tweets_neuralNetworks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tweetlerin veritabanından alınması\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import nltk\n",
    "import zemberek\n",
    "import jpype,os\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "from nltk.corpus import stopwords \n",
    "durakwords= set(stopwords.words(\"turkish\"))\n",
    "import string\n",
    "import re\n",
    "from typing import List\n",
    "from jpype import JClass, JString, getDefaultJVMPath, shutdownJVM, startJVM, java\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Embedding,SpatialDropout1D,LSTM,Dense\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "%matplotlib inline\n",
    "df=pd.DataFrame()\n",
    "df=pd.read_csv(\"C:/Users/user/tweets/tweets2.csv\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tweetlerin küçük harfe dönüştürülmesi\n",
    "df= df.rename(columns = {'hash,content,class': 'tweets'})\n",
    "df['tweets']=df['tweets'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2\n",
       "1        8\n",
       "2        4\n",
       "3        2\n",
       "4        5\n",
       "        ..\n",
       "34995    1\n",
       "34996    1\n",
       "34997    1\n",
       "34998    1\n",
       "34999    1\n",
       "Name: label, Length: 35000, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tweetlerin son değerlerine göre yeni bir dataframe e yerleştirilmesi\n",
    "#1 tek,2 spor,eko,pol,mag,sağ,kült\n",
    "#df[df.tweets.str.endswith('2')]\n",
    "df['icerik']='icerik'\n",
    "df['cleantext']='cleantext'\n",
    "df.loc[df['tweets'].str.endswith('1'),'icerik']=\"teknoloji\"\n",
    "df.loc[df['tweets'].str.endswith('2'),'icerik']=\"spor\"\n",
    "df.loc[df['tweets'].str.endswith('3'),'icerik']=\"ekonomi\"\n",
    "df.loc[df['tweets'].str.endswith('4'),'icerik']=\"politika\"\n",
    "df.loc[df['tweets'].str.endswith('5'),'icerik']=\"magazin\"\n",
    "df.loc[df['tweets'].str.endswith('8'),'icerik']=\"kultur\"\n",
    "df.loc[df['tweets'].str.endswith('7'),'icerik']=\"saglik\"\n",
    "df.loc[df['tweets'].str.endswith('1'),'label']=\"1\"\n",
    "df.loc[df['tweets'].str.endswith('2'),'label']=\"2\"\n",
    "df.loc[df['tweets'].str.endswith('3'),'label']=\"3\"\n",
    "df.loc[df['tweets'].str.endswith('4'),'label']=\"4\"\n",
    "df.loc[df['tweets'].str.endswith('5'),'label']=\"5\"\n",
    "df.loc[df['tweets'].str.endswith('8'),'label']=\"8\"\n",
    "df.loc[df['tweets'].str.endswith('7'),'label']=\"7\"\n",
    "pd.to_numeric(df.label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Zemberek kütüphanesinin kullanılabilmesi için Jpype ile JVM oluşturulması\n",
    "ZEMBEREK_PATH = r'C:\\Users\\user\\Desktop\\dersler\\tweets\\zemberek-full.jar'\n",
    "startJVM(getDefaultJVMPath(), '-ea', '-Djava.class.path=%s' % (ZEMBEREK_PATH))\n",
    "#Morphology kütüphanesini kullanacağımızdan burada oluşturuyoruz\n",
    "TurkishMorphology = JClass('zemberek.morphology.TurkishMorphology')\n",
    "morphology = TurkishMorphology.createWithDefaults()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mentionRemove(text):\n",
    "    text = re.sub(r'@\\w+', '', text)#mentionların çıkartırılması\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def siteRemove(text):\n",
    "    text = re.sub(r'http.?://[^\\s]+[\\s]?', '', text)#bağlantıların çıkarılması\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def puncRemove(text):\n",
    "    text = re.sub('[^a-zığüşiöç\\s]', '', text)#özel karakterlerin ve sayıların çıkarılması\n",
    "    text = re.sub(r\"Â\", \"A\", text)#umlautlu karakterlerinn değiştirilmesi\n",
    "    text = re.sub(r\"â\", \"a\", text)\n",
    "    text = re.sub(r\"Î\", \"I\", text)\n",
    "    text = re.sub(r\"î\", \"ı\", text)\n",
    "    text = re.sub(r\"Û\", \"U\", text)\n",
    "    text = re.sub(r\"û\", \"u\", text)\n",
    "\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def removeSw(text):\n",
    "    text=[w for w in tweet if w.lower() not in durakwords]#stopwordlerin çıkarılması\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rootFinder(text):\n",
    "    pos=[]\n",
    "    textlist=text.split(\" \")\n",
    "    analysis: java.util.ArrayList = ( morphology.analyzeAndDisambiguate(text).bestAnalysis() )\n",
    "    for i, analysis in enumerate(analysis, start=1):\n",
    "        f'\\nAnalysis {i}: {analysis}',\n",
    "        f'\\nPrimary POS {i}: {analysis.getPos()}' \n",
    "        if (str(analysis.getLemmas()[0]) == \"UNK\"):\n",
    "            pos.append(textlist[i-1])\n",
    "        else:     \n",
    "            pos.append(f'{str(analysis.getLemmas()[0])}')\n",
    "    return pos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def emojiRemove(tweet):\n",
    "    # Smile -- :), : ), :-), (:, ( :, (-:, :')\n",
    "    tweet = re.sub(r'(:\\s?\\)|:-\\)|\\(\\s?:|\\(-:|:\\'\\))', ' EMO_POS ', tweet)\n",
    "    # Laugh -- :D, : D, :-D, xD, x-D, XD, X-D\n",
    "    tweet = re.sub(r'(:\\s?D|:-D|x-?D|X-?D)', ' EMO_POS ', tweet)\n",
    "    # Love -- <3, :*\n",
    "    tweet = re.sub(r'(<3|:\\*)', ' EMO_POS ', tweet)\n",
    "    # Wink -- ;-), ;), ;-D, ;D, (;,  (-;\n",
    "    tweet = re.sub(r'(;-?\\)|;-?D|\\(-?;)', ' EMO_POS ', tweet)\n",
    "    # Sad -- :-(, : (, :(, ):, )-:\n",
    "    tweet = re.sub(r'(:\\s?\\(|:-\\(|\\)\\s?:|\\)-:)', ' EMO_NEG ', tweet)\n",
    "    # Cry -- :,(, :'(, :\"(\n",
    "    tweet = re.sub(r'(:,\\(|:\\'\\(|:\"\\()', ' EMO_NEG ', tweet)\n",
    "    return tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hash kısmının ve sondaki içerik belirteci ve tweet bağlantısının kaldırılması\n",
    "#utf-16 karakterlerin elenmesi\n",
    "#kelimelerin ayrıştırılması\n",
    "#tweet içerisindeki bağlantıların çıkarılması\n",
    "#tweet içindeki mentionların çıkarılması\n",
    "p=set(string.punctuation)\n",
    "\n",
    "def cleaner(text):\n",
    "    text=text\n",
    "    text=mentionRemove(text)#mentionların çıkartırılması\n",
    "    text=siteRemove(text)#bağlantıların çıkarılması\n",
    "    text=puncRemove(text)#özel karakterlerin ve sayıların çıkarılması\n",
    "    text=emojiRemove(text)\n",
    "    text=rootFinder(text)#kelime köklerinin bulunması\n",
    "    text=removeSw(text)#durak kelimelerin elenmesi\n",
    "    return text\n",
    "for x in range(0,len(df)):\n",
    "    #tweet=\"ve ya da için baba veya\"\n",
    "    tweet=df.iloc[x,0]\n",
    "    tweet=mentionRemove(tweet)#mentionların çıkartırılması\n",
    "    tweet=siteRemove(tweet)#bağlantıların çıkarılması\n",
    "    #tweet.replace(\"aracılığıyla\",\"a\")\n",
    "    tweet=puncRemove(tweet)#özel karakterlerin ve sayıların çıkarılması\n",
    "    tweet=rootFinder(tweet)#kelime köklerinin bulunması\n",
    "    tweet=removeSw(tweet)#durak kelimelerin elenmesi\n",
    "    tweet=' '.join(tweet)\n",
    "    #tweet=cleaner(tweet)\n",
    "    df.iloc[x,2]=tweet\n",
    "    pos=[]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import re\n",
    "import shutil\n",
    "import string\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import regularizers\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras import losses\n",
    "from collections import Counter\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from sklearn.metrics import accuracy_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.1\n",
      "please install gpu version of tf\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n",
    "if tf.test.gpu_device_name():\n",
    "    print(\"gpu device :{}\".format(tf.test.gpu_device_name()))\n",
    "else:\n",
    "    print(\"please install gpu version of tf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def counter_words(df):\n",
    "    count = Counter()\n",
    "    for i in df:\n",
    "        for w in i.split():\n",
    "            count[w] += 1\n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=counter_words(df.cleantext)\n",
    "num_words = len(c)\n",
    "max_seq_lenght = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "tweets=df.cleantext\n",
    "labels=df.icerik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[23, 68, 334]]\n"
     ]
    }
   ],
   "source": [
    "tokenizer=Tokenizer(num_words=num_words)\n",
    "tokenizer.fit_on_texts(tweets.tolist())\n",
    "\n",
    "print(str(tokenizer.texts_to_sequences([\"fenerbahçe ben sevgili\"])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data len: 24500\n",
      "labels:  Counter({'teknoloji': 3500, 'spor': 3500, 'politika': 3500, 'kultur': 3500, 'magazin': 3500, 'saglik': 3500, 'ekonomi': 3500})\n",
      "test data len: 10500\n",
      "labels:  Counter({'magazin': 1500, 'teknoloji': 1500, 'saglik': 1500, 'politika': 1500, 'kultur': 1500, 'ekonomi': 1500, 'spor': 1500})\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(tweets.tolist(),labels.tolist(), test_size = 0.3,stratify=labels.tolist(),\n",
    "                                                    random_state = 44)\n",
    "print('Train data len: '+ str(len(X_train)))\n",
    "print('labels: ', Counter(Y_train))\n",
    "print('test data len: '+ str(len(X_test)))\n",
    "print('labels: ' ,Counter(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train=np.array(tokenizer.texts_to_sequences(X_train))\n",
    "X_test=np.array(tokenizer.texts_to_sequences(X_test))\n",
    "#Y_train=np.array(tokenizer.texts_to_sequences(Y_train))\n",
    "X_train=pad_sequences(X_train, padding = 'post',maxlen=max_seq_lenght,truncating=\"post\")\n",
    "X_test=pad_sequences(X_test, padding = 'post',maxlen=max_seq_lenght,truncating=\"post\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  122  2777   207 10413   807   477   734   207    54    21     0     0]\n",
      "['teknoloji', 'spor', 'politika', 'kultur', 'kultur', 'magazin', 'teknoloji', 'spor', 'teknoloji', 'politika']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function print>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train[1])\n",
    "print(Y_train[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 5 3 ... 5 4 6]\n",
      "[[0. 0. 0. ... 0. 0. 1.]\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 1. 0.]\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['ekonomi', 'kultur', 'magazin', 'politika', 'saglik', 'spor', 'teknoloji']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "train_labels=le.fit_transform(Y_train)\n",
    "print(train_labels)\n",
    "train_labels=np.asarray(tf.keras.utils.to_categorical(train_labels))\n",
    "print(train_labels)\n",
    "test_labels=le.fit_transform(Y_test)\n",
    "test_labels=np.asarray(tf.keras.utils.to_categorical(test_labels))\n",
    "list(le.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds=tf.data.Dataset.from_tensor_slices((X_train,train_labels))\n",
    "test_ds=tf.data.Dataset.from_tensor_slices((X_test,test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 1.],\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 1., 0.],\n",
       "       [0., 0., 0., ..., 1., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 1.]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels=np.asarray(tf.keras.utils.to_categorical(le.fit_transform(Y_train)))\n",
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18507\n",
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 12, 100)           1850800   \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 6, 128)            89728     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d (Global (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 7)                 903       \n",
      "=================================================================\n",
      "Total params: 1,941,431\n",
      "Trainable params: 1,941,431\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "num_words = len(c)\n",
    "# Max number of words in each complaint.\n",
    "max_seq_lenght = 12\n",
    "# This is fixed.\n",
    "dim = 100\n",
    "print(len(c))\n",
    "model = tf.keras.Sequential()\n",
    "model.add(tf.keras.layers.Embedding(num_words+1,dim,input_length=max_seq_lenght,embeddings_regularizer=regularizers.l2(0.0001)))\n",
    "model.add(tf.keras.layers.Conv1D(128,7,activation='relu',kernel_regularizer=regularizers.l2(0.0001),bias_regularizer=regularizers.l2(0.0001)))\n",
    "model.add(tf.keras.layers.GlobalMaxPooling1D())\n",
    "model.add(tf.keras.layers.Dropout(0.2))\n",
    "model.add(tf.keras.layers.Dense(7,activation='sigmoid',kernel_regularizer=regularizers.l2(0.001),bias_regularizer=regularizers.l2(0.001)))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),optimizer='Adam',metrics=[\"CategoricalAccuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\indexed_slices.py:432: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "192/192 [==============================] - 3s 18ms/step - loss: 1.4553 - categorical_accuracy: 0.5222 - val_loss: 0.7982 - val_categorical_accuracy: 0.7841\n",
      "Epoch 2/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.6752 - categorical_accuracy: 0.8269 - val_loss: 0.7133 - val_categorical_accuracy: 0.8143\n",
      "Epoch 3/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.5078 - categorical_accuracy: 0.8949 - val_loss: 0.7417 - val_categorical_accuracy: 0.8150\n",
      "Epoch 4/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.4347 - categorical_accuracy: 0.9252 - val_loss: 0.7836 - val_categorical_accuracy: 0.8116\n",
      "Epoch 5/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.3936 - categorical_accuracy: 0.9454 - val_loss: 0.8155 - val_categorical_accuracy: 0.8078\n",
      "Epoch 6/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.3640 - categorical_accuracy: 0.9571 - val_loss: 0.8405 - val_categorical_accuracy: 0.8067\n",
      "Epoch 7/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.3483 - categorical_accuracy: 0.9648 - val_loss: 0.8622 - val_categorical_accuracy: 0.8052\n",
      "Epoch 8/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.3379 - categorical_accuracy: 0.9688 - val_loss: 0.8786 - val_categorical_accuracy: 0.8055\n",
      "Epoch 9/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.3256 - categorical_accuracy: 0.9740 - val_loss: 0.8969 - val_categorical_accuracy: 0.7988\n",
      "Epoch 10/10\n",
      "192/192 [==============================] - 3s 17ms/step - loss: 0.3183 - categorical_accuracy: 0.9764 - val_loss: 0.9011 - val_categorical_accuracy: 0.8019\n"
     ]
    }
   ],
   "source": [
    "epochs=10\n",
    "history=model.fit(train_ds.batch(128),epochs=epochs,validation_data=test_ds.batch(128),verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tokenizer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-9a62286cc1ad>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mnew_complaint\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'erdoğan hastane millet millet oy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mseq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtexts_to_sequences\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_complaint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mpadded\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpad_sequences\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxlen\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_seq_lenght\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpadded\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'ekonomi'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'kultur'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'magazin'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'politika'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'saglik'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'spor'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'teknoloji'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'tokenizer' is not defined"
     ]
    }
   ],
   "source": [
    "new_complaint = ['erdoğan hastane millet millet oy']\n",
    "seq = tokenizer.texts_to_sequences(new_complaint)\n",
    "padded = pad_sequences(seq, maxlen=max_seq_lenght)\n",
    "pred = model.predict(padded)\n",
    "labels = ['ekonomi', 'kultur', 'magazin', 'politika', 'saglik', 'spor', 'teknoloji']\n",
    "print(pred, labels[np.argmax(pred)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy score is 0.8019047619047619\n"
     ]
    }
   ],
   "source": [
    "preds=model.predict(X_test)\n",
    "preds=preds.argmax(axis=1)\n",
    "actual=le.fit_transform(Y_test)\n",
    "print(\"accuracy score is\",accuracy_score(preds,actual))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "     ekonomi       0.77      0.76      0.76      1500\n",
      "      kultur       0.74      0.74      0.74      1500\n",
      "     magazin       0.74      0.77      0.75      1500\n",
      "    politika       0.82      0.83      0.83      1500\n",
      "      saglik       0.86      0.79      0.82      1500\n",
      "        spor       0.87      0.89      0.88      1500\n",
      "   teknoloji       0.82      0.85      0.83      1500\n",
      "\n",
      "    accuracy                           0.80     10500\n",
      "   macro avg       0.80      0.80      0.80     10500\n",
      "weighted avg       0.80      0.80      0.80     10500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(actual, preds,target_names=['ekonomi', 'kultur', 'magazin', 'politika', 'saglik', 'spor', 'teknoloji']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-01-25 21:51:16,849 - numexpr.utils - INFO\n",
      "Msg: NumExpr defaulting to 8 threads.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_mat = confusion_matrix(actual,preds)\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "sns.heatmap(conf_mat, annot=True, cmap=\"Blues\", fmt='d',\n",
    "            xticklabels={'ekonomi', 'kultur', 'magazin', 'politika', 'saglik', 'spor', 'teknoloji'},\n",
    "            yticklabels={'ekonomi', 'kultur', 'magazin', 'politika', 'saglik', 'spor', 'teknoloji'})\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.title(\"CONFUSION MATRIX\\n\", size=16);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred=model.predict()"
   ]
  },
  {
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}