{ "cells": [ { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [], "source": [ "import lzma\n", "import sys\n", "from io import StringIO\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "import pandas as pd\n", "import numpy\n", "\n", "pathX = \"./train/in.tsv.xz\"\n", "# pathX = \"./train/in.tsv\"\n", "pathY = \"./train/expected.tsv\"\n", "nrows = 10000" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [], "source": [ "# data = lzma.open(pathX, mode='rt', encoding='utf-8').read()\n", "# stringIO = StringIO(data)\n", "# df = pd.read_csv(stringIO, sep=\"\\t\", header=None)\n", "df = pd.read_csv(pathX, sep='\\t', nrows=nrows, header=None)\n", "df = df.drop(df.columns[1], axis=1)\n", "topics = pd.read_csv(pathY, sep='\\t', nrows=nrows, header=None)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000\n", "10000\n" ] } ], "source": [ "print(len(df.index))\n", "\n", "print(len(topics.index))\n" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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8910What? It isn't a fake memo. It's a real memo. ...
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" ], "text/plain": [ " 0\n", "8910 What? It isn't a fake memo. It's a real memo. ..." ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sample()" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['00', '000', '00000001', ..., 'αsynuclein', 'ಠ_ಠ', 'fibrosis'],\n", " dtype=object)" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vectorizer = TfidfVectorizer(lowercase=True, stop_words=['english'])\n", "X = vectorizer.fit_transform(df.to_numpy().ravel())\n", "vectorizer.get_feature_names_out()\n" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [], "source": [ "# vectorizer.transform(\"Ala ma kotka\".lower().split())" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "df = df.reset_index()" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [], "source": [ "tfidfVector = vectorizer.transform(df[0])\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " y = column_or_1d(y, warn=True)\n", "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", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "GaussianNB()" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.model_selection import train_test_split\n", "from sklearn.naive_bayes import GaussianNB\n", "\n", "gnb = GaussianNB()\n", "gnb.fit(tfidfVector.todense(), topics)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [], "source": [ "testXPath = \"./dev-0/in.tsv.xz\"\n", "testYPath = \"./dev-0/expected.tsv\"\n", "\n", "testX = pd.read_csv(testXPath, sep='\\t', nrows=nrows, header=None)\n", "\n", "testY = pd.read_csv(testYPath, sep='\\t', nrows=nrows, header=None)\n", "testXtfidfVector = vectorizer.transform(testX[0])\n" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [], "source": [ "testXPath = \"./test-A/in.tsv.xz\"\n", "testYPath = \"./test-A/expected.tsv\"\n", "\n", "testX = pd.read_csv(testXPath, sep='\\t', nrows=nrows, header=None)\n", "\n", "# testY = pd.read_csv(testYPath, sep='\\t', nrows=nrows, header=None)\n", "testXtfidfVector = vectorizer.transform(testX[0])\n" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[0 1 0 ... 0 0 1]\n" ] } ], "source": [ "pred = gnb.predict(testXtfidfVector.todense())\n", "print(pred)\n", "\n", "import csv\n", "with open(testYPath, 'w', newline='') as f_output:\n", " tsv_output = csv.writer(f_output, delimiter='\\n')\n", " tsv_output.writerow(pred)" ] } ], "metadata": { "interpreter": { "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c" }, "kernelspec": { "display_name": "Python 3.10.4 64-bit", "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": "3.10.4" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }