merge master

examples/trials/ga_squad/data.py

@@ -19,6 +19,10 @@
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+'''
+Data processing script for the QA model.
+'''
+
 import csv
 import json
 from random import shuffle
@@ -73,19 +77,19 @@ def load_from_file(path, fmt=None, is_training=True):
             for doc in data:
                 for paragraph in doc['paragraphs']:
                     passage = paragraph['context']
-                    for qa in paragraph['qas']:
-                        question = qa['question']
-                        id = qa['id']
+                    for qa_pair in paragraph['qas']:
+                        question = qa_pair['question']
+                        qa_id = qa_pair['id']
                         if not is_training:
                             qp_pairs.append(
-                                {'passage': passage, 'question': question, 'id': id})
+                                {'passage': passage, 'question': question, 'id': qa_id})
                         else:
-                            for answer in qa['answers']:
+                            for answer in qa_pair['answers']:
                                 answer_begin = int(answer['answer_start'])
                                 answer_end = answer_begin + len(answer['text'])
                                 qp_pairs.append({'passage': passage,
                                                  'question': question,
-                                                 'id': id,
+                                                 'id': qa_id,
                                                  'answer_begin': answer_begin,
                                                  'answer_end': answer_end})
     else:
@@ -121,21 +125,21 @@ def collect_vocab(qp_pairs):
     Build the vocab from corpus.
     '''
     vocab = set()
-    for qp in qp_pairs:
-        for word in qp['question_tokens']:
+    for qp_pair in qp_pairs:
+        for word in qp_pair['question_tokens']:
             vocab.add(word['word'])
-        for word in qp['passage_tokens']:
+        for word in qp_pair['passage_tokens']:
             vocab.add(word['word'])
     return vocab
 
 
-def shuffle_step(l, step):
+def shuffle_step(entries, step):
     '''
     Shuffle the step
     '''
     answer = []
-    for i in range(0, len(l), step):
-        sub = l[i:i+step]
+    for i in range(0, len(entries), step):
+        sub = entries[i:i+step]
         shuffle(sub)
         answer += sub
     return answer
@@ -163,13 +167,13 @@ def get_char_input(data, char_dict, max_char_length):
     char_id = np.zeros((max_char_length, sequence_length,
                         batch_size), dtype=np.int32)
     char_lengths = np.zeros((sequence_length, batch_size), dtype=np.float32)
-    for b in range(0, min(len(data), batch_size)):
-        d = data[b]
-        for s in range(0, min(len(d), sequence_length)):
-            word = d[s]['word']
-            char_lengths[s, b] = min(len(word), max_char_length)
+    for batch_idx in range(0, min(len(data), batch_size)):
+        batch_data = data[batch_idx]
+        for sample_idx in range(0, min(len(batch_data), sequence_length)):
+            word = batch_data[sample_idx]['word']
+            char_lengths[sample_idx, batch_idx] = min(len(word), max_char_length)
             for i in range(0, min(len(word), max_char_length)):
-                char_id[i, s, b] = get_id(char_dict, word[i])
+                char_id[i, sample_idx, batch_idx] = get_id(char_dict, word[i])
     return char_id, char_lengths
 
 
@@ -180,26 +184,26 @@ def get_word_input(data, word_dict, embed, embed_dim):
     batch_size = len(data)
     max_sequence_length = max(len(d) for d in data)
     sequence_length = max_sequence_length
-    t = np.zeros((max_sequence_length, batch_size,
-                  embed_dim), dtype=np.float32)
+    word_input = np.zeros((max_sequence_length, batch_size,
+                           embed_dim), dtype=np.float32)
     ids = np.zeros((sequence_length, batch_size), dtype=np.int32)
     masks = np.zeros((sequence_length, batch_size), dtype=np.float32)
     lengths = np.zeros([batch_size], dtype=np.int32)
 
-    for b in range(0, min(len(data), batch_size)):
-        d = data[b]
+    for batch_idx in range(0, min(len(data), batch_size)):
+        batch_data = data[batch_idx]
 
-        lengths[b] = len(d)
+        lengths[batch_idx] = len(batch_data)
 
-        for s in range(0, min(len(d), sequence_length)):
-            word = d[s]['word'].lower()
+        for sample_idx in range(0, min(len(batch_data), sequence_length)):
+            word = batch_data[sample_idx]['word'].lower()
             if word in word_dict.keys():
-                t[s, b] = embed[word_dict[word]]
-                ids[s, b] = word_dict[word]
-            masks[s, b] = 1
+                word_input[sample_idx, batch_idx] = embed[word_dict[word]]
+                ids[sample_idx, batch_idx] = word_dict[word]
+            masks[sample_idx, batch_idx] = 1
 
-    t = np.reshape(t, (-1, embed_dim))
-    return t, ids, masks, lengths
+    word_input = np.reshape(word_input, (-1, embed_dim))
+    return word_input, ids, masks, lengths
 
 
 def get_word_index(tokens, char_index):

examples/trials/ga_squad/download.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
+wget http://nlp.stanford.edu/data/glove.840B.300d.zip
+unzip glove.840B.300d.zip

examples/trials/ga_squad/evaluate.py

@@ -19,6 +19,10 @@
 # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 
+'''
+Evaluation scripts for QA model.
+'''
+
 from __future__ import print_function
 from collections import Counter
 import string
@@ -68,8 +72,8 @@ def f1_score(prediction, ground_truth):
         return 0
     precision = 1.0 * num_same / len(prediction_tokens)
     recall = 1.0 * num_same / len(ground_truth_tokens)
-    f1 = (2 * precision * recall) / (precision + recall)
-    return f1
+    f1_result = (2 * precision * recall) / (precision + recall)
+    return f1_result
 
 def exact_match_score(prediction, ground_truth):
     '''
@@ -91,28 +95,25 @@ def _evaluate(dataset, predictions):
     '''
     Evaluate function.
     '''
-    f1 = exact_match = total = 0
+    f1_result = exact_match = total = 0
     count = 0
     for article in dataset:
         for paragraph in article['paragraphs']:
-            for qa in paragraph['qas']:
+            for qa_pair in paragraph['qas']:
                 total += 1
-                if qa['id'] not in predictions:
-                    message = 'Unanswered question ' + qa['id'] + \
-                              ' will receive score 0.'
-                    #print(message, file=sys.stderr)
+                if qa_pair['id'] not in predictions:
                     count += 1
                     continue
-                ground_truths = list(map(lambda x: x['text'], qa['answers']))
-                prediction = predictions[qa['id']]
+                ground_truths = list(map(lambda x: x['text'], qa_pair['answers']))
+                prediction = predictions[qa_pair['id']]
                 exact_match += metric_max_over_ground_truths(
                     exact_match_score, prediction, ground_truths)
-                f1 += metric_max_over_ground_truths(
+                f1_result += metric_max_over_ground_truths(
                     f1_score, prediction, ground_truths)
     print('total', total, 'exact_match', exact_match, 'unanswer_question ', count)
     exact_match = 100.0 * exact_match / total
-    f1 = 100.0 * f1 / total
-    return {'exact_match': exact_match, 'f1': f1}
+    f1_result = 100.0 * f1_result / total
+    return {'exact_match': exact_match, 'f1': f1_result}
 
 def evaluate(data_file, pred_file):
     '''

examples/trials/ga_squad/graph.py

@@ -43,8 +43,8 @@ class Layer(object):
     '''
     Layer class, which contains the information of graph.
     '''
-    def __init__(self, graph_type, input=None, output=None, size=None):
-        self.input = input if input is not None else []
+    def __init__(self, graph_type, inputs=None, output=None, size=None):
+        self.input = inputs if inputs is not None else []
         self.output = output if output is not None else []
         self.graph_type = graph_type
         self.is_delete = False
@@ -117,11 +117,11 @@ class Graph(object):
     '''
     Customed Graph class.
     '''
-    def __init__(self, max_layer_num, input, output, hide):
+    def __init__(self, max_layer_num, inputs, output, hide):
         self.layers = []
         self.max_layer_num = max_layer_num
 
-        for layer in input:
+        for layer in inputs:
             self.layers.append(layer)
         for layer in output:
             self.layers.append(layer)
@@ -240,7 +240,7 @@ def mutation(self, only_add=False):
             if graph_type <= 1:
                 new_id = len(layers)
                 out = random.choice(layers_out)
-                input = []
+                inputs = []
                 output = [out]
                 pos = random.randint(0, len(layers[out].input) - 1)
                 last_in = layers[out].input[pos]
@@ -250,13 +250,13 @@ def mutation(self, only_add=False):
                 if graph_type == 1:
                     layers[last_in].output.remove(out)
                     layers[last_in].output.append(new_id)
-                    input = [last_in]
-                lay = Layer(graph_type=layer_type, input=input, output=output)
-                while len(input) < lay.input_size:
+                    inputs = [last_in]
+                lay = Layer(graph_type=layer_type, inputs=inputs, output=output)
+                while len(inputs) < lay.input_size:
                     layer1 = random.choice(layers_in)
-                    input.append(layer1)
+                    inputs.append(layer1)
                     layers[layer1].output.append(new_id)
-                lay.input = input
+                lay.input = inputs
                 layers.append(lay)
             else:
                 layer1 = random.choice(layers_del)

examples/trials/ga_squad/readme.md

@@ -1,9 +1,32 @@
-## How to download data
+# Download data
+
+## Use downloading script
+
+Execute the following command to download needed files
+using the downloading script:
+
+```
+chmod +x ./download.sh
+./download.sh
+```
+
+## Download manually
 
 1. download "dev-v1.1.json" and "train-v1.1.json" in https://rajpurkar.github.io/SQuAD-explorer/
-2. download "glove.840B.300d.txt" in "https://nlp.stanford.edu/projects/glove/"
 
-## How to submit this job
+```
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json
+wget https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
+```
+
+2. download "glove.840B.300d.txt" in https://nlp.stanford.edu/projects/glove/
+
+```
+wget http://nlp.stanford.edu/data/glove.840B.300d.zip
+unzip glove.840B.300d.zip
+```
+
+# How to submit this job
 
 1. run "$NNI_ROOT_DIR/auto_run.py" as "$NNI_ROOT_DIR/README-AUTO.md" said.
 2. use the dockerImage openpai.azurecr.io/nni_v0.0.1, which means it use a tensorflow cpu-version.

examples/trials/ga_squad/train_model.py

@@ -32,12 +32,13 @@
 
 
 class GAGConfig:
+    """The class for model hyper-parameter configuration."""
     def __init__(self):
         self.batch_size = 128
 
         self.dropout = 0.1
 
-        self.char_vcb_size = 1371
+        self.char_vcb_size = 1500
         self.max_char_length = 20
         self.char_embed_dim = 100
 
@@ -56,6 +57,7 @@ def __init__(self):
 
 
 class GAG:
+    """The class for the computation graph based QA model."""
     def __init__(self, cfg, embed, graph):
         self.cfg = cfg
         self.embed = embed
@@ -83,6 +85,7 @@ def __init__(self, cfg, embed, graph):
 
 
     def build_net(self, is_training):
+        """Build the whole neural network for the QA model."""
         cfg = self.cfg
         with tf.device('/cpu:0'):
             word_embed = tf.get_variable(
@@ -202,6 +205,7 @@ def build_net(self, is_training):
 
         if is_training:
             def label_smoothing(inputs, masks, epsilon=0.1):
+                """Modify target for label smoothing."""
                 epsilon = cfg.labelsmoothing
                 num_of_channel = tf.shape(inputs)[-1]  # number of channels
                 inputs = tf.cast(inputs, tf.float32)
@@ -229,6 +233,7 @@ def label_smoothing(inputs, masks, epsilon=0.1):
         return tf.stack([self.begin_prob, self.end_prob])
 
     def build_char_states(self, char_embed, is_training, reuse, char_ids, char_lengths):
+        """Build char embedding network for the QA model."""
         max_char_length = self.cfg.max_char_length
 
         inputs = dropout(tf.nn.embedding_lookup(char_embed, char_ids),

examples/trials/ga_squad/trial.py

@@ -58,7 +58,7 @@ def get_config():
                         default='./glove.840B.300d.txt', help='dev file')
     parser.add_argument('--root_path', default='./data/',
                         type=str, help='Root path of models')
-    parser.add_argument('--batch_size', type=int, default=2, help='batch size')
+    parser.add_argument('--batch_size', type=int, default=64, help='batch size')
     parser.add_argument('--save_path', type=str,
                         default='./save', help='save path dir')
     parser.add_argument('--learning_rate', type=float, default=0.0001,
@@ -88,11 +88,13 @@ def load_embedding(path):
     '''
     return embedding for a specif file by given file path.
     '''
+    EMBEDDING_DIM = 300
     embedding_dict = {}
     with open(path, 'r', encoding='utf-8') as file:
         pairs = [line.strip('\r\n').split() for line in file.readlines()]
         for pair in pairs:
-            embedding_dict[pair[0]] = [float(x) for x in pair[1:]]
+            if len(pair) == EMBEDDING_DIM + 1:
+                embedding_dict[pair[0]] = [float(x) for x in pair[1:]]
     logger.debug('embedding_dict size: %d', len(embedding_dict))
     return embedding_dict
 
@@ -241,8 +243,6 @@ def run_epoch(batches, answer_net, is_training):
             if count % 100 == 0:
                 logger.debug('%d %g except:%g' %
                              (count, used, used / count * len(batches)))
-        if count % 100 == 0:
-            break
     loss = loss_sum / len(batches)
     if is_training:
         return loss

examples/trials/mnist-batch-tune-keras/config.yml

@@ -0,0 +1,20 @@
+authorName: default
+experimentName: example_mnist-keras
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 6
+#choice: local, remote
+trainingServicePlatform: local
+searchSpacePath: ~/nni/examples/trials/mnist-batch-tune-keras/search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  builtinTunerName: BatchTuner
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  command: python3 mnist-keras.py
+  codeDir: ~/nni/examples/trials/mnist-batch-tune-keras
+  gpuNum: 0