out

.ipynb_checkpoints/Untitled-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "from nltk.tokenize import RegexpTokenizer\n",
+    "from stop_words import get_stop_words\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=pd.read_csv('dev-0/in.tsv', sep='\\t', header=None)\n",
+    "expected_data=pd.read_csv('dev-0/expected.tsv', sep='\\t', header=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[0] = data[0].str.lower()\n",
+    "filtered_words = [word for word in data[0] if word not in get_stop_words('polish')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "token = RegexpTokenizer(r'[a-zA-Z0-9]+')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv = CountVectorizer(lowercase=True,ngram_range = (1,1),tokenizer = token.tokenize)\n",
+    "text_counts= cv.fit_transform(data[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<1x5048 sparse matrix of type '<class 'numpy.int64'>'\n",
+       "\twith 234 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    text_counts, expected_data[0], test_size=0.3, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MultinomialNB Accuracy: 0.6296296296296297\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn import metrics\n",
+    "clf = MultinomialNB().fit(X_train, y_train)\n",
+    "predicted= clf.predict(X_test)\n",
+    "print(\"MultinomialNB Accuracy:\",metrics.accuracy_score(y_test, predicted))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "tf=TfidfVectorizer()\n",
+    "text_tf= tf.fit_transform(filtered_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    text_tf, expected_data[0], test_size=0.3, random_state=123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MultinomialNB Accuracy: 0.2222222222222222\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn import metrics\n",
+    "clf = MultinomialNB().fit(X_train, y_train)\n",
+    "predicted= clf.predict(X_test)\n",
+    "print(\"MultinomialNB Accuracy:\",metrics.accuracy_score(y_test, predicted))"
+   ]
+  },
+  {
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

dev-0/out.tsv

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