notebook_showing_steps.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import fasttext\n",
    "import pandas as pd\n",
    "import gensim\n",
    "from gensim.test.utils import get_tmpfile\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense\n",
    "import time\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "lstm_neurons = 80\n",
    "SAVE_PROGRESSIVE = False\n",
    "\n",
    "#THE PAPER WAS TRAINED USING VECSIZE 100 AND WINDOWSIZE 2\n",
    "vecSize = 100 \n",
    "windowSize = 2\n",
    "timesteps = 100\n",
    "pretrainedModelDimms = 300\n",
    "keras_iterations = 30\n",
    "batch_size = 128\n",
    "flag_verbose = 1\n",
    "\n",
    "# DEFINE PRETRAINED MODEL\n",
    "\n",
    "#from gensim.models import KeyedVectors\n",
    "#fname=\"GoogleNews-vectors-negative300.bin\"\n",
    "fname_pretrained=\"crawl-300d-2M.vec\"\n",
    "\n",
    "if not os.path.exists(fname_pretrained):\n",
    "    !wget https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip\n",
    "    !unzip crawl-300d-2M.vec.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'c#': 1, 'sql': 9, 'javascript': 7, 'php': 10, 'r': 4, 'perl': 6, 'python': 3, 'java': 2, 'vb.net': 5, 'c++': 8}\n"
     ]
    }
   ],
   "source": [
    "df_overall = pd.read_csv(\"dataset.csv\", index_col=0)\n",
    "df_overall.head()\n",
    "\n",
    "tags = list(df_overall.tags.unique())\n",
    "ref = {}\n",
    "index=1\n",
    "for tag in tags:\n",
    "    if str(tag) == 'nan':\n",
    "        continue\n",
    "    ref[tag]=index\n",
    "    index+=1\n",
    "print(ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plow the text from the dataset into a text file, and the train a custom model on that text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Custom Model..\n",
      "Done..\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "sentences = np.array(df_overall[\"body\"]).tolist()\n",
    "\n",
    "for i in range(len(sentences)):\n",
    "    sentences[i] = str(sentences[i])\n",
    "training_string = '\\n'.join(sentences)\n",
    "\n",
    "with open(\"training_file.txt\", \"w\") as text_file:\n",
    "    text_file.write(training_string)\n",
    "\n",
    "print(\"Training Custom Model..\")\n",
    "\n",
    "# customw2v = fasttext.skipgram('training_file.txt', 'model', ws=windowSize, dim=vecSize, min_count=3)\n",
    "model_custom = fasttext.train_unsupervised('training_file.txt', model='skipgram', dim=vecSize, ws=windowSize, epoch=5)\n",
    "# customw2v = Word2Vec(sentences, size=vecSize, window=windowSize, min_count=3, workers=4)\n",
    "\n",
    "fname = get_tmpfile(\"model_custom.model\")\n",
    "model_custom.save_model(fname)\n",
    "model_custom = fasttext.load_model(fname)\n",
    "print(\"Done..\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define method to get custom vectors from custom language model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Custom Vectors Generation\n",
      "row 1000\n",
      "row 2000\n",
      "row 3000\n",
      "row 4000\n",
      "row 5000\n",
      "row 6000\n",
      "row 7000\n",
      "row 8000\n",
      "row 9000\n",
      "row 10000\n",
      "Custom Vectors Generated.\n"
     ]
    }
   ],
   "source": [
    "def sentVectorsCustom(keywordListIn):\n",
    "    global model_custom\n",
    "    sentenceVectorComponents = []\n",
    "    \n",
    "    pattern_vector = False\n",
    "    n_words = 0\n",
    "    for keyword in keywordListIn:\n",
    "        #try splitting into two words\n",
    "        if not keyword in model_custom:\n",
    "            for splitIndex in range(len(keyword)):\n",
    "                firstWord=keyword[0:splitIndex]\n",
    "                secondWord=keyword[splitIndex:]\n",
    "                if(firstWord in model_custom):\n",
    "                    vector = model_custom[firstWord]\n",
    "                    sentenceVectorComponents.append(vector)\n",
    "                    n_words+=1 #NOTE: this case messes with the ratio a bit. One word can be counted as 2 after it is unfused\n",
    "                    break\n",
    "                if(secondWord in model_custom):\n",
    "                    vector = model_custom[secondWord]\n",
    "                    sentenceVectorComponents.append(vector)\n",
    "                    n_words+=1 #NOTE: this case messes with the ratio a bit. One word can be counted as 2 after it is unfused\n",
    "                    break\n",
    "                \n",
    "        if(keyword in model_custom):\n",
    "            vector = model_custom[keyword]\n",
    "            sentenceVectorComponents.append(vector)\n",
    "            n_words += 1\n",
    "    #print(n_words,pattern_vector)\n",
    "    return sentenceVectorComponents #pattern_vector    #,n_words,\n",
    "\n",
    "def embedCustom(row):\n",
    "    global popo\n",
    "    popo += 1\n",
    "    if(popo % 1000 == 0):\n",
    "        print(\"row \" + str(popo))\n",
    "    return [sentVectorsCustom(str(row[\"body\"]).split())]\n",
    "\n",
    "print(\"Starting Custom Vectors Generation\")\n",
    "popo = 0\n",
    "df_overall['vector_custom'] = df_overall.apply(embedCustom, axis=1)\n",
    "del model_custom\n",
    "print(\"Custom Vectors Generated.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load pre-trained model and get vectors from it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Pretrained Model..\n",
      "Pretrained Model Loaded.\n",
      "Starting Pretrained Vectors Generation\n",
      "row 1000\n",
      "row 2000\n",
      "row 3000\n",
      "row 4000\n",
      "row 5000\n",
      "row 6000\n",
      "row 7000\n",
      "row 8000\n",
      "row 9000\n",
      "row 10000\n",
      "Pretrained Vectors Generated\n"
     ]
    }
   ],
   "source": [
    "print(\"Loading Pretrained Model..\")\n",
    "model_pretrained = gensim.models.KeyedVectors.load_word2vec_format(fname_pretrained, binary=False) #false for fasttext & true for googleNews\n",
    "# model_pretrained = fasttext.load_model(fname_pretrained)\n",
    "print(\"Pretrained Model Loaded.\")\n",
    "\n",
    "def avgVectors(keywordListIn):\n",
    "    global model_pretrained\n",
    "    sentenceVectorComponents = []\n",
    "    \n",
    "    n_words = 0\n",
    "    for keyword in keywordListIn:\n",
    "        \n",
    "        #try splitting into two words\n",
    "        if not keyword in model_pretrained:\n",
    "            for splitIndex in range(len(keyword)):\n",
    "                firstWord=keyword[0:splitIndex]\n",
    "                secondWord=keyword[splitIndex:]\n",
    "                #print(firstWord,secondWord)\n",
    "                if(firstWord in model_pretrained):\n",
    "                    vector = model_pretrained[firstWord]\n",
    "                    sentenceVectorComponents.append(vector)\n",
    "                    n_words+=1 #NOTE: this case messes with the ratio a bit. One word can be counted as 2 after it is unfused\n",
    "                    break\n",
    "                if(secondWord in model_pretrained):\n",
    "                    vector = model_pretrained[secondWord]\n",
    "                    sentenceVectorComponents.append(vector)\n",
    "                    n_words+=1 #NOTE: this case messes with the ratio a bit. One word can be counted as 2 after it is unfused\n",
    "                    break\n",
    "                \n",
    "        if(keyword in model_pretrained):\n",
    "            vector = model_pretrained[keyword]\n",
    "            sentenceVectorComponents.append(vector)\n",
    "            n_words += 1\n",
    "\n",
    "    #print(n_words,pattern_vector)\n",
    "    return sentenceVectorComponents #pattern_vector    #,n_words,\n",
    "\n",
    "def embed(row):\n",
    "    global popo\n",
    "    popo += 1\n",
    "    if(popo % 1000 == 0):\n",
    "        print(\"row \" + str(popo))\n",
    "    return [avgVectors(str(row[\"body\"]).split())]\n",
    "\n",
    "print(\"Starting Pretrained Vectors Generation\")\n",
    "popo = 0\n",
    "df_overall['vector'] = df_overall.apply(embed, axis=1)\n",
    "del model_pretrained\n",
    "print(\"Pretrained Vectors Generated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10001 entries, 0 to 999\n",
      "Data columns (total 5 columns):\n",
      "[LocalizedFileNames]    1 non-null float64\n",
      "body                    10000 non-null object\n",
      "tags                    10000 non-null object\n",
      "vector_custom           10001 non-null object\n",
      "vector                  10001 non-null object\n",
      "dtypes: float64(1), object(4)\n",
      "memory usage: 14.2 MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "10001"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_overall.to_pickle('df_overall_fastText_vectors.p')\n",
    "display(df_overall.info(memory_usage='deep'))\n",
    "display(len(df_overall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>[LocalizedFileNames]</th>\n",
       "      <th>body</th>\n",
       "      <th>tags</th>\n",
       "      <th>vector_custom</th>\n",
       "      <th>vector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;p&gt;How i can convert word file (.docx &amp;amp; do...</td>\n",
       "      <td>c#</td>\n",
       "      <td>[[[0.46398896, 0.16047312, -0.5810341, 0.00351...</td>\n",
       "      <td>[[[0.3435, 0.6339, -0.0938, -0.1206, -0.0436, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;p&gt;I essentially have the following:&lt;/p&gt;\\n\\n&lt;p...</td>\n",
       "      <td>c#</td>\n",
       "      <td>[[[-0.27910337, 0.9925494, -0.39375988, 0.4042...</td>\n",
       "      <td>[[[-0.7691, -0.2869, 0.0241, 0.2612, 0.17, 0.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;p&gt;I have a variable which I am populating wit...</td>\n",
       "      <td>c#</td>\n",
       "      <td>[[[-0.27910337, 0.9925494, -0.39375988, 0.4042...</td>\n",
       "      <td>[[[-0.7691, -0.2869, 0.0241, 0.2612, 0.17, 0.2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;pre&gt;&lt;code&gt;public new int AdjustedBaseValue\\n&lt;...</td>\n",
       "      <td>c#</td>\n",
       "      <td>[[[-0.39814365, 0.20550673, 0.40518367, -0.452...</td>\n",
       "      <td>[[[0.3435, 0.6339, -0.0938, -0.1206, -0.0436, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;p&gt;Sorry for this question, but I've been Goog...</td>\n",
       "      <td>c#</td>\n",
       "      <td>[[[0.2184001, 0.52190584, -0.6915303, 0.285176...</td>\n",
       "      <td>[[[0.3435, 0.6339, -0.0938, -0.1206, -0.0436, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   [LocalizedFileNames]                                               body  \\\n",
       "0                   NaN  <p>How i can convert word file (.docx &amp; do...   \n",
       "1                   NaN  <p>I essentially have the following:</p>\\n\\n<p...   \n",
       "2                   NaN  <p>I have a variable which I am populating wit...   \n",
       "3                   NaN  <pre><code>public new int AdjustedBaseValue\\n<...   \n",
       "4                   NaN  <p>Sorry for this question, but I've been Goog...   \n",
       "\n",
       "  tags                                      vector_custom  \\\n",
       "0   c#  [[[0.46398896, 0.16047312, -0.5810341, 0.00351...   \n",
       "1   c#  [[[-0.27910337, 0.9925494, -0.39375988, 0.4042...   \n",
       "2   c#  [[[-0.27910337, 0.9925494, -0.39375988, 0.4042...   \n",
       "3   c#  [[[-0.39814365, 0.20550673, 0.40518367, -0.452...   \n",
       "4   c#  [[[0.2184001, 0.52190584, -0.6915303, 0.285176...   \n",
       "\n",
       "                                              vector  \n",
       "0  [[[0.3435, 0.6339, -0.0938, -0.1206, -0.0436, ...  \n",
       "1  [[[-0.7691, -0.2869, 0.0241, 0.2612, 0.17, 0.2...  \n",
       "2  [[[-0.7691, -0.2869, 0.0241, 0.2612, 0.17, 0.2...  \n",
       "3  [[[0.3435, 0.6339, -0.0938, -0.1206, -0.0436, ...  \n",
       "4  [[[0.3435, 0.6339, -0.0938, -0.1206, -0.0436, ...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_overall = pd.read_pickle('df_overall_fastText_vectors.p')\n",
    "df_overall.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PRE-TRAINED WORD EMBEDDING X AND Y CREATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y.shape = (10001, 10)\n",
      "Xshape = (1, 26, 300)\n",
      "row 0\n",
      "xs.shape = (100, 300)\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xs.shape = (100, 300)\n",
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      "newarr[i].shape = (100, 300)\n",
      "row 3000\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "newarr[i].shape = (100, 300)\n",
      "row 4000\n",
      "xs.shape = (100, 300)\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xs.shape = (100, 300)\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "row 8000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xs.shape = (100, 300)\n",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xs.shape = (100, 300)\n",
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      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "row 10000\n",
      "xs.shape = (100, 300)\n",
      "newarr[i].shape = (100, 300)\n",
      "X.shape = (10001, 100, 300)\n"
     ]
    }
   ],
   "source": [
    "Y = pd.get_dummies(df_overall['tags']).fillna(0)\n",
    "print(\"Y.shape = \" + str(Y.shape))\n",
    "Y = np.array(Y)\n",
    "\n",
    "X = df_overall[[\"vector\"]].fillna(0)\n",
    "rows=X.shape[0]\n",
    "print(\"Xshape = \" + str(np.array(np.array(X)[0][0]).shape))\n",
    "X=np.array(X)\n",
    "\n",
    "def column(matrix, i):\n",
    "    return (row[i] for row in matrix)\n",
    "\n",
    "#X[i][2][0]\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "newarr=np.empty([rows,timesteps,pretrainedModelDimms])\n",
    "for i in range(rows):\n",
    "    if(i%1000 == 0):\n",
    "        print(\"row \" + str(i))\n",
    "    if len(X[i][0][0])==0:\n",
    "        xs=np.zeros([1,timesteps,pretrainedModelDimms])\n",
    "    else:\n",
    "        xd=np.stack(X[i][0][0], axis=0)\n",
    "        xd=xd.swapaxes(0,1)#np.swapaxes(xd,0,1)\n",
    "        xs=pad_sequences(xd, maxlen=timesteps, dtype='float32')\n",
    "        xs=np.swapaxes(xs,0,1)\n",
    "    print(\"xs.shape = \" + str(xs.shape))\n",
    "    print(\"newarr[i].shape = \" + str(newarr[i].shape))\n",
    "    newarr[i]=xs\n",
    "X=newarr\n",
    "print(\"X.shape = \" + str(X.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Xshape = 400\n"
     ]
    }
   ],
   "source": [
    "print(\"Xshape = \" + str(np.array(np.array(X)[0][0]).shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242869764"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10001, 100, 400) (10001, 10)\n"
     ]
    }
   ],
   "source": [
    "#CUSTOM WORD EMBEDDING X AND Y CREATION\n",
    "X2 = df_overall[[\"vector_custom\"]].fillna(0)\n",
    "rows=X2.shape[0]\n",
    "X2=np.array(X2)\n",
    "\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "newarr=np.empty([rows,timesteps,vecSize])\n",
    "for i in range(rows):\n",
    "    if len(X2[i][0][0])==0:\n",
    "        xs=np.zeros([1,timesteps,vecSize])\n",
    "    else:\n",
    "        xd=np.stack(X2[i][0][0], axis=0)\n",
    "        xd=np.swapaxes(xd,0,1)\n",
    "        xs=pad_sequences(xd, maxlen=vecSize, dtype='float32')\n",
    "        xs=np.swapaxes(xs,0,1)\n",
    "    newarr[i]=xs\n",
    "\n",
    "X2=newarr\n",
    "X=np.swapaxes(X,1,2)\n",
    "X2=np.swapaxes(X2,1,2)\n",
    "X = np.hstack((X,X2))\n",
    "X=np.swapaxes(X,1,2)\n",
    "\n",
    "print(X.shape,Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "323849264"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TEST/TRAIN SPLIT ON concatenated PRE-TRAINED + CUSTOM vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9500, 100, 400) (501, 100, 400) (9500, 10) (501, 10)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test=train_test_split(X, Y, test_size=0.05, random_state=42)\n",
    "\n",
    "print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "data_dim = pretrainedModelDimms + vecSize\n",
    "num_classes = len(ref.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 LSTM neurons \n",
      "Iteration: 0\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 48s 5ms/step - loss: 1.8231 - acc: 0.3281\n",
      "501/501 [==============================] - 2s 3ms/step\n",
      "SCORE: 0.465\n",
      "Iteration: 1\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 43s 5ms/step - loss: 1.3377 - acc: 0.5195\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.569\n",
      "Iteration: 2\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 40s 4ms/step - loss: 1.0967 - acc: 0.6136\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.631\n",
      "Iteration: 3\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 40s 4ms/step - loss: 0.9679 - acc: 0.6655\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.649\n",
      "Iteration: 4\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 38s 4ms/step - loss: 0.8785 - acc: 0.6934\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.645\n",
      "Iteration: 5\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 39s 4ms/step - loss: 0.8317 - acc: 0.7123\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.655\n",
      "Iteration: 6\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 40s 4ms/step - loss: 0.7921 - acc: 0.7263\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.705\n",
      "Iteration: 7\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 39s 4ms/step - loss: 0.7210 - acc: 0.7559\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.699\n",
      "Iteration: 8\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 38s 4ms/step - loss: 0.7343 - acc: 0.7522\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.709\n",
      "Iteration: 9\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 40s 4ms/step - loss: 0.6494 - acc: 0.7812\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.721\n",
      "Iteration: 10\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 40s 4ms/step - loss: 0.5920 - acc: 0.8036\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.743\n",
      "Iteration: 11\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 41s 4ms/step - loss: 0.5609 - acc: 0.8163\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.764\n",
      "Iteration: 12\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 40s 4ms/step - loss: 0.5147 - acc: 0.8325\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.752\n",
      "Iteration: 13\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 39s 4ms/step - loss: 0.4891 - acc: 0.8418\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.766\n",
      "Iteration: 14\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 41s 4ms/step - loss: 0.4717 - acc: 0.8492\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.764\n",
      "Iteration: 15\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 39s 4ms/step - loss: 0.4224 - acc: 0.8631\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.786\n",
      "Iteration: 16\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 41s 4ms/step - loss: 0.4054 - acc: 0.8706\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.776\n",
      "Iteration: 17\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 41s 4ms/step - loss: 0.3729 - acc: 0.8802\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.756\n",
      "Iteration: 18\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 42s 4ms/step - loss: 0.3303 - acc: 0.8953\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.758\n",
      "Iteration: 19\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 42s 4ms/step - loss: 0.3681 - acc: 0.8815\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.780\n",
      "Iteration: 20\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 41s 4ms/step - loss: 0.3778 - acc: 0.8796\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.756\n",
      "Iteration: 21\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 43s 5ms/step - loss: 0.3064 - acc: 0.9032\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.768\n",
      "Iteration: 22\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 41s 4ms/step - loss: 0.2936 - acc: 0.9056\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.780\n",
      "Iteration: 23\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 42s 4ms/step - loss: 0.2623 - acc: 0.9163\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.750\n",
      "Iteration: 24\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 43s 4ms/step - loss: 0.2477 - acc: 0.9225\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.760\n",
      "Iteration: 25\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 43s 5ms/step - loss: 0.2170 - acc: 0.9322\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.780\n",
      "Iteration: 26\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 42s 4ms/step - loss: 0.1918 - acc: 0.9397\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.776\n",
      "Iteration: 27\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 42s 4ms/step - loss: 0.1715 - acc: 0.9455\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.776\n",
      "Iteration: 28\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 45s 5ms/step - loss: 0.1997 - acc: 0.9373\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.745\n",
      "Iteration: 29\n",
      "Epoch 1/1\n",
      "9500/9500 [==============================] - 41s 4ms/step - loss: 0.2286 - acc: 0.9261\n",
      "501/501 [==============================] - 1s 2ms/step\n",
      "SCORE: 0.780\n",
      "Ok.\n"
     ]
    }
   ],
   "source": [
    "K.clear_session()\n",
    "print(str(lstm_neurons) + \" LSTM neurons \")\n",
    "model = Sequential()\n",
    "model.add(LSTM(lstm_neurons, return_sequences=True,\n",
    "               input_shape=(timesteps, data_dim)))  # returns a sequence of vectors of dimension 32\n",
    "model.add(LSTM(lstm_neurons, return_sequences=True))  # returns a sequence of vectors of dimension 32\n",
    "model.add(LSTM(lstm_neurons))  # return a single vector of dimension 32\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "import time\n",
    "for i in range(keras_iterations):\n",
    "    print('Iteration: '+str(i))\n",
    "    model.fit(x_train, y_train, epochs=1,  batch_size=batch_size, verbose=flag_verbose)#, class_weight=\"auto\"\n",
    "    score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=flag_verbose)\n",
    "    print(\"SCORE: {0:.3f}\".format(score[1]))\n",
    "    if i == 20 and SAVE_PROGRESSIVE:\n",
    "        model.save('model_' + str(lstm_neurons) + '_20_iter.h5')\n",
    "\n",
    "fname_sidecar='model_' + str(lstm_neurons) + '.h5'\n",
    "model.save(fname_sidecar)\n",
    "print(\"Ok.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_80.h5\n",
      "501/501 [==============================] - 1s 3ms/step\n",
      "0.78\n"
     ]
    }
   ],
   "source": [
    "K.clear_session()\n",
    "print(fname_sidecar)\n",
    "model = keras.models.load_model(fname_sidecar)\n",
    "score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=flag_verbose)\n",
    "print(np.around(score[1], decimals=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ok.\n"
     ]
    }
   ],
   "source": [
    "fname_sidecar='model_' + str(lstm_neurons) + '.h5'\n",
    "model.save(fname_sidecar)\n",
    "print(\"Ok.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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