Milestone_05_v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Milestone 5: Final Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import ndimage, misc\n",
    "\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.models import load_model\n",
    "from keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D\n",
    "from keras.optimizers import SGD, RMSprop\n",
    "from keras.utils import plot_model\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from sklearn.metrics import roc_curve, hamming_loss, average_precision_score, recall_score, precision_score, f1_score, classification_report, roc_auc_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.ensemble import RandomForestClassifier as RFC\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import preprocessing\n",
    "\n",
    "#from IPython.display import SVG\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import string\n",
    "import time\n",
    "from datetime import datetime\n",
    "from __future__ import division\n",
    "\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This notebook is an extension of Milestones 3 & 4, adding topic modeling variables,\n",
    "improving the data processing pipeline, and extending the deep learning model.\n",
    "\n",
    "Specifically, these issues were addressed:\n",
    "\n",
    "    1.  The test set must be set aside to start with, \n",
    "        so subsequently generated training sets don't dip into it   \n",
    "            \n",
    "    2.  Implement training by \"maxi-batches\": \n",
    "            - set aside test\n",
    "            - load one big train batch (~5k)\n",
    "            - do one epoch of training\n",
    "    \n",
    "    4.  Manual image pre-processing - use scipy to load images in with \n",
    "        uniform shape and formatting (and downsample, if desired)\n",
    "        \n",
    "    5.  Use Precision and Recall custom fcns for metrics\n",
    "    \n",
    "    6.  Save Keras model and associated metadata automatically\n",
    "    \n",
    "    7.  Log results for TensorBoard viz\n",
    "    \n",
    "    8.  Functionalize calls for model building and fitting, so we can sweep configs\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Data loading, munging, and topic modeling of movie overviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%cd ~/data/\n",
    "\n",
    "### Set whether to generate dataset from scratch, or load from CSVs ###\n",
    "LOAD_FROM_CSV = True\n",
    "####################\n",
    "\n",
    "\n",
    "if LOAD_FROM_CSV:\n",
    "    \n",
    "    ### Read tabular data from CSVs ###\n",
    "    X_train = pd.read_csv('X_train.csv').as_matrix()\n",
    "    X_test = pd.read_csv('X_test.csv').as_matrix()\n",
    "    X_valid = pd.read_csv('X_valid.csv').as_matrix()\n",
    "    y_train = pd.read_csv('y_train.csv', header=None).as_matrix()\n",
    "    y_test = pd.read_csv('y_test.csv', header=None).as_matrix()\n",
    "    y_valid = pd.read_csv('y_valid.csv', header=None).as_matrix()\n",
    "    ids_train = pd.read_csv('ids_train.csv', header=None).as_matrix()\n",
    "    ids_test = pd.read_csv('ids_test.csv', header=None).as_matrix()\n",
    "    ids_valid = pd.read_csv('ids_validate.csv', header=None).as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "### Loading and munging of tabular data ###\n",
    "\n",
    "# We'll start by loading in all the tabular data and the multi-category labels.\n",
    "# Then we'll process the data all together, to remove any movies with missing data.\n",
    "# Then we'll split into train, validate, test groups, and isolate movie IDs for each\n",
    "\n",
    "\n",
    "if LOAD_FROM_CSV == False:\n",
    "\n",
    "    # Let's load all the raw data\n",
    "    features = pd.read_csv('features_V1.csv')\n",
    "    labels = pd.read_csv('multilabels.csv')\n",
    "\n",
    "    #delete text-based variables, except for overview\n",
    "    features = features.drop(features.columns[[1, 2, 3, 6, 12]], axis=1)\n",
    "\n",
    "    #convert release date string to numeric year\n",
    "    rd = features['release_date'].str[:4]\n",
    "    rd = pd.to_numeric(rd)\n",
    "\n",
    "    features['release_date'] = rd\n",
    "\n",
    "    #drop adult since it is always false\n",
    "    del features['adult']\n",
    "\n",
    "    # drop missing data, duplicates and pop off labels\n",
    "    feat2 = pd.concat([features, labels], axis=1)\n",
    "    feat2 = feat2.drop_duplicates(subset=['id'])\n",
    "    feat2 = feat2.dropna()\n",
    "\n",
    "    labels2 = feat2.iloc[:, 515:].as_matrix()\n",
    "    feat2 = feat2.iloc[:, :515]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "### Partition dataset into train, validate, and test sets ###\n",
    "\n",
    "# We have a final cleaned dataset of 133,851 movies. Along with this, we start with\n",
    "# 150,854 images and do a 50:25:25 split. We assume image data will be missing at\n",
    "# random, so data loss should be approximately equal across splits.\n",
    "\n",
    "if LOAD_FROM_CSV == False:\n",
    "\n",
    "    # Split our data 50:25:25 into training, validation, and testing sets\n",
    "    X_train, X_test, y_train, y_test = train_test_split(feat2, labels2, test_size=0.50, random_state=42)\n",
    "    X_valid, X_test, y_valid, y_test = train_test_split(X_test, y_test, test_size=0.50, random_state=42)\n",
    "    # We will use these splits to keep the test data in a \"lockbox\" until final model evaluation.\n",
    "\n",
    "    # Now pop off movie IDs from each group to use as keys\n",
    "    train_ids = X_train.pop('id').as_matrix()\n",
    "    valid_ids = X_valid.pop('id').as_matrix()\n",
    "    test_ids = X_test.pop('id').as_matrix()\n",
    "\n",
    "    # Extract overview text from each movie\n",
    "    overview_train = X_train['overview'].tolist()\n",
    "    overview_valid = X_valid['overview'].tolist()\n",
    "    overview_test = X_test['overview'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Tokenize overview text for topic modeling\n",
    "\n",
    "def movie_tokenizer(raw, stop_words, stemmer):\n",
    "\n",
    "        try:\n",
    "            \n",
    "            # Tokenize using simple whitespace\n",
    "            tokens = raw.lower().encode('ascii', 'ignore').split()\n",
    "\n",
    "            # Strip useless unicode remnants, numeric, and stop words\n",
    "            # **adapted from http://stackoverflow.com/questions/265960/best-way-to-strip-punctuation-from-a-string-in-python\n",
    "            stripped_tokens = [t.translate(None, string.punctuation+string.digits) for t in tokens if ((not t in stop_words) and (len(t) > 2))]\n",
    "            \n",
    "            # Stem tokens\n",
    "            return [stemmer.stem(t) for t in stripped_tokens]\n",
    "\n",
    "\n",
    "        except:\n",
    "        \n",
    "            # Fill in non-english or missing observations with 'unknown' value\n",
    "            return ['unknown']\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Do corpus preparation for LDA fitting\n",
    "\n",
    "def get_overview_corpus(overview_text):\n",
    "    \n",
    "    # Create English stop words list (hardly any overview are in another language)\n",
    "    en_stop = get_stop_words('en')\n",
    "\n",
    "    # Eliminate genres as words to include, so as not to cheat\n",
    "    full_stop = en_stop + [u'comedy', u'action', u'adventure', \n",
    "                           u'drama', u'family', u'horror', \n",
    "                           u'mystery', u'thriller', u'romance', \n",
    "                           u'crime', u'science fiction', u'fantasy', \n",
    "                           u'animation', u'history', u'war', \n",
    "                           u'music', u'documentary', u'western', u'tv movie']\n",
    "\n",
    "    # Create stemmer\n",
    "    s_stemmer = SnowballStemmer('english')\n",
    "\n",
    "\n",
    "    # Loop through overview list\n",
    "    texts = []\n",
    "    for i in overview_text:\n",
    "        texts.append(movie_tokenizer(i, full_stop, s_stemmer))\n",
    "\n",
    "    # Put tokenized overviews into a id <-> term dictionary\n",
    "    dictionary = corpora.Dictionary(texts)\n",
    "\n",
    "    # Convert tokenized overviews into a document-term matrix - FYI, this is a SPARSE matrix!!\n",
    "    corpus = [dictionary.doc2bow(text) for text in texts]\n",
    "    \n",
    "    return corpus, dictionary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Fit a topic model using Latent Dirichlet Allocation.  Fitting the model can \n",
    "# take > 1hr, so we'll save the gensim model by default, to be loaded later\n",
    "\n",
    "def fit_topic_model(overview_text, n_topics=20, doSave=True):\n",
    "\n",
    "    print 'Constructing dictionary'\n",
    "    corpus, dictionary = get_overview_corpus(overview_text)\n",
    "    \n",
    "    # Fit topic model using LDA\n",
    "    print 'Fitting topic model'\n",
    "    overview_topic_mdl = gensim.models.ldamulticore.LdaMulticore(corpus, num_topics=n_topics, id2word=dictionary, passes=20)\n",
    "\n",
    "    if doSave: overview_topic_mdl.save('./topic_mdl')\n",
    "    \n",
    "    return overview_topic_mdl, corpus\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "### Topic modeling using Overview text ###\n",
    "\n",
    "# We perform Latent Dirichlet Allocation to try to utilize the Overview text.\n",
    "# We fit the topic model to the training data, and then apply model to all\n",
    "# so there's no \"leakage\" into the test and validation sets.\n",
    "\n",
    "\n",
    "if LOAD_FROM_CSV == False:\n",
    "\n",
    "\n",
    "    FIT_LDA = False\n",
    "    n_topics = 20\n",
    "\n",
    "\n",
    "    if FIT_LDA:\n",
    "         \n",
    "        from stop_words import get_stop_words\n",
    "        from nltk.stem.porter import PorterStemmer\n",
    "        from nltk.stem.snowball import SnowballStemmer\n",
    "        from gensim import corpora, models\n",
    "        import gensim\n",
    "\n",
    "        # Fit a topic model to the training data.  The resulting model will be \n",
    "        # applied to validation and test data as well. Note: takes 1h+ to fit!\n",
    "        overview_topic_mdl, corpus = fit_topic_model(overview_train, n_topics)\n",
    "\n",
    "    else:\n",
    "\n",
    "        # load existing model\n",
    "        overview_topic_mdl = gensim.models.ldamodel.LdaModel.load('./topic_mdl')\n",
    "        corpus, dictionary = get_overview_corpus(overview_train)\n",
    "\n",
    "\n",
    "\n",
    "    # Now apply topic model to each dataset partition to get estimated topic loadings\n",
    "    # and append these values as to data matrix as new features\n",
    "\n",
    "    # --- Training set\n",
    "    tpx = gensim.matutils.corpus2dense(overview_topic_mdl[corpus], n_topics)\n",
    "\n",
    "    # Now append to existing data matrix\n",
    "    tpx_df = pd.DataFrame(tpx.transpose(), index=range(len(corpus)))\n",
    "    X_train.index = range(len(corpus))\n",
    "    X_train = pd.concat([X_train, tpx_df], ignore_index=True, axis=1)\n",
    "    X_train = X_train.drop(X_train.columns[[1]], axis=1)\n",
    "\n",
    "\n",
    "    # --- Validation set\n",
    "    corpus, dictionary = get_overview_corpus(overview_valid)\n",
    "    tpx = gensim.matutils.corpus2dense(overview_topic_mdl[corpus], n_topics)\n",
    "\n",
    "    # Now append to existing data matrix\n",
    "    tpx_df = pd.DataFrame(tpx.transpose(), index=range(len(corpus)))\n",
    "    X_valid.index = range(len(corpus))\n",
    "    X_valid = pd.concat([X_valid, tpx_df], ignore_index=True, axis=1)\n",
    "    X_valid = X_valid.drop(X_valid.columns[[1]], axis=1)\n",
    "\n",
    "\n",
    "    # --- Test set\n",
    "    corpus, dictionary = get_overview_corpus(overview_test)\n",
    "    tpx = gensim.matutils.corpus2dense(overview_topic_mdl[corpus], n_topics)\n",
    "\n",
    "    # Now append to existing data matrix\n",
    "    tpx_df = pd.DataFrame(tpx.transpose(), index=range(len(corpus)))\n",
    "    X_test.index = range(len(corpus))\n",
    "    X_test = pd.concat([X_test, tpx_df], ignore_index=True, axis=1)\n",
    "    X_test = X_test.drop(X_test.columns[[1]], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now, at this point, we've assembled our final data matrices for each of the sets, complete with topic model loadings.  Next we'll start dealing with the image data.  Most, but not all, movies have posters. Similarly, some movies with posters have been excluded because of other missing features.  We'll need to reconcile these discrepancies by loading images and checking their formatting.  Any exceptions will cause the movie to be excluded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Create multi-label metrics to evaluate performance.\n",
    "# These functionas calculate metric within each observation, \n",
    "# and average across observations -just what we need.\n",
    "\n",
    "def multi_recall(y_true, y_pred):\n",
    "    return recall_score(y_true, y_pred, average='samples')\n",
    "\n",
    "def multi_precision(y_true, y_pred):\n",
    "    return precision_score(y_true, y_pred, average='samples')\n",
    "\n",
    "def multi_f1(y_true, y_pred):\n",
    "    return f1_score(y_true, y_pred, average='samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Fit RFC on non-image features set and calculate multilabel performance metrics on validation set\n",
    "# We'll hold out the test set until the very end\n",
    "\n",
    "DO_PCA = False\n",
    "\n",
    "if DO_PCA:\n",
    "    N_PCs = 100\n",
    "    # Standardize data\n",
    "    scaler = preprocessing.StandardScaler().fit(X_train)\n",
    "    \n",
    "    # Do PCA, retaining the first N_PCs components\n",
    "    pca = PCA(n_components=N_PCs)\n",
    "    pca.fit(scaler.transform(X_train))\n",
    "    \n",
    "    # Fit RFC on PCs using optimal hyperparameters found from tuning on validation set\n",
    "    rfc_mdl = RFC(n_estimators=300, max_depth=20, max_features=N_PCs, n_jobs=-1).fit(pca.transform(scaler.transform(X_train)), y_train)\n",
    "    rf_pred = rfc_mdl.predict(pca.transform(scaler.transform(X_valid)))\n",
    "\n",
    "else:\n",
    "    # Fit RFC using optimal hyperparameters found from tuning on validation set\n",
    "    rfc_mdl = RFC(n_estimators=300, max_depth=20, max_features=533, n_jobs=-1).fit(X_train, y_train)\n",
    "    rf_pred = rfc_mdl.predict(X_valid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.415387269018\n",
      "0.526327893768\n",
      "0.445691701807\n"
     ]
    }
   ],
   "source": [
    "print multi_recall(y_valid, rf_pred)\n",
    "print multi_precision(y_valid, rf_pred)\n",
    "print multi_f1(y_valid, rf_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 26min 59s, sys: 12.5 s, total: 27min 11s\n",
      "Wall time: 7min 51s\n",
      "n_estimators = 300, max_depth = 20, max_features = 533\n",
      "0.348556782259\n",
      "0.435060005957\n",
      "0.369707389456\n"
     ]
    }
   ],
   "source": [
    "### Tune RFC model using validation set ###\n",
    "\n",
    "# n_estimators=300, max_depth=20, and max_features=533 seems to be optimal (8min run time)\n",
    "\n",
    "r = []\n",
    "p = []\n",
    "f1 = []\n",
    "\n",
    "for n in [10, 20, 50, 100, 200, 300]:\n",
    "    \n",
    "    for d in [10, 20, 50, 100, 200, 300]:\n",
    "        \n",
    "        for f in [10, 25, 50, 100, 200, 300, 500, 533]:\n",
    "\n",
    "            rfc_mdl = RFC(n_estimators=n, max_depth=d, max_features=f, n_jobs=-1).fit(X_train, y_train)\n",
    "            rf_pred = rfc_mdl.predict(X_valid)\n",
    "\n",
    "            r.append(multi_recall(y_valid, rf_pred))\n",
    "            p.append(multi_precision(y_valid, rf_pred))\n",
    "            f1.append(multi_f1(y_valid, rf_pred))\n",
    "\n",
    "            print 'n_estimators = {n}, max_depth = {d}, max_features = {f}'.format(n=n,d=d,f=f)\n",
    "            print multi_recall(y_valid, rf_pred)\n",
    "            print multi_precision(y_valid, rf_pred)\n",
    "            print multi_f1(y_valid, rf_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.447149198663\n",
      "0.349975498674\n",
      "0.376083411573\n",
      "0.192596893479\n",
      "\n",
      "\n",
      "0.265651062685\n",
      "0.303943752642\n",
      "0.258254682018\n",
      "0.300929815025\n"
     ]
    }
   ],
   "source": [
    "### Evaluate RFC on held out test data ###\n",
    "rf_pred = rfc_mdl.predict(X_test)\n",
    "\n",
    "print multi_precision(y_test, rf_pred)\n",
    "print multi_recall(y_test, rf_pred)\n",
    "print multi_f1(y_test, rf_pred)\n",
    "print hamming_loss(y_test, rf_pred)\n",
    "\n",
    "### Compare metrics with random data ###\n",
    "y_probs = y_train.mean(axis=0)\n",
    "\n",
    "random_data = np.column_stack((np.random.binomial(1,y_probs[0],(y_test.shape[0],1)),\n",
    "                             np.random.binomial(1,y_probs[1],(y_test.shape[0],1)),\n",
    "                             np.random.binomial(1,y_probs[2],(y_test.shape[0],1)),\n",
    "                             np.random.binomial(1,y_probs[3],(y_test.shape[0],1)),\n",
    "                             np.random.binomial(1,y_probs[4],(y_test.shape[0],1)),\n",
    "                             np.random.binomial(1,y_probs[5],(y_test.shape[0],1)),\n",
    "                             np.random.binomial(1,y_probs[6],(y_test.shape[0],1))))\n",
    "\n",
    "print '\\n'\n",
    "print multi_precision(y_test, random_data)\n",
    "print multi_recall(y_test, random_data)\n",
    "print multi_f1(y_test, random_data)\n",
    "print hamming_loss(y_test, random_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Generate maxi-batches\n",
    "def get_train_batch(batch_size=3200, verbose=True):\n",
    "    \n",
    "    STANDARDIZE_IMAGES = True\n",
    "\n",
    "    # Randomly select a maxi-batch of images\n",
    "    idx_batch = np.random.choice(np.arange(len(ids_train)), size=batch_size, replace=False)\n",
    "\n",
    "    # Get movie IDs\n",
    "    ids_batch = ids_train[idx_batch]\n",
    "\n",
    "    # Get labels\n",
    "    y_batch = y_train[idx_batch]\n",
    "\n",
    "    # Read in images\n",
    "    X_batch = np.zeros([batch_size, 300, 185, 3])\n",
    "\n",
    "    ct = 0\n",
    "    for i in ids_batch:\n",
    "        IM = ndimage.imread('posters/train/{}.jpg'.format(i[0]))\n",
    "        \n",
    "        try:\n",
    "            \n",
    "            # Standardize **within** each image\n",
    "            if STANDARDIZE_IMAGES:    \n",
    "                # Here we just want to standardize the overall image intensity, \n",
    "                # so we convert to HSV, standardize V-channel, and then convert back to RGB\n",
    "                tmp_im = matplotlib.colors.rgb_to_hsv(IM)\n",
    "                tmp_im[:,:,2] = (tmp_im[:,:,2] - tmp_im[:,:,2].mean()) / tmp_im[:,:,2].std()\n",
    "                IM = matplotlib.colors.hsv_to_rgb(tmp_im)\n",
    "                #IM = (IM - IM.mean()) / IM.std()\n",
    "\n",
    "            X_batch[ct,:,:,:] = IM\n",
    "        \n",
    "        except:\n",
    "            print'Something bad happened when loading movie {}'.format(i[0])\n",
    "\n",
    "        if verbose and (ct % 100 == 0):\n",
    "            print 'poster {i}/{n} loaded'.format(i=ct+1, n=batch_size)\n",
    "\n",
    "        ct += 1\n",
    "\n",
    "    if verbose: print 'batch loaded'\n",
    "\n",
    "    return X_batch, y_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Generate maxi-batches\n",
    "def get_validate_batch(batch_size=3200, verbose=True):\n",
    "\n",
    "    STANDARDIZE_IMAGES = True\n",
    "    \n",
    "    # Randomly select a maxi-batch of images\n",
    "    idx_batch = np.random.choice(np.arange(len(ids_valid)), size=batch_size, replace=False)\n",
    "\n",
    "    # Get movie IDs\n",
    "    ids_batch = ids_valid[idx_batch]\n",
    "\n",
    "    # Get labels\n",
    "    y_batch = y_train[idx_batch]\n",
    "\n",
    "    # Read in images\n",
    "    X_batch = np.zeros([batch_size, 300, 185, 3])\n",
    "\n",
    "    ct = 0\n",
    "    for i in ids_batch:\n",
    "        IM = ndimage.imread('posters/validate/{}.jpg'.format(i[0]))\n",
    "\n",
    "        try:\n",
    "                        \n",
    "            # Standardize **within** each image\n",
    "            if STANDARDIZE_IMAGES:    \n",
    "                # Here we just want to standardize the overall image intensity, \n",
    "                # so we convert to HSV, standardize V-channel, and then convert back to RGB\n",
    "                tmp_im = matplotlib.colors.rgb_to_hsv(IM)\n",
    "                tmp_im[:,:,2] = (tmp_im[:,:,2] - tmp_im[:,:,2].mean()) / tmp_im[:,:,2].std()\n",
    "                IM = matplotlib.colors.hsv_to_rgb(tmp_im)\n",
    "                #IM = (IM - IM.mean()) / IM.std()\n",
    "\n",
    "            X_batch[ct,:,:,:] = IM\n",
    "            \n",
    "        except:\n",
    "            print'Something bad happened when loading movie {}'.format(i[0])\n",
    "\n",
    "        if verbose and (ct % 100 == 0):\n",
    "            print 'poster {i}/{n} loaded'.format(i=ct+1, n=batch_size)\n",
    "\n",
    "        ct += 1\n",
    "\n",
    "    if verbose: print 'batch loaded'\n",
    "\n",
    "    return X_batch, y_batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_49 (Conv2D)           (None, 298, 183, 32)      896       \n",
      "_________________________________________________________________\n",
      "conv2d_50 (Conv2D)           (None, 296, 181, 32)      9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_25 (MaxPooling (None, 148, 90, 32)       0         \n",
      "_________________________________________________________________\n",
      "dropout_37 (Dropout)         (None, 148, 90, 32)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_51 (Conv2D)           (None, 146, 88, 64)       18496     \n",
      "_________________________________________________________________\n",
      "conv2d_52 (Conv2D)           (None, 144, 86, 64)       36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_26 (MaxPooling (None, 72, 43, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_38 (Dropout)         (None, 72, 43, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_13 (Flatten)         (None, 198144)            0         \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 256)               50725120  \n",
      "_________________________________________________________________\n",
      "dropout_39 (Dropout)         (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 7)                 1799      \n",
      "=================================================================\n",
      "Total params: 50,792,487.0\n",
      "Trainable params: 50,792,487.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build CNN model\n",
    "\n",
    "model_1 = Sequential()\n",
    "\n",
    "# input: 300x185 images with 3 channels -> (300, 185, 3) tensors.\n",
    "# this applies 32 convolution filters of size 3x3 each.\n",
    "# NoteL we're not completely covering the images here\n",
    "model_1.add(Conv2D(32, (3, 3), activation='relu', input_shape=(300, 185, 3)))\n",
    "model_1.add(Conv2D(32, (3, 3), activation='relu'))\n",
    "model_1.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model_1.add(Dropout(0.25))\n",
    "\n",
    "model_1.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model_1.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model_1.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model_1.add(Dropout(0.25))\n",
    "\n",
    "model_1.add(Flatten())\n",
    "model_1.add(Dense(256, activation='relu'))\n",
    "model_1.add(Dropout(0.5))\n",
    "model_1.add(Dense(7, activation='sigmoid'))\n",
    "\n",
    "model_1.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['binary_accuracy'])\n",
    "\n",
    "# Other optimizer parameters\n",
    "#model_1.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['binary_accuracy'])\n",
    "#sgd = SGD(lr=0.0001, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#model_1.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['binary_accuracy'])\n",
    "\n",
    "model_1.summary()\n",
    "\n",
    "# Visualize network graph\n",
    "#SVG(model_to_dot(model_1).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### Fit CNN model using maxi-batches\n",
    "\n",
    "# 200 iterations over maxi-batches gives us about 1X coverage of all training data\n",
    "n_it = 300\n",
    "\n",
    "# Write to persistent storage on EFS\n",
    "TB_callback = keras.callbacks.TensorBoard(log_dir='/home/ubuntu/efs/logs')\n",
    "valid_auc = []\n",
    "\n",
    "for i in range(n_it):\n",
    "    \n",
    "    print i\n",
    "    X_batch, y_batch = get_train_batch(320, verbose=False)\n",
    "\n",
    "    # Run in mini-batches of size=32\n",
    "    history = model_1.fit(X_batch, y_batch, batch_size=32, verbose=1, epochs=10, callbacks=[TB_callback])\n",
    "    \n",
    "    # Test on validation batch after each maxi-batch\n",
    "    X_batch, y_batch = get_validate_batch(1024, verbose=False)\n",
    "    y_pred = model_1.predict(X_batch)\n",
    "    auc = roc_auc_score(y_batch, y_pred)\n",
    "    valid_auc.append(auc)\n",
    "    print 'ROC AUC = {}'.format(auc)\n",
    "\n",
    "    # Save model periodically to avoid catastrophic loss\n",
    "    if i % 5 == 0:\n",
    "        print 'saving'\n",
    "        model_1.save('/home/ubuntu/efs/conv_movie_model.h5')\n",
    "\n",
    "# Save model at very end, too\n",
    "model_1.save('/home/ubuntu/efs/conv_movie_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X_batch, y_batch = get_validate_batch(10000, verbose=False)\n",
    "y_pred = model_1.predict(X_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc904e0c310>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi4AAAFCCAYAAADWlFZjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc904e407d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is for large batch size, high iteration model - all predicted the same\n",
    "plt.figure(figsize=[10,5])\n",
    "plt.pcolor(y_pred[:1000])\n",
    "plt.colorbar()\n",
    "plt.title('Predicted multi-label probabilities for 1000 test observations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc9abf1b450>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmAAAAE4CAYAAAD8Xk/XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9b5b21b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Long run saved as Model_3 = maxi-batch=320, epochs=10, valid-batch=1000\n",
    "plt.figure(figsize=[10,5])\n",
    "plt.plot(valid_auc_long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc90583a390>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAicAAAFCCAYAAAAqgX2pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9058f9790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is for small batch size, low iteration model\n",
    "plt.figure(figsize=[10,5])\n",
    "plt.pcolor(y_pred[:1000])\n",
    "plt.colorbar()\n",
    "plt.title('Predicted multi-label probabilities for 1000 test observations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fc90633ae90>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmUAAAFRCAYAAAA1jNoBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJKkN/P+19CiJ9DLQpAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc907cc05d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is for small batch size, low iteration model\n",
    "plt.figure(figsize=[10, 5])\n",
    "plt.plot(history.history['loss'])\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.title('Training loss of reduced CNN model (trained on 1024 posters)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "0.497124822411\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAJoCAYAAAAj5EbEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc9062e7690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curves for each genre category\n",
    "plt.figure(figsize=[10,10])\n",
    "plt.hold\n",
    "\n",
    "for i in range(7):\n",
    "    fpr, tpr, thresholds = roc_curve(y_batch[:,i], y_pred[:,i])\n",
    "    score = roc_auc_score(y_batch[:,i], y_pred[:,i])\n",
    "    plt.plot(fpr, tpr, lw=2, label='Class {c} (AUC={auc:.3f})'.format(c=i, auc=score))\n",
    "\n",
    "plt.legend(loc=4)\n",
    "plt.title('ROC curves for individual genre categories')\n",
    "plt.xlabel('false positive rate'); plt.ylabel('true positive rate')\n",
    "\n",
    "score = roc_auc_score(y_batch, y_pred)\n",
    "print '\\n'\n",
    "print score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}