Wyklad 12 - Klasyfikacja tekstu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Klasyfikacja tekstu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# encode document\n",
    "vector = vectorizer.transform([text[0]])\n",
    "# summarize encoded vector\n",
    "print(vector.shape)\n",
    "print(vector.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.feature_extraction.text import HashingVectorizer\n",
    "# list of text documents\n",
    "text = [\"The quick brown fox jumped over the lazy dog.\"]\n",
    "# create the transform\n",
    "vectorizer = HashingVectorizer(n_features=20)\n",
    "# encode document\n",
    "vector = vectorizer.transform(text)\n",
    "# summarize encoded vector\n",
    "print(vector.shape)\n",
    "print(vector.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " categories = ['alt.atheism', 'soc.religion.christian','comp.graphics', 'sci.med']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "twenty_train = fetch_20newsgroups(data_home='c:\\\\work', subset='train', categories=categories, shuffle=True, random_state=42, download_if_missing=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "twenty_train.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(twenty_train.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"\\n\".join(twenty_train.data[0].split(\"\\n\")[:15]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " print(twenty_train.target_names[twenty_train.target[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "count_vect = CountVectorizer()\n",
    "X_train_counts = count_vect.fit_transform(twenty_train.data)\n",
    "X_train_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "# list of text documents\n",
    "text = [\"The quick brown fox jumped over the lazy dog.\"]\n",
    "# create the transform\n",
    "vectorizer = CountVectorizer()\n",
    "# tokenize and build vocab\n",
    "vectorizer.fit(text)\n",
    "# summarize\n",
    "print(vectorizer.vocabulary_)\n",
    "# encode document\n",
    "vector = vectorizer.transform(text)\n",
    "# summarize encoded vector\n",
    "print(vector.shape)\n",
    "print(type(vector))\n",
    "print(vector.toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "# list of text documents\n",
    "text = [\"The quick brown fox jumped over the lazy dog.\",\n",
    "\t\t\"The dog.\",\n",
    "\t\t\"The fox\"]\n",
    "# create the transform\n",
    "vectorizer = TfidfVectorizer()\n",
    "# tokenize and build vocab\n",
    "vectorizer.fit(text)\n",
    "# summarize\n",
    "\n",
    "print(vectorizer.idf_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ tf-idf_{ij} = tf_{ij} \\times idf_i $$\n",
    "\n",
    "$$ tf_{ij} = \\frac{n_{ij}}{\\sum_{k} n_{kj}} n_{ij} - ilość~wystąpień~t_i~w~dokumencie~d_j $$ \n",
    "\n",
    "$$ idf_i = log \\frac{ |D| }{|{d : t_i \\in d }|} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "tfidf_transformer = TfidfTransformer(use_idf=True).fit(X_train_counts)\n",
    "X_train_tfidf = tfidf_transformer.transform(X_train_counts)\n",
    "X_train_tfidf.shape\n",
    "\n",
    "clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs_new = ['God is love', 'OpenGL on the GPU is fast']\n",
    "X_new_counts = count_vect.transform(docs_new)\n",
    "X_new_tfidf = tfidf_transformer.transform(X_new_counts)\n",
    "\n",
    "predicted = clf.predict(X_new_tfidf)\n",
    "\n",
    "for doc, category in zip(docs_new, predicted):\n",
    "    print('%r => %s' % (doc, twenty_train.target_names[category]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "text_clf = Pipeline([\n",
    "    ('vect', CountVectorizer()),\n",
    "     ('tfidf', TfidfTransformer()),\n",
    "     ('clf', MultinomialNB()),\n",
    " ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_clf.fit(twenty_train.data, twenty_train.target)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "twenty_test = fetch_20newsgroups(data_home='c:\\\\work', subset='train', categories=categories, shuffle=True, random_state=42, download_if_missing=False)\n",
    "docs_test = twenty_test.data\n",
    "predicted = text_clf.predict(docs_test)\n",
    "print np.mean(predicted == twenty_test.target)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(twenty_test.target, predicted, target_names=twenty_test.target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "text_clf = Pipeline([\n",
    "('vect', CountVectorizer()),\n",
    "('tfidf', TfidfTransformer()),\n",
    "('clf', SGDClassifier(loss='hinge', penalty='l2',\n",
    "alpha=1e-3, random_state=42,\n",
    "max_iter=5, tol=None)),\n",
    "])\n",
    "\n",
    "text_clf.fit(twenty_train.data, twenty_train.target)  \n",
    "\n",
    "predicted = text_clf.predict(docs_test)\n",
    "np.mean(predicted == twenty_test.target)            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "print(metrics.classification_report(twenty_test.target, predicted, target_names=twenty_test.target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics.confusion_matrix(twenty_test.target, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {\n",
    "'vect__ngram_range': [(1, 1), (1, 2)],\n",
    "'tfidf__use_idf': (True, False),\n",
    "'clf__alpha': (1e-2, 1e-3),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_clf = GridSearchCV(text_clf, parameters, cv=5, iid=False, n_jobs=-1)\n",
    "gs_clf.__dict__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Intern](\"img/se.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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