dim_reduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "o-1z4fRpobGa",
    "outputId": "858ef526-e6e6-44aa-ab4f-1494a14de579"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /Users/tyler/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "YEE1fP0Qu2rK",
    "outputId": "81108150-0a12-4302-fd61-b4202bb30e34"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>toxic</th>\n",
       "      <th>severe_toxic</th>\n",
       "      <th>obscene</th>\n",
       "      <th>threat</th>\n",
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       "      <td>You should be ashamed of yourself \\n\\nThat is ...</td>\n",
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       "      <td>Spitzer \\n\\nUmm, theres no actual article for ...</td>\n",
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      ],
      "text/plain": [
       "                      id                                       comment_text  \\\n",
       "0       0000997932d777bf  Explanation\\nWhy the edits made under my usern...   \n",
       "1       000103f0d9cfb60f  D'aww! He matches this background colour I'm s...   \n",
       "2       000113f07ec002fd  Hey man, I'm really not trying to edit war. It...   \n",
       "3       0001b41b1c6bb37e  \"\\nMore\\nI can't make any real suggestions on ...   \n",
       "4       0001d958c54c6e35  You, sir, are my hero. Any chance you remember...   \n",
       "...                  ...                                                ...   \n",
       "159566  ffe987279560d7ff  \":::::And for the second time of asking, when ...   \n",
       "159567  ffea4adeee384e90  You should be ashamed of yourself \\n\\nThat is ...   \n",
       "159568  ffee36eab5c267c9  Spitzer \\n\\nUmm, theres no actual article for ...   \n",
       "159569  fff125370e4aaaf3  And it looks like it was actually you who put ...   \n",
       "159570  fff46fc426af1f9a  \"\\nAnd ... I really don't think you understand...   \n",
       "\n",
       "        toxic  severe_toxic  obscene  threat  insult  identity_hate  \n",
       "0           0             0        0       0       0              0  \n",
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       "2           0             0        0       0       0              0  \n",
       "3           0             0        0       0       0              0  \n",
       "4           0             0        0       0       0              0  \n",
       "...       ...           ...      ...     ...     ...            ...  \n",
       "159566      0             0        0       0       0              0  \n",
       "159567      0             0        0       0       0              0  \n",
       "159568      0             0        0       0       0              0  \n",
       "159569      0             0        0       0       0              0  \n",
       "159570      0             0        0       0       0              0  \n",
       "\n",
       "[159571 rows x 8 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stops = stopwords.words('english')\n",
    "countVec = CountVectorizer(stop_words=stops, lowercase=True)\n",
    "tfidfVec = TfidfVectorizer(stop_words=stops, lowercase=True)\n",
    "\n",
    "df = pd.read_csv('train.csv')\n",
    "display(df)\n",
    "\n",
    "x = df.comment_text\n",
    "countX = countVec.fit_transform(x)\n",
    "tfidfX = tfidfVec.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "colab_type": "code",
    "id": "vH_kjQwfu5MF",
    "outputId": "d7e43bae-beb1-4c21-b30c-372a922d6f1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{25: 0.48807839407077636, 50: 0.635050806586107, 75: 0.711902858146491, 100: 0.7582709223918476, 125: 0.7867228302539461, 150: 0.8072790043268541, 175: 0.8212795147737014, 200: 0.8323784672677401, 225: 0.8410582762327463, 250: 0.8480277685604551, 275: 0.8539428300824223, 300: 0.8591483280383536, 325: 0.8637267023686883, 350: 0.8678510642604479, 375: 0.8715771802105259, 400: 0.8750028743039472, 425: 0.8781195887406992, 450: 0.8810461872187334, 475: 0.8837820693622329}\n"
     ]
    },
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_components = list(range(25,500,25))\n",
    "retained_variance_ratios = []\n",
    "for i in n_components:\n",
    "  TSVD = TruncatedSVD(n_components=i)\n",
    "  principalComponents = TSVD.fit_transform(countX)\n",
    "  retained_variance_ratio = np.sum(TSVD.explained_variance_ratio_)\n",
    "  retained_variance_ratios.append(retained_variance_ratio)\n",
    "\n",
    "print(dict(zip(n_components,retained_variance_ratios)))\n",
    "plt.plot(n_components, retained_variance_ratios, 'bx-') \n",
    "plt.xlabel('# Principal Components') \n",
    "plt.ylabel('Retained Variance Ratio') \n",
    "plt.title('Retained Variance per Principal Component using Counts') \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332
    },
    "colab_type": "code",
    "id": "f_By-1JpzLKT",
    "outputId": "58588bc3-87ac-4e4a-f517-4ba19c4fd3c0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{25: 0.052328035234170865, 50: 0.07788518992016878, 75: 0.09806223722370218, 100: 0.11573259761405186, 125: 0.1315135360267699, 150: 0.14575027807137794, 175: 0.15882137524853307, 200: 0.1710056606646177, 225: 0.18221646194868682, 250: 0.1928711726730826, 275: 0.20282441906115245, 300: 0.21214371388944742, 325: 0.22105897816909323, 350: 0.2294857973939744, 375: 0.2375935898091864, 400: 0.24537738123807593, 425: 0.25283032253160664, 450: 0.2600326453377644, 475: 0.26696161042063304}\n"
     ]
    },
    {
     "data": {
      "image/png": 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Qrh2MHh1GavUE4VxmuVxJzAe+ljSQMDQHAGZ2ad6icm4FdOoUekyn6g9GjICT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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_components = list(range(25,500,25))\n",
    "retained_variance_ratios = []\n",
    "for i in n_components:\n",
    "  TSVD = TruncatedSVD(n_components=i)\n",
    "  principalComponents = TSVD.fit_transform(tfidfX)\n",
    "  retained_variance_ratio = np.sum(TSVD.explained_variance_ratio_)\n",
    "  retained_variance_ratios.append(retained_variance_ratio)\n",
    "\n",
    "print(dict(zip(n_components,retained_variance_ratios)))\n",
    "plt.plot(n_components, retained_variance_ratios, 'bx-') \n",
    "plt.xlabel('# Principal Components') \n",
    "plt.ylabel('Retained Variance Ratio') \n",
    "plt.title('Retained Variance per Principal Component using TFIDF') \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tkHMx71rf7LU"
   },
   "outputs": [],
   "source": [
    "TSVD = TruncatedSVD(n_components=400)\n",
    "principalComponents = TSVD.fit_transform(countX)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IGWIgclRf8GF"
   },
   "outputs": [],
   "source": [
    "f=open('dim_reduced_count_vector.pickle','wb')\n",
    "pickle.dump(principalComponents, f)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "dim_reduction.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}