preprocess_recsys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ayusham/.conda/envs/poincare/lib/python3.6/site-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.25.10) or chardet (3.0.4) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/ayusham/.local/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "I1122 13:26:47.298351 139956583294784 utils_rec.py:213] Downloading raw data\n",
      "I1122 13:26:47.299415 139956583294784 utils_rec.py:217] Processing book data\n",
      "I1122 13:27:02.716694 139956583294784 utils_rec.py:219] Processing interaction data\n",
      "/home/ayusham/Semi_Supervised_LFs/utils_rec.py:223: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df_interactions_nz[\"rating_4_5\"] = df_interactions_nz.rating.map(ratings_map)\n",
      "I1122 13:29:01.853140 139956583294784 utils_rec.py:224] Processing review data\n",
      "I1122 13:29:46.784573 139956583294784 utils_rec.py:226] Joining interaction data\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import os\n",
    "\n",
    "logging.basicConfig(level=logging.INFO)\n",
    "\n",
    "\n",
    "if os.path.basename(os.getcwd()) == \"snorkel-tutorials\":\n",
    "    os.chdir(\"recsys\")\n",
    "\n",
    "\n",
    "# %% [markdown]\n",
    "# ## Loading Data\n",
    "\n",
    "# %% [markdown]\n",
    "# We start by running the `download_and_process_data` function.\n",
    "# The function returns the `df_train`, `df_test`, `df_dev`, `df_valid` dataframes, which correspond to our training, test, development, and validation sets.\n",
    "# Each of those dataframes has the following fields:\n",
    "# * `user_idx`: A unique identifier for a user.\n",
    "# * `book_idx`: A unique identifier for a book that is being rated by the user.\n",
    "# * `book_idxs`: The set of books that the user has interacted with (read or planned to read).\n",
    "# * `review_text`: Optional text review written by the user for the book.\n",
    "# * `rating`: Either `0` (which means the user did not read or did not like the book) or `1` (which means the user read and liked the book). The `rating` field is missing for `df_train`.\n",
    "# Our objective is to predict whether a given user (represented by the set of book_idxs the user has interacted with) will read and like any given book.\n",
    "# That is, we want to train a model that takes a set of `book_idxs` (the user) and a single `book_idx` (the book to rate) and predicts the `rating`.\n",
    "#\n",
    "# In addition, `download_and_process_data` also returns the `df_books` dataframe, which contains one row per book, along with metadata for that book (such as `title` and `first_author`).\n",
    "\n",
    "# %% {\"tags\": [\"md-exclude-output\"]}\n",
    "from utils_rec import download_and_process_data\n",
    "\n",
    "(df_train, df_test, df_dev, df_valid), df_books = download_and_process_data()\n",
    "\n",
    "df_books.head()\n",
    "\n",
    "# %% [markdown]\n",
    "# We look at a sample of the labeled development set.\n",
    "# As an example, we want our final recommendations model to be able to predict that a user who has interacted with `book_idxs` (25743, 22318, 7662, 6857, 83, 14495, 30664, ...) would either not read or not like the book with `book_idx` 22764 (first row), while a user who has interacted with `book_idxs` (3880, 18078, 9092, 29933, 1511, 8560, ...) would read and like the book with `book_idx` 3181 (second row).\n",
    "\n",
    "# %%\n",
    "df_dev.sample(frac=1, random_state=12).head()\n",
    "\n",
    "# %% [markdown]\n",
    "# ## Writing Labeling Functions\n",
    "\n",
    "# %%\n",
    "POSITIVE = 1\n",
    "NEGATIVE = 0\n",
    "ABSTAIN = -1\n",
    "\n",
    "# %% [markdown]\n",
    "# If a user has interacted with several books written by an author, there is a good chance that the user will read and like other books by the same author.\n",
    "# We express this as a labeling function, using the `first_author` field in the `df_books` dataframe.\n",
    "# We picked the threshold 15 by plotting histograms and running error analysis using the dev set.\n",
    "\n",
    "# %%\n",
    "from snorkel.labeling.lf import labeling_function\n",
    "\n",
    "book_to_first_author = dict(zip(df_books.book_idx, df_books.first_author))\n",
    "first_author_to_books_df = df_books.groupby(\"first_author\")[[\"book_idx\"]].agg(set)\n",
    "first_author_to_books = dict(\n",
    "    zip(first_author_to_books_df.index, first_author_to_books_df.book_idx)\n",
    ")\n",
    "\n",
    "\n",
    "@labeling_function(\n",
    "    resources=dict(\n",
    "        book_to_first_author=book_to_first_author,\n",
    "        first_author_to_books=first_author_to_books,\n",
    "    )\n",
    ")\n",
    "def shared_first_author(x, book_to_first_author, first_author_to_books):\n",
    "    author = book_to_first_author[x.book_idx]\n",
    "    same_author_books = first_author_to_books[author]\n",
    "    num_read = len(set(x.book_idxs).intersection(same_author_books))\n",
    "    return POSITIVE if num_read > 15 else ABSTAIN\n",
    "\n",
    "\n",
    "# %% [markdown]\n",
    "# We can also leverage the long text reviews written by users to guess whether they liked or disliked a book.\n",
    "# For example, the third `df_dev` entry above has a review with the text `'4.5 STARS'`, which indicates that the user liked the book.\n",
    "# We write a simple LF that looks for similar phrases to guess the user's rating of a book.\n",
    "# We interpret >= 4 stars to indicate a positive rating, while < 4 stars is negative.\n",
    "\n",
    "# %%\n",
    "low_rating_strs = [\n",
    "    \"one star\",\n",
    "    \"1 star\",\n",
    "    \"two star\",\n",
    "    \"2 star\",\n",
    "    \"3 star\",\n",
    "    \"three star\",\n",
    "    \"3.5 star\",\n",
    "    \"2.5 star\",\n",
    "    \"1 out of 5\",\n",
    "    \"2 out of 5\",\n",
    "    \"3 out of 5\",\n",
    "]\n",
    "high_rating_strs = [\"5 stars\", \"five stars\", \"four stars\", \"4 stars\", \"4.5 stars\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 795499/795499 [13:51<00:00, 957.10it/s]  \n",
      "100%|██████████| 7849/7849 [00:07<00:00, 1020.67it/s]\n",
      "100%|██████████| 7819/7819 [00:07<00:00, 1035.92it/s]\n",
      "100%|██████████| 44560/44560 [00:52<00:00, 856.92it/s] \n"
     ]
    }
   ],
   "source": [
    "@labeling_function(\n",
    "    resources=dict(low_rating_strs=low_rating_strs)\n",
    ")\n",
    "def low_stars_in_review(x, low_rating_strs):\n",
    "    if not isinstance(x.review_text, str):\n",
    "        return ABSTAIN\n",
    "    for low_rating_str in low_rating_strs:\n",
    "        if low_rating_str in x.review_text.lower():\n",
    "            return NEGATIVE\n",
    "    return ABSTAIN\n",
    "\n",
    "@labeling_function(\n",
    "    resources=dict( high_rating_strs=high_rating_strs)\n",
    ")\n",
    "def high_stars_in_review(x, high_rating_strs):\n",
    "    if not isinstance(x.review_text, str):\n",
    "        return ABSTAIN\n",
    "    for high_rating_str in high_rating_strs:\n",
    "        if high_rating_str in x.review_text.lower():\n",
    "            return POSITIVE\n",
    "    return ABSTAIN\n",
    "\n",
    "\n",
    "# %% [markdown]\n",
    "# We can also run [TextBlob](https://textblob.readthedocs.io/en/dev/index.html), a tool that provides a pretrained sentiment analyzer, on the reviews, and use its polarity and subjectivity scores to estimate the user's rating for the book.\n",
    "# As usual, these thresholds were picked by analyzing the score distributions and running error analysis.\n",
    "\n",
    "# %%\n",
    "from snorkel.preprocess import preprocessor\n",
    "from textblob import TextBlob\n",
    "\n",
    "\n",
    "@preprocessor(memoize=True)\n",
    "def textblob_polarity(x):\n",
    "    if isinstance(x.review_text, str):\n",
    "        x.blob = TextBlob(x.review_text)\n",
    "    else:\n",
    "        x.blob = None\n",
    "    return x\n",
    "\n",
    "\n",
    "# Label high polarity reviews as positive.\n",
    "@labeling_function(pre=[textblob_polarity])\n",
    "def polarity_positive(x):\n",
    "    if x.blob:\n",
    "        if x.blob.polarity > 0.3:\n",
    "            return POSITIVE\n",
    "    return ABSTAIN\n",
    "\n",
    "\n",
    "# Label high subjectivity reviews as positive.\n",
    "@labeling_function(pre=[textblob_polarity])\n",
    "def subjectivity_positive(x):\n",
    "    if x.blob:\n",
    "        if x.blob.subjectivity > 0.75:\n",
    "            return POSITIVE\n",
    "    return ABSTAIN\n",
    "\n",
    "\n",
    "# Label low polarity reviews as negative.\n",
    "@labeling_function(pre=[textblob_polarity])\n",
    "def polarity_negative(x):\n",
    "    if x.blob:\n",
    "        if x.blob.polarity < 0.0:\n",
    "            return NEGATIVE\n",
    "    return ABSTAIN\n",
    "\n",
    "\n",
    "# %% {\"tags\": [\"md-exclude-output\"]}\n",
    "from snorkel.labeling import PandasLFApplier, LFAnalysis\n",
    "\n",
    "lfs = [\n",
    "    low_stars_in_review,\n",
    "    high_stars_in_review,\n",
    "    shared_first_author,\n",
    "    polarity_positive,\n",
    "    subjectivity_positive,\n",
    "    polarity_negative,\n",
    "]\n",
    "\n",
    "applier = PandasLFApplier(lfs)\n",
    "#L_dev = applier.apply(df_dev)\n",
    "L_train = applier.apply(df=df_train)\n",
    "L_dev = applier.apply(df=df_dev)\n",
    "L_valid = applier.apply(df=df_valid)\n",
    "L_test = applier.apply(df=df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((795499, 6), (795499, 6), (7849, 6), (7819, 6), (44560, 6))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_train.shape, L_train.shape, L_dev.shape, L_valid.shape, L_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 109,  111,  111, ..., 7846, 7847, 7848]),\n",
       " array([4, 1, 3, ..., 2, 2, 2]))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.where(L_dev==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -1, -1, -1,  1, -1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_dev[109]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% [markdown]\n",
    "# ### Applying labeling functions to the training set\n",
    "#\n",
    "# We apply the labeling functions to the training set, and then filter out data points unlabeled by any LF to form our final training set.\n",
    "\n",
    "# %% {\"tags\": [\"md-exclude-output\"]}\n",
    "from snorkel.labeling.model import LabelModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0901 00:43:16.650498 139997562042176 label_model.py:852] Computing O...\n",
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      "I0901 00:43:26.587674 139997562042176 logger.py:79] [4740 epochs]: TRAIN:[loss=0.000]\n",
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      "I0901 00:43:26.704190 139997562042176 logger.py:79] [4800 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:26.745422 139997562042176 logger.py:79] [4820 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:26.789527 139997562042176 logger.py:79] [4840 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:26.830900 139997562042176 logger.py:79] [4860 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:26.872744 139997562042176 logger.py:79] [4880 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:26.914206 139997562042176 logger.py:79] [4900 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:26.954806 139997562042176 logger.py:79] [4920 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:26.999433 139997562042176 logger.py:79] [4940 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:27.039019 139997562042176 logger.py:79] [4960 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:27.078399 139997562042176 logger.py:79] [4980 epochs]: TRAIN:[loss=0.000]\n",
      "I0901 00:43:27.119831 139997562042176 label_model.py:915] Finished Training\n",
      "/home/ayusham/.conda/envs/poincare/lib/python3.6/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "\n",
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    {
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     ]
    }
   ],
   "source": [
    "# L_train = applier.apply(df_train)\n",
    "label_model = LabelModel(cardinality=2, verbose=True)\n",
    "label_model.fit(L_train, n_epochs=5000, seed=123, log_freq=20, lr=0.01)\n",
    "preds_train = label_model.predict(L_train)\n",
    "\n",
    "from snorkel.labeling import filter_unlabeled_dataframe\n",
    "\n",
    "df_train_filtered, preds_train_filtered = filter_unlabeled_dataframe(\n",
    "    df_train, preds_train, L_train\n",
    ")\n",
    "df_train_filtered[\"rating\"] = preds_train_filtered\n",
    "L_train_filtered = applier.apply(df_train_filtered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=\"./Data/rec\"\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction import DictVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ayusham/.conda/envs/poincare/lib/python3.6/site-packages/pandas/core/generic.py:5208: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "# df_train_filtered.review_text = df_train_filtered.review_text.fillna('no text')\n",
    "# df_dev.review_text = df_dev.review_text.fillna('no text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_valid.review_text = df_valid.review_text.fillna('no text')\n",
    "# df_test.review_text = df_test.review_text.fillna('no text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer.transform(df_train_filtered[0:10].book_idxs)\n",
    "vectorizer = CountVectorizer()#ngram_range=(1, 2),max_features=10000)\n",
    "# vectorizer = DictVectorizer()\n",
    "X_train = vectorizer.fit_transform(df_train_filtered.book_idxs)\n",
    "X_dev = vectorizer.transform(df_dev.book_idxs).toarray()\n",
    "X_valid = vectorizer.transform(df_valid.book_idxs.tolist())\n",
    "X_test = vectorizer.transform(df_test.book_idxs.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#============ our changes ==================#\n",
    "def lsnork_to_l_m(lsnork, num_classes):\n",
    "\tm = 1 - np.equal(lsnork,-1).astype(int)\n",
    "\tl = m*lsnork + (1-m)*num_classes\n",
    "\treturn l,m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_features(df):\n",
    "    t = df.book_idxs.values\n",
    "    u = 200#[len(i) for i in t]\n",
    "    v = [np.pad(i,(0,u-len(i)),'constant') for i in t]\n",
    "    return np.asarray(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(795499, 6)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1, -1, -1, -1, -1, -1],\n",
       "       [-1, -1, -1, -1, -1, -1],\n",
       "       [-1, -1, -1, -1, -1, -1],\n",
       "       ...,\n",
       "       [-1, -1,  1, -1, -1, -1],\n",
       "       [-1, -1,  1, -1, -1, -1],\n",
       "       [-1, -1,  1, -1, -1, -1]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_l1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.equal(L_dev[0],-1).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#============ our changes ==================#\n",
    "def lsnork_to_l_m(lsnork, num_classes):\n",
    "\tm = 1 - np.equal(lsnork,0).astype(int)\n",
    "\tl = m*lsnork + (1-m)*num_classes\n",
    "\treturn l,m\n",
    "d_l1, d_m1 = lsnork_to_l_m(L_dev,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_l, d_m = lsnork_to_l_m(L_dev,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_x = get_features(df_dev)\n",
    "# d_x = df_dev.book_idxs.values#.toarray()\n",
    "d_L = df_dev.rating.values\n",
    "d_l = L_dev\n",
    "\n",
    "d_l, d_m = lsnork_to_l_m(d_l,2)\n",
    "d_d = np.array([1.0] * len(d_x))\n",
    "d_r = np.zeros(d_l.shape) #rule exemplar coupling unavailable\n",
    "\n",
    "\n",
    "    with open(path+\"/\"+\"d_processed.p\",\"wb\") as f:\n",
    "        pickle.dump(d_x,f)\n",
    "        pickle.dump(d_l,f)\n",
    "        pickle.dump(d_m,f)\n",
    "        pickle.dump(d_L,f)\n",
    "        pickle.dump(d_d,f)\n",
    "        pickle.dump(d_r,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# U_x = X_train.toarray()\n",
    "U_x = get_features(df_train_filtered)# toarray()\n",
    "U_L = df_train_filtered.rating.values\n",
    "U_l = L_train_filtered\n",
    "U_l, U_m = lsnork_to_l_m(U_l,2)\n",
    "U_d = np.array([0.0] * len(U_x))\n",
    "U_r = np.zeros(U_l.shape)\n",
    "\n",
    "with open(path+\"/\"+\"U_processed.p\",\"wb\") as f:\n",
    "    pickle.dump(U_x,f)\n",
    "    pickle.dump(U_l,f)\n",
    "    pickle.dump(U_m,f)\n",
    "    pickle.dump(U_L,f)\n",
    "    pickle.dump(U_d,f)\n",
    "    pickle.dump(U_r,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_features' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-6d476c6eafc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvalid_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_valid\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      2\u001b[0m \u001b[0mvalid_L\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_valid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrating\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mvalid_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mL_valid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mvalid_l\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid_m\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlsnork_to_l_m\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_l\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mvalid_d\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid_x\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;31mNameError\u001b[0m: name 'get_features' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "valid_x = get_features(df_valid)\n",
    "valid_L = df_valid.rating.values\n",
    "valid_l = L_valid\n",
    "valid_l, valid_m = lsnork_to_l_m(valid_l,2)\n",
    "valid_d = np.array([0.0] * len(valid_x))\n",
    "valid_r = np.zeros(valid_l.shape) #rule exemplar coupling unavailable\n",
    "with open(path+\"/\"+\"validation_processed.p\",\"wb\") as f:\n",
    "\tpickle.dump(valid_x,f)\n",
    "\tpickle.dump(valid_l,f)\n",
    "\tpickle.dump(valid_m,f)\n",
    "\tpickle.dump(valid_L,f)\n",
    "\tpickle.dump(valid_d,f)\n",
    "\tpickle.dump(valid_r,f)\n",
    "\n",
    "\n",
    "test_x = get_features(df_test)\n",
    "test_L = df_test.rating.values\n",
    "test_l = L_test\n",
    "test_l, test_m = lsnork_to_l_m(test_l,2)\n",
    "test_d = np.array([0.0] * len(test_x))\n",
    "test_r = np.zeros(test_l.shape) #rule exemplar coupling unavailable\n",
    "with open(path+\"/\"+\"test_processed.p\",\"wb\") as f:\n",
    "\tpickle.dump(test_x,f)\n",
    "\tpickle.dump(test_l,f)\n",
    "\tpickle.dump(test_m,f)\n",
    "\tpickle.dump(test_L,f)\n",
    "\tpickle.dump(test_d,f)\n",
    "\tpickle.dump(test_r,f)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "exit()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'LFAnalysis' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-c14064d8d4bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mLFAnalysis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mL_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlfs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlf_summary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_dev\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrating\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'LFAnalysis' is not defined"
     ]
    }
   ],
   "source": [
    "LFAnalysis(L_dev, lfs).lf_summary(df_dev.rating.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% [markdown]\n",
    "# ### Applying labeling functions to the training set\n",
    "#np.where(d_m==1)\n",
    "# We apply the labeling functions to the training set, and then filter out data points unlabeled by any LF to form our final training set.\n",
    "\n",
    "# %% {\"t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %% [markdown]\n",
    "# ### Applying labeling functions to the training set\n",
    "#\n",
    "# We apply the labeling functions to the training set, and then filter out data points unlabeled by any LF to form our final training set.\n",
    "\n",
    "# %% {\"tags\": [\"md-exclude-output\"]}\n",
    "from snorkel.labeling.model import LabelModel\n",
    "\n",
    "L_train = applier.apply(df_train)\n",
    "label_model = LabelModel(cardinality=2, verbose=True)\n",
    "label_model.fit(L_train, n_epochs=5000, seed=123, log_freq=20, lr=0.01)\n",
    "preds_train = label_model.predict(L_train)\n",
    "\n",
    "from snorkel.labeling import filter_unlabeled_dataframe\n",
    "\n",
    "df_train_filtered, preds_train_filtered = filter_unlabeled_dataframe(\n",
    "    df_train, preds_train, L_train\n",
    ")\n",
    "df_train_filtered[\"rating\"] = preds_train_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df_train_filtered' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-1b60e25ff6bb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_train_filtered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'df_train_filtered' is not defined"
     ]
    }
   ],
   "source": [
    "df_tr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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