test_gio/FirstTest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import texthero as hero\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "/Users/giovanniliotta/Dev/texthero/test_gio\n"
    }
   ],
   "source": [
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\n",
    "   \"../dataset/bbcsport.csv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                                text      topic\n0  Claxton hunting first major medal\\n\\nBritish h...  athletics\n1  O'Sullivan could run in Worlds\\n\\nSonia O'Sull...  athletics",
      "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>text</th>\n      <th>topic</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Claxton hunting first major medal\\n\\nBritish h...</td>\n      <td>athletics</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>O'Sullivan could run in Worlds\\n\\nSonia O'Sull...</td>\n      <td>athletics</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['clean_text'] = hero.clean(df['text'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                                                text      topic  \\\n0  Claxton hunting first major medal\\n\\nBritish h...  athletics   \n1  O'Sullivan could run in Worlds\\n\\nSonia O'Sull...  athletics   \n\n                                          clean_text  \n0  claxton hunting first major medal british hurd...  \n1  sullivan could run worlds sonia sullivan indic...  ",
      "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>text</th>\n      <th>topic</th>\n      <th>clean_text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Claxton hunting first major medal\\n\\nBritish h...</td>\n      <td>athletics</td>\n      <td>claxton hunting first major medal british hurd...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>O'Sullivan could run in Worlds\\n\\nSonia O'Sull...</td>\n      <td>athletics</td>\n      <td>sullivan could run worlds sonia sullivan indic...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Category:\n",
    "    BOOKS= \"BOOKS\"\n",
    "    CLOTHING = \"CLOTHING\"\n",
    "    \n",
    "train_x = [\"I love the book\", \"this is a great book\", \"the fit is great\", \"I love the shoes\"]\n",
    "train_y = [Category.BOOKS, Category.BOOKS, Category.CLOTHING, Category.CLOTHING]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 0 0 1 0 1 0]\n",
      " [1 0 1 1 0 0 0 1]\n",
      " [0 1 1 1 0 0 1 0]\n",
      " [0 0 0 0 1 1 1 0]]\n",
      "['book', 'fit', 'great', 'is', 'love', 'shoes', 'the', 'this']\n"
     ]
    }
   ],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "train_x_vectors = vectorizer.fit_transform(train_x)\n",
    "print(vectors.toarray())\n",
    "print(vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_svm = svm.SVC(kernel='linear')\n",
    "clf_svm.fit(train_x_vectors, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BOOKS'], dtype='<U8')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_x = vectorizer.transform(['I like the book'])\n",
    "\n",
    "clf_svm.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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