lab2 TEXT (Конфликтующая копия с компьютера W7HomePC 2019-01-13).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib\n",
    "from bs4 import BeautifulSoup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "model = MLPClassifier()\n",
    "vectorizer = CountVectorizer()\n",
    "data = pd.read_csv(\"train.txt\",delimiter=';',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vectorizer.fit_transform(data[0])\n",
    "y = data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py:562: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       n_iter_no_change=10, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ЭТО ЧИСТО ЗАЦЕНИТЬ КЛАССИФИКАТОР\n",
    "X_pred = vectorizer.transform(['Мы купили Сане педигри', 'Мы купили Сане зачет'])\n",
    "y_pred = model.predict(X_pred)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from natasha import (\n",
    "    NamesExtractor,\n",
    "    LocationExtractor\n",
    ")\n",
    "import pymorphy2\n",
    "from nltk.tokenize import PunktSentenceTokenizer as PST\n",
    "from nltk.tokenize import WordPunctTokenizer as WPT\n",
    "st = PST()\n",
    "wt = WPT()\n",
    "geos = []\n",
    "names = []\n",
    "pets = []\n",
    "marks = []\n",
    "morph = pymorphy2.MorphAnalyzer()\n",
    "extractor = NamesExtractor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#старое\n",
    "paragraphs = []\n",
    "url = 'https://ilibrary.ru/text/477/p.1/index.html'\n",
    "page = urllib.request.urlopen(url)\n",
    "soup = BeautifulSoup(page, 'html.parser') #получаем страницу\n",
    "\n",
    "messages = soup.findAll('span', attrs={'class': 'p'}) #берем то, что надо (сообщения)\n",
    "for m in messages: #для каждого сообщения\n",
    "    if 'span' not in str(m.contents[1]):\n",
    "        parts = m.contents[1].split('.')\n",
    "        for p in parts:\n",
    "            paragraphs.append(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def do_natasha(text, arr):\n",
    "    extractor = LocationExtractor()\n",
    "    matches = extractor(text)\n",
    "    for match in matches:\n",
    "        start, stop = match.span\n",
    "        if text[start:stop] not in arr:\n",
    "            arr.append(text[start:stop])\n",
    "    return arr\n",
    "\n",
    "def do_pymorphy(text, arr, search_type):\n",
    "    for word in wt.tokenize(text):\n",
    "        for morphx in morph.parse(word):\n",
    "            if search_type in morphx.tag and morphx.score>=0.3:\n",
    "                if morphx.normal_form not in arr:\n",
    "                    arr+=[morphx.normal_form]\n",
    "    \n",
    "\n",
    "def text_classification(path):\n",
    "    with open(path, 'r', encoding='utf-8') as file:\n",
    "        text = file.read()\n",
    "    text_=st.sentences_from_text(text)\n",
    "    paragraphs = []\n",
    "    paragraphs += text_\n",
    "    X_pred = vectorizer.transform(paragraphs)\n",
    "    y_pred = model.predict(X_pred)\n",
    "    for i in range(len(paragraphs)):\n",
    "        if y_pred[i] == 1:\n",
    "            #do_natasha(paragraphs[i], geos)\n",
    "            do_pymorphy(paragraphs[i], geos, \"Geox\")      \n",
    "        elif y_pred[i] == 2:\n",
    "            do_pymorphy(paragraphs[i], names, \"Name\")\n",
    "        elif y_pred[i] == 3:\n",
    "            do_pymorphy(paragraphs[i], pets, \"Name\")\n",
    "        elif y_pred[i] == 4:\n",
    "            do_pymorphy(paragraphs[i], marks, \"Trad\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(7):\n",
    "    path = 'pushkin/' + str(i+1) + '.txt'    \n",
    "    text_classification(path)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(geos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "try: \n",
    "    geofile = open('geox.txt', 'r', encoding='utf-8')\n",
    "    geos = [line.strip() for line in geofile]\n",
    "except FileNotFoundError:\n",
    "    geos.sort()\n",
    "    geofile = open('geox.txt', \"w\", encoding='utf-8')\n",
    "    for i in geos:\n",
    "        geofile.write(i+'\\n')\n",
    "    geofile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt5Agg\n"
     ]
    }
   ],
   "source": [
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "GeoD={}\n",
    "coord=[]\n",
    "\n",
    "try: #считываю координаты из файла\n",
    "    geocoord = open('coord.txt','r', encoding='utf-8')\n",
    "    coord=[]\n",
    "    temp = []\n",
    "    k=0\n",
    "    for line in geocoord:\n",
    "        if k==2:\n",
    "            coord+=[temp]\n",
    "            k=0\n",
    "            temp=[]\n",
    "        temp+=[float(line)]\n",
    "        k+=1\n",
    "    coord+=[temp]\n",
    "except FileNotFoundError:\n",
    "    geocoord_Real = open('coordReal.txt', \"w\", encoding='utf-8')\n",
    "    for i in geos:\n",
    "        x=float(np.random.uniform(-180, 180, 1))\n",
    "        y=float(np.random.uniform(-90, 90, 1))\n",
    "        geocoord_Real.write(i+'\\n')\n",
    "        geocoord_Real.write(str(x)+'\\n')\n",
    "        geocoord_Real.write(str(y)+'\\n')\n",
    "\n",
    "        x=((x+180)*2)/(180+180)-1\n",
    "        y=((y+90)*2)/(90+90)-1\n",
    "        X=[x,y]\n",
    "        coord+=[X]\n",
    "        GeoD.update({i:X})\n",
    "    geocoord_Real.close()\n",
    "    geocoord = open('coord.txt', \"w\", encoding='utf-8')\n",
    "    for i in coord:\n",
    "        for j in i:\n",
    "            geocoord.write(str(j)+'\\n')\n",
    "    geocoord.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(720, 1280, 3)\n",
      "63\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import ImageFont, ImageDraw, Image \n",
    "#img = cv2.imread('konturnaya-karta-mira320016002.jpg')\n",
    "#img = np.zeros((500,1000,3), np.uint8)\n",
    "img = cv2.imread('white.jpg')\n",
    "shape = img.shape\n",
    "print(shape)\n",
    "print(len(coord))\n",
    "y = shape[0]\n",
    "x = shape[1] \n",
    "\n",
    "def norm_coordinates(x1, y1):\n",
    "    if x1 < 100:\n",
    "        x1 += 25\n",
    "    if x1 > 650:\n",
    "        x1 -= 80\n",
    "    if y1 < 100:\n",
    "        y1 += 25\n",
    "    if y1 > 1200:\n",
    "        y1 -=80\n",
    "    return x1, y1\n",
    "            \n",
    "\n",
    "for j in coord:\n",
    "    x1 = j[0]\n",
    "    y1 = j[1]\n",
    "    x1 = int((x1*x+x)/2)\n",
    "    y1 = y - int((y1*y+y)/2)\n",
    "    x1, y1 = norm_coordinates(x1,y1)\n",
    "    cv2.circle(img,(x1,y1), 20, (255,0,0), -1)\n",
    "    #cv2.putText(img, geos[i], (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (0, 0, 255), 1)\n",
    "    \n",
    "    \n",
    "# Convert the image to RGB (OpenCV uses BGR)\n",
    "cv2_im_rgb = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)  \n",
    " # Pass the image to PIL  \n",
    "pil_im = Image.fromarray(cv2_im_rgb)    \n",
    "draw = ImageDraw.Draw(pil_im)  \n",
    " # use a truetype font  \n",
    "font = ImageFont.truetype(\"cour.ttf\", 30) \n",
    "\n",
    "i = 0\n",
    "for j in coord:\n",
    "    x1 = j[0] \n",
    "    y1 = j[1] \n",
    "    x1 = int((x1*x+x)/2)\n",
    "    y1 = y - int((y1*y+y)/2)\n",
    "    x1, y1 = norm_coordinates(x1,y1)\n",
    "    draw.text((x1+10, y1+10), geos[i], font=font, fill=(0,0,0,255)) \n",
    "    i +=1\n",
    "    \n",
    " # Get back the image to OpenCV  \n",
    "cv2_im_processed = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)  \n",
    "\n",
    "cv2.imshow('GeoMap', cv2_im_processed) \n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}