50 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_50\n", + "Training model with size 100 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_100\n", + "Training model with size 5 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_5\n", + "Training model with size 10 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_10\n", + "Training model with size 20 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_20\n", + "Training model with size 50 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_50\n", + "Training model with size 100 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_100\n", + "Training model with size 5 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_5\n", + "Training model with size 10 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_10\n", + "Training model with size 20 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_20\n", + "Training model with size 50 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_50\n", + "Training model with size 100 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_100\n" + ] + } + ], + "source": [ + "# Possibly re-write with permutations instead of nested loops?\n", + "for epochs_ in epochs:\n", + " for threads_ in threads:\n", + " for neg_size in neg_sizes:\n", + " model_name = 'cpp_epochs_%d_threads_%d_neg_%d' % (epochs_, threads_, neg_size)\n", + " lp_model_files[model_name] = {}\n", + " for model_size in model_sizes:\n", + " output_file_name = '%s_dim_%d' % (model_name, model_size)\n", + " output_file = os.path.join(models_directory, output_file_name)\n", + " print('Training model with size %d neg %d threads %d epochs %d, saving to %s' %\n", + " (model_size, neg_size, threads_, epochs_, output_file))\n", + " out = train_cpp_model(\n", + " cpp_binary_path, wordnet_train_file, output_file,\n", + " model_size, epochs_, neg_size, threads_, seed=0)\n", + " lp_model_files[model_name][model_size] = output_file" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "lp_embeddings = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "for model_name, models in lp_model_files.items():\n", + " lp_embeddings[model_name] = {}\n", + " for model_size, model_file in models.items():\n", + " lp_embeddings[model_name][model_size] = PoincareEmbedding.load_poincare_cpp(model_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.2.3 Evaluating models" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "class LinkPredictionEvaluation(object):\n", + " \"\"\"Evaluating reconstruction on given network for given embedding\"\"\"\n", + " def __init__(self, train_path, test_path, embedding):\n", + " \"\"\"Initialize evaluation instance with tsv file containing relation pairs and embedding to be evaluated\n", + " \n", + " Args:\n", + " train_path (str): path to tsv file containing relation pairs used for training\n", + " test_path (str): path to tsv file containing relation pairs to evaluate\n", + " embedding (PoincareEmbedding instance): embedding to be evaluated\n", + " \n", + " Returns\n", + " ReconstructionEvaluation instance\n", + "\n", + " \"\"\"\n", + " items = set()\n", + " embedding_vocab = embedding.kv.vocab\n", + " relations = {'known': defaultdict(set), 'unknown': defaultdict(set)}\n", + " data_files = {'known': train_path, 'unknown': test_path}\n", + " for relation_type, data_file in data_files.items():\n", + " with smart_open(data_file, 'r') as f:\n", + " reader = csv.reader(f, delimiter='\\t')\n", + " for row in reader:\n", + " assert len(row) == 2, 'Hypernym pair has more than two items'\n", + " item_1_index = embedding_vocab[row[0]].index\n", + " item_2_index = embedding_vocab[row[1]].index\n", + " relations[relation_type][item_1_index].add(item_2_index)\n", + " items.update([item_1_index, item_2_index])\n", + " self.items = items\n", + " self.relations = relations\n", + " self.embedding = embedding\n", + " \n", + " \n", + " @staticmethod\n", + " def get_unknown_relation_ranks(distances, unknown_relations, known_relations):\n", + " \"\"\"\n", + " Given a numpy array of distances and indices of known and unknown positive relations,\n", + " compute ranks of unknown positive relations\n", + " \n", + " Args:\n", + " distances (numpy float array): np array of all distances for a specific item\n", + " unknown_relations (list): list of indices of unknown positive relations\n", + " known_relations (list): list of indices of known positive relations\n", + " \n", + " Returns:\n", + " list of ranks of unknown relations in the same order as `unknown_relations`\n", + " \"\"\"\n", + " unknown_relation_distances = distances[unknown_relations]\n", + " negative_relation_distances = np.ma.array(distances, mask=False)\n", + " negative_relation_distances.mask[unknown_relations] = True\n", + " negative_relation_distances.mask[known_relations] = True\n", + " # Compute how many negative relation distances are less than each unknown relation distance, plus 1 for rank\n", + " ranks = (negative_relation_distances < unknown_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n", + " return list(ranks) \n", + " \n", + " def evaluate_metric(self, metric, max_n=None):\n", + " \"\"\"Evaluate given metric for the reconstruction task\n", + " \n", + " Args:\n", + " metric (str): accepted values are 'mean_rank' and 'MAP'\n", + " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", + " \n", + " Returns:\n", + " Computed value of given metric (float)\n", + "\n", + " \"\"\"\n", + " if metric == 'mean_rank':\n", + " return self.evaluate_mean_rank(max_n)\n", + " elif metric == 'MAP':\n", + " return self.evaluate_map(max_n)\n", + " else:\n", + " raise ValueError('Invalid value for metric')\n", + "\n", + " def evaluate_mean_rank(self, max_n=None):\n", + " \"\"\"Evaluate mean rank and MAP for reconstruction\n", + " \n", + " Args:\n", + " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", + " \n", + " Returns:\n", + " Computed value of mean rank (float)\n", + "\n", + " \"\"\"\n", + " ranks = []\n", + " for i, item in enumerate(self.items, start=1):\n", + " if item not in self.relations['unknown']: # No positive relations to predict for this node\n", + " continue\n", + " unknown_relations = list(self.relations['unknown'][item])\n", + " known_relations = list(self.relations['known'][item])\n", + " item_term = self.embedding.kv.index2word[item]\n", + " item_distances = self.embedding.get_all_distances(item_term)\n", + " unknown_relation_ranks = self.get_unknown_relation_ranks(item_distances, unknown_relations, known_relations)\n", + " ranks += unknown_relation_ranks\n", + " if max_n is not None and i > max_n:\n", + " break\n", + " return np.mean(ranks)\n", + " \n", + " def evaluate_map(self, max_n=None):\n", + " \"\"\"Evaluate MAP (Mean Average Precision) for reconstruction\n", + " \n", + " Args:\n", + " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", + " \n", + " Returns:\n", + " Computed value of MAP (float)\n", + "\n", + " \"\"\"\n", + " raise NotImplementedError" + ] + }, + { + "cell_type": "code", + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ - "# On second thoughts, this might be trickier than anticipated - the test split cannot have root or leaf nodes\n", - "# Find root and leaf nodes and put them in train set first?" + "lp_results = {}\n", + "metrics = ['mean_rank']" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 20\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 10\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 100\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 50\n", + "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 5\n", + "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 20\n", + "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 10\n", + "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 100\n", + "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 50\n", + "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 5\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 20\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 10\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 100\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 50\n", + "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 5\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 20\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 10\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 100\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 50\n", + "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 5\n", + "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 20\n", + "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 10\n", + "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 100\n", + "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 50\n", + "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 5\n", + "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 20\n", + "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 10\n", + "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 100\n", + "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 50\n", + "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 5\n", + "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 20\n", + "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 10\n", + "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 100\n", + "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 50\n", + "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 5\n", + "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 20\n", + "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 10\n", + "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 100\n", + "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 50\n", + "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 5\n" + ] + } + ], + "source": [ + "for model_name, models in lp_embeddings.items():\n", + " lp_results[model_name] = {}\n", + " for metric in metrics:\n", + " lp_results[model_name][metric] = {}\n", + " for model_size, embedding in models.items():\n", + " print('Evaluating model %s of size %d' % (model_name, model_size))\n", + " eval_instance = LinkPredictionEvaluation(wordnet_train_file, wordnet_test_file, embedding)\n", + " for metric in metrics:\n", + " lp_results[model_name][metric][model_size] = eval_instance.evaluate_metric(metric, max_n=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for WordNet Link Prediction task\n", + "+--------------------------------------------+---------------------------------+\n", + "| | Model Dimensions |\n", + "+---------------------------------+-----------+--------+--------+-------+-------+-------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 |\n", + "+---------------------------------+-----------+--------+--------+-------+-------+-------+\n", + "| cpp_epochs_50_threads_8_neg_20 | mean_rank | 197.63 | 85.96 | 63.21 | 51.94 | 58.72 |\n", + "| cpp_epochs_50_threads_1_neg_20 | mean_rank | 191.36 | 92.09 | 53.68 | 47.57 | 46.26 |\n", + "| cpp_epochs_50_threads_1_neg_10 | mean_rank | 232.96 | 87.99 | 62.41 | 46.84 | 47.23 |\n", + "| cpp_epochs_50_threads_8_neg_10 | mean_rank | 208.80 | 104.13 | 77.91 | 63.97 | 75.42 |\n", + "| cpp_epochs_100_threads_8_neg_10 | mean_rank | 202.66 | 99.25 | 77.94 | 67.56 | 66.49 |\n", + "| cpp_epochs_100_threads_1_neg_20 | mean_rank | 147.81 | 79.82 | 48.12 | 42.29 | 41.96 |\n", + "| cpp_epochs_100_threads_8_neg_20 | mean_rank | 160.67 | 105.16 | 70.21 | 56.91 | 46.13 |\n", + "| cpp_epochs_100_threads_1_neg_10 | mean_rank | 157.65 | 72.03 | 50.60 | 39.28 | 39.18 |\n", + "+---------------------------------+-----------+--------+--------+-------+-------+-------+\n" + ] + } + ], + "source": [ + "display_results('WordNet Link Prediction', lp_results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4.3 HyperLex lexical entailment" + "### 4.3 HyperLex Lexical Entailment" ] }, { @@ -730,7 +1183,7 @@ " embedding (PoincareEmbedding instance): embedding for which evaluation is to be done\n", " \n", " Returns:\n", - " ??\n", + " spearman correlation score (float)\n", "\n", " \"\"\"\n", " predicted_scores = []\n", From efd1fe015e3fab9ba1894650fb48824df1cfe0bc Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 18 Oct 2017 13:12:19 +0200 Subject: [PATCH 07/28] Adds patch for C++ poincare implementation --- .../poincare_data/poincare_burn_in_eps.patch | 197 ++++++++++++++++++ 1 file changed, 197 insertions(+) create mode 100644 docs/notebooks/poincare_data/poincare_burn_in_eps.patch diff --git a/docs/notebooks/poincare_data/poincare_burn_in_eps.patch b/docs/notebooks/poincare_data/poincare_burn_in_eps.patch new file mode 100644 index 0000000000..7464919209 --- /dev/null +++ b/docs/notebooks/poincare_data/poincare_burn_in_eps.patch @@ -0,0 +1,197 @@ +diff --git a/include/poincare_embedding.hpp b/include/poincare_embedding.hpp +index f1f1c5f..e1d68cc 100644 +--- a/include/poincare_embedding.hpp ++++ b/include/poincare_embedding.hpp +@@ -22,7 +22,7 @@ + + namespace poincare_disc{ + +- constexpr float EPS = 1e-6; ++ // constexpr float EPS = 1e-6; + + /////////////////////////////////////////////////////////////////////////////////////////// + // Initializer +@@ -114,11 +114,12 @@ namespace poincare_disc{ + return *this; + } + +- Vector& add_clip_(const real c, const Vector& v, const real thresh=1.0-EPS) ++ Vector& add_clip_(const real c, const Vector& v, const real eps) + { + real uu = this->squared_sum(), uv = this->dot(v), vv = v.squared_sum(); + real C = uu + 2*c*uv + c*c*vv; // resulting norm + real scale = 1.0; ++ real thresh = 1.0 - eps; + if(C > thresh * thresh){ + scale = thresh / sqrt(C); + } +@@ -132,7 +133,7 @@ namespace poincare_disc{ + data_.get()[i] = (data_.get()[i] + c * v.data_.get()[i]) * scale; + } + } +- assert(this->squared_sum() <= (thresh + EPS) * (thresh+EPS)); ++ assert(this->squared_sum() <= (thresh + eps) * (thresh+eps)); + return *this; + } + +@@ -251,7 +252,7 @@ namespace poincare_disc{ + using real = RealType; + public: + Distance(): u_(), v_(), uu_(), vv_(), uv_(), alpha_(), beta_(), gamma_() {} +- real operator()(const Vector& u, const Vector& v) ++ real operator()(const Vector& u, const Vector& v, real eps) + { + u_ = u; + v_ = v; +@@ -259,11 +260,11 @@ namespace poincare_disc{ + vv_ = v_.squared_sum(); + uv_ = u_.dot(v_); + alpha_ = 1 - uu_; +- if(alpha_ <= 0){ alpha_ = EPS; } // TODO: ensure 0 <= uu_ <= 1-EPS; ++ if(alpha_ <= 0){ alpha_ = eps; } // TODO: ensure 0 <= uu_ <= 1-eps; + // if(!(alpha_ > 0)){ std::cout << "uu_: " << uu_ << ", alpha_: " << alpha_ << std::endl; } + // assert(alpha_ > 0); + beta_ = 1 - vv_; +- if(beta_ <= 0){ beta_ = EPS; } // TODO: ensure 0 <= vv_ <= 1-EPS; ++ if(beta_ <= 0){ beta_ = eps; } // TODO: ensure 0 <= vv_ <= 1-eps; + // if(!(beta_ > 0)){ std::cout << "vv_: " << vv_ << ", beta_: " << beta_ << std::endl; } + // assert(beta_ > 0); + gamma_ = 1 + 2 * (uu_ - 2 * uv_ + vv_) / alpha_ / beta_; +@@ -406,11 +407,14 @@ namespace poincare_disc{ + char delim = '\t'; + real lr0 = 0.01; // learning rate + real lr1 = 0.0001; // learning rate ++ real eps = 1e-6; // epsilon for numerical stability in clipping ++ std::size_t burn_in = 0; // whether to use burn-in for initialization + }; + + template +- void clip(Vector& v, const RealType& thresh = 1-EPS) ++ void clip(Vector& v, const RealType& eps) + { ++ RealType thresh = 1.0 - eps; + RealType vv = v.squared_sum(); + if(vv >= thresh*thresh){ + v.mult_(thresh / std::sqrt(vv)); +@@ -500,7 +504,7 @@ namespace poincare_disc{ + + // clip + for(std::size_t i = 0, I = embeddings.nrow(); i < I; ++i){ +- clip(embeddings[i]); ++ clip(embeddings[i], config.eps); + } + + // construct negative sampler +@@ -551,7 +555,7 @@ namespace poincare_disc{ + auto i = left_indices[0] = itr->first; + auto j = right_indices[0] = itr->second; + +- exp_neg_dist_values[0] = std::exp(-dists[0](embeddings[i], embeddings[j])); ++ exp_neg_dist_values[0] = std::exp(-dists[0](embeddings[i], embeddings[j], config.eps)); + for(std::size_t k = 0; k < config.neg_size; ++k){ + #if SAMPLING_STRATEGY == LEFT_SAMPLING + auto i = left_indices[k + 1] = negative_sampler(); +@@ -563,7 +567,7 @@ namespace poincare_disc{ + auto i = left_indices[k + 1] = negative_sampler(); + auto j = right_indices[k + 1] = negative_sampler(); + #endif +- exp_neg_dist_values[k + 1] = std::exp(-dists[k + 1](embeddings[i], embeddings[j])); ++ exp_neg_dist_values[k + 1] = std::exp(-dists[k + 1](embeddings[i], embeddings[j], config.eps)); + } + + // compute gradient +@@ -589,8 +593,8 @@ namespace poincare_disc{ + // update + for(std::size_t k = 0; k < 1 + config.neg_size; ++k){ + auto i = left_indices[k], j = right_indices[k]; +- embeddings[i].add_clip_(-lr(), left_grads[k]); +- embeddings[j].add_clip_(-lr(), right_grads[k]); ++ embeddings[i].add_clip_(-lr(), left_grads[k], config.eps); ++ embeddings[j].add_clip_(-lr(), right_grads[k], config.eps); + } + + lr.update(); +@@ -634,6 +638,22 @@ namespace poincare_disc{ + + std::cout << "embedding size: " << embeddings.nrow() << " x " << embeddings.ncol() << std::endl; + ++ if(config.burn_in > 0){ ++ std::cout << "burn in start" << std::endl; ++ // burn in ++ LinearLearningRate lr_init(config.lr0 / 10., config.lr1 / 10., data_size * config.burn_in); ++ for(std::size_t epoch = 0; epoch < config.burn_in; ++epoch){ ++ std::cout << "epoch " << epoch+1 << "/" << config.max_epoch << " start" << std::endl; ++ // std::cout << "random shuffle data" << std::endl; ++ std::random_shuffle(data.begin(), data.end()); ++ // single thread ++ const unsigned int thread_seed = engine(); ++ train_thread(embeddings, dict.counts(), data.begin(), data.end(), config, lr_init, 0, thread_seed); ++ } ++ ++ std::cout << "burn in end" << std::endl; ++ } ++ + // fit + LinearLearningRate lr(config.lr0, config.lr1, data_size * config.max_epoch); + std::vector > fake_pairs(config.neg_size); +diff --git a/src/poincare_embedding.cpp b/src/poincare_embedding.cpp +index 7eb7293..a67626e 100644 +--- a/src/poincare_embedding.cpp ++++ b/src/poincare_embedding.cpp +@@ -35,6 +35,8 @@ struct Arguments + real uniform_range = 0.001; + real lr0 = 0.01; + real lr1 = 0.0001; ++ real eps = 1e-6; // epsilon for numerical stability in clipping ++ std::size_t burn_in = 0; // number of epochs for burn-in initialization + }; + + Arguments parse_args(int narg, char** argv) +@@ -92,7 +94,20 @@ Arguments parse_args(int narg, char** argv) + if( x <= 0 ){ goto HELP; } + result.uniform_range = static_cast(x); + continue; +- }else if(arg == "-h" || arg == "--help"){ ++ }else if(arg == "-E" || arg == "--epsilon"){ ++ arg = argv[++i]; ++ double x = std::stod(arg); ++ if( x <= 0 ){ goto HELP; } ++ result.eps = static_cast(x); ++ continue; ++ }else if(arg == "-b" || arg == "--burn_in"){ ++ arg = argv[++i]; ++ int n = std::stoi(arg); ++ if( n < 0 ){ goto HELP; } ++ result.burn_in = static_cast(n); ++ continue; ++ } ++ else if(arg == "-h" || arg == "--help"){ + goto HELP; + } + +@@ -134,6 +149,8 @@ Arguments parse_args(int narg, char** argv) + " -u, --uniform_range : float > 0 embedding uniform initializer range\n" + " -l, --learning_rate_init : float > 0 initial learning rate\n" + " -L, --learning_rate_final : float > 0 final learning rate\n" ++ " -E, --epsilon : float > 0 epsilon for clipping vectors\n" ++ " -b, --burn_in : int >= 0 number of epochs for burn-in (0 => no burn-in)\n" + << std::endl; + exit(0); + } +@@ -159,6 +176,8 @@ int main(int narg, char** argv) + config.dim = args.dim; + config.lr0 = args.lr0; + config.lr1 = args.lr1; ++ config.eps = args.eps; ++ config.burn_in = args.burn_in; + config.initializer = UniformInitializer(-args.uniform_range, args.uniform_range); + + std::cout << "settings:" << "\n" +@@ -172,6 +191,7 @@ int main(int narg, char** argv) + << " " << "lr0 : " << config.lr0 << "\n" + << " " << "lr1 : " << config.lr1 << "\n" + << " " << "uniform_range : " << args.uniform_range << "\n" ++ << " " << "epsilon : " << args.eps << "\n" + << std::endl; + + From c062814490f5163201c881a4dd3e459deee12f07 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 18 Oct 2017 23:25:30 +0200 Subject: [PATCH 08/28] Adds patch file to setup and all results --- docs/notebooks/Poincare Evaluation.ipynb | 691 +++++++++++++---------- 1 file changed, 403 insertions(+), 288 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 850185ddcd..8cfa5de8ca 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -26,26 +26,111 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Tables" + "## 1. Setup\n", + "\n", + "The following code clones the `poincare-embedding` repository containing the C++ implementation of the Poincare embeddings, and applies a patch containing minor additions to the implementation. Please set the variable `parent_directory` below to define the directory to which the repository is cloned." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 1, "metadata": {}, + "outputs": [], "source": [ - "## 1. Setup\n", - "\n", - "Clone the [poincare-embedding](https://github.com/TatsuyaShirakawa/poincare-embedding) repository and follow the README to compile the sources into a binary. Set the variable below to the directory containing the `poincare-embedding` directory." + "# Set this to the path of the directory that is to contain the poincare-embedding directory\n", + "parent_directory = '/home/jayant/projects/'" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# Set this to the path of the directory containing the poincare-embedding directory\n", - "parent_directory = '/home/jayant/projects/'" + "import os\n", + "current_directory = os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jayant/projects\n", + "Cloning into 'poincare-embedding'...\n", + "remote: Counting objects: 96, done.\u001b[K\n", + "remote: Total 96 (delta 0), reused 0 (delta 0), pack-reused 96\u001b[K\n", + "Unpacking objects: 100% (96/96), done.\n", + "Checking connectivity... done.\n", + "/home/jayant/projects/poincare-embedding\n" + ] + } + ], + "source": [ + "# Clone repo\n", + "% cd {parent_directory}\n", + "repo_name = 'poincare-embedding'\n", + "! git clone https://github.com/TatsuyaShirakawa/poincare-embedding.git {repo_name}\n", + "% cd {repo_name}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2017-10-18 20:37:11-- https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_eval/docs/notebooks/poincare_data/poincare_burn_in_eps.patch\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.112.133\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.112.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 7999 (7.8K) [text/plain]\n", + "Saving to: ‘./poincare_burn_in_eps.patch’\n", + "\n", + "./poincare_burn_in_ 100%[===================>] 7.81K --.-KB/s in 0s \n", + "\n", + "2017-10-18 20:37:11 (59.5 MB/s) - ‘./poincare_burn_in_eps.patch’ saved [7999/7999]\n", + "\n" + ] + } + ], + "source": [ + "# Download and apply patch\n", + "patch_url = \"https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_eval/docs/notebooks/poincare_data/poincare_burn_in_eps.patch\"\n", + "! wget {patch_url} -O ./poincare_burn_in_eps.patch\n", + "! git apply poincare_burn_in_eps.patch\n", + "! rm poincare_burn_in_eps.patch" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jayant/projects/gensim/docs/notebooks\n" + ] + } + ], + "source": [ + "% cd {current_directory}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Follow the instructions in the README to compile the sources and create the binaries." ] }, { @@ -59,12 +144,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "import os\n", - "\n", "# These directories are auto created in the current directory for storing poincare datasets and models\n", "data_directory = 'poincare_data'\n", "models_directory = os.path.join(data_directory, 'models')\n", @@ -76,25 +159,68 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82115 nouns\n", + "743241 hypernyms\n" + ] + } + ], "source": [ - "# Create the WordNet data\n", + "# Prepare the WordNet data\n", "wordnet_file = os.path.join(data_directory, 'wordnet_noun_hypernyms.tsv')\n", - "! python {parent_directory}/poincare-embedding/scripts/create_wordnet_noun_hierarchy.py {wordnet_data_file}" + "! python {parent_directory}/poincare-embedding/scripts/create_wordnet_noun_hierarchy.py {wordnet_file}" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 23, "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2017-10-18 20:44:13-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", + "Resolving people.ds.cam.ac.uk (people.ds.cam.ac.uk)... 131.111.3.47\n", + "Connecting to people.ds.cam.ac.uk (people.ds.cam.ac.uk)|131.111.3.47|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 183900 (180K) [application/zip]\n", + "Saving to: ‘poincare_data/hyperlex-data.zip’\n", + "\n", + "poincare_data/hyper 100%[===================>] 179.59K --.-KB/s in 0.06s \n", + "\n", + "2017-10-18 20:44:13 (2.75 MB/s) - ‘poincare_data/hyperlex-data.zip’ saved [183900/183900]\n", + "\n", + "Archive: poincare_data/hyperlex-data.zip\n", + " creating: poincare_data/nouns-verbs/\n", + " inflating: poincare_data/nouns-verbs/hyperlex-verbs.txt \n", + " inflating: poincare_data/nouns-verbs/hyperlex-nouns.txt \n", + " creating: poincare_data/splits/\n", + " creating: poincare_data/splits/random/\n", + " inflating: poincare_data/splits/random/hyperlex_training_all_random.txt \n", + " inflating: poincare_data/splits/random/hyperlex_test_all_random.txt \n", + " inflating: poincare_data/splits/random/hyperlex_dev_all_random.txt \n", + " creating: poincare_data/splits/lexical/\n", + " inflating: poincare_data/splits/lexical/hyperlex_dev_all_lexical.txt \n", + " inflating: poincare_data/splits/lexical/hyperlex_test_all_lexical.txt \n", + " inflating: poincare_data/splits/lexical/hyperlex_training_all_lexical.txt \n", + " inflating: poincare_data/hyperlex-all.txt \n", + " inflating: poincare_data/README.txt \n" + ] + } + ], "source": [ + "# Prepare the HyperLex data\n", "hyperlex_url = \"http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\"\n", - "! wget {hyperlex_url} -P {data_directory}\n", + "! wget {hyperlex_url} -O {data_directory}/hyperlex-data.zip\n", "! unzip {data_directory}/hyperlex-data.zip -d {data_directory}\n", "hyperlex_file = os.path.join(data_directory, 'nouns-verbs', 'hyperlex-nouns.txt')" ] @@ -103,29 +229,46 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2.2 Traing C++ embeddings" + "### 2.2 Training C++ embeddings" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "from gensim.utils import check_output\n", "\n", - "def train_cpp_model(binary_path, data_file, output_file, dim, epochs, neg, num_threads, seed=0):\n", + "def train_cpp_model(\n", + " binary_path, data_file, output_file, dim, epochs, neg,\n", + " num_threads, epsilon, burn_in, seed=0):\n", " \"\"\"Train a poincare embedding using the c++ implementation\n", " \n", " Args:\n", " binary_path (str): Path to the compiled c++ implementation binary\n", - " \n", + " data_file (str): Path to tsv file containing relation pairs\n", + " output_file (str): Path to output file containing model\n", + " dim (int): Number of dimensions of the trained model\n", + " epochs (int): Number of epochs to use\n", + " neg (int): Number of negative samples to use\n", + " num_threads (int): Number of threads to use for training the model\n", + " epsilon (float): Constant used for clipping below a norm of one\n", + " burn_in (int): Number of epochs to use for burn-in init (0 means no burn-in)\n", + " \n", + " Notes: \n", + " If `output_file` already exists, skips training\n", " \"\"\"\n", + " if os.path.exists(output_file):\n", + " print('File %s exists, skipping' % output_file)\n", + " return\n", " args = {\n", " 'dim': dim,\n", " 'max_epoch': epochs,\n", " 'neg_size': neg,\n", " 'num_thread': num_threads,\n", + " 'epsilon': epsilon,\n", + " 'burn_in': burn_in,\n", " 'learning_rate_init': 0.1,\n", " 'learning_rate_final': 0.0001,\n", " }\n", @@ -139,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -148,19 +291,31 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "model_sizes = [5, 10, 20, 50, 100]\n", - "neg_sizes = [10, 20]\n", - "epochs = [50, 100]\n", - "threads = [8, 1]" + "model_sizes = [5, 10, 20, 50, 100, 200]\n", + "default_params = {\n", + " 'neg': 20,\n", + " 'epochs': 50,\n", + " 'threads': 8,\n", + " 'eps': 1e-6,\n", + " 'burn_in': 0,\n", + "}\n", + "\n", + "non_default_params = {\n", + " 'neg': [10],\n", + " 'epochs': [100, 200],\n", + " 'threads': [1],\n", + " 'eps': [1e-5],\n", + " 'burn_in': [5, 10]\n", + "}" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -169,74 +324,66 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 28, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training model with size 5 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_5\n", - "Training model with size 10 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_10\n", - "Training model with size 20 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_20\n", - "Training model with size 50 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_50\n", - "Training model with size 100 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_100\n", - "Training model with size 5 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_5\n", - "Training model with size 10 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_10\n", - "Training model with size 20 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_20\n", - "Training model with size 50 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_50\n", - "Training model with size 100 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_100\n", - "Training model with size 5 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_5\n", - "Training model with size 10 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_10\n", - "Training model with size 20 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_20\n", - "Training model with size 50 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_50\n", - "Training model with size 100 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_100\n", - "Training model with size 5 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_5\n", - "Training model with size 10 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_10\n", - "Training model with size 20 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_20\n", - "Training model with size 50 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_50\n", - "Training model with size 100 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_100\n", - "Training model with size 5 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_5\n", - "Training model with size 10 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_10\n", - "Training model with size 20 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_20\n", - "Training model with size 50 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_50\n", - "Training model with size 100 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_100\n", - "Training model with size 5 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_5\n", - "Training model with size 10 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_10\n", - "Training model with size 20 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_20\n", - "Training model with size 50 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_50\n", - "Training model with size 100 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_100\n", - "Training model with size 5 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_5\n", - "Training model with size 10 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_10\n", - "Training model with size 20 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_20\n", - "Training model with size 50 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_50\n", - "Training model with size 100 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_100\n", - "Training model with size 5 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_5\n", - "Training model with size 10 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_10\n", - "Training model with size 20 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_20\n", - "Training model with size 50 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_50\n", - "Training model with size 100 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_100\n" - ] - } - ], + "outputs": [], + "source": [ + "def model_name_from_params(params, prefix):\n", + " name = ['%s_%s' % (key, params[key]) for key in sorted(params.keys())]\n", + " return '%s_%s' % (prefix, '_'.join(name))\n", + "\n", + "\n", + "def train_model_with_params(params, train_file, model_sizes, prefix):\n", + " \"\"\"Trains models with given params for multiple model sizes using the C++ implementation\n", + " \n", + " Args:\n", + " params (dict): parameters to train the model with, passed to `train_cpp_model`\n", + " train_file (str): Path to tsv file containing relation pairs\n", + " model_sizes (list): list of dimension sizes (integer) to train the model with\n", + " prefix (str): prefix to use for the saved model filenames\n", + " Returns:\n", + " tuple (model_name, model_files)\n", + " model_files is a dict of (size, filename) pairs\n", + " Example: ('cpp_model_epochs_50', {5: 'models/cpp_model_epochs_50_dim_5'})\n", + " \"\"\"\n", + " files = {}\n", + " model_name = model_name_from_params(params, prefix)\n", + " for model_size in model_sizes:\n", + " output_file_name = '%s_dim_%d' % (model_name, model_size)\n", + " output_file = os.path.join(models_directory, output_file_name)\n", + " print('Training model %s' % output_file)\n", + " out = train_cpp_model(\n", + " cpp_binary_path, train_file, output_file, model_size,\n", + " params['epochs'], params['neg'], params['threads'],\n", + " params['eps'], params['burn_in'], seed=0)\n", + " files[model_size] = output_file\n", + " return (model_name, files)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ - "# Possibly re-write with permutations instead of nested loops?\n", - "for epochs_ in epochs:\n", - " for threads_ in threads:\n", - " for neg_size in neg_sizes:\n", - " model_name = 'cpp_epochs_%d_threads_%d_neg_%d' % (epochs_, threads_, neg_size)\n", - " model_files[model_name] = {}\n", - " for model_size in model_sizes:\n", - " output_file_name = '%s_dim_%d' % (model_name, model_size)\n", - " output_file = os.path.join(models_directory, output_file_name)\n", - " print('Training model with size %d neg %d threads %d epochs %d, saving to %s' %\n", - " (model_size, neg_size, threads_, epochs_, output_file))\n", - " out = train_cpp_model(\n", - " cpp_binary_path, wordnet_data_file, output_file,\n", - " model_size, epochs_, neg_size, threads_, seed=0)\n", - " model_files[model_name][model_size] = output_file" + "# Train models with default params\n", + "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'cpp_model')\n", + "model_files[model_name] = {}\n", + "for dim, filepath in files.items():\n", + " model_files[model_name][dim] = filepath\n", + "# Train models with non-default params\n", + "for param, values in non_default_params.items():\n", + " params = default_params.copy()\n", + " for value in values:\n", + " params[param] = value\n", + " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'cpp_model')\n", + " model_files[model_name] = {}\n", + " for dim, filepath in files.items():\n", + " model_files[model_name][dim] = filepath" ] }, { @@ -248,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -264,11 +411,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "import os\n", "import pickle\n", "import re\n", "\n", @@ -398,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -425,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -439,10 +585,6 @@ " results (dict): mapping between embeddings and corresponding results\n", " \n", " \"\"\"\n", - " header = PrettyTable()\n", - " # TODO: infer widths from table rather than hard-coding\n", - " header.field_names = [\" \" * 42, \" \" * 7 + \"Model Dimensions\" + \" \" * 8]\n", - "\n", " data = PrettyTable()\n", " data.field_names = [\"Model Description\", \"Metric\"] + [str(dim) for dim in sorted(model_sizes)]\n", " for model_name, model_results in results.items():\n", @@ -453,7 +595,20 @@ " scores = ['%.2f' % model_results[metric][dim] for metric in metrics]\n", " row.append('\\n'.join(scores))\n", " data.add_row(row)\n", + "\n", + " data_cols = data.get_string().split('\\n')[0].split('+')[1:-1]\n", + " col_lengths = [len(col) for col in data_cols]\n", + " header_col_1_length = col_lengths[0] + col_lengths[1] - 1\n", + " header_col_2_length = sum(col_lengths[2:]) + len(col_lengths[2:-1]) - 2\n", " \n", + " header_col_2_content = \"Model Dimensions\"\n", + " header_col_2_left_margin = (header_col_2_length - len(header_col_2_content)) // 2\n", + " header_col_2_right_margin = header_col_2_length - len(header_col_2_content) - header_col_2_left_margin\n", + " header_col_2_string = \"%s%s%s\" % (\n", + " \" \" * header_col_2_left_margin, header_col_2_content, \" \" * header_col_2_right_margin)\n", + "\n", + " header = PrettyTable()\n", + " header.field_names = [\" \" * header_col_1_length, header_col_2_string]\n", " header_lines = header.get_string(start=0, end=0).split(\"\\n\")[:2]\n", " print('Results for %s task' % task_name)\n", " print(\"\\n\".join(header_lines))\n", @@ -469,7 +624,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -583,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -593,31 +748,11 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 20\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 10\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 100\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 50\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 5\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 20\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 10\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 100\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 50\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 5\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 20\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 10\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 100\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 50\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 5\n" - ] - } - ], + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "for model_name, models in embeddings.items():\n", " reconstruction_results[model_name] = {}\n", @@ -632,7 +767,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -640,15 +775,20 @@ "output_type": "stream", "text": [ "Results for WordNet Reconstruction task\n", - "+--------------------------------------------+---------------------------------+\n", - "| | Model Dimensions |\n", - "+--------------------------------+-----------+--------+--------+-------+-------+-------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 |\n", - "+--------------------------------+-----------+--------+--------+-------+-------+-------+\n", - "| cpp_epochs_50_threads_8_neg_10 | mean_rank | 268.44 | 129.66 | 86.78 | 76.58 | 71.15 |\n", - "| cpp_epochs_50_threads_8_neg_20 | mean_rank | 251.71 | 145.56 | 96.18 | 72.44 | 57.26 |\n", - "| cpp_epochs_50_threads_1_neg_10 | mean_rank | 325.21 | 107.59 | 71.23 | 61.48 | 60.01 |\n", - "+--------------------------------+-----------+--------+--------+-------+-------+-------+\n" + "+-----------------------------------------------------------------------+-----------------------------------------------------+\n", + "| | Model Dimensions |\n", + "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 308.69 | 101.92 | 73.20 | 56.17 | 52.97 | 51.37 |\n", + "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", + "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 228.70 | 112.40 | 81.00 | 64.81 | 57.15 | 70.87 |\n", + "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 227.69 | 175.64 | 171.60 | 144.47 | 146.84 | 139.28 |\n", + "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 245.48 | 104.66 | 88.33 | 65.40 | 74.88 | 55.66 |\n", + "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" ] } ], @@ -672,7 +812,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -701,6 +841,7 @@ " and node_2 not in leaf_nodes\n", " and node_1 not in root_nodes\n", " and node_2 not in root_nodes\n", + " and node_1 != node_2\n", " ):\n", " test_line_candidates.append(i)\n", " line_count += 1\n", @@ -734,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -768,14 +909,14 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Choosing 74324 test lines from 126730 candidates\n" + "Choosing 74324 test lines from 109577 candidates\n" ] } ], @@ -792,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -802,74 +943,24 @@ }, { "cell_type": "code", - "execution_count": 88, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training model with size 5 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_5\n", - "Training model with size 10 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_10\n", - "Training model with size 20 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_20\n", - "Training model with size 50 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_50\n", - "Training model with size 100 neg 10 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_10_dim_100\n", - "Training model with size 5 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_5\n", - "Training model with size 10 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_10\n", - "Training model with size 20 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_20\n", - "Training model with size 50 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_50\n", - "Training model with size 100 neg 20 threads 8 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_8_neg_20_dim_100\n", - "Training model with size 5 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_5\n", - "Training model with size 10 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_10\n", - "Training model with size 20 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_20\n", - "Training model with size 50 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_50\n", - "Training model with size 100 neg 10 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_10_dim_100\n", - "Training model with size 5 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_5\n", - "Training model with size 10 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_10\n", - "Training model with size 20 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_20\n", - "Training model with size 50 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_50\n", - "Training model with size 100 neg 20 threads 1 epochs 50, saving to poincare_data/models/cpp_epochs_50_threads_1_neg_20_dim_100\n", - "Training model with size 5 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_5\n", - "Training model with size 10 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_10\n", - "Training model with size 20 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_20\n", - "Training model with size 50 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_50\n", - "Training model with size 100 neg 10 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_10_dim_100\n", - "Training model with size 5 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_5\n", - "Training model with size 10 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_10\n", - "Training model with size 20 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_20\n", - "Training model with size 50 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_50\n", - "Training model with size 100 neg 20 threads 8 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_8_neg_20_dim_100\n", - "Training model with size 5 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_5\n", - "Training model with size 10 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_10\n", - "Training model with size 20 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_20\n", - "Training model with size 50 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_50\n", - "Training model with size 100 neg 10 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_10_dim_100\n", - "Training model with size 5 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_5\n", - "Training model with size 10 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_10\n", - "Training model with size 20 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_20\n", - "Training model with size 50 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_50\n", - "Training model with size 100 neg 20 threads 1 epochs 100, saving to poincare_data/models/cpp_epochs_100_threads_1_neg_20_dim_100\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "# Possibly re-write with permutations instead of nested loops?\n", - "for epochs_ in epochs:\n", - " for threads_ in threads:\n", - " for neg_size in neg_sizes:\n", - " model_name = 'cpp_epochs_%d_threads_%d_neg_%d' % (epochs_, threads_, neg_size)\n", - " lp_model_files[model_name] = {}\n", - " for model_size in model_sizes:\n", - " output_file_name = '%s_dim_%d' % (model_name, model_size)\n", - " output_file = os.path.join(models_directory, output_file_name)\n", - " print('Training model with size %d neg %d threads %d epochs %d, saving to %s' %\n", - " (model_size, neg_size, threads_, epochs_, output_file))\n", - " out = train_cpp_model(\n", - " cpp_binary_path, wordnet_train_file, output_file,\n", - " model_size, epochs_, neg_size, threads_, seed=0)\n", - " lp_model_files[model_name][model_size] = output_file" + "# Train models with default params\n", + "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'cpp_lp_model')\n", + "model_files[model_name] = {}\n", + "for dim, filepath in files.items():\n", + " lp_model_files[model_name][dim] = filepath\n", + "# Train models with non-default params\n", + "for param, values in non_default_params.items():\n", + " params = default_params.copy()\n", + " for value in values:\n", + " params[param] = value\n", + " model_name, files = train_model_with_params(params, wordnet_train_file, model_sizes, 'cpp_lp_model')\n", + " lp_model_files[model_name] = {}\n", + " for dim, filepath in files.items():\n", + " lp_model_files[model_name][dim] = filepath" ] }, { @@ -1027,56 +1118,9 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 20\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 10\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 100\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 50\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 5\n", - "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 20\n", - "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 10\n", - "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 100\n", - "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 50\n", - "Evaluating model cpp_epochs_50_threads_1_neg_20 of size 5\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 20\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 10\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 100\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 50\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 5\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 20\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 10\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 100\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 50\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 5\n", - "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 20\n", - "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 10\n", - "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 100\n", - "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 50\n", - "Evaluating model cpp_epochs_100_threads_8_neg_10 of size 5\n", - "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 20\n", - "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 10\n", - "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 100\n", - "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 50\n", - "Evaluating model cpp_epochs_100_threads_1_neg_20 of size 5\n", - "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 20\n", - "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 10\n", - "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 100\n", - "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 50\n", - "Evaluating model cpp_epochs_100_threads_8_neg_20 of size 5\n", - "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 20\n", - "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 10\n", - "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 100\n", - "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 50\n", - "Evaluating model cpp_epochs_100_threads_1_neg_10 of size 5\n" - ] - } - ], + "outputs": [], "source": [ "for model_name, models in lp_embeddings.items():\n", " lp_results[model_name] = {}\n", @@ -1129,7 +1173,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -1206,7 +1250,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -1216,42 +1260,108 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 91, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 20\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 50\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 100\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 5\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 200\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 20\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 10\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 50\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 100\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 5\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 200\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 20\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 10\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 50\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 10\n", + "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 100\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 100\n", + "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 5\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 50\n", + "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 200\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_8_neg_10 of size 5\n", + "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 20\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 20\n", + "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 10\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 10\n", + "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 50\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 100\n", + "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 100\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 50\n", + "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 5\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_8_neg_20 of size 5\n", + "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 200\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 20\n", + "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 20\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 10\n", + "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 10\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 100\n", + "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 50\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 50\n", + "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 100\n", "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_epochs_50_threads_1_neg_10 of size 5\n", + "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 5\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 200\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 20\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 10\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 50\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 100\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 5\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 200\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 20\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 10\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 50\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 100\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 5\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 200\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 20\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 10\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 50\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 100\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 5\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 200\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 20\n", + "Skipped pairs: 182 out of 2163\n", + "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 10\n", "Skipped pairs: 182 out of 2163\n" ] } @@ -1267,7 +1377,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -1275,15 +1385,20 @@ "output_type": "stream", "text": [ "Results for Lexical Entailment (HyperLex) task\n", - "+--------------------------------------------+---------------------------------+\n", - "| | Model Dimensions |\n", - "+--------------------------------+----------+------+------+------+------+------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 |\n", - "+--------------------------------+----------+------+------+------+------+------+\n", - "| cpp_epochs_50_threads_8_neg_10 | spearman | 0.44 | 0.43 | 0.44 | 0.44 | 0.44 |\n", - "| cpp_epochs_50_threads_8_neg_20 | spearman | 0.47 | 0.45 | 0.46 | 0.46 | 0.46 |\n", - "| cpp_epochs_50_threads_1_neg_10 | spearman | 0.45 | 0.48 | 0.47 | 0.47 | 0.47 |\n", - "+--------------------------------+----------+------+------+------+------+------+\n" + "+----------------------------------------------------------------------+-----------------------------------------+\n", + "| | Model Dimensions |\n", + "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | spearman | 0.43 | 0.44 | 0.44 | 0.44 | 0.45 | 0.45 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | spearman | 0.46 | 0.48 | 0.46 | 0.47 | 0.47 | 0.47 |\n", + "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.47 | 0.46 | 0.46 | 0.46 | 0.47 |\n", + "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.45 | 0.45 | 0.47 | 0.46 | 0.46 |\n", + "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.43 | 0.44 | 0.44 | 0.43 | 0.45 | 0.45 |\n", + "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.43 | 0.43 | 0.44 | 0.45 | 0.45 | 0.45 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.44 | 0.48 | 0.45 | 0.47 | 0.46 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | spearman | 0.45 | 0.46 | 0.46 | 0.47 | 0.47 | 0.46 |\n", + "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n" ] } ], From 1f15aeb0822520d142ae7dca469bf6cce475fde7 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Mon, 23 Oct 2017 13:04:49 +0200 Subject: [PATCH 09/28] Minor fixes to poincare nb - change in variable name, relative path, misaligned header --- docs/notebooks/Poincare Evaluation.ipynb | 121 +++++++----------- .../poincare_burn_in_eps.patch | 0 2 files changed, 47 insertions(+), 74 deletions(-) rename docs/notebooks/{poincare_data => poincare}/poincare_burn_in_eps.patch (100%) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 8cfa5de8ca..236046ca52 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -37,8 +37,9 @@ "metadata": {}, "outputs": [], "source": [ - "# Set this to the path of the directory that is to contain the poincare-embedding directory\n", - "parent_directory = '/home/jayant/projects/'" + "# The poincare datasets, models and c++ source code are downloaded to this directory\n", + "parent_directory = './poincare/'\n", + "! mkdir -p {parent_directory}" ] }, { @@ -60,13 +61,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "/home/jayant/projects\n", + "/home/jayant/projects/gensim/docs/notebooks/poincare\n", "Cloning into 'poincare-embedding'...\n", "remote: Counting objects: 96, done.\u001b[K\n", "remote: Total 96 (delta 0), reused 0 (delta 0), pack-reused 96\u001b[K\n", "Unpacking objects: 100% (96/96), done.\n", "Checking connectivity... done.\n", - "/home/jayant/projects/poincare-embedding\n" + "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-embedding\n" ] } ], @@ -82,37 +83,6 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2017-10-18 20:37:11-- https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_eval/docs/notebooks/poincare_data/poincare_burn_in_eps.patch\n", - "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.112.133\n", - "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.112.133|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 7999 (7.8K) [text/plain]\n", - "Saving to: ‘./poincare_burn_in_eps.patch’\n", - "\n", - "./poincare_burn_in_ 100%[===================>] 7.81K --.-KB/s in 0s \n", - "\n", - "2017-10-18 20:37:11 (59.5 MB/s) - ‘./poincare_burn_in_eps.patch’ saved [7999/7999]\n", - "\n" - ] - } - ], - "source": [ - "# Download and apply patch\n", - "patch_url = \"https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_eval/docs/notebooks/poincare_data/poincare_burn_in_eps.patch\"\n", - "! wget {patch_url} -O ./poincare_burn_in_eps.patch\n", - "! git apply poincare_burn_in_eps.patch\n", - "! rm poincare_burn_in_eps.patch" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, "outputs": [ { "name": "stdout", @@ -123,6 +93,8 @@ } ], "source": [ + "# Apply patch\n", + "! git apply ../poincare_burn_in_eps.patch\n", "% cd {current_directory}" ] }, @@ -130,7 +102,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Follow the instructions in the README to compile the sources and create the binaries." + "Follow the instructions in the [README](https://github.com/TatsuyaShirakawa/poincare-embedding/blob/master/README.md) to compile the sources in the poincare directory and create the binaries." ] }, { @@ -144,22 +116,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# These directories are auto created in the current directory for storing poincare datasets and models\n", - "data_directory = 'poincare_data'\n", - "models_directory = os.path.join(data_directory, 'models')\n", + "data_directory = os.path.join(parent_directory, 'data')\n", + "models_directory = os.path.join(parent_directory, 'models')\n", "\n", "# Create directories\n", - "! mkdir {data_directory}\n", - "! mkdir {models_directory}" + "! mkdir -p {data_directory}\n", + "! mkdir -p {models_directory}" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -179,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 8, "metadata": { "scrolled": true }, @@ -188,32 +160,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2017-10-18 20:44:13-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", + "--2017-10-23 12:50:28-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", "Resolving people.ds.cam.ac.uk (people.ds.cam.ac.uk)... 131.111.3.47\n", "Connecting to people.ds.cam.ac.uk (people.ds.cam.ac.uk)|131.111.3.47|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 183900 (180K) [application/zip]\n", - "Saving to: ‘poincare_data/hyperlex-data.zip’\n", + "Saving to: ‘./poincare/data/hyperlex-data.zip’\n", "\n", - "poincare_data/hyper 100%[===================>] 179.59K --.-KB/s in 0.06s \n", + "./poincare/data/hyp 100%[===================>] 179.59K --.-KB/s in 0.06s \n", "\n", - "2017-10-18 20:44:13 (2.75 MB/s) - ‘poincare_data/hyperlex-data.zip’ saved [183900/183900]\n", + "2017-10-23 12:50:28 (2.93 MB/s) - ‘./poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", "\n", - "Archive: poincare_data/hyperlex-data.zip\n", - " creating: poincare_data/nouns-verbs/\n", - " inflating: poincare_data/nouns-verbs/hyperlex-verbs.txt \n", - " inflating: poincare_data/nouns-verbs/hyperlex-nouns.txt \n", - " creating: poincare_data/splits/\n", - " creating: poincare_data/splits/random/\n", - " inflating: poincare_data/splits/random/hyperlex_training_all_random.txt \n", - " inflating: poincare_data/splits/random/hyperlex_test_all_random.txt \n", - " inflating: poincare_data/splits/random/hyperlex_dev_all_random.txt \n", - " creating: poincare_data/splits/lexical/\n", - " inflating: poincare_data/splits/lexical/hyperlex_dev_all_lexical.txt \n", - " inflating: poincare_data/splits/lexical/hyperlex_test_all_lexical.txt \n", - " inflating: poincare_data/splits/lexical/hyperlex_training_all_lexical.txt \n", - " inflating: poincare_data/hyperlex-all.txt \n", - " inflating: poincare_data/README.txt \n" + "Archive: ./poincare/data/hyperlex-data.zip\n", + " creating: ./poincare/data/nouns-verbs/\n", + " inflating: ./poincare/data/nouns-verbs/hyperlex-verbs.txt \n", + " inflating: ./poincare/data/nouns-verbs/hyperlex-nouns.txt \n", + " creating: ./poincare/data/splits/\n", + " creating: ./poincare/data/splits/random/\n", + " inflating: ./poincare/data/splits/random/hyperlex_training_all_random.txt \n", + " inflating: ./poincare/data/splits/random/hyperlex_test_all_random.txt \n", + " inflating: ./poincare/data/splits/random/hyperlex_dev_all_random.txt \n", + " creating: ./poincare/data/splits/lexical/\n", + " inflating: ./poincare/data/splits/lexical/hyperlex_dev_all_lexical.txt \n", + " inflating: ./poincare/data/splits/lexical/hyperlex_test_all_lexical.txt \n", + " inflating: ./poincare/data/splits/lexical/hyperlex_training_all_lexical.txt \n", + " inflating: ./poincare/data/hyperlex-all.txt \n", + " inflating: ./poincare/data/README.txt \n" ] } ], @@ -234,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -276,7 +248,7 @@ " for option, value in args.items():\n", " cmd.append(\"--%s\" % option)\n", " cmd.append(str(value))\n", - "\n", + " \n", " return check_output(args=cmd)" ] }, @@ -324,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 118, "metadata": { "scrolled": true }, @@ -343,7 +315,8 @@ " train_file (str): Path to tsv file containing relation pairs\n", " model_sizes (list): list of dimension sizes (integer) to train the model with\n", " prefix (str): prefix to use for the saved model filenames\n", - " Returns:\n", + " \n", + " Returns:\n", " tuple (model_name, model_files)\n", " model_files is a dict of (size, filename) pairs\n", " Example: ('cpp_model_epochs_50', {5: 'models/cpp_model_epochs_50_dim_5'})\n", @@ -571,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -663,7 +636,7 @@ " \n", " \n", " @staticmethod\n", - " def get_positive_relation_ranks(distances, positive_relations):\n", + " def get_positive_relation_ranks(all_distances, positive_relations):\n", " \"\"\"\n", " Given a numpy array of all distances from an item and indices of its positive relations,\n", " compute ranks of positive relations\n", @@ -675,8 +648,8 @@ " Returns:\n", " list of ranks of positive items in the same order as `positive_indices`\n", " \"\"\"\n", - " positive_relation_distances = distances[positive_relations]\n", - " negative_relation_distances = np.ma.array(distances, mask=False)\n", + " positive_relation_distances = all_distances[positive_relations]\n", + " negative_relation_distances = np.ma.array(all_distances, mask=False)\n", " negative_relation_distances.mask[positive_relations] = True\n", " # Compute how many negative relation distances are less than each positive relation distance, plus 1 for rank\n", " ranks = (negative_relation_distances < positive_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n", @@ -812,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -933,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1143,8 +1116,8 @@ "output_type": "stream", "text": [ "Results for WordNet Link Prediction task\n", - "+--------------------------------------------+---------------------------------+\n", - "| | Model Dimensions |\n", + "+--------------------------------------------+------------------------------------------+\n", + "| | Model Dimensions |\n", "+---------------------------------+-----------+--------+--------+-------+-------+-------+\n", "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 |\n", "+---------------------------------+-----------+--------+--------+-------+-------+-------+\n", diff --git a/docs/notebooks/poincare_data/poincare_burn_in_eps.patch b/docs/notebooks/poincare/poincare_burn_in_eps.patch similarity index 100% rename from docs/notebooks/poincare_data/poincare_burn_in_eps.patch rename to docs/notebooks/poincare/poincare_burn_in_eps.patch From a415c65fc60c7a632587c628a83072b6baf5e08b Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Tue, 24 Oct 2017 08:07:06 +0200 Subject: [PATCH 10/28] Adds results for all models on link prediction --- docs/notebooks/Poincare Evaluation.ipynb | 64 ++++++++++++------------ 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 236046ca52..32c394c6c0 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -287,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -296,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 22, "metadata": { "scrolled": true }, @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -544,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -597,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -785,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -848,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -882,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -906,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -922,7 +922,7 @@ "source": [ "# Train models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'cpp_lp_model')\n", - "model_files[model_name] = {}\n", + "lp_model_files[model_name] = {}\n", "for dim, filepath in files.items():\n", " lp_model_files[model_name][dim] = filepath\n", "# Train models with non-default params\n", @@ -938,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -947,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -966,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1081,7 +1081,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1108,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1116,20 +1116,20 @@ "output_type": "stream", "text": [ "Results for WordNet Link Prediction task\n", - "+--------------------------------------------+------------------------------------------+\n", - "| | Model Dimensions |\n", - "+---------------------------------+-----------+--------+--------+-------+-------+-------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 |\n", - "+---------------------------------+-----------+--------+--------+-------+-------+-------+\n", - "| cpp_epochs_50_threads_8_neg_20 | mean_rank | 197.63 | 85.96 | 63.21 | 51.94 | 58.72 |\n", - "| cpp_epochs_50_threads_1_neg_20 | mean_rank | 191.36 | 92.09 | 53.68 | 47.57 | 46.26 |\n", - "| cpp_epochs_50_threads_1_neg_10 | mean_rank | 232.96 | 87.99 | 62.41 | 46.84 | 47.23 |\n", - "| cpp_epochs_50_threads_8_neg_10 | mean_rank | 208.80 | 104.13 | 77.91 | 63.97 | 75.42 |\n", - "| cpp_epochs_100_threads_8_neg_10 | mean_rank | 202.66 | 99.25 | 77.94 | 67.56 | 66.49 |\n", - "| cpp_epochs_100_threads_1_neg_20 | mean_rank | 147.81 | 79.82 | 48.12 | 42.29 | 41.96 |\n", - "| cpp_epochs_100_threads_8_neg_20 | mean_rank | 160.67 | 105.16 | 70.21 | 56.91 | 46.13 |\n", - "| cpp_epochs_100_threads_1_neg_10 | mean_rank | 157.65 | 72.03 | 50.60 | 39.28 | 39.18 |\n", - "+---------------------------------+-----------+--------+--------+-------+-------+-------+\n" + "+--------------------------------------------------------------------------+-----------------------------------------------------+\n", + "| | Model Dimensions |\n", + "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", + "| cpp_lp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 224.50 | 199.29 | 184.29 | 178.89 | 155.52 | 157.39 |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 232.82 | 98.78 | 53.79 | 49.79 | 46.52 | 47.03 |\n", + "| cpp_lp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 190.93 | 116.63 | 68.68 | 43.67 | 55.01 | 66.02 |\n", + "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 182.03 | 107.04 | 63.29 | 72.67 | 73.64 | 60.35 |\n", + "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", + "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" ] } ], From 0d593f05654393305334448b7cd05ee067151c0a Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Tue, 24 Oct 2017 10:46:39 +0200 Subject: [PATCH 11/28] Adds initial implementation of MAP and MAP scores --- docs/notebooks/Poincare Evaluation.ipynb | 183 +++++++++++------------ 1 file changed, 89 insertions(+), 94 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 32c394c6c0..773baa532b 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -206,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +254,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -287,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -296,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 9, "metadata": { "scrolled": true }, @@ -368,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -517,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -544,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -597,12 +597,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "import csv\n", - "from collections import defaultdict\n", + "from collections import defaultdict, OrderedDict\n", "import itertools\n", "\n", "\n", @@ -636,111 +636,100 @@ " \n", " \n", " @staticmethod\n", - " def get_positive_relation_ranks(all_distances, positive_relations):\n", + " def get_positive_relation_ranks_and_avg_prec(all_distances, positive_relations):\n", " \"\"\"\n", " Given a numpy array of all distances from an item and indices of its positive relations,\n", - " compute ranks of positive relations\n", + " compute ranks and Average Precision of positive relations\n", " \n", " Args:\n", " distances (numpy float array): np array of all distances for a specific item\n", " positive_relations (list): list of indices of positive relations for the item\n", " \n", " Returns:\n", - " list of ranks of positive items in the same order as `positive_indices`\n", + " tuple of (ranks, avg_precision)\n", + " `ranks` is a list of ranks (int) of positive relations in the same order as `positive_relations`\n", + " `avg_precision` is a float representing the Average Precision of the ranking\n", " \"\"\"\n", " positive_relation_distances = all_distances[positive_relations]\n", " negative_relation_distances = np.ma.array(all_distances, mask=False)\n", " negative_relation_distances.mask[positive_relations] = True\n", " # Compute how many negative relation distances are less than each positive relation distance, plus 1 for rank\n", " ranks = (negative_relation_distances < positive_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n", - " return list(ranks) \n", + " avg_precision = ((np.arange(1, len(ranks) + 1) / np.sort(ranks)).mean()) / len(ranks)\n", + " return list(ranks), avg_precision\n", " \n", - " def evaluate_metric(self, metric, max_n=None):\n", - " \"\"\"Evaluate given metric for the reconstruction task\n", + " def evaluate(self, max_n=None):\n", + " \"\"\"Evaluate all defined metrics for the reconstruction task\n", " \n", " Args:\n", - " metric (str): accepted values are 'mean_rank' and 'MAP'\n", " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", " \n", " Returns:\n", - " Computed value of given metric (float)\n", + " dict containing (metric_name, metric_value) pairs\n", + " e.g. {'mean_rank': 50.3, 'MAP': 0.31}\n", "\n", " \"\"\"\n", - " if metric == 'mean_rank':\n", - " return self.evaluate_mean_rank(max_n)\n", - " elif metric == 'MAP':\n", - " return self.evaluate_map(max_n)\n", - " else:\n", - " raise ValueError('Invalid value for metric')\n", + " mean_rank, map_ = self.evaluate_mean_rank_and_map(max_n)\n", + " return {'mean_rank': mean_rank, 'MAP': map_}\n", "\n", - " def evaluate_mean_rank(self, max_n=None):\n", + " def evaluate_mean_rank_and_map(self, max_n=None):\n", " \"\"\"Evaluate mean rank and MAP for reconstruction\n", " \n", " Args:\n", " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", " \n", " Returns:\n", - " Computed value of mean rank (float)\n", + " tuple of (mean_rank, MAP)\n", "\n", " \"\"\"\n", " ranks = []\n", + " avg_precision_scores = []\n", " for i, item in enumerate(self.items, start=1):\n", " if item not in self.relations:\n", " continue\n", " item_relations = list(self.relations[item])\n", " item_term = self.embedding.kv.index2word[item]\n", " item_distances = self.embedding.get_all_distances(item_term)\n", - " positive_relation_ranks = self.get_positive_relation_ranks(item_distances, item_relations)\n", + " positive_relation_ranks, avg_precision = self.get_positive_relation_ranks_and_avg_prec(item_distances, item_relations)\n", " ranks += positive_relation_ranks\n", + " avg_precision_scores.append(avg_precision)\n", " if max_n is not None and i > max_n:\n", " break\n", - " return np.mean(ranks)\n", - " \n", - " def evaluate_map(self, max_n=None):\n", - " \"\"\"Evaluate MAP (Mean Average Precision) for reconstruction\n", - " \n", - " Args:\n", - " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", - " \n", - " Returns:\n", - " Computed value of MAP (float)\n", - "\n", - " \"\"\"\n", - " raise NotImplementedError" + " return np.mean(ranks), np.mean(avg_precision_scores)\n", + " " ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "reconstruction_results = {}\n", - "metrics = ['mean_rank']" + "metrics = ['mean_rank', 'MAP']" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "for model_name, models in embeddings.items():\n", - " reconstruction_results[model_name] = {}\n", + " reconstruction_results[model_name] = OrderedDict()\n", " for metric in metrics:\n", " reconstruction_results[model_name][metric] = {}\n", " for model_size, embedding in models.items():\n", " print('Evaluating model %s of size %d' % (model_name, model_size))\n", " eval_instance = ReconstructionEvaluation(wordnet_file, embedding)\n", + " eval_result = eval_instance.evaluate(max_n=1000)\n", " for metric in metrics:\n", - " reconstruction_results[model_name][metric][model_size] = eval_instance.evaluate_metric(metric, max_n=1000)" + " reconstruction_results[model_name][metric][model_size] = eval_result[metric]" ] }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -753,14 +742,22 @@ "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 308.69 | 101.92 | 73.20 | 56.17 | 52.97 | 51.37 |\n", - "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", - "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 228.70 | 112.40 | 81.00 | 64.81 | 57.15 | 70.87 |\n", - "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 227.69 | 175.64 | 171.60 | 144.47 | 146.84 | 139.28 |\n", + "| | MAP | 0.05 | 0.10 | 0.14 | 0.18 | 0.20 | 0.21 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", + "| | MAP | 0.05 | 0.08 | 0.12 | 0.14 | 0.15 | 0.15 |\n", "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", + "| | MAP | 0.05 | 0.07 | 0.07 | 0.08 | 0.08 | 0.08 |\n", + "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 227.69 | 175.64 | 171.60 | 144.47 | 146.84 | 139.28 |\n", + "| | MAP | 0.05 | 0.07 | 0.07 | 0.08 | 0.08 | 0.09 |\n", "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", + "| | MAP | 0.05 | 0.09 | 0.12 | 0.15 | 0.16 | 0.16 |\n", + "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", + "| | MAP | 0.07 | 0.10 | 0.13 | 0.15 | 0.16 | 0.17 |\n", + "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 228.70 | 112.40 | 81.00 | 64.81 | 57.15 | 70.87 |\n", + "| | MAP | 0.06 | 0.09 | 0.12 | 0.14 | 0.15 | 0.15 |\n", "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 245.48 | 104.66 | 88.33 | 65.40 | 74.88 | 55.66 |\n", + "| | MAP | 0.05 | 0.09 | 0.12 | 0.15 | 0.15 | 0.16 |\n", "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" ] } @@ -966,7 +963,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ @@ -1003,56 +1000,56 @@ " \n", " \n", " @staticmethod\n", - " def get_unknown_relation_ranks(distances, unknown_relations, known_relations):\n", + " def get_unknown_relation_ranks_and_avg_prec(all_distances, unknown_relations, known_relations):\n", " \"\"\"\n", " Given a numpy array of distances and indices of known and unknown positive relations,\n", - " compute ranks of unknown positive relations\n", + " compute ranks and Average Precision of unknown positive relations\n", " \n", " Args:\n", - " distances (numpy float array): np array of all distances for a specific item\n", + " all_distances (numpy float array): np array of all distances for a specific item\n", " unknown_relations (list): list of indices of unknown positive relations\n", " known_relations (list): list of indices of known positive relations\n", " \n", " Returns:\n", - " list of ranks of unknown relations in the same order as `unknown_relations`\n", + " tuple of (ranks, avg_precision)\n", + " `ranks` is a list of ranks (int) of unknown relations in the same order as `unknown_relations`\n", + " `avg_precision` is a float representing the Average Precision of the ranking\n", " \"\"\"\n", - " unknown_relation_distances = distances[unknown_relations]\n", - " negative_relation_distances = np.ma.array(distances, mask=False)\n", + " unknown_relation_distances = all_distances[unknown_relations]\n", + " negative_relation_distances = np.ma.array(all_distances, mask=False)\n", " negative_relation_distances.mask[unknown_relations] = True\n", " negative_relation_distances.mask[known_relations] = True\n", " # Compute how many negative relation distances are less than each unknown relation distance, plus 1 for rank\n", " ranks = (negative_relation_distances < unknown_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n", - " return list(ranks) \n", + " avg_precision = ((np.arange(1, len(ranks) + 1) / np.sort(ranks)).mean()) / len(ranks)\n", + " return list(ranks), avg_precision\n", " \n", - " def evaluate_metric(self, metric, max_n=None):\n", - " \"\"\"Evaluate given metric for the reconstruction task\n", + " def evaluate(self, max_n=None):\n", + " \"\"\"Evaluate all defined metrics for the reconstruction task\n", " \n", " Args:\n", - " metric (str): accepted values are 'mean_rank' and 'MAP'\n", " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", " \n", " Returns:\n", - " Computed value of given metric (float)\n", + " dict containing (metric_name, metric_value) pairs\n", + " e.g. {'mean_rank': 50.3, 'MAP': 0.31}\n", "\n", " \"\"\"\n", - " if metric == 'mean_rank':\n", - " return self.evaluate_mean_rank(max_n)\n", - " elif metric == 'MAP':\n", - " return self.evaluate_map(max_n)\n", - " else:\n", - " raise ValueError('Invalid value for metric')\n", + " mean_rank, map_ = self.evaluate_mean_rank_and_map(max_n)\n", + " return {'mean_rank': mean_rank, 'MAP': map_}\n", "\n", - " def evaluate_mean_rank(self, max_n=None):\n", + " def evaluate_mean_rank_and_map(self, max_n=None):\n", " \"\"\"Evaluate mean rank and MAP for reconstruction\n", " \n", " Args:\n", " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", " \n", " Returns:\n", - " Computed value of mean rank (float)\n", + " tuple of (mean_rank, MAP)\n", "\n", " \"\"\"\n", " ranks = []\n", + " avg_precision_scores = []\n", " for i, item in enumerate(self.items, start=1):\n", " if item not in self.relations['unknown']: # No positive relations to predict for this node\n", " continue\n", @@ -1060,33 +1057,22 @@ " known_relations = list(self.relations['known'][item])\n", " item_term = self.embedding.kv.index2word[item]\n", " item_distances = self.embedding.get_all_distances(item_term)\n", - " unknown_relation_ranks = self.get_unknown_relation_ranks(item_distances, unknown_relations, known_relations)\n", + " unknown_relation_ranks, avg_precision = self.get_unknown_relation_ranks_and_avg_prec(item_distances, unknown_relations, known_relations)\n", " ranks += unknown_relation_ranks\n", + " avg_precision_scores.append(avg_precision)\n", " if max_n is not None and i > max_n:\n", " break\n", - " return np.mean(ranks)\n", - " \n", - " def evaluate_map(self, max_n=None):\n", - " \"\"\"Evaluate MAP (Mean Average Precision) for reconstruction\n", - " \n", - " Args:\n", - " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", - " \n", - " Returns:\n", - " Computed value of MAP (float)\n", - "\n", - " \"\"\"\n", - " raise NotImplementedError" + " return np.mean(ranks), np.mean(avg_precision_scores)\n" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "lp_results = {}\n", - "metrics = ['mean_rank']" + "metrics = ['mean_rank', 'MAP']" ] }, { @@ -1096,19 +1082,20 @@ "outputs": [], "source": [ "for model_name, models in lp_embeddings.items():\n", - " lp_results[model_name] = {}\n", + " lp_results[model_name] = OrderedDict()\n", " for metric in metrics:\n", " lp_results[model_name][metric] = {}\n", " for model_size, embedding in models.items():\n", " print('Evaluating model %s of size %d' % (model_name, model_size))\n", " eval_instance = LinkPredictionEvaluation(wordnet_train_file, wordnet_test_file, embedding)\n", + " eval_result = eval_instance.evaluate(max_n=1000)\n", " for metric in metrics:\n", - " lp_results[model_name][metric][model_size] = eval_instance.evaluate_metric(metric, max_n=1000)" + " lp_results[model_name][metric][model_size] = eval_result[metric]" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -1122,13 +1109,21 @@ "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", + "| | MAP | 0.05 | 0.06 | 0.13 | 0.15 | 0.16 | 0.15 |\n", "| cpp_lp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 224.50 | 199.29 | 184.29 | 178.89 | 155.52 | 157.39 |\n", + "| | MAP | 0.04 | 0.06 | 0.06 | 0.06 | 0.07 | 0.07 |\n", "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 232.82 | 98.78 | 53.79 | 49.79 | 46.52 | 47.03 |\n", + "| | MAP | 0.06 | 0.10 | 0.14 | 0.17 | 0.18 | 0.18 |\n", "| cpp_lp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 190.93 | 116.63 | 68.68 | 43.67 | 55.01 | 66.02 |\n", + "| | MAP | 0.07 | 0.10 | 0.12 | 0.16 | 0.16 | 0.15 |\n", "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", + "| | MAP | 0.04 | 0.05 | 0.05 | 0.06 | 0.07 | 0.07 |\n", "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", + "| | MAP | 0.06 | 0.09 | 0.11 | 0.13 | 0.13 | 0.14 |\n", "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 182.03 | 107.04 | 63.29 | 72.67 | 73.64 | 60.35 |\n", + "| | MAP | 0.06 | 0.10 | 0.12 | 0.15 | 0.15 | 0.16 |\n", "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", + "| | MAP | 0.06 | 0.10 | 0.13 | 0.15 | 0.15 | 0.15 |\n", "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" ] } From d7f1840d99a502f24f344455a93af2225440555c Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Tue, 24 Oct 2017 13:26:51 +0200 Subject: [PATCH 12/28] Corrects implementation of MAP, updated results --- docs/notebooks/Poincare Evaluation.ipynb | 72 +++++++++++++----------- 1 file changed, 38 insertions(+), 34 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 773baa532b..14d211f2ff 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -655,7 +655,8 @@ " negative_relation_distances.mask[positive_relations] = True\n", " # Compute how many negative relation distances are less than each positive relation distance, plus 1 for rank\n", " ranks = (negative_relation_distances < positive_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n", - " avg_precision = ((np.arange(1, len(ranks) + 1) / np.sort(ranks)).mean()) / len(ranks)\n", + " map_ranks = np.sort(ranks) + np.arange(len(ranks))\n", + " avg_precision = ((np.arange(1, len(map_ranks) + 1) / np.sort(map_ranks)).mean())\n", " return list(ranks), avg_precision\n", " \n", " def evaluate(self, max_n=None):\n", @@ -701,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -712,7 +713,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "for model_name, models in embeddings.items():\n", @@ -729,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -743,21 +746,21 @@ "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 308.69 | 101.92 | 73.20 | 56.17 | 52.97 | 51.37 |\n", - "| | MAP | 0.05 | 0.10 | 0.14 | 0.18 | 0.20 | 0.21 |\n", + "| | MAP | 0.27 | 0.43 | 0.53 | 0.61 | 0.64 | 0.65 |\n", "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", - "| | MAP | 0.05 | 0.08 | 0.12 | 0.14 | 0.15 | 0.15 |\n", + "| | MAP | 0.27 | 0.40 | 0.49 | 0.53 | 0.56 | 0.56 |\n", "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", - "| | MAP | 0.05 | 0.07 | 0.07 | 0.08 | 0.08 | 0.08 |\n", + "| | MAP | 0.26 | 0.32 | 0.34 | 0.36 | 0.36 | 0.36 |\n", "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 227.69 | 175.64 | 171.60 | 144.47 | 146.84 | 139.28 |\n", - "| | MAP | 0.05 | 0.07 | 0.07 | 0.08 | 0.08 | 0.09 |\n", + "| | MAP | 0.27 | 0.33 | 0.35 | 0.37 | 0.37 | 0.38 |\n", "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", - "| | MAP | 0.05 | 0.09 | 0.12 | 0.15 | 0.16 | 0.16 |\n", + "| | MAP | 0.28 | 0.41 | 0.49 | 0.56 | 0.58 | 0.59 |\n", "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", - "| | MAP | 0.07 | 0.10 | 0.13 | 0.15 | 0.16 | 0.17 |\n", + "| | MAP | 0.34 | 0.43 | 0.51 | 0.57 | 0.59 | 0.59 |\n", "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 228.70 | 112.40 | 81.00 | 64.81 | 57.15 | 70.87 |\n", - "| | MAP | 0.06 | 0.09 | 0.12 | 0.14 | 0.15 | 0.15 |\n", + "| | MAP | 0.31 | 0.42 | 0.49 | 0.53 | 0.56 | 0.54 |\n", "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 245.48 | 104.66 | 88.33 | 65.40 | 74.88 | 55.66 |\n", - "| | MAP | 0.05 | 0.09 | 0.12 | 0.15 | 0.15 | 0.16 |\n", + "| | MAP | 0.28 | 0.42 | 0.50 | 0.56 | 0.56 | 0.57 |\n", "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" ] } @@ -782,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -845,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -903,7 +906,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -935,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -944,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -963,7 +966,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -978,7 +981,7 @@ " embedding (PoincareEmbedding instance): embedding to be evaluated\n", " \n", " Returns\n", - " ReconstructionEvaluation instance\n", + " LinkPredictionEvaluation instance\n", "\n", " \"\"\"\n", " items = set()\n", @@ -1021,7 +1024,8 @@ " negative_relation_distances.mask[known_relations] = True\n", " # Compute how many negative relation distances are less than each unknown relation distance, plus 1 for rank\n", " ranks = (negative_relation_distances < unknown_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n", - " avg_precision = ((np.arange(1, len(ranks) + 1) / np.sort(ranks)).mean()) / len(ranks)\n", + " map_ranks = np.sort(ranks) + np.arange(len(ranks))\n", + " avg_precision = ((np.arange(1, len(map_ranks) + 1) / np.sort(map_ranks)).mean())\n", " return list(ranks), avg_precision\n", " \n", " def evaluate(self, max_n=None):\n", @@ -1067,7 +1071,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -1095,7 +1099,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -1108,22 +1112,22 @@ "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", - "| | MAP | 0.05 | 0.06 | 0.13 | 0.15 | 0.16 | 0.15 |\n", - "| cpp_lp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 224.50 | 199.29 | 184.29 | 178.89 | 155.52 | 157.39 |\n", - "| | MAP | 0.04 | 0.06 | 0.06 | 0.06 | 0.07 | 0.07 |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 182.03 | 107.04 | 63.29 | 72.67 | 73.64 | 60.35 |\n", + "| | MAP | 0.16 | 0.25 | 0.31 | 0.34 | 0.36 | 0.37 |\n", + "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", + "| | MAP | 0.10 | 0.13 | 0.14 | 0.15 | 0.16 | 0.16 |\n", "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 232.82 | 98.78 | 53.79 | 49.79 | 46.52 | 47.03 |\n", - "| | MAP | 0.06 | 0.10 | 0.14 | 0.17 | 0.18 | 0.18 |\n", + "| | MAP | 0.15 | 0.25 | 0.33 | 0.39 | 0.40 | 0.41 |\n", "| cpp_lp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 190.93 | 116.63 | 68.68 | 43.67 | 55.01 | 66.02 |\n", - "| | MAP | 0.07 | 0.10 | 0.12 | 0.16 | 0.16 | 0.15 |\n", - "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", - "| | MAP | 0.04 | 0.05 | 0.05 | 0.06 | 0.07 | 0.07 |\n", + "| | MAP | 0.17 | 0.24 | 0.29 | 0.36 | 0.37 | 0.35 |\n", "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", - "| | MAP | 0.06 | 0.09 | 0.11 | 0.13 | 0.13 | 0.14 |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 182.03 | 107.04 | 63.29 | 72.67 | 73.64 | 60.35 |\n", - "| | MAP | 0.06 | 0.10 | 0.12 | 0.15 | 0.15 | 0.16 |\n", + "| | MAP | 0.14 | 0.22 | 0.28 | 0.31 | 0.33 | 0.34 |\n", + "| cpp_lp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 224.50 | 199.29 | 184.29 | 178.89 | 155.52 | 157.39 |\n", + "| | MAP | 0.09 | 0.14 | 0.15 | 0.16 | 0.17 | 0.18 |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", + "| | MAP | 0.12 | 0.15 | 0.31 | 0.35 | 0.36 | 0.36 |\n", "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", - "| | MAP | 0.06 | 0.10 | 0.13 | 0.15 | 0.15 | 0.15 |\n", + "| | MAP | 0.15 | 0.24 | 0.31 | 0.35 | 0.36 | 0.36 |\n", "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" ] } From 53fcf2340fc54c90801a2849b4ce801a415c683a Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Tue, 24 Oct 2017 18:19:08 +0200 Subject: [PATCH 13/28] More readable results --- docs/notebooks/Poincare Evaluation.ipynb | 200 +++++++---------------- 1 file changed, 56 insertions(+), 144 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 14d211f2ff..4694555866 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -563,12 +563,12 @@ " for model_name, model_results in results.items():\n", " metrics = [metric for metric in model_results.keys()]\n", " dims = sorted([dim for dim in model_results[metrics[0]].keys()])\n", - " row = [model_name, '\\n'.join(metrics)]\n", + " row = [model_name, '\\n'.join(metrics) + '\\n']\n", " for dim in dims:\n", " scores = ['%.2f' % model_results[metric][dim] for metric in metrics]\n", " row.append('\\n'.join(scores))\n", " data.add_row(row)\n", - "\n", + " data.align = 'r'\n", " data_cols = data.get_string().split('\\n')[0].split('+')[1:-1]\n", " col_lengths = [len(col) for col in data_cols]\n", " header_col_1_length = col_lengths[0] + col_lengths[1] - 1\n", @@ -579,7 +579,6 @@ " header_col_2_right_margin = header_col_2_length - len(header_col_2_content) - header_col_2_left_margin\n", " header_col_2_string = \"%s%s%s\" % (\n", " \" \" * header_col_2_left_margin, header_col_2_content, \" \" * header_col_2_right_margin)\n", - "\n", " header = PrettyTable()\n", " header.field_names = [\" \" * header_col_1_length, header_col_2_string]\n", " header_lines = header.get_string(start=0, end=0).split(\"\\n\")[:2]\n", @@ -597,7 +596,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -702,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -732,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -743,24 +742,32 @@ "+-----------------------------------------------------------------------+-----------------------------------------------------+\n", "| | Model Dimensions |\n", "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 308.69 | 101.92 | 73.20 | 56.17 | 52.97 | 51.37 |\n", - "| | MAP | 0.27 | 0.43 | 0.53 | 0.61 | 0.64 | 0.65 |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", - "| | MAP | 0.27 | 0.40 | 0.49 | 0.53 | 0.56 | 0.56 |\n", - "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", - "| | MAP | 0.26 | 0.32 | 0.34 | 0.36 | 0.36 | 0.36 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", + "| | MAP | 0.28 | 0.41 | 0.49 | 0.56 | 0.58 | 0.59 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 228.70 | 112.40 | 81.00 | 64.81 | 57.15 | 70.87 |\n", + "| | MAP | 0.31 | 0.42 | 0.49 | 0.53 | 0.56 | 0.54 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", + "| | MAP | 0.27 | 0.40 | 0.49 | 0.53 | 0.56 | 0.56 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 308.69 | 101.92 | 73.20 | 56.17 | 52.97 | 51.37 |\n", + "| | MAP | 0.27 | 0.43 | 0.53 | 0.61 | 0.64 | 0.65 |\n", + "| | | | | | | | |\n", "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 227.69 | 175.64 | 171.60 | 144.47 | 146.84 | 139.28 |\n", - "| | MAP | 0.27 | 0.33 | 0.35 | 0.37 | 0.37 | 0.38 |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", - "| | MAP | 0.28 | 0.41 | 0.49 | 0.56 | 0.58 | 0.59 |\n", - "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", - "| | MAP | 0.34 | 0.43 | 0.51 | 0.57 | 0.59 | 0.59 |\n", - "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 228.70 | 112.40 | 81.00 | 64.81 | 57.15 | 70.87 |\n", - "| | MAP | 0.31 | 0.42 | 0.49 | 0.53 | 0.56 | 0.54 |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 245.48 | 104.66 | 88.33 | 65.40 | 74.88 | 55.66 |\n", - "| | MAP | 0.28 | 0.42 | 0.50 | 0.56 | 0.56 | 0.57 |\n", + "| | MAP | 0.27 | 0.33 | 0.35 | 0.37 | 0.37 | 0.38 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 245.48 | 104.66 | 88.33 | 65.40 | 74.88 | 55.66 |\n", + "| | MAP | 0.28 | 0.42 | 0.50 | 0.56 | 0.56 | 0.57 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", + "| | MAP | 0.34 | 0.43 | 0.51 | 0.57 | 0.59 | 0.59 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", + "| | MAP | 0.26 | 0.32 | 0.34 | 0.36 | 0.36 | 0.36 |\n", + "| | | | | | | | |\n", "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" ] } @@ -1099,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -1110,24 +1117,32 @@ "+--------------------------------------------------------------------------+-----------------------------------------------------+\n", "| | Model Dimensions |\n", "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 182.03 | 107.04 | 63.29 | 72.67 | 73.64 | 60.35 |\n", - "| | MAP | 0.16 | 0.25 | 0.31 | 0.34 | 0.36 | 0.37 |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 182.03 | 107.04 | 63.29 | 72.67 | 73.64 | 60.35 |\n", + "| | MAP | 0.16 | 0.25 | 0.31 | 0.34 | 0.36 | 0.37 |\n", + "| | | | | | | | |\n", "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", - "| | MAP | 0.10 | 0.13 | 0.14 | 0.15 | 0.16 | 0.16 |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 232.82 | 98.78 | 53.79 | 49.79 | 46.52 | 47.03 |\n", - "| | MAP | 0.15 | 0.25 | 0.33 | 0.39 | 0.40 | 0.41 |\n", - "| cpp_lp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 190.93 | 116.63 | 68.68 | 43.67 | 55.01 | 66.02 |\n", - "| | MAP | 0.17 | 0.24 | 0.29 | 0.36 | 0.37 | 0.35 |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", - "| | MAP | 0.14 | 0.22 | 0.28 | 0.31 | 0.33 | 0.34 |\n", - "| cpp_lp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 224.50 | 199.29 | 184.29 | 178.89 | 155.52 | 157.39 |\n", - "| | MAP | 0.09 | 0.14 | 0.15 | 0.16 | 0.17 | 0.18 |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", - "| | MAP | 0.12 | 0.15 | 0.31 | 0.35 | 0.36 | 0.36 |\n", - "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", - "| | MAP | 0.15 | 0.24 | 0.31 | 0.35 | 0.36 | 0.36 |\n", + "| | MAP | 0.10 | 0.13 | 0.14 | 0.15 | 0.16 | 0.16 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 232.82 | 98.78 | 53.79 | 49.79 | 46.52 | 47.03 |\n", + "| | MAP | 0.15 | 0.25 | 0.33 | 0.39 | 0.40 | 0.41 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 190.93 | 116.63 | 68.68 | 43.67 | 55.01 | 66.02 |\n", + "| | MAP | 0.17 | 0.24 | 0.29 | 0.36 | 0.37 | 0.35 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", + "| | MAP | 0.14 | 0.22 | 0.28 | 0.31 | 0.33 | 0.34 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 224.50 | 199.29 | 184.29 | 178.89 | 155.52 | 157.39 |\n", + "| | MAP | 0.09 | 0.14 | 0.15 | 0.16 | 0.17 | 0.18 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", + "| | MAP | 0.12 | 0.15 | 0.31 | 0.35 | 0.36 | 0.36 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", + "| | MAP | 0.15 | 0.24 | 0.31 | 0.35 | 0.36 | 0.36 |\n", + "| | | | | | | | |\n", "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" ] } @@ -1232,112 +1247,9 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 50\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 100\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 5\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 200\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 20\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 of size 10\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 50\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 100\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 5\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 200\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 20\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 of size 10\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 50\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 100\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 5\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 200\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 20\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 of size 10\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 50\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 100\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 5\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 200\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 20\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 of size 10\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 50\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 100\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 5\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 200\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 20\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 of size 10\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 50\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 100\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 5\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 200\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 20\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 of size 10\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 50\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 100\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 5\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 200\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 20\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 of size 10\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 50\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 100\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 5\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 200\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 20\n", - "Skipped pairs: 182 out of 2163\n", - "Evaluating model cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 of size 10\n", - "Skipped pairs: 182 out of 2163\n" - ] - } - ], + "outputs": [], "source": [ "for model_name, models in embeddings.items():\n", " entailment_results[model_name] = {}\n", From 5d72642f56c7e823714e1a1fecce91a4f2a2b88a Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 25 Oct 2017 07:59:34 +0200 Subject: [PATCH 14/28] Adds code for training and loading external numpy models, results --- docs/notebooks/Poincare Evaluation.ipynb | 329 ++++++++++++++++------- 1 file changed, 238 insertions(+), 91 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 4694555866..2acd14ba19 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -37,7 +37,7 @@ "metadata": {}, "outputs": [], "source": [ - "# The poincare datasets, models and c++ source code are downloaded to this directory\n", + "# The poincare datasets, models and source code for external models are downloaded to this directory\n", "parent_directory = './poincare/'\n", "! mkdir -p {parent_directory}" ] @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -62,39 +62,44 @@ "output_type": "stream", "text": [ "/home/jayant/projects/gensim/docs/notebooks/poincare\n", - "Cloning into 'poincare-embedding'...\n", + "Cloning into 'poincare-np-embedding'...\n", + "remote: Counting objects: 20, done.\u001b[K\n", + "remote: Compressing objects: 100% (18/18), done.\u001b[K\n", + "remote: Total 20 (delta 2), reused 20 (delta 2), pack-reused 0\u001b[K\n", + "Unpacking objects: 100% (20/20), done.\n", + "Checking connectivity... done.\n", + "Cloning into 'poincare-cpp-embedding'...\n", "remote: Counting objects: 96, done.\u001b[K\n", "remote: Total 96 (delta 0), reused 0 (delta 0), pack-reused 96\u001b[K\n", "Unpacking objects: 100% (96/96), done.\n", "Checking connectivity... done.\n", - "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-embedding\n" + "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-cpp-embedding\n" ] } ], "source": [ "# Clone repo\n", "% cd {parent_directory}\n", - "repo_name = 'poincare-embedding'\n", - "! git clone https://github.com/TatsuyaShirakawa/poincare-embedding.git {repo_name}\n", - "% cd {repo_name}" + "np_repo_name = 'poincare-np-embedding'\n", + "! git clone https://github.com/nishnik/poincare_embeddings.git {np_repo_name}\n", + "\n", + "cpp_repo_name = 'poincare-cpp-embedding'\n", + "! git clone https://github.com/TatsuyaShirakawa/poincare-embedding.git {cpp_repo_name}" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jayant/projects/gensim/docs/notebooks\n" - ] - } - ], + "outputs": [], "source": [ - "# Apply patch\n", + "# Apply patches\n", + "% cd {cpp_repo_name}\n", "! git apply ../poincare_burn_in_eps.patch\n", + "\n", + "% cd ../{np_repo_name}\n", + "! git apply ../poincare_numpy.patch\n", + "\n", "% cd {current_directory}" ] }, @@ -102,7 +107,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Follow the instructions in the [README](https://github.com/TatsuyaShirakawa/poincare-embedding/blob/master/README.md) to compile the sources in the poincare directory and create the binaries." + "Follow the instructions in the [README](https://github.com/TatsuyaShirakawa/poincare-embedding/blob/master/README.md) to compile the sources in the C++ repo in the `poincare` directory and create the binaries." ] }, { @@ -204,6 +209,15 @@ "### 2.2 Training C++ embeddings" ] }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "cpp_binary_path = os.path.join(parent_directory, cpp_repo_name, 'work', 'poincare_embedding')" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -252,15 +266,6 @@ " return check_output(args=cmd)" ] }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "cpp_binary_path = os.path.join(parent_directory, 'poincare-embedding', 'work', 'poincare_embedding')" - ] - }, { "cell_type": "code", "execution_count": 7, @@ -296,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 35, "metadata": { "scrolled": true }, @@ -307,14 +312,16 @@ " return '%s_%s' % (prefix, '_'.join(name))\n", "\n", "\n", - "def train_model_with_params(params, train_file, model_sizes, prefix):\n", - " \"\"\"Trains models with given params for multiple model sizes using the C++ implementation\n", + "def train_model_with_params(params, train_file, model_sizes, prefix, implementation):\n", + " \"\"\"Trains models with given params for multiple model sizes using the given implementation\n", " \n", " Args:\n", - " params (dict): parameters to train the model with, passed to `train_cpp_model`\n", + " params (dict): parameters to train the model with\n", " train_file (str): Path to tsv file containing relation pairs\n", " model_sizes (list): list of dimension sizes (integer) to train the model with\n", " prefix (str): prefix to use for the saved model filenames\n", + " implementation (str): whether to use the python or c++ implementation,\n", + " allowed values: 'python', 'c++'\n", " \n", " Returns:\n", " tuple (model_name, model_files)\n", @@ -322,15 +329,26 @@ " Example: ('cpp_model_epochs_50', {5: 'models/cpp_model_epochs_50_dim_5'})\n", " \"\"\"\n", " files = {}\n", - " model_name = model_name_from_params(params, prefix)\n", + " if implementation == 'c++':\n", + " model_name = cpp_model_name_from_params(params, prefix)\n", + " elif implementation == 'python':\n", + " model_name = np_model_name_from_params(params, prefix)\n", + " \n", " for model_size in model_sizes:\n", " output_file_name = '%s_dim_%d' % (model_name, model_size)\n", " output_file = os.path.join(models_directory, output_file_name)\n", " print('Training model %s' % output_file)\n", - " out = train_cpp_model(\n", - " cpp_binary_path, train_file, output_file, model_size,\n", - " params['epochs'], params['neg'], params['threads'],\n", - " params['eps'], params['burn_in'], seed=0)\n", + " if implementation == 'c++':\n", + " out = train_cpp_model(\n", + " cpp_binary_path, train_file, output_file, model_size,\n", + " params['epochs'], params['neg'], params['threads'],\n", + " params['eps'], params['burn_in'], seed=0)\n", + " elif implementation == 'python':\n", + " train_external_numpy_model(\n", + " python_script_path, train_file, output_file, model_size,\n", + " params['epochs'], params['neg'], seed=0)\n", + " else:\n", + " raise ValueError('Given implementation %s not found' % implementation)\n", " files[model_size] = output_file\n", " return (model_name, files)" ] @@ -344,7 +362,7 @@ "outputs": [], "source": [ "# Train models with default params\n", - "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'cpp_model')\n", + "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'cpp_model', 'c++')\n", "model_files[model_name] = {}\n", "for dim, filepath in files.items():\n", " model_files[model_name][dim] = filepath\n", @@ -353,7 +371,7 @@ " params = default_params.copy()\n", " for value in values:\n", " params[param] = value\n", - " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'cpp_model')\n", + " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'cpp_model', 'c++')\n", " model_files[model_name] = {}\n", " for dim, filepath in files.items():\n", " model_files[model_name][dim] = filepath" @@ -363,23 +381,100 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. Loading the embeddings" + "### 2.3 Training [numpy embeddings](https://github.com/nishnik/poincare_embeddings) (non-gensim)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ - "embeddings = {}" + "python_script_path = os.path.join(parent_directory, np_repo_name, 'poincare.py')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "from gensim.utils import check_output\n", + "\n", + "def np_model_name_from_params(params, prefix):\n", + " param_keys = ['neg', 'epochs']\n", + " name = ['%s_%s' % (key, params[key]) for key in sorted(param_keys)]\n", + " return '%s_%s' % (prefix, '_'.join(name))\n", + "\n", + "def train_external_numpy_model(\n", + " script_path, data_file, output_file, dim, epochs, neg, seed=0):\n", + " \"\"\"Train a poincare embedding using an external numpy implementation\n", + " \n", + " Args:\n", + " script_path (str): Path to the Python training script\n", + " data_file (str): Path to tsv file containing relation pairs\n", + " output_file (str): Path to output file containing model\n", + " dim (int): Number of dimensions of the trained model\n", + " epochs (int): Number of epochs to use\n", + " neg (int): Number of negative samples to use\n", + " \n", + " Notes: \n", + " If `output_file` already exists, skips training\n", + " \"\"\"\n", + " if os.path.exists(output_file):\n", + " print('File %s exists, skipping' % output_file)\n", + " return\n", + " args = {\n", + " 'input-file': data_file,\n", + " 'output-file': output_file,\n", + " 'dimensions': dim,\n", + " 'epochs': epochs,\n", + " 'learning-rate': 0.01,\n", + " 'num-negative': neg,\n", + " }\n", + " cmd = ['python', script_path]\n", + " for option, value in args.items():\n", + " cmd.append(\"--%s\" % option)\n", + " cmd.append(str(value))\n", + " \n", + " return check_output(args=cmd)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "np_model_files = {}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training model ./poincare/models/np_model_epochs_50_neg_20_dim_5\n" + ] + } + ], + "source": [ + "# Train models with default params\n", + "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'np_model', 'python')\n", + "np_model_files[model_name] = {}\n", + "for dim, filepath in files.items():\n", + " np_model_files[model_name][dim] = filepath" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.1 C++ embeddings" + "## 3. Loading the embeddings" ] }, { @@ -387,6 +482,15 @@ "execution_count": 12, "metadata": {}, "outputs": [], + "source": [ + "embeddings = {}" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], "source": [ "import pickle\n", "import re\n", @@ -412,6 +516,18 @@ " for line in lines:\n", " f.write(line.replace('\\t', ' '))\n", "\n", + "def transform_numpy_embedding_to_kv(input_file, output_file, encoding='utf8'):\n", + " \"\"\"Given a numpy poincare embedding pkl filepath, converts it to a KeyedVector-supported file\"\"\"\n", + " np_embeddings = pickle.load(open(input_file, 'rb'))\n", + " random_embedding = np_embeddings[list(np_embeddings.keys())[0]]\n", + " \n", + " model_size = random_embedding.shape[0]\n", + " vocab_size = len(np_embeddings)\n", + " with open(output_file, 'w') as f:\n", + " f.write('%d %d\\n' % (vocab_size, model_size))\n", + " for key, vector in np_embeddings.items():\n", + " vector_string = ' '.join('%.6f' % value for value in vector)\n", + " f.write('%s %s\\n' % (key, vector_string))\n", " \n", "class PoincareEmbedding(object):\n", " \"\"\"Load and perform distance operations on poincare embedding\"\"\"\n", @@ -515,9 +631,16 @@ " return self.poincare_dist(vector_1, vector_2)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 C++ embeddings" + ] + }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -531,8 +654,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 3.2 Numpy embeddings\n", - "TODO" + "### 3.2 Numpy embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [], + "source": [ + "for model_name, models in np_model_files.items():\n", + " embeddings[model_name] = {}\n", + " for model_size, model_file in models.items():\n", + " embeddings[model_name][model_size] = PoincareEmbedding.load_poincare_numpy(model_file)" ] }, { @@ -544,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -596,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -731,7 +865,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -739,36 +873,39 @@ "output_type": "stream", "text": [ "Results for WordNet Reconstruction task\n", - "+-----------------------------------------------------------------------+-----------------------------------------------------+\n", - "| | Model Dimensions |\n", - "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", - "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", - "| | MAP | 0.28 | 0.41 | 0.49 | 0.56 | 0.58 | 0.59 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 228.70 | 112.40 | 81.00 | 64.81 | 57.15 | 70.87 |\n", - "| | MAP | 0.31 | 0.42 | 0.49 | 0.53 | 0.56 | 0.54 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", - "| | MAP | 0.27 | 0.40 | 0.49 | 0.53 | 0.56 | 0.56 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 308.69 | 101.92 | 73.20 | 56.17 | 52.97 | 51.37 |\n", - "| | MAP | 0.27 | 0.43 | 0.53 | 0.61 | 0.64 | 0.65 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 227.69 | 175.64 | 171.60 | 144.47 | 146.84 | 139.28 |\n", - "| | MAP | 0.27 | 0.33 | 0.35 | 0.37 | 0.37 | 0.38 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 245.48 | 104.66 | 88.33 | 65.40 | 74.88 | 55.66 |\n", - "| | MAP | 0.28 | 0.42 | 0.50 | 0.56 | 0.56 | 0.57 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", - "| | MAP | 0.34 | 0.43 | 0.51 | 0.57 | 0.59 | 0.59 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", - "| | MAP | 0.26 | 0.32 | 0.34 | 0.36 | 0.36 | 0.36 |\n", - "| | | | | | | | |\n", - "+-----------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" + "+-----------------------------------------------------------------------+---------------------------------------------------------+\n", + "| | Model Dimensions |\n", + "+-----------------------------------------------------------+-----------+---------+---------+---------+---------+--------+--------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "+-----------------------------------------------------------+-----------+---------+---------+---------+---------+--------+--------+\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", + "| | MAP | 0.28 | 0.41 | 0.49 | 0.56 | 0.58 | 0.59 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 228.70 | 112.40 | 81.00 | 64.81 | 57.15 | 70.87 |\n", + "| | MAP | 0.31 | 0.42 | 0.49 | 0.53 | 0.56 | 0.54 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", + "| | MAP | 0.27 | 0.40 | 0.49 | 0.53 | 0.56 | 0.56 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 308.69 | 101.92 | 73.20 | 56.17 | 52.97 | 51.37 |\n", + "| | MAP | 0.27 | 0.43 | 0.53 | 0.61 | 0.64 | 0.65 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 227.69 | 175.64 | 171.60 | 144.47 | 146.84 | 139.28 |\n", + "| | MAP | 0.27 | 0.33 | 0.35 | 0.37 | 0.37 | 0.38 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 245.48 | 104.66 | 88.33 | 65.40 | 74.88 | 55.66 |\n", + "| | MAP | 0.28 | 0.42 | 0.50 | 0.56 | 0.56 | 0.57 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", + "| | MAP | 0.34 | 0.43 | 0.51 | 0.57 | 0.59 | 0.59 |\n", + "| | | | | | | | |\n", + "| np_model_epochs_50_neg_20 | mean_rank | 9396.18 | 5651.94 | 3523.95 | 1053.29 | 505.56 | 364.93 |\n", + "| | MAP | 0.14 | 0.16 | 0.19 | 0.25 | 0.30 | 0.35 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", + "| | MAP | 0.26 | 0.32 | 0.34 | 0.36 | 0.36 | 0.36 |\n", + "| | | | | | | | |\n", + "+-----------------------------------------------------------+-----------+---------+---------+---------+---------+--------+--------+\n" ] } ], @@ -928,7 +1065,7 @@ "outputs": [], "source": [ "# Train models with default params\n", - "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'cpp_lp_model')\n", + "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'cpp_lp_model', 'c++')\n", "lp_model_files[model_name] = {}\n", "for dim, filepath in files.items():\n", " lp_model_files[model_name][dim] = filepath\n", @@ -937,7 +1074,7 @@ " params = default_params.copy()\n", " for value in values:\n", " params[param] = value\n", - " model_name, files = train_model_with_params(params, wordnet_train_file, model_sizes, 'cpp_lp_model')\n", + " model_name, files = train_model_with_params(params, wordnet_train_file, model_sizes, 'cpp_lp_model', 'c++')\n", " lp_model_files[model_name] = {}\n", " for dim, filepath in files.items():\n", " lp_model_files[model_name][dim] = filepath" @@ -1160,7 +1297,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -1237,7 +1374,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -1261,7 +1398,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -1272,16 +1409,26 @@ "+----------------------------------------------------------------------+-----------------------------------------+\n", "| | Model Dimensions |\n", "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | spearman | 0.43 | 0.44 | 0.44 | 0.44 | 0.45 | 0.45 |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | spearman | 0.46 | 0.48 | 0.46 | 0.47 | 0.47 | 0.47 |\n", - "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.47 | 0.46 | 0.46 | 0.46 | 0.47 |\n", - "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.45 | 0.45 | 0.47 | 0.46 | 0.46 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.45 | 0.48 | 0.45 | 0.47 | 0.46 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.45 | 0.46 | 0.47 | 0.46 | 0.47 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | spearman | 0.44 | 0.44 | 0.45 | 0.44 | 0.45 | 0.45 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | spearman | 0.46 | 0.48 | 0.47 | 0.47 | 0.48 | 0.47 |\n", + "| | | | | | | | |\n", "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.43 | 0.44 | 0.44 | 0.43 | 0.45 | 0.45 |\n", - "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.43 | 0.43 | 0.44 | 0.45 | 0.45 | 0.45 |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.44 | 0.48 | 0.45 | 0.47 | 0.46 |\n", + "| | | | | | | | |\n", "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | spearman | 0.45 | 0.46 | 0.46 | 0.47 | 0.47 | 0.46 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.47 | 0.46 | 0.46 | 0.46 | 0.47 |\n", + "| | | | | | | | |\n", + "| np_model_epochs_50_neg_20 | spearman | 0.15 | 0.20 | 0.21 | 0.21 | 0.25 | 0.27 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.43 | 0.43 | 0.44 | 0.45 | 0.44 | 0.45 |\n", + "| | | | | | | | |\n", "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n" ] } From 7cbd6b9081f36d7404ae458822e0ead5bd49a139 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 25 Oct 2017 08:43:37 +0200 Subject: [PATCH 15/28] Adds patch for external numply implementation to repo --- docs/notebooks/Poincare Evaluation.ipynb | 10 +- docs/notebooks/poincare/poincare_numpy.patch | 324 +++++++++++++++++++ 2 files changed, 325 insertions(+), 9 deletions(-) create mode 100644 docs/notebooks/poincare/poincare_numpy.patch diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 2acd14ba19..68309cc6db 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -453,15 +453,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training model ./poincare/models/np_model_epochs_50_neg_20_dim_5\n" - ] - } - ], + "outputs": [], "source": [ "# Train models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'np_model', 'python')\n", diff --git a/docs/notebooks/poincare/poincare_numpy.patch b/docs/notebooks/poincare/poincare_numpy.patch new file mode 100644 index 0000000000..8134bea4a3 --- /dev/null +++ b/docs/notebooks/poincare/poincare_numpy.patch @@ -0,0 +1,324 @@ +diff --git a/poincare.py b/poincare.py +index ecae36e..f85bf22 100644 +--- a/poincare.py ++++ b/poincare.py +@@ -1,160 +1,169 @@ ++import argparse ++import csv + import nltk + from nltk.corpus import wordnet as wn + from math import * ++import pickle + import random ++import re + import numpy as np +-import matplotlib.pyplot as plt +-import matplotlib.lines as mlines ++import time ++from collections import defaultdict ++from smart_open import smart_open ++ + + STABILITY = 0.00001 # to avoid overflow while dividing +-network = {} # representation of network (here it is hierarchical) + +-last_level = 4 ++# network: the actual network of which node is connected to whom ++network = defaultdict(list) # representation of network (here it is hierarchical) + +-# plots the embedding of all the nodes of network +-def plotall(ii): +- fig = plt.figure() +- # plot all the nodes +- for a in emb: +- plt.plot(emb[a][0], emb[a][1], marker = 'o', color = [levelOfNode[a]/(last_level+1),levelOfNode[a]/(last_level+1),levelOfNode[a]/(last_level+1)]) +- # plot the relationship, black line means root level relationship +- # consecutive relationship lines fade out in color ++ ++def load_wordnet(wordnet_path): ++ with smart_open(wordnet_path, 'r') as f: ++ reader = csv.reader(f, delimiter='\t') ++ for row in reader: ++ assert len(row) == 2, 'Hypernym pair has more than two items' ++ network[row[0]].append(row[1]) ++ ++def main(input_file, output_file, embedding_size, num_epochs, num_negs, lr): ++ print('Creating wordnet dataset') ++ load_wordnet(input_file) ++ print('Created wordnet dataset') ++ ++ # embedding of nodes of network ++ emb = {} ++ ++ # Randomly uniform distribution + for a in network: + for b in network[a]: +- plt.plot([emb[a][0], emb[b][0]], [emb[a][1], emb[b][1]], color = [levelOfNode[a]/(last_level+1),levelOfNode[a]/(last_level+1),levelOfNode[a]/(last_level+1)]) +- # plt.show() +- fig.savefig(str(last_level) + '_' + str(ii) + '.png', dpi=fig.dpi) ++ emb[b] = np.random.uniform(low=-0.001, high=0.001, size=(embedding_size,)) ++ emb[a] = np.random.uniform(low=-0.001, high=0.001, size=(embedding_size,)) + +-# network: the actual network of which node is connected to whom +-# levelOfNode: level of the node in hierarchical data +-levelOfNode = {} +- +-# recursive function to popoulate the hyponyms of a root node in `network` +-# synset: the root node +-# last_level: the level till which we consider the hyponyms +-def get_hyponyms(synset, level): +- if (level == last_level): +- levelOfNode[str(synset)] = level +- return +- # BFS +- if not str(synset) in network: +- network[str(synset)] = [str(s) for s in synset.hyponyms()] +- levelOfNode[str(synset)] = level +- for hyponym in synset.hyponyms(): +- get_hyponyms(hyponym, level + 1) +- +-mammal = wn.synset('mammal.n.01') +-get_hyponyms(mammal, 0) +-levelOfNode[str(mammal)] = 0 +- +-# embedding of nodes of network +-emb = {} +- +-# Randomly uniform distribution +-for a in network: +- for b in network[a]: +- emb[b] = np.random.uniform(low=-0.001, high=0.001, size=(2,)) +- emb[a] = np.random.uniform(low=-0.001, high=0.001, size=(2,)) +- +-vocab = list(emb.keys()) +-random.shuffle(vocab) +- +-# the leave nodes are not connected to anything +-for a in emb: +- if not a in network: +- network[a] = [] +- +- +-# Partial derivative as given in the paper wrt theta +-def partial_der(theta, x, gamma): #eqn4 +- alpha = (1.0-np.dot(theta, theta)) +- norm_x = np.dot(x, x) +- beta = (1-norm_x) +- gamma = gamma +- return 4.0/(beta * sqrt(gamma*gamma - 1) + STABILITY)*((norm_x- 2*np.dot(theta, x)+1)/(pow(alpha,2)+STABILITY)*theta - x/(alpha + STABILITY)) +- +-lr = 0.01 +- +-# the update equation as given in the paper +-def update(emb, error_): #eqn5 +- try: +- update = lr*pow((1 - np.dot(emb,emb)), 2)*error_/4 +- emb = emb - update +- if (np.dot(emb, emb) >= 1): +- emb = emb/sqrt(np.dot(emb, emb)) - STABILITY +- return emb +- except Exception as e: +- print (e) +- +-# Distance in poincare disk model +-def dist(vec1, vec2): # eqn1 +- return 1 + 2*np.dot(vec1 - vec2, vec1 - vec2)/ \ +- ((1-np.dot(vec1, vec1))*(1-np.dot(vec2, vec2)) + STABILITY) +- +-num_negs = 5 +- +-# The plot of initialized embeddings +-plotall("init") +- +-for epoch in range(200): +- # pos2 is related to pos1 +- # negs are not related to pos1 +- for pos1 in vocab: +- if not network[pos1]: # a leaf node +- continue +- pos2 = random.choice(network[pos1]) # pos2 and pos1 are related +- dist_p_init = dist(emb[pos1], emb[pos2]) # distance between the related nodes +- if (dist_p_init > 700): # this causes overflow, so I clipped it here +- print ("got one very high") # if you have reached this zone, the training is unstable now +- dist_p_init = 700 +- elif (dist_p_init < -700): +- print ("got one very high") +- dist_p_init = -700 +- dist_p = cosh(dist_p_init) # this is the actual distance, it is always positive +- # print ("distance between related nodes", dist_p) +- negs = [] # pairs of not related nodes, the first node in the pair is `pos1` +- dist_negs_init = [] # distances without taking cosh on it (for not related nodes) +- dist_negs = [] # distances with taking cosh on it (for not related nodes) +- while (len(negs) < num_negs): +- neg1 = pos1 +- neg2 = random.choice(vocab) +- if not (neg2 in network[neg1] or neg1 in network[neg2] or neg2 == neg1): # neg2 should not be related to neg1 and vice versa +- dist_neg_init = dist(emb[neg1], emb[neg2]) +- if (dist_neg_init > 700 or dist_neg_init < -700): # already dist is good, leave it +- continue +- negs.append([neg1, neg2]) +- dist_neg = cosh(dist_neg_init) +- dist_negs_init.append(dist_neg_init) # saving it for faster computation +- dist_negs.append(dist_neg) +- # print ("distance between non related nodes", dist_neg) +- loss_den = 0.0 +- # eqn6 +- for dist_neg in dist_negs: +- loss_den += exp(-1*dist_neg) +- loss = -1*dist_p - log(loss_den + STABILITY) +- # derivative of loss wrt positive relation [d(u, v)] +- der_p = -1 +- der_negs = [] +- # derivative of loss wrt negative relation [d(u, v')] +- for dist_neg in dist_negs: +- der_negs.append(exp(-1*dist_neg)/(loss_den + STABILITY)) +- # derivative of loss wrt pos1 +- der_p_pos1 = der_p * partial_der(emb[pos1], emb[pos2], dist_p_init) +- # derivative of loss wrt pos2 +- der_p_pos2 = der_p * partial_der(emb[pos2], emb[pos1], dist_p_init) +- der_negs_final = [] +- for (der_neg, neg, dist_neg_init) in zip(der_negs, negs, dist_negs_init): +- # derivative of loss wrt second element of the pair in neg +- der_neg1 = der_neg * partial_der(emb[neg[1]], emb[neg[0]], dist_neg_init) +- # derivative of loss wrt first element of the pair in neg +- der_neg0 = der_neg * partial_der(emb[neg[0]], emb[neg[1]], dist_neg_init) +- der_negs_final.append([der_neg0, der_neg1]) +- # update embeddings now +- emb[pos1] = update(emb[pos1], -1*der_p_pos1) +- emb[pos2] = update(emb[pos2], -1*der_p_pos2) +- for (neg, der_neg) in zip(negs, der_negs_final): +- emb[neg[0]] = update(emb[neg[0]], -1*der_neg[0]) +- emb[neg[1]] = update(emb[neg[1]], -1*der_neg[1]) +- # plot the embeddings +- if ((epoch)%20 == 0): +- plotall(epoch+1) +\ No newline at end of file ++ vocab = list(emb.keys()) ++ random.shuffle(vocab) ++ ++ # the leave nodes are not connected to anything ++ for a in emb: ++ if not a in network: ++ network[a] = [] ++ ++ # Partial derivative as given in the paper wrt theta ++ def partial_der(theta, x, gamma): #eqn4 ++ alpha = (1.0-np.dot(theta, theta)) ++ norm_x = np.dot(x, x) ++ beta = (1-norm_x) ++ gamma = gamma ++ return 4.0/(beta * sqrt(gamma*gamma - 1) + STABILITY)*((norm_x- 2*np.dot(theta, x)+1)/(pow(alpha,2)+STABILITY)*theta - x/(alpha + STABILITY)) ++ ++ # the update equation as given in the paper ++ def update(emb, error_): #eqn5 ++ try: ++ update = lr*pow((1 - np.dot(emb,emb)), 2)*error_/4 ++ emb = emb - update ++ if (np.dot(emb, emb) >= 1): ++ emb = emb/sqrt(np.dot(emb, emb)) - STABILITY ++ return emb ++ except Exception as e: ++ print (e) ++ ++ # Distance in poincare disk model ++ def dist(vec1, vec2): # eqn1 ++ return 1 + 2*np.dot(vec1 - vec2, vec1 - vec2)/ \ ++ ((1-np.dot(vec1, vec1))*(1-np.dot(vec2, vec2)) + STABILITY) ++ ++ ++ # The plot of initialized embeddings ++ # plotall("init") ++ ++ last_time = time.time() ++ for epoch in range(num_epochs): ++ # pos2 is related to pos1 ++ # negs are not related to pos1 ++ for pos1 in vocab: ++ if not network[pos1]: # a leaf node ++ continue ++ pos2 = random.choice(network[pos1]) # pos2 and pos1 are related ++ dist_p_init = dist(emb[pos1], emb[pos2]) # distance between the related nodes ++ if (dist_p_init > 700): # this causes overflow, so I clipped it here ++ print ("got one very high") # if you have reached this zone, the training is unstable now ++ dist_p_init = 700 ++ elif (dist_p_init < -700): ++ print ("got one very high") ++ dist_p_init = -700 ++ dist_p = cosh(dist_p_init) # this is the actual distance, it is always positive ++ # print ("distance between related nodes", dist_p) ++ negs = [] # pairs of not related nodes, the first node in the pair is `pos1` ++ dist_negs_init = [] # distances without taking cosh on it (for not related nodes) ++ dist_negs = [] # distances with taking cosh on it (for not related nodes) ++ while (len(negs) < num_negs): ++ neg1 = pos1 ++ neg2 = random.choice(vocab) ++ if not (neg2 in network[neg1] or neg1 in network[neg2] or neg2 == neg1): # neg2 should not be related to neg1 and vice versa ++ dist_neg_init = dist(emb[neg1], emb[neg2]) ++ if (dist_neg_init > 700 or dist_neg_init < -700): # already dist is good, leave it ++ continue ++ negs.append([neg1, neg2]) ++ dist_neg = cosh(dist_neg_init) ++ dist_negs_init.append(dist_neg_init) # saving it for faster computation ++ dist_negs.append(dist_neg) ++ # print ("distance between non related nodes", dist_neg) ++ loss_den = 0.0 ++ # eqn6 ++ for dist_neg in dist_negs: ++ loss_den += exp(-1*dist_neg) ++ loss = -1*dist_p - log(loss_den + STABILITY) ++ # derivative of loss wrt positive relation [d(u, v)] ++ der_p = -1 ++ der_negs = [] ++ # derivative of loss wrt negative relation [d(u, v')] ++ for dist_neg in dist_negs: ++ der_negs.append(exp(-1*dist_neg)/(loss_den + STABILITY)) ++ # derivative of loss wrt pos1 ++ der_p_pos1 = der_p * partial_der(emb[pos1], emb[pos2], dist_p_init) ++ # derivative of loss wrt pos2 ++ der_p_pos2 = der_p * partial_der(emb[pos2], emb[pos1], dist_p_init) ++ der_negs_final = [] ++ for (der_neg, neg, dist_neg_init) in zip(der_negs, negs, dist_negs_init): ++ # derivative of loss wrt second element of the pair in neg ++ der_neg1 = der_neg * partial_der(emb[neg[1]], emb[neg[0]], dist_neg_init) ++ # derivative of loss wrt first element of the pair in neg ++ der_neg0 = der_neg * partial_der(emb[neg[0]], emb[neg[1]], dist_neg_init) ++ der_negs_final.append([der_neg0, der_neg1]) ++ # update embeddings now ++ emb[pos1] = update(emb[pos1], -1*der_p_pos1) ++ emb[pos2] = update(emb[pos2], -1*der_p_pos2) ++ for (neg, der_neg) in zip(negs, der_negs_final): ++ emb[neg[0]] = update(emb[neg[0]], -1*der_neg[0]) ++ emb[neg[1]] = update(emb[neg[1]], -1*der_neg[1]) ++ print('Epoch #%d, time taken: %.2f seconds' % (epoch + 1, time.time() - last_time)) ++ last_time = time.time() ++ pickle.dump(emb, open(output_file, 'wb')) ++ ++ ++if __name__ == "__main__": ++ # check and process cmdline input ++ parser = argparse.ArgumentParser() ++ parser.add_argument( ++ '-i', '--input-file', required=True, ++ help="Input tsv file containing relation pairs") ++ parser.add_argument( ++ '-o', '--output-file', required=True, ++ help="Where to save the trained model") ++ parser.add_argument( ++ '-d', '--dimensions', required=True, type=int, ++ help="Dimensionality of the trained vectors") ++ parser.add_argument( ++ '-e', '--epochs', required=True, type=int, ++ help="Number of epochs to train the model for") ++ parser.add_argument( ++ '-l', '--learning-rate', required=True, type=float, ++ help="Learning rate to use for training the model") ++ parser.add_argument( ++ '-n', '--num-negative', required=True, type=int, ++ help="Number of negative samples to use for each node") ++ args = parser.parse_args() ++ ++ main( ++ args.input_file, args.output_file, args.dimensions, ++ args.epochs, args.num_negative, args.learning_rate, ++ ) +\ No newline at end of file From c07d58236b47e94489be5b22c55b88460e759750 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 25 Oct 2017 11:03:15 +0200 Subject: [PATCH 16/28] Adds cleaner setup --- docs/notebooks/Poincare Evaluation.ipynb | 171 ++++++++++++++--------- 1 file changed, 108 insertions(+), 63 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 68309cc6db..8f87ce4293 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -33,28 +33,24 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ + "import os\n", + "current_directory = os.getcwd()\n", + "\n", + "# Change this variable to `False` to not remove and re-download repos for external implementations\n", + "force_setup = True\n", + "\n", "# The poincare datasets, models and source code for external models are downloaded to this directory\n", - "parent_directory = './poincare/'\n", + "parent_directory = os.path.join(current_directory, 'poincare')\n", "! mkdir -p {parent_directory}" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "current_directory = os.getcwd()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -72,34 +68,82 @@ "remote: Counting objects: 96, done.\u001b[K\n", "remote: Total 96 (delta 0), reused 0 (delta 0), pack-reused 96\u001b[K\n", "Unpacking objects: 100% (96/96), done.\n", - "Checking connectivity... done.\n", - "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-cpp-embedding\n" + "Checking connectivity... done.\n" ] } ], "source": [ - "# Clone repo\n", "% cd {parent_directory}\n", + "\n", + "# Clone repos\n", "np_repo_name = 'poincare-np-embedding'\n", - "! git clone https://github.com/nishnik/poincare_embeddings.git {np_repo_name}\n", + "if force_setup and os.path.exists(np_repo_name):\n", + " ! rm -rf {np_repo_name}\n", + "if not os.path.exists(np_repo_name):\n", + " ! git clone https://github.com/nishnik/poincare_embeddings.git {np_repo_name}\n", "\n", "cpp_repo_name = 'poincare-cpp-embedding'\n", - "! git clone https://github.com/TatsuyaShirakawa/poincare-embedding.git {cpp_repo_name}" + "if force_setup and os.path.exists(cpp_repo_name):\n", + " ! rm -rf {cpp_repo_name}\n", + "if not os.path.exists(cpp_repo_name):\n", + " ! git clone https://github.com/TatsuyaShirakawa/poincare-embedding.git {cpp_repo_name}\n", + "\n", + "patches_applied = False" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-cpp-embedding\n", + "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-np-embedding\n", + "/home/jayant/projects/gensim/docs/notebooks\n" + ] + } + ], "source": [ "# Apply patches\n", - "% cd {cpp_repo_name}\n", - "! git apply ../poincare_burn_in_eps.patch\n", - "\n", - "% cd ../{np_repo_name}\n", - "! git apply ../poincare_numpy.patch\n", + "if not patches_applied:\n", + " % cd {cpp_repo_name}\n", + " ! git apply ../poincare_burn_in_eps.patch\n", "\n", + " % cd ../{np_repo_name}\n", + " ! git apply ../poincare_numpy.patch\n", + " \n", + " patches_applied = True" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-cpp-embedding\n", + "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-cpp-embedding/work\n", + "-- Configuring done\n", + "-- Generating done\n", + "-- Build files have been written to: /home/jayant/projects/gensim/docs/notebooks/poincare/poincare-cpp-embedding/work\n", + "[100%] Built target poincare_embedding\n", + "/home/jayant/projects/gensim/docs/notebooks\n" + ] + } + ], + "source": [ + "# Compile the code for the external c++ implementation into a binary\n", + "% cd {parent_directory}/{cpp_repo_name}\n", + "! mkdir -p work\n", + "% cd work\n", + "! cmake ..\n", + "! make\n", "% cd {current_directory}" ] }, @@ -107,7 +151,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Follow the instructions in the [README](https://github.com/TatsuyaShirakawa/poincare-embedding/blob/master/README.md) to compile the sources in the C++ repo in the `poincare` directory and create the binaries." + "You might need to install an updated version of `cmake` to be able to compile the source code. Please make sure that the binary `poincare-embedding` has been created before proceeding." ] }, { @@ -121,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -156,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 137, "metadata": { "scrolled": true }, @@ -165,32 +209,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2017-10-23 12:50:28-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", + "--2017-10-25 10:56:39-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", "Resolving people.ds.cam.ac.uk (people.ds.cam.ac.uk)... 131.111.3.47\n", "Connecting to people.ds.cam.ac.uk (people.ds.cam.ac.uk)|131.111.3.47|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 183900 (180K) [application/zip]\n", - "Saving to: ‘./poincare/data/hyperlex-data.zip’\n", + "Saving to: ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’\n", "\n", - "./poincare/data/hyp 100%[===================>] 179.59K --.-KB/s in 0.06s \n", + "/home/jayant/projec 100%[===================>] 179.59K --.-KB/s in 0.06s \n", "\n", - "2017-10-23 12:50:28 (2.93 MB/s) - ‘./poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", + "2017-10-25 10:56:39 (2.82 MB/s) - ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", "\n", - "Archive: ./poincare/data/hyperlex-data.zip\n", - " creating: ./poincare/data/nouns-verbs/\n", - " inflating: ./poincare/data/nouns-verbs/hyperlex-verbs.txt \n", - " inflating: ./poincare/data/nouns-verbs/hyperlex-nouns.txt \n", - " creating: ./poincare/data/splits/\n", - " creating: ./poincare/data/splits/random/\n", - " inflating: ./poincare/data/splits/random/hyperlex_training_all_random.txt \n", - " inflating: ./poincare/data/splits/random/hyperlex_test_all_random.txt \n", - " inflating: ./poincare/data/splits/random/hyperlex_dev_all_random.txt \n", - " creating: ./poincare/data/splits/lexical/\n", - " inflating: ./poincare/data/splits/lexical/hyperlex_dev_all_lexical.txt \n", - " inflating: ./poincare/data/splits/lexical/hyperlex_test_all_lexical.txt \n", - " inflating: ./poincare/data/splits/lexical/hyperlex_training_all_lexical.txt \n", - " inflating: ./poincare/data/hyperlex-all.txt \n", - " inflating: ./poincare/data/README.txt \n" + "Archive: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip\n", + " creating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/nouns-verbs/\n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/nouns-verbs/hyperlex-verbs.txt \n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/nouns-verbs/hyperlex-nouns.txt \n", + " creating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/splits/\n", + " creating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/splits/random/\n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/splits/random/hyperlex_training_all_random.txt \n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/splits/random/hyperlex_test_all_random.txt \n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/splits/random/hyperlex_dev_all_random.txt \n", + " creating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/splits/lexical/\n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/splits/lexical/hyperlex_dev_all_lexical.txt \n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/splits/lexical/hyperlex_test_all_lexical.txt \n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/splits/lexical/hyperlex_training_all_lexical.txt \n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/hyperlex-all.txt \n", + " inflating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/README.txt \n" ] } ], @@ -198,8 +242,9 @@ "# Prepare the HyperLex data\n", "hyperlex_url = \"http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\"\n", "! wget {hyperlex_url} -O {data_directory}/hyperlex-data.zip\n", - "! unzip {data_directory}/hyperlex-data.zip -d {data_directory}\n", - "hyperlex_file = os.path.join(data_directory, 'nouns-verbs', 'hyperlex-nouns.txt')" + "! rm -r {data_directory}/hyperlex\n", + "! unzip {data_directory}/hyperlex-data.zip -d {data_directory}/hyperlex/\n", + "hyperlex_file = os.path.join(data_directory, 'hyperlex', 'nouns-verbs', 'hyperlex-nouns.txt')" ] }, { @@ -220,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 138, "metadata": {}, "outputs": [], "source": [ @@ -268,7 +313,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -301,13 +346,13 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 150, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "def model_name_from_params(params, prefix):\n", + "def cpp_model_name_from_params(params, prefix):\n", " name = ['%s_%s' % (key, params[key]) for key in sorted(params.keys())]\n", " return '%s_%s' % (prefix, '_'.join(name))\n", "\n", @@ -320,8 +365,8 @@ " train_file (str): Path to tsv file containing relation pairs\n", " model_sizes (list): list of dimension sizes (integer) to train the model with\n", " prefix (str): prefix to use for the saved model filenames\n", - " implementation (str): whether to use the python or c++ implementation,\n", - " allowed values: 'python', 'c++'\n", + " implementation (str): whether to use the numpy or c++ implementation,\n", + " allowed values: 'numpy', 'c++'\n", " \n", " Returns:\n", " tuple (model_name, model_files)\n", @@ -331,19 +376,19 @@ " files = {}\n", " if implementation == 'c++':\n", " model_name = cpp_model_name_from_params(params, prefix)\n", - " elif implementation == 'python':\n", + " elif implementation == 'numpy':\n", " model_name = np_model_name_from_params(params, prefix)\n", " \n", " for model_size in model_sizes:\n", " output_file_name = '%s_dim_%d' % (model_name, model_size)\n", " output_file = os.path.join(models_directory, output_file_name)\n", - " print('Training model %s' % output_file)\n", + " print('Training model %s of size %d' % (model_name, model_size))\n", " if implementation == 'c++':\n", " out = train_cpp_model(\n", " cpp_binary_path, train_file, output_file, model_size,\n", " params['epochs'], params['neg'], params['threads'],\n", " params['eps'], params['burn_in'], seed=0)\n", - " elif implementation == 'python':\n", + " elif implementation == 'numpy':\n", " train_external_numpy_model(\n", " python_script_path, train_file, output_file, model_size,\n", " params['epochs'], params['neg'], seed=0)\n", @@ -386,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 145, "metadata": {}, "outputs": [], "source": [ @@ -395,7 +440,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 152, "metadata": {}, "outputs": [], "source": [ @@ -442,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 153, "metadata": {}, "outputs": [], "source": [ @@ -456,7 +501,7 @@ "outputs": [], "source": [ "# Train models with default params\n", - "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'np_model', 'python')\n", + "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'np_model', 'numpy')\n", "np_model_files[model_name] = {}\n", "for dim, filepath in files.items():\n", " np_model_files[model_name][dim] = filepath" From 17390ac93e5c53f13f69c0a6fc743ead49bfd16f Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 25 Oct 2017 19:28:33 +0200 Subject: [PATCH 17/28] Adds results of numpy poincare embeddings on link prediction, minor improvements --- docs/notebooks/Poincare Evaluation.ipynb | 157 ++++++++++++++--------- 1 file changed, 97 insertions(+), 60 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 8f87ce4293..110edb6a1e 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -11,12 +11,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This notebook demonstrates how well poincare embeddings trained using this [implementation](https://github.com/TatsuyaShirakawa/poincare-embedding) perform on the tasks detailed in the [original paper](https://arxiv.org/pdf/1705.08039.pdf).\n", + "This notebook demonstrates how well Poincare embeddings perform on the tasks detailed in the [original paper](https://arxiv.org/pdf/1705.08039.pdf) about the embeddings.\n", + "\n", + "The following two external, open-source implementations are used - \n", + "1. [C++](https://github.com/TatsuyaShirakawa/poincare-embedding)\n", + "2. [Numpy](https://github.com/nishnik/poincare_embeddings)\n", "\n", "This is the list of tasks - \n", "1. WordNet reconstruction\n", "2. WordNet link prediction\n", - "3. Link prediction in collaboration networks\n", + "3. Link prediction in collaboration networks (evaluation incomplete)\n", "4. Lexical entailment on HyperLex\n", "\n", "A more detailed explanation of the tasks and the evaluation methodology is present in the individual evaluation subsections." @@ -33,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -251,12 +255,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 2.2 Training C++ embeddings" + "### 2.2 Training [C++ embeddings](https://github.com/TatsuyaShirakawa/poincare-embedding)" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -265,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -313,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -337,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -346,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 24, "metadata": { "scrolled": true }, @@ -378,7 +382,8 @@ " model_name = cpp_model_name_from_params(params, prefix)\n", " elif implementation == 'numpy':\n", " model_name = np_model_name_from_params(params, prefix)\n", - " \n", + " else:\n", + " raise ValueError('Given implementation %s not found' % implementation)\n", " for model_size in model_sizes:\n", " output_file_name = '%s_dim_%d' % (model_name, model_size)\n", " output_file = os.path.join(models_directory, output_file_name)\n", @@ -431,7 +436,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -440,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -487,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -525,7 +530,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -715,7 +720,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -767,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -966,7 +971,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -982,6 +987,11 @@ " Returns\n", " (train_file, test_file): tuple of strings with train file and test file paths\n", " \"\"\"\n", + " train_filename = data_file + '.train'\n", + " test_filename = data_file + '.test'\n", + " if os.path.exists(train_filename) and os.path.exists(test_filename):\n", + " print('Train and test files already exist, skipping')\n", + " return (train_filename, test_filename)\n", " root_nodes, leaf_nodes = get_root_and_leaf_nodes(data_file)\n", " test_line_candidates = []\n", " line_count = 0\n", @@ -1007,8 +1017,6 @@ " test_line_indices = set(random.sample(test_line_candidates, num_test_lines))\n", " train_line_indices = set(l for l in range(line_count) if l not in test_line_indices)\n", " \n", - " train_filename = data_file + '.train'\n", - " test_filename = data_file + '.test'\n", " train_set_nodes = set()\n", " with open(data_file, 'rb') as f:\n", " train_file = open(train_filename, 'wb')\n", @@ -1029,7 +1037,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -1063,14 +1071,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Choosing 74324 test lines from 109577 candidates\n" + "Train and test files already exist, skipping\n" ] } ], @@ -1101,12 +1109,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Train models with default params\n", + "# Train c++ models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'cpp_lp_model', 'c++')\n", "lp_model_files[model_name] = {}\n", "for dim, filepath in files.items():\n", " lp_model_files[model_name][dim] = filepath\n", - "# Train models with non-default params\n", + "# Train c++ models with non-default params\n", "for param, values in non_default_params.items():\n", " params = default_params.copy()\n", " for value in values:\n", @@ -1119,7 +1127,21 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lp_np_model_files = {}\n", + "# Train numpy models with default params\n", + "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'np_lp_model', 'numpy')\n", + "lp_np_model_files[model_name] = {}\n", + "for dim, filepath in files.items():\n", + " lp_np_model_files[model_name][dim] = filepath" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1128,7 +1150,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1138,6 +1160,18 @@ " lp_embeddings[model_name][model_size] = PoincareEmbedding.load_poincare_cpp(model_file)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for model_name, models in lp_np_model_files.items():\n", + " lp_embeddings[model_name] = {}\n", + " for model_size, model_file in models.items():\n", + " lp_embeddings[model_name][model_size] = PoincareEmbedding.load_poincare_numpy(model_file)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1147,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1252,7 +1286,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1280,7 +1314,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1288,36 +1322,39 @@ "output_type": "stream", "text": [ "Results for WordNet Link Prediction task\n", - "+--------------------------------------------------------------------------+-----------------------------------------------------+\n", - "| | Model Dimensions |\n", - "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", - "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 182.03 | 107.04 | 63.29 | 72.67 | 73.64 | 60.35 |\n", - "| | MAP | 0.16 | 0.25 | 0.31 | 0.34 | 0.36 | 0.37 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", - "| | MAP | 0.10 | 0.13 | 0.14 | 0.15 | 0.16 | 0.16 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 232.82 | 98.78 | 53.79 | 49.79 | 46.52 | 47.03 |\n", - "| | MAP | 0.15 | 0.25 | 0.33 | 0.39 | 0.40 | 0.41 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 190.93 | 116.63 | 68.68 | 43.67 | 55.01 | 66.02 |\n", - "| | MAP | 0.17 | 0.24 | 0.29 | 0.36 | 0.37 | 0.35 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", - "| | MAP | 0.14 | 0.22 | 0.28 | 0.31 | 0.33 | 0.34 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 224.50 | 199.29 | 184.29 | 178.89 | 155.52 | 157.39 |\n", - "| | MAP | 0.09 | 0.14 | 0.15 | 0.16 | 0.17 | 0.18 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", - "| | MAP | 0.12 | 0.15 | 0.31 | 0.35 | 0.36 | 0.36 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", - "| | MAP | 0.15 | 0.24 | 0.31 | 0.35 | 0.36 | 0.36 |\n", - "| | | | | | | | |\n", - "+--------------------------------------------------------------+-----------+--------+--------+--------+--------+--------+--------+\n" + "+--------------------------------------------------------------------------+-----------------------------------------------------------+\n", + "| | Model Dimensions |\n", + "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+--------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+--------+\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", + "| | MAP | 0.14 | 0.22 | 0.28 | 0.31 | 0.33 | 0.34 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 224.50 | 199.29 | 184.29 | 178.89 | 155.52 | 157.39 |\n", + "| | MAP | 0.09 | 0.14 | 0.15 | 0.16 | 0.17 | 0.18 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 182.03 | 107.04 | 63.29 | 72.67 | 73.64 | 60.35 |\n", + "| | MAP | 0.16 | 0.25 | 0.31 | 0.34 | 0.36 | 0.37 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", + "| | MAP | 0.15 | 0.24 | 0.31 | 0.35 | 0.36 | 0.36 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", + "| | MAP | 0.12 | 0.15 | 0.31 | 0.35 | 0.36 | 0.36 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 232.82 | 98.78 | 53.79 | 49.79 | 46.52 | 47.03 |\n", + "| | MAP | 0.15 | 0.25 | 0.33 | 0.39 | 0.40 | 0.41 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 190.93 | 116.63 | 68.68 | 43.67 | 55.01 | 66.02 |\n", + "| | MAP | 0.17 | 0.24 | 0.29 | 0.36 | 0.37 | 0.35 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", + "| | MAP | 0.10 | 0.13 | 0.14 | 0.15 | 0.16 | 0.16 |\n", + "| | | | | | | | |\n", + "| np_lp_model_epochs_50_neg_20 | mean_rank | 14443.26 | 7432.58 | 4299.32 | 2617.49 | 1398.49 | 842.81 |\n", + "| | MAP | 0.00 | 0.02 | 0.03 | 0.07 | 0.08 | 0.13 |\n", + "| | | | | | | | |\n", + "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+--------+\n" ] } ], From e80a834b9ca984a005afa22e187b171bfdf243a4 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Thu, 26 Oct 2017 09:50:37 +0200 Subject: [PATCH 18/28] Minor fixes to poincare eval notebook --- docs/notebooks/Poincare Evaluation.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index 110edb6a1e..d1df222692 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -32,7 +32,7 @@ "source": [ "## 1. Setup\n", "\n", - "The following code clones the `poincare-embedding` repository containing the C++ implementation of the Poincare embeddings, and applies a patch containing minor additions to the implementation. Please set the variable `parent_directory` below to define the directory to which the repository is cloned." + "The following code clones the repositories containing the C++ and Numpy implementations of the Poincare embeddings, and applies a patch containing minor changes to the implementations. Please set the variable `parent_directory` below to change the directory to which the repositories is cloned." ] }, { @@ -199,7 +199,7 @@ "source": [ "# Prepare the WordNet data\n", "wordnet_file = os.path.join(data_directory, 'wordnet_noun_hypernyms.tsv')\n", - "! python {parent_directory}/poincare-embedding/scripts/create_wordnet_noun_hierarchy.py {wordnet_file}" + "! python {parent_directory}/{cpp_repo_name}/scripts/create_wordnet_noun_hierarchy.py {wordnet_file}" ] }, { From 1e7ddd8616d66ea3d5556029774f4e8efd666a57 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Thu, 26 Oct 2017 14:05:26 +0200 Subject: [PATCH 19/28] Adds poincare nb requirements, moves imports to beginning --- docs/notebooks/Poincare Evaluation.ipynb | 191 ++++++++++++++--------- docs/notebooks/poincare/requirements.txt | 4 + 2 files changed, 121 insertions(+), 74 deletions(-) create mode 100644 docs/notebooks/poincare/requirements.txt diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index d1df222692..b1b39c245f 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -32,20 +32,91 @@ "source": [ "## 1. Setup\n", "\n", - "The following code clones the repositories containing the C++ and Numpy implementations of the Poincare embeddings, and applies a patch containing minor changes to the implementations. Please set the variable `parent_directory` below to change the directory to which the repositories is cloned." + "The following section performs the following - \n", + "1. Imports required python libraries and downloads the wordnet data\n", + "2. Clones the repositories containing the C++ and Numpy implementations of the Poincare embeddings\n", + "3. Applies patches containing minor changes to the implementations.\n", + "4. Compiles the C++ sources to create a binary" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package wordnet to /home/jayant/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "import csv\n", + "from collections import defaultdict, OrderedDict\n", + "import itertools\n", "import os\n", - "current_directory = os.getcwd()\n", + "import pickle\n", + "import random\n", + "import re\n", + "\n", + "import click\n", + "from gensim.models.keyedvectors import KeyedVectors\n", + "from gensim.utils import check_output\n", + "import nltk\n", + "import numpy as np\n", + "from prettytable import PrettyTable\n", + "from pygtrie import Trie\n", + "from scipy.spatial.distance import euclidean, pdist\n", + "from scipy.stats import spearmanr\n", + "from smart_open import smart_open\n", "\n", + "nltk.download('wordnet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that not all the above libraries are part of the gensim dependencies, so they might need to be installed separately. These requirements are listed in the poincare [requirements.txt](poincare/requirements.txt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Please set the variable `parent_directory` below to change the directory to which the repositories are cloned." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "current_directory = os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ "# Change this variable to `False` to not remove and re-download repos for external implementations\n", - "force_setup = True\n", + "force_setup = False\n", "\n", "# The poincare datasets, models and source code for external models are downloaded to this directory\n", "parent_directory = os.path.join(current_directory, 'poincare')\n", @@ -83,13 +154,15 @@ "np_repo_name = 'poincare-np-embedding'\n", "if force_setup and os.path.exists(np_repo_name):\n", " ! rm -rf {np_repo_name}\n", - "if not os.path.exists(np_repo_name):\n", + "clone_np_repo = not os.path.exists(np_repo_name)\n", + "if clone_np_repo:\n", " ! git clone https://github.com/nishnik/poincare_embeddings.git {np_repo_name}\n", "\n", "cpp_repo_name = 'poincare-cpp-embedding'\n", "if force_setup and os.path.exists(cpp_repo_name):\n", " ! rm -rf {cpp_repo_name}\n", - "if not os.path.exists(cpp_repo_name):\n", + "clone_cpp_repo = not os.path.exists(cpp_repo_name)\n", + "if clone_cpp_repo:\n", " ! git clone https://github.com/TatsuyaShirakawa/poincare-embedding.git {cpp_repo_name}\n", "\n", "patches_applied = False" @@ -97,34 +170,25 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 42, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-cpp-embedding\n", - "/home/jayant/projects/gensim/docs/notebooks/poincare/poincare-np-embedding\n", - "/home/jayant/projects/gensim/docs/notebooks\n" - ] - } - ], + "outputs": [], "source": [ "# Apply patches\n", - "if not patches_applied:\n", + "if clone_cpp_repo and not patches_applied:\n", " % cd {cpp_repo_name}\n", " ! git apply ../poincare_burn_in_eps.patch\n", "\n", + "if clone_np_repo and not patches_applied:\n", " % cd ../{np_repo_name}\n", " ! git apply ../poincare_numpy.patch\n", " \n", - " patches_applied = True" + "patches_applied = True" ] }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -136,6 +200,7 @@ "-- Configuring done\n", "-- Generating done\n", "-- Build files have been written to: /home/jayant/projects/gensim/docs/notebooks/poincare/poincare-cpp-embedding/work\n", + "[ 50%] \u001b[32m\u001b[1mLinking CXX executable poincare_embedding\u001b[0m\n", "[100%] Built target poincare_embedding\n", "/home/jayant/projects/gensim/docs/notebooks\n" ] @@ -155,7 +220,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You might need to install an updated version of `cmake` to be able to compile the source code. Please make sure that the binary `poincare-embedding` has been created before proceeding." + "You might need to install an updated version of `cmake` to be able to compile the source code. Please make sure that the binary `poincare_embedding` has been created before proceeding by verifying the above cell does not raise an error." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "cpp_binary_path = os.path.join(parent_directory, cpp_repo_name, 'work', 'poincare_embedding')\n", + "assert(os.path.exists(cpp_binary_path)), 'Binary file doesnt exist at %s' % cpp_binary_path" ] }, { @@ -169,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -184,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -204,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": 54, "metadata": { "scrolled": true }, @@ -213,7 +288,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2017-10-25 10:56:39-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", + "--2017-10-26 14:00:59-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", "Resolving people.ds.cam.ac.uk (people.ds.cam.ac.uk)... 131.111.3.47\n", "Connecting to people.ds.cam.ac.uk (people.ds.cam.ac.uk)|131.111.3.47|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", @@ -222,7 +297,7 @@ "\n", "/home/jayant/projec 100%[===================>] 179.59K --.-KB/s in 0.06s \n", "\n", - "2017-10-25 10:56:39 (2.82 MB/s) - ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", + "2017-10-26 14:00:59 (2.94 MB/s) - ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", "\n", "Archive: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip\n", " creating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/nouns-verbs/\n", @@ -246,7 +321,8 @@ "# Prepare the HyperLex data\n", "hyperlex_url = \"http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\"\n", "! wget {hyperlex_url} -O {data_directory}/hyperlex-data.zip\n", - "! rm -r {data_directory}/hyperlex\n", + "if os.path.exists(os.path.join(data_directory, 'hyperlex')):\n", + " ! rm -r {data_directory}/hyperlex\n", "! unzip {data_directory}/hyperlex-data.zip -d {data_directory}/hyperlex/\n", "hyperlex_file = os.path.join(data_directory, 'hyperlex', 'nouns-verbs', 'hyperlex-nouns.txt')" ] @@ -260,21 +336,10 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 55, "metadata": {}, "outputs": [], "source": [ - "cpp_binary_path = os.path.join(parent_directory, cpp_repo_name, 'work', 'poincare_embedding')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from gensim.utils import check_output\n", - "\n", "def train_cpp_model(\n", " binary_path, data_file, output_file, dim, epochs, neg,\n", " num_threads, epsilon, burn_in, seed=0):\n", @@ -317,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -341,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +415,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 58, "metadata": { "scrolled": true }, @@ -436,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -445,12 +510,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ - "from gensim.utils import check_output\n", - "\n", "def np_model_name_from_params(params, prefix):\n", " param_keys = ['neg', 'epochs']\n", " name = ['%s_%s' % (key, params[key]) for key in sorted(param_keys)]\n", @@ -492,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -521,7 +584,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 64, "metadata": {}, "outputs": [], "source": [ @@ -530,19 +593,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ - "import pickle\n", - "import re\n", - "\n", - "from gensim.models.keyedvectors import KeyedVectors\n", - "import numpy as np\n", - "from pygtrie import Trie\n", - "from scipy.spatial.distance import euclidean, pdist\n", - "from smart_open import smart_open\n", - "\n", "def transform_cpp_embedding_to_kv(input_file, output_file, encoding='utf8'):\n", " \"\"\"Given a C++ embedding tsv filepath, converts it to a KeyedVector-supported file\"\"\"\n", " with smart_open(input_file, 'rb') as f:\n", @@ -682,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -724,8 +778,6 @@ "metadata": {}, "outputs": [], "source": [ - "from prettytable import PrettyTable\n", - "\n", "def display_results(task_name, results):\n", " \"\"\"Display evaluation results of multiple embeddings on a single task in a tabular format\n", " \n", @@ -772,15 +824,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "import csv\n", - "from collections import defaultdict, OrderedDict\n", - "import itertools\n", - "\n", - "\n", "class ReconstructionEvaluation(object):\n", " \"\"\"Evaluating reconstruction on given network for given embedding\"\"\"\n", " def __init__(self, filepath, embedding):\n", @@ -971,12 +1018,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "import random\n", - "\n", "def train_test_split(data_file, test_ratio=0.1):\n", " \"\"\"Creates train and test files from given data file, returns train/test file names\n", " \n", @@ -1371,12 +1416,10 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "from scipy.stats import spearmanr\n", - "\n", "class LexicalEntailmentEvaluation(object):\n", " \"\"\"Evaluating reconstruction on given network for any embedding\"\"\"\n", " def __init__(self, filepath):\n", diff --git a/docs/notebooks/poincare/requirements.txt b/docs/notebooks/poincare/requirements.txt new file mode 100644 index 0000000000..faaea1399b --- /dev/null +++ b/docs/notebooks/poincare/requirements.txt @@ -0,0 +1,4 @@ +click==6.7 +nltk==3.2.5 +prettytable==0.7.2 +pygtrie==2.2 From 99089a5faa9e6aa7a6880ea65714005a066dfac1 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Thu, 26 Oct 2017 14:46:18 +0200 Subject: [PATCH 20/28] Doesnt load all models into memory at once --- docs/notebooks/Poincare Evaluation.ipynb | 280 +++++++++-------------- 1 file changed, 110 insertions(+), 170 deletions(-) diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index b1b39c245f..b9dff89bf2 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -188,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -225,7 +225,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -259,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 13, "metadata": { "scrolled": true }, @@ -288,16 +288,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2017-10-26 14:00:59-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", + "--2017-10-26 14:09:50-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", "Resolving people.ds.cam.ac.uk (people.ds.cam.ac.uk)... 131.111.3.47\n", "Connecting to people.ds.cam.ac.uk (people.ds.cam.ac.uk)|131.111.3.47|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 183900 (180K) [application/zip]\n", "Saving to: ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’\n", "\n", - "/home/jayant/projec 100%[===================>] 179.59K --.-KB/s in 0.06s \n", + "/home/jayant/projec 100%[===================>] 179.59K --.-KB/s in 0.08s \n", "\n", - "2017-10-26 14:00:59 (2.94 MB/s) - ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", + "2017-10-26 14:09:50 (2.23 MB/s) - ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", "\n", "Archive: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip\n", " creating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/nouns-verbs/\n", @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -406,16 +406,7 @@ }, { "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "model_files = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 58, + "execution_count": 20, "metadata": { "scrolled": true }, @@ -468,6 +459,15 @@ " return (model_name, files)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_files = {}" + ] + }, { "cell_type": "code", "execution_count": null, @@ -476,20 +476,21 @@ }, "outputs": [], "source": [ - "# Train models with default params\n", + "model_files['c++'] = {}\n", + "# Train c++ models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'cpp_model', 'c++')\n", - "model_files[model_name] = {}\n", + "model_files['c++'][model_name] = {}\n", "for dim, filepath in files.items():\n", - " model_files[model_name][dim] = filepath\n", - "# Train models with non-default params\n", + " model_files['c++'][model_name][dim] = filepath\n", + "# Train c++ models with non-default params\n", "for param, values in non_default_params.items():\n", " params = default_params.copy()\n", " for value in values:\n", " params[param] = value\n", " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'cpp_model', 'c++')\n", - " model_files[model_name] = {}\n", + " model_files['c++'][model_name] = {}\n", " for dim, filepath in files.items():\n", - " model_files[model_name][dim] = filepath" + " model_files['c++'][model_name][dim] = filepath" ] }, { @@ -555,24 +556,16 @@ }, { "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "np_model_files = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ + "model_files['numpy'] = {}\n", "# Train models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'np_model', 'numpy')\n", - "np_model_files[model_name] = {}\n", + "model_files['numpy'][model_name] = {}\n", "for dim, filepath in files.items():\n", - " np_model_files[model_name][dim] = filepath" + " model_files['numpy'][model_name][dim] = filepath" ] }, { @@ -584,16 +577,7 @@ }, { "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "embeddings = {}" - ] - }, - { - "cell_type": "code", - "execution_count": 65, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -724,45 +708,27 @@ "\n", " \"\"\"\n", " vector_1, vector_2 = self.kv[term_1], self.kv[term_2]\n", - " return self.poincare_dist(vector_1, vector_2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.1 C++ embeddings" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for model_name, models in model_files.items():\n", - " embeddings[model_name] = {}\n", - " for model_size, model_file in models.items():\n", - " embeddings[model_name][model_size] = PoincareEmbedding.load_poincare_cpp(model_file)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Numpy embeddings" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "for model_name, models in np_model_files.items():\n", - " embeddings[model_name] = {}\n", - " for model_size, model_file in models.items():\n", - " embeddings[model_name][model_size] = PoincareEmbedding.load_poincare_numpy(model_file)" + " return self.poincare_dist(vector_1, vector_2)\n", + "\n", + "def load_model(implementation, model_file):\n", + " \"\"\"Convenience function over PoincareEmbedding to load models from different implementations\n", + " \n", + " Args:\n", + " implementation (str): implementation used to create model file ('c++'/'numpy')\n", + " model_file (str): Path to model file\n", + " \n", + " Returns:\n", + " PoincareEmbedding instance loaded from `model_file`\n", + " \n", + " Notes:\n", + " Raises ValueError in case of invalid value for `implementation`\n", + " \"\"\"\n", + " if implementation == 'c++':\n", + " return PoincareEmbedding.load_poincare_cpp(model_file)\n", + " elif implementation == 'numpy':\n", + " return PoincareEmbedding.load_poincare_numpy(model_file)\n", + " else:\n", + " raise ValueError('Invalid implementation %s' % implementation)" ] }, { @@ -774,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -824,7 +790,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -924,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -940,21 +906,23 @@ }, "outputs": [], "source": [ - "for model_name, models in embeddings.items():\n", - " reconstruction_results[model_name] = OrderedDict()\n", - " for metric in metrics:\n", - " reconstruction_results[model_name][metric] = {}\n", - " for model_size, embedding in models.items():\n", - " print('Evaluating model %s of size %d' % (model_name, model_size))\n", - " eval_instance = ReconstructionEvaluation(wordnet_file, embedding)\n", - " eval_result = eval_instance.evaluate(max_n=1000)\n", + "for implementation, models in model_files.items():\n", + " for model_name, files in models.items():\n", + " reconstruction_results[model_name] = OrderedDict()\n", " for metric in metrics:\n", - " reconstruction_results[model_name][metric][model_size] = eval_result[metric]" + " reconstruction_results[model_name][metric] = {}\n", + " for model_size, model_file in files.items():\n", + " print('Evaluating model %s of size %d' % (model_name, model_size))\n", + " embedding = load_model(implementation, model_file)\n", + " eval_instance = ReconstructionEvaluation(wordnet_file, embedding)\n", + " eval_result = eval_instance.evaluate(max_n=1000)\n", + " for metric in metrics:\n", + " reconstruction_results[model_name][metric][model_size] = eval_result[metric]" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -1018,7 +986,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1082,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1116,7 +1084,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -1135,12 +1103,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 4.2.2 Training and loading models" + "#### 4.2.2 Training models" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1150,71 +1118,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ + "lp_model_files['c++'] = {}\n", "# Train c++ models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'cpp_lp_model', 'c++')\n", - "lp_model_files[model_name] = {}\n", + "lp_model_files['c++'][model_name] = {}\n", "for dim, filepath in files.items():\n", - " lp_model_files[model_name][dim] = filepath\n", + " lp_model_files['c++'][model_name][dim] = filepath\n", "# Train c++ models with non-default params\n", "for param, values in non_default_params.items():\n", " params = default_params.copy()\n", " for value in values:\n", " params[param] = value\n", " model_name, files = train_model_with_params(params, wordnet_train_file, model_sizes, 'cpp_lp_model', 'c++')\n", - " lp_model_files[model_name] = {}\n", + " lp_model_files['c++'][model_name] = {}\n", " for dim, filepath in files.items():\n", - " lp_model_files[model_name][dim] = filepath" + " lp_model_files['c++'][model_name][dim] = filepath" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "lp_np_model_files = {}\n", + "lp_model_files['numpy'] = {}\n", "# Train numpy models with default params\n", "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'np_lp_model', 'numpy')\n", - "lp_np_model_files[model_name] = {}\n", + "lp_model_files['numpy'][model_name] = {}\n", "for dim, filepath in files.items():\n", - " lp_np_model_files[model_name][dim] = filepath" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lp_embeddings = {}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for model_name, models in lp_model_files.items():\n", - " lp_embeddings[model_name] = {}\n", - " for model_size, model_file in models.items():\n", - " lp_embeddings[model_name][model_size] = PoincareEmbedding.load_poincare_cpp(model_file)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for model_name, models in lp_np_model_files.items():\n", - " lp_embeddings[model_name] = {}\n", - " for model_size, model_file in models.items():\n", - " lp_embeddings[model_name][model_size] = PoincareEmbedding.load_poincare_numpy(model_file)" + " lp_model_files['numpy'][model_name][dim] = filepath" ] }, { @@ -1226,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -1331,7 +1267,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ @@ -1345,21 +1281,23 @@ "metadata": {}, "outputs": [], "source": [ - "for model_name, models in lp_embeddings.items():\n", - " lp_results[model_name] = OrderedDict()\n", - " for metric in metrics:\n", - " lp_results[model_name][metric] = {}\n", - " for model_size, embedding in models.items():\n", - " print('Evaluating model %s of size %d' % (model_name, model_size))\n", - " eval_instance = LinkPredictionEvaluation(wordnet_train_file, wordnet_test_file, embedding)\n", - " eval_result = eval_instance.evaluate(max_n=1000)\n", + "for implementation, models in lp_model_files.items():\n", + " for model_name, files in models.items():\n", + " lp_results[model_name] = OrderedDict()\n", " for metric in metrics:\n", - " lp_results[model_name][metric][model_size] = eval_result[metric]" + " lp_results[model_name][metric] = {}\n", + " for model_size, model_file in files.items():\n", + " print('Evaluating model %s of size %d' % (model_name, model_size))\n", + " embedding = load_model(implementation, model_file)\n", + " eval_instance = LinkPredictionEvaluation(wordnet_train_file, wordnet_test_file, embedding)\n", + " eval_result = eval_instance.evaluate(max_n=1000)\n", + " for metric in metrics:\n", + " lp_results[model_name][metric][model_size] = eval_result[metric]" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1416,7 +1354,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -1491,7 +1429,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -1505,17 +1443,19 @@ "metadata": {}, "outputs": [], "source": [ - "for model_name, models in embeddings.items():\n", - " entailment_results[model_name] = {}\n", - " entailment_results[model_name]['spearman'] = {}\n", - " for model_size, embedding in models.items():\n", - " print('Evaluating model %s of size %d' % (model_name, model_size))\n", - " entailment_results[model_name]['spearman'][model_size] = eval_instance.evaluate_spearman(embedding)" + "for implementation, models in model_files.items():\n", + " for model_name, files in models.items():\n", + " entailment_results[model_name] = OrderedDict()\n", + " entailment_results[model_name]['spearman'] = {}\n", + " for model_size, model_file in files.items():\n", + " print('Evaluating model %s of size %d' % (model_name, model_size))\n", + " embedding = load_model(implementation, model_file)\n", + " entailment_results[model_name]['spearman'][model_size] = eval_instance.evaluate_spearman(embedding)" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 51, "metadata": {}, "outputs": [ { From 0ae0f96b52c815278c0781c5af9d963a7fcd5057 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Wed, 15 Nov 2017 19:07:56 +0530 Subject: [PATCH 21/28] Add Poincare model (#1696) * Initial classes and loading data for poincare model * Initial implementation of training using autograd * faster negative sampling, bugfix in vector updates * allows poincare dist function to be differentiable by autograd * batched gradient descent initial implementation * minor changes to batch poincare distance computation * Adds calculation of gradients for poincare model * Correct implementation of clipping of updated vectors * Fixes error in gradient computation * Better messages while training * Renames PoincareDistance to PoincareExample for clarity * Compares computed gradients to autograd gradients every few iterations * Avoids doing some numpy computations twice * Avoids creating copies of numpy vectors * Only calls nan_to_num when gamma has at least one value equal to 1 * Simply sets nan gradients to zero instead of nan_to_num * Adds batch-wise implementation of training and gradient computations * Minor correction in clipping * Fixes typo in clip_vectors * Prints average loss every few iterations instead of current loss * Adds weighted negative sampling * Ensures positive edges are not returned by negative sampling * Poincare model stores node indices in relations instead of node keys * Minor renaming; uses node indices for batch training instead of node keys * Changes shapes of vectors passed to PoincareBatch * Minor bugfixes related to batch size * Corrects implementation of negative sampling for batch training * Adds option to check gradients in batchwise training * Checks gradients only every few iterations * Handles multiple occurrence of same node across and within batches * Removes unused section of code * Implements slightly different clipping method * Fixes bugs with wrong reshape in batchwise training * Example-wise training takes into account multiple occurrences of same node in an example too * Batchwise training prints average loss over many iterations instead of current batch * Fixes bug in updating vector for batchwise training * Faster implementation of negative sampling * Negative sampling for a node follows different paths depending on fraction of positive relations * Uses a buffer for negative samples to reduce calls to np.random.choice * Cleans up poincare.py, removes unused code * Adds shapes to PoincareBatch, more documentation * Adds more documentation to PoincareModel * Stores indices for nodes in a batch in PoincareBatch for better encapsulation * More documentation for poincare module * Implements burn-in for poincare model * Slightly better logging for poincare model * Uses np.random.random and np.searchsorted for random sampling rather than np.random.choice * Removes duplicates in negative samples * Moves helper classes in poincare after PoincareModel * Change in PoincareModel API to allow initializing from an iterable, separate class for streaming from file * Adds failing test for handling encoding in PoincareData * Fixes encoding handling in PoincareData * Adds docstrings to PoincareData, PoincareData streams tuples now * More unittests for PoincareModel * Changes handle_duplicates to staticmethod, adds test * Adds batch size and print_every parameters to train method * Renames print_check to should_print * Adds separate parameter for checking gradients * Minor fixes for coding style * Removes default values from docstrings, redundant * Adds example to PoincareModel init docstring * Extracts buffer for negatives out into a separate class * More detailed logging, fix to check_gradients * Minor fixes to documentation in poincare.py * Adds tests for gradients checking * Raise AssertionError if gradients check fails * Adds failing tests for saving/loading PoincareModel instances * Fixes bug with saving/loading PoincareModel to disk * Adds test and fix for raising error on invalid input data * Adds test and fix for no duplicates and positives in negative sample * Bugfix with NegativesBuffer having less than items left * Uses larger data for poincare tests, adds data files * Bugfix with incorrect use of random state * Minor fixes in documentation style * Renames PoincareData to PoincareRelations * Change in the order of conditions checked before resampling * Imports datapath from test.utils instead of defining own * Adds working examples and a more detailed description in docstring * Renames term_relations to node_relations * Removes unused imports * Moves iter parameter to train instead of __init__, renames to epochs * Fixes term_relations in tests * Adds option to disable gradient check, disabled by default * Extracts gradient checking code into a separate method * Conditionally import autograd only if gradient checking is enabled * Marks private methods in poincare module with leading underscore * Adds init_range as an API parameter to PoincareModel * Marks private properties with a leading underscore * Fixes bug with burn-in happening on subsequent calls to train * Adds test for training multiple times * Adds autograd to test dependencies * Renames wv to kv in PoincareModel * add numpy==1.12 as test dependency * add missing quote * try to run tests without autograd * fix PEP8 in poincare.py * fix PEP8 in test_poincare * PoincareRelations handles python2 correctly * Bugfix with int division for python2 * Imports mock module for tests correctly in python2 * Cleaner implementation of __iter__ for PoincareRelations * Adds rst file and updates apiref.rst for poincare module * Adds clarifying comment to PoincareRelations.__iter__ * Updates rst file for poincare * Renames hypernym pair to relations everywhere * Simpler way of detecting duplicates * Minor documentation updates in poincare.py * Skips gradients test if autograd not installed, adds test for bytes input data * Fix flake8 (noqa + remove unused var) * Fix missing mock dependency for win * Fix links in docstrings * Changes error message for negative sampling failing * Adds option to specify dtype for PoincareModel and corresponding unittest * Extends test for dtype to check after training, updates docstring --- appveyor.yml | 2 +- docs/src/apiref.rst | 1 + docs/src/models/poincare.rst | 10 + gensim/models/poincare.py | 848 ++++++++++++++++++ gensim/test/test_data/poincare_cp852.tsv | 2 + gensim/test/test_data/poincare_hypernyms.tsv | 5 + .../test_data/poincare_hypernyms_large.tsv | 95 ++ gensim/test/test_data/poincare_utf8.tsv | 2 + gensim/test/test_poincare.py | 216 +++++ setup.py | 1 + 10 files changed, 1181 insertions(+), 1 deletion(-) create mode 100644 docs/src/models/poincare.rst create mode 100644 gensim/models/poincare.py create mode 100644 gensim/test/test_data/poincare_cp852.tsv create mode 100644 gensim/test/test_data/poincare_hypernyms.tsv create mode 100644 gensim/test/test_data/poincare_hypernyms_large.tsv create mode 100644 gensim/test/test_data/poincare_utf8.tsv create mode 100644 gensim/test/test_poincare.py diff --git a/appveyor.yml b/appveyor.yml index 50de6882d8..994b638433 100644 --- a/appveyor.yml +++ b/appveyor.yml @@ -79,7 +79,7 @@ test_script: # installed library. - "mkdir empty_folder" - "cd empty_folder" - - "pip install pyemd testfixtures sklearn Morfessor==2.0.2a4" + - "pip install pyemd mock testfixtures sklearn Morfessor==2.0.2a4" - "pip freeze" - "python -c \"import nose; nose.main()\" -s -v gensim" # Move back to the project folder diff --git a/docs/src/apiref.rst b/docs/src/apiref.rst index 3538dca954..9003258d25 100644 --- a/docs/src/apiref.rst +++ b/docs/src/apiref.rst @@ -44,6 +44,7 @@ Modules: models/doc2vec models/fasttext models/phrases + models/poincare models/coherencemodel models/basemodel models/callbacks diff --git a/docs/src/models/poincare.rst b/docs/src/models/poincare.rst new file mode 100644 index 0000000000..c4e057367f --- /dev/null +++ b/docs/src/models/poincare.rst @@ -0,0 +1,10 @@ +:mod:`models.poincare` -- Train and use Poincare embeddings +============================================================= + +.. automodule:: gensim.models.poincare + :synopsis: Train and use Poincare embeddings + :members: + :inherited-members: + :special-members: __iter__, __getitem__, __contains__ + :undoc-members: + :show-inheritance: diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py new file mode 100644 index 0000000000..6a5b5f0ccd --- /dev/null +++ b/gensim/models/poincare.py @@ -0,0 +1,848 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Author: Jayant Jain +# Copyright (C) 2017 Radim Rehurek +# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html + + +"""Python implementation of Poincare Embeddings [1]_, an embedding that is better at capturing latent hierarchical +information better than traditional Euclidean embeddings. The method is described in more detail in [1]_. + +The main use-case is to automatically learn hierarchical representations of nodes from a tree-like structure, +such as a Directed Acyclic Graph, using the transitive closure of the relations. Representations of nodes in a +symmetric graph can also be learned, using an iterable of the relations in the graph. + +This module allows training a Poincare Embedding from a training file containing relations of graph in a +csv-like format. + +.. [1] Maximilian Nickel, Douwe Kiela - "PoincarĂ© Embeddings for Learning Hierarchical Representations" + https://arxiv.org/pdf/1705.08039.pdf + +Examples: +--------- +Initialize and train a model from a list: + +>>> from gensim.models.poincare import PoincareModel +>>> relations = [('kangaroo', 'marsupial'), ('kangaroo', 'mammal'), ('gib', 'cat')] +>>> model = PoincareModel(relations, negative=2) +>>> model.train(epochs=50) + +Initialize and train a model from a file containing one relation per line: + +>>> from gensim.models.poincare import PoincareModel, PoincareRelations +>>> from gensim.test.utils import datapath +>>> file_path = datapath('poincare_hypernyms.tsv') +>>> model = PoincareModel(PoincareRelations(file_path), negative=2) +>>> model.train(epochs=50) + +""" + + +import csv +import logging +import sys +import time + +import numpy as np +from collections import defaultdict, Counter +from numpy import random as np_random +from smart_open import smart_open + +from gensim import utils +from gensim.models.keyedvectors import KeyedVectors, Vocab + + +logger = logging.getLogger(__name__) + + +class PoincareModel(utils.SaveLoad): + """Class for training, using and evaluating Poincare Embeddings. + + The model can be stored/loaded via its :meth:`~gensim.models.poincare.PoincareModel.save` + and :meth:`~gensim.models.poincare.PoincareModel.load` methods, or stored/loaded in the word2vec format + via `model.kv.save_word2vec_format` and :meth:`~gensim.models.keyedvectors.KeyedVectors.load_word2vec_format`. + + Note that training cannot be resumed from a model loaded via `load_word2vec_format`, if you wish to train further, + use :meth:`~gensim.models.poincare.PoincareModel.save` and :meth:`~gensim.models.poincare.PoincareModel.load` + methods instead. + + """ + def __init__(self, train_data, size=50, alpha=0.1, negative=10, workers=1, epsilon=1e-5, + burn_in=10, burn_in_alpha=0.01, init_range=(-0.001, 0.001), dtype=np.float64, seed=0): + """Initialize and train a Poincare embedding model from an iterable of relations. + + Parameters + ---------- + train_data : iterable of (str, str) + Iterable of relations, e.g. a list of tuples, or a PoincareRelations instance streaming from a file. + Note that the relations are treated as ordered pairs, i.e. a relation (a, b) does not imply the + opposite relation (b, a). In case the relations are symmetric, the data should contain both relations + (a, b) and (b, a). + size : int, optional + Number of dimensions of the trained model. + alpha : float, optional + Learning rate for training. + negative : int, optional + Number of negative samples to use. + workers : int, optional + Number of threads to use for training the model. + epsilon : float, optional + Constant used for clipping embeddings below a norm of one. + burn_in : int, optional + Number of epochs to use for burn-in initialization (0 means no burn-in). + burn_in_alpha : float, optional + Learning rate for burn-in initialization, ignored if `burn_in` is 0. + init_range : 2-tuple (float, float) + Range within which the vectors are randomly initialized. + dtype : numpy.dtype + The numpy dtype to use for the vectors in the model (numpy.float64, numpy.float32 etc). + Using lower precision floats may be useful in increasing training speed and reducing memory usage. + seed : int, optional + Seed for random to ensure reproducibility. + + Examples + -------- + Initialize a model from a list: + + >>> from gensim.models.poincare import PoincareModel + >>> relations = [('kangaroo', 'marsupial'), ('kangaroo', 'mammal'), ('gib', 'cat')] + >>> model = PoincareModel(relations, negative=2) + + Initialize a model from a file containing one relation per line: + + >>> from gensim.models.poincare import PoincareModel, PoincareRelations + >>> from gensim.test.utils import datapath + >>> file_path = datapath('poincare_hypernyms.tsv') + >>> model = PoincareModel(PoincareRelations(file_path), negative=2) + + See :class:`~gensim.models.poincare.PoincareRelations` for more options. + + """ + self.train_data = train_data + self.kv = KeyedVectors() + self.size = size + self.train_alpha = alpha # Learning rate for training + self.burn_in_alpha = burn_in_alpha # Learning rate for burn-in + self.alpha = alpha # Current learning rate + self.negative = negative + self.workers = workers + self.epsilon = epsilon + self.burn_in = burn_in + self._burn_in_done = False + self.dtype = dtype + self.seed = seed + self._np_random = np_random.RandomState(seed) + self.init_range = init_range + self._loss_grad = None + self._load_relations() + self._init_embeddings() + + def _load_relations(self): + """Load relations from the train data and build vocab.""" + vocab = {} + index2word = [] + all_relations = [] # List of all relation pairs + node_relations = defaultdict(set) # Mapping from node index to its related node indices + + logger.info("Loading relations from train data..") + for relation in self.train_data: + if len(relation) != 2: + raise ValueError('Relation pair "%s" should have exactly two items' % repr(relation)) + for item in relation: + if item in vocab: + vocab[item].count += 1 + else: + vocab[item] = Vocab(count=1, index=len(index2word)) + index2word.append(item) + node_1, node_2 = relation + node_1_index, node_2_index = vocab[node_1].index, vocab[node_2].index + node_relations[node_1_index].add(node_2_index) + relation = (node_1_index, node_2_index) + all_relations.append(relation) + logger.info("Loaded %d relations from train data, %d unique terms", len(all_relations), len(vocab)) + self.kv.vocab = vocab + self.kv.index2word = index2word + self.indices_set = set((range(len(index2word)))) # Set of all node indices + self.indices_array = np.array(range(len(index2word))) # Numpy array of all node indices + counts = np.array([self.kv.vocab[index2word[i]].count for i in range(len(index2word))], dtype=np.float64) + self._node_probabilities = counts / counts.sum() + self._node_probabilities_cumsum = np.cumsum(self._node_probabilities) + self.all_relations = all_relations + self.node_relations = node_relations + self._negatives_buffer = NegativesBuffer([]) # Buffer for negative samples, to reduce calls to sampling method + self._negatives_buffer_size = 2000 + + def _init_embeddings(self): + """Randomly initialize vectors for the items in the vocab.""" + shape = (len(self.kv.index2word), self.size) + self.kv.syn0 = self._np_random.uniform(self.init_range[0], self.init_range[1], shape).astype(self.dtype) + + def _get_candidate_negatives(self): + """Returns candidate negatives of size `self.negative` from the negative examples buffer. + + Returns + -------- + numpy.array + Array of shape (`self.negative`,) containing indices of negative nodes. + + """ + + if self._negatives_buffer.num_items() < self.negative: + # Note: np.random.choice much slower than random.sample for large populations, possible bottleneck + uniform_numbers = self._np_random.random_sample(self._negatives_buffer_size) + cumsum_table_indices = np.searchsorted(self._node_probabilities_cumsum, uniform_numbers) + self._negatives_buffer = NegativesBuffer(cumsum_table_indices) + return self._negatives_buffer.get_items(self.negative) + + def _sample_negatives(self, node_index): + """Return a sample of negatives for the given node. + + Parameters + ---------- + node_index : int + Index of the positive node for which negative samples are to be returned. + + Returns + -------- + numpy.array + Array of shape (self.negative,) containing indices of negative nodes for the given node index. + + """ + node_relations = self.node_relations[node_index] + num_remaining_nodes = len(self.kv.vocab) - len(node_relations) + if num_remaining_nodes < self.negative: + raise ValueError( + 'Cannot sample %d negative nodes from a set of %d negative nodes for %s' % + (self.negative, num_remaining_nodes, self.kv.index2word[node_index]) + ) + + positive_fraction = len(node_relations) / len(self.kv.vocab) + if positive_fraction < 0.01: + # If number of positive relations is a small fraction of total nodes + # re-sample till no positively connected nodes are chosen + indices = self._get_candidate_negatives() + unique_indices = set(indices) + times_sampled = 1 + while (len(indices) != len(unique_indices)) or (unique_indices & node_relations): + times_sampled += 1 + indices = self._get_candidate_negatives() + unique_indices = set(indices) + if times_sampled > 1: + logger.debug('Sampled %d times, positive fraction %.5f', times_sampled, positive_fraction) + else: + # If number of positive relations is a significant fraction of total nodes + # subtract positively connected nodes from set of choices and sample from the remaining + valid_negatives = np.array(list(self.indices_set - node_relations)) + probs = self._node_probabilities[valid_negatives] + probs /= probs.sum() + indices = self._np_random.choice(valid_negatives, size=self.negative, p=probs, replace=False) + + return list(indices) + + @staticmethod + def _loss_fn(matrix): + """Given a numpy array with vectors for u, v and negative samples, computes loss value. + + Parameters + ---------- + matrix : numpy.array + Array containing vectors for u, v and negative samples, of shape (2 + negative_size, dim). + + Returns + ------- + float + Computed loss value. + + Warnings + -------- + Only used for autograd gradients, since autograd requires a specific function signature. + + """ + # Loaded only if gradients are to be checked to avoid dependency + from autograd import numpy as grad_np + + vector_u = matrix[0] + vectors_v = matrix[1:] + euclidean_dists = grad_np.linalg.norm(vector_u - vectors_v, axis=1) + norm = grad_np.linalg.norm(vector_u) + all_norms = grad_np.linalg.norm(vectors_v, axis=1) + poincare_dists = grad_np.arccosh( + 1 + 2 * ( + (euclidean_dists ** 2) / ((1 - norm ** 2) * (1 - all_norms ** 2)) + ) + ) + exp_negative_distances = grad_np.exp(-poincare_dists) + return -grad_np.log(exp_negative_distances[0] / (exp_negative_distances.sum())) + + @staticmethod + def _clip_vectors(vectors, epsilon): + """Clip vectors to have a norm of less than one. + + Parameters + ---------- + vectors : numpy.array + Can be 1-D,or 2-D (in which case the norm for each row is checked). + epsilon : float + Parameter for numerical stability, each dimension of the vector is reduced by `epsilon` + if the norm of the vector is greater than or equal to 1. + + Returns + ------- + numpy.array + Array with norms clipped below 1. + + """ + one_d = len(vectors.shape) == 1 + threshold = 1 - epsilon + if one_d: + norm = np.linalg.norm(vectors) + if norm < threshold: + return vectors + else: + return vectors / norm - (np.sign(vectors) * epsilon) + else: + norms = np.linalg.norm(vectors, axis=1) + if (norms < threshold).all(): + return vectors + else: + vectors[norms >= threshold] *= (threshold / norms[norms >= threshold])[:, np.newaxis] + vectors[norms >= threshold] -= np.sign(vectors[norms >= threshold]) * epsilon + return vectors + + def save(self, *args, **kwargs): + """Save complete model to disk, inherited from :class:`gensim.utils.SaveLoad`.""" + self._loss_grad = None # Can't pickle autograd fn to disk + super(PoincareModel, self).save(*args, **kwargs) + + @classmethod + def load(cls, *args, **kwargs): + """Load model from disk, inherited from :class:`~gensim.utils.SaveLoad`.""" + model = super(PoincareModel, cls).load(*args, **kwargs) + return model + + def _prepare_training_batch(self, relations, all_negatives, check_gradients=False): + """Creates training batch and computes gradients and loss for the batch. + + Parameters + ---------- + + relations : list of tuples + List of tuples of positive examples of the form (node_1_index, node_2_index). + all_negatives : list of lists + List of lists of negative samples for each node_1 in the positive examples. + check_gradients : bool, optional + Whether to compare the computed gradients to autograd gradients for this batch. + + Returns + ------- + :class:`~gensim.models.poincare.PoincareBatch` + Contains node indices, computed gradients and loss for the batch. + + """ + batch_size = len(relations) + indices_u, indices_v = [], [] + for relation, negatives in zip(relations, all_negatives): + u, v = relation + indices_u.append(u) + indices_v.append(v) + indices_v.extend(negatives) + + vectors_u = self.kv.syn0[indices_u] + vectors_v = self.kv.syn0[indices_v].reshape((batch_size, 1 + self.negative, self.size)) + vectors_v = vectors_v.swapaxes(0, 1).swapaxes(1, 2) + batch = PoincareBatch(vectors_u, vectors_v, indices_u, indices_v) + batch.compute_all() + + if check_gradients: + self._check_gradients(relations, all_negatives, batch) + + return batch + + def _check_gradients(self, relations, all_negatives, batch, tol=1e-8): + """Compare computed gradients for batch to autograd gradients. + + Parameters + ---------- + batch : PoincareBatch instance + Batch for which computed gradients are to checked. + relations : list of tuples + List of tuples of positive examples of the form (node_1_index, node_2_index). + all_negatives : list of lists + List of lists of negative samples for each node_1 in the positive examples. + + """ + try: # Loaded only if gradients are to be checked to avoid dependency + from autograd import grad + except ImportError: + logger.warning('autograd could not be imported, skipping checking of gradients') + logger.warning('please install autograd to enable gradient checking') + return + + if self._loss_grad is None: + self._loss_grad = grad(PoincareModel._loss_fn) + + max_diff = 0.0 + for i, (relation, negatives) in enumerate(zip(relations, all_negatives)): + u, v = relation + auto_gradients = self._loss_grad(np.vstack((self.kv.syn0[u], self.kv.syn0[[v] + negatives]))) + computed_gradients = np.vstack((batch.gradients_u[:, i], batch.gradients_v[:, :, i])) + diff = np.abs(auto_gradients - computed_gradients).max() + if diff > max_diff: + max_diff = diff + logger.info('Max difference between computed gradients and autograd gradients: %.10f', max_diff) + assert max_diff < tol, ( + 'Max difference between computed gradients and autograd gradients %.10f, ' + 'greater than tolerance %.10f' % (max_diff, tol)) + + def _sample_negatives_batch(self, nodes): + """Return negative examples for each node in the given nodes. + + Parameters + ---------- + nodes : list + List of node indices for which negative samples are to be returned. + + Returns + ------- + list of lists + Each inner list is a list of negative sample for a single node in the input list. + + """ + all_indices = [self._sample_negatives(node) for node in nodes] + return all_indices + + def _train_on_batch(self, relations, check_gradients=False): + """Performs training for a single training batch. + + Parameters + ---------- + relations : list of tuples + List of tuples of positive examples of the form (node_1_index, node_2_index). + check_gradients : bool, optional + Whether to compare the computed gradients to autograd gradients for this batch. + + Returns + ------- + :class:`~gensim.models.poincare.PoincareBatch` + The batch that was just trained on, contains computed loss for the batch. + + """ + all_negatives = self._sample_negatives_batch([relation[0] for relation in relations]) + batch = self._prepare_training_batch(relations, all_negatives, check_gradients) + self._update_vectors_batch(batch) + return batch + + @staticmethod + def _handle_duplicates(vector_updates, node_indices): + """Handles occurrences of multiple updates to the same node in a batch of vector updates. + + Parameters + ---------- + vector_updates : numpy.array + Array with each row containing updates to be performed on a certain node. + node_indices : list + Node indices on which the above updates are to be performed on. + + Notes + ----- + Mutates the `vector_updates` array. + + Required because vectors[[2, 1, 2]] += np.array([-0.5, 1.0, 0.5]) performs only the last update + on the row at index 2. + + """ + counts = Counter(node_indices) + for node_index, count in counts.items(): + if count == 1: + continue + positions = [i for i, index in enumerate(node_indices) if index == node_index] + # Move all updates to the same node to the last such update, zeroing all the others + vector_updates[positions[-1]] = vector_updates[positions].sum(axis=0) + vector_updates[positions[:-1]] = 0 + + def _update_vectors_batch(self, batch): + """Updates vectors for nodes in the given batch. + + Parameters + ---------- + batch : :class:`~gensim.models.poincare.PoincareBatch` + Batch containing computed gradients and node indices of the batch for which updates are to be done. + + """ + grad_u, grad_v = batch.gradients_u, batch.gradients_v + indices_u, indices_v = batch.indices_u, batch.indices_v + batch_size = len(indices_u) + + u_updates = (self.alpha * (batch.alpha ** 2) / 4 * grad_u).T + self._handle_duplicates(u_updates, indices_u) + + self.kv.syn0[indices_u] -= u_updates + self.kv.syn0[indices_u] = self._clip_vectors(self.kv.syn0[indices_u], self.epsilon) + + v_updates = self.alpha * (batch.beta ** 2)[:, np.newaxis] / 4 * grad_v + v_updates = v_updates.swapaxes(1, 2).swapaxes(0, 1) + v_updates = v_updates.reshape(((1 + self.negative) * batch_size, self.size)) + self._handle_duplicates(v_updates, indices_v) + + self.kv.syn0[indices_v] -= v_updates + self.kv.syn0[indices_v] = self._clip_vectors(self.kv.syn0[indices_v], self.epsilon) + + def train(self, epochs, batch_size=10, print_every=1000, check_gradients_every=None): + """Trains Poincare embeddings using loaded data and model parameters. + + Parameters + ---------- + + batch_size : int, optional + Number of examples to train on in a single batch. + epochs : int + Number of iterations (epochs) over the corpus. + print_every : int, optional + Prints progress and average loss after every `print_every` batches. + check_gradients_every : int or None, optional + Compares computed gradients and autograd gradients after every `check_gradients_every` batches. + Useful for debugging, doesn't compare by default. + + Examples + -------- + >>> from gensim.models.poincare import PoincareModel + >>> relations = [('kangaroo', 'marsupial'), ('kangaroo', 'mammal'), ('gib', 'cat')] + >>> model = PoincareModel(relations, negative=2) + >>> model.train(epochs=50) + + """ + if self.workers > 1: + raise NotImplementedError("Multi-threaded version not implemented yet") + + logger.info( + "training model of size %d with %d workers on %d relations for %d epochs and %d burn-in epochs, " + "using lr=%.5f burn-in lr=%.5f negative=%d", + self.size, self.workers, len(self.all_relations), epochs, self.burn_in, + self.alpha, self.burn_in_alpha, self.negative + ) + + if self.burn_in > 0 and not self._burn_in_done: + logger.info("Starting burn-in (%d epochs)----------------------------------------", self.burn_in) + self.alpha = self.burn_in_alpha + self._train_batchwise( + epochs=self.burn_in, batch_size=batch_size, print_every=print_every, + check_gradients_every=check_gradients_every) + self._burn_in_done = True + logger.info("Burn-in finished") + + self.alpha = self.train_alpha + logger.info("Starting training (%d epochs)----------------------------------------", epochs) + self._train_batchwise( + epochs=epochs, batch_size=batch_size, print_every=print_every, + check_gradients_every=check_gradients_every) + logger.info("Training finished") + + def _train_batchwise(self, epochs, batch_size=10, print_every=1000, check_gradients_every=None): + """Trains Poincare embeddings using specified parameters. + + Parameters + ---------- + epochs : int + Number of iterations (epochs) over the corpus. + batch_size : int, optional + Number of examples to train on in a single batch. + print_every : int, optional + Prints progress and average loss after every `print_every` batches. + check_gradients_every : int or None, optional + Compares computed gradients and autograd gradients after every `check_gradients_every` batches. + Useful for debugging, doesn't compare by default. + + """ + if self.workers > 1: + raise NotImplementedError("Multi-threaded version not implemented yet") + for epoch in range(1, epochs + 1): + indices = list(range(len(self.all_relations))) + self._np_random.shuffle(indices) + avg_loss = 0.0 + last_time = time.time() + for batch_num, i in enumerate(range(0, len(indices), batch_size), start=1): + should_print = not (batch_num % print_every) + check_gradients = bool(check_gradients_every) and (batch_num % check_gradients_every) == 0 + batch_indices = indices[i:i + batch_size] + relations = [self.all_relations[idx] for idx in batch_indices] + result = self._train_on_batch(relations, check_gradients=check_gradients) + avg_loss += result.loss + if should_print: + avg_loss /= print_every + time_taken = time.time() - last_time + speed = print_every * batch_size / time_taken + logger.info( + 'Training on epoch %d, examples #%d-#%d, loss: %.2f' + % (epoch, i, i + batch_size, avg_loss)) + logger.info( + 'Time taken for %d examples: %.2f s, %.2f examples / s' + % (print_every * batch_size, time_taken, speed)) + last_time = time.time() + avg_loss = 0.0 + + +class PoincareBatch(object): + """Compute Poincare distances, gradients and loss for a training batch. + + Class for computing Poincare distances, gradients and loss for a training batch, + and storing intermediate state to avoid recomputing multiple times. + + """ + def __init__(self, vectors_u, vectors_v, indices_u, indices_v): + """ + Initialize instance with sets of vectors for which distances are to be computed. + + Parameters + ---------- + vectors_u : numpy.array + Vectors of all nodes `u` in the batch. + Expected shape (batch_size, dim). + vectors_v : numpy.array + Vectors of all positively related nodes `v` and negatively sampled nodes `v'`, + for each node `u` in the batch. + Expected shape (1 + neg_size, dim, batch_size). + indices_u : list + List of node indices for each of the vectors in `vectors_u`. + indices_v : list + Nested list of lists, each of which is a list of node indices + for each of the vectors in `vectors_v` for a specific node `u`. + + """ + self.vectors_u = vectors_u.T[np.newaxis, :, :] # (1, dim, batch_size) + self.vectors_v = vectors_v # (1 + neg_size, dim, batch_size) + self.indices_u = indices_u + self.indices_v = indices_v + + self.poincare_dists = None + self.euclidean_dists = None + + self.norms_u = None + self.norms_v = None + self.alpha = None + self.beta = None + self.gamma = None + + self.gradients_u = None + self.distance_gradients_u = None + self.gradients_v = None + self.distance_gradients_v = None + + self.loss = None + + self._distances_computed = False + self._gradients_computed = False + self._distance_gradients_computed = False + self._loss_computed = False + + def compute_all(self): + """Convenience method to perform all computations.""" + self.compute_distances() + self.compute_distance_gradients() + self.compute_gradients() + self.compute_loss() + + def compute_distances(self): + """Compute and store norms, euclidean distances and poincare distances between input vectors.""" + if self._distances_computed: + return + euclidean_dists = np.linalg.norm(self.vectors_u - self.vectors_v, axis=1) # (1 + neg_size, batch_size) + norms_u = np.linalg.norm(self.vectors_u, axis=1) # (1, batch_size) + norms_v = np.linalg.norm(self.vectors_v, axis=1) # (1 + neg_size, batch_size) + alpha = 1 - norms_u ** 2 # (1, batch_size) + beta = 1 - norms_v ** 2 # (1 + neg_size, batch_size) + gamma = 1 + 2 * ( + (euclidean_dists ** 2) / (alpha * beta) + ) # (1 + neg_size, batch_size) + poincare_dists = np.arccosh(gamma) # (1 + neg_size, batch_size) + exp_negative_distances = np.exp(-poincare_dists) # (1 + neg_size, batch_size) + Z = exp_negative_distances.sum(axis=0) # (batch_size) + + self.euclidean_dists = euclidean_dists + self.poincare_dists = poincare_dists + self.exp_negative_distances = exp_negative_distances + self.Z = Z + self.gamma = gamma + self.norms_u = norms_u + self.norms_v = norms_v + self.alpha = alpha + self.beta = beta + self.gamma = gamma + + self._distances_computed = True + + def compute_gradients(self): + """Compute and store gradients of loss function for all input vectors.""" + if self._gradients_computed: + return + self.compute_distances() + self.compute_distance_gradients() + + gradients_v = -self.exp_negative_distances[:, np.newaxis, :] * self.distance_gradients_v # (1 + neg_size, dim, batch_size) + gradients_v /= self.Z # (1 + neg_size, dim, batch_size) + gradients_v[0] += self.distance_gradients_v[0] + + gradients_u = -self.exp_negative_distances[:, np.newaxis, :] * self.distance_gradients_u # (1 + neg_size, dim, batch_size) + gradients_u /= self.Z # (1 + neg_size, dim, batch_size) + gradients_u = gradients_u.sum(axis=0) # (dim, batch_size) + gradients_u += self.distance_gradients_u[0] + + assert(not np.isnan(gradients_u).any()) + assert(not np.isnan(gradients_v).any()) + self.gradients_u = gradients_u + self.gradients_v = gradients_v + + self._gradients_computed = True + + def compute_distance_gradients(self): + """Compute and store partial derivatives of poincare distance d(u, v) w.r.t all u and all v.""" + if self._distance_gradients_computed: + return + self.compute_distances() + + euclidean_dists_squared = self.euclidean_dists ** 2 # (1 + neg_size, batch_size) + c_ = (4 / (self.alpha * self.beta * np.sqrt(self.gamma ** 2 - 1)))[:, np.newaxis, :] # (1 + neg_size, 1, batch_size) + u_coeffs = ((euclidean_dists_squared + self.alpha) / self.alpha)[:, np.newaxis, :] # (1 + neg_size, 1, batch_size) + distance_gradients_u = u_coeffs * self.vectors_u - self.vectors_v # (1 + neg_size, dim, batch_size) + distance_gradients_u *= c_ # (1 + neg_size, dim, batch_size) + + nan_gradients = self.gamma == 1 # (1 + neg_size, batch_size) + if nan_gradients.any(): + distance_gradients_u.swapaxes(1, 2)[nan_gradients] = 0 + self.distance_gradients_u = distance_gradients_u + + v_coeffs = ((euclidean_dists_squared + self.beta) / self.beta)[:, np.newaxis, :] # (1 + neg_size, 1, batch_size) + distance_gradients_v = v_coeffs * self.vectors_v - self.vectors_u # (1 + neg_size, dim, batch_size) + distance_gradients_v *= c_ # (1 + neg_size, dim, batch_size) + + if nan_gradients.any(): + distance_gradients_v.swapaxes(1, 2)[nan_gradients] = 0 + self.distance_gradients_v = distance_gradients_v + + self._distance_gradients_computed = True + + def compute_loss(self): + """Compute and store loss value for the given batch of examples.""" + if self._loss_computed: + return + self.compute_distances() + + self.loss = -np.log(self.exp_negative_distances[0] / self.Z).sum() # scalar + self._loss_computed = True + + +class PoincareKeyedVectors(KeyedVectors): + """Class to contain vectors and vocab for the :class:`~gensim.models.poincare.PoincareModel` training class. + + Used to perform operations on the vectors such as vector lookup, distance etc. + + """ + @staticmethod + def poincare_dist(vector_1, vector_2): + """Return poincare distance between two vectors.""" + norm_1 = np.linalg.norm(vector_1) + norm_2 = np.linalg.norm(vector_2) + euclidean_dist = np.linalg.norm(vector_1 - vector_2) + if euclidean_dist == 0.0: + return 0.0 + else: + return np.arccosh( + 1 + 2 * ( + (euclidean_dist ** 2) / ((1 - norm_1 ** 2) * (1 - norm_2 ** 2)) + ) + ) + # TODO: Add other KeyedVector supported methods - most_similar, etc. + + +class PoincareRelations(object): + """Class to stream relations for `PoincareModel` from a tsv-like file.""" + + def __init__(self, file_path, encoding='utf8', delimiter='\t'): + """Initialize instance from file containing a pair of nodes (a relation) per line. + + Parameters + ---------- + file_path : str + Path to file containing a pair of nodes (a relation) per line, separated by `delimiter`. + encoding : str, optional + Character encoding of the input file. + delimiter : str, optional + Delimiter character for each relation. + + """ + + self.file_path = file_path + self.encoding = encoding + self.delimiter = delimiter + + def __iter__(self): + """Streams relations from self.file_path decoded into unicode strings. + + Yields + ------- + 2-tuple (unicode, unicode) + Relation from input file. + + """ + if sys.version_info[0] < 3: + lines = smart_open(self.file_path, 'rb') + else: + lines = (l.decode(self.encoding) for l in smart_open(self.file_path, 'rb')) + # csv.reader requires bytestring input in python2, unicode input in python3 + reader = csv.reader(lines, delimiter=self.delimiter) + for row in reader: + if sys.version_info[0] < 3: + row = [value.decode(self.encoding) for value in row] + yield tuple(row) + + +class NegativesBuffer(object): + """Class to buffer and return negative samples.""" + + def __init__(self, items): + """Initialize instance from list or numpy array of samples. + + Parameters + ---------- + items : list/numpy.array + List or array containing negative samples. + + """ + + self._items = items + self._current_index = 0 + + def num_items(self): + """Returns number of items remaining in the buffer. + + Returns + ------- + int + Number of items in the buffer that haven't been consumed yet. + + """ + return len(self._items) - self._current_index + + def get_items(self, num_items): + """Returns next `num_items` from buffer. + + Parameters + ---------- + num_items : int + number of items to fetch. + + Returns + ------- + numpy.array or list + Slice containing `num_items` items from the original data. + + Notes + ----- + No error is raised if less than `num_items` items are remaining, + simply all the remaining items are returned. + + """ + start_index = self._current_index + end_index = start_index + num_items + self._current_index += num_items + return self._items[start_index:end_index] diff --git a/gensim/test/test_data/poincare_cp852.tsv b/gensim/test/test_data/poincare_cp852.tsv new file mode 100644 index 0000000000..49fd37436a --- /dev/null +++ b/gensim/test/test_data/poincare_cp852.tsv @@ -0,0 +1,2 @@ +t¡mto budeç +budem byli \ No newline at end of file diff --git a/gensim/test/test_data/poincare_hypernyms.tsv b/gensim/test/test_data/poincare_hypernyms.tsv new file mode 100644 index 0000000000..b920bb0dab --- /dev/null +++ b/gensim/test/test_data/poincare_hypernyms.tsv @@ -0,0 +1,5 @@ +kangaroo.n.01 marsupial.n.01 +kangaroo.n.01 metatherian.n.01 +kangaroo.n.01 mammal.n.01 +gib.n.02 cat.n.01 +striped_skunk.n.01 mammal.n.01 \ No newline at end of file diff --git a/gensim/test/test_data/poincare_hypernyms_large.tsv b/gensim/test/test_data/poincare_hypernyms_large.tsv new file mode 100644 index 0000000000..3d93ca1ef7 --- /dev/null +++ b/gensim/test/test_data/poincare_hypernyms_large.tsv @@ -0,0 +1,95 @@ +kangaroo.n.01 marsupial.n.01 +kangaroo.n.01 metatherian.n.01 +kangaroo.n.01 mammal.n.01 +gib.n.02 cat.n.01 +striped_skunk.n.01 mammal.n.01 +domestic_goat.n.01 even-toed_ungulate.n.01 +rock_squirrel.n.01 ground_squirrel.n.02 +vizsla.n.01 dog.n.01 +dandie_dinmont.n.01 mammal.n.01 +broodmare.n.01 horse.n.01 +spotted_skunk.n.01 spotted_skunk.n.01 +hispid_pocket_mouse.n.01 hispid_pocket_mouse.n.01 +lesser_kudu.n.01 placental.n.01 +water_shrew.n.01 insectivore.n.01 +silky_anteater.n.01 placental.n.01 +giant_kangaroo.n.01 metatherian.n.01 +bronco.n.01 bronco.n.01 +pekinese.n.01 pekinese.n.01 +seattle_slew.n.01 thoroughbred.n.02 +kinkajou.n.01 kinkajou.n.01 +boxer.n.04 mammal.n.01 +rabbit.n.01 placental.n.01 +longhorn.n.01 bovid.n.01 +blue_fox.n.01 fox.n.01 +woolly_monkey.n.01 new_world_monkey.n.01 +jungle_cat.n.01 jungle_cat.n.01 +vole.n.01 mammal.n.01 +western_big-eared_bat.n.01 long-eared_bat.n.01 +leopard.n.02 leopard.n.02 +hackney.n.02 hackney.n.02 +shetland_sheepdog.n.01 placental.n.01 +coati.n.01 carnivore.n.01 +wild_boar.n.01 mammal.n.01 +post_horse.n.01 placental.n.01 +porker.n.01 porker.n.01 +mouflon.n.01 mouflon.n.01 +australian_sea_lion.n.01 seal.n.09 +coondog.n.01 placental.n.01 +schipperke.n.01 mammal.n.01 +black_rat.n.01 rodent.n.01 +waterbuck.n.01 placental.n.01 +hack.n.06 odd-toed_ungulate.n.01 +central_chimpanzee.n.01 anthropoid_ape.n.01 +harrier.n.02 harrier.n.02 +lesser_panda.n.01 mammal.n.01 +wether.n.01 ruminant.n.01 +collie.n.01 shepherd_dog.n.01 +prancer.n.01 horse.n.01 +doberman.n.01 placental.n.01 +pygmy_marmoset.n.01 monkey.n.01 +phalanger.n.01 metatherian.n.01 +black-and-tan_coonhound.n.01 black-and-tan_coonhound.n.01 +woolly_monkey.n.01 primate.n.02 +ferret_badger.n.01 badger.n.02 +mountain_chinchilla.n.01 placental.n.01 +english_foxhound.n.01 english_foxhound.n.01 +leveret.n.01 leporid.n.01 +shetland_sheepdog.n.01 canine.n.02 +beagle.n.01 beagle.n.01 +tibetan_mastiff.n.01 tibetan_mastiff.n.01 +bouvier_des_flandres.n.01 canine.n.02 +wheel_horse.n.01 placental.n.01 +pocket_rat.n.01 rat.n.01 +malinois.n.01 working_dog.n.01 +white_elephant.n.02 white_elephant.n.02 +camel.n.01 camel.n.01 +mexican_pocket_mouse.n.01 rat.n.01 +vaquita.n.01 toothed_whale.n.01 +manchester_terrier.n.01 hunting_dog.n.01 +chacma.n.01 monkey.n.01 +binturong.n.01 viverrine.n.01 +mastiff_bat.n.01 mammal.n.01 +goat.n.01 mammal.n.01 +pembroke.n.01 canine.n.02 +steenbok.n.01 ungulate.n.01 +tarsius_syrichta.n.01 mammal.n.01 +maltese.n.03 domestic_cat.n.01 +pacific_bottlenose_dolphin.n.01 toothed_whale.n.01 +tamandua.n.01 tamandua.n.01 +murine.n.01 rodent.n.01 +coyote.n.01 canine.n.02 +king_charles_spaniel.n.01 placental.n.01 +basset.n.01 canine.n.02 +pygmy_mouse.n.01 pygmy_mouse.n.01 +toy_spaniel.n.01 carnivore.n.01 +cactus_mouse.n.01 mouse.n.01 +hart.n.03 ruminant.n.01 +broodmare.n.01 equine.n.01 +sussex_spaniel.n.01 sporting_dog.n.01 +omaha.n.04 odd-toed_ungulate.n.01 +alaska_fur_seal.n.01 placental.n.01 +cattalo.n.01 bovine.n.01 +soft-coated_wheaten_terrier.n.01 mammal.n.01 +harness_horse.n.01 horse.n.01 +banteng.n.01 even-toed_ungulate.n.01 \ No newline at end of file diff --git a/gensim/test/test_data/poincare_utf8.tsv b/gensim/test/test_data/poincare_utf8.tsv new file mode 100644 index 0000000000..97003c93c1 --- /dev/null +++ b/gensim/test/test_data/poincare_utf8.tsv @@ -0,0 +1,2 @@ +tĂ­mto budeÅ¡ +budem byli \ No newline at end of file diff --git a/gensim/test/test_poincare.py b/gensim/test/test_poincare.py new file mode 100644 index 0000000000..12b301043b --- /dev/null +++ b/gensim/test/test_poincare.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Author: Jayant Jain +# Copyright (C) 2017 Radim Rehurek +# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html + +""" +Automated tests for checking the poincare module from the models package. +""" + +import logging +import os +import tempfile +import unittest +try: + from mock import Mock +except ImportError: + from unittest.mock import Mock + +import numpy as np +try: + import autograd # noqa:F401 + autograd_installed = True +except ImportError: + autograd_installed = False + +from gensim.models.poincare import PoincareRelations, PoincareModel +from gensim.test.utils import datapath + + +logger = logging.getLogger(__name__) + + +def testfile(): + # temporary data will be stored to this file + return os.path.join(tempfile.gettempdir(), 'gensim_word2vec.tst') + + +class TestPoincareData(unittest.TestCase): + def test_encoding_handling(self): + """Tests whether utf8 and non-utf8 data loaded correctly.""" + non_utf8_file = datapath('poincare_cp852.tsv') + relations = [relation for relation in PoincareRelations(non_utf8_file, encoding='cp852')] + self.assertEqual(len(relations), 2) + self.assertEqual(relations[0], (u'tĂ­mto', u'budeÅ¡')) + + utf8_file = datapath('poincare_utf8.tsv') + relations = [relation for relation in PoincareRelations(utf8_file)] + self.assertEqual(len(relations), 2) + self.assertEqual(relations[0], (u'tĂ­mto', u'budeÅ¡')) + + +class TestPoincareModel(unittest.TestCase): + def setUp(self): + self.data = PoincareRelations(datapath('poincare_hypernyms.tsv')) + self.data_large = PoincareRelations(datapath('poincare_hypernyms_large.tsv')) + + def models_equal(self, model_1, model_2): + self.assertEqual(len(model_1.kv.vocab), len(model_2.kv.vocab)) + self.assertEqual(set(model_1.kv.vocab.keys()), set(model_2.kv.vocab.keys())) + self.assertTrue(np.allclose(model_1.kv.syn0, model_2.kv.syn0)) + + def test_data_counts(self): + """Tests whether data has been loaded correctly and completely.""" + model = PoincareModel(self.data) + self.assertEqual(len(model.all_relations), 5) + self.assertEqual(len(model.node_relations[model.kv.vocab['kangaroo.n.01'].index]), 3) + self.assertEqual(len(model.kv.vocab), 7) + self.assertTrue('mammal.n.01' not in model.node_relations) + + def test_data_counts_with_bytes(self): + """Tests whether input bytes data is loaded correctly and completely.""" + model = PoincareModel([(b'\x80\x01c', b'\x50\x71a'), (b'node.1', b'node.2')]) + self.assertEqual(len(model.all_relations), 2) + self.assertEqual(len(model.node_relations[model.kv.vocab[b'\x80\x01c'].index]), 1) + self.assertEqual(len(model.kv.vocab), 4) + self.assertTrue(b'\x50\x71a' not in model.node_relations) + + def test_persistence(self): + """Tests whether the model is saved and loaded correctly.""" + model = PoincareModel(self.data, burn_in=0, negative=3) + model.train(epochs=1) + model.save(testfile()) + loaded = PoincareModel.load(testfile()) + self.models_equal(model, loaded) + + def test_persistence_separate_file(self): + """Tests whether the model is saved and loaded correctly when the arrays are stored separately.""" + model = PoincareModel(self.data, burn_in=0, negative=3) + model.train(epochs=1) + model.save(testfile(), sep_limit=1) + loaded = PoincareModel.load(testfile()) + self.models_equal(model, loaded) + + def test_invalid_data_raises_error(self): + """Tests that error is raised on invalid input data.""" + with self.assertRaises(ValueError): + PoincareModel([("a", "b", "c")]) + with self.assertRaises(ValueError): + PoincareModel(["a", "b", "c"]) + with self.assertRaises(ValueError): + PoincareModel("ab") + + def test_vector_shape(self): + """Tests whether vectors are initialized with the correct size.""" + model = PoincareModel(self.data, size=20) + self.assertEqual(model.kv.syn0.shape, (7, 20)) + + def test_vector_dtype(self): + """Tests whether vectors have the correct dtype before and after training.""" + model = PoincareModel(self.data_large, dtype=np.float32, burn_in=0, negative=3) + self.assertEqual(model.kv.syn0.dtype, np.float32) + model.train(epochs=1) + self.assertEqual(model.kv.syn0.dtype, np.float32) + + def test_training(self): + """Tests that vectors are different before and after training.""" + model = PoincareModel(self.data_large, burn_in=0, negative=3) + old_vectors = np.copy(model.kv.syn0) + model.train(epochs=2) + self.assertFalse(np.allclose(old_vectors, model.kv.syn0)) + + def test_training_multiple(self): + """Tests that calling train multiple times results in different vectors.""" + model = PoincareModel(self.data_large, burn_in=0, negative=3) + model.train(epochs=2) + old_vectors = np.copy(model.kv.syn0) + + model.train(epochs=1) + self.assertFalse(np.allclose(old_vectors, model.kv.syn0)) + + old_vectors = np.copy(model.kv.syn0) + model.train(epochs=0) + self.assertTrue(np.allclose(old_vectors, model.kv.syn0)) + + def test_gradients_check(self): + """Tests that the model is trained successfully with gradients check enabled.""" + model = PoincareModel(self.data, negative=3) + try: + model.train(epochs=1, batch_size=1, check_gradients_every=1) + except Exception as e: + self.fail('Exception %s raised unexpectedly while training with gradient checking' % repr(e)) + + @unittest.skipIf(not autograd_installed, 'autograd needs to be installed for this test') + def test_wrong_gradients_raises_assertion(self): + """Tests that discrepancy in gradients raises an error.""" + model = PoincareModel(self.data, negative=3) + model._loss_grad = Mock(return_value=np.zeros((2 + model.negative, model.size))) + with self.assertRaises(AssertionError): + model.train(epochs=1, batch_size=1, check_gradients_every=1) + + def test_reproducible(self): + """Tests that vectors are same for two independent models trained with the same seed.""" + model_1 = PoincareModel(self.data_large, seed=1, negative=3, burn_in=1) + model_1.train(epochs=2) + + model_2 = PoincareModel(self.data_large, seed=1, negative=3, burn_in=1) + model_2.train(epochs=2) + self.assertTrue(np.allclose(model_1.kv.syn0, model_2.kv.syn0)) + + def test_burn_in(self): + """Tests that vectors are different after burn-in.""" + model = PoincareModel(self.data, burn_in=1, negative=3) + original_vectors = np.copy(model.kv.syn0) + model.train(epochs=0) + self.assertFalse(np.allclose(model.kv.syn0, original_vectors)) + + def test_burn_in_only_done_once(self): + """Tests that burn-in does not happen when train is called a second time.""" + model = PoincareModel(self.data, negative=3, burn_in=1) + model.train(epochs=0) + original_vectors = np.copy(model.kv.syn0) + model.train(epochs=0) + self.assertTrue(np.allclose(model.kv.syn0, original_vectors)) + + def test_negatives(self): + """Tests that correct number of negatives are sampled.""" + model = PoincareModel(self.data, negative=5) + self.assertEqual(len(model._get_candidate_negatives()), 5) + + def test_error_if_negative_more_than_population(self): + """Tests error is rased if number of negatives to sample is more than remaining nodes.""" + model = PoincareModel(self.data, negative=5) + with self.assertRaises(ValueError): + model.train(epochs=1) + + def test_no_duplicates_and_positives_in_negative_sample(self): + """Tests that no duplicates or positively related nodes are present in negative samples.""" + model = PoincareModel(self.data_large, negative=3) + positive_nodes = model.node_relations[0] # Positive nodes for node 0 + num_samples = 100 # Repeat experiment multiple times + for i in range(num_samples): + negatives = model._sample_negatives(0) + self.assertFalse(positive_nodes & set(negatives)) + self.assertEqual(len(negatives), len(set(negatives))) + + def test_handle_duplicates(self): + """Tests that correct number of negatives are used.""" + vector_updates = np.array([[0.5, 0.5], [0.1, 0.2], [0.3, -0.2]]) + node_indices = [0, 1, 0] + PoincareModel._handle_duplicates(vector_updates, node_indices) + vector_updates_expected = np.array([[0.0, 0.0], [0.1, 0.2], [0.8, 0.3]]) + self.assertTrue((vector_updates == vector_updates_expected).all()) + + @classmethod + def tearDownClass(cls): + try: + os.unlink(testfile()) + except OSError: + pass + + +if __name__ == '__main__': + logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) + unittest.main() diff --git a/setup.py b/setup.py index 405174093a..0897bbba95 100644 --- a/setup.py +++ b/setup.py @@ -233,6 +233,7 @@ def finalize_options(self): 'annoy', 'tensorflow <= 1.3.0', 'keras >= 2.0.4', + 'mock==2.0.0', ] setup( From 1ac5a26896cbcfad97076d763ad291d57d99a674 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Mon, 4 Dec 2017 17:38:17 +0530 Subject: [PATCH 22/28] [MRG] Add poincare vectors, tests and evaluation (#1700) * Initial classes and loading data for poincare model * Initial implementation of training using autograd * faster negative sampling, bugfix in vector updates * allows poincare dist function to be differentiable by autograd * batched gradient descent initial implementation * minor changes to batch poincare distance computation * Adds calculation of gradients for poincare model * Correct implementation of clipping of updated vectors * Fixes error in gradient computation * Better messages while training * Renames PoincareDistance to PoincareExample for clarity * Compares computed gradients to autograd gradients every few iterations * Avoids doing some numpy computations twice * Avoids creating copies of numpy vectors * Only calls nan_to_num when gamma has at least one value equal to 1 * Simply sets nan gradients to zero instead of nan_to_num * Adds batch-wise implementation of training and gradient computations * Minor correction in clipping * Fixes typo in clip_vectors * Prints average loss every few iterations instead of current loss * Adds weighted negative sampling * Ensures positive edges are not returned by negative sampling * Poincare model stores node indices in relations instead of node keys * Minor renaming; uses node indices for batch training instead of node keys * Changes shapes of vectors passed to PoincareBatch * Minor bugfixes related to batch size * Corrects implementation of negative sampling for batch training * Adds option to check gradients in batchwise training * Checks gradients only every few iterations * Handles multiple occurrence of same node across and within batches * Removes unused section of code * Implements slightly different clipping method * Fixes bugs with wrong reshape in batchwise training * Example-wise training takes into account multiple occurrences of same node in an example too * Batchwise training prints average loss over many iterations instead of current batch * Fixes bug in updating vector for batchwise training * Faster implementation of negative sampling * Negative sampling for a node follows different paths depending on fraction of positive relations * Uses a buffer for negative samples to reduce calls to np.random.choice * Cleans up poincare.py, removes unused code * Adds shapes to PoincareBatch, more documentation * Adds more documentation to PoincareModel * Stores indices for nodes in a batch in PoincareBatch for better encapsulation * More documentation for poincare module * Implements burn-in for poincare model * Slightly better logging for poincare model * Uses np.random.random and np.searchsorted for random sampling rather than np.random.choice * Removes duplicates in negative samples * Moves helper classes in poincare after PoincareModel * Change in PoincareModel API to allow initializing from an iterable, separate class for streaming from file * Adds failing test for handling encoding in PoincareData * Fixes encoding handling in PoincareData * Adds docstrings to PoincareData, PoincareData streams tuples now * More unittests for PoincareModel * Changes handle_duplicates to staticmethod, adds test * Adds batch size and print_every parameters to train method * Renames print_check to should_print * Adds separate parameter for checking gradients * Minor fixes for coding style * Removes default values from docstrings, redundant * Adds example to PoincareModel init docstring * Extracts buffer for negatives out into a separate class * More detailed logging, fix to check_gradients * Minor fixes to documentation in poincare.py * Adds support for most_similar to PoincareKeyedVectors * Refactors most_similar and loss_fn to use PoincareKeyedVectors.poincare_dists * Adds tests for gradients checking * Raise AssertionError if gradients check fails * Adds failing tests for saving/loading PoincareModel instances * Fixes bug with saving/loading PoincareModel to disk * Adds test and fix for raising error on invalid input data * Adds test and fix for no duplicates and positives in negative sample * Bugfix with NegativesBuffer having less than items left * Uses larger data for poincare tests, adds data files * Bugfix with incorrect use of random state * Minor fixes in documentation style * Renames PoincareData to PoincareRelations * Change in the order of conditions checked before resampling * Imports datapath from test.utils instead of defining own * Adds working examples and a more detailed description in docstring * Renames term_relations to node_relations * Removes unused imports * Moves iter parameter to train instead of __init__, renames to epochs * Fixes term_relations in tests * Adds option to disable gradient check, disabled by default * Extracts gradient checking code into a separate method * Conditionally import autograd only if gradient checking is enabled * Marks private methods in poincare module with leading underscore * Adds init_range as an API parameter to PoincareModel * Marks private properties with a leading underscore * Fixes bug with burn-in happening on subsequent calls to train * Adds test for training multiple times * Adds autograd to test dependencies * Renames wv to kv in PoincareModel * add numpy==1.12 as test dependency * add missing quote * Moves methods for evaluating poincare embeddings to poincare.py * Updates docstrings for newly added classes * Moves trie-related methods to LexicalEntailmentEvaluation * Moves code for loading PoincareEmbedding into notebook * Removes PoincareEmbedding class, adds functionality to PoincareKeyedVectors * Updates eval nb with code and evaluation results for gensim models * Minor documentation updates + bugfix in distance * Adds methods for rank and nodes_closer_than to PoincareKeyedVectors * Adds methods to return closest child, parent, and ancestor and descendant chain for an input node * Updates LE and reconstruction results for gensim models in eval nb * Adds notebook detailing Poincare embedding operations and report * Adds images for poincare embedding report * Updates image links in poincare report nb * try to run tests without autograd * fix PEP8 in poincare.py * fix PEP8 in test_poincare * PoincareRelations handles python2 correctly * Bugfix with int division for python2 * Imports mock module for tests correctly in python2 * Cleaner implementation of __iter__ for PoincareRelations * Adds rst file and updates apiref.rst for poincare module * Adds clarifying comment to PoincareRelations.__iter__ * Adds functions for visualization to poincare_visualization.py * Suppresses certain numpy warnings while training model * Updates rst file for poincare * Updates poincare report nb with reduced code, section on training, better visualization labels and titles * Renames hypernym pair to relations everywhere * Simpler way of detecting duplicates * Minor documentation updates in poincare.py * Skips gradients test if autograd not installed, adds test for bytes input data * Adds results of gensim models on link prediction to eval notebook * Adds link prediction results to report, more information about training * Adds further details to concept and motivation sections, section on future work, and images * Fix flake8 (noqa + remove unused var) * Fix missing mock dependency for win * Fix links in docstrings * Refactors KeyedVectors into KeyedVectorsBase and EuclideanKeyedVectors * Changes error message for negative sampling failing * Adds option to specify dtype for PoincareModel and corresponding unittest * Extends test for dtype to check after training, updates docstring * Adds tests for new methods in PoincareKeyedVectors * Fixes bug in closest_child implementation * Adds similarity and distance to KeyedVectorsBase interface, implementation and tests for similarity for PoincareKeyedVectors * Minor fixes to Poincare report notebook * Adds method to compute all distances to KeyedVectorsBase, moves most_similar from EuclideanKeyedVectors to KeyedVectorsBase * Allows PoincareKeyedVectors.distances to accept an optional list of words * Adds implementation of PoincareKeyedVectors.similarities and tests * Adds restrict_vocab option to most_similar and tests for EuclideanKeyedVectors.most_similar * Adds docstring for tests * Adds implementation of EuclideanKeyedVectors.distances and tests, updates docstrings * Moves most_similar_to_given to KeyedVectorsBase, adds tests * Moves similar_by_vector and similar_by_word to KeyedVectorsBase, adds tests * Adds failing tests for similar_by_word and similar_by_vector to PoincareKeyedVector tests * Moves multiple methods out of KeyedVectorsBase back to EuclideanKeyedVectors, removes tests * Adds test for most_similar with vector input for EuclideanKeyedVectors * Adds failing test for vector input for most_similar for PoincareKeyedVectors * Allows passing in vector input to most_similar and distances methods in PoincareKeyedVectors * Removes precompute_max_distance and uses simpler formula for similarity in PoincareKeyedVectors * Renames PoincareKeyedVectors.poincare_dists to PoincareKeyedVectors.poincare_distance_batch * Fixes error with unclosed file in PoincareRelations * Adds tests and method for computing poincare distance between two input vectors * Adds methods and tests for finding position and difference in hierarchical positions of input vectors * Fixes unused import, pep8 and docstring issues * More intuitive naming of arguments for methods in PoincareKeyedVectors * Uses w1 and w2 consistently across KeyedVectors methods * Removes most_similar from KeyedVectorsBase * Adds failing tests for words_closer_than and rank for EuclideanKeyedVectors and PoincareKeyedVectors * Adds distances method to KeyedVectorsBase and EuclideanKeyedVectors, fixes tests * Makes default argument for distances immutable * Uses conditional import for pygtrie in LexicalEntailmentEvaluation * Renames position_in_hierarchy to norm with minor change in behaviour, updates tests * Renames poincare_distance and poincare_distance_batch to vector_distance and vector_distance_batch * Forces float division for positive_fraction in _sample_negatives * Removes unused method from PoincareKeyedVectors * Updates report notebook with usage examples of new API methods * Minor pep8 fix * Fixes pep8 issues, unused imports and typo * Adds example of saving and loading model to notebook * Updates docstrings in poincare.py * Moves poincare visualization methods to new gensim.viz module * Updates rst files for poincare viz * Adds newline at the end of poincare.py in viz package * Adds link to original paper to poincare notebook * fix viz.poincare & update docs dependencies * add link to init file * fix PEP8 * fixes for poincare.py --- docs/notebooks/Poincare Evaluation.ipynb | 859 +- docs/notebooks/Poincare Report.ipynb | 107575 +++++++++++++++ docs/notebooks/poincare/entailment_eval.png | Bin 0 -> 132846 bytes docs/notebooks/poincare/entailment_paper.png | Bin 0 -> 32675 bytes docs/notebooks/poincare/example_tree.png | Bin 0 -> 20777 bytes .../poincare/link_prediction_eval.png | Bin 0 -> 202645 bytes .../poincare/link_prediction_paper.png | Bin 0 -> 172214 bytes .../poincare/reconstruction_eval.png | Bin 0 -> 201202 bytes .../poincare/reconstruction_paper.png | Bin 0 -> 93664 bytes docs/src/apiref.rst | 1 + docs/src/viz/poincare.rst | 9 + gensim/models/keyedvectors.py | 382 +- gensim/models/poincare.py | 828 +- gensim/test/test_data/euclidean_vectors.bin | Bin 0 -> 130531 bytes gensim/test/test_data/poincare_vectors.bin | Bin 0 -> 66969 bytes gensim/test/test_keyedvectors.py | 127 + gensim/test/test_poincare.py | 151 +- gensim/viz/__init__.py | 3 + gensim/viz/poincare.py | 186 + setup.py | 2 +- 20 files changed, 109452 insertions(+), 671 deletions(-) create mode 100644 docs/notebooks/Poincare Report.ipynb create mode 100644 docs/notebooks/poincare/entailment_eval.png create mode 100644 docs/notebooks/poincare/entailment_paper.png create mode 100644 docs/notebooks/poincare/example_tree.png create mode 100644 docs/notebooks/poincare/link_prediction_eval.png create mode 100644 docs/notebooks/poincare/link_prediction_paper.png create mode 100644 docs/notebooks/poincare/reconstruction_eval.png create mode 100644 docs/notebooks/poincare/reconstruction_paper.png create mode 100644 docs/src/viz/poincare.rst create mode 100644 gensim/test/test_data/euclidean_vectors.bin create mode 100644 gensim/test/test_data/poincare_vectors.bin create mode 100644 gensim/test/test_keyedvectors.py create mode 100644 gensim/viz/__init__.py create mode 100644 gensim/viz/poincare.py diff --git a/docs/notebooks/Poincare Evaluation.ipynb b/docs/notebooks/Poincare Evaluation.ipynb index b9dff89bf2..839e54c760 100644 --- a/docs/notebooks/Poincare Evaluation.ipynb +++ b/docs/notebooks/Poincare Evaluation.ipynb @@ -41,7 +41,41 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jayant/projects/gensim\n" + ] + } + ], + "source": [ + "% cd ../.." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jayant/projects/gensim/docs/notebooks\n" + ] + } + ], + "source": [ + "% cd docs/notebooks/" + ] + }, + { + "cell_type": "code", + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -58,31 +92,30 @@ "True" ] }, - "execution_count": 1, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import csv\n", - "from collections import defaultdict, OrderedDict\n", - "import itertools\n", + "from collections import OrderedDict\n", + "import logging\n", "import os\n", "import pickle\n", "import random\n", "import re\n", "\n", "import click\n", - "from gensim.models.keyedvectors import KeyedVectors\n", + "from gensim.models.poincare import PoincareModel, PoincareRelations, \\\n", + " ReconstructionEvaluation, LinkPredictionEvaluation, \\\n", + " LexicalEntailmentEvaluation, PoincareKeyedVectors\n", "from gensim.utils import check_output\n", "import nltk\n", - "import numpy as np\n", "from prettytable import PrettyTable\n", - "from pygtrie import Trie\n", - "from scipy.spatial.distance import euclidean, pdist\n", - "from scipy.stats import spearmanr\n", "from smart_open import smart_open\n", "\n", + "logging.basicConfig(level=logging.INFO)\n", "nltk.download('wordnet')" ] }, @@ -102,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 65, "metadata": {}, "outputs": [], "source": [ @@ -111,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 66, "metadata": {}, "outputs": [], "source": [ @@ -125,25 +158,14 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "/home/jayant/projects/gensim/docs/notebooks/poincare\n", - "Cloning into 'poincare-np-embedding'...\n", - "remote: Counting objects: 20, done.\u001b[K\n", - "remote: Compressing objects: 100% (18/18), done.\u001b[K\n", - "remote: Total 20 (delta 2), reused 20 (delta 2), pack-reused 0\u001b[K\n", - "Unpacking objects: 100% (20/20), done.\n", - "Checking connectivity... done.\n", - "Cloning into 'poincare-cpp-embedding'...\n", - "remote: Counting objects: 96, done.\u001b[K\n", - "remote: Total 96 (delta 0), reused 0 (delta 0), pack-reused 96\u001b[K\n", - "Unpacking objects: 100% (96/96), done.\n", - "Checking connectivity... done.\n" + "/home/jayant/projects/gensim/docs/notebooks/poincare\n" ] } ], @@ -170,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ @@ -188,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 69, "metadata": {}, "outputs": [ { @@ -200,7 +222,6 @@ "-- Configuring done\n", "-- Generating done\n", "-- Build files have been written to: /home/jayant/projects/gensim/docs/notebooks/poincare/poincare-cpp-embedding/work\n", - "[ 50%] \u001b[32m\u001b[1mLinking CXX executable poincare_embedding\u001b[0m\n", "[100%] Built target poincare_embedding\n", "/home/jayant/projects/gensim/docs/notebooks\n" ] @@ -225,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -244,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -259,27 +280,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 72, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "82115 nouns\n", - "743241 hypernyms\n" - ] - } - ], + "outputs": [], "source": [ "# Prepare the WordNet data\n", "wordnet_file = os.path.join(data_directory, 'wordnet_noun_hypernyms.tsv')\n", - "! python {parent_directory}/{cpp_repo_name}/scripts/create_wordnet_noun_hierarchy.py {wordnet_file}" + "if not os.path.exists(wordnet_file):\n", + " ! python {parent_directory}/{cpp_repo_name}/scripts/create_wordnet_noun_hierarchy.py {wordnet_file}" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 73, "metadata": { "scrolled": true }, @@ -288,16 +301,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2017-10-26 14:09:50-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", + "--2017-11-14 11:15:54-- http://people.ds.cam.ac.uk/iv250/paper/hyperlex/hyperlex-data.zip\n", "Resolving people.ds.cam.ac.uk (people.ds.cam.ac.uk)... 131.111.3.47\n", "Connecting to people.ds.cam.ac.uk (people.ds.cam.ac.uk)|131.111.3.47|:80... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 183900 (180K) [application/zip]\n", "Saving to: ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’\n", "\n", - "/home/jayant/projec 100%[===================>] 179.59K --.-KB/s in 0.08s \n", + "/home/jayant/projec 100%[===================>] 179.59K --.-KB/s in 0.06s \n", "\n", - "2017-10-26 14:09:50 (2.23 MB/s) - ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", + "2017-11-14 11:15:54 (2.94 MB/s) - ‘/home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip’ saved [183900/183900]\n", "\n", "Archive: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex-data.zip\n", " creating: /home/jayant/projects/gensim/docs/notebooks/poincare/data/hyperlex/nouns-verbs/\n", @@ -336,7 +349,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 74, "metadata": {}, "outputs": [], "source": [ @@ -382,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 75, "metadata": {}, "outputs": [], "source": [ @@ -393,30 +406,29 @@ " 'threads': 8,\n", " 'eps': 1e-6,\n", " 'burn_in': 0,\n", + " 'batch_size': 10,\n", "}\n", "\n", "non_default_params = {\n", " 'neg': [10],\n", - " 'epochs': [100, 200],\n", - " 'threads': [1],\n", - " 'eps': [1e-5],\n", - " 'burn_in': [5, 10]\n", + " 'epochs': [200],\n", + " 'burn_in': [10]\n", "}" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 76, "metadata": { "scrolled": true }, "outputs": [], "source": [ "def cpp_model_name_from_params(params, prefix):\n", - " name = ['%s_%s' % (key, params[key]) for key in sorted(params.keys())]\n", + " param_keys = ['burn_in', 'epochs', 'neg', 'eps', 'threads']\n", + " name = ['%s_%s' % (key, params[key]) for key in sorted(param_keys)]\n", " return '%s_%s' % (prefix, '_'.join(name))\n", "\n", - "\n", "def train_model_with_params(params, train_file, model_sizes, prefix, implementation):\n", " \"\"\"Trains models with given params for multiple model sizes using the given implementation\n", " \n", @@ -438,6 +450,8 @@ " model_name = cpp_model_name_from_params(params, prefix)\n", " elif implementation == 'numpy':\n", " model_name = np_model_name_from_params(params, prefix)\n", + " elif implementation == 'gensim':\n", + " model_name = gensim_model_name_from_params(params, prefix)\n", " else:\n", " raise ValueError('Given implementation %s not found' % implementation)\n", " for model_size in model_sizes:\n", @@ -453,6 +467,10 @@ " train_external_numpy_model(\n", " python_script_path, train_file, output_file, model_size,\n", " params['epochs'], params['neg'], seed=0)\n", + " elif implementation == 'gensim':\n", + " train_gensim_model(\n", + " train_file, output_file, model_size, params['epochs'],\n", + " params['neg'], params['burn_in'], params['batch_size'], seed=0)\n", " else:\n", " raise ValueError('Given implementation %s not found' % implementation)\n", " files[model_size] = output_file\n", @@ -461,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "metadata": {}, "outputs": [], "source": [ @@ -502,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -511,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -556,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -568,6 +586,85 @@ " model_files['numpy'][model_name][dim] = filepath" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4 Training gensim embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [], + "source": [ + "def gensim_model_name_from_params(params, prefix):\n", + " param_keys = ['neg', 'epochs', 'burn_in', 'batch_size']\n", + " name = ['%s_%s' % (key, params[key]) for key in sorted(param_keys)]\n", + " return '%s_%s' % (prefix, '_'.join(name))\n", + "\n", + "def train_gensim_model(\n", + " data_file, output_file, dim, epochs, neg, burn_in, batch_size, seed=0):\n", + " \"\"\"Train a poincare embedding using gensim implementation\n", + " \n", + " Args:\n", + " data_file (str): Path to tsv file containing relation pairs\n", + " output_file (str): Path to output file containing model\n", + " dim (int): Number of dimensions of the trained model\n", + " epochs (int): Number of epochs to use\n", + " neg (int): Number of negative samples to use\n", + " burn_in (int): Number of epochs to use for burn-in initialization\n", + " batch_size (int): Size of batch to use for training\n", + " \n", + " Notes: \n", + " If `output_file` already exists, skips training\n", + " \"\"\"\n", + " if os.path.exists(output_file):\n", + " print('File %s exists, skipping' % output_file)\n", + " return\n", + " train_data = PoincareRelations(data_file)\n", + " model = PoincareModel(train_data, size=dim, negative=neg, burn_in=burn_in)\n", + " model.train(epochs=epochs, batch_size=batch_size)\n", + " model.save(output_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [], + "source": [ + "non_default_params_gensim = {\n", + " 'neg': [10],\n", + " 'burn_in': [10],\n", + " 'batch_size': [50]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model_files['gensim'] = {}\n", + "# Train models with default params\n", + "model_name, files = train_model_with_params(default_params, wordnet_file, model_sizes, 'gensim_model', 'gensim')\n", + "model_files['gensim'][model_name] = {}\n", + "for dim, filepath in files.items():\n", + " model_files['gensim'][model_name][dim] = filepath\n", + "# Train models with non-default params\n", + "for param, values in non_default_params_gensim.items():\n", + " params = default_params.copy()\n", + " for value in values:\n", + " params[param] = value\n", + " model_name, files = train_model_with_params(params, wordnet_file, model_sizes, 'gensim_model', 'gensim')\n", + " model_files['gensim'][model_name] = {}\n", + " for dim, filepath in files.items():\n", + " model_files['gensim'][model_name][dim] = filepath" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -577,7 +674,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -608,125 +705,84 @@ " for key, vector in np_embeddings.items():\n", " vector_string = ' '.join('%.6f' % value for value in vector)\n", " f.write('%s %s\\n' % (key, vector_string))\n", - " \n", - "class PoincareEmbedding(object):\n", - " \"\"\"Load and perform distance operations on poincare embedding\"\"\"\n", "\n", - " def __init__(self, keyed_vectors):\n", - " \"\"\"Initialize PoincareEmbedding via a KeyedVectors instance\"\"\"\n", - " self.kv = keyed_vectors\n", - " self.init_key_trie()\n", - " \n", - " def init_key_trie(self):\n", - " \"\"\"Setup trie containing vocab keys for quick prefix lookups\"\"\"\n", - " self.key_trie = Trie()\n", - " for key in self.kv.vocab:\n", - " self.key_trie[key] = True\n", - " \n", - " @staticmethod\n", - " def poincare_dist(vector_1, vector_2):\n", - " \"\"\"Return poincare distance between two vectors\"\"\"\n", - " norm_1 = np.linalg.norm(vector_1)\n", - " norm_2 = np.linalg.norm(vector_2)\n", - " euclidean_dist = euclidean(vector_1, vector_2)\n", - " return np.arccosh(\n", - " 1 + 2 * (\n", - " (euclidean_dist ** 2) / ((1 - norm_1 ** 2) * (1 - norm_2 ** 2))\n", - " )\n", - " )\n", - " \n", - " @classmethod\n", - " def load_poincare_cpp(cls, input_filename):\n", - " \"\"\"Load embedding trained via C++ Poincare model\n", + "def load_poincare_cpp(input_filename):\n", + " \"\"\"Load embedding trained via C++ Poincare model.\n", "\n", - " Args:\n", - " filepath (str): Path to tsv file containing embedding\n", + " Parameters\n", + " ----------\n", + " filepath : str\n", + " Path to tsv file containing embedding.\n", "\n", - " Returns:\n", - " PoincareEmbedding instance\n", + " Returns\n", + " -------\n", + " PoincareKeyedVectors instance.\n", "\n", - " \"\"\"\n", - " keyed_vectors_filename = input_filename + '.kv'\n", - " transform_cpp_embedding_to_kv(input_filename, keyed_vectors_filename)\n", - " keyed_vectors = KeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", - " os.unlink(keyed_vectors_filename)\n", - " return cls(keyed_vectors)\n", + " \"\"\"\n", + " keyed_vectors_filename = input_filename + '.kv'\n", + " transform_cpp_embedding_to_kv(input_filename, keyed_vectors_filename)\n", + " embedding = PoincareKeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", + " os.unlink(keyed_vectors_filename)\n", + " return embedding\n", "\n", - " @classmethod\n", - " def load_poincare_numpy(cls, input_filename):\n", - " \"\"\"Load embedding trained via Python numpy Poincare model\n", + "def load_poincare_numpy(input_filename):\n", + " \"\"\"Load embedding trained via Python numpy Poincare model.\n", "\n", - " Args:\n", - " filepath (str): Path to pkl file containing embedding\n", + " Parameters\n", + " ----------\n", + " filepath : str\n", + " Path to pkl file containing embedding.\n", "\n", - " Returns:\n", - " PoincareEmbedding instance\n", + " Returns:\n", + " PoincareKeyedVectors instance.\n", "\n", - " \"\"\"\n", - " keyed_vectors_filename = input_filename + '.kv'\n", - " transform_numpy_embedding_to_kv(input_filename, keyed_vectors_filename)\n", - " keyed_vectors = KeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", - " os.unlink(keyed_vectors_filename)\n", - " return cls(keyed_vectors)\n", - " \n", - " def find_matching_keys(self, word):\n", - " \"\"\"Find all senses of given word in embedding vocabulary\"\"\"\n", - " matches = self.key_trie.items('%s.' % word)\n", - " matching_keys = [''.join(key_chars) for key_chars, value in matches]\n", - " return matching_keys\n", + " \"\"\"\n", + " keyed_vectors_filename = input_filename + '.kv'\n", + " transform_numpy_embedding_to_kv(input_filename, keyed_vectors_filename)\n", + " embedding = PoincareKeyedVectors.load_word2vec_format(keyed_vectors_filename)\n", + " os.unlink(keyed_vectors_filename)\n", + " return embedding\n", "\n", - " def get_vector(self, term):\n", - " \"\"\"Return vector for given term\"\"\"\n", - " return self.kv.word_vec(term)\n", - " \n", - " def get_all_distances(self, term):\n", - " \"\"\"Return distances to all terms for given term, including itself\"\"\"\n", - " term_vector = self.kv.word_vec(term)\n", - " all_vectors = self.kv.syn0\n", - " \n", - " euclidean_dists = np.linalg.norm(term_vector - all_vectors, axis=1)\n", - " norm = np.linalg.norm(term_vector)\n", - " all_norms = np.linalg.norm(all_vectors, axis=1)\n", - " return np.arccosh(\n", - " 1 + 2 * (\n", - " (euclidean_dists ** 2) / ((1 - norm ** 2) * (1 - all_norms ** 2))\n", - " )\n", - " )\n", - " \n", - " def get_distance(self, term_1, term_2):\n", - " \"\"\"Returns distance between vectors for input terms\n", + "def load_poincare_gensim(input_filename):\n", + " \"\"\"Load embedding trained via Gensim PoincareModel.\n", "\n", - " Args:\n", - " term_1 (str)\n", - " term_2 (str)\n", + " Parameters\n", + " ----------\n", + " filepath : str\n", + " Path to model file.\n", "\n", - " Returns:\n", - " Poincare distance between the two terms (float)\n", - " \n", - " Note:\n", - " Raises KeyError if either term_1 or term_2 is absent from vocabulary\n", + " Returns:\n", + " PoincareKeyedVectors instance.\n", "\n", - " \"\"\"\n", - " vector_1, vector_2 = self.kv[term_1], self.kv[term_2]\n", - " return self.poincare_dist(vector_1, vector_2)\n", + " \"\"\"\n", + " model = PoincareModel.load(input_filename)\n", + " return model.kv\n", "\n", "def load_model(implementation, model_file):\n", - " \"\"\"Convenience function over PoincareEmbedding to load models from different implementations\n", + " \"\"\"Convenience function over functions to load models from different implementations.\n", " \n", - " Args:\n", - " implementation (str): implementation used to create model file ('c++'/'numpy')\n", - " model_file (str): Path to model file\n", + " Parameters\n", + " ----------\n", + " implementation : str\n", + " Implementation used to create model file ('c++'/'numpy'/'gensim').\n", + " model_file : str\n", + " Path to model file.\n", " \n", - " Returns:\n", - " PoincareEmbedding instance loaded from `model_file`\n", + " Returns\n", + " -------\n", + " PoincareKeyedVectors instance\n", " \n", - " Notes:\n", - " Raises ValueError in case of invalid value for `implementation`\n", + " Notes\n", + " -----\n", + " Raises ValueError in case of invalid value for `implementation`\n", + "\n", " \"\"\"\n", " if implementation == 'c++':\n", - " return PoincareEmbedding.load_poincare_cpp(model_file)\n", + " return load_poincare_cpp(model_file)\n", " elif implementation == 'numpy':\n", - " return PoincareEmbedding.load_poincare_numpy(model_file)\n", + " return load_poincare_numpy(model_file)\n", + " elif implementation == 'gensim':\n", + " return load_poincare_gensim(model_file)\n", " else:\n", " raise ValueError('Invalid implementation %s' % implementation)" ] @@ -740,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -790,111 +846,11 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "class ReconstructionEvaluation(object):\n", - " \"\"\"Evaluating reconstruction on given network for given embedding\"\"\"\n", - " def __init__(self, filepath, embedding):\n", - " \"\"\"Initialize evaluation instance with tsv file containing relation pairs and embedding to be evaluated\n", - " \n", - " Args:\n", - " filepath (str): path to tsv file containing relation pairs\n", - " embedding (PoincareEmbedding instance): embedding to be evaluated\n", - " \n", - " Returns\n", - " ReconstructionEvaluation instance\n", - "\n", - " \"\"\"\n", - " items = set()\n", - " embedding_vocab = embedding.kv.vocab\n", - " relations = defaultdict(set)\n", - " with smart_open(filepath, 'r') as f:\n", - " reader = csv.reader(f, delimiter='\\t')\n", - " for row in reader:\n", - " assert len(row) == 2, 'Hypernym pair has more than two items'\n", - " item_1_index = embedding_vocab[row[0]].index\n", - " item_2_index = embedding_vocab[row[1]].index\n", - " relations[item_1_index].add(item_2_index)\n", - " items.update([item_1_index, item_2_index])\n", - " self.items = items\n", - " self.relations = relations\n", - " self.embedding = embedding\n", - " \n", - " \n", - " @staticmethod\n", - " def get_positive_relation_ranks_and_avg_prec(all_distances, positive_relations):\n", - " \"\"\"\n", - " Given a numpy array of all distances from an item and indices of its positive relations,\n", - " compute ranks and Average Precision of positive relations\n", - " \n", - " Args:\n", - " distances (numpy float array): np array of all distances for a specific item\n", - " positive_relations (list): list of indices of positive relations for the item\n", - " \n", - " Returns:\n", - " tuple of (ranks, avg_precision)\n", - " `ranks` is a list of ranks (int) of positive relations in the same order as `positive_relations`\n", - " `avg_precision` is a float representing the Average Precision of the ranking\n", - " \"\"\"\n", - " positive_relation_distances = all_distances[positive_relations]\n", - " negative_relation_distances = np.ma.array(all_distances, mask=False)\n", - " negative_relation_distances.mask[positive_relations] = True\n", - " # Compute how many negative relation distances are less than each positive relation distance, plus 1 for rank\n", - " ranks = (negative_relation_distances < positive_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n", - " map_ranks = np.sort(ranks) + np.arange(len(ranks))\n", - " avg_precision = ((np.arange(1, len(map_ranks) + 1) / np.sort(map_ranks)).mean())\n", - " return list(ranks), avg_precision\n", - " \n", - " def evaluate(self, max_n=None):\n", - " \"\"\"Evaluate all defined metrics for the reconstruction task\n", - " \n", - " Args:\n", - " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", - " \n", - " Returns:\n", - " dict containing (metric_name, metric_value) pairs\n", - " e.g. {'mean_rank': 50.3, 'MAP': 0.31}\n", - "\n", - " \"\"\"\n", - " mean_rank, map_ = self.evaluate_mean_rank_and_map(max_n)\n", - " return {'mean_rank': mean_rank, 'MAP': map_}\n", - "\n", - " def evaluate_mean_rank_and_map(self, max_n=None):\n", - " \"\"\"Evaluate mean rank and MAP for reconstruction\n", - " \n", - " Args:\n", - " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", - " \n", - " Returns:\n", - " tuple of (mean_rank, MAP)\n", - "\n", - " \"\"\"\n", - " ranks = []\n", - " avg_precision_scores = []\n", - " for i, item in enumerate(self.items, start=1):\n", - " if item not in self.relations:\n", - " continue\n", - " item_relations = list(self.relations[item])\n", - " item_term = self.embedding.kv.index2word[item]\n", - " item_distances = self.embedding.get_all_distances(item_term)\n", - " positive_relation_ranks, avg_precision = self.get_positive_relation_ranks_and_avg_prec(item_distances, item_relations)\n", - " ranks += positive_relation_ranks\n", - " avg_precision_scores.append(avg_precision)\n", - " if max_n is not None and i > max_n:\n", - " break\n", - " return np.mean(ranks), np.mean(avg_precision_scores)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 33, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ - "reconstruction_results = {}\n", + "reconstruction_results = OrderedDict()\n", "metrics = ['mean_rank', 'MAP']" ] }, @@ -906,8 +862,10 @@ }, "outputs": [], "source": [ - "for implementation, models in model_files.items():\n", + "for implementation, models in sorted(model_files.items()):\n", " for model_name, files in models.items():\n", + " if model_name in reconstruction_results:\n", + " continue\n", " reconstruction_results[model_name] = OrderedDict()\n", " for metric in metrics:\n", " reconstruction_results[model_name][metric] = {}\n", @@ -922,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 53, "metadata": {}, "outputs": [ { @@ -930,39 +888,39 @@ "output_type": "stream", "text": [ "Results for WordNet Reconstruction task\n", - "+-----------------------------------------------------------------------+---------------------------------------------------------+\n", - "| | Model Dimensions |\n", - "+-----------------------------------------------------------+-----------+---------+---------+---------+---------+--------+--------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", - "+-----------------------------------------------------------+-----------+---------+---------+---------+---------+--------+--------+\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", - "| | MAP | 0.28 | 0.41 | 0.49 | 0.56 | 0.58 | 0.59 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 228.70 | 112.40 | 81.00 | 64.81 | 57.15 | 70.87 |\n", - "| | MAP | 0.31 | 0.42 | 0.49 | 0.53 | 0.56 | 0.54 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", - "| | MAP | 0.27 | 0.40 | 0.49 | 0.53 | 0.56 | 0.56 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 308.69 | 101.92 | 73.20 | 56.17 | 52.97 | 51.37 |\n", - "| | MAP | 0.27 | 0.43 | 0.53 | 0.61 | 0.64 | 0.65 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 227.69 | 175.64 | 171.60 | 144.47 | 146.84 | 139.28 |\n", - "| | MAP | 0.27 | 0.33 | 0.35 | 0.37 | 0.37 | 0.38 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 245.48 | 104.66 | 88.33 | 65.40 | 74.88 | 55.66 |\n", - "| | MAP | 0.28 | 0.42 | 0.50 | 0.56 | 0.56 | 0.57 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", - "| | MAP | 0.34 | 0.43 | 0.51 | 0.57 | 0.59 | 0.59 |\n", - "| | | | | | | | |\n", - "| np_model_epochs_50_neg_20 | mean_rank | 9396.18 | 5651.94 | 3523.95 | 1053.29 | 505.56 | 364.93 |\n", - "| | MAP | 0.14 | 0.16 | 0.19 | 0.25 | 0.30 | 0.35 |\n", - "| | | | | | | | |\n", - "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", - "| | MAP | 0.26 | 0.32 | 0.34 | 0.36 | 0.36 | 0.36 |\n", - "| | | | | | | | |\n", - "+-----------------------------------------------------------+-----------+---------+---------+---------+---------+--------+--------+\n" + "+-----------------------------------------------------------------------+----------------------------------------------------------+\n", + "| | Model Dimensions |\n", + "+-----------------------------------------------------------+-----------+----------+---------+---------+---------+--------+--------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "+-----------------------------------------------------------+-----------+----------+---------+---------+---------+--------+--------+\n", + "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 252.86 | 195.73 | 182.57 | 165.33 | 157.37 | 155.78 |\n", + "| | MAP | 0.26 | 0.32 | 0.34 | 0.36 | 0.36 | 0.36 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 265.72 | 116.94 | 90.81 | 59.47 | 55.14 | 54.31 |\n", + "| | MAP | 0.28 | 0.41 | 0.49 | 0.56 | 0.58 | 0.59 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 280.17 | 129.46 | 92.06 | 80.41 | 71.42 | 69.30 |\n", + "| | MAP | 0.27 | 0.40 | 0.49 | 0.53 | 0.56 | 0.56 |\n", + "| | | | | | | | |\n", + "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 191.69 | 97.65 | 72.07 | 55.48 | 46.76 | 49.62 |\n", + "| | MAP | 0.34 | 0.43 | 0.51 | 0.57 | 0.59 | 0.59 |\n", + "| | | | | | | | |\n", + "| gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20 | mean_rank | 154.41 | 62.77 | 27.32 | 20.22 | 16.15 | 13.20 |\n", + "| | MAP | 0.40 | 0.63 | 0.72 | 0.77 | 0.78 | 0.79 |\n", + "| | | | | | | | |\n", + "| gensim_model_batch_size_50_burn_in_0_epochs_50_neg_20 | mean_rank | 148.51 | 63.67 | 28.36 | 20.23 | 15.75 | 13.59 |\n", + "| | MAP | 0.38 | 0.62 | 0.72 | 0.76 | 0.78 | 0.79 |\n", + "| | | | | | | | |\n", + "| gensim_model_batch_size_10_burn_in_10_epochs_50_neg_20 | mean_rank | 108.01 | 100.73 | 97.38 | 94.49 | 94.68 | 89.66 |\n", + "| | MAP | 0.37 | 0.47 | 0.48 | 0.49 | 0.48 | 0.49 |\n", + "| | | | | | | | |\n", + "| gensim_model_batch_size_10_burn_in_0_epochs_50_neg_10 | mean_rank | 211.71 | 54.42 | 24.90 | 21.42 | 15.80 | 15.13 |\n", + "| | MAP | 0.33 | 0.60 | 0.72 | 0.76 | 0.78 | 0.79 |\n", + "| | | | | | | | |\n", + "| np_model_epochs_50_neg_20 | mean_rank | 10169.24 | 5602.84 | 3631.57 | 1088.31 | 537.43 | 345.45 |\n", + "| | MAP | 0.14 | 0.16 | 0.20 | 0.25 | 0.30 | 0.36 |\n", + "| | | | | | | | |\n", + "+-----------------------------------------------------------+-----------+----------+---------+---------+---------+--------+--------+\n" ] } ], @@ -986,7 +944,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -1050,7 +1008,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -1084,7 +1042,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -1108,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -1118,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1141,7 +1099,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1154,124 +1112,42 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "#### 4.2.3 Evaluating models" + "lp_model_files['gensim'] = {}\n", + "# Train models with default params\n", + "model_name, files = train_model_with_params(default_params, wordnet_train_file, model_sizes, 'gensim_lp_model', 'gensim')\n", + "lp_model_files['gensim'][model_name] = {}\n", + "for dim, filepath in files.items():\n", + " lp_model_files['gensim'][model_name][dim] = filepath\n", + "# Train models with non-default params\n", + "for param, values in non_default_params_gensim.items():\n", + " params = default_params.copy()\n", + " for value in values:\n", + " params[param] = value\n", + " model_name, files = train_model_with_params(params, wordnet_train_file, model_sizes, 'gensim_lp_model', 'gensim')\n", + " lp_model_files['gensim'][model_name] = {}\n", + " for dim, filepath in files.items():\n", + " lp_model_files['gensim'][model_name][dim] = filepath" ] }, { - "cell_type": "code", - "execution_count": 44, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "class LinkPredictionEvaluation(object):\n", - " \"\"\"Evaluating reconstruction on given network for given embedding\"\"\"\n", - " def __init__(self, train_path, test_path, embedding):\n", - " \"\"\"Initialize evaluation instance with tsv file containing relation pairs and embedding to be evaluated\n", - " \n", - " Args:\n", - " train_path (str): path to tsv file containing relation pairs used for training\n", - " test_path (str): path to tsv file containing relation pairs to evaluate\n", - " embedding (PoincareEmbedding instance): embedding to be evaluated\n", - " \n", - " Returns\n", - " LinkPredictionEvaluation instance\n", - "\n", - " \"\"\"\n", - " items = set()\n", - " embedding_vocab = embedding.kv.vocab\n", - " relations = {'known': defaultdict(set), 'unknown': defaultdict(set)}\n", - " data_files = {'known': train_path, 'unknown': test_path}\n", - " for relation_type, data_file in data_files.items():\n", - " with smart_open(data_file, 'r') as f:\n", - " reader = csv.reader(f, delimiter='\\t')\n", - " for row in reader:\n", - " assert len(row) == 2, 'Hypernym pair has more than two items'\n", - " item_1_index = embedding_vocab[row[0]].index\n", - " item_2_index = embedding_vocab[row[1]].index\n", - " relations[relation_type][item_1_index].add(item_2_index)\n", - " items.update([item_1_index, item_2_index])\n", - " self.items = items\n", - " self.relations = relations\n", - " self.embedding = embedding\n", - " \n", - " \n", - " @staticmethod\n", - " def get_unknown_relation_ranks_and_avg_prec(all_distances, unknown_relations, known_relations):\n", - " \"\"\"\n", - " Given a numpy array of distances and indices of known and unknown positive relations,\n", - " compute ranks and Average Precision of unknown positive relations\n", - " \n", - " Args:\n", - " all_distances (numpy float array): np array of all distances for a specific item\n", - " unknown_relations (list): list of indices of unknown positive relations\n", - " known_relations (list): list of indices of known positive relations\n", - " \n", - " Returns:\n", - " tuple of (ranks, avg_precision)\n", - " `ranks` is a list of ranks (int) of unknown relations in the same order as `unknown_relations`\n", - " `avg_precision` is a float representing the Average Precision of the ranking\n", - " \"\"\"\n", - " unknown_relation_distances = all_distances[unknown_relations]\n", - " negative_relation_distances = np.ma.array(all_distances, mask=False)\n", - " negative_relation_distances.mask[unknown_relations] = True\n", - " negative_relation_distances.mask[known_relations] = True\n", - " # Compute how many negative relation distances are less than each unknown relation distance, plus 1 for rank\n", - " ranks = (negative_relation_distances < unknown_relation_distances[:, np.newaxis]).sum(axis=1) + 1\n", - " map_ranks = np.sort(ranks) + np.arange(len(ranks))\n", - " avg_precision = ((np.arange(1, len(map_ranks) + 1) / np.sort(map_ranks)).mean())\n", - " return list(ranks), avg_precision\n", - " \n", - " def evaluate(self, max_n=None):\n", - " \"\"\"Evaluate all defined metrics for the reconstruction task\n", - " \n", - " Args:\n", - " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", - " \n", - " Returns:\n", - " dict containing (metric_name, metric_value) pairs\n", - " e.g. {'mean_rank': 50.3, 'MAP': 0.31}\n", - "\n", - " \"\"\"\n", - " mean_rank, map_ = self.evaluate_mean_rank_and_map(max_n)\n", - " return {'mean_rank': mean_rank, 'MAP': map_}\n", - "\n", - " def evaluate_mean_rank_and_map(self, max_n=None):\n", - " \"\"\"Evaluate mean rank and MAP for reconstruction\n", - " \n", - " Args:\n", - " max_n (int or None): Maximum number of positive relations to evaluate, all if max_n is None\n", - " \n", - " Returns:\n", - " tuple of (mean_rank, MAP)\n", - "\n", - " \"\"\"\n", - " ranks = []\n", - " avg_precision_scores = []\n", - " for i, item in enumerate(self.items, start=1):\n", - " if item not in self.relations['unknown']: # No positive relations to predict for this node\n", - " continue\n", - " unknown_relations = list(self.relations['unknown'][item])\n", - " known_relations = list(self.relations['known'][item])\n", - " item_term = self.embedding.kv.index2word[item]\n", - " item_distances = self.embedding.get_all_distances(item_term)\n", - " unknown_relation_ranks, avg_precision = self.get_unknown_relation_ranks_and_avg_prec(item_distances, unknown_relations, known_relations)\n", - " ranks += unknown_relation_ranks\n", - " avg_precision_scores.append(avg_precision)\n", - " if max_n is not None and i > max_n:\n", - " break\n", - " return np.mean(ranks), np.mean(avg_precision_scores)\n" + "#### 4.2.3 Evaluating models" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ - "lp_results = {}\n", + "lp_results = OrderedDict()\n", "metrics = ['mean_rank', 'MAP']" ] }, @@ -1281,7 +1157,7 @@ "metadata": {}, "outputs": [], "source": [ - "for implementation, models in lp_model_files.items():\n", + "for implementation, models in sorted(lp_model_files.items()):\n", " for model_name, files in models.items():\n", " lp_results[model_name] = OrderedDict()\n", " for metric in metrics:\n", @@ -1297,7 +1173,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 105, "metadata": {}, "outputs": [ { @@ -1305,39 +1181,39 @@ "output_type": "stream", "text": [ "Results for WordNet Link Prediction task\n", - "+--------------------------------------------------------------------------+-----------------------------------------------------------+\n", - "| | Model Dimensions |\n", - "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+--------+\n", - "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", - "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+--------+\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", - "| | MAP | 0.14 | 0.22 | 0.28 | 0.31 | 0.33 | 0.34 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 224.50 | 199.29 | 184.29 | 178.89 | 155.52 | 157.39 |\n", - "| | MAP | 0.09 | 0.14 | 0.15 | 0.16 | 0.17 | 0.18 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | mean_rank | 182.03 | 107.04 | 63.29 | 72.67 | 73.64 | 60.35 |\n", - "| | MAP | 0.16 | 0.25 | 0.31 | 0.34 | 0.36 | 0.37 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", - "| | MAP | 0.15 | 0.24 | 0.31 | 0.35 | 0.36 | 0.36 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", - "| | MAP | 0.12 | 0.15 | 0.31 | 0.35 | 0.36 | 0.36 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | mean_rank | 232.82 | 98.78 | 53.79 | 49.79 | 46.52 | 47.03 |\n", - "| | MAP | 0.15 | 0.25 | 0.33 | 0.39 | 0.40 | 0.41 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | mean_rank | 190.93 | 116.63 | 68.68 | 43.67 | 55.01 | 66.02 |\n", - "| | MAP | 0.17 | 0.24 | 0.29 | 0.36 | 0.37 | 0.35 |\n", - "| | | | | | | | |\n", - "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", - "| | MAP | 0.10 | 0.13 | 0.14 | 0.15 | 0.16 | 0.16 |\n", - "| | | | | | | | |\n", - "| np_lp_model_epochs_50_neg_20 | mean_rank | 14443.26 | 7432.58 | 4299.32 | 2617.49 | 1398.49 | 842.81 |\n", - "| | MAP | 0.00 | 0.02 | 0.03 | 0.07 | 0.08 | 0.13 |\n", - "| | | | | | | | |\n", - "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+--------+\n" + "+--------------------------------------------------------------------------+------------------------------------------------------------+\n", + "| | Model Dimensions |\n", + "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+---------+\n", + "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", + "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+---------+\n", + "| cpp_lp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | mean_rank | 218.26 | 99.09 | 60.50 | 52.24 | 60.81 | 69.13 |\n", + "| | MAP | 0.15 | 0.24 | 0.31 | 0.35 | 0.36 | 0.36 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | mean_rank | 230.34 | 123.24 | 75.62 | 65.97 | 55.33 | 56.89 |\n", + "| | MAP | 0.14 | 0.22 | 0.28 | 0.31 | 0.33 | 0.34 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 687.48 | 281.88 | 72.95 | 57.37 | 52.56 | 61.42 |\n", + "| | MAP | 0.12 | 0.15 | 0.31 | 0.35 | 0.36 | 0.36 |\n", + "| | | | | | | | |\n", + "| cpp_lp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | mean_rank | 236.31 | 214.85 | 193.30 | 180.27 | 169.00 | 163.22 |\n", + "| | MAP | 0.10 | 0.13 | 0.14 | 0.15 | 0.16 | 0.16 |\n", + "| | | | | | | | |\n", + "| gensim_lp_model_batch_size_10_burn_in_0_epochs_50_neg_10 | mean_rank | 141.52 | 58.89 | 31.66 | 22.13 | 21.29 | 19.38 |\n", + "| | MAP | 0.18 | 0.34 | 0.46 | 0.51 | 0.52 | 0.53 |\n", + "| | | | | | | | |\n", + "| gensim_lp_model_batch_size_10_burn_in_0_epochs_50_neg_20 | mean_rank | 121.42 | 52.51 | 24.61 | 19.96 | 20.44 | 19.55 |\n", + "| | MAP | 0.19 | 0.37 | 0.46 | 0.52 | 0.50 | 0.54 |\n", + "| | | | | | | | |\n", + "| gensim_lp_model_batch_size_10_burn_in_10_epochs_50_neg_20 | mean_rank | 154.95 | 138.12 | 122.06 | 117.96 | 112.99 | 110.84 |\n", + "| | MAP | 0.16 | 0.21 | 0.24 | 0.26 | 0.25 | 0.26 |\n", + "| | | | | | | | |\n", + "| gensim_lp_model_batch_size_50_burn_in_0_epochs_50_neg_20 | mean_rank | 144.19 | 53.65 | 25.21 | 20.68 | 21.32 | 18.97 |\n", + "| | MAP | 0.19 | 0.35 | 0.47 | 0.52 | 0.51 | 0.53 |\n", + "| | | | | | | | |\n", + "| np_lp_model_epochs_50_neg_20 | mean_rank | 14100.99 | 7538.02 | 5297.38 | 2365.27 | 1552.72 | 1741.87 |\n", + "| | MAP | 0.00 | 0.02 | 0.03 | 0.08 | 0.10 | 0.13 |\n", + "| | | | | | | | |\n", + "+--------------------------------------------------------------+-----------+----------+---------+---------+---------+---------+---------+\n" ] } ], @@ -1352,88 +1228,13 @@ "### 4.3 HyperLex Lexical Entailment" ] }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": {}, - "outputs": [], - "source": [ - "class LexicalEntailmentEvaluation(object):\n", - " \"\"\"Evaluating reconstruction on given network for any embedding\"\"\"\n", - " def __init__(self, filepath):\n", - " \"\"\"Initialize evaluation instance with HyperLex text file containing relation pairs\n", - " \n", - " Args:\n", - " filepath (str): path to HyperLex text file\n", - " \n", - " Returns\n", - " LexicalEntailmentEvaluation instance\n", - "\n", - " \"\"\"\n", - " expected_scores = {}\n", - " with smart_open(filepath, 'r') as f:\n", - " reader = csv.DictReader(f, delimiter=' ')\n", - " for row in reader:\n", - " word_1, word_2 = row['WORD1'], row['WORD2']\n", - " expected_scores[(word_1, word_2)] = float(row['AVG_SCORE'])\n", - " self.scores = expected_scores\n", - " self.alpha = 1000\n", - " \n", - " def score_function(self, embedding, word_1, word_2):\n", - " \"\"\"Given an embedding and two terms, return the predicted score for them (extent to which term_1 is a type of term_2)\"\"\"\n", - " try:\n", - " word_1_terms = embedding.find_matching_keys(word_1)\n", - " word_2_terms = embedding.find_matching_keys(word_2)\n", - " except KeyError:\n", - " raise ValueError(\"No matching terms found for either %s or %s\" % (word_1, word_2))\n", - " min_distance = np.inf\n", - " min_term_1, min_term_2 = None, None\n", - " for term_1 in word_1_terms:\n", - " for term_2 in word_2_terms:\n", - " distance = embedding.get_distance(term_1, term_2)\n", - " if distance < min_distance:\n", - " min_term_1, min_term_2 = term_1, term_2\n", - " min_distance = distance\n", - " assert min_term_1 is not None and min_term_2 is not None\n", - " vector_1, vector_2 = embedding.get_vector(min_term_1), embedding.get_vector(min_term_2)\n", - " norm_1, norm_2 = np.linalg.norm(vector_1), np.linalg.norm(vector_2)\n", - " return -1 * (1 + self.alpha * (norm_2 - norm_1)) * distance\n", - " \n", - " def evaluate_spearman(self, embedding):\n", - " \"\"\"Evaluate spearman scores for lexical entailment for given embedding\n", - " \n", - " Args:\n", - " embedding (PoincareEmbedding instance): embedding for which evaluation is to be done\n", - " \n", - " Returns:\n", - " spearman correlation score (float)\n", - "\n", - " \"\"\"\n", - " predicted_scores = []\n", - " expected_scores = []\n", - " skipped = 0\n", - " count = 0\n", - " for (word_1, word_2), expected_score in self.scores.items():\n", - " try:\n", - " predicted_score = self.score_function(embedding, word_1, word_2)\n", - " except ValueError:\n", - " skipped += 1\n", - " continue\n", - " count += 1\n", - " predicted_scores.append(predicted_score)\n", - " expected_scores.append(expected_score)\n", - " print('Skipped pairs: %d out of %d' % (skipped, len(self.scores)))\n", - " spearman = spearmanr(expected_scores, predicted_scores)\n", - " return spearman.correlation\n" - ] - }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ - "entailment_results = {}\n", + "entailment_results = OrderedDict()\n", "eval_instance = LexicalEntailmentEvaluation(hyperlex_file)" ] }, @@ -1443,8 +1244,10 @@ "metadata": {}, "outputs": [], "source": [ - "for implementation, models in model_files.items():\n", + "for implementation, models in sorted(model_files.items()):\n", " for model_name, files in models.items():\n", + " if model_name in entailment_results:\n", + " continue\n", " entailment_results[model_name] = OrderedDict()\n", " entailment_results[model_name]['spearman'] = {}\n", " for model_size, model_file in files.items():\n", @@ -1455,7 +1258,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -1468,23 +1271,23 @@ "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n", "| Model Description | Metric | 5 | 10 | 20 | 50 | 100 | 200 |\n", "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.45 | 0.48 | 0.45 | 0.47 | 0.46 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.45 | 0.44 | 0.47 | 0.44 | 0.46 | 0.45 |\n", "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_100_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.45 | 0.46 | 0.47 | 0.46 | 0.47 |\n", + "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.42 | 0.42 | 0.44 | 0.44 | 0.44 | 0.45 |\n", "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | spearman | 0.44 | 0.44 | 0.45 | 0.44 | 0.45 | 0.45 |\n", + "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_10_threads_8 | spearman | 0.42 | 0.43 | 0.44 | 0.43 | 0.44 | 0.44 |\n", "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-06_neg_20_threads_1 | spearman | 0.46 | 0.48 | 0.47 | 0.47 | 0.48 | 0.47 |\n", + "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | spearman | 0.45 | 0.46 | 0.45 | 0.46 | 0.45 | 0.47 |\n", "| | | | | | | | |\n", - "| cpp_model_burn_in_5_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.43 | 0.44 | 0.44 | 0.43 | 0.45 | 0.45 |\n", + "| gensim_model_batch_size_10_burn_in_0_epochs_50_neg_10 | spearman | 0.46 | 0.47 | 0.46 | 0.47 | 0.48 | 0.48 |\n", "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_50_eps_1e-05_neg_20_threads_8 | spearman | 0.45 | 0.46 | 0.46 | 0.47 | 0.47 | 0.46 |\n", + "| gensim_model_batch_size_50_burn_in_0_epochs_50_neg_20 | spearman | 0.47 | 0.47 | 0.47 | 0.47 | 0.48 | 0.48 |\n", "| | | | | | | | |\n", - "| cpp_model_burn_in_0_epochs_200_eps_1e-06_neg_20_threads_8 | spearman | 0.46 | 0.47 | 0.46 | 0.46 | 0.46 | 0.47 |\n", + "| gensim_model_batch_size_10_burn_in_10_epochs_50_neg_20 | spearman | 0.45 | 0.46 | 0.45 | 0.46 | 0.45 | 0.46 |\n", "| | | | | | | | |\n", - "| np_model_epochs_50_neg_20 | spearman | 0.15 | 0.20 | 0.21 | 0.21 | 0.25 | 0.27 |\n", + "| gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20 | spearman | 0.47 | 0.46 | 0.47 | 0.48 | 0.48 | 0.48 |\n", "| | | | | | | | |\n", - "| cpp_model_burn_in_10_epochs_50_eps_1e-06_neg_20_threads_8 | spearman | 0.43 | 0.43 | 0.44 | 0.45 | 0.44 | 0.45 |\n", + "| np_model_epochs_50_neg_20 | spearman | 0.15 | 0.20 | 0.21 | 0.21 | 0.25 | 0.27 |\n", "| | | | | | | | |\n", "+-----------------------------------------------------------+----------+------+------+------+------+------+------+\n" ] diff --git a/docs/notebooks/Poincare Report.ipynb b/docs/notebooks/Poincare Report.ipynb new file mode 100644 index 0000000000..45b3f1519c --- /dev/null +++ b/docs/notebooks/Poincare Report.ipynb @@ -0,0 +1,107575 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Report on Poincare Embeddings" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook discusses the motivation and idea behind Poincare embeddings and demonstrates what kind of operations can be done with them. It also presents quantitative evaluation results on evaluation tasks mentioned in the [original paper](https://arxiv.org/pdf/1705.08039.pdf), and compares them to results obtained from other, non-gensim implementations. Lastly, it tries to provide some intuition behind the nature of the embeddings and hyperbolic space through visualizations.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Introduction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1.1 Concept and use-case\n", + "\n", + "Poincare embeddings are a method to learn vector representations of nodes in a graph. The input data is of the form of a list of relations (edges) between nodes, and the model tries to learn representations such that the vectors for the nodes accurately represent the distances between them.\n", + "\n", + "The learnt embeddings capture notions of both _hierarchy_ and _similarity_ - similarity by placing connected nodes close to each other and unconnected nodes far from each other; hierarchy by placing nodes lower in the hierarchy farther from the origin, i.e. with higher norms.\n", + "\n", + "The paper uses this model to learn embeddings of nodes in the WordNet noun hierarchy, and evaluates these on 3 tasks - reconstruction, link prediction and lexical entailment, which are described in the section on evaluation. We have compared the results of our Poincare model implementation on these tasks to other open-source implementations and the results mentioned in the paper.\n", + "\n", + "The paper also describes a variant of the Poincare model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n", + "\n", + "This variant has not been implemented yet, and is therefore not a part of our experiments.\n", + "\n", + "\n", + "### 1.2 Motivation\n", + "\n", + "The main innovation here is that these embeddings are learnt in hyperbolic space, as opposed to the commonly used Euclidean space. The reason behind this is that hyperbolic space is more suitable for capturing any hierarchical information inherently present in the graph. Embedding nodes into a Euclidean space while preserving the distance between the nodes usually requires a very high number of dimensions. A simple illustration of this can be seen below - \n", + " \n", + " ![Example tree](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/example_tree.png)\n", + "\n", + "Here, the positions of nodes represent their position in 2-D euclidean space. Ideally, the distances between the nodes `(A, D)` should be the same as that between `(D, H)` and as that between `H` and its child nodes. Similarly, all the child nodes of `H` must be equally far away from node `A`. It becomes progressively hard to accurately preserve these distances in Euclidean space as the degree and depth of the tree grows larger. Hierarchical structures may also have cross-connections (effectively a directed graph).\n", + "\n", + "There is no representation of this simple tree in 2-dimensional Euclidean space which can reflect these distances correctly. This can be solved by adding more dimensions, but this becomes computationally infeasible as the number of required dimensions grows exponentially. \n", + "Hyperbolic space is a metric space in which distances aren't straight lines - they are curves, and this allows such tree-like hierarchical structures to have a representation that captures the distances more accurately even in low dimensions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Training the embedding" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jayant/projects/gensim\n" + ] + } + ], + "source": [ + "# Import required modules and train an example embedding\n", + "% cd ../.." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload \n", + "%autoreload 2\n", + "\n", + "import os\n", + "import logging\n", + "import numpy as np\n", + "\n", + "from gensim.models.poincare import PoincareModel, PoincareKeyedVectors, PoincareRelations\n", + "\n", + "logging.basicConfig(level=logging.INFO)\n", + "\n", + "poincare_directory = os.path.join(os.getcwd(), 'docs', 'notebooks', 'poincare')\n", + "data_directory = os.path.join(poincare_directory, 'data')\n", + "wordnet_mammal_file = os.path.join(data_directory, 'wordnet_mammal_hypernyms.tsv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model can be initialized using an iterable of relations, where a relation is simply a pair of nodes - " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.models.poincare:Loading relations from train data..\n", + "INFO:gensim.models.poincare:Loaded 2 relations from train data, 3 nodes\n" + ] + } + ], + "source": [ + "model = PoincareModel(train_data=[('node.1', 'node.2'), ('node.2', 'node.3')])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model can also be initialized from a csv-like file containing one relation per line. The module provides a convenience class `PoincareRelations` to do so." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.models.poincare:Loading relations from train data..\n", + "INFO:gensim.models.poincare:Loaded 7724 relations from train data, 1182 unique terms\n" + ] + } + ], + "source": [ + "relations = PoincareRelations(file_path=wordnet_mammal_file, delimiter='\\t')\n", + "model = PoincareModel(train_data=relations)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the above only initializes the model and does not begin training. To train the model - " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.models.poincare:Loading relations from train data..\n", + "INFO:gensim.models.poincare:Loaded 7724 relations from train data, 1182 unique terms\n", + "INFO:gensim.models.poincare:training model of size 2 with 1 workers on 7724 relations for 1 epochs and 0 burn-in epochs, using lr=0.10000 burn-in lr=0.01000 negative=10\n", + "INFO:gensim.models.poincare:Starting training (1 epochs)----------------------------------------\n", + "INFO:gensim.models.poincare:Training on epoch 1, examples #4990-#5000, loss: 23.57\n", + "INFO:gensim.models.poincare:Time taken for 5000 examples: 0.47 s, 10562.18 examples / s\n", + "INFO:gensim.models.poincare:Training finished\n" + ] + } + ], + "source": [ + "model = PoincareModel(train_data=relations, size=2, burn_in=0)\n", + "model.train(epochs=1, print_every=500)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same model can be trained further on more epochs in case the user decides that the model hasn't converged yet." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.models.poincare:training model of size 2 with 1 workers on 7724 relations for 1 epochs and 0 burn-in epochs, using lr=0.10000 burn-in lr=0.01000 negative=10\n", + "INFO:gensim.models.poincare:Starting training (1 epochs)----------------------------------------\n", + "INFO:gensim.models.poincare:Training on epoch 1, examples #4990-#5000, loss: 21.98\n", + "INFO:gensim.models.poincare:Time taken for 5000 examples: 0.48 s, 10442.40 examples / s\n", + "INFO:gensim.models.poincare:Training finished\n" + ] + } + ], + "source": [ + "model.train(epochs=1, print_every=500)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model can be saved and loaded using two different methods - " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.utils:saving PoincareModel object under /tmp/test_model, separately None\n", + "INFO:gensim.utils:saved /tmp/test_model\n", + "INFO:gensim.utils:loading PoincareModel object from /tmp/test_model\n", + "INFO:gensim.utils:loading kv recursively from /tmp/test_model.kv.* with mmap=None\n", + "INFO:gensim.utils:loaded /tmp/test_model\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Saves the entire PoincareModel instance, the loaded model can be trained further\n", + "model.save('/tmp/test_model')\n", + "PoincareModel.load('/tmp/test_model')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.models.keyedvectors:storing 3x50 projection weights into /tmp/test_vectors\n", + "INFO:gensim.models.keyedvectors:loading projection weights from /tmp/test_vectors\n", + "INFO:gensim.models.keyedvectors:loaded (3, 50) matrix from /tmp/test_vectors\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Saves only the vectors from the PoincareModel instance, in the commonly used word2vec format\n", + "model.kv.save_word2vec_format('/tmp/test_vectors')\n", + "PoincareKeyedVectors.load_word2vec_format('/tmp/test_vectors')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. What the embedding can be used for" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.utils:loading PoincareModel object from /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20_dim_50\n", + "INFO:gensim.utils:loading kv recursively from /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20_dim_50.kv.* with mmap=None\n", + "INFO:gensim.utils:loaded /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20_dim_50\n" + ] + } + ], + "source": [ + "# Load an example model\n", + "models_directory = os.path.join(poincare_directory, 'models')\n", + "test_model_path = os.path.join(models_directory, 'gensim_model_batch_size_10_burn_in_0_epochs_50_neg_20_dim_50')\n", + "model = PoincareModel.load(test_model_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The learnt representations can be used to perform various kinds of useful operations. This section is split into two - some simple operations that are directly mentioned in the paper, as well as some experimental operations that are hinted at, and might require more work to refine.\n", + "\n", + "The models that are used in this section have been trained on the transitive closure of the WordNet hypernym graph. The transitive closure is the list of all the direct and indirect hypernyms in the WordNet graph. An example of a direct hypernym is `(seat.n.03, furniture.n.01)` while an example of an indirect hypernym is `(seat.n.03, physical_entity.n.01)`.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Simple operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All the following operations are based simply on the notion of distance between two nodes in hyperbolic space." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.9232418343441235" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Distance between any two nodes\n", + "model.kv.distance('plant.n.02', 'tree.n.01')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.5111423377921103" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.kv.distance('plant.n.02', 'animal.n.01')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('phenomenon.n.01', 2.0296901412261614),\n", + " ('natural_phenomenon.n.01', 2.1052921648852934),\n", + " ('physical_phenomenon.n.01', 2.1084626073820045),\n", + " ('photoelectricity.n.01', 2.4527217652991005),\n", + " ('piezoelectricity.n.01', 2.4687111939575397),\n", + " ('galvanism.n.01', 2.9496409087300357),\n", + " ('cloud.n.02', 3.164090455102602),\n", + " ('electrical_phenomenon.n.01', 3.2563741920630225),\n", + " ('pressure.n.01', 3.3063009504377368),\n", + " ('atmospheric_phenomenon.n.01', 3.313970950348909)]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Nodes most similar to a given input node\n", + "model.kv.most_similar('electricity.n.01')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('male.n.02', 1.725430794111438),\n", + " ('physical_entity.n.01', 3.5532684790327624),\n", + " ('whole.n.02', 3.5663516391532815),\n", + " ('object.n.01', 3.5885342299888077),\n", + " ('adult.n.01', 3.6422291495399124),\n", + " ('organism.n.01', 4.096498630105297),\n", + " ('causal_agent.n.01', 4.127447093914292),\n", + " ('living_thing.n.01', 4.198756842588067),\n", + " ('person.n.01', 4.371831459784078),\n", + " ('lawyer.n.01', 4.581830548066727)]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.kv.most_similar('man.n.01')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['domestic_animal.n.01',\n", + " 'canine.n.02',\n", + " 'terrier.n.01',\n", + " 'hunting_dog.n.01',\n", + " 'hound.n.01']" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Nodes closer to node 1 than node 2 is from node 1\n", + "model.kv.nodes_closer_than('dog.n.01', 'carnivore.n.01')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "6" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Rank of distance of node 2 from node 1 in relation to distances of all nodes from node 1\n", + "model.kv.rank('dog.n.01', 'carnivore.n.01')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.24618276804\n", + "[ 0.20492232 0.21622492 0.22568267 0.20813361 0.26086168]\n" + ] + } + ], + "source": [ + "# Finding Poincare distance between input vectors\n", + "vector_1 = np.random.uniform(size=(100,))\n", + "vector_2 = np.random.uniform(size=(100,))\n", + "vectors_multiple = np.random.uniform(size=(5, 100))\n", + "\n", + "# Distance between vector_1 and vector_2\n", + "print(PoincareKeyedVectors.vector_distance(vector_1, vector_2))\n", + "# Distance between vector_1 and each vector in vectors_multiple\n", + "print(PoincareKeyedVectors.vector_distance_batch(vector_1, vectors_multiple))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Experimental operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These operations are based on the notion that the norm of a vector represents its hierarchical position. Leaf nodes typically tend to have the highest norms, and as we move up the hierarchy, the norm decreases, with the root node being close to the center (or origin)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'writer.n.01'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Closest child node\n", + "model.kv.closest_child('person.n.01')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'causal_agent.n.01'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Closest parent node\n", + "model.kv.closest_parent('person.n.01')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.940798238231\n", + "0.967868985302\n" + ] + } + ], + "source": [ + "# Position in hierarchy - lower values represent that the node is higher in the hierarchy\n", + "print(model.kv.norm('person.n.01'))\n", + "print(model.kv.norm('teacher.n.01'))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.027070747071\n" + ] + } + ], + "source": [ + "# Difference in hierarchy between the first node and the second node\n", + "# Positive values indicate the first node is higher in the hierarchy\n", + "print(model.kv.difference_in_hierarchy('person.n.01', 'teacher.n.01'))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['carnivore.n.01',\n", + " 'dog.n.01',\n", + " 'hunting_dog.n.01',\n", + " 'terrier.n.01',\n", + " 'sporting_dog.n.01']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# One possible descendant chain\n", + "model.kv.descendants('mammal.n.01')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['canine.n.02',\n", + " 'domestic_animal.n.01',\n", + " 'placental.n.01',\n", + " 'ungulate.n.01',\n", + " 'chordate.n.01',\n", + " 'animal.n.01',\n", + " 'physical_entity.n.01']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# One possible ancestor chain\n", + "model.kv.ancestors('dog.n.01')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the chains are not symmetric - while descending to the closest child recursively, starting with `mammal`, the closest child of `carnivore` is `dog`, however, while ascending from `dog` to the closest parent, the closest parent to `dog` is `canine`. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is despite the fact that Poincare distance is symmetric (like any distance in a metric space). The asymmetry stems from the fact that even if node `Y` is the closest node to node `X` amongst all nodes with a higher norm (lower in the hierarchy) than `X`, node `X` may not be the closest node to node `Y` amongst all the nodes with a lower norm (higher in the hierarchy) than `Y`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following section presents the results of Poincare models trained using some open-source implementations of the model, as well as the Gensim implementation. Original results as mentioned in the paper are also provided for reference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following two external, open-source implementations are used - \n", + "1. [C++](https://github.com/TatsuyaShirakawa/poincare-embedding)\n", + "2. [Numpy](https://github.com/nishnik/poincare_embeddings)\n", + "\n", + "This is the list of tasks from the paper that have been evaluated - \n", + "1. WordNet reconstruction\n", + "2. WordNet link prediction\n", + "4. Lexical entailment on HyperLex" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 WordNet Reconstruction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this task, embeddings are learnt using the entire transitive closure of the WordNet noun hypernym hierarchy. Subsequently, for every hypernym pair `(u, v)`, the rank of `v` amongst all nodes that do not have a positive edge with `v` is computed. The final metric `mean_rank` is the average of all these ranks. The `MAP` metric is the mean of the Average Precision of the rankings for all positive nodes for a given node `u`.\n", + "\n", + "Note that this task tests representation capacity of the learnt embeddings, and not the generalization ability.\n", + "\n", + "The prefix in the model names denote - \n", + "1. `cpp_`: Model trained using [open source C++ implementation](https://github.com/TatsuyaShirakawa/poincare-embedding)\n", + "2. `numpy_`: Model trained using [open source Numpy implementation](https://github.com/nishnik/poincare_embeddings)\n", + "3. `gensim_`: Model trained using Gensim implementation\n", + "\n", + "The rest of the model name contains information about the model hyperparameters used for training. \n", + "\n", + "For more details about the exact evaluation method, and to reproduce the results described above, refer to the detailed [evaluation notebook](http://nbviewer.jupyter.org/github/RaRe-Technologies/gensim/blob/9e3190f0e4e38f1c93804feb0a62ce025a8489d3/docs/notebooks/Poincare%20Evaluation.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our results -" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Reconstruction Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/reconstruction_eval.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Results from the paper -\n", + "![Reconstruction Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/reconstruction_paper.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The figures above illustrate a few things - \n", + "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n", + "2. The results from the original paper have not been achieved completely. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the reconstruction task.\n", + "3. Using a higher number of negatives or a different batch size does not have a consistently significant impact on the results.\n", + "4. Using burn-in leads to better results with low model sizes, however the results do not improve significantly with increasing model size." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.2 Link Prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This task is similar to the reconstruction task described above, except that the list of relations is split into a training and testing set, and the mean rank reported is for the edges in the test set.\n", + "\n", + "Therefore, this tests the ability of the model to predict unseen edges between nodes, i.e. generalization ability, as opposed to the representation capacity tested in the Reconstruction task\n", + "\n", + "Our results -" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Link Prediction Eval](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/link_prediction_eval.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Link Prediction Paper](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/link_prediction_paper.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These results follow similar trends to the reconstruction results. Specifically - \n", + "1. The gensim implementation does significantly better for all model sizes and hyperparameters than both the other implementations.\n", + "2. The results from the original paper have not been achieved completely. Especially for models with lower dimensions, the paper mentions significantly better mean rank and MAP for the reconstruction task.\n", + "3. Using a higher number of negatives or a different batch size does not have a consistently significant impact on the results.\n", + "4. Using burn-in leads to better results with low model sizes, however the results do not improve significantly with increasing model size.\n", + "\n", + "The main difference from the reconstruction results is that mean ranks for link prediction are slightly worse most of the time than the corresponding reconstruction results. This is to be expected, as link prediction is performed on a held-out test set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.3 Lexical Entailment" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Lexical Entailment task is performed using the HyperLex dataset, a collection of 2163 noun pairs and scores that denote \"To what degree is noun A a type of noun Y\". For example - \n", + " \n", + "`girl person 9.85`\n", + "\n", + "These scores are out of 10.\n", + "\n", + "The [spearman's correlation score](https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient) is computed for the predicted and actual similarity scores, with the models trained on the entire WordNet noun hierarchy.\n", + "\n", + "Our results - \n", + "![LE Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/entailment_eval.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Results from paper (for Poincare Embeddings, as well as other embeddings from previous papers) - \n", + "![LE Results](https://raw.githubusercontent.com/RaRe-Technologies/gensim/poincare_model_keyedvectors/docs/notebooks/poincare/entailment_paper.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some observations - \n", + "1. We achieve a max spearman score of 0.48, fairly close to the spearman score of 0.512 mentioned in the paper.\n", + "2. The best results are obtained with 20 negative examples, a batch size of 10, and no burn-in, however the differences are too low to make a meaningful conclusion.\n", + "\n", + "However, there are a few ambiguities and caveats - \n", + "1. The paper does not mention which hyperparameters and model size have been used for the above mentioned result. Hence it is possible that the results are achieved with a significantly lower model size than the one we use, which would imply that our implementation still has some way to go.\n", + "2. The same word can have multiple nodes in the WordNet dataset for different senses of the word, and it is unclear in the paper how to decide which node to pick. For the above results, we have gone with the sane default of picking the particular sense that has the maximum similarity score with the target word.\n", + "3. Certain words in the HyperLex dataset seem to be absent from the WordNet data - the paper does not mention any such thing. Pairs containing missing words have been omitted from the evaluation (182/2163).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.4 Link Prediction on Collaboration Networks\n", + "\n", + "The paper also describes a variant of the Poincare model to learn embeddings of nodes in a symmetric graph, unlike the WordNet noun hierarchy, which is directed and asymmetric. The datasets used in the paper for this model are scientific collaboration networks, in which the nodes are researchers and an edge represents that the two researchers have co-authored a paper.\n", + "\n", + "This variant has not been implemented yet, and is therefore not a part of our experiments." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Visualization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The paper presents a visual representation of a 2-D model trained on the mammals subtree of the WordNet noun hierarchy. This is a useful tool to get an intuitive sense of the model, and is also helpful for the purposes of debugging where the model could be learning incorrect representations. Visualizations for some models are presented below." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/vnd.plotly.v1+html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pickle\n", + "from plotly.offline import init_notebook_mode, iplot\n", + "from gensim.viz.poincare import poincare_2d_visualization, poincare_distance_heatmap\n", + "\n", + "init_notebook_mode(connected=True)\n", + "\n", + "show_node_labels = [\n", + " 'mammal.n.01', 'placental.n.01', 'ungulate.n.01', 'carnivore.n.01', 'rodent.n.01',\n", + " 'canine.n.02', 'even-toed_ungulate.n.01', 'odd-toed_ungulate.n.01', 'elephant.n.01',\n", + " 'rhinoceros.n.01', 'german_shepherd.n.01', 'feline.n.01', 'tiger.n.02', 'homo_sapiens.n.01']" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "tree = pickle.load(open(os.path.join(poincare_directory, 'data', 'mammal_tree.pkl'), 'rb'))" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:gensim.utils:loading PoincareModel object from /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_mammals_epochs_50_dim_2\n", + "INFO:gensim.utils:loading kv recursively from /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_mammals_epochs_50_dim_2.kv.* with mmap=None\n", + "INFO:gensim.utils:loaded /home/jayant/projects/gensim/docs/notebooks/poincare/models/gensim_mammals_epochs_50_dim_2\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "hoverinfo": false, + "line": { + "color": "rgb(50,50,50)", + "width": 1 + }, + "mode": "line", + "type": "scatter", + "x": [ + 0.9101090324281224, + 0.853485929661836, + null, + -0.9407291128909836, + -0.8819992981021165, + null, + 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+ "tatouay.n.01", + "white-footed_mouse.n.01", + "gnu.n.01", + "tiglon.n.01", + "golden_hamster.n.01", + "appaloosa.n.01", + "pricket.n.02", + "neandertal_man.n.01", + "angora.n.03", + "ass.n.03", + "basset.n.01", + "marten.n.01", + "golden_mole.n.01", + "common_dolphin.n.01", + "sivapithecus.n.01", + "friesian.n.01", + "bovine.n.01", + "water_buffalo.n.01", + "short-tailed_shrew.n.01", + "serval.n.01", + "war_admiral.n.01", + "dachshund.n.01", + "pomeranian.n.01", + "spitz.n.01", + "gaur.n.01", + "cynopterus_sphinx.n.01", + "cotton_mouse.n.01", + "mouse.n.01", + "steller_sea_lion.n.01", + "brocket.n.02", + "shrew.n.02", + "bucking_bronco.n.01", + "rock_wallaby.n.01", + "carthorse.n.01", + "tamarau.n.01", + "buckskin.n.01", + "brahman.n.04", + "bonnet_macaque.n.01", + "anthropoid.n.02", + "entlebucher.n.01", + "pinniped_mammal.n.01", + "newfoundland.n.01", + "durham.n.02", + "wood_mouse.n.01", + "assault.n.03", + "aardvark.n.01", + "giant_schnauzer.n.01", + "common_wallaby.n.01", + "mantled_ground_squirrel.n.01", + "bullterrier.n.01", + "homo.n.02", + "gorilla.n.01", + "great_ape.n.01", + "jackrabbit.n.01", + "roe_deer.n.01", + "imperial_mammoth.n.01", + "elephant.n.01", + "rabbit-eared_bandicoot.n.01", + "chickeree.n.01", + "flickertail.n.01", + "reynard.n.01", + "clydesdale_terrier.n.01", + "bear_cub.n.01", + "bear.n.01", + "stoat.n.01", + "rhodesian_man.n.01", + "ram.n.05", + "sheep.n.01", + "santa_gertrudis.n.01", + "mountain_sheep.n.01", + "eastern_chimpanzee.n.01", + "pouched_mouse.n.01", + "pacific_bottlenose_dolphin.n.01", + "attack_dog.n.01", + "ungulata.n.01", + "deer.n.01", + "domestic_sheep.n.01", + "welsh.n.03", + "solo_man.n.01", + "great_dane.n.01", + "pithecanthropus.n.01", + "lhasa.n.02", + "italian_greyhound.n.01", + "bull.n.11", + "jersey.n.05", + "polo_pony.n.01", + "hare.n.01", + "working_dog.n.01", + "trotting_horse.n.01", + "wirehair.n.01", + "lionet.n.01", + "zebu.n.01", + "glutton.n.02", + "irish_water_spaniel.n.01", + "sealyham_terrier.n.01", + "musk_kangaroo.n.01", + "water_chevrotain.n.01", + "baronduki.n.01", + "guinea_pig.n.02", + "english_springer.n.01", + "viscacha.n.01", + "harnessed_antelope.n.01", + "chesapeake_bay_retriever.n.01", + "numbat.n.01", + "dasyurid_marsupial.n.01", + "large_poodle.n.01", + "roan.n.02", + "entellus.n.01", + "old_world_monkey.n.01", + "squirrel_monkey.n.01", + "wild_goat.n.01", + "anoa.n.01", + "native_cat.n.01", + "bandicoot.n.01", + "mare.n.01", + "river_dolphin.n.01", + "wharf_rat.n.02", + "cardigan.n.02", + "saki.n.03", + "zoril.n.01", + "dark_horse.n.02", + "world.n.08", + "schnauzer.n.01", + "irish_terrier.n.01", + "keeshond.n.01", + "aardwolf.n.01", + "pembroke.n.01", + "foxhound.n.01", + "red_fox.n.03", + "hooded_skunk.n.01", + "woolly_indris.n.01", + "miniature_poodle.n.01", + "pocket_rat.n.01", + "japanese_deer.n.01", + "lesser_ape.n.01", + "slender_loris.n.01", + "cow.n.02", + "merino.n.01", + "bullock.n.01", + "cavy.n.01", + "fur_seal.n.02", + "jird.n.01", + "protohippus.n.01", + "greater_swiss_mountain_dog.n.01", + "capuchin.n.02", + "chigetai.n.01", + "southern_flying_squirrel.n.01", + "mangabey.n.01", + "sled_dog.n.01", + "spouter.n.03", + "tennessee_walker.n.01", + "pangolin.n.01", + "old_world_porcupine.n.01", + "sewer_rat.n.01", + "brown_rat.n.01", + "swamp_rabbit.n.02", + "cheviot.n.01", + "collared_peccary.n.01", + "mountain_goat.n.01", + "golden_retriever.n.01", + "yorkshire_terrier.n.01", + "pachyderm.n.01", + "fisher.n.02", + "tenrec.n.01", + "jumping_mouse.n.01", + "cinnamon_bear.n.01", + "american_black_bear.n.01", + "liver_chestnut.n.01", + "howler_monkey.n.01", + "sow.n.01", + "manchester_terrier.n.01", + "king_charles_spaniel.n.01", + "mouser.n.01", + "american_shrew_mole.n.01", + "peba.n.01", + "asiatic_shrew_mole.n.01", + "hudson_bay_collared_lemming.n.01", + "bunny.n.02", + "rat_terrier.n.01", + "langur.n.01", + "european_wood_mouse.n.01", + "nude_mouse.n.01", + "mole.n.06", + "mule.n.01", + "barren_ground_caribou.n.01", + "galago.n.01", + "paca.n.01", + "wolfhound.n.01", + "mountain_nyala.n.01", + "bezoar_goat.n.01", + "muntjac.n.01", + "packrat.n.02", + "skye_terrier.n.01", + "douroucouli.n.01", + "prairie_dog.n.01", + "lucy.n.01", + "hack.n.07", + "seeing_eye_dog.n.01", + "grivet.n.01", + "rhodesian_ridgeback.n.01", + "manatee.n.01", + "farm_horse.n.01", + "quagga.n.01", + "bouvier_des_flandres.n.01", + "poodle.n.01", + "stepper.n.03", + "rottweiler.n.01", + "tarsius_syrichta.n.01", + "affenpinscher.n.01", + "tailless_tenrec.n.01", + "eastern_dasyure.n.01", + "dasyure.n.01", + "elk.n.01", + "boarhound.n.01", + "woolly_rhinoceros.n.01", + "rambouillet.n.01", + "bowhead.n.01", + "grasshopper_mouse.n.01", + "dall_sheep.n.01", + "gerbil.n.01", + "prairie_vole.n.01", + "finback.n.01", + "polar_hare.n.01", + "hairy-legged_vampire_bat.n.01", + "white-lipped_peccary.n.01", + "peccary.n.01", + "dwarf_sperm_whale.n.01", + "mule_deer.n.01", + "sausage_dog.n.01", + "female_mammal.n.01", + "digitigrade_mammal.n.01", + "siamang.n.01", + "kanchil.n.01", + "chevrotain.n.01", + "giant_eland.n.01", + "timber_wolf.n.01", + "european_hare.n.01", + "guernsey.n.02", + "warrigal.n.01", + "european_rabbit.n.01", + "pug.n.01", + "white_wolf.n.01", + "new_world_mouse.n.01", + "beaked_whale.n.01", + "leonberg.n.01", + "pinche.n.01", + "big_brown_bat.n.01", + "mexican_hairless.n.01", + "american_marten.n.01", + "asian_wild_ox.n.01", + "steller's_sea_cow.n.01", + "old_world_buffalo.n.01", + "ord_kangaroo_rat.n.01", + "kangaroo_rat.n.01", + "lynx.n.02", + "alley_cat.n.01", + "serow.n.01", + "grison.n.01", + "common_eland.n.01", + "phyllostomus_hastatus.n.01", + "grizzly.n.01", + "eland.n.01", + "silky_pocket_mouse.n.01", + "elephant_seal.n.01", + "starnose_mole.n.01", + "gazella_subgutturosa.n.01", + "kerry_blue_terrier.n.01", + "vespertilian_bat.n.01", + "colobus.n.01", + "sea_otter.n.01", + "flying_fox.n.01", + "black-tailed_deer.n.01", + "mouse-eared_bat.n.01", + "chestnut.n.06", + "burro.n.01", + "kid.n.05", + "hereford.n.01", + "snow_leopard.n.01", + "indri.n.01", + "cocker_spaniel.n.01", + "kob.n.01", + "european_lemming.n.01", + "carabao.n.01", + "harbor_seal.n.01", + "shetland_pony.n.01", + "water_spaniel.n.01", + "grey_whale.n.01", + "papillon.n.01", + "toy_dog.n.01", + "draft_horse.n.01", + "eastern_grey_squirrel.n.01", + "arabian.n.02", + "northern_bog_lemming.n.01", + "lemming.n.01", + "weimaraner.n.01", + "civet.n.01", + "saiga.n.01", + "jennet.n.01", + "edentate.n.01", + "pilot_whale.n.01", + "dolphin.n.02", + "grey_fox.n.01", + "grampus.n.02", + "devon.n.02", + "walker_hound.n.01", + "shrew_mole.n.01", + "chihuahua.n.03", + "redbone.n.01", + "borzoi.n.01", + "mapinguari.n.01", + "binturong.n.01", + "scottish_deerhound.n.01", + "greyhound.n.01", + "field_spaniel.n.01", + "griffon.n.03", + "forest_goat.n.01", + "pocket_mouse.n.01", + "hart.n.03", + "pere_david's_deer.n.01", + "algeripithecus_minutus.n.01", + "red_bat.n.01", + "vixen.n.02", + "malamute.n.01", + "western_grey_squirrel.n.01", + "striped_muishond.n.01", + "gallant_fox.n.01", + "margay.n.01", + "saber-toothed_tiger.n.01", + "groenendael.n.01", + "silver_fox.n.01", + "jack.n.12", + "bengal_tiger.n.01", + "mandrill.n.01", + "asiatic_black_bear.n.01", + "pied_lemming.n.01", + "lioness.n.01", + "bloodhound.n.01", + "australopithecus_boisei.n.01", + "milking_shorthorn.n.01", + "jerboa.n.01", + "bongo.n.02", + "norfolk_terrier.n.01", + "serotine.n.01", + "rock_hyrax.n.01", + "welsh_terrier.n.01", + "west_highland_white_terrier.n.01", + "water_dog.n.02", + "red_deer.n.01", + "takin.n.01", + "blue_whale.n.01", + "hooded_seal.n.01", + "secretariat.n.02", + "cape_buffalo.n.01", + "stone_marten.n.01", + "whippet.n.01", + "aoudad.n.01", + "bottlenose_dolphin.n.01", + "lechwe.n.01", + "lion.n.01", + "hartebeest.n.01", + "red_poll.n.01", + "eastern_cottontail.n.01", + "murine.n.01", + "sussex_spaniel.n.01", + "leopard_cat.n.01", + "coydog.n.01", + "big-eared_bat.n.01", + "java_man.n.01", + "coyote.n.01", + "macaque.n.01", + "american_harvest_mouse.n.01", + "belgian_sheepdog.n.01", + "eohippus.n.01", + "ibex.n.01", + "longwool.n.01", + "nilgai.n.01", + "quarter_horse.n.01", + "africander.n.01", + "stalking-horse.n.04", + "apar.n.01", + "three-toed_sloth.n.01", + "indian_elephant.n.01", + "gayal.n.01", + "malayan_tapir.n.01", + "cairn.n.02", + "chiacoan_peccary.n.01", + "rhinoceros.n.01", + "naked_mole_rat.n.01", + "loir.n.01", + "hack.n.08", + "eskimo_dog.n.01", + "queen.n.09", + "south_american_sea_lion.n.01", + "lippizan.n.01", + "smiledon_californicus.n.01", + "norwegian_elkhound.n.01", + "stirk.n.01", + "mustang.n.01", + "cayuse.n.01", + "courser.n.03", + "paranthropus.n.01", + "hominoid.n.01", + "napu.n.01", + "bushbuck.n.01", + "atlantic_bottlenose_dolphin.n.01", + "ayrshire.n.01", + "border_collie.n.01", + "norwich_terrier.n.01", + "liger.n.01", + "oryx.n.01", + "pine_vole.n.01", + "european_water_shrew.n.01", + "two-toed_sloth.n.02", + "sloth.n.02", + "bison.n.01", + "burmese_cat.n.01", + "wolverine.n.03", + "aye-aye.n.01", + "puku.n.01", + "mastiff.n.01", + "common_opossum.n.01", + "potto.n.02", + "typical_jerboa.n.01", + "house_mouse.n.01", + "tarsius_glis.n.01", + "tarsier.n.01", + "slender-tailed_meerkat.n.01", + "cow_pony.n.01", + "fox_squirrel.n.01", + "bay.n.07", + "madagascar_cat.n.01", + "australopithecus_afarensis.n.01", + "australopithecine.n.01", + "livestock.n.01", + "lerot.n.01", + "pole_horse.n.01", + "banded_palm_civet.n.01", + "american_red_squirrel.n.01", + "mara.n.02", + "pteropus_hypomelanus.n.01", + "meerkat.n.01", + "bernese_mountain_dog.n.01", + "przewalski's_horse.n.01", + "sea_lion.n.01", + "black_rhinoceros.n.01", + "onager.n.02", + "common_zebra.n.01", + "narwhal.n.01", + "pouched_mole.n.01", + "crab-eating_opossum.n.01", + "pollard.n.02", + "porpoise.n.01", + "brush-tailed_phalanger.n.01", + "arabian_camel.n.01", + "camel.n.01", + "sei_whale.n.01", + "field_mouse.n.02", + "packhorse.n.01", + "lapin.n.02", + "palomino.n.01", + "prosimian.n.01", + "greater_kudu.n.01", + "macrotus.n.01", + "horseshoe_bat.n.02", + "pademelon.n.01", + "indian_buffalo.n.01", + "new_world_beaver.n.01", + "charger.n.01", + "irish_setter.n.01", + "hog-nosed_skunk.n.01", + "cow.n.01", + "ox.n.01", + "workhorse.n.02", + "tree_wallaby.n.01", + "pygmy_mouse.n.01", + "bearded_seal.n.01", + "guenon.n.01", + "brood_bitch.n.01", + "masked_shrew.n.01", + "sir_barton.n.01", + "cheetah.n.01", + "columbian_mammoth.n.01", + "giant_panda.n.01", + "american_bison.n.01", + "round-tailed_muskrat.n.01", + "gerenuk.n.01", + "smooth-haired_fox_terrier.n.01", + "platypus.n.01", + "monotreme.n.01", + "feist.n.01", + "jaculus_jaculus.n.01", + "wolf.n.01", + "coypu.n.01", + "remount.n.01", + "kit_fox.n.02", + "jaguarundi.n.01", + "american_mink.n.01", + "arctic_ground_squirrel.n.01", + "weasel.n.02", + "abrocome.n.01", + "pteropus_capestratus.n.01", + "fissiped_mammal.n.01", + "eurasian_badger.n.01", + "little_chief_hare.n.01", + "fossorial_mammal.n.01", + "phenacomys.n.01", + "gee-gee.n.01", + "blacktail_prairie_dog.n.01", + "little_brown_bat.n.01", + "hog_badger.n.01", + "pointer.n.04", + "large_civet.n.01", + "roebuck.n.01", + "bandicoot_rat.n.01", + "common_lynx.n.01", + "razorback.n.01", + "puppy.n.01", + "kelpie.n.02", + "pipistrelle.n.01", + "sable.n.05", + "pacer.n.01", + "brocket.n.01", + "alaska_fur_seal.n.01", + "hoary_marmot.n.01", + "marmot.n.01", + "alpaca.n.03", + "spotted_lynx.n.01", + "setter.n.02", + "dusky-footed_woodrat.n.01", + "stag.n.02", + "dik-dik.n.01", + "hippopotamus.n.01", + "mountain_beaver.n.01", + "argali.n.01", + "impala.n.01", + "mastodon.n.01", + "dryopithecine.n.01", + "pole_horse.n.02", + "true_marmoset.n.01", + "royal.n.02", + "plains_pocket_gopher.n.01", + "rat_kangaroo.n.01", + "ferret.n.02", + "australopithecus_robustus.n.01", + "patas.n.01", + "orange_bat.n.01", + "harvest_mouse.n.02", + "dinoceras.n.01", + 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+ "baboon.n.01", + "bluetick.n.01", + "vaquita.n.01", + "tiger_cat.n.01", + "wildcat.n.03", + "wheel_horse.n.01", + "flying_mouse.n.01", + "hound.n.01", + "bighorn.n.02", + "chinchilla.n.03", + "raccoon_dog.n.01", + "palfrey.n.01", + "rorqual.n.01", + "sassaby.n.01", + "sloth_bear.n.01", + "rogue_elephant.n.01", + "white_elephant.n.02", + "virginia_deer.n.01", + "count_fleet.n.01", + "lakeland_terrier.n.01", + "billy.n.02", + "omaha.n.04", + "soft-coated_wheaten_terrier.n.01", + "terrier.n.01", + "bedlington_terrier.n.01", + "wisent.n.01", + "sable_antelope.n.01", + "staffordshire_bullterrier.n.01", + "american_staffordshire_terrier.n.01", + "belgian_hare.n.01", + "aquatic_mammal.n.01", + "tibetan_terrier.n.01", + "exmoor.n.01", + "procyonid.n.01", + "canada_lynx.n.01", + "old_english_sheepdog.n.01", + "raccoon.n.02", + "leafnose_bat.n.01", + "bat.n.01", + "pygmy_sperm_whale.n.01", + "cetacean.n.01", + "african_hunting_dog.n.01", + "spearnose_bat.n.01", + "pooch.n.01", + "percheron.n.01", + "blenheim_spaniel.n.01", + "persian_cat.n.01", + "dun.n.01", + "nail-tailed_wallaby.n.01", + "tabby.n.01", + "three-year-old_horse.n.01", + "broadtail.n.02", + "springbok.n.01", + "sporting_dog.n.01", + "new_world_tapir.n.01", + "muskrat.n.02", + "banteng.n.01", + "yak.n.02", + "hog.n.03", + "basenji.n.01", + "panther.n.02", + "gibbon.n.02", + "ape.n.01", + "tamarisk_gerbil.n.01", + "beagle.n.01", + "small_civet.n.01", + "hind.n.02", + "blue_point_siamese.n.01", + "babirusa.n.01", + "swine.n.01", + "wire-haired_fox_terrier.n.01", + "wood_rabbit.n.01", + "pacific_walrus.n.01", + "walrus.n.01", + "long-tailed_porcupine.n.01", + "hyrax.n.01", + "airedale.n.01", + "silky_tamarin.n.01", + "gomphothere.n.01", + "nanny.n.02", + "urial.n.01", + "bullock.n.02", + "lapdog.n.01", + "german_short-haired_pointer.n.01", + "northern_flying_squirrel.n.01", + "eastern_chipmunk.n.01", + "warhorse.n.03", + "saddle_horse.n.01", + "llama.n.01", + "javanthropus.n.01", + "squirrel.n.01", + "harness_horse.n.01", + "toy_manchester.n.01", + "homo_habilis.n.01", + "chimpanzee.n.01", + "egyptian_cat.n.01", + "domestic_cat.n.01", + "standard_poodle.n.01", + "sambar.n.01", + "potoroo.n.01", + "chow.n.03", + "lincoln.n.03", + "black_squirrel.n.01", + "common_raccoon.n.01", + "wild_sheep.n.01", + "goat_antelope.n.01", + "pygmy_chimpanzee.n.01", + "eared_seal.n.01", + "lesser_rorqual.n.01", + "woodland_caribou.n.01", + "common_shrew.n.01", + "gemsbok.n.01", + "tabby.n.02", + "marmoset.n.01", + "english_toy_spaniel.n.01", + "fawn.n.02", + "pacer.n.02", + "deer_mouse.n.01", + "beef.n.01", + "ox.n.02", + "shire.n.02", + "american_saddle_horse.n.01", + "bassarisk.n.01", + "coach_horse.n.01", + "greater_pichiciego.n.01", + "armadillo.n.01", + "water_rat.n.03", + "guereza.n.01", + "carnivorous_bat.n.01", + "thomson's_gazelle.n.01", + "stablemate.n.01", + "leveret.n.01", + "eurasian_hamster.n.01", + "steed.n.01", + "harpy.n.03", + "fruit_bat.n.01", + "irish_wolfhound.n.01", + "talapoin.n.01", + "stallion.n.01", + "ermine.n.02", + "musteline_mammal.n.01", + "leporid.n.01", + "african_elephant.n.01", + "wood_rat.n.01", + "megatherian.n.01", + "kangaroo_mouse.n.01", + "dairy_cattle.n.01", + "boston_bull.n.01", + "harbor_porpoise.n.01", + "pichiciago.n.01", + "domestic_ass.n.01", + "homo_soloensis.n.01", + "kuvasz.n.01", + "watchdog.n.02", + "black-footed_ferret.n.01", + "german_shepherd.n.01", + "cavalry_horse.n.01", + "mylodontid.n.01", + "wild_ass.n.01", + "big_cat.n.01", + "clumber.n.01", + "spaniel.n.01", + "ant_bear.n.01", + "tamarin.n.01", + "rat.n.01", + "sinanthropus.n.01", + "charolais.n.01", + "citation.n.06", + "liver-spotted_dalmatian.n.01", + "tapir.n.01", + "flying_phalanger.n.01", + "atlantic_walrus.n.01", + "gelding.n.01", + "alaskan_brown_bear.n.01", + "jackass_bat.n.01", + "bull.n.01", + "jaguar.n.01", + "affirmed.n.01", + "false_vampire.n.01", + "stud.n.04", + "male_horse.n.01", + "bird_dog.n.01", + "cattalo.n.01", + "two-year-old_horse.n.01", + "ewe.n.03", + "standard_schnauzer.n.01", + "lemur.n.01", + "zinjanthropus.n.01", + "genet.n.03", + "gordon_setter.n.01", + "groundhog.n.01", + "earless_seal.n.01", + "killer_whale.n.01", + "toothed_whale.n.01", + "jerboa_rat.n.01", + "plains_pocket_mouse.n.01", + "curly-coated_retriever.n.01", + "retriever.n.01", + "fanaloka.n.01", + "viverrine.n.01", + "muishond.n.01", + "corgi.n.01", + "grevy's_zebra.n.01", + "zebra.n.01", + "homo_sapiens_sapiens.n.01", + "homo_sapiens.n.01", + "english_setter.n.01", + "wombat.n.01", + "whirlaway.n.01", + "heifer.n.01", + "uakari.n.01", + "hognose_bat.n.01", + "northern_pocket_gopher.n.01", + "gopher.n.04", + "crabeater_seal.n.01", + "kiang.n.01", + "sennenhunde.n.01", + "pony.n.02", + "tatouay.n.01", + "white-footed_mouse.n.01", + "gnu.n.01", + "tiglon.n.01", + "golden_hamster.n.01", + "appaloosa.n.01", + "pricket.n.02", + "neandertal_man.n.01", + "angora.n.03", + "ass.n.03", + "basset.n.01", + "marten.n.01", + "golden_mole.n.01", + "common_dolphin.n.01", + "sivapithecus.n.01", + "friesian.n.01", + "bovine.n.01", + "water_buffalo.n.01", + "short-tailed_shrew.n.01", + "serval.n.01", + "war_admiral.n.01", + "dachshund.n.01", + "pomeranian.n.01", + "spitz.n.01", + "gaur.n.01", + "cynopterus_sphinx.n.01", + "cotton_mouse.n.01", + "mouse.n.01", + "steller_sea_lion.n.01", + "brocket.n.02", + "shrew.n.02", + "bucking_bronco.n.01", + "rock_wallaby.n.01", + "carthorse.n.01", + "tamarau.n.01", + "buckskin.n.01", + "brahman.n.04", + "bonnet_macaque.n.01", + "anthropoid.n.02", + "entlebucher.n.01", + "pinniped_mammal.n.01", + "newfoundland.n.01", + "durham.n.02", + "wood_mouse.n.01", + "assault.n.03", + "aardvark.n.01", + "giant_schnauzer.n.01", + "common_wallaby.n.01", + "mantled_ground_squirrel.n.01", + "bullterrier.n.01", + "homo.n.02", + "gorilla.n.01", + "great_ape.n.01", + "jackrabbit.n.01", + "roe_deer.n.01", + "imperial_mammoth.n.01", + "elephant.n.01", + "rabbit-eared_bandicoot.n.01", + "chickeree.n.01", + "flickertail.n.01", + "reynard.n.01", + "clydesdale_terrier.n.01", + "bear_cub.n.01", + "bear.n.01", + "stoat.n.01", + "rhodesian_man.n.01", + "ram.n.05", + "sheep.n.01", + "santa_gertrudis.n.01", + "mountain_sheep.n.01", + "eastern_chimpanzee.n.01", + "pouched_mouse.n.01", + "pacific_bottlenose_dolphin.n.01", + "attack_dog.n.01", + "ungulata.n.01", + "deer.n.01", + "domestic_sheep.n.01", + "welsh.n.03", + "solo_man.n.01", + "great_dane.n.01", + "pithecanthropus.n.01", + "lhasa.n.02", + "italian_greyhound.n.01", + "bull.n.11", + "jersey.n.05", + "polo_pony.n.01", + "hare.n.01", + "working_dog.n.01", + "trotting_horse.n.01", + "wirehair.n.01", + "lionet.n.01", + "zebu.n.01", + "glutton.n.02", + "irish_water_spaniel.n.01", + "sealyham_terrier.n.01", + "musk_kangaroo.n.01", + "water_chevrotain.n.01", + "baronduki.n.01", + "guinea_pig.n.02", + "english_springer.n.01", + "viscacha.n.01", + "harnessed_antelope.n.01", + "chesapeake_bay_retriever.n.01", + "numbat.n.01", + "dasyurid_marsupial.n.01", + "large_poodle.n.01", + "roan.n.02", + "entellus.n.01", + "old_world_monkey.n.01", + "squirrel_monkey.n.01", + "wild_goat.n.01", + "anoa.n.01", + "native_cat.n.01", + "bandicoot.n.01", + "mare.n.01", + "river_dolphin.n.01", + "wharf_rat.n.02", + "cardigan.n.02", + "saki.n.03", + "zoril.n.01", + "dark_horse.n.02", + "world.n.08", + "schnauzer.n.01", + "irish_terrier.n.01", + "keeshond.n.01", + "aardwolf.n.01", + "pembroke.n.01", + "foxhound.n.01", + "red_fox.n.03", + "hooded_skunk.n.01", + "woolly_indris.n.01", + "miniature_poodle.n.01", + "pocket_rat.n.01", + "japanese_deer.n.01", + "lesser_ape.n.01", + "slender_loris.n.01", + "cow.n.02", + "merino.n.01", + "bullock.n.01", + "cavy.n.01", + "fur_seal.n.02", + "jird.n.01", + "protohippus.n.01", + "greater_swiss_mountain_dog.n.01", + "capuchin.n.02", + "chigetai.n.01", + "southern_flying_squirrel.n.01", + "mangabey.n.01", + "sled_dog.n.01", + "spouter.n.03", + "tennessee_walker.n.01", + "pangolin.n.01", + "old_world_porcupine.n.01", + "sewer_rat.n.01", + "brown_rat.n.01", + "swamp_rabbit.n.02", + "cheviot.n.01", + "collared_peccary.n.01", + "mountain_goat.n.01", + "golden_retriever.n.01", + "yorkshire_terrier.n.01", + "pachyderm.n.01", + "fisher.n.02", + "tenrec.n.01", + "jumping_mouse.n.01", + "cinnamon_bear.n.01", + "american_black_bear.n.01", + "liver_chestnut.n.01", + "howler_monkey.n.01", + "sow.n.01", + "manchester_terrier.n.01", + "king_charles_spaniel.n.01", + "mouser.n.01", + "american_shrew_mole.n.01", + "peba.n.01", + "asiatic_shrew_mole.n.01", + "hudson_bay_collared_lemming.n.01", + "bunny.n.02", + "rat_terrier.n.01", + "langur.n.01", + "european_wood_mouse.n.01", + "nude_mouse.n.01", + "mole.n.06", + "mule.n.01", + "barren_ground_caribou.n.01", + "galago.n.01", + "paca.n.01", + "wolfhound.n.01", + "mountain_nyala.n.01", + "bezoar_goat.n.01", + "muntjac.n.01", + "packrat.n.02", + "skye_terrier.n.01", + "douroucouli.n.01", + "prairie_dog.n.01", + "lucy.n.01", + "hack.n.07", + "seeing_eye_dog.n.01", + "grivet.n.01", + "rhodesian_ridgeback.n.01", + "manatee.n.01", + "farm_horse.n.01", + "quagga.n.01", + "bouvier_des_flandres.n.01", + "poodle.n.01", + "stepper.n.03", + "rottweiler.n.01", + "tarsius_syrichta.n.01", + "affenpinscher.n.01", + "tailless_tenrec.n.01", + "eastern_dasyure.n.01", + "dasyure.n.01", + "elk.n.01", + "boarhound.n.01", + "woolly_rhinoceros.n.01", + "rambouillet.n.01", + "bowhead.n.01", + "grasshopper_mouse.n.01", + "dall_sheep.n.01", + "gerbil.n.01", + "prairie_vole.n.01", + "finback.n.01", + "polar_hare.n.01", + "hairy-legged_vampire_bat.n.01", + "white-lipped_peccary.n.01", + "peccary.n.01", + "dwarf_sperm_whale.n.01", + "mule_deer.n.01", + "sausage_dog.n.01", + "female_mammal.n.01", + "digitigrade_mammal.n.01", + "siamang.n.01", + "kanchil.n.01", + "chevrotain.n.01", + "giant_eland.n.01", + "timber_wolf.n.01", + "european_hare.n.01", + "guernsey.n.02", + "warrigal.n.01", + "european_rabbit.n.01", + "pug.n.01", + "white_wolf.n.01", + "new_world_mouse.n.01", + "beaked_whale.n.01", + "leonberg.n.01", + "pinche.n.01", + "big_brown_bat.n.01", + "mexican_hairless.n.01", + "american_marten.n.01", + "asian_wild_ox.n.01", + "steller's_sea_cow.n.01", + "old_world_buffalo.n.01", + "ord_kangaroo_rat.n.01", + "kangaroo_rat.n.01", + "lynx.n.02", + "alley_cat.n.01", + "serow.n.01", + "grison.n.01", + "common_eland.n.01", + "phyllostomus_hastatus.n.01", + "grizzly.n.01", + "eland.n.01", + "silky_pocket_mouse.n.01", + "elephant_seal.n.01", + "starnose_mole.n.01", + "gazella_subgutturosa.n.01", + "kerry_blue_terrier.n.01", + "vespertilian_bat.n.01", + "colobus.n.01", + "sea_otter.n.01", + "flying_fox.n.01", + "black-tailed_deer.n.01", + "mouse-eared_bat.n.01", + "chestnut.n.06", + "burro.n.01", + "kid.n.05", + "hereford.n.01", + "snow_leopard.n.01", + "indri.n.01", + "cocker_spaniel.n.01", + "kob.n.01", + "european_lemming.n.01", + "carabao.n.01", + "harbor_seal.n.01", + "shetland_pony.n.01", + "water_spaniel.n.01", + "grey_whale.n.01", + "papillon.n.01", + "toy_dog.n.01", + "draft_horse.n.01", + "eastern_grey_squirrel.n.01", + "arabian.n.02", + "northern_bog_lemming.n.01", + "lemming.n.01", + "weimaraner.n.01", + "civet.n.01", + "saiga.n.01", + "jennet.n.01", + "edentate.n.01", + "pilot_whale.n.01", + "dolphin.n.02", + "grey_fox.n.01", + "grampus.n.02", + "devon.n.02", + "walker_hound.n.01", + "shrew_mole.n.01", + "chihuahua.n.03", + "redbone.n.01", + "borzoi.n.01", + "mapinguari.n.01", + "binturong.n.01", + "scottish_deerhound.n.01", + "greyhound.n.01", + "field_spaniel.n.01", + "griffon.n.03", + "forest_goat.n.01", + "pocket_mouse.n.01", + "hart.n.03", + "pere_david's_deer.n.01", + "algeripithecus_minutus.n.01", + "red_bat.n.01", + "vixen.n.02", + "malamute.n.01", + "western_grey_squirrel.n.01", + "striped_muishond.n.01", + "gallant_fox.n.01", + "margay.n.01", + "saber-toothed_tiger.n.01", + "groenendael.n.01", + "silver_fox.n.01", + "jack.n.12", + "bengal_tiger.n.01", + "mandrill.n.01", + 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"rodent.n.01", + "canine.n.02", + "even-toed_ungulate.n.01", + "odd-toed_ungulate.n.01", + "elephant.n.01", + "rhinoceros.n.01", + "german_shepherd.n.01", + "feline.n.01", + "tiger.n.02", + "homo_sapiens.n.01" + ], + "textposition": "bottom", + "type": "scatter", + "x": [ + -0.05010500674502724, + 0.11451627274425251, + -0.756037492343237, + 0.9372625138073847, + -0.658548383207663, + 0.9188692857569626, + -0.9040980095816089, + -0.17267991240321653, + 0.11165685971397364, + 0.19179824692125683, + 0.8152021551925026, + 0.4227710646614852, + 0.49875044878193364, + -0.14298519566678491 + ], + "y": [ + -0.1032154295293315, + -0.054762350091481775, + 0.5896586298883478, + -0.1663959934735289, + -0.7432746646633447, + -0.3546995178707962, + 0.4017775071927024, + 0.9769155725982757, + -0.9935877638397855, + 0.9784790304472676, + -0.5791081650976017, + 0.9017731736266421, + 0.866457977409469, + -0.9890550501668381 + ] + } + ], + "layout": { + "height": 800, + "hovermode": "closest", + "showlegend": false, + "title": "\n2-D Visualization of model trained on mammals subtree
\n200 epochs, model is closer to convergence", + "width": 800 + } + }, + "text/html": [ + "
" + ], + "text/vnd.plotly.v1+html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = PoincareModel.load(os.path.join(models_directory, 'gensim_mammals_epochs_200_dim_2'))\n", + "figure_title = \"\"\"\n", + "2-D Visualization of model trained on mammals subtree
\n", + "200 epochs, model is closer to convergence\"\"\"\n", + "iplot(poincare_2d_visualization(model, tree, figure_title, show_node_labels=show_node_labels))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is slightly different from the representation shown in the paper. Some key differences are - \n", + "1. Some of the nodes fairly high in the hierarchy (carnivore, canine, odd-toed ungulate) are much closer to the boundary than in the paper. As a result, it is likely that distances from them to certain positive nodes will be higher than they should be.\n", + "2. Some nodes very close to the boundary have an edge with a node which is also very close to the boundary, but on the other side of the disk. This is certainly an incorrect placement, as the distance between these two nodes is going be much higher.\n", + "\n", + "Note that the actual distance between two nodes is not the same as the \"naked-eye distance\" seen in the above representation. The actual distance is the Poincare distance, whereas the \"naked-eye distance\" is the Euclidean distance.\n", + "\n", + "To get a better sense of Poincare distance, a visualization is presented below." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "marker": { + "color": [ + 7.140956639559867, + 5.936527629641392, + 5.449964500555572, + 5.157886659013344, + 4.960443770623403, + 4.820682578168151, + 4.721070602881184, + 4.652180583089688, + 4.608667030835196, + 4.587573855895887, + 4.58757386106396, + 4.608667046656536, + 4.652180610584842, + 4.7210706440155175, + 4.8206826363984625, + 4.960443852127046, + 5.157886775775228, + 5.449964679934476, + 5.936527958167716, + 7.14095785502446, + 6.723979765587077, + 5.5515046016534635, + 5.0544233193020895, + 4.745089491653391, + 4.526739420273301, + 4.362948960205523, + 4.2360776763275485, + 4.136293726117703, + 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"x": [ + 0.7 + ], + "y": [ + 0.7 + ] + } + ], + "layout": { + "height": 800, + "hovermode": "closest", + "showlegend": false, + "title": "Poincare Distances from (0.70, 0.70)", + "width": 900 + } + }, + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "iplot(poincare_distance_heatmap([0.7, 0.7]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some interesting things to note here are - \n", + "1. Distances increase much more quickly when the origin point is closer to the boundary - this can be seen visually from the fact that the darker region around the origin point grows smaller and smaller (though the changing scales make this slightly error-prone, the fact still holds). \n", + "This is also borne out from the mathematical formula for Poincare distance.\n", + "\n", + "2. For points close to the boundary, distance to other points close to the boundary is relatively low to points with a low euclidean distance, but then increases significantly and saturates - this explains the positioning of children of parent node close to the boundary in a small arc around the parent node, and the presence of all negative nodes to the parent node (nodes without an edge to the parent node) outside of that small arc." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Next steps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. The model can be investigated further to understand why it doesn't produce results as good as the paper. It is possible that this might be due to training details not present in the paper, or due to us incorrectly interpreting some ambiguous parts of the paper. We have not been able to clarify all such ambiguitities in communication with the authors.\n", + "2. Optimizing the training process further - with a model size of 50 dimensions and a dataset with ~700k relations and ~80k nodes, the Gensim implementation takes around 70 seconds to complete an epoch (~10k relations per second), whereas the open source C++ implementation takes around 1/10th the time (~95k relations per second).\n", + "3. Implementing the variant of the model mentioned in the paper for symmetric graphs and evaluating on the scientific collaboration datasets described earlier in the report." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/notebooks/poincare/entailment_eval.png b/docs/notebooks/poincare/entailment_eval.png new file mode 100644 index 0000000000000000000000000000000000000000..ae841cfaf06aa54aaa144c8d745fc96ec24d2480 GIT binary patch literal 132846 zcmdqJby$>b_cls*Bi*2MigZaMC<4MT^Z78F^z>;DXu9g!V7MLaN{($ 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zd+P9-b+HmR0%k`Kwl-l!48b{`;AHdukX~@D2-&I58#QTJ0S18@Hn(L>Y1WCRba}pt z6N>Q&NdeR7@A;i~TBGbV+bXq+N3x(Cy-hbu<6ZdM2!Kko?m3sVZq|~Kj zT0%{k0*#qSZw8p#*RL*Z`E5jvS$HQXXoA$rXNDIhS_rbc!4k9?+DB?uLgn7DWtCmq z>6+X&7`e9Pi(k>hdK}<@O0lABC zkakurP->z~P75`p8%TIRfUTq5eJLe5*2vM~A-GL;TiQ9<3GY02u@SKjfA)lfq&#?^a>JU}E@bK$~dWb}>KA37lif|LOWiyx&vk9nw>shzY}M)c=H+?xv}P zs-gg^U;nYOPyZQ4QJ2M>36^}K1bQY2I{&B-i^%`>0dqoC7`C9pU`hvG^tUp~ z_rL5Bmy%G$JD~EPas2FnI)8&3fBSEP|E-;&#uMN~{_VE9CY6ndOUc|g(s>imz# zumvP=|83*nZ5eXCgtW90G!zuexLsgILVLiRid7#J=*S!c>> trained_model.word_vec('office') + array([ -1.40128313e-02, ...]) + + """ + if word in self.vocab: + result = self.syn0[self.vocab[word].index] + result.setflags(write=False) + return result + else: + raise KeyError("word '%s' not in vocabulary" % word) + + def __getitem__(self, words): + """ + Accept a single word or a list of words as input. + + If a single word: returns the word's representations in vector space, as + a 1D numpy array. + + Multiple words: return the words' representations in vector space, as a + 2d numpy array: #words x #vector_size. Matrix rows are in the same order + as in input. + + Example:: + + >>> trained_model['office'] + array([ -1.40128313e-02, ...]) + + >>> trained_model[['office', 'products']] + array([ -1.40128313e-02, ...] + [ -1.70425311e-03, ...] + ...) + + """ + if isinstance(words, string_types): + # allow calls like trained_model['office'], as a shorthand for trained_model[['office']] + return self.word_vec(words) + + return vstack([self.word_vec(word) for word in words]) + + def __contains__(self, word): + return word in self.vocab + + def most_similar_to_given(self, w1, word_list): + """Return the word from word_list most similar to w1. + + Args: + w1 (str): a word + word_list (list): list of words containing a word most similar to w1 + + Returns: + the word in word_list with the highest similarity to w1 + + Raises: + KeyError: If w1 or any word in word_list is not in the vocabulary + + Example:: + + >>> trained_model.most_similar_to_given('music', ['water', 'sound', 'backpack', 'mouse']) + 'sound' + + >>> trained_model.most_similar_to_given('snake', ['food', 'pencil', 'animal', 'phone']) + 'animal' + + """ + return word_list[argmax([self.similarity(w1, word) for word in word_list])] + + def words_closer_than(self, w1, w2): + """ + Returns all words that are closer to `w1` than `w2` is to `w1`. + + Parameters + ---------- + w1 : str + Input word. + w2 : str + Input word. + + Returns + ------- + list (str) + List of words that are closer to `w1` than `w2` is to `w1`. + + Examples + -------- + + >>> model.words_closer_than('carnivore.n.01', 'mammal.n.01') + ['dog.n.01', 'canine.n.02'] + + """ + all_distances = self.distances(w1) + w1_index = self.vocab[w1].index + w2_index = self.vocab[w2].index + closer_node_indices = np.where(all_distances < all_distances[w2_index])[0] + return [self.index2word[index] for index in closer_node_indices if index != w1_index] + + def rank(self, w1, w2): + """ + Rank of the distance of `w2` from `w1`, in relation to distances of all words from `w1`. + + Parameters + ---------- + w1 : str + Input word. + w2 : str + Input word. + + Returns + ------- + int + Rank of `w2` from `w1` in relation to all other nodes. + + Examples + -------- + + >>> model.rank('mammal.n.01', 'carnivore.n.01') + 3 + + """ + return len(self.words_closer_than(w1, w2)) + 1 + + +class EuclideanKeyedVectors(KeyedVectorsBase): + """ + Class to contain vectors and vocab for the Word2Vec training class and other w2v methods not directly + involved in training such as most_similar() + """ + + def __init__(self): + super(EuclideanKeyedVectors, self).__init__() + self.syn0norm = None + + @property + def wv(self): + return self + + def save(self, *args, **kwargs): + # don't bother storing the cached normalized vectors + kwargs['ignore'] = kwargs.get('ignore', ['syn0norm']) + super(EuclideanKeyedVectors, self).save(*args, **kwargs) + def word_vec(self, word, use_norm=False): """ Accept a single word as input. @@ -356,6 +519,44 @@ def most_similar(self, positive=None, negative=None, topn=10, restrict_vocab=Non result = [(self.index2word[sim], float(dists[sim])) for sim in best if sim not in all_words] return result[:topn] + def similar_by_word(self, word, topn=10, restrict_vocab=None): + """ + Find the top-N most similar words. + + If topn is False, similar_by_word returns the vector of similarity scores. + + `restrict_vocab` is an optional integer which limits the range of vectors which + are searched for most-similar values. For example, restrict_vocab=10000 would + only check the first 10000 word vectors in the vocabulary order. (This may be + meaningful if you've sorted the vocabulary by descending frequency.) + + Example:: + + >>> trained_model.similar_by_word('graph') + [('user', 0.9999163150787354), ...] + + """ + return self.most_similar(positive=[word], topn=topn, restrict_vocab=restrict_vocab) + + def similar_by_vector(self, vector, topn=10, restrict_vocab=None): + """ + Find the top-N most similar words by vector. + + If topn is False, similar_by_vector returns the vector of similarity scores. + + `restrict_vocab` is an optional integer which limits the range of vectors which + are searched for most-similar values. For example, restrict_vocab=10000 would + only check the first 10000 word vectors in the vocabulary order. (This may be + meaningful if you've sorted the vocabulary by descending frequency.) + + Example:: + + >>> trained_model.similar_by_vector([1,2]) + [('survey', 0.9942699074745178), ...] + + """ + return self.most_similar(positive=[vector], topn=topn, restrict_vocab=restrict_vocab) + def wmdistance(self, document1, document2): """ Compute the Word Mover's Distance between two documents. When using this @@ -511,46 +712,6 @@ def most_similar_cosmul(self, positive=None, negative=None, topn=10): result = [(self.index2word[sim], float(dists[sim])) for sim in best if sim not in all_words] return result[:topn] - def similar_by_word(self, word, topn=10, restrict_vocab=None): - """ - Find the top-N most similar words. - - If topn is False, similar_by_word returns the vector of similarity scores. - - `restrict_vocab` is an optional integer which limits the range of vectors which - are searched for most-similar values. For example, restrict_vocab=10000 would - only check the first 10000 word vectors in the vocabulary order. (This may be - meaningful if you've sorted the vocabulary by descending frequency.) - - Example:: - - >>> trained_model.similar_by_word('graph') - [('user', 0.9999163150787354), ...] - - """ - - return self.most_similar(positive=[word], topn=topn, restrict_vocab=restrict_vocab) - - def similar_by_vector(self, vector, topn=10, restrict_vocab=None): - """ - Find the top-N most similar words by vector. - - If topn is False, similar_by_vector returns the vector of similarity scores. - - `restrict_vocab` is an optional integer which limits the range of vectors which - are searched for most-similar values. For example, restrict_vocab=10000 would - only check the first 10000 word vectors in the vocabulary order. (This may be - meaningful if you've sorted the vocabulary by descending frequency.) - - Example:: - - >>> trained_model.similar_by_vector([1,2]) - [('survey', 0.9942699074745178), ...] - - """ - - return self.most_similar(positive=[vector], topn=topn, restrict_vocab=restrict_vocab) - def doesnt_match(self, words): """ Which word from the given list doesn't go with the others? @@ -574,36 +735,83 @@ def doesnt_match(self, words): dists = dot(vectors, mean) return sorted(zip(dists, used_words))[0][1] - def __getitem__(self, words): + @staticmethod + def cosine_similarities(vector_1, vectors_all): """ - Accept a single word or a list of words as input. + Return cosine similarities between one vector and a set of other vectors. + + Parameters + ---------- + vector_1 : numpy.array + vector from which similarities are to be computed. + expected shape (dim,) + vectors_all : numpy.array + for each row in vectors_all, distance from vector_1 is computed. + expected shape (num_vectors, dim) + + Returns + ------- + numpy.array + Contains cosine distance between vector_1 and each row in vectors_all. + shape (num_vectors,) - If a single word: returns the word's representations in vector space, as - a 1D numpy array. + """ + norm = np.linalg.norm(vector_1) + all_norms = np.linalg.norm(vectors_all, axis=1) + dot_products = dot(vectors_all, vector_1) + similarities = dot_products / (norm * all_norms) + return similarities - Multiple words: return the words' representations in vector space, as a - 2d numpy array: #words x #vector_size. Matrix rows are in the same order - as in input. + def distances(self, word_or_vector, other_words=()): + """ + Compute cosine distances from given word or vector to all words in `other_words`. + If `other_words` is empty, return distance between `word_or_vectors` and all words in vocab. - Example:: + Parameters + ---------- + word_or_vector : str or numpy.array + Word or vector from which distances are to be computed. - >>> trained_model['office'] - array([ -1.40128313e-02, ...]) + other_words : iterable(str) or None + For each word in `other_words` distance from `word_or_vector` is computed. + If None or empty, distance of `word_or_vector` from all words in vocab is computed (including itself). - >>> trained_model[['office', 'products']] - array([ -1.40128313e-02, ...] - [ -1.70425311e-03, ...] - ...) + Returns + ------- + numpy.array + Array containing distances to all words in `other_words` from input `word_or_vector`, + in the same order as `other_words`. + + Notes + ----- + Raises KeyError if either `word_or_vector` or any word in `other_words` is absent from vocab. """ - if isinstance(words, string_types): - # allow calls like trained_model['office'], as a shorthand for trained_model[['office']] - return self.word_vec(words) + if isinstance(word_or_vector, string_types): + input_vector = self.word_vec(word_or_vector) + else: + input_vector = word_or_vector + if not other_words: + other_vectors = self.syn0 + else: + other_indices = [self.vocab[word].index for word in other_words] + other_vectors = self.syn0[other_indices] + return 1 - self.cosine_similarities(input_vector, other_vectors) - return vstack([self.word_vec(word) for word in words]) + def distance(self, w1, w2): + """ + Compute cosine distance between two words. - def __contains__(self, word): - return word in self.vocab + Example:: + + >>> trained_model.distance('woman', 'man') + 0.34 + + >>> trained_model.distance('woman', 'woman') + 0.0 + + """ + return 1 - self.similarity(w1, w2) def similarity(self, w1, w2): """ @@ -620,30 +828,6 @@ def similarity(self, w1, w2): """ return dot(matutils.unitvec(self[w1]), matutils.unitvec(self[w2])) - def most_similar_to_given(self, w1, word_list): - """Return the word from word_list most similar to w1. - - Args: - w1 (str): a word - word_list (list): list of words containing a word most similar to w1 - - Returns: - the word in word_list with the highest similarity to w1 - - Raises: - KeyError: If w1 or any word in word_list is not in the vocabulary - - Example:: - - >>> trained_model.most_similar_to_given('music', ['water', 'sound', 'backpack', 'mouse']) - 'sound' - - >>> trained_model.most_similar_to_given('snake', ['food', 'pencil', 'animal', 'phone']) - 'animal' - - """ - return word_list[argmax([self.similarity(w1, word) for word in word_list])] - def n_similarity(self, ws1, ws2): """ Compute cosine similarity between two sets of words. @@ -873,3 +1057,7 @@ def get_keras_embedding(self, train_embeddings=False): weights=[weights], trainable=train_embeddings ) return layer + + +# For backward compatibility +KeyedVectors = EuclideanKeyedVectors diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index 6a5b5f0ccd..b5506a67ef 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -6,18 +6,18 @@ # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html -"""Python implementation of Poincare Embeddings [1]_, an embedding that is better at capturing latent hierarchical -information better than traditional Euclidean embeddings. The method is described in more detail in [1]_. +"""Python implementation of PoincarĂ© Embeddings [1]_, an embedding that is better at capturing latent hierarchical +information than traditional Euclidean embeddings. The method is described in more detail in [1]_. The main use-case is to automatically learn hierarchical representations of nodes from a tree-like structure, -such as a Directed Acyclic Graph, using the transitive closure of the relations. Representations of nodes in a -symmetric graph can also be learned, using an iterable of the relations in the graph. +such as a Directed Acyclic Graph, using a transitive closure of the relations. Representations of nodes in a +symmetric graph can also be learned, using an iterable of the direct relations in the graph. -This module allows training a Poincare Embedding from a training file containing relations of graph in a -csv-like format. +This module allows training a PoincarĂ© Embedding from a training file containing relations of graph in a +csv-like format, or a Python iterable of relations. .. [1] Maximilian Nickel, Douwe Kiela - "PoincarĂ© Embeddings for Learning Hierarchical Representations" - https://arxiv.org/pdf/1705.08039.pdf + https://arxiv.org/abs/1705.08039 Examples: --------- @@ -47,10 +47,12 @@ import numpy as np from collections import defaultdict, Counter from numpy import random as np_random +from scipy.stats import spearmanr +from six import string_types from smart_open import smart_open -from gensim import utils -from gensim.models.keyedvectors import KeyedVectors, Vocab +from gensim import utils, matutils +from gensim.models.keyedvectors import KeyedVectorsBase, Vocab logger = logging.getLogger(__name__) @@ -61,7 +63,7 @@ class PoincareModel(utils.SaveLoad): The model can be stored/loaded via its :meth:`~gensim.models.poincare.PoincareModel.save` and :meth:`~gensim.models.poincare.PoincareModel.load` methods, or stored/loaded in the word2vec format - via `model.kv.save_word2vec_format` and :meth:`~gensim.models.keyedvectors.KeyedVectors.load_word2vec_format`. + via `model.kv.save_word2vec_format` and :meth:`~gensim.models.poincare.PoincareKeyedVectors.load_word2vec_format`. Note that training cannot be resumed from a model loaded via `load_word2vec_format`, if you wish to train further, use :meth:`~gensim.models.poincare.PoincareModel.save` and :meth:`~gensim.models.poincare.PoincareModel.load` @@ -120,7 +122,7 @@ def __init__(self, train_data, size=50, alpha=0.1, negative=10, workers=1, epsil """ self.train_data = train_data - self.kv = KeyedVectors() + self.kv = PoincareKeyedVectors() self.size = size self.train_alpha = alpha # Learning rate for training self.burn_in_alpha = burn_in_alpha # Learning rate for burn-in @@ -160,7 +162,7 @@ def _load_relations(self): node_relations[node_1_index].add(node_2_index) relation = (node_1_index, node_2_index) all_relations.append(relation) - logger.info("Loaded %d relations from train data, %d unique terms", len(all_relations), len(vocab)) + logger.info("Loaded %d relations from train data, %d nodes", len(all_relations), len(vocab)) self.kv.vocab = vocab self.kv.index2word = index2word self.indices_set = set((range(len(index2word)))) # Set of all node indices @@ -217,7 +219,7 @@ def _sample_negatives(self, node_index): (self.negative, num_remaining_nodes, self.kv.index2word[node_index]) ) - positive_fraction = len(node_relations) / len(self.kv.vocab) + positive_fraction = float(len(node_relations)) / len(self.kv.vocab) if positive_fraction < 0.01: # If number of positive relations is a small fraction of total nodes # re-sample till no positively connected nodes are chosen @@ -514,6 +516,8 @@ def train(self, epochs, batch_size=10, print_every=1000, check_gradients_every=N """ if self.workers > 1: raise NotImplementedError("Multi-threaded version not implemented yet") + # Some divide-by-zero results are handled explicitly + old_settings = np.seterr(divide='ignore', invalid='ignore') logger.info( "training model of size %d with %d workers on %d relations for %d epochs and %d burn-in epochs, " @@ -538,6 +542,8 @@ def train(self, epochs, batch_size=10, print_every=1000, check_gradients_every=N check_gradients_every=check_gradients_every) logger.info("Training finished") + np.seterr(**old_settings) + def _train_batchwise(self, epochs, batch_size=10, print_every=1000, check_gradients_every=None): """Trains Poincare embeddings using specified parameters. @@ -678,17 +684,19 @@ def compute_gradients(self): self.compute_distances() self.compute_distance_gradients() - gradients_v = -self.exp_negative_distances[:, np.newaxis, :] * self.distance_gradients_v # (1 + neg_size, dim, batch_size) + # (1 + neg_size, dim, batch_size) + gradients_v = -self.exp_negative_distances[:, np.newaxis, :] * self.distance_gradients_v gradients_v /= self.Z # (1 + neg_size, dim, batch_size) gradients_v[0] += self.distance_gradients_v[0] - gradients_u = -self.exp_negative_distances[:, np.newaxis, :] * self.distance_gradients_u # (1 + neg_size, dim, batch_size) + # (1 + neg_size, dim, batch_size) + gradients_u = -self.exp_negative_distances[:, np.newaxis, :] * self.distance_gradients_u gradients_u /= self.Z # (1 + neg_size, dim, batch_size) gradients_u = gradients_u.sum(axis=0) # (dim, batch_size) gradients_u += self.distance_gradients_u[0] - assert(not np.isnan(gradients_u).any()) - assert(not np.isnan(gradients_v).any()) + assert not np.isnan(gradients_u).any() + assert not np.isnan(gradients_v).any() self.gradients_u = gradients_u self.gradients_v = gradients_v @@ -701,8 +709,10 @@ def compute_distance_gradients(self): self.compute_distances() euclidean_dists_squared = self.euclidean_dists ** 2 # (1 + neg_size, batch_size) - c_ = (4 / (self.alpha * self.beta * np.sqrt(self.gamma ** 2 - 1)))[:, np.newaxis, :] # (1 + neg_size, 1, batch_size) - u_coeffs = ((euclidean_dists_squared + self.alpha) / self.alpha)[:, np.newaxis, :] # (1 + neg_size, 1, batch_size) + # (1 + neg_size, 1, batch_size) + c_ = (4 / (self.alpha * self.beta * np.sqrt(self.gamma ** 2 - 1)))[:, np.newaxis, :] + # (1 + neg_size, 1, batch_size) + u_coeffs = ((euclidean_dists_squared + self.alpha) / self.alpha)[:, np.newaxis, :] distance_gradients_u = u_coeffs * self.vectors_u - self.vectors_v # (1 + neg_size, dim, batch_size) distance_gradients_u *= c_ # (1 + neg_size, dim, batch_size) @@ -711,7 +721,8 @@ def compute_distance_gradients(self): distance_gradients_u.swapaxes(1, 2)[nan_gradients] = 0 self.distance_gradients_u = distance_gradients_u - v_coeffs = ((euclidean_dists_squared + self.beta) / self.beta)[:, np.newaxis, :] # (1 + neg_size, 1, batch_size) + # (1 + neg_size, 1, batch_size) + v_coeffs = ((euclidean_dists_squared + self.beta) / self.beta)[:, np.newaxis, :] distance_gradients_v = v_coeffs * self.vectors_v - self.vectors_u # (1 + neg_size, dim, batch_size) distance_gradients_v *= c_ # (1 + neg_size, dim, batch_size) @@ -731,27 +742,389 @@ def compute_loss(self): self._loss_computed = True -class PoincareKeyedVectors(KeyedVectors): +class PoincareKeyedVectors(KeyedVectorsBase): """Class to contain vectors and vocab for the :class:`~gensim.models.poincare.PoincareModel` training class. Used to perform operations on the vectors such as vector lookup, distance etc. """ + + def __init__(self): + super(PoincareKeyedVectors, self).__init__() + self.max_distance = 0 + @staticmethod - def poincare_dist(vector_1, vector_2): - """Return poincare distance between two vectors.""" - norm_1 = np.linalg.norm(vector_1) - norm_2 = np.linalg.norm(vector_2) - euclidean_dist = np.linalg.norm(vector_1 - vector_2) - if euclidean_dist == 0.0: - return 0.0 - else: - return np.arccosh( - 1 + 2 * ( - (euclidean_dist ** 2) / ((1 - norm_1 ** 2) * (1 - norm_2 ** 2)) - ) + def vector_distance(vector_1, vector_2): + """ + Return poincare distance between two input vectors. Convenience method over `vector_distance_batch`. + + Parameters + ---------- + vector_1 : numpy.array + input vector + vector_2 : numpy.array + input vector + + Returns + ------- + numpy.float + Poincare distance between `vector_1` and `vector_2`. + + """ + return PoincareKeyedVectors.vector_distance_batch(vector_1, vector_2[np.newaxis, :])[0] + + @staticmethod + def vector_distance_batch(vector_1, vectors_all): + """ + Return poincare distances between one vector and a set of other vectors. + + Parameters + ---------- + vector_1 : numpy.array + vector from which Poincare distances are to be computed. + expected shape (dim,) + vectors_all : numpy.array + for each row in vectors_all, distance from vector_1 is computed. + expected shape (num_vectors, dim) + + Returns + ------- + numpy.array + Contains Poincare distance between vector_1 and each row in vectors_all. + shape (num_vectors,) + + """ + euclidean_dists = np.linalg.norm(vector_1 - vectors_all, axis=1) + norm = np.linalg.norm(vector_1) + all_norms = np.linalg.norm(vectors_all, axis=1) + return np.arccosh( + 1 + 2 * ( + (euclidean_dists ** 2) / ((1 - norm ** 2) * (1 - all_norms ** 2)) ) - # TODO: Add other KeyedVector supported methods - most_similar, etc. + ) + + def closest_child(self, node): + """ + Returns the node closest to `node` that is lower in the hierarchy than `node`. + + Parameters + ---------- + node : str or int + Key for node for which closest child is to be found. + + Returns + ------- + str or None + Node closest to `node` that is lower in the hierarchy than `node`. + If there are no nodes lower in the hierarchy, None is returned. + + """ + all_distances = self.distances(node) + all_norms = np.linalg.norm(self.syn0, axis=1) + node_norm = all_norms[self.vocab[node].index] + mask = node_norm >= all_norms + if mask.all(): # No nodes lower in the hierarchy + return None + all_distances = np.ma.array(all_distances, mask=mask) + closest_child_index = np.ma.argmin(all_distances) + return self.index2word[closest_child_index] + + def closest_parent(self, node): + """ + Returns the node closest to `node` that is higher in the hierarchy than `node`. + + Parameters + ---------- + node : str or int + Key for node for which closest parent is to be found. + + Returns + ------- + str or None + Node closest to `node` that is higher in the hierarchy than `node`. + If there are no nodes higher in the hierarchy, None is returned. + + """ + all_distances = self.distances(node) + all_norms = np.linalg.norm(self.syn0, axis=1) + node_norm = all_norms[self.vocab[node].index] + mask = node_norm <= all_norms + if mask.all(): # No nodes higher in the hierarchy + return None + all_distances = np.ma.array(all_distances, mask=mask) + closest_child_index = np.ma.argmin(all_distances) + return self.index2word[closest_child_index] + + def descendants(self, node, max_depth=5): + """ + Returns the list of recursively closest children from the given node, upto a max depth of `max_depth`. + + Parameters + ---------- + node : str or int + Key for node for which descendants are to be found. + max_depth : int + Maximum number of descendants to return. + + Returns + ------- + list (str) + Descendant nodes from the node `node`. + + """ + depth = 0 + descendants = [] + current_node = node + while depth < max_depth: + descendants.append(self.closest_child(current_node)) + current_node = descendants[-1] + depth += 1 + return descendants + + def ancestors(self, node): + """ + Returns the list of recursively closest parents from the given node. + + Parameters + ---------- + node : str or int + Key for node for which ancestors are to be found. + + Returns + ------- + list (str) + Ancestor nodes of the node `node`. + + """ + ancestors = [] + current_node = node + ancestor = self.closest_parent(current_node) + while ancestor is not None: + ancestors.append(ancestor) + ancestor = self.closest_parent(ancestors[-1]) + return ancestors + + def distance(self, w1, w2): + """ + Return Poincare distance between vectors for nodes `w1` and `w2`. + + Parameters + ---------- + w1 : str or int + Key for first node. + w2 : str or int + Key for second node. + + Returns + ------- + float + Poincare distance between the vectors for nodes `w1` and `w2`. + + Examples + -------- + + >>> model.distance('mammal.n.01', 'carnivore.n.01') + 2.13 + + Notes + ----- + Raises KeyError if either of `w1` and `w2` is absent from vocab. + + """ + vector_1 = self.word_vec(w1) + vector_2 = self.word_vec(w2) + return self.vector_distance(vector_1, vector_2) + + def similarity(self, w1, w2): + """ + Return similarity based on Poincare distance between vectors for nodes `w1` and `w2`. + + Parameters + ---------- + w1 : str or int + Key for first node. + w2 : str or int + Key for second node. + + Returns + ------- + float + Similarity between the between the vectors for nodes `w1` and `w2` (between 0 and 1). + + Examples + -------- + + >>> model.similarity('mammal.n.01', 'carnivore.n.01') + 0.73 + + Notes + ----- + Raises KeyError if either of `w1` and `w2` is absent from vocab. + Similarity lies between 0 and 1. + + """ + return 1 / (1 + self.distance(w1, w2)) + + def most_similar(self, node_or_vector, topn=10, restrict_vocab=None): + """ + Find the top-N most similar nodes to the given node or vector, sorted in increasing order of distance. + + Parameters + ---------- + + node_or_vector : str/int or numpy.array + node key or vector for which similar nodes are to be found. + topn : int or None, optional + number of similar nodes to return, if `None`, returns all. + restrict_vocab : int or None, optional + Optional integer which limits the range of vectors which are searched for most-similar values. + For example, restrict_vocab=10000 would only check the first 10000 node vectors in the vocabulary order. + This may be meaningful if vocabulary is sorted by descending frequency. + + Returns + -------- + list of tuples (str, float) + List of tuples containing (node, distance) pairs in increasing order of distance. + + Examples + -------- + >>> vectors.most_similar('lion.n.01') + [('lion_cub.n.01', 0.4484), ('lionet.n.01', 0.6552), ...] + + """ + if not restrict_vocab: + all_distances = self.distances(node_or_vector) + else: + nodes_to_use = self.index2word[:restrict_vocab] + all_distances = self.distances(node_or_vector, nodes_to_use) + + if isinstance(node_or_vector, string_types + (int,)): + node_index = self.vocab[node_or_vector].index + else: + node_index = None + if not topn: + closest_indices = matutils.argsort(all_distances) + else: + closest_indices = matutils.argsort(all_distances, topn=1 + topn) + result = [ + (self.index2word[index], float(all_distances[index])) + for index in closest_indices if (not node_index or index != node_index) # ignore the input node + ] + if topn: + result = result[:topn] + return result + + def distances(self, node_or_vector, other_nodes=()): + """ + Compute Poincare distances from given node or vector to all nodes in `other_nodes`. + If `other_nodes` is empty, return distance between `node_or_vector` and all nodes in vocab. + + Parameters + ---------- + node_or_vector : str/int or numpy.array + Node key or vector from which distances are to be computed. + + other_nodes : iterable of str/int or None + For each node in `other_nodes` distance from `node_or_vector` is computed. + If None or empty, distance of `node_or_vector` from all nodes in vocab is computed (including itself). + + Returns + ------- + numpy.array + Array containing distances to all nodes in `other_nodes` from input `node_or_vector`, + in the same order as `other_nodes`. + + Examples + -------- + + >>> model.distances('mammal.n.01', ['carnivore.n.01', 'dog.n.01']) + np.array([2.1199, 2.0710] + + >>> model.distances('mammal.n.01') + np.array([0.43753847, 3.67973852, ..., 6.66172886]) + + Notes + ----- + Raises KeyError if either `node_or_vector` or any node in `other_nodes` is absent from vocab. + + """ + if isinstance(node_or_vector, string_types): + input_vector = self.word_vec(node_or_vector) + else: + input_vector = node_or_vector + if not other_nodes: + other_vectors = self.syn0 + else: + other_indices = [self.vocab[node].index for node in other_nodes] + other_vectors = self.syn0[other_indices] + return self.vector_distance_batch(input_vector, other_vectors) + + def norm(self, node_or_vector): + """ + Return absolute position in hierarchy of input node or vector. + Values range between 0 and 1. A lower value indicates the input node or vector is higher in the hierarchy. + + Parameters + ---------- + node_or_vector : str/int or numpy.array + Input node key or vector for which position in hierarchy is to be returned. + + Returns + ------- + float + Absolute position in the hierarchy of the input vector or node. + + Examples + -------- + + >>> model.norm('mammal.n.01') + 0.9 + + Notes + ----- + The position in hierarchy is based on the norm of the vector for the node. + + """ + if isinstance(node_or_vector, string_types): + input_vector = self.word_vec(node_or_vector) + else: + input_vector = node_or_vector + return np.linalg.norm(input_vector) + + def difference_in_hierarchy(self, node_or_vector_1, node_or_vector_2): + """ + Relative position in hierarchy of `node_or_vector_1` relative to `node_or_vector_2`. + A positive value indicates `node_or_vector_1` is higher in the hierarchy than `node_or_vector_2`. + + Parameters + ---------- + node_or_vector_1 : str/int or numpy.array + Input node key or vector. + + node_or_vector_2 : str/int or numpy.array + Input node key or vector. + + Returns + ------- + float + Relative position in hierarchy of `node_or_vector_1` relative to `node_or_vector_2`. + + Examples + -------- + + >>> model.difference_in_hierarchy('mammal.n.01', 'dog.n.01') + 0.51 + + >>> model.difference_in_hierarchy('dog.n.01', 'mammal.n.01') + -0.51 + + Notes + ----- + The returned value can be positive or negative, depending on whether `node_or_vector_1` is higher + or lower in the hierarchy than `node_or_vector_2`. + + """ + return self.norm(node_or_vector_2) - self.norm(node_or_vector_1) class PoincareRelations(object): @@ -784,16 +1157,17 @@ def __iter__(self): Relation from input file. """ - if sys.version_info[0] < 3: - lines = smart_open(self.file_path, 'rb') - else: - lines = (l.decode(self.encoding) for l in smart_open(self.file_path, 'rb')) - # csv.reader requires bytestring input in python2, unicode input in python3 - reader = csv.reader(lines, delimiter=self.delimiter) - for row in reader: + with smart_open(self.file_path) as file_obj: if sys.version_info[0] < 3: - row = [value.decode(self.encoding) for value in row] - yield tuple(row) + lines = file_obj + else: + lines = (l.decode(self.encoding) for l in file_obj) + # csv.reader requires bytestring input in python2, unicode input in python3 + reader = csv.reader(lines, delimiter=self.delimiter) + for row in reader: + if sys.version_info[0] < 3: + row = [value.decode(self.encoding) for value in row] + yield tuple(row) class NegativesBuffer(object): @@ -829,7 +1203,7 @@ def get_items(self, num_items): Parameters ---------- num_items : int - number of items to fetch. + Number of items to fetch. Returns ------- @@ -846,3 +1220,369 @@ def get_items(self, num_items): end_index = start_index + num_items self._current_index += num_items return self._items[start_index:end_index] + + +class ReconstructionEvaluation(object): + """Evaluating reconstruction on given network for given embedding.""" + + def __init__(self, file_path, embedding): + """Initialize evaluation instance with tsv file containing relation pairs and embedding to be evaluated. + + Parameters + ---------- + file_path : str + Path to tsv file containing relation pairs. + embedding : PoincareKeyedVectors instance + Embedding to be evaluated. + + """ + items = set() + embedding_vocab = embedding.vocab + relations = defaultdict(set) + with smart_open(file_path, 'r') as f: + reader = csv.reader(f, delimiter='\t') + for row in reader: + assert len(row) == 2, 'Hypernym pair has more than two items' + item_1_index = embedding_vocab[row[0]].index + item_2_index = embedding_vocab[row[1]].index + relations[item_1_index].add(item_2_index) + items.update([item_1_index, item_2_index]) + self.items = items + self.relations = relations + self.embedding = embedding + + @staticmethod + def get_positive_relation_ranks_and_avg_prec(all_distances, positive_relations): + """ + Given a numpy array of all distances from an item and indices of its positive relations, + compute ranks and Average Precision of positive relations. + + Parameters + ---------- + all_distances : numpy.array (float) + Array of all distances (floats) for a specific item. + positive_relations : list + List of indices of positive relations for the item. + + Returns + ------- + tuple (list, float) + The list contains ranks (int) of positive relations in the same order as `positive_relations`. + The float is the Average Precision of the ranking. + e.g. ([1, 2, 3, 20], 0.610). + + """ + positive_relation_distances = all_distances[positive_relations] + negative_relation_distances = np.ma.array(all_distances, mask=False) + negative_relation_distances.mask[positive_relations] = True + # Compute how many negative relation distances are less than each positive relation distance, plus 1 for rank + ranks = (negative_relation_distances < positive_relation_distances[:, np.newaxis]).sum(axis=1) + 1 + map_ranks = np.sort(ranks) + np.arange(len(ranks)) + avg_precision = ((np.arange(1, len(map_ranks) + 1) / np.sort(map_ranks)).mean()) + return list(ranks), avg_precision + + def evaluate(self, max_n=None): + """Evaluate all defined metrics for the reconstruction task. + + Parameters + ---------- + max_n : int or None + Maximum number of positive relations to evaluate, all if `max_n` is None. + + Returns + ------- + dict + Contains (metric_name, metric_value) pairs. + e.g. {'mean_rank': 50.3, 'MAP': 0.31}. + + """ + mean_rank, map_ = self.evaluate_mean_rank_and_map(max_n) + return {'mean_rank': mean_rank, 'MAP': map_} + + def evaluate_mean_rank_and_map(self, max_n=None): + """Evaluate mean rank and MAP for reconstruction. + + Parameters + ---------- + max_n : int or None + Maximum number of positive relations to evaluate, all if `max_n` is None. + + Returns + ------- + tuple (float, float) + Contains (mean_rank, MAP). + e.g (50.3, 0.31) + + """ + ranks = [] + avg_precision_scores = [] + for i, item in enumerate(self.items, start=1): + if item not in self.relations: + continue + item_relations = list(self.relations[item]) + item_term = self.embedding.index2word[item] + item_distances = self.embedding.distances(item_term) + positive_relation_ranks, avg_precision = self.get_positive_relation_ranks_and_avg_prec(item_distances, item_relations) + ranks += positive_relation_ranks + avg_precision_scores.append(avg_precision) + if max_n is not None and i > max_n: + break + return np.mean(ranks), np.mean(avg_precision_scores) + + +class LinkPredictionEvaluation(object): + """Evaluating reconstruction on given network for given embedding.""" + + def __init__(self, train_path, test_path, embedding): + """Initialize evaluation instance with tsv file containing relation pairs and embedding to be evaluated. + + Parameters + ---------- + train_path : str + Path to tsv file containing relation pairs used for training. + test_path : str + Path to tsv file containing relation pairs to evaluate. + embedding : PoincareKeyedVectors instance + Embedding to be evaluated. + + """ + items = set() + embedding_vocab = embedding.vocab + relations = {'known': defaultdict(set), 'unknown': defaultdict(set)} + data_files = {'known': train_path, 'unknown': test_path} + for relation_type, data_file in data_files.items(): + with smart_open(data_file, 'r') as f: + reader = csv.reader(f, delimiter='\t') + for row in reader: + assert len(row) == 2, 'Hypernym pair has more than two items' + item_1_index = embedding_vocab[row[0]].index + item_2_index = embedding_vocab[row[1]].index + relations[relation_type][item_1_index].add(item_2_index) + items.update([item_1_index, item_2_index]) + self.items = items + self.relations = relations + self.embedding = embedding + + @staticmethod + def get_unknown_relation_ranks_and_avg_prec(all_distances, unknown_relations, known_relations): + """ + Given a numpy array of distances and indices of known and unknown positive relations, + compute ranks and Average Precision of unknown positive relations. + + Parameters + ---------- + all_distances : numpy.array (float) + Array of all distances for a specific item. + unknown_relations : list + List of indices of unknown positive relations. + known_relations : list + List of indices of known positive relations. + + Returns + ------- + tuple (list, float) + The list contains ranks (int) of positive relations in the same order as `positive_relations`. + The float is the Average Precision of the ranking. + e.g. ([1, 2, 3, 20], 0.610). + + """ + unknown_relation_distances = all_distances[unknown_relations] + negative_relation_distances = np.ma.array(all_distances, mask=False) + negative_relation_distances.mask[unknown_relations] = True + negative_relation_distances.mask[known_relations] = True + # Compute how many negative relation distances are less than each unknown relation distance, plus 1 for rank + ranks = (negative_relation_distances < unknown_relation_distances[:, np.newaxis]).sum(axis=1) + 1 + map_ranks = np.sort(ranks) + np.arange(len(ranks)) + avg_precision = ((np.arange(1, len(map_ranks) + 1) / np.sort(map_ranks)).mean()) + return list(ranks), avg_precision + + def evaluate(self, max_n=None): + """Evaluate all defined metrics for the link prediction task. + + Parameters + ---------- + max_n : int or None + Maximum number of positive relations to evaluate, all if `max_n` is None. + + Returns + ------- + dict + Contains (metric_name, metric_value) pairs. + e.g. {'mean_rank': 50.3, 'MAP': 0.31}. + + """ + mean_rank, map_ = self.evaluate_mean_rank_and_map(max_n) + return {'mean_rank': mean_rank, 'MAP': map_} + + def evaluate_mean_rank_and_map(self, max_n=None): + """Evaluate mean rank and MAP for link prediction. + + Parameters + ---------- + max_n : int or None + Maximum number of positive relations to evaluate, all if `max_n` is None. + + Returns + ------- + tuple (float, float) + Contains (mean_rank, MAP). + e.g (50.3, 0.31). + + """ + ranks = [] + avg_precision_scores = [] + for i, item in enumerate(self.items, start=1): + if item not in self.relations['unknown']: # No positive relations to predict for this node + continue + unknown_relations = list(self.relations['unknown'][item]) + known_relations = list(self.relations['known'][item]) + item_term = self.embedding.index2word[item] + item_distances = self.embedding.distances(item_term) + unknown_relation_ranks, avg_precision = self.get_unknown_relation_ranks_and_avg_prec(item_distances, unknown_relations, known_relations) + ranks += unknown_relation_ranks + avg_precision_scores.append(avg_precision) + if max_n is not None and i > max_n: + break + return np.mean(ranks), np.mean(avg_precision_scores) + + +class LexicalEntailmentEvaluation(object): + """Evaluating reconstruction on given network for any embedding.""" + + def __init__(self, filepath): + """Initialize evaluation instance with HyperLex text file containing relation pairs. + + Parameters + ---------- + filepath : str + Path to HyperLex text file. + + """ + expected_scores = {} + with smart_open(filepath, 'r') as f: + reader = csv.DictReader(f, delimiter=' ') + for row in reader: + word_1, word_2 = row['WORD1'], row['WORD2'] + expected_scores[(word_1, word_2)] = float(row['AVG_SCORE']) + self.scores = expected_scores + self.alpha = 1000 + + def score_function(self, embedding, trie, term_1, term_2): + """ + Given an embedding and two terms, return the predicted score for them - + extent to which `term_1` is a type of `term_2`. + + Parameters + ---------- + embedding : PoincareKeyedVectors instance + Embedding to use for computing predicted score. + trie : pygtrie.Trie instance + Trie to use for finding matching vocab terms for input terms. + term_1 : str + Input term. + term_2 : str + Input term. + + Returns + ------- + float + Predicted score (the extent to which `term_1` is a type of `term_2`). + + """ + try: + word_1_terms = self.find_matching_terms(trie, term_1) + word_2_terms = self.find_matching_terms(trie, term_2) + except KeyError: + raise ValueError("No matching terms found for either %s or %s" % (term_1, term_2)) + min_distance = np.inf + min_term_1, min_term_2 = None, None + for term_1 in word_1_terms: + for term_2 in word_2_terms: + distance = embedding.distance(term_1, term_2) + if distance < min_distance: + min_term_1, min_term_2 = term_1, term_2 + min_distance = distance + assert min_term_1 is not None and min_term_2 is not None + vector_1, vector_2 = embedding.word_vec(min_term_1), embedding.word_vec(min_term_2) + norm_1, norm_2 = np.linalg.norm(vector_1), np.linalg.norm(vector_2) + return -1 * (1 + self.alpha * (norm_2 - norm_1)) * distance + + @staticmethod + def find_matching_terms(trie, word): + """ + Given a trie and a word, find terms in the trie beginning with the word. + + Parameters + ---------- + trie : pygtrie.Trie instance + Trie to use for finding matching terms. + word : str + Input word to use for prefix search. + + Returns + ------- + list (str) + List of matching terms. + + """ + matches = trie.items('%s.' % word) + matching_terms = [''.join(key_chars) for key_chars, value in matches] + return matching_terms + + @staticmethod + def create_vocab_trie(embedding): + """Create trie with vocab terms of the given embedding to enable quick prefix searches. + + Parameters + ---------- + embedding : PoincareKeyedVectors instance + Embedding for which trie is to be created. + + Returns + ------- + pygtrie.Trie instance + Trie containing vocab terms of the input embedding. + + """ + try: + from pygtrie import Trie + except ImportError: + raise ImportError( + 'pygtrie could not be imported, please install pygtrie in order to use LexicalEntailmentEvaluation') + + vocab_trie = Trie() + for key in embedding.vocab: + vocab_trie[key] = True + return vocab_trie + + def evaluate_spearman(self, embedding): + """Evaluate spearman scores for lexical entailment for given embedding. + + Parameters + ---------- + embedding : PoincareKeyedVectors instance + Embedding for which evaluation is to be done. + + Returns + ------- + float + Spearman correlation score for the task for input embedding. + + """ + predicted_scores = [] + expected_scores = [] + skipped = 0 + count = 0 + vocab_trie = self.create_vocab_trie(embedding) + for (word_1, word_2), expected_score in self.scores.items(): + try: + predicted_score = self.score_function(embedding, vocab_trie, word_1, word_2) + except ValueError: + skipped += 1 + continue + count += 1 + predicted_scores.append(predicted_score) + expected_scores.append(expected_score) + print('Skipped pairs: %d out of %d' % (skipped, len(self.scores))) + spearman = spearmanr(expected_scores, 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z+K`#_PJkV3tvC(IPvrQDwG{e;tT`jmj3pf-@ut+RXP-Eat>SxXrC%|6t1?l$mxrK! zAwM*$U+%eT7T$5Y#nOfsetYhcW9BK)YU_$!`%KU?)PnxkKb=_UBB9q@3|ZHhT=jz( zn13^fV^E6K-$tR|#6&I5Xs^TVSqHE!HUk~kT0-glIx4HR!<7ZhKCf^I_RSOWEDie0 zWi%k@I4%>tNy6X1jGEe8C@Z=`RI*@DI*DPm?G@Z&pGnCyOFo)8EV5V}AJ20?48$J6 kBJ|c>R@<1gLv-HD=o(gq@z)+<#nYph9()z8nO5Nc0DXpAr2qf` literal 0 HcmV?d00001 diff --git a/gensim/test/test_keyedvectors.py b/gensim/test/test_keyedvectors.py new file mode 100644 index 0000000000..8789ec38b3 --- /dev/null +++ b/gensim/test/test_keyedvectors.py @@ -0,0 +1,127 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Author: Jayant Jain +# Copyright (C) 2017 Radim Rehurek +# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html + +""" +Automated tests for checking the poincare module from the models package. +""" + +import logging +import unittest + +import numpy as np + +from gensim.models.keyedvectors import EuclideanKeyedVectors +from gensim.test.utils import datapath + + +logger = logging.getLogger(__name__) + + +class TestEuclideanKeyedVectors(unittest.TestCase): + def setUp(self): + self.vectors = EuclideanKeyedVectors.load_word2vec_format( + datapath('euclidean_vectors.bin'), binary=True, datatype=np.float64) + + def test_most_similar(self): + """Test most_similar returns expected results.""" + expected = [ + 'conflict', + 'administration', + 'terrorism', + 'call', + 'israel' + ] + predicted = [result[0] for result in self.vectors.most_similar('war', topn=5)] + self.assertEqual(expected, predicted) + + def test_most_similar_topn(self): + """Test most_similar returns correct results when `topn` is specified.""" + self.assertEqual(len(self.vectors.most_similar('war', topn=5)), 5) + self.assertEqual(len(self.vectors.most_similar('war', topn=10)), 10) + + predicted = self.vectors.most_similar('war', topn=None) + self.assertEqual(len(predicted), len(self.vectors.vocab)) + + def test_most_similar_raises_keyerror(self): + """Test most_similar raises KeyError when input is out of vocab.""" + with self.assertRaises(KeyError): + self.vectors.most_similar('not_in_vocab') + + def test_most_similar_restrict_vocab(self): + """Test most_similar returns handles restrict_vocab correctly.""" + expected = set(self.vectors.index2word[:5]) + predicted = set(result[0] for result in self.vectors.most_similar('war', topn=5, restrict_vocab=5)) + self.assertEqual(expected, predicted) + + def test_most_similar_with_vector_input(self): + """Test most_similar returns expected results with an input vector instead of an input word.""" + expected = [ + 'war', + 'conflict', + 'administration', + 'terrorism', + 'call', + ] + input_vector = self.vectors['war'] + predicted = [result[0] for result in self.vectors.most_similar([input_vector], topn=5)] + self.assertEqual(expected, predicted) + + def test_most_similar_to_given(self): + """Test most_similar_to_given returns correct results.""" + predicted = self.vectors.most_similar_to_given('war', ['terrorism', 'call', 'waging']) + self.assertEqual(predicted, 'terrorism') + + def test_similar_by_word(self): + """Test similar_by_word returns expected results.""" + expected = [ + 'conflict', + 'administration', + 'terrorism', + 'call', + 'israel' + ] + predicted = [result[0] for result in self.vectors.similar_by_word('war', topn=5)] + self.assertEqual(expected, predicted) + + def test_similar_by_vector(self): + """Test similar_by_word returns expected results.""" + expected = [ + 'war', + 'conflict', + 'administration', + 'terrorism', + 'call', + ] + input_vector = self.vectors['war'] + predicted = [result[0] for result in self.vectors.similar_by_vector(input_vector, topn=5)] + self.assertEqual(expected, predicted) + + def test_distance(self): + """Test that distance returns expected values.""" + self.assertTrue(np.allclose(self.vectors.distance('war', 'conflict'), 0.06694602)) + self.assertEqual(self.vectors.distance('war', 'war'), 0) + + def test_similarity(self): + """Test similarity returns expected value for two words, and for identical words.""" + self.assertTrue(np.allclose(self.vectors.similarity('war', 'war'), 1)) + self.assertTrue(np.allclose(self.vectors.similarity('war', 'conflict'), 0.93305397)) + + def test_words_closer_than(self): + """Test words_closer_than returns expected value for distinct and identical nodes.""" + self.assertEqual(self.vectors.words_closer_than('war', 'war'), []) + expected = set(['conflict', 'administration']) + self.assertEqual(set(self.vectors.words_closer_than('war', 'terrorism')), expected) + + def test_rank(self): + """Test rank returns expected value for distinct and identical nodes.""" + self.assertEqual(self.vectors.rank('war', 'war'), 1) + self.assertEqual(self.vectors.rank('war', 'terrorism'), 3) + + +if __name__ == '__main__': + logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) + unittest.main() diff --git a/gensim/test/test_poincare.py b/gensim/test/test_poincare.py index 12b301043b..226add5da2 100644 --- a/gensim/test/test_poincare.py +++ b/gensim/test/test_poincare.py @@ -25,7 +25,7 @@ except ImportError: autograd_installed = False -from gensim.models.poincare import PoincareRelations, PoincareModel +from gensim.models.poincare import PoincareRelations, PoincareModel, PoincareKeyedVectors from gensim.test.utils import datapath @@ -211,6 +211,155 @@ def tearDownClass(cls): pass +class TestPoincareKeyedVectors(unittest.TestCase): + def setUp(self): + self.vectors = PoincareKeyedVectors.load_word2vec_format(datapath('poincare_vectors.bin'), binary=True) + + def test_most_similar(self): + """Test most_similar returns expected results.""" + expected = [ + 'canine.n.02', + 'hunting_dog.n.01', + 'carnivore.n.01', + 'placental.n.01', + 'mammal.n.01' + ] + predicted = [result[0] for result in self.vectors.most_similar('dog.n.01', topn=5)] + self.assertEqual(expected, predicted) + + def test_most_similar_topn(self): + """Test most_similar returns correct results when `topn` is specified.""" + self.assertEqual(len(self.vectors.most_similar('dog.n.01', topn=5)), 5) + self.assertEqual(len(self.vectors.most_similar('dog.n.01', topn=10)), 10) + + predicted = self.vectors.most_similar('dog.n.01', topn=None) + self.assertEqual(len(predicted), len(self.vectors.vocab) - 1) + self.assertEqual(predicted[-1][0], 'gallant_fox.n.01') + + def test_most_similar_raises_keyerror(self): + """Test most_similar raises KeyError when input is out of vocab.""" + with self.assertRaises(KeyError): + self.vectors.most_similar('not_in_vocab') + + def test_most_similar_restrict_vocab(self): + """Test most_similar returns handles restrict_vocab correctly.""" + expected = set(self.vectors.index2word[:5]) + predicted = set(result[0] for result in self.vectors.most_similar('dog.n.01', topn=5, restrict_vocab=5)) + self.assertEqual(expected, predicted) + + def test_most_similar_to_given(self): + """Test most_similar_to_given returns correct results.""" + predicted = self.vectors.most_similar_to_given('dog.n.01', ['carnivore.n.01', 'placental.n.01', 'mammal.n.01']) + self.assertEqual(predicted, 'carnivore.n.01') + + def test_most_similar_with_vector_input(self): + """Test most_similar returns expected results with an input vector instead of an input word.""" + expected = [ + 'dog.n.01', + 'canine.n.02', + 'hunting_dog.n.01', + 'carnivore.n.01', + 'placental.n.01', + ] + input_vector = self.vectors['dog.n.01'] + predicted = [result[0] for result in self.vectors.most_similar([input_vector], topn=5)] + self.assertEqual(expected, predicted) + + def test_distance(self): + """Test that distance returns expected values.""" + self.assertTrue(np.allclose(self.vectors.distance('dog.n.01', 'mammal.n.01'), 4.5278745)) + self.assertEqual(self.vectors.distance('dog.n.01', 'dog.n.01'), 0) + + def test_distances(self): + """Test that distances between one word and multiple other words have expected values.""" + distances = self.vectors.distances('dog.n.01', ['mammal.n.01', 'dog.n.01']) + self.assertTrue(np.allclose(distances, [4.5278745, 0])) + + distances = self.vectors.distances('dog.n.01') + self.assertEqual(len(distances), len(self.vectors.vocab)) + self.assertTrue(np.allclose(distances[-1], 10.04756)) + + def test_distances_with_vector_input(self): + """Test that distances between input vector and a list of words have expected values.""" + input_vector = self.vectors['dog.n.01'] + distances = self.vectors.distances(input_vector, ['mammal.n.01', 'dog.n.01']) + self.assertTrue(np.allclose(distances, [4.5278745, 0])) + + distances = self.vectors.distances(input_vector) + self.assertEqual(len(distances), len(self.vectors.vocab)) + self.assertTrue(np.allclose(distances[-1], 10.04756)) + + def test_poincare_distances_batch(self): + """Test that poincare_distance_batch returns correct distances.""" + vector_1 = self.vectors['dog.n.01'] + vectors_2 = self.vectors[['mammal.n.01', 'dog.n.01']] + distances = self.vectors.vector_distance_batch(vector_1, vectors_2) + self.assertTrue(np.allclose(distances, [4.5278745, 0])) + + def test_poincare_distance(self): + """Test that poincare_distance returns correct distance between two input vectors.""" + vector_1 = self.vectors['dog.n.01'] + vector_2 = self.vectors['mammal.n.01'] + + distance = self.vectors.vector_distance(vector_1, vector_2) + self.assertTrue(np.allclose(distance, 4.5278745)) + + distance = self.vectors.vector_distance(vector_1, vector_1) + self.assertTrue(np.allclose(distance, 0)) + + def test_closest_child(self): + """Test closest_child returns expected value and returns None for lowest node in hierarchy.""" + self.assertEqual(self.vectors.closest_child('dog.n.01'), 'terrier.n.01') + self.assertEqual(self.vectors.closest_child('harbor_porpoise.n.01'), None) + + def test_closest_parent(self): + """Test closest_parent returns expected value and returns None for highest node in hierarchy.""" + self.assertEqual(self.vectors.closest_parent('dog.n.01'), 'canine.n.02') + self.assertEqual(self.vectors.closest_parent('mammal.n.01'), None) + + def test_ancestors(self): + """Test ancestors returns expected list and returns empty list for highest node in hierarchy.""" + expected = ['canine.n.02', 'carnivore.n.01', 'placental.n.01', 'mammal.n.01'] + self.assertEqual(self.vectors.ancestors('dog.n.01'), expected) + expected = [] + self.assertEqual(self.vectors.ancestors('mammal.n.01'), expected) + + def test_descendants(self): + """Test descendants returns expected list and returns empty list for lowest node in hierarchy.""" + expected = [ + 'terrier.n.01', 'sporting_dog.n.01', 'spaniel.n.01', 'water_spaniel.n.01', 'irish_water_spaniel.n.01' + ] + self.assertEqual(self.vectors.descendants('dog.n.01'), expected) + self.assertEqual(self.vectors.descendants('dog.n.01', max_depth=3), expected[:3]) + + def test_similarity(self): + """Test similarity returns expected value for two nodes, and for identical nodes.""" + self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'dog.n.01'), 1)) + self.assertTrue(np.allclose(self.vectors.similarity('dog.n.01', 'mammal.n.01'), 0.180901358)) + + def norm(self): + """Test norm returns expected value.""" + self.assertTrue(np.allclose(self.vectors.norm('dog.n.01'), 0.97757602)) + self.assertTrue(np.allclose(self.vectors.norm('mammal.n.01'), 0.03914723)) + + def test_difference_in_hierarchy(self): + """Test difference_in_hierarchy returns expected value for two nodes, and for identical nodes.""" + self.assertTrue(np.allclose(self.vectors.difference_in_hierarchy('dog.n.01', 'dog.n.01'), 0)) + self.assertTrue(np.allclose(self.vectors.difference_in_hierarchy('mammal.n.01', 'dog.n.01'), 0.9384287)) + self.assertTrue(np.allclose(self.vectors.difference_in_hierarchy('dog.n.01', 'mammal.n.01'), -0.9384287)) + + def test_words_closer_than(self): + """Test words_closer_than returns expected value for distinct and identical nodes.""" + self.assertEqual(self.vectors.words_closer_than('dog.n.01', 'dog.n.01'), []) + expected = set(['canine.n.02', 'hunting_dog.n.01']) + self.assertEqual(set(self.vectors.words_closer_than('dog.n.01', 'carnivore.n.01')), expected) + + def test_rank(self): + """Test rank returns expected value for distinct and identical nodes.""" + self.assertEqual(self.vectors.rank('dog.n.01', 'dog.n.01'), 1) + self.assertEqual(self.vectors.rank('dog.n.01', 'carnivore.n.01'), 3) + + if __name__ == '__main__': logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG) unittest.main() diff --git a/gensim/viz/__init__.py b/gensim/viz/__init__.py new file mode 100644 index 0000000000..aa3f80b1ba --- /dev/null +++ b/gensim/viz/__init__.py @@ -0,0 +1,3 @@ +""" +This package contains functions to visualize different models from :mod:`gensim.models`. +""" diff --git a/gensim/viz/poincare.py b/gensim/viz/poincare.py new file mode 100644 index 0000000000..9de048cd6a --- /dev/null +++ b/gensim/viz/poincare.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Author: Jayant Jain +# Copyright (C) 2017 Radim Rehurek +# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html + + +""" +Utilities for creating 2-D visualizations of Poincare models and Poincare distance heatmaps. + +""" + +import logging + +from collections import Counter +import numpy as np +import plotly.graph_objs as go + +from gensim.models.poincare import PoincareKeyedVectors + + +logger = logging.getLogger(__name__) + + +def poincare_2d_visualization(model, tree, figure_title, num_nodes=50, show_node_labels=()): + """Create a 2-d plot of the nodes and edges of a 2-d poincare embedding. + + Parameters + ---------- + model : :class:`~gensim.models.poincare.PoincareModel` + The model to visualize, model size must be 2. + tree : set + Set of tuples containing the direct edges present in the original dataset. + figure_title : str + Title of the plotted figure. + num_nodes : int or None + Number of nodes for which edges are to be plotted. + If `None`, all edges are plotted. + Helpful to limit this in case the data is too large to avoid a messy plot. + show_node_labels : iterable + Iterable of nodes for which to show labels by default. + + Returns + ------- + :class:`plotly.graph_objs.Figure` + Plotly figure that contains plot + + """ + vectors = model.kv.syn0 + if vectors.shape[1] != 2: + raise ValueError('Can only plot 2-D vectors') + + node_labels = model.kv.index2word + nodes_x = list(vectors[:, 0]) + nodes_y = list(vectors[:, 1]) + nodes = go.Scatter( + x=nodes_x, y=nodes_y, + mode='markers', + marker=dict(color='rgb(30, 100, 200)'), + text=node_labels, + textposition='bottom' + ) + + nodes_x, nodes_y, node_labels = [], [], [] + for node in show_node_labels: + vector = model.kv[node] + nodes_x.append(vector[0]) + nodes_y.append(vector[1]) + node_labels.append(node) + nodes_with_labels = go.Scatter( + x=nodes_x, y=nodes_y, + mode='markers+text', + marker=dict(color='rgb(200, 100, 200)'), + text=node_labels, + textposition='bottom' + ) + + node_out_degrees = Counter(hypernym_pair[1] for hypernym_pair in tree) + if num_nodes is None: + chosen_nodes = list(node_out_degrees.keys()) + else: + chosen_nodes = list(sorted(node_out_degrees.keys(), key=lambda k: -node_out_degrees[k]))[:num_nodes] + + edges_x = [] + edges_y = [] + for u, v in tree: + if not(u in chosen_nodes or v in chosen_nodes): + continue + vector_u = model.kv[u] + vector_v = model.kv[v] + edges_x += [vector_u[0], vector_v[0], None] + edges_y += [vector_u[1], vector_v[1], None] + edges = go.Scatter( + x=edges_x, y=edges_y, mode="line", hoverinfo=False, + line=dict(color='rgb(50,50,50)', width=1)) + + layout = go.Layout( + title=figure_title, showlegend=False, hovermode='closest', width=800, height=800) + return go.Figure(data=[edges, nodes, nodes_with_labels], layout=layout) + + +def poincare_distance_heatmap(origin_point, x_range=(-1.0, 1.0), y_range=(-1.0, 1.0), num_points=100): + """Create a heatmap of Poincare distances from `origin_point` for each point (x, y), + where x and y lie in `x_range` and `y_range` respectively, with `num_points` points chosen uniformly in both ranges. + + Parameters + ---------- + origin_point : tuple (int, int) + (x, y) from which distances are to be measured and plotted. + x_range : tuple (int, int) + Range for x-axis from which to choose `num_points` points. + y_range : tuple (int, int) + Range for y-axis from which to choose `num_points` points. + num_points : int + Number of points to choose from `x_range` and `y_range`. + + Notes + ----- + Points outside the unit circle are ignored, since the Poincare distance is defined + only for points inside the circle boundaries (exclusive of the boundary). + + Returns + ------- + :class:`plotly.graph_objs.Figure` + Plotly figure that contains plot + + """ + epsilon = 1e-8 # Can't choose (-1.0, -1.0) or (1.0, 1.0), distance undefined + x_range, y_range = list(x_range), list(y_range) + if x_range[0] == -1.0 and y_range[0] == -1.0: + x_range[0] += epsilon + y_range[0] += epsilon + if x_range[0] == 1.0 and y_range[0] == 1.0: + x_range[0] -= epsilon + y_range[0] -= epsilon + + x_axis_values = np.linspace(x_range[0], x_range[1], num=num_points) + y_axis_values = np.linspace(x_range[0], x_range[1], num=num_points) + x, y = np.meshgrid(x_axis_values, y_axis_values) + all_points = np.dstack((x, y)).swapaxes(1, 2).swapaxes(0, 1).reshape(2, num_points ** 2).T + norms = np.linalg.norm(all_points, axis=1) + all_points = all_points[norms < 1] + + origin_point = np.array(origin_point) + all_distances = PoincareKeyedVectors.poincare_dists(origin_point, all_points) + + distances = go.Scatter( + x=all_points[:, 0], + y=all_points[:, 1], + mode='markers', + marker=dict( + size='9', + color=all_distances, + colorscale='Viridis', + showscale=True, + colorbar=go.ColorBar( + title='Poincare Distance' + ), + ), + text=[ + 'Distance from (%.2f, %.2f): %.2f' % (origin_point[0], origin_point[1], d) + for d in all_distances], + name='', # To avoid the default 'trace 0' + ) + + origin = go.Scatter( + x=[origin_point[0]], + y=[origin_point[1]], + name='Distance from (%.2f, %.2f)' % (origin_point[0], origin_point[1]), + mode='markers+text', + marker=dict( + size='10', + color='rgb(200, 50, 50)' + ) + ) + + layout = go.Layout( + width=900, + height=800, + showlegend=False, + title='Poincare Distances from (%.2f, %.2f)' % (origin_point[0], origin_point[1]), + hovermode='closest', + ) + + return go.Figure(data=[distances, origin], layout=layout) diff --git a/setup.py b/setup.py index 0897bbba95..449cc3a6df 100644 --- a/setup.py +++ b/setup.py @@ -296,7 +296,7 @@ def finalize_options(self): extras_require={ 'distributed': ['Pyro4 >= 4.27'], 'test': test_env, - 'docs': test_env + ['Pyro4 >= 4.27', 'sphinx', 'sphinxcontrib-napoleon', 'annoy'], + 'docs': test_env + ['Pyro4 >= 4.27', 'sphinx', 'sphinxcontrib-napoleon', 'annoy', 'plotly'], }, include_package_data=True, From 4ab63c64c42dac6fbd76c28012daf7705c60f263 Mon Sep 17 00:00:00 2001 From: Jayant Jain Date: Mon, 4 Dec 2017 19:29:30 +0530 Subject: [PATCH 23/28] Add L2 regularization for poincare model (#1734) * Initial classes and loading data for poincare model * Initial implementation of training using autograd * faster negative sampling, bugfix in vector updates * allows poincare dist function to be differentiable by autograd * batched gradient descent initial implementation * minor changes to batch poincare distance computation * Adds calculation of gradients for poincare model * Correct implementation of clipping of updated vectors * Fixes error in gradient computation * Better messages while training * Renames PoincareDistance to PoincareExample for clarity * Compares computed gradients to autograd gradients every few iterations * Avoids doing some numpy computations twice * Avoids creating copies of numpy vectors * Only calls nan_to_num when gamma has at least one value equal to 1 * Simply sets nan gradients to zero instead of nan_to_num * Adds batch-wise implementation of training and gradient computations * Minor correction in clipping * Fixes typo in clip_vectors * Prints average loss every few iterations instead of current loss * Adds weighted negative sampling * Ensures positive edges are not returned by negative sampling * Poincare model stores node indices in relations instead of node keys * Minor renaming; uses node indices for batch training instead of node keys * Changes shapes of vectors passed to PoincareBatch * Minor bugfixes related to batch size * Corrects implementation of negative sampling for batch training * Adds option to check gradients in batchwise training * Checks gradients only every few iterations * Handles multiple occurrence of same node across and within batches * Removes unused section of code * Implements slightly different clipping method * Fixes bugs with wrong reshape in batchwise training * Example-wise training takes into account multiple occurrences of same node in an example too * Batchwise training prints average loss over many iterations instead of current batch * Fixes bug in updating vector for batchwise training * Faster implementation of negative sampling * Negative sampling for a node follows different paths depending on fraction of positive relations * Uses a buffer for negative samples to reduce calls to np.random.choice * Cleans up poincare.py, removes unused code * Adds shapes to PoincareBatch, more documentation * Adds more documentation to PoincareModel * Stores indices for nodes in a batch in PoincareBatch for better encapsulation * More documentation for poincare module * Implements burn-in for poincare model * Slightly better logging for poincare model * Uses np.random.random and np.searchsorted for random sampling rather than np.random.choice * Removes duplicates in negative samples * Moves helper classes in poincare after PoincareModel * Change in PoincareModel API to allow initializing from an iterable, separate class for streaming from file * Adds failing test for handling encoding in PoincareData * Fixes encoding handling in PoincareData * Adds docstrings to PoincareData, PoincareData streams tuples now * More unittests for PoincareModel * Changes handle_duplicates to staticmethod, adds test * Adds batch size and print_every parameters to train method * Renames print_check to should_print * Adds separate parameter for checking gradients * Minor fixes for coding style * Removes default values from docstrings, redundant * Adds example to PoincareModel init docstring * Extracts buffer for negatives out into a separate class * More detailed logging, fix to check_gradients * Minor fixes to documentation in poincare.py * Adds support for most_similar to PoincareKeyedVectors * Refactors most_similar and loss_fn to use PoincareKeyedVectors.poincare_dists * Adds tests for gradients checking * Raise AssertionError if gradients check fails * Adds failing tests for saving/loading PoincareModel instances * Fixes bug with saving/loading PoincareModel to disk * Adds test and fix for raising error on invalid input data * Adds test and fix for no duplicates and positives in negative sample * Bugfix with NegativesBuffer having less than items left * Uses larger data for poincare tests, adds data files * Bugfix with incorrect use of random state * Minor fixes in documentation style * Renames PoincareData to PoincareRelations * Change in the order of conditions checked before resampling * Imports datapath from test.utils instead of defining own * Adds working examples and a more detailed description in docstring * Renames term_relations to node_relations * Removes unused imports * Moves iter parameter to train instead of __init__, renames to epochs * Fixes term_relations in tests * Adds option to disable gradient check, disabled by default * Extracts gradient checking code into a separate method * Conditionally import autograd only if gradient checking is enabled * Marks private methods in poincare module with leading underscore * Adds init_range as an API parameter to PoincareModel * Marks private properties with a leading underscore * Fixes bug with burn-in happening on subsequent calls to train * Adds test for training multiple times * Adds autograd to test dependencies * Renames wv to kv in PoincareModel * add numpy==1.12 as test dependency * add missing quote * Moves methods for evaluating poincare embeddings to poincare.py * Updates docstrings for newly added classes * Moves trie-related methods to LexicalEntailmentEvaluation * Moves code for loading PoincareEmbedding into notebook * Removes PoincareEmbedding class, adds functionality to PoincareKeyedVectors * Updates eval nb with code and evaluation results for gensim models * Minor documentation updates + bugfix in distance * Adds methods for rank and nodes_closer_than to PoincareKeyedVectors * Adds methods to return closest child, parent, and ancestor and descendant chain for an input node * Updates LE and reconstruction results for gensim models in eval nb * Adds notebook detailing Poincare embedding operations and report * Adds images for poincare embedding report * Updates image links in poincare report nb * try to run tests without autograd * fix PEP8 in poincare.py * fix PEP8 in test_poincare * PoincareRelations handles python2 correctly * Bugfix with int division for python2 * Imports mock module for tests correctly in python2 * Cleaner implementation of __iter__ for PoincareRelations * Adds rst file and updates apiref.rst for poincare module * Adds clarifying comment to PoincareRelations.__iter__ * Adds functions for visualization to poincare_visualization.py * Suppresses certain numpy warnings while training model * Updates rst file for poincare * Updates poincare report nb with reduced code, section on training, better visualization labels and titles * Renames hypernym pair to relations everywhere * Simpler way of detecting duplicates * Minor documentation updates in poincare.py * Skips gradients test if autograd not installed, adds test for bytes input data * Adds results of gensim models on link prediction to eval notebook * Adds link prediction results to report, more information about training * Adds further details to concept and motivation sections, section on future work, and images * Fix flake8 (noqa + remove unused var) * Fix missing mock dependency for win * Fix links in docstrings * Refactors KeyedVectors into KeyedVectorsBase and EuclideanKeyedVectors * Changes error message for negative sampling failing * Adds option to specify dtype for PoincareModel and corresponding unittest * Extends test for dtype to check after training, updates docstring * Adds tests for new methods in PoincareKeyedVectors * Fixes bug in closest_child implementation * Adds similarity and distance to KeyedVectorsBase interface, implementation and tests for similarity for PoincareKeyedVectors * Minor fixes to Poincare report notebook * Adds method to compute all distances to KeyedVectorsBase, moves most_similar from EuclideanKeyedVectors to KeyedVectorsBase * Allows PoincareKeyedVectors.distances to accept an optional list of words * Adds implementation of PoincareKeyedVectors.similarities and tests * Adds restrict_vocab option to most_similar and tests for EuclideanKeyedVectors.most_similar * Adds docstring for tests * Adds implementation of EuclideanKeyedVectors.distances and tests, updates docstrings * Moves most_similar_to_given to KeyedVectorsBase, adds tests * Moves similar_by_vector and similar_by_word to KeyedVectorsBase, adds tests * Adds failing tests for similar_by_word and similar_by_vector to PoincareKeyedVector tests * Moves multiple methods out of KeyedVectorsBase back to EuclideanKeyedVectors, removes tests * Adds test for most_similar with vector input for EuclideanKeyedVectors * Adds failing test for vector input for most_similar for PoincareKeyedVectors * Allows passing in vector input to most_similar and distances methods in PoincareKeyedVectors * Removes precompute_max_distance and uses simpler formula for similarity in PoincareKeyedVectors * Renames PoincareKeyedVectors.poincare_dists to PoincareKeyedVectors.poincare_distance_batch * Fixes error with unclosed file in PoincareRelations * Adds tests and method for computing poincare distance between two input vectors * Adds methods and tests for finding position and difference in hierarchical positions of input vectors * Fixes unused import, pep8 and docstring issues * More intuitive naming of arguments for methods in PoincareKeyedVectors * Uses w1 and w2 consistently across KeyedVectors methods * Removes most_similar from KeyedVectorsBase * Adds failing tests for words_closer_than and rank for EuclideanKeyedVectors and PoincareKeyedVectors * Adds distances method to KeyedVectorsBase and EuclideanKeyedVectors, fixes tests * Makes default argument for distances immutable * Uses conditional import for pygtrie in LexicalEntailmentEvaluation * Renames position_in_hierarchy to norm with minor change in behaviour, updates tests * Renames poincare_distance and poincare_distance_batch to vector_distance and vector_distance_batch * Forces float division for positive_fraction in _sample_negatives * Removes unused method from PoincareKeyedVectors * Updates report notebook with usage examples of new API methods * Minor pep8 fix * Adds l2 regularization to poincare model training * Cleaner way to avoid dependency on autograd * Fixes pep8 issues, unused imports and typo * Adds example of saving and loading model to notebook * Updates docstrings in poincare.py * Updates docstring for regularization coefficient in PoincareModel * Moves poincare visualization methods to new gensim.viz module * Updates rst files for poincare viz * Adds newline at the end of poincare.py in viz package * Adds link to original paper to poincare notebook * Adds l2 regularization to poincare model training * Cleaner way to avoid dependency on autograd * Updates docstring for regularization coefficient in PoincareModel --- gensim/models/poincare.py | 38 +++++++++++++++++++++++++------------- gensim/viz/__init__.py | 2 +- gensim/viz/poincare.py | 2 +- 3 files changed, 27 insertions(+), 15 deletions(-) diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index b5506a67ef..458e61b512 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -54,6 +54,12 @@ from gensim import utils, matutils from gensim.models.keyedvectors import KeyedVectorsBase, Vocab +try: + from autograd import grad # Only required for optionally verifying gradients while training + from autograd import numpy as grad_np + AUTOGRAD_PRESENT = True +except ImportError: + AUTOGRAD_PRESENT = False logger = logging.getLogger(__name__) @@ -70,7 +76,7 @@ class PoincareModel(utils.SaveLoad): methods instead. """ - def __init__(self, train_data, size=50, alpha=0.1, negative=10, workers=1, epsilon=1e-5, + def __init__(self, train_data, size=50, alpha=0.1, negative=10, workers=1, epsilon=1e-5, regularization_coeff=1.0, burn_in=10, burn_in_alpha=0.01, init_range=(-0.001, 0.001), dtype=np.float64, seed=0): """Initialize and train a Poincare embedding model from an iterable of relations. @@ -91,6 +97,8 @@ def __init__(self, train_data, size=50, alpha=0.1, negative=10, workers=1, epsil Number of threads to use for training the model. epsilon : float, optional Constant used for clipping embeddings below a norm of one. + regularization_coeff : float, optional + Coefficient used for l2-regularization while training (0 effectively disables regularization). burn_in : int, optional Number of epochs to use for burn-in initialization (0 means no burn-in). burn_in_alpha : float, optional @@ -130,6 +138,7 @@ def __init__(self, train_data, size=50, alpha=0.1, negative=10, workers=1, epsil self.negative = negative self.workers = workers self.epsilon = epsilon + self.regularization_coeff = regularization_coeff self.burn_in = burn_in self._burn_in_done = False self.dtype = dtype @@ -243,13 +252,15 @@ def _sample_negatives(self, node_index): return list(indices) @staticmethod - def _loss_fn(matrix): + def _loss_fn(matrix, regularization_coeff=1.0): """Given a numpy array with vectors for u, v and negative samples, computes loss value. Parameters ---------- matrix : numpy.array Array containing vectors for u, v and negative samples, of shape (2 + negative_size, dim). + regularization_coeff : float + Coefficient to use for l2-regularization Returns ------- @@ -261,9 +272,6 @@ def _loss_fn(matrix): Only used for autograd gradients, since autograd requires a specific function signature. """ - # Loaded only if gradients are to be checked to avoid dependency - from autograd import numpy as grad_np - vector_u = matrix[0] vectors_v = matrix[1:] euclidean_dists = grad_np.linalg.norm(vector_u - vectors_v, axis=1) @@ -275,7 +283,8 @@ def _loss_fn(matrix): ) ) exp_negative_distances = grad_np.exp(-poincare_dists) - return -grad_np.log(exp_negative_distances[0] / (exp_negative_distances.sum())) + regularization_term = regularization_coeff * grad_np.linalg.norm(vectors_v[0]) ** 2 + return -grad_np.log(exp_negative_distances[0] / (exp_negative_distances.sum())) + regularization_term @staticmethod def _clip_vectors(vectors, epsilon): @@ -353,7 +362,7 @@ def _prepare_training_batch(self, relations, all_negatives, check_gradients=Fals vectors_u = self.kv.syn0[indices_u] vectors_v = self.kv.syn0[indices_v].reshape((batch_size, 1 + self.negative, self.size)) vectors_v = vectors_v.swapaxes(0, 1).swapaxes(1, 2) - batch = PoincareBatch(vectors_u, vectors_v, indices_u, indices_v) + batch = PoincareBatch(vectors_u, vectors_v, indices_u, indices_v, self.regularization_coeff) batch.compute_all() if check_gradients: @@ -374,10 +383,8 @@ def _check_gradients(self, relations, all_negatives, batch, tol=1e-8): List of lists of negative samples for each node_1 in the positive examples. """ - try: # Loaded only if gradients are to be checked to avoid dependency - from autograd import grad - except ImportError: - logger.warning('autograd could not be imported, skipping checking of gradients') + if not AUTOGRAD_PRESENT: + logger.warning('autograd could not be imported, cannot do gradient checking') logger.warning('please install autograd to enable gradient checking') return @@ -387,7 +394,8 @@ def _check_gradients(self, relations, all_negatives, batch, tol=1e-8): max_diff = 0.0 for i, (relation, negatives) in enumerate(zip(relations, all_negatives)): u, v = relation - auto_gradients = self._loss_grad(np.vstack((self.kv.syn0[u], self.kv.syn0[[v] + negatives]))) + auto_gradients = self._loss_grad( + np.vstack((self.kv.syn0[u], self.kv.syn0[[v] + negatives])), self.regularization_coeff) computed_gradients = np.vstack((batch.gradients_u[:, i], batch.gradients_v[:, :, i])) diff = np.abs(auto_gradients - computed_gradients).max() if diff > max_diff: @@ -595,7 +603,7 @@ class PoincareBatch(object): and storing intermediate state to avoid recomputing multiple times. """ - def __init__(self, vectors_u, vectors_v, indices_u, indices_v): + def __init__(self, vectors_u, vectors_v, indices_u, indices_v, regularization_coeff=1.0): """ Initialize instance with sets of vectors for which distances are to be computed. @@ -613,12 +621,15 @@ def __init__(self, vectors_u, vectors_v, indices_u, indices_v): indices_v : list Nested list of lists, each of which is a list of node indices for each of the vectors in `vectors_v` for a specific node `u`. + regularization_coeff : float + Coefficient to use for l2-regularization """ self.vectors_u = vectors_u.T[np.newaxis, :, :] # (1, dim, batch_size) self.vectors_v = vectors_v # (1 + neg_size, dim, batch_size) self.indices_u = indices_u self.indices_v = indices_v + self.regularization_coeff = regularization_coeff self.poincare_dists = None self.euclidean_dists = None @@ -688,6 +699,7 @@ def compute_gradients(self): gradients_v = -self.exp_negative_distances[:, np.newaxis, :] * self.distance_gradients_v gradients_v /= self.Z # (1 + neg_size, dim, batch_size) gradients_v[0] += self.distance_gradients_v[0] + gradients_v[0] += self.regularization_coeff * 2 * self.vectors_v[0] # (1 + neg_size, dim, batch_size) gradients_u = -self.exp_negative_distances[:, np.newaxis, :] * self.distance_gradients_u diff --git a/gensim/viz/__init__.py b/gensim/viz/__init__.py index aa3f80b1ba..3f968b01e4 100644 --- a/gensim/viz/__init__.py +++ b/gensim/viz/__init__.py @@ -1,3 +1,3 @@ """ -This package contains functions to visualize different models from :mod:`gensim.models`. +This package contains functions to visualize different models from `gensim.models`. """ diff --git a/gensim/viz/poincare.py b/gensim/viz/poincare.py index 9de048cd6a..41ea3d2eab 100644 --- a/gensim/viz/poincare.py +++ b/gensim/viz/poincare.py @@ -44,7 +44,7 @@ def poincare_2d_visualization(model, tree, figure_title, num_nodes=50, show_node Returns ------- :class:`plotly.graph_objs.Figure` - Plotly figure that contains plot + Plotly figure that contains plot. """ vectors = model.kv.syn0 From de112eee8abadbf7224dccbbdcca4cd32bbbc65f Mon Sep 17 00:00:00 2001 From: ivan Date: Mon, 4 Dec 2017 19:19:35 +0500 Subject: [PATCH 24/28] fix PEP8 --- gensim/models/poincare.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index 458e61b512..933da73fb6 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -1334,7 +1334,8 @@ def evaluate_mean_rank_and_map(self, max_n=None): item_relations = list(self.relations[item]) item_term = self.embedding.index2word[item] item_distances = self.embedding.distances(item_term) - positive_relation_ranks, avg_precision = self.get_positive_relation_ranks_and_avg_prec(item_distances, item_relations) + positive_relation_ranks, avg_precision = \ + self.get_positive_relation_ranks_and_avg_prec(item_distances, item_relations) ranks += positive_relation_ranks avg_precision_scores.append(avg_precision) if max_n is not None and i > max_n: @@ -1450,7 +1451,8 @@ def evaluate_mean_rank_and_map(self, max_n=None): known_relations = list(self.relations['known'][item]) item_term = self.embedding.index2word[item] item_distances = self.embedding.distances(item_term) - unknown_relation_ranks, avg_precision = self.get_unknown_relation_ranks_and_avg_prec(item_distances, unknown_relations, known_relations) + unknown_relation_ranks, avg_precision = \ + self.get_unknown_relation_ranks_and_avg_prec(item_distances, unknown_relations, known_relations) ranks += unknown_relation_ranks avg_precision_scores.append(avg_precision) if max_n is not None and i > max_n: From 433cd1def5f703c3bf5dc2084202d08668862e54 Mon Sep 17 00:00:00 2001 From: ivan Date: Mon, 4 Dec 2017 19:19:45 +0500 Subject: [PATCH 25/28] add missing deps - mock --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 49f0cc59b5..1c8c00168b 100644 --- a/setup.py +++ b/setup.py @@ -229,6 +229,7 @@ def finalize_options(self): win_testenv = [ 'pytest', 'pytest-rerunfailures', + 'mock', 'cython', 'pyemd', 'testfixtures', From 4bd0ccbd334415b32ae6a7e477f015fa578a3bc1 Mon Sep 17 00:00:00 2001 From: Menshikh Ivan Date: Mon, 4 Dec 2017 19:37:34 +0500 Subject: [PATCH 26/28] remove 'mock' duplication --- setup.py | 1 - 1 file changed, 1 deletion(-) diff --git a/setup.py b/setup.py index 1c8c00168b..480f0242d0 100644 --- a/setup.py +++ b/setup.py @@ -241,7 +241,6 @@ def finalize_options(self): 'annoy', 'tensorflow <= 1.3.0', 'keras >= 2.0.4', - 'mock==2.0.0', ] setup( From e547bb8551c63f3e171259b82bb708c2ee6cd18d Mon Sep 17 00:00:00 2001 From: ivan Date: Wed, 6 Dec 2017 19:47:39 +0500 Subject: [PATCH 27/28] remove useless requirements.txt --- docs/notebooks/poincare/requirements.txt | 4 ---- 1 file changed, 4 deletions(-) delete mode 100644 docs/notebooks/poincare/requirements.txt diff --git a/docs/notebooks/poincare/requirements.txt b/docs/notebooks/poincare/requirements.txt deleted file mode 100644 index faaea1399b..0000000000 --- a/docs/notebooks/poincare/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -click==6.7 -nltk==3.2.5 -prettytable==0.7.2 -pygtrie==2.2 From 14fc8a80696fd8167d0bdc6afec666282cd2c2d4 Mon Sep 17 00:00:00 2001 From: ivan Date: Wed, 6 Dec 2017 19:58:13 +0500 Subject: [PATCH 28/28] fix typo in docstring --- gensim/models/poincare.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/gensim/models/poincare.py b/gensim/models/poincare.py index 933da73fb6..921e7e74f6 100644 --- a/gensim/models/poincare.py +++ b/gensim/models/poincare.py @@ -19,8 +19,8 @@ .. [1] Maximilian Nickel, Douwe Kiela - "Poincaré Embeddings for Learning Hierarchical Representations" https://arxiv.org/abs/1705.08039 -Examples: ---------- +Examples +-------- Initialize and train a model from a list: >>> from gensim.models.poincare import PoincareModel @@ -193,7 +193,7 @@ def _get_candidate_negatives(self): """Returns candidate negatives of size `self.negative` from the negative examples buffer. Returns - -------- + ------- numpy.array Array of shape (`self.negative`,) containing indices of negative nodes. @@ -215,7 +215,7 @@ def _sample_negatives(self, node_index): Index of the positive node for which negative samples are to be returned. Returns - -------- + ------- numpy.array Array of shape (self.negative,) containing indices of negative nodes for the given node index.