628 lines
50 KiB
Plaintext
628 lines
50 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Kkolejna część zajęć będzie wprowadzeniem do drugiej, szeroko używanej biblioteki w Pythonie: `sklearn`. Zajęcia będą miały charaktere case-study poprzeplatane zadaniami do wykonania. Zacznijmy od załadowania odpowiednich bibliotek."
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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": 3,
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# ! pip install matplotlib"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Zacznijmy od załadowania danych. Na dzisiejszych zajęciach będziemy korzystać z danych z portalu [gapminder.org](https://www.gapminder.org/data/)."
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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": 23,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv('gapminder.csv', index_col=0)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Dane zawierają różne informacje z większość państw świata (z roku 2008). Poniżej znajduje się opis kolumn:\n",
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" * female_BMI - średnie BMI u kobiet\n",
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" * male_BMI - średnie BMI u mężczyzn\n",
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" * gdp - PKB na obywatela\n",
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" * population - wielkość populacji\n",
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" * under5mortality - wskaźnik śmiertelności dzieni pon. 5 roku życia (na 1000 urodzonych dzieci)\n",
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" * life_expectancy - średnia długość życia\n",
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" * fertility - wskaźnik dzietności"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**zad. 1**\n",
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"Na podstawie danych zawartych w `df` odpowiedz na następujące pytania:\n",
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" * Jaki był współczynniki dzietności w Polsce w 2018?\n",
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" * W którym kraju ludzie żyją najdłużej?\n",
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" * Z ilu krajów zostały zebrane dane?"
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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": 26,
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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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"175"
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]
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},
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"execution_count": 26,
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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.head(10)\n",
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"\n",
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"df.loc['Poland', 'fertility']\n",
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"df[df['life_expectancy'].max() == df['life_expectancy']]\n",
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"len(df.index)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**zad. 2** Stwórz kolumnę `gdp_log`, która powstanie z kolumny `gdp` poprzez zastowanie funkcji `log` (logarytm). \n",
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"\n",
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"Hint 1: Wykorzystaj funkcję `apply` (https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.apply.html#pandas.Series.apply).\n",
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"\n",
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"Hint 2: Wykorzystaj fukcję `log` z pakietu `np`."
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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": 30,
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"metadata": {},
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"outputs": [],
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"source": [
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"df['gdp_log'] = df['gdp'].apply(np.log)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Naszym zadaniem będzie oszacowanie długości życia (kolumna `life_expectancy`) na podstawie pozostałych zmiennych. Na samym początku, zastosujemy regresje jednowymiarową na `fertility`."
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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": 31,
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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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"Y shape: (175,)\n",
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"X shape: (175,)\n"
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]
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}
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],
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"source": [
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"y = df['life_expectancy'].values\n",
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"X = df['fertility'].values\n",
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"\n",
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"print(\"Y shape:\", y.shape)\n",
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"print(\"X shape:\", X.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Będziemy korzystać z gotowej implementacji regreji liniowej z pakietu sklearn. Żeby móc wykorzystać, musimy napierw zmienić shape na dwuwymiarowy."
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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": 32,
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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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"Y shape: (175, 1)\n",
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"X shape: (175, 1)\n"
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]
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}
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],
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"source": [
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"y = y.reshape(-1, 1)\n",
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"X = X.reshape(-1, 1)\n",
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"\n",
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"print(\"Y shape:\", y.shape)\n",
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"print(\"X shape:\", X.shape)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Jeszcze przed właściwą analizą, narysujmy wykres i zobaczny czy istnieje \"wizualny\" związek pomiędzy kolumnami."
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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": 98,
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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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"<Axes: xlabel='gdp_log', ylabel='gdp'>"
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]
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},
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"execution_count": 98,
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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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"data": {
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.plot.scatter('gdp_log', 'gdp')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**zad. 3** Zaimportuj `LinearRegression` z pakietu `sklearn.linear_model`."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Tworzymy obiekt modelu regresji liniowej."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 35,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from sklearn.linear_model import LinearRegression\n",
|
|
"model = LinearRegression()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Trening modelu ogranicza się do wywołania metodu `fit`, która przyjmuje dwa argumenty:"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 36,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
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],
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"text/plain": [
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"LinearRegression()"
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]
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},
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"execution_count": 36,
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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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"model.fit(X, y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Współczynniki modelu:"
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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": 37,
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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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"Wyraz wolny (bias): [83.2025629]\n",
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"Współczynniki cech: [[-4.41400624]]\n"
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]
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}
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],
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"source": [
|
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"print(\"Wyraz wolny (bias):\", model.intercept_)\n",
|
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"print(\"Współczynniki cech:\", model.coef_)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**zad. 4** Wytrenuj nowy model `model2`, który będzie jako X przyjmie kolumnę `gdp_log`. Wyświetl parametry nowego modelu."
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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": 38,
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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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"Wyraz wolny (bias): [20.24520889]\n",
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"Współczynniki cech: [[5.47719379]]\n"
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]
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}
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],
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"source": [
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"y = df['life_expectancy'].values\n",
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"X = df['gdp_log'].values\n",
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"\n",
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"y = y.reshape(-1, 1)\n",
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"X = X.reshape(-1, 1)\n",
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"\n",
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"model.fit(X, y)\n",
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"\n",
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"print(\"Wyraz wolny (bias):\", model.intercept_)\n",
|
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"print(\"Współczynniki cech:\", model.coef_)"
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]
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},
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{
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"cell_type": "markdown",
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|
"metadata": {},
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"source": [
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"Mając wytrenowany model możemy wykorzystać go do predykcji. Wystarczy wywołać metodę `predict`."
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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": 42,
|
|
"metadata": {},
|
|
"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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"input: 7.1785454837637\t predicted: 59.56349361537759\t expected: 52.8\n",
|
|
"input: 9.064620717626777\t predicted: 69.89389316839483\t expected: 76.8\n",
|
|
"input: 9.418492105471156\t predicted: 71.83211533534711\t expected: 75.5\n",
|
|
"input: 8.86827250899781\t predicted: 68.81845597997369\t expected: 56.7\n",
|
|
"input: 10.155646068918863\t predicted: 75.86965044411781\t expected: 75.5\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"X_test = X[:5,:]\n",
|
|
"y_test = y[:5,:]\n",
|
|
"output = model.predict(X_test)\n",
|
|
"\n",
|
|
"for i in range(5):\n",
|
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" print(\"input: {}\\t predicted: {}\\t expected: {}\".format(X_test[i,0], output[i,0], y_test[i,0]))"
|
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]
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},
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{
|
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"cell_type": "markdown",
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|
"metadata": {},
|
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"source": [
|
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"## Sprawdzenie jakości modelu - metryki: $MSE$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
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|
"Istnieją 3 metryki, które określają jak dobry jest nasz model:\n",
|
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" * $MSE$: [błąd średnio-kwadratowy](https://pl.wikipedia.org/wiki/B%C5%82%C4%85d_%C5%9Bredniokwadratowy) \n",
|
|
" * $RMSE = \\sqrt{MSE}$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 44,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Root Mean Squared Error: 5.542126033117308\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.metrics import mean_squared_error\n",
|
|
"\n",
|
|
"rmse = np.sqrt(mean_squared_error(y, model.predict(X)))\n",
|
|
"print(\"Root Mean Squared Error: {}\".format(rmse))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 45,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"R^2: 0.768485708231896\n",
|
|
"Root Mean Squared Error: 4.108807300711791\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Import necessary modules\n",
|
|
"from sklearn.linear_model import LinearRegression\n",
|
|
"from sklearn.metrics import mean_squared_error\n",
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"\n",
|
|
"# Create training and test sets\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state=42)\n",
|
|
"\n",
|
|
"# Create the regressor: reg_all\n",
|
|
"reg_all = LinearRegression() \n",
|
|
"\n",
|
|
"# Fit the regressor to the training data\n",
|
|
"reg_all.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"# Predict on the test data: y_pred\n",
|
|
"y_pred = reg_all.predict(X_test)\n",
|
|
"\n",
|
|
"# Compute and print R^2 and RMSE\n",
|
|
"print(\"R^2: {}\".format(reg_all.score(X_test, y_test)))\n",
|
|
"rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
|
|
"print(\"Root Mean Squared Error: {}\".format(rmse))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Regresja wielu zmiennych"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"Model regresji liniowej wielu zmiennych nie różni się istotnie od modelu jednej zmiennej. Np. chcąc zbudować model oparty o dwie kolumny: `fertility` i `gdp` wystarczy zmienić X (cechy wejściowe):"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 46,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(175, 2)\n",
|
|
"Wyraz wolny (bias): [78.39388437]\n",
|
|
"Współczynniki cech: [[-3.68816683e+00 1.38298454e-04]]\n",
|
|
"Root Mean Squared Error: 4.347105512793037\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"X = df[['fertility', 'gdp']]\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state=42)\n",
|
|
"\n",
|
|
"print(X.shape)\n",
|
|
"\n",
|
|
"model_mv = LinearRegression()\n",
|
|
"model_mv.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"print(\"Wyraz wolny (bias):\", model_mv.intercept_)\n",
|
|
"print(\"Współczynniki cech:\", model_mv.coef_)\n",
|
|
"\n",
|
|
"y_pred = model_mv.predict(X_test)\n",
|
|
"\n",
|
|
"rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
|
|
"print(\"Root Mean Squared Error: {}\".format(rmse))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**zad. 7** \n",
|
|
" * Zbuduj model regresji liniowej, która oszacuje wartność kolumny `life_expectancy` na podstawie pozostałych kolumn.\n",
|
|
" * Wyświetl współczynniki modelu.\n",
|
|
" * Oblicz wartości metryki rmse na zbiorze trenującym.\n",
|
|
" "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 87,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Wyraz wolny (bias): 51.77966340645703\n",
|
|
"Współczynniki cech: [-1.26558913e+00 1.58457647e+00 -1.19465585e-05 8.99682207e-10\n",
|
|
" -1.32027358e-01 3.09413223e-01 1.74214537e+00]\n",
|
|
"Root Mean Sqared Error: 3.42188778846474\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from sklearn.linear_model import LogisticRegression, LinearRegression, LogisticRegressionCV\n",
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
"\n",
|
|
"X = df.drop('life_expectancy', axis='columns')\n",
|
|
"y = df['life_expectancy']\n",
|
|
"\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3)\n",
|
|
"\n",
|
|
"model = LinearRegression()\n",
|
|
"model.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"print(\"Wyraz wolny (bias):\", model.intercept_)\n",
|
|
"print(\"Współczynniki cech:\", model.coef_)\n",
|
|
"\n",
|
|
"y_pred = model.predict(X_test)\n",
|
|
"\n",
|
|
"rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n",
|
|
"print(f\"Root Mean Sqared Error: {rmse}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"**zad. 6**\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
" Zaimplementuj metrykę $RMSE$ jako fukcję rmse (szablon poniżej). Fukcja rmse przyjmuje dwa parametry typu list i ma zwrócić wartość metryki $RMSE$ ."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 96,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "ValueError",
|
|
"evalue": "Found input variables with inconsistent numbers of samples: [175, 176]",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[1;32mj:\\Python\\2023-programowanie-w-pythonie\\zajecia3\\sklearn cz. 1.ipynb Cell 39\u001b[0m line \u001b[0;36m2\n\u001b[0;32m <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia3/sklearn%20cz.%201.ipynb#X53sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m expected\u001b[39m.\u001b[39mappend(\u001b[39m1\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia3/sklearn%20cz.%201.ipynb#X53sZmlsZQ%3D%3D?line=27'>28</a>\u001b[0m \u001b[39m# print(rmse(predicted,expected))\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/j%3A/Python/2023-programowanie-w-pythonie/zajecia3/sklearn%20cz.%201.ipynb#X53sZmlsZQ%3D%3D?line=28'>29</a>\u001b[0m \u001b[39mprint\u001b[39m(np\u001b[39m.\u001b[39msqrt(mean_squared_error(predicted, expected)))\n",
|
|
"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\sklearn\\utils\\_param_validation.py:211\u001b[0m, in \u001b[0;36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 205\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 206\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[0;32m 207\u001b[0m skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[0;32m 208\u001b[0m prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[0;32m 209\u001b[0m )\n\u001b[0;32m 210\u001b[0m ):\n\u001b[1;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 212\u001b[0m \u001b[39mexcept\u001b[39;00m InvalidParameterError \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m 213\u001b[0m \u001b[39m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[0;32m 214\u001b[0m \u001b[39m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[0;32m 215\u001b[0m \u001b[39m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[0;32m 216\u001b[0m \u001b[39m# message to avoid confusion.\u001b[39;00m\n\u001b[0;32m 217\u001b[0m msg \u001b[39m=\u001b[39m re\u001b[39m.\u001b[39msub(\n\u001b[0;32m 218\u001b[0m \u001b[39mr\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m\\\u001b[39m\u001b[39mw+ must be\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 219\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mparameter of \u001b[39m\u001b[39m{\u001b[39;00mfunc\u001b[39m.\u001b[39m\u001b[39m__qualname__\u001b[39m\u001b[39m}\u001b[39;00m\u001b[39m must be\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m 220\u001b[0m \u001b[39mstr\u001b[39m(e),\n\u001b[0;32m 221\u001b[0m )\n",
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"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\sklearn\\metrics\\_regression.py:474\u001b[0m, in \u001b[0;36mmean_squared_error\u001b[1;34m(y_true, y_pred, sample_weight, multioutput, squared)\u001b[0m\n\u001b[0;32m 404\u001b[0m \u001b[39m@validate_params\u001b[39m(\n\u001b[0;32m 405\u001b[0m {\n\u001b[0;32m 406\u001b[0m \u001b[39m\"\u001b[39m\u001b[39my_true\u001b[39m\u001b[39m\"\u001b[39m: [\u001b[39m\"\u001b[39m\u001b[39marray-like\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 415\u001b[0m y_true, y_pred, \u001b[39m*\u001b[39m, sample_weight\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, multioutput\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39muniform_average\u001b[39m\u001b[39m\"\u001b[39m, squared\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m\n\u001b[0;32m 416\u001b[0m ):\n\u001b[0;32m 417\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Mean squared error regression loss.\u001b[39;00m\n\u001b[0;32m 418\u001b[0m \n\u001b[0;32m 419\u001b[0m \u001b[39m Read more in the :ref:`User Guide <mean_squared_error>`.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 472\u001b[0m \u001b[39m 0.825...\u001b[39;00m\n\u001b[0;32m 473\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 474\u001b[0m y_type, y_true, y_pred, multioutput \u001b[39m=\u001b[39m _check_reg_targets(\n\u001b[0;32m 475\u001b[0m y_true, y_pred, multioutput\n\u001b[0;32m 476\u001b[0m )\n\u001b[0;32m 477\u001b[0m check_consistent_length(y_true, y_pred, sample_weight)\n\u001b[0;32m 478\u001b[0m output_errors \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39maverage((y_true \u001b[39m-\u001b[39m y_pred) \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m \u001b[39m2\u001b[39m, axis\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, weights\u001b[39m=\u001b[39msample_weight)\n",
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"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\sklearn\\metrics\\_regression.py:99\u001b[0m, in \u001b[0;36m_check_reg_targets\u001b[1;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_check_reg_targets\u001b[39m(y_true, y_pred, multioutput, dtype\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mnumeric\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m 66\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Check that y_true and y_pred belong to the same regression task.\u001b[39;00m\n\u001b[0;32m 67\u001b[0m \n\u001b[0;32m 68\u001b[0m \u001b[39m Parameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 97\u001b[0m \u001b[39m correct keyword.\u001b[39;00m\n\u001b[0;32m 98\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 99\u001b[0m check_consistent_length(y_true, y_pred)\n\u001b[0;32m 100\u001b[0m y_true \u001b[39m=\u001b[39m check_array(y_true, ensure_2d\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, dtype\u001b[39m=\u001b[39mdtype)\n\u001b[0;32m 101\u001b[0m y_pred \u001b[39m=\u001b[39m check_array(y_pred, ensure_2d\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, dtype\u001b[39m=\u001b[39mdtype)\n",
|
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"File \u001b[1;32mc:\\software\\python3\\lib\\site-packages\\sklearn\\utils\\validation.py:409\u001b[0m, in \u001b[0;36mcheck_consistent_length\u001b[1;34m(*arrays)\u001b[0m\n\u001b[0;32m 407\u001b[0m uniques \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39munique(lengths)\n\u001b[0;32m 408\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(uniques) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m--> 409\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 410\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFound input variables with inconsistent numbers of samples: \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m 411\u001b[0m \u001b[39m%\u001b[39m [\u001b[39mint\u001b[39m(l) \u001b[39mfor\u001b[39;00m l \u001b[39min\u001b[39;00m lengths]\n\u001b[0;32m 412\u001b[0m )\n",
|
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"\u001b[1;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [175, 176]"
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]
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}
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],
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"source": [
|
|
"def rmse(expected, predicted):\n",
|
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" \"\"\"\n",
|
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" argumenty:\n",
|
|
" expected (type: list): poprawne wartości\n",
|
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" predicted (type: list): oszacowanie z modelu\n",
|
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" \"\"\"\n",
|
|
"\n",
|
|
" if len(expected) != len(predicted):\n",
|
|
" raise ValueError(\"Lists have to be equal length, can't proceed.\")\n",
|
|
"\n",
|
|
" mse = 0\n",
|
|
" for i in range(len(expected)):\n",
|
|
" mse += pow((expected[i] - predicted[i]),2)\n",
|
|
" return np.sqrt(mse/len(expected))\n",
|
|
" \n",
|
|
" \n",
|
|
"\n",
|
|
"y = df['life_expectancy'].values\n",
|
|
"X = df[['fertility', 'gdp']].values\n",
|
|
"\n",
|
|
"test_model = LinearRegression()\n",
|
|
"test_model.fit(X, y)\n",
|
|
"\n",
|
|
"predicted = list(test_model.predict(X))\n",
|
|
"expected = list(y)\n",
|
|
"# expected.append(1)\n",
|
|
"\n",
|
|
"print(rmse(predicted,expected))\n",
|
|
"print(np.sqrt(mean_squared_error(predicted, expected)))\n"
|
|
]
|
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.10.11"
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}
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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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