DeepCross.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/linchenxiao/.pyenv/versions/3.8.5/lib/python3.8/site-packages/pandas/compat/__init__.py:97: UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Crossing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training samples path, change to your local path\n",
    "training_samples_file_path = tf.keras.utils.get_file(\"trainingSamples.csv\", \"file:/Users/linchenxiao/Python_Code/Spark/SparrowRecsys/sampledata/trainingSamples.csv\")\n",
    "# Test samples path, change to your local path\n",
    "test_samples_file_path = tf.keras.utils.get_file(\"testSamples.csv\", \"file:/Users/linchenxiao/Python_Code/Spark/SparrowRecsys/sampledata/testSamples.csv\")\n",
    "\n",
    "\n",
    "# load sample as tf dataset\n",
    "def get_dataset(file_path):\n",
    "    dataset = tf.data.experimental.make_csv_dataset(\n",
    "        file_path,\n",
    "        batch_size=12,\n",
    "        label_name='label',\n",
    "        na_value=\"0\",\n",
    "        num_epochs=1,\n",
    "        ignore_errors=True)\n",
    "    return dataset\n",
    "\n",
    "\n",
    "# split as test dataset and training dataset\n",
    "train_dataset = get_dataset(training_samples_file_path)\n",
    "test_dataset = get_dataset(test_samples_file_path)\n",
    "\n",
    "# genre features vocabulary\n",
    "genre_vocab = ['Film-Noir', 'Action', 'Adventure', 'Horror', 'Romance', 'War', 'Comedy', 'Western', 'Documentary',\n",
    "               'Sci-Fi', 'Drama', 'Thriller',\n",
    "               'Crime', 'Fantasy', 'Animation', 'IMAX', 'Mystery', 'Children', 'Musical']\n",
    "\n",
    "GENRE_FEATURES = {\n",
    "    'userGenre1': genre_vocab,\n",
    "    'userGenre2': genre_vocab,\n",
    "    'userGenre3': genre_vocab,\n",
    "    'userGenre4': genre_vocab,\n",
    "    'userGenre5': genre_vocab,\n",
    "    'movieGenre1': genre_vocab,\n",
    "    'movieGenre2': genre_vocab,\n",
    "    'movieGenre3': genre_vocab\n",
    "}\n",
    "\n",
    "# all categorical features\n",
    "# 都需要onehot + Embedding化\n",
    "categorical_columns = []\n",
    "for feature, vocab in GENRE_FEATURES.items():\n",
    "    # One hot\n",
    "    cat_col = tf.feature_column.categorical_column_with_vocabulary_list(\n",
    "        key=feature, vocabulary_list=vocab)\n",
    "    # embedding10维\n",
    "    emb_col = tf.feature_column.embedding_column(cat_col, 10)\n",
    "    categorical_columns.append(emb_col)\n",
    "# movie id embedding feature\n",
    "# Hash bucket 数为1001\n",
    "movie_col = tf.feature_column.categorical_column_with_identity(key='movieId', num_buckets=1001)\n",
    "movie_emb_col = tf.feature_column.embedding_column(movie_col, 10)\n",
    "categorical_columns.append(movie_emb_col)\n",
    "# user id embedding feature\n",
    "user_col = tf.feature_column.categorical_column_with_identity(key='userId', num_buckets=30001)\n",
    "user_emb_col = tf.feature_column.embedding_column(user_col, 10)\n",
    "categorical_columns.append(user_emb_col)\n",
    "\n",
    "# # all numerical features\n",
    "numerical_columns = [tf.feature_column.numeric_column('releaseYear'),\n",
    "                     tf.feature_column.numeric_column('movieRatingCount'),\n",
    "                     tf.feature_column.numeric_column('movieAvgRating'),\n",
    "                     tf.feature_column.numeric_column('movieRatingStddev'),\n",
    "                     tf.feature_column.numeric_column('userRatingCount'),\n",
    "                     tf.feature_column.numeric_column('userAvgRating'),\n",
    "                     tf.feature_column.numeric_column('userRatingStddev')]\n",
    "\n",
    "# embedding + MLP model architecture\n",
    "# Sequential Model\n",
    "# 第一层是Concat层\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.DenseFeatures(numerical_columns + categorical_columns),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(1, activation='sigmoid'),\n",
    "])\n",
    "\n",
    "# compile the model, set loss function, optimizer and evaluation metrics\n",
    "model.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy', tf.keras.metrics.AUC(curve='ROC'), tf.keras.metrics.AUC(curve='PR')])\n",
    "\n",
    "# train the model\n",
    "model.fit(train_dataset, epochs=5)\n",
    "\n",
    "# evaluate the model\n",
    "test_loss, test_accuracy, test_roc_auc, test_pr_auc = model.evaluate(test_dataset)\n",
    "print('\\n\\nTest Loss {}, Test Accuracy {}, Test ROC AUC {}, Test PR AUC {}'.format(test_loss, test_accuracy,\n",
    "                                                                                   test_roc_auc, test_pr_auc))\n",
    "\n",
    "# print some predict results\n",
    "predictions = model.predict(test_dataset)\n",
    "for prediction, goodRating in zip(predictions[:12], list(test_dataset)[0][1][:12]):\n",
    "    print(\"Predicted good rating: {:.2%}\".format(prediction[0]),\n",
    "          \" | Actual rating label: \",\n",
    "          (\"Good Rating\" if bool(goodRating) else \"Bad Rating\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functional API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "7403/7403 [==============================] - 22s 3ms/step - loss: 5.8763 - accuracy: 0.5596 - auc_12: 0.5632 - auc_13: 0.6141\n",
      "Epoch 2/5\n",
      "7403/7403 [==============================] - 18s 2ms/step - loss: 0.6698 - accuracy: 0.6580 - auc_12: 0.7034 - auc_13: 0.7309\n",
      "Epoch 3/5\n",
      "7403/7403 [==============================] - 18s 2ms/step - loss: 0.5549 - accuracy: 0.7189 - auc_12: 0.7834 - auc_13: 0.8052\n",
      "Epoch 4/5\n",
      "7403/7403 [==============================] - 17s 2ms/step - loss: 0.5314 - accuracy: 0.7356 - auc_12: 0.8053 - auc_13: 0.8287\n",
      "Epoch 5/5\n",
      "7403/7403 [==============================] - 17s 2ms/step - loss: 0.5198 - accuracy: 0.7420 - auc_12: 0.8153 - auc_13: 0.8394\n",
      "1870/1870 [==============================] - 3s 1ms/step - loss: 0.6150 - accuracy: 0.6834 - auc_12: 0.7401 - auc_13: 0.7685\n",
      "\n",
      "\n",
      "Test Loss 0.6149641871452332, Test Accuracy 0.6834224462509155, Test ROC AUC 0.7400882244110107, Test PR AUC 0.768450915813446\n",
      "Predicted good rating: 79.47%  | Actual rating label:  Good Rating\n",
      "Predicted good rating: 52.79%  | Actual rating label:  Good Rating\n",
      "Predicted good rating: 84.37%  | Actual rating label:  Bad Rating\n",
      "Predicted good rating: 9.55%  | Actual rating label:  Good Rating\n",
      "Predicted good rating: 96.20%  | Actual rating label:  Bad Rating\n",
      "Predicted good rating: 30.51%  | Actual rating label:  Bad Rating\n",
      "Predicted good rating: 65.39%  | Actual rating label:  Good Rating\n",
      "Predicted good rating: 90.57%  | Actual rating label:  Bad Rating\n",
      "Predicted good rating: 66.38%  | Actual rating label:  Bad Rating\n",
      "Predicted good rating: 58.84%  | Actual rating label:  Good Rating\n",
      "Predicted good rating: 7.75%  | Actual rating label:  Bad Rating\n",
      "Predicted good rating: 69.32%  | Actual rating label:  Bad Rating\n"
     ]
    }
   ],
   "source": [
    "# Training samples path, change to your local path\n",
    "training_samples_file_path = tf.keras.utils.get_file(\"trainingSamples.csv\", \"file:/Users/linchenxiao/Python_Code/Spark/SparrowRecsys/sampledata/trainingSamples.csv\")\n",
    "# Test samples path, change to your local path\n",
    "test_samples_file_path = tf.keras.utils.get_file(\"testSamples.csv\", \"file:/Users/linchenxiao/Python_Code/Spark/SparrowRecsys/sampledata/testSamples.csv\")\n",
    "\n",
    "\n",
    "# load sample as tf dataset\n",
    "def get_dataset(file_path):\n",
    "    dataset = tf.data.experimental.make_csv_dataset(\n",
    "        file_path,\n",
    "        batch_size=12,\n",
    "        label_name='label',\n",
    "        na_value=\"0\",\n",
    "        num_epochs=1,\n",
    "        ignore_errors=True)\n",
    "    return dataset\n",
    "\n",
    "\n",
    "# split as test dataset and training dataset\n",
    "train_dataset = get_dataset(training_samples_file_path)\n",
    "test_dataset = get_dataset(test_samples_file_path)\n",
    "\n",
    "# embedding + MLP model architecture\n",
    "\n",
    "# Functional API\n",
    "# https://github.com/tensorflow/community/blob/master/rfcs/20191212-keras-categorical-inputs.md\n",
    "genre_vocab = ['Film-Noir', 'Action', 'Adventure', 'Horror', 'Romance', 'War', 'Comedy', 'Western', 'Documentary',\n",
    "               'Sci-Fi', 'Drama', 'Thriller',\n",
    "               'Crime', 'Fantasy', 'Animation', 'IMAX', 'Mystery', 'Children', 'Musical']\n",
    "\n",
    "GENRE_FEATURES = {\n",
    "    'userGenre1': genre_vocab,\n",
    "    'userGenre2': genre_vocab,\n",
    "    'userGenre3': genre_vocab,\n",
    "    'userGenre4': genre_vocab,\n",
    "    'userGenre5': genre_vocab,\n",
    "    'movieGenre1': genre_vocab,\n",
    "    'movieGenre2': genre_vocab,\n",
    "    'movieGenre3': genre_vocab\n",
    "}\n",
    "\n",
    "keras_inputs = {}\n",
    "keras_preproc_inputs = []\n",
    "# Categorical data\n",
    "#  都需要onehot + Embedding化\n",
    "for feature, vocab in GENRE_FEATURES.items():\n",
    "    keras_input = tf.keras.Input(shape=(1,), name=feature, dtype=tf.dtypes.string)\n",
    "    keras_inputs[feature] = keras_input\n",
    "    keras_preproc_input = tf.keras.layers.experimental.preprocessing.StringLookup(vocabulary=vocab)(keras_input)\n",
    "    # input_dim Size of the vocabulary, i.e. maximum integer index + 1. 再加一因为unknown value\n",
    "    keras_preproc_input = tf.keras.layers.Embedding(input_dim=len(vocab)+2, output_dim=10)(keras_preproc_input)\n",
    "    keras_preproc_input = tf.keras.layers.Flatten()(keras_preproc_input)\n",
    "    keras_preproc_inputs.append(keras_preproc_input)\n",
    "\n",
    "# movide id\n",
    "keras_input = tf.keras.Input(shape=(1,), name='movieId', dtype=tf.dtypes.int32)\n",
    "keras_inputs['movieId'] = keras_input\n",
    "keras_preproc_input = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=1001)(keras_input)\n",
    "keras_preproc_input = tf.keras.layers.Embedding(input_dim=1001, output_dim=10)(keras_preproc_input)\n",
    "keras_preproc_input = tf.keras.layers.Flatten()(keras_preproc_input)\n",
    "keras_preproc_inputs.append(keras_preproc_input)\n",
    "\n",
    "# user id\n",
    "keras_input = tf.keras.Input(shape=(1,), name='userId', dtype=tf.dtypes.int32)\n",
    "keras_inputs['userId'] = keras_input\n",
    "keras_preproc_input = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=30001)(keras_input)\n",
    "keras_preproc_input = tf.keras.layers.Embedding(input_dim=30001, output_dim=10)(keras_preproc_input)\n",
    "keras_preproc_input = tf.keras.layers.Flatten()(keras_preproc_input)\n",
    "keras_preproc_inputs.append(keras_preproc_input)\n",
    "\n",
    "# numeric values\n",
    "for key in ['releaseYear','movieRatingCount', 'movieAvgRating', 'movieRatingStddev', 'userRatingCount', 'userAvgRating', 'userRatingStddev']:\n",
    "    keras_input = tf.keras.Input(shape=(1,), dtype=tf.float32, name=key)\n",
    "    keras_inputs[key] = keras_input\n",
    "    keras_preproc_inputs.append(keras_input)\n",
    "\n",
    "\n",
    "# Construct the model\n",
    "# Merge all available features into a single large vector via concatenation\n",
    "x = tf.keras.layers.concatenate(keras_preproc_inputs)\n",
    "for units in [128, 128]:\n",
    "    x = tf.keras.layers.Dense(units, activation='relu')(x)\n",
    "prediction = tf.keras.layers.Dense(1, activation='sigmoid')(x)\n",
    "\n",
    "model = tf.keras.Model(keras_inputs, prediction)\n",
    "# compile the model, set loss function, optimizer and evaluation metrics\n",
    "model.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer='adam',\n",
    "    metrics=['accuracy', tf.keras.metrics.AUC(curve='ROC'), tf.keras.metrics.AUC(curve='PR')])\n",
    "\n",
    "# # train the model\n",
    "model.fit(train_dataset, epochs=5)\n",
    "\n",
    "# evaluate the model\n",
    "test_loss, test_accuracy, test_roc_auc, test_pr_auc = model.evaluate(test_dataset)\n",
    "print('\\n\\nTest Loss {}, Test Accuracy {}, Test ROC AUC {}, Test PR AUC {}'.format(test_loss, test_accuracy,\n",
    "                                                                                   test_roc_auc, test_pr_auc))\n",
    "\n",
    "# print some predict results\n",
    "predictions = model.predict(test_dataset)\n",
    "for prediction, goodRating in zip(predictions[:12], list(test_dataset)[0][1][:12]):\n",
    "    print(\"Predicted good rating: {:.2%}\".format(prediction[0]),\n",
    "          \" | Actual rating label: \",\n",
    "          (\"Good Rating\" if bool(goodRating) else \"Bad Rating\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.keras.utils.plot_model(model, to_file=\"DeepCross.png\", show_shapes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}