auta-public/.ipynb_checkpoints/solution-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TRENING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NAMES = [\"Price\",\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
    "TRAIN_BASE = pd.read_csv(\"train/train.tsv\", sep ='\\t', names=NAMES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_TRAIN = np.array(TRAIN_BASE[\"Price\"])\n",
    "X_TRAIN = np.array(TRAIN_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL = LinearRegression().fit(X_TRAIN,Y_TRAIN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DEV-0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "NAMES = [\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
    "FILE_BASE = pd.read_csv(\"dev-0/in.tsv\", sep ='\\t', names=NAMES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_TEST = np.array(FILE_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_TEST = MODEL.predict(X_TEST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "VALUES = np.array2string(Y_TEST, precision=5, separator='\\n',suppress_small=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "VALUES = VALUES.split(\".\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUTFILE = open(\"dev-0/out.tsv\", \"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "for x in VALUES:\n",
    "    RESULT = x.replace(\" \",\"\")\n",
    "    RESULT = RESULT.replace(\"[\",\"\")\n",
    "    RESULT = RESULT.replace(\"]\",\"\")\n",
    "    OUTFILE.write(str(RESULT)+\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUTFILE.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TEST A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "NAMES = [\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
    "FILE_BASE = pd.read_csv(\"test-A/in.tsv\", sep ='\\t', names=NAMES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_TEST = np.array(FILE_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_TEST = MODEL.predict(X_TEST)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "VALUES = np.array2string(Y_TEST, precision=5, separator='\\n',suppress_small=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "VALUES = VALUES.split(\".\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUTFILE = open(\"test-A/out.tsv\", \"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in VALUES:\n",
    "    RESULT = x.replace(\" \",\"\").replace(\"[\",\"\").replace(\"]\",\"\")\n",
    "    OUTFILE.write(str(RESULT) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "OUTFILE.close()"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}