01-TextClassification_with_sklearn_spaCy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Classification\n",
    "### A Getting Started Tutorial for Self Learners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  # numpy package is for fast numerical computation in Python\n",
    "import spacy\n",
    "from sklearn.datasets import fetch_20newsgroups  # import \\\\n    packages which help us download dataset and load intp python\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the filenames and data from the 20 newsgroups dataset\n",
    "# This will download the datasets if you don't have them already\n",
    "twenty_train = fetch_20newsgroups(\n",
    "    subset=\"train\", shuffle=True, download_if_missing=True\n",
    ")\n",
    "twenty_test = fetch_20newsgroups(subset=\"test\", shuffle=True, download_if_missing=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"From: lerxst@wam.umd.edu (where's my thing)\\nSubject: WHAT car is this!?\\nNntp-Posting-Host: rac3.wam.umd.edu\\nOrganization: University of Maryland, College Park\\nLines: 15\\n\\n I was wondering if anyone out there could enlighten me on this car I saw\\nthe other day. It was a 2-door sports car, looked to be from the late 60s/\\nearly 70s. It was called a Bricklin. The doors were really small. In addition,\\nthe front bumper was separate from the rest of the body. This is \\nall I know. If anyone can tellme a model name, engine specs, years\\nof production, where this car is made, history, or whatever info you\\nhave on this funky looking car, please e-mail.\\n\\nThanks,\\n- IL\\n   ---- brought to you by your neighborhood Lerxst ----\\n\\n\\n\\n\\n\",\n",
       " \"From: guykuo@carson.u.washington.edu (Guy Kuo)\\nSubject: SI Clock Poll - Final Call\\nSummary: Final call for SI clock reports\\nKeywords: SI,acceleration,clock,upgrade\\nArticle-I.D.: shelley.1qvfo9INNc3s\\nOrganization: University of Washington\\nLines: 11\\nNNTP-Posting-Host: carson.u.washington.edu\\n\\nA fair number of brave souls who upgraded their SI clock oscillator have\\nshared their experiences for this poll. Please send a brief message detailing\\nyour experiences with the procedure. Top speed attained, CPU rated speed,\\nadd on cards and adapters, heat sinks, hour of usage per day, floppy disk\\nfunctionality with 800 and 1.4 m floppies are especially requested.\\n\\nI will be summarizing in the next two days, so please add to the network\\nknowledge base if you have done the clock upgrade and haven't answered this\\npoll. Thanks.\\n\\nGuy Kuo <guykuo@u.washington.edu>\\n\"]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is this dataset about?\n",
    "twenty_train.data[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at what are all the classes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.os.ms-windows.misc',\n",
       " 'comp.sys.ibm.pc.hardware',\n",
       " 'comp.sys.mac.hardware',\n",
       " 'comp.windows.x',\n",
       " 'misc.forsale',\n",
       " 'rec.autos',\n",
       " 'rec.motorcycles',\n",
       " 'rec.sport.baseball',\n",
       " 'rec.sport.hockey',\n",
       " 'sci.crypt',\n",
       " 'sci.electronics',\n",
       " 'sci.med',\n",
       " 'sci.space',\n",
       " 'soc.religion.christian',\n",
       " 'talk.politics.guns',\n",
       " 'talk.politics.mideast',\n",
       " 'talk.politics.misc',\n",
       " 'talk.religion.misc']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.target_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why do we need to convert text to numbers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Term Frequency Matrix: (11314, 130107)\n"
     ]
    }
   ],
   "source": [
    "# Extracting features from text files\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "count_vect = CountVectorizer()\n",
    "X_train_counts = count_vect.fit_transform(twenty_train.data)\n",
    "print(f\"Shape of Term Frequency Matrix: {X_train_counts.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of TFIDF Matrix: (11314, 130107)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
    "print(f\"Shape of TFIDF Matrix: {X_train_tfidf.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Machine Learning\n",
    "# Training Naive Bayes (NB) classifier on training data.\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a pipeline: We can write less code and do all of the above, by building a pipeline as follows:\n",
    "text_nb_clf = Pipeline(\n",
    "    [\n",
    "        (\"vect\", CountVectorizer()),\n",
    "        (\"tfidf\", TfidfTransformer()),\n",
    "        (\"clf\", MultinomialNB()),\n",
    "    ]\n",
    ")\n",
    "text_nb_clf = text_nb_clf.fit(twenty_train.data, twenty_train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted = text_nb_clf.predict(twenty_test.data)\n",
    "naivebayes_clf_accuracy = np.mean(predicted == twenty_test.target) * 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy is 77.38980350504514 %\n"
     ]
    }
   ],
   "source": [
    "print(f\"Test Accuracy is {naivebayes_clf_accuracy} %\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Logistic Regression (LR) instead of Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression as LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 42s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "text_lr_clf = Pipeline(\n",
    "    [(\"vect\", CountVectorizer()), (\"tfidf\", TfidfTransformer()), (\"clf\", LR())]\n",
    ")\n",
    "text_lr_clf = text_lr_clf.fit(twenty_train.data, twenty_train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy is 82.74030801911843\n"
     ]
    }
   ],
   "source": [
    "lr_predicted = text_lr_clf.predict(twenty_test.data)\n",
    "lr_clf_accuracy = np.mean(lr_predicted == twenty_test.target) * 100.0\n",
    "print(f\"Test Accuracy is {lr_clf_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "cf = confusion_matrix(y_true=twenty_test.target, y_pred=lr_predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  [\n",
      "    237,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    1,\n",
      "    4,\n",
      "    0,\n",
      "    3,\n",
      "    3,\n",
      "    0,\n",
      "    1,\n",
      "    2,\n",
      "    8,\n",
      "    2,\n",
      "    33,\n",
      "    2,\n",
      "    4,\n",
      "    1,\n",
      "    15\n",
      "  ],\n",
      "  [\n",
      "    1,\n",
      "    307,\n",
      "    14,\n",
      "    8,\n",
      "    8,\n",
      "    17,\n",
      "    6,\n",
      "    1,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    4,\n",
      "    10,\n",
      "    1,\n",
      "    4,\n",
      "    2,\n",
      "    1,\n",
      "    1,\n",
      "    2,\n",
      "    1\n",
      "  ],\n",
      "  [\n",
      "    2,\n",
      "    21,\n",
      "    289,\n",
      "    34,\n",
      "    12,\n",
      "    11,\n",
      "    2,\n",
      "    2,\n",
      "    2,\n",
      "    4,\n",
      "    0,\n",
      "    1,\n",
      "    1,\n",
      "    1,\n",
      "    3,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    6,\n",
      "    1\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    13,\n",
      "    23,\n",
      "    284,\n",
      "    21,\n",
      "    2,\n",
      "    14,\n",
      "    3,\n",
      "    0,\n",
      "    1,\n",
      "    1,\n",
      "    1,\n",
      "    24,\n",
      "    0,\n",
      "    3,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    1\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    5,\n",
      "    6,\n",
      "    22,\n",
      "    319,\n",
      "    1,\n",
      "    10,\n",
      "    0,\n",
      "    0,\n",
      "    5,\n",
      "    1,\n",
      "    0,\n",
      "    12,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    3,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    43,\n",
      "    38,\n",
      "    5,\n",
      "    4,\n",
      "    292,\n",
      "    3,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    3,\n",
      "    2,\n",
      "    4,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    3,\n",
      "    2,\n",
      "    12,\n",
      "    7,\n",
      "    0,\n",
      "    351,\n",
      "    3,\n",
      "    2,\n",
      "    1,\n",
      "    1,\n",
      "    0,\n",
      "    7,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    1,\n",
      "    1,\n",
      "    4,\n",
      "    0,\n",
      "    2,\n",
      "    14,\n",
      "    352,\n",
      "    3,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    12,\n",
      "    1,\n",
      "    1,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    2,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    6,\n",
      "    10,\n",
      "    378,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    2,\n",
      "    1,\n",
      "    6,\n",
      "    1,\n",
      "    0,\n",
      "    367,\n",
      "    15,\n",
      "    0,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    1\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    3,\n",
      "    2,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    9,\n",
      "    379,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    2,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    1,\n",
      "    8,\n",
      "    3,\n",
      "    0,\n",
      "    3,\n",
      "    4,\n",
      "    5,\n",
      "    2,\n",
      "    1,\n",
      "    5,\n",
      "    0,\n",
      "    349,\n",
      "    6,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    4,\n",
      "    0,\n",
      "    4,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    9,\n",
      "    5,\n",
      "    20,\n",
      "    9,\n",
      "    3,\n",
      "    8,\n",
      "    5,\n",
      "    2,\n",
      "    2,\n",
      "    0,\n",
      "    9,\n",
      "    314,\n",
      "    4,\n",
      "    2,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    4,\n",
      "    10,\n",
      "    1,\n",
      "    2,\n",
      "    2,\n",
      "    3,\n",
      "    15,\n",
      "    3,\n",
      "    4,\n",
      "    4,\n",
      "    0,\n",
      "    0,\n",
      "    13,\n",
      "    327,\n",
      "    0,\n",
      "    2,\n",
      "    1,\n",
      "    2,\n",
      "    3,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    11,\n",
      "    0,\n",
      "    0,\n",
      "    2,\n",
      "    2,\n",
      "    2,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    2,\n",
      "    6,\n",
      "    361,\n",
      "    1,\n",
      "    2,\n",
      "    0,\n",
      "    3,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    2,\n",
      "    4,\n",
      "    2,\n",
      "    1,\n",
      "    0,\n",
      "    1,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    2,\n",
      "    1,\n",
      "    3,\n",
      "    373,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    6\n",
      "  ],\n",
      "  [\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    1,\n",
      "    1,\n",
      "    6,\n",
      "    4,\n",
      "    2,\n",
      "    4,\n",
      "    1,\n",
      "    5,\n",
      "    1,\n",
      "    4,\n",
      "    2,\n",
      "    0,\n",
      "    319,\n",
      "    1,\n",
      "    9,\n",
      "    3\n",
      "  ],\n",
      "  [\n",
      "    10,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    9,\n",
      "    1,\n",
      "    1,\n",
      "    1,\n",
      "    3,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    0,\n",
      "    4,\n",
      "    3,\n",
      "    328,\n",
      "    12,\n",
      "    0\n",
      "  ],\n",
      "  [\n",
      "    1,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    1,\n",
      "    4,\n",
      "    1,\n",
      "    1,\n",
      "    0,\n",
      "    0,\n",
      "    2,\n",
      "    2,\n",
      "    4,\n",
      "    8,\n",
      "    3,\n",
      "    95,\n",
      "    2,\n",
      "    182,\n",
      "    1\n",
      "  ],\n",
      "  [\n",
      "    39,\n",
      "    4,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    0,\n",
      "    4,\n",
      "    0,\n",
      "    0,\n",
      "    1,\n",
      "    2,\n",
      "    0,\n",
      "    0,\n",
      "    8,\n",
      "    3,\n",
      "    41,\n",
      "    13,\n",
      "    2,\n",
      "    10,\n",
      "    124\n",
      "  ]\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "print(json.dumps(cf.tolist(), indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "ax = sns.heatmap(cf, annot=True, fmt=\"d\", linewidths=0.5, center=90, vmax=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tricks of Trade\n",
    "#### Removing Stop Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_sm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there is an error above, try:\n",
    "- Windows:```python -m spacy download en_core_web_sm``` as **Administrator**\n",
    "- Linux:```sudo python -m spacy download en_core_web_sm```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring spaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\"'d\",\n",
       " \"'ll\",\n",
       " \"'m\",\n",
       " \"'re\",\n",
       " \"'s\",\n",
       " \"'ve\",\n",
       " 'a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'across',\n",
       " 'after',\n",
       " 'afterwards',\n",
       " 'again',\n",
       " 'against',\n",
       " 'all',\n",
       " 'almost',\n",
       " 'alone',\n",
       " 'along',\n",
       " 'already',\n",
       " 'also',\n",
       " 'although',\n",
       " 'always',\n",
       " 'am',\n",
       " 'among',\n",
       " 'amongst',\n",
       " 'amount',\n",
       " 'an',\n",
       " 'and',\n",
       " 'another',\n",
       " 'any',\n",
       " 'anyhow',\n",
       " 'anyone',\n",
       " 'anything',\n",
       " 'anyway',\n",
       " 'anywhere',\n",
       " 'are',\n",
       " 'around',\n",
       " 'as',\n",
       " 'at',\n",
       " 'back',\n",
       " 'be',\n",
       " 'became',\n",
       " 'because',\n",
       " 'become',\n",
       " 'becomes',\n",
       " 'becoming',\n",
       " 'been',\n",
       " 'before',\n",
       " 'beforehand',\n",
       " 'behind',\n",
       " 'being',\n",
       " 'below',\n",
       " 'beside',\n",
       " 'besides',\n",
       " 'between',\n",
       " 'beyond',\n",
       " 'both',\n",
       " 'bottom',\n",
       " 'but',\n",
       " 'by',\n",
       " 'ca',\n",
       " 'call',\n",
       " 'can',\n",
       " 'cannot',\n",
       " 'could',\n",
       " 'did',\n",
       " 'do',\n",
       " 'does',\n",
       " 'doing',\n",
       " 'done',\n",
       " 'down',\n",
       " 'due',\n",
       " 'during',\n",
       " 'each',\n",
       " 'eight',\n",
       " 'either',\n",
       " 'eleven',\n",
       " 'else',\n",
       " 'elsewhere',\n",
       " 'empty',\n",
       " 'enough',\n",
       " 'even',\n",
       " 'ever',\n",
       " 'every',\n",
       " 'everyone',\n",
       " 'everything',\n",
       " 'everywhere',\n",
       " 'except',\n",
       " 'few',\n",
       " 'fifteen',\n",
       " 'fifty',\n",
       " 'first',\n",
       " 'five',\n",
       " 'for',\n",
       " 'former',\n",
       " 'formerly',\n",
       " 'forty',\n",
       " 'four',\n",
       " 'from',\n",
       " 'front',\n",
       " 'full',\n",
       " 'further',\n",
       " 'get',\n",
       " 'give',\n",
       " 'go',\n",
       " 'had',\n",
       " 'has',\n",
       " 'have',\n",
       " 'he',\n",
       " 'hence',\n",
       " 'her',\n",
       " 'here',\n",
       " 'hereafter',\n",
       " 'hereby',\n",
       " 'herein',\n",
       " 'hereupon',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'him',\n",
       " 'himself',\n",
       " 'his',\n",
       " 'how',\n",
       " 'however',\n",
       " 'hundred',\n",
       " 'i',\n",
       " 'if',\n",
       " 'in',\n",
       " 'indeed',\n",
       " 'into',\n",
       " 'is',\n",
       " 'it',\n",
       " 'its',\n",
       " 'itself',\n",
       " 'just',\n",
       " 'keep',\n",
       " 'last',\n",
       " 'latter',\n",
       " 'latterly',\n",
       " 'least',\n",
       " 'less',\n",
       " 'made',\n",
       " 'make',\n",
       " 'many',\n",
       " 'may',\n",
       " 'me',\n",
       " 'meanwhile',\n",
       " 'might',\n",
       " 'mine',\n",
       " 'more',\n",
       " 'moreover',\n",
       " 'most',\n",
       " 'mostly',\n",
       " 'move',\n",
       " 'much',\n",
       " 'must',\n",
       " 'my',\n",
       " 'myself',\n",
       " \"n't\",\n",
       " 'name',\n",
       " 'namely',\n",
       " 'neither',\n",
       " 'never',\n",
       " 'nevertheless',\n",
       " 'next',\n",
       " 'nine',\n",
       " 'no',\n",
       " 'nobody',\n",
       " 'none',\n",
       " 'noone',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'nothing',\n",
       " 'now',\n",
       " 'nowhere',\n",
       " 'n‘t',\n",
       " 'n’t',\n",
       " 'of',\n",
       " 'off',\n",
       " 'often',\n",
       " 'on',\n",
       " 'once',\n",
       " 'one',\n",
       " 'only',\n",
       " 'onto',\n",
       " 'or',\n",
       " 'other',\n",
       " 'others',\n",
       " 'otherwise',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'out',\n",
       " 'over',\n",
       " 'own',\n",
       " 'part',\n",
       " 'per',\n",
       " 'perhaps',\n",
       " 'please',\n",
       " 'put',\n",
       " 'quite',\n",
       " 'rather',\n",
       " 're',\n",
       " 'really',\n",
       " 'regarding',\n",
       " 'same',\n",
       " 'say',\n",
       " 'see',\n",
       " 'seem',\n",
       " 'seemed',\n",
       " 'seeming',\n",
       " 'seems',\n",
       " 'serious',\n",
       " 'several',\n",
       " 'she',\n",
       " 'should',\n",
       " 'show',\n",
       " 'side',\n",
       " 'since',\n",
       " 'six',\n",
       " 'sixty',\n",
       " 'so',\n",
       " 'some',\n",
       " 'somehow',\n",
       " 'someone',\n",
       " 'something',\n",
       " 'sometime',\n",
       " 'sometimes',\n",
       " 'somewhere',\n",
       " 'still',\n",
       " 'such',\n",
       " 'take',\n",
       " 'ten',\n",
       " 'than',\n",
       " 'that',\n",
       " 'the',\n",
       " 'their',\n",
       " 'them',\n",
       " 'themselves',\n",
       " 'then',\n",
       " 'thence',\n",
       " 'there',\n",
       " 'thereafter',\n",
       " 'thereby',\n",
       " 'therefore',\n",
       " 'therein',\n",
       " 'thereupon',\n",
       " 'these',\n",
       " 'they',\n",
       " 'third',\n",
       " 'this',\n",
       " 'those',\n",
       " 'though',\n",
       " 'three',\n",
       " 'through',\n",
       " 'throughout',\n",
       " 'thru',\n",
       " 'thus',\n",
       " 'to',\n",
       " 'together',\n",
       " 'too',\n",
       " 'top',\n",
       " 'toward',\n",
       " 'towards',\n",
       " 'twelve',\n",
       " 'twenty',\n",
       " 'two',\n",
       " 'under',\n",
       " 'unless',\n",
       " 'until',\n",
       " 'up',\n",
       " 'upon',\n",
       " 'us',\n",
       " 'used',\n",
       " 'using',\n",
       " 'various',\n",
       " 'very',\n",
       " 'via',\n",
       " 'was',\n",
       " 'we',\n",
       " 'well',\n",
       " 'were',\n",
       " 'what',\n",
       " 'whatever',\n",
       " 'when',\n",
       " 'whence',\n",
       " 'whenever',\n",
       " 'where',\n",
       " 'whereafter',\n",
       " 'whereas',\n",
       " 'whereby',\n",
       " 'wherein',\n",
       " 'whereupon',\n",
       " 'wherever',\n",
       " 'whether',\n",
       " 'which',\n",
       " 'while',\n",
       " 'whither',\n",
       " 'who',\n",
       " 'whoever',\n",
       " 'whole',\n",
       " 'whom',\n",
       " 'whose',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'within',\n",
       " 'without',\n",
       " 'would',\n",
       " 'yet',\n",
       " 'you',\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " '‘d',\n",
       " '‘ll',\n",
       " '‘m',\n",
       " '‘re',\n",
       " '‘s',\n",
       " '‘ve',\n",
       " '’d',\n",
       " '’ll',\n",
       " '’m',\n",
       " '’re',\n",
       " '’s',\n",
       " '’ve'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from spacy.lang.en.stop_words import STOP_WORDS\n",
    "STOP_WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'There are 326 stopwords in spaCy'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f'There are {len(STOP_WORDS)} stopwords in spaCy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'After adding your own stop words, spaCy will use 327 stopwords'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can add your own corpora specific STOPWORDS using the .add syntax\n",
    "STOP_WORDS.add(\"your_additional_stop_word_here\")\n",
    "f'After adding your own stop words, spaCy will use {len(STOP_WORDS)} stopwords'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am learning the most important ideas Natural Language Processing ideas using Python\n"
     ]
    }
   ],
   "source": [
    "doc = nlp(\"I am learning the most important ideas Natural Language Processing ideas using Python\")\n",
    "print(doc)  # doc is a spaCy object which stores the entire document string"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**About spaCy objects**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I\n",
      "am\n",
      "learning\n",
      "the\n",
      "most\n",
      "important\n",
      "ideas\n",
      "Natural\n",
      "Language\n",
      "Processing\n",
      "ideas\n",
      "using\n",
      "Python\n"
     ]
    }
   ],
   "source": [
    "for token in doc:\n",
    "    print(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[learning, important, ideas, Natural, Language, Processing, ideas, Python]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simplified_doc = [token for token in doc if not token.is_punct | token.is_stop]\n",
    "simplified_doc\n",
    "# please note that .orth_ attribute returns the unicode string representation of the token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also check what other things we know about these tags in the simplified_doc:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token:learning\tLemmatized:learn\tPart-of-Speech-Tag:VERB\n",
      "Token:important\tLemmatized:important\tPart-of-Speech-Tag:ADJ\n",
      "Token:ideas\tLemmatized:idea\tPart-of-Speech-Tag:NOUN\n",
      "Token:Natural\tLemmatized:Natural\tPart-of-Speech-Tag:PROPN\n",
      "Token:Language\tLemmatized:Language\tPart-of-Speech-Tag:PROPN\n",
      "Token:Processing\tLemmatized:Processing\tPart-of-Speech-Tag:PROPN\n",
      "Token:ideas\tLemmatized:idea\tPart-of-Speech-Tag:NOUN\n",
      "Token:Python\tLemmatized:Python\tPart-of-Speech-Tag:PROPN\n"
     ]
    }
   ],
   "source": [
    "for token in simplified_doc:\n",
    "    print(f'Token:{token.orth_}\\tLemmatized:{token.lemma_}\\tPart-of-Speech-Tag:{token.pos_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Integrating back with our classifier pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construction 1\n",
    "from spacy.tokenizer import Tokenizer\n",
    "from spacy.lang.en import English\n",
    "nlp = English()\n",
    "# Create a blank Tokenizer with just the English vocab\n",
    "tokenizer = Tokenizer(nlp.vocab)\n",
    "def spacy_tokenizer(document):\n",
    "    return [token.orth_ for token in tokenizer(document)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first run it without any spaCy tokenizer usage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 48s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "text_lr_clf = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf',LR())])\n",
    "text_lr_clf = text_lr_clf.fit(twenty_train.data, twenty_train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy is 82.74030801911843\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "82.74030801911843"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calc_print_accuracy(text_clf, test):\n",
    "    predictions = text_clf.predict(test.data)\n",
    "    clf_accuracy = np.mean(predictions == test.target) * 100.\n",
    "    print(f'Test Accuracy is {clf_accuracy}')\n",
    "    return clf_accuracy\n",
    "\n",
    "calc_print_accuracy(text_lr_clf, twenty_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we run it by passing a custom tokenizer -- in our case, from spaCy and a list of stop words -- again from spaCy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\nirantk\\miniconda3\\envs\\book\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:765: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy is 79.5539033457249\n",
      "Wall time: 4min 37s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "79.5539033457249"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "text_lr_clf = Pipeline([('vect', CountVectorizer(tokenizer=spacy_tokenizer, stop_words=list(STOP_WORDS))), ('tfidf', TfidfTransformer()), ('clf',LR())])\n",
    "text_lr_clf = text_lr_clf.fit(twenty_train.data, twenty_train.target)\n",
    "calc_print_accuracy(text_lr_clf, twenty_test)"
   ]
  }
 ],
 "metadata": {
  "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}