diff --git a/.gitignore b/.gitignore
index 336d663..79aac1b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,5 @@
*~
*.swp
*.pyc
+geval
+.ipynb_checkpoints/*
\ No newline at end of file
diff --git a/cars price prediction.ipynb b/cars price prediction.ipynb
new file mode 100644
index 0000000..65b7df7
--- /dev/null
+++ b/cars price prediction.ipynb
@@ -0,0 +1,935 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Requirement already satisfied: scikit-learn==0.24.2 in c:\\users\\ania\\appdata\\roaming\\python\\python38\\site-packages (0.24.2)\n",
+ "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn==0.24.2) (2.1.0)\n",
+ "Requirement already satisfied: scipy>=0.19.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn==0.24.2) (1.5.2)\n",
+ "Requirement already satisfied: joblib>=0.11 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn==0.24.2) (0.17.0)\n",
+ "Requirement already satisfied: numpy>=1.13.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn==0.24.2) (1.19.2)\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install scikit-learn==0.24.2 --user"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from math import sqrt\n",
+ "from sklearn.linear_model import LinearRegression\n",
+ "from sklearn.metrics import mean_squared_error\n",
+ "np.set_printoptions(formatter={'float_kind':'{:f}'.format})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "header = None\n",
+ "with open('names') as f:\n",
+ " header = f.read().replace('\\n', '').split('\\t')\n",
+ "cars_train = pd.read_csv('train/train.tsv', sep=\"\\t\", names=header)\n",
+ "cars_train_X = cars_train[[\"mileage\", \"year\", \"brand\", \"engineType\", \"engineCapacity\"]]\n",
+ "cars_train_X = pd.get_dummies(cars_train_X)\n",
+ "cars_train_Y = cars_train[\"price\"]\n",
+ "input_columns = cars_train_X.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "30118.8791272898"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Trenowanie modelu i błąd na train\n",
+ "model = LinearRegression(positive=True)\n",
+ "model.fit(cars_train_X, cars_train_Y)\n",
+ "predictions = model.predict(cars_train_X)\n",
+ "sqrt(mean_squared_error(predictions, cars_train_Y))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cars_dev_0_X = pd.read_csv('dev-0/in.tsv', sep=\"\\t\", names=header[1:])\n",
+ "cars_dev_0_Y = pd.read_csv('dev-0/expected.tsv', sep=\"\\t\", header=None).to_numpy().flatten('F')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
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+ " brand_star engineType_benzyna engineType_diesel engineType_gaz \n",
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+ "[1000 rows x 96 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Przygotowanie inputu dla DEV_0\n",
+ "cars_dev_0_X = pd.get_dummies(cars_dev_0_X)\n",
+ "columns_to_add = [x for x in input_columns if x not in cars_dev_0_X.columns]\n",
+ "for column in columns_to_add:\n",
+ " cars_dev_0_X[column] = 0\n",
+ "cars_dev_0_X = cars_dev_0_X[input_columns]\n",
+ "cars_dev_0_X"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "33193.54683638966"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Wynik dla DEV_0\n",
+ "predictions_dev = model.predict(cars_dev_0_X)\n",
+ "np.savetxt(\"dev-0/out.tsv\", predictions_dev, fmt='%f')\n",
+ "sqrt(mean_squared_error(predictions_dev, cars_dev_0_Y))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cars_test_A_X = pd.read_csv('test-A/in.tsv', sep=\"\\t\", names=header[1:])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
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1000 rows × 96 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " mileage year engineCapacity brand_Abarth brand_Aixam brand_Alfa \\\n",
+ "0 203000 2010 1500 0 0 0 \n",
+ "1 39000 2008 1000 0 0 0 \n",
+ "2 190000 2005 1600 0 0 0 \n",
+ "3 230000 2001 1598 0 0 0 \n",
+ "4 189000 2000 1600 0 0 0 \n",
+ ".. ... ... ... ... ... ... \n",
+ "995 465000 2005 2500 0 0 0 \n",
+ "996 89074 2014 2000 0 0 0 \n",
+ "997 21711 2014 1329 0 0 0 \n",
+ "998 144000 2014 1500 0 0 0 \n",
+ "999 113606 2000 4000 0 0 0 \n",
+ "\n",
+ " brand_Aston brand_Audi brand_Austin brand_BMW ... brand_Uaz \\\n",
+ "0 0 0 0 0 ... 0 \n",
+ "1 0 0 0 0 ... 0 \n",
+ "2 0 0 0 0 ... 0 \n",
+ "3 0 0 0 0 ... 0 \n",
+ "4 0 0 0 1 ... 0 \n",
+ ".. ... ... ... ... ... ... \n",
+ "995 0 0 0 0 ... 0 \n",
+ "996 0 0 0 1 ... 0 \n",
+ "997 0 0 0 0 ... 0 \n",
+ "998 0 0 0 0 ... 0 \n",
+ "999 0 0 0 0 ... 0 \n",
+ "\n",
+ " brand_Vauxhall brand_Volkswagen brand_Volvo brand_Warszawa brand_dla \\\n",
+ "0 0 0 0 0 0 \n",
+ "1 0 0 0 0 0 \n",
+ "2 0 0 0 0 0 \n",
+ "3 0 1 0 0 0 \n",
+ "4 0 0 0 0 0 \n",
+ ".. ... ... ... ... ... \n",
+ "995 0 0 0 0 0 \n",
+ "996 0 0 0 0 0 \n",
+ "997 0 0 0 0 0 \n",
+ "998 0 0 0 0 0 \n",
+ "999 0 0 0 0 0 \n",
+ "\n",
+ " brand_star engineType_benzyna engineType_diesel engineType_gaz \n",
+ "0 0 0 1 0 \n",
+ "1 0 1 0 0 \n",
+ "2 0 0 1 0 \n",
+ "3 0 1 0 0 \n",
+ "4 0 1 0 0 \n",
+ ".. ... ... ... ... \n",
+ "995 0 0 1 0 \n",
+ "996 0 0 1 0 \n",
+ "997 0 1 0 0 \n",
+ "998 0 0 1 0 \n",
+ "999 0 1 0 0 \n",
+ "\n",
+ "[1000 rows x 96 columns]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Dostosowanie inputu dla testu\n",
+ "cars_test_A_X = pd.get_dummies(cars_test_A_X)\n",
+ "columns_to_add = [x for x in input_columns if x not in cars_test_A_X.columns]\n",
+ "for column in columns_to_add:\n",
+ " cars_test_A_X[column] = 0\n",
+ "cars_test_A_X = cars_test_A_X[input_columns]\n",
+ "cars_test_A_X"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Predykcja i zapisywanie wyniku dla testu\n",
+ "predictions_test = model.predict(cars_test_A_X)\n",
+ "np.savetxt(\"test-A/out.tsv\", predictions_test, fmt='%f')"
+ ]
+ }
+ ],
+ "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
+}
diff --git a/dev-0/out.tsv b/dev-0/out.tsv
new file mode 100644
index 0000000..99045fb
--- /dev/null
+++ b/dev-0/out.tsv
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diff --git a/in.tsv b/in.tsv
new file mode 100644
index 0000000..98aebe1
--- /dev/null
+++ b/in.tsv
@@ -0,0 +1,1000 @@
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diff --git a/test-A/out.tsv b/test-A/out.tsv
new file mode 100644
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