282 lines
7.1 KiB
Plaintext
282 lines
7.1 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 100,
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"metadata": {},
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"outputs": [],
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"source": [
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"import lzma\n",
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"import sys\n",
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"from io import StringIO\n",
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"from sklearn.feature_extraction.text import TfidfVectorizer\n",
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"import pandas as pd\n",
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"import numpy\n",
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"\n",
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"pathX = \"./train/in.tsv.xz\"\n",
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"# pathX = \"./train/in.tsv\"\n",
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"pathY = \"./train/expected.tsv\"\n",
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"nrows = 10000"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 101,
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"metadata": {},
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"outputs": [],
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"source": [
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"# data = lzma.open(pathX, mode='rt', encoding='utf-8').read()\n",
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"# stringIO = StringIO(data)\n",
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"# df = pd.read_csv(stringIO, sep=\"\\t\", header=None)\n",
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"df = pd.read_csv(pathX, sep='\\t', nrows=nrows, header=None)\n",
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"df = df.drop(df.columns[1], axis=1)\n",
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"topics = pd.read_csv(pathY, sep='\\t', nrows=nrows, header=None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 102,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10000\n",
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"10000\n"
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]
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}
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],
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"source": [
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"print(len(df.index))\n",
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"\n",
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"print(len(topics.index))\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 103,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>0</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>8910</th>\n",
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" <td>What? It isn't a fake memo. It's a real memo. ...</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 0\n",
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"8910 What? It isn't a fake memo. It's a real memo. ..."
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]
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},
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"execution_count": 103,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.sample()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 104,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(['00', '000', '00000001', ..., 'αsynuclein', 'ಠ_ಠ', 'fibrosis'],\n",
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" dtype=object)"
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]
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},
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"execution_count": 104,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"vectorizer = TfidfVectorizer(lowercase=True, stop_words=['english'])\n",
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"X = vectorizer.fit_transform(df.to_numpy().ravel())\n",
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"vectorizer.get_feature_names_out()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 105,
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"metadata": {},
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"outputs": [],
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"source": [
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"# vectorizer.transform(\"Ala ma kotka\".lower().split())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 106,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = df.reset_index()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 107,
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"metadata": {},
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"outputs": [],
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"source": [
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"tfidfVector = vectorizer.transform(df[0])\n",
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"\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 108,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
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" y = column_or_1d(y, warn=True)\n",
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"c:\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:593: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n",
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" warnings.warn(\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"GaussianNB()"
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]
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},
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"execution_count": 108,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.naive_bayes import GaussianNB\n",
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"\n",
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"gnb = GaussianNB()\n",
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"gnb.fit(tfidfVector.todense(), topics)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 109,
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"metadata": {},
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"outputs": [],
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"source": [
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"testXPath = \"./dev-0/in.tsv.xz\"\n",
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"testYPath = \"./dev-0/expected.tsv\"\n",
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"\n",
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"testX = pd.read_csv(testXPath, sep='\\t', nrows=nrows, header=None)\n",
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"\n",
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"testY = pd.read_csv(testYPath, sep='\\t', nrows=nrows, header=None)\n",
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"testXtfidfVector = vectorizer.transform(testX[0])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 115,
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"metadata": {},
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"outputs": [],
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"source": [
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"testXPath = \"./test-A/in.tsv.xz\"\n",
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"testYPath = \"./test-A/expected.tsv\"\n",
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"\n",
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"testX = pd.read_csv(testXPath, sep='\\t', nrows=nrows, header=None)\n",
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"\n",
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"# testY = pd.read_csv(testYPath, sep='\\t', nrows=nrows, header=None)\n",
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"testXtfidfVector = vectorizer.transform(testX[0])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 118,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Python310\\lib\\site-packages\\sklearn\\utils\\validation.py:593: FutureWarning: np.matrix usage is deprecated in 1.0 and will raise a TypeError in 1.2. Please convert to a numpy array with np.asarray. For more information see: https://numpy.org/doc/stable/reference/generated/numpy.matrix.html\n",
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" warnings.warn(\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0 1 0 ... 0 0 1]\n"
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]
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}
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],
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"source": [
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"pred = gnb.predict(testXtfidfVector.todense())\n",
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"print(pred)\n",
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"\n",
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"import csv\n",
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"with open(testYPath, 'w', newline='') as f_output:\n",
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" tsv_output = csv.writer(f_output, delimiter='\\n')\n",
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" tsv_output.writerow(pred)"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
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},
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"kernelspec": {
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"display_name": "Python 3.10.4 64-bit",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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