paranormal-or-skeptic-ISI-public-bayess/bayess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "import pandas as pd\n",
    "import csv\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = make_pipeline(TfidfVectorizer(),MultinomialNB())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py:73: 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",
      "  return f(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "#training\n",
    "all_train_data_in = pd.read_csv('train/in.tsv.xz', compression='xz', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
    "train_data_ex = pd.read_csv('train/expected.tsv', header=None, error_bad_lines=False, quoting=csv.QUOTE_NONE, sep='\\t')\n",
    "train_data_in = []\n",
    "for value in all_train_data_in.values:\n",
    "    temp = \"\"\n",
    "    for el in value:\n",
    "        if(temp == \"\"):\n",
    "            temp = str(el)\n",
    "        else:\n",
    "            temp += '\\t' + str(el)\n",
    "    train_data_in.append(temp)\n",
    "    \n",
    "nb=steps.fit(train_data_in, LabelEncoder().fit_transform(train_data_ex.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dev0\n",
    "all_dev0_data = pd.read_csv('dev-0/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
    "dev0_data = []\n",
    "for value in all_dev0_data.values:\n",
    "    temp = \"\"\n",
    "    for el in value:\n",
    "        if(temp == \"\"):\n",
    "            temp = str(el)\n",
    "        else:\n",
    "            temp += '\\t' + str(el)\n",
    "    dev0_data.append(temp)\n",
    "\n",
    "\n",
    "dev0_y = nb.predict(dev0_data)\n",
    "\n",
    "#zapis wyników\n",
    "dev0_y.tofile('dev-0/out.tsv', sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test-A\n",
    "all_testA_data = pd.read_csv('test-A/in.tsv.xz', compression='xz', header=None, quoting=csv.QUOTE_NONE, sep='\\t')\n",
    "testA_data = []\n",
    "for value in all_testA_data.values:\n",
    "    temp = \"\"\n",
    "    for el in value:\n",
    "        if(temp == \"\"):\n",
    "            temp = str(el)\n",
    "        else:\n",
    "            temp += '\\t' + str(el)\n",
    "    testA_data.append(temp)\n",
    "\n",
    "\n",
    "testA_y = nb.predict(testA_data)\n",
    "\n",
    "#zapis wyników\n",
    "testA_y.tofile('test-A/out.tsv', sep='\\n')"
   ]
  }
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