q4PreProcessing_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's Create the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Please wait while we do some pre-processing.....\n",
      "\n",
      "\n",
      "Creating the data...\n",
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    },
    {
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from elasticsearch import Elasticsearch\n",
    "import pickle\n",
    "\n",
    "def computeTfIdf(atlas):\n",
    "    ''' This function computes the tf-idf vectors for every word \n",
    "    and returns atlas_tfidf using the dictionary \n",
    "    atlas we created in  getTfIdf()''' \n",
    "    atlas_df = pd.DataFrame.from_dict({(i,j): atlas[i][j] \n",
    "                           for i in atlas.keys() \n",
    "                           for j in atlas[i].keys()},\n",
    "                       orient='index').sort_index()\n",
    "    \n",
    "    #doc_count = 9125 movies\n",
    "    # tf-idf = term_freq * idf\n",
    "    # idf = log(doc_count / doc_freq)\n",
    "    atlas_df[\"idf\"] = np.log( 9125 / atlas_df[\"doc_freq\"])\n",
    "    atlas_df[\"tf-idf\"] = np.multiply(atlas_df[\"term_freq\"], atlas_df[\"idf\"])\n",
    "\n",
    "    atlas_df.index.names = [\"movieId\", \"word\"]\n",
    "    \n",
    "    atlas_tfidf = atlas_df.reset_index()\n",
    "    atlas_tfidf.drop([\"term_freq\",\"doc_freq\",\"idf\"],axis=1, inplace=True)\n",
    "    atlas_tfidf.movieId = atlas_tfidf.movieId.astype(\"int64\")\n",
    "    #Bu I have a memory problem when trying to One-Hot-Encode all words in above dataframe\n",
    "    #So let's try. Creating a different dataframe per user\n",
    "    #That only has the movie-word-tfidf info of the movies each user has seen\n",
    "\n",
    "    return atlas_tfidf\n",
    "\n",
    "\n",
    "def getTfIdf():\n",
    "    ''' This function returns atlas_tfidf which has all movieId \n",
    "    and corresponding tfidf with their words ''' \n",
    "    # Dictionary with (es_id : movieId) pairs, where es_id is the _id from Elasticsearch\n",
    "    ids = dict()\n",
    "    #atlas is a dict with keys = movieId and values = { words : { 'doc_freq' : doc_freq , 'term_freq' : term_freq} }\n",
    "    atlas = dict()\n",
    "    # and word_params_dict = { words : { 'doc_freq' : doc_freq , 'term_freq' : term_freq} }\n",
    "    word_params_dict = dict()\n",
    "\n",
    "    res = es.search(index=\"movies\", body={\"query\": {\"match_all\":{} }}, size = 10000)\n",
    "    total_hits = res['hits']['total']['value']\n",
    "    #here I create a dictionary with key-value pairs = (_id, movieId), where _id is from Elasticsearch\n",
    "    for hit in range(total_hits):\n",
    "        movieId = res['hits']['hits'][hit]['_source']['movieId']\n",
    "        es_id = res['hits']['hits'][hit]['_id']\n",
    "        ids[es_id] = movieId\n",
    "\n",
    "    # every doc is a distinct movie. Thus, it has it's own _id from Elasticsearch\n",
    "    # Out of every doc I need to get term_freq and doc_freq\n",
    "    for movie in es.mtermvectors(index=\"movies\",body=dict(ids=list(ids.keys()),parameters=dict(term_statistics=True,field_statistics=True,fields=[\"title\"])))['docs']:                                                                                                                                                            \n",
    "        title = movie['term_vectors'] \n",
    "        #get the movieId of the movie we are currently at\n",
    "        movieId = ids.get( movie['_id'], None ) \n",
    "        #get the words in the title of the movie we are curently at\n",
    "        words = title['title']['terms'].keys()\n",
    "        # for each word get the parameters and save them on a dictionary\n",
    "        for word in words:\n",
    "            #for each word get the term and document frequency\n",
    "            term_freq = title['title']['terms'][word]['term_freq']\n",
    "            doc_freq = title['title']['terms'][word]['doc_freq']\n",
    "            word_params_dict[word] = {'term_freq': term_freq, 'doc_freq' : doc_freq}\n",
    "        # save said dictionary on the correct key of the atlas dictionary\n",
    "        atlas[movieId] = word_params_dict\n",
    "        #reset the dictionary\n",
    "        word_params_dict = dict()\n",
    "    atlas_tfidf = computeTfIdf(atlas)\n",
    "    return atlas_tfidf\n",
    "\n",
    "\n",
    "def createDataFrames(movies_dummy_genre, tfidf_df, movie_rating_df_pu):\n",
    "    '''This function creates the training and prediction data for each user and saves the to their \n",
    "    corresponding pickle files'''\n",
    "    print(\"\\nCreating the data...\")\n",
    "    training_data = dict()\n",
    "    prediction_data = dict()\n",
    "    #create it\n",
    "    tfidf_df_pivoted = tfidf_df.pivot(index='movieId', columns='word', values='tf-idf')\n",
    "    tfidf_df_pivoted = tfidf_df_pivoted.astype(np.float16)\n",
    "    #compute row avg\n",
    "    mean_of_words = tfidf_df_pivoted.mean()\n",
    "    tfidf_df_pivoted_noNaN = tfidf_df_pivoted.fillna(mean_of_words)\n",
    "    #add genre data\n",
    "    tfidf_df_pivoted_noNaN = tfidf_df_pivoted_noNaN.merge(movies_dummy_genre, on='movieId', how='left')\n",
    "\n",
    "    for userId in list(movie_rating_df_pu['userId'].unique()):\n",
    "        print(\"\\ncurrently on user : \", userId)\n",
    "        user_data = tfidf_df_pivoted_noNaN.merge(movie_rating_df_pu[movie_rating_df_pu['userId'] == userId],on='movieId', how = 'outer')\n",
    "\n",
    "        # I do not drop any words for now because it would mess up the model structure and I have to make sure that\n",
    "        # training and testing and prediction data all have the same columns\n",
    "        #create prediction data\n",
    "        user_prediction_data = user_data[user_data['rating'].isnull()]\n",
    "        user_prediction_data.drop('userId',axis=1,inplace=True)\n",
    "        #create training data\n",
    "        user_training_data = user_data[~user_data['rating'].isnull()]\n",
    "        user_training_data.drop('userId',axis=1,inplace=True)\n",
    "        #set the index\n",
    "        user_training_data.set_index('movieId', inplace=True)\n",
    "        user_prediction_data.set_index('movieId', inplace=True)\n",
    "        #dump the data on the correct folder\n",
    "        \n",
    "        pickle.dump(user_training_data, open(\"./data/training/user_{}_training_data.p\".format(userId), \"wb\"))\n",
    "        pickle.dump(user_prediction_data, open(\"./data/prediction/user_{}_prediction_data.p\".format(userId), \"wb\"))\n",
    "    return \n",
    "\n",
    "\n",
    "\n",
    "###################################################################################################################################\n",
    "# start Elasticsearch server\n",
    "es = Elasticsearch()\n",
    "print('\\nPlease wait while we do some pre-processing.....\\n')\n",
    "rating_df = pd.read_csv('./data/ratings.csv')  \n",
    "movies_df = pd.read_csv('./data/movies.csv')\n",
    "#one hot encoding movie genres\n",
    "temp = movies_df['genres'].str.get_dummies()\n",
    "temp.columns = ['Genre_' + str(col) for col in temp.columns ]\n",
    "# movies_dummy_genre has dummy variables for all genres and also has movieId, title\n",
    "movies_dummy_genre = movies_df.merge(temp, left_index=True, right_index=True)\n",
    "movies_dummy_genre.drop(['title','genres'],axis=1,inplace=True)\n",
    "tfidf_df = getTfIdf()\n",
    "\n",
    "#pu = per user\n",
    "movie_rating_df_pu = rating_df[['userId','movieId','rating']]\n",
    "# At this point I have all the info per movie I need so let's create the training dataframe per user\n",
    "createDataFrames(movies_dummy_genre, tfidf_df, movie_rating_df_pu)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now let's Train the Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics \n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_training_data = pickle.load( open(\"./data/training/user_1_training_data.p\", \"rb\")) \n",
    "user_prediction_data = pickle.load( open(\"./data/prediction/user_1_prediction_data.p\", \"rb\")) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "currently on user :  637\n",
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     ]
    }
   ],
   "source": [
    "for userId in list(movie_rating_df_pu['userId'].unique()):\n",
    "    print(\"\\ncurrently on user : \", userId)\n",
    "    user_training_data = pickle.load( open(\"./data/training/user_{}_training_data.p\".format(userId), \"rb\")) \n",
    "    user_prediction_data = pickle.load( open(\"./data/prediction/user_{}_prediction_data.p\".format(userId), \"rb\")) \n",
    "    X = user_training_data.loc[:, user_training_data.columns != 'rating']\n",
    "    y = user_training_data['rating']\n",
    "    #encoding y_train ( because I had some errors : ValueError: Unknown label type: 'continuous' )\n",
    "    lab_enc = LabelEncoder()\n",
    "    X_train = X\n",
    "    y_train = y.ravel()\n",
    "    y_train = lab_enc.fit_transform(y_train)\n",
    "\n",
    "    #Create a RandomForestClassifier\n",
    "    clf = RandomForestClassifier(n_estimators=100, random_state = 42)\n",
    "    #Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict(user_prediction_data.loc[:, user_prediction_data.columns != 'rating'])\n",
    "    user_prediction_data['rating'] = y_pred\n",
    "    user_predicted_data = user_prediction_data.append(user_training_data).reset_index()\n",
    "    user_predicted_data = user_predicted_data[['movieId', 'rating']]\n",
    "    pickle.dump(user_predicted_data, open(\"./data/predicted_result/user_{}_predicted_data.p\".format(userId), \"wb\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Below is just a test for a single file ( was user 1 when run)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.75\n"
     ]
    }
   ],
   "source": [
    "X = user_training_data.loc[:, user_training_data.columns != 'rating']\n",
    "y = user_training_data['rating']\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=20) # 70% training and 30% test\n",
    "#encoding y_train ( because I had some errors : ValueError: Unknown label type: 'continuous' )\n",
    "lab_enc = LabelEncoder()\n",
    "y_train = lab_enc.fit_transform(y_train)\n",
    "y_test = lab_enc.fit_transform(y_test)\n",
    " \n",
    "#Create a Gaussian Classifier\n",
    "clf = RandomForestClassifier(n_estimators=100, random_state = 42)\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "clf.fit(X_train,y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "if metrics.accuracy_score(y_test, y_pred) < 0.51 :\n",
    "    print(\"Horrible\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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