From d292d1c31c4439e7c9887679f41a7948868194c0 Mon Sep 17 00:00:00 2001 From: przemekolender <56126908+przemekolender@users.noreply.github.com> Date: Tue, 9 Mar 2021 14:15:00 +0100 Subject: [PATCH 1/2] Add files via upload --- .../PrzemyslawOlenderPD1.ipynb | 381 ++++++++++++++++++ .../PrzemyslawOlenderPD1.pdf | Bin 0 -> 642233 bytes 2 files changed, 381 insertions(+) create mode 100644 Prace_domowe/Praca_domowa1/Grupa3/Przemyslaw_Olender/PrzemyslawOlenderPD1.ipynb create mode 100644 Prace_domowe/Praca_domowa1/Grupa3/Przemyslaw_Olender/PrzemyslawOlenderPD1.pdf diff --git a/Prace_domowe/Praca_domowa1/Grupa3/Przemyslaw_Olender/PrzemyslawOlenderPD1.ipynb b/Prace_domowe/Praca_domowa1/Grupa3/Przemyslaw_Olender/PrzemyslawOlenderPD1.ipynb new file mode 100644 index 000000000..3d4fceb3d --- /dev/null +++ b/Prace_domowe/Praca_domowa1/Grupa3/Przemyslaw_Olender/PrzemyslawOlenderPD1.ipynb @@ -0,0 +1,381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "declared-compilation", + "metadata": {}, + "source": [ + "# Praca domowa 1\n", + "\n", + "### Przemysław Olender" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mighty-roads", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "associate-federal", + "metadata": {}, + "outputs": [], + "source": [ + "fires = pd.read_csv('forest_fires_dataset.csv')\n", + "\n", + "fires.head()" + ] + }, + { + "cell_type": "markdown", + "id": "norman-tiffany", + "metadata": {}, + "source": [ + "Analizujemy zbiór danych o pożarach w północnej Portugalii. Według opisu autora cała ramka służy po przewidywania powierzchni pożary za pomocą pozostałych zminnych, są to:\n", + "\n", + " X - x-owa współrzedna wewnątrz parku\n", + " \n", + " Y - y-owa współrzędna wewnątrz parku\n", + "\n", + "month - miesiąc\n", + "\n", + "day - dzień tygodnia\n", + "\n", + "FFMC - wskaźnik wilgotności paliw* ze ściółki leśnej\n", + "\n", + "DMC - wskaźnik wilgotności paliw* znajdujących się poniżej ściółki leśnej\n", + "\n", + "DC - wskaźnik wilgotności paliw* wgłąb gleby\n", + "\n", + "ISI - wskażnik łączocy wilgotność martwych paliw* i prędkości wiatru, pomaga w oszacowaniu prędkości roznoszenia sie pożaru\n", + "\n", + "temp - temperatura w stopniach celsiujsza\n", + "\n", + "RH - względna wilgotność powietrza wyrażona w %\n", + "\n", + "wind - prędkość wiatru w km/godzinę\n", + "\n", + "rain - wielkość opadów w mm/$m^2$\n", + "\n", + "area - spalona powierzchnia w hektarach\n", + "\n", + "FFMc, DMC, DC, ISI to indeks z międzynaroowego systemu FWI (Fire Weather Index) określającego zagrożenie pożarowe (informazje ze strony https://www.nwcg.gov/publications/pms437/cffdrs/fire-weather-index-system). \n", + "\n", + "\\* - paliwo oznacza materiał podatny na spalenie, w tym przyapdku rośliny\n", + "\n", + "Źródło danych: https://www.apispreadsheets.com/datasets/129\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "meaningful-sequence", + "metadata": {}, + "outputs": [], + "source": [ + "fires.info()" + ] + }, + { + "cell_type": "markdown", + "id": "innocent-onion", + "metadata": {}, + "source": [ + "W danych nie ma żadnych braków, nie będziemy musieli się poźniej martwić wypełniem dziur." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "painful-scholarship", + "metadata": {}, + "outputs": [], + "source": [ + "fires.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "copyrighted-brand", + "metadata": {}, + "outputs": [], + "source": [ + "fires.hist(bins = 20, figsize=(18, 12))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "funded-approval", + "metadata": {}, + "source": [ + "Jak widać na wykresach, temperatura ma rozkład bliski normalnemu, wskaźniki FWI i RH mają rozkłady skośne.\n", + "Prawie wszytskie wartości w kolumnie area są badrzo bliskie zero, użyjmy więc skali logarytmicznej, żeby lepiej przyjrzeć się wykresowi." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "suffering-farmer", + "metadata": {}, + "outputs": [], + "source": [ + "sns.displot(np.log1p(fires['area']))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "olympic-trouble", + "metadata": {}, + "source": [ + "Sprawdżmy ile wartości jest równych 0:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cathedral-affiliate", + "metadata": {}, + "outputs": [], + "source": [ + "sum(fires['area'] == 0)" + ] + }, + { + "cell_type": "markdown", + "id": "rocky-circular", + "metadata": {}, + "source": [ + "Aż 247 z 517 wartości w kolumnie area to 0, może to oznaczać błąd w danych lub to, że większość pożarów w zaookrągleniu nie stawiło nawet jednego hektara lasu." + ] + }, + { + "cell_type": "markdown", + "id": "distinct-photographer", + "metadata": {}, + "source": [ + "Sprawdźmy jakie korelacje zachodzą miedzy danymi:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "answering-reality", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 10))\n", + "sns.heatmap(fires.corr(), annot=True, square=True)\n", + "#fig = plt.figure(fizsize= (10, 10))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "vanilla-theta", + "metadata": {}, + "source": [ + "Największe korelacje zachodzą między wskaźnikami FWI i temperaturę, nie jest to duże zaskoczenie, ponieważ im wyższa temperatura tym rośliny są suchsze. Podobnej korelacji można było sie spodziewać między wielkościa opadów (kolumna rain) a wkaźnikamim jednak jak widzieliśmy wcześniej opady prawie nie występują w badanym czasie. Sprawdźmy jak wyglądają scatterploty dla tych zmiennych:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "accurate-registrar", + "metadata": {}, + "outputs": [], + "source": [ + "cols = ['FFMC', 'DMC', 'DC', 'ISI', 'temp']\n", + "\n", + "sns.pairplot(fires, y_vars=fires[cols], x_vars=fires[cols])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "extensive-thong", + "metadata": {}, + "outputs": [], + "source": [ + "sns.pairplot?" + ] + }, + { + "cell_type": "markdown", + "id": "crazy-empire", + "metadata": {}, + "source": [ + "Na wykresach ze zminnymi ISI i FFMC można zauważyć odstające wartość, usuńmy je, żeby nie zaburzały wykresów." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ultimate-anniversary", + "metadata": {}, + "outputs": [], + "source": [ + "tmp = fires[fires['ISI'] < 50 ]\n", + "tmp = tmp[tmp['FFMC'] > 60]\n", + "\n", + "\n", + "sns.pairplot(tmp, y_vars=fires[cols], x_vars=fires[cols])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "following-bridges", + "metadata": {}, + "source": [ + "Przyjzyjmy się jeszcze zależnościom zmiennej area od innych zmiennych numerycznych, użyjemu loarytmicznego przekształcenia powirzchni.:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "olympic-hayes", + "metadata": {}, + "outputs": [], + "source": [ + "fires_log = fires\n", + "fires_log['area'] = np.log1p(fires['area'])\n", + "\n", + "sns.pairplot(fires_log, y_vars=\"area\", x_vars=fires.columns.values)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "normal-detective", + "metadata": {}, + "source": [ + "Sprawdźmy jak pożary o danej powierzchni rozkładaja się w poszczególnych dniach tygodnia i miesiącach. Zoabczmy też, ile pożarów występuje w danych dnia i miesiącach." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "standard-electricity", + "metadata": {}, + "outputs": [], + "source": [ + "day_order = ['mon', 'tue', 'wed', 'thu', 'fri', 'sat', 'sun']\n", + "month_order = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']\n", + "\n", + "\n", + "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(13, 10))\n", + "sns.countplot(data=fires, x='day', order = day_order, color = 'dodgerblue', ax = ax1)\n", + "sns.boxplot(data=fires_log, x='day', y = 'area', order = day_order, color = 'dodgerblue', ax = ax2)\n", + "sns.countplot(data=fires, x='month', order = month_order, color = 'dodgerblue', ax = ax3)\n", + "sns.boxplot(data=fires_log, x='month', y= 'area', order = month_order, color = 'dodgerblue', ax = ax4)\n", + "\n", + "ax1.set_title('Liczba pożarów w poszczególne dni tygodnia')\n", + "ax2.set_title('Rozkład powierzchni pożarów w poszczególne dni tygodnia')\n", + "ax3.set_title('Liczba pożarów w poszczególnych miesiącach')\n", + "ax4.set_title('Rozkład powierzchni pożarów w poszczególnych miesiącach')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "decimal-berry", + "metadata": {}, + "source": [ + "Liczba pożarów jest największa w weekeny, może się to wiązać z działalnością człowieka i większą liczbą odwiedzających park w weekend.\n", + "\n", + "Jeśli chodzi o rozłożenie pożarów w ciągu roku, to w lipicu i sierpieniu liczba pożarów znacznie większa niż w pozostałych miesiącach. Sprawdźmy jak wygląda rozłożenie temperatury i wilgotności w ciągu roku, żeby ocenić powiązanie tych cech z liczbą pożarów.\n", + "\n", + "Jenak i w ciągu poszczególnych dni jak i miesięcy powierzchnia pożarów utrzymuje się na podobnym poziomie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mental-appendix", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(13, 10))\n", + "sns.boxplot(data = fires, x = 'month', y = 'temp', order = month_order, ax = ax1, color = 'white')\n", + "sns.boxplot(data = fires, x = 'month', y = 'RH', order = month_order, ax = ax2, color = 'white')\n", + "sns.boxplot(data = fires, x = 'month', y = 'wind', order = month_order, ax = ax3, color = 'white')\n", + "sns.boxplot(data = fires, x = 'month', y = 'rain', order = month_order, ax = ax4, color = 'white')\n", + "\n", + "ax1.set_title('Rozkład temperatury w ciągu roku')\n", + "ax2.set_title('Rozkład wilgotności w ciągu roku')\n", + "ax3.set_title('Rozkład prędkości wiatru w ciągu roku')\n", + "ax4.set_title('Rozkład opadów wiatru w ciągu roku')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "devoted-anger", + "metadata": {}, + "source": [ + "Jak widać temperatura w lipcu i sierpniu jest najwyższa w ciągu rokum jednak nie jest dużo większa niż w czerwcu i wrześniu. Wilgotność również znacząco nie odstaje od innych miesięcy letnich.\n", + "\n", + "Widać korelację dużej prekości wiatru w grudniu i dużej powierzchni strawionej pożarem (z poprzedniego wykresu), nawet przy małej liczbie pożarów (mimo, że pożarów było mało to każdy był stosunkowo duży).\n", + "\n", + "Sprawdźmy jeszcze rozłożenie wskaźników FWI w ciągu roku." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "metallic-interest", + "metadata": {}, + "outputs": [], + "source": [ + "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(13, 10))\n", + "sns.boxplot(data = fires, x = 'month', y = 'FFMC', order = month_order, ax = ax1, color = 'white')\n", + "sns.boxplot(data = fires, x = 'month', y = 'DMC', order = month_order, ax = ax2, color = 'white')\n", + "sns.boxplot(data = fires, x = 'month', y = 'DC', order = month_order, ax = ax3, color = 'white')\n", + "sns.boxplot(data = fires, x = 'month', y = 'ISI', order = month_order, ax = ax4, color = 'white')\n", + "\n", + "ax1.set_title('Rozkład wskaźnika FFMC w ciągu roku')\n", + "ax2.set_title('Rozkład wskaźnika DMC w ciągu roku')\n", + "ax3.set_title('Rozkład wskaźnika DC w ciągu roku')\n", + "ax4.set_title('Rozkład wskaźnika ISI w ciągu roku')\n", + "\n", + "plt.show()" + ] + } + ], + "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": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Prace_domowe/Praca_domowa1/Grupa3/Przemyslaw_Olender/PrzemyslawOlenderPD1.pdf b/Prace_domowe/Praca_domowa1/Grupa3/Przemyslaw_Olender/PrzemyslawOlenderPD1.pdf new file mode 100644 index 0000000000000000000000000000000000000000..057dc0c2e40d187f574a90db5d77269eb141422b GIT binary patch literal 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zN?iS2K3CYIiL#mvfD&1vXYz9ZU5wKAt)@{Uuu==OP@c;pE0tB24J%8@BI~{8>j7#x zQCZTRC~rmft`*q$_3&}~wDU6~fygJ2dH#p7I+5pe5O&@F%ur5;-+mW_qy!&5xE9WN zuN!U$ozUtzAGH!=_r=hDXK5c|FR7*c(H74=EB0dykmqXc4B0LKCyjdF%X+4d{vhZ7 zq}{l*YC%1{sye{0GF(+CZgkL52Oq?zGF($AYUI(>hWt=~Y!5@CRqtzEnTKDwzv3u) z&}(c%gI&N}oiC!btgjs(zDMza<;_wa?9!?Tw}`lK{p-(NL0a%XRWu{Ze^9~yUm2~T zWNJpQWN+_6@L%V#O7xO;X7&XCrM3UvifHpNnHn>2uo#&c8?iDmu^BU(nVGOLG8(dS zGMI33{1>X~|N9MHdSy#5Qz%9jR(f@9MgnF6Cf)ywOlSG;$IPsYQ_?AhDB}-L*^QZ; zmYG^%`(X6pc6CfTYhPgn6v#q z-H2z(vMPJn;ayIi>c w$qrus1^5ef4@z_ufSgEq_J3|bXBR^!7Y`>>GblEef2cD~C{j{Uc`>N}2Ny&0c>n+a literal 0 HcmV?d00001 From d5e2ab31540173e1f182619d4bae8a6daed49832 Mon Sep 17 00:00:00 2001 From: przemekolender <56126908+przemekolender@users.noreply.github.com> Date: Tue, 30 Mar 2021 14:23:33 +0200 Subject: [PATCH 2/2] add milestone2 --- .../Olender_Pawlak/FeatureEngineering.ipynb | 524 ++++++++++++++++++ .../Olender_Pawlak/FeatureEngineering.pdf | Bin 0 -> 113421 bytes 2 files changed, 524 insertions(+) create mode 100644 Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.ipynb create mode 100644 Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.pdf diff --git a/Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.ipynb b/Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.ipynb new file mode 100644 index 000000000..136c7ad36 --- /dev/null +++ b/Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.ipynb @@ -0,0 +1,524 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Feature engineering\n", + "## Milestone 2\n", + "\n", + "### Dominik Pawlak, Przemysław Olender" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import category_encoders as ce\n", + "import warnings\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", + "from statistics import stdev\n", + "import xgboost as xgb\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.ensemble import BaggingClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.model_selection import train_test_split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grades_df = pd.read_csv('school_grades_dataset.csv')\n", + "\n", + "print(grades_df.shape)\n", + "grades_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regresja liniowa dla G1 i G2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (12, 5))\n", + "\n", + "sns.scatterplot(data = grades_df, x = 'G1', y = 'G3', ax = ax1)\n", + "sns.scatterplot(data = grades_df, x = 'G2', y = 'G3', ax = ax2)\n", + "\n", + "plt.show()\n", + "\n", + "# liniowa zalezcnosc miedzy G1, G2, i G3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sprawdźmy jak sprawdzi się prsoty model regresji liniowej dla G1, G2 oraz G1 i G2 jednocześnie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X1 = grades_df[['G1']]\n", + "Y = grades_df['G3']\n", + "\n", + "model1 = LinearRegression()\n", + "X1_train, X1_test, Y1_train, Y1_test = train_test_split(X1, Y, test_size = 0.3, random_state = 1)\n", + "model1.fit(X1_train, Y1_train)\n", + "Y1_test_predicted = model1.predict(X1_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(X1_test, Y1_test, label = 'testing data')\n", + "plt.plot(X1_test, Y1_test_predicted, label = 'prediction', linewidth = 3, color = 'red')\n", + "plt.xlabel('G1')\n", + "plt.ylabel('G3')\n", + "plt.legend(loc = 'upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'RMSE: {mean_squared_error(Y1_test, Y1_test_predicted, squared = False)}')\n", + "print(f'R-squared: {model1.score(X1_test, Y1_test)}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X2 = grades_df[['G2']]\n", + "model2 = LinearRegression()\n", + "X2_train, X2_test, Y2_train, Y2_test = train_test_split(X2, Y, test_size = 0.3, random_state = 1)\n", + "model2.fit(X2_train, Y2_train)\n", + "Y2_test_predicted = model2.predict(X2_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.scatter(X2_test, Y2_test, label = 'testing data')\n", + "plt.plot(X2_test, Y2_test_predicted, label = 'prediction', linewidth = 3, color = 'red')\n", + "plt.xlabel('G2')\n", + "plt.ylabel('G3')\n", + "plt.legend(loc = 'upper left')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'RMSE: {mean_squared_error(Y2_test, Y2_test_predicted, squared = False)}')\n", + "print(f'R-squared: {model2.score(X2_test, Y2_test)}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X3 = grades_df[['G1', 'G2']]\n", + "model3 = LinearRegression()\n", + "X3_train, X3_test, Y3_train, Y3_test = train_test_split(X3, Y, test_size = 0.3, random_state = 1)\n", + "model3.fit(X3_train, Y3_train)\n", + "Y3_test_predicted = model3.predict(X3_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f'RMSE: {mean_squared_error(Y3_test, Y3_test_predicted, squared = False)}')\n", + "print(f'R-squared: {model3.score(X3_test, Y3_test)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Najlepeij wypadła regresja oparta na G1 i G2, model oparty tylko na G2 jest minimalnie gorszy, najsłabeij prezentuje się model oparty na G1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Przewidywanie G3 za pomocą innych zmiennych oraz bez G1 i G2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grades_df[grades_df['G3'] == 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cieżko jest nie zdobyć żadnego punktu na egazminie, zakładamy, że te osoby nie podeszły i nie można przewidzieć ilości punktów, usuwamy rekordy z ramki." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grades_df = grades_df[grades_df['G3'] != 0]\n", + "\n", + "grades_df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " W naszej ramce mamy też dużo zmienncyh kategorycznych, zakodujmy je, żeby usprawnić działanie modeli." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cat_cols = ['school', 'sex', 'address', 'famsize', 'Mjob', 'Fjob', 'reason', 'guardian', 'Pstatus', 'sex', 'school']\n", + "bin_cols = ['famsup', 'activities', 'nursery', 'internet', 'romantic', 'higher', 'paid', 'schoolsup']\n", + "\n", + "grades_df_new = grades_df.drop(columns = cat_cols)\n", + "grades_df_new = grades_df.drop(columns = bin_cols)\n", + "\n", + "for i in cat_cols:\n", + " means = grades_df.groupby(i)['G3'].mean()\n", + " grades_df_new[i] = grades_df[i].map(means)\n", + " \n", + "for i in bin_cols:\n", + " encoder = ce.OrdinalEncoder(mapping = [{'col': i, 'mapping': {'yes': 1, 'no': 0}},])\n", + " grades_df_new[i] = encoder.fit_transform(grades_df)[i]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pogrupujmy rezultat G3 w przedział, aby nie trzeba było przewidywać dokłądniej liczby - zadanie będzie prostsze." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "names = ['failed', 'weak', 'ok', 'good', 'excellent']\n", + "\n", + "grades_df_new['result'] = pd.cut(grades_df['G3'], bins=[-1, 9, 11, 13, 15, 21], labels = names)\n", + "\n", + "sns.countplot(grades_df_new['result'], order = names)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# funkcja liczby accuracy dla 4 modeli i za pomocą xgboost wybiera najważniejsze cechy\n", + "\n", + "def modelScores(X, X_train, X_test, y_train, y_test):\n", + " \n", + " importance = pd.DataFrame()\n", + " importance['col'] = X.columns \n", + " importance['xgb'] = 0\n", + "\n", + " model_xgb = xgb.XGBClassifier(\n", + " max_depth=4\n", + " ,learning_rate=0.2\n", + " ,reg_lambda=1\n", + " ,n_estimators=150\n", + " ,subsample = 0.9\n", + " ,colsample_bytree = 0.9)\n", + " model_xgb.fit(X_train, y_train)\n", + "\n", + " #print(model_xgb.feature_importances_)\n", + " #print(sum(model_xgb.feature_importances_))\n", + " #print(len(model_xgb.feature_importances_))\n", + " #print(grades_df_new.shape)\n", + "\n", + " importance['xgb'] = importance['xgb'] + model_xgb.feature_importances_ / 100\n", + " importance = importance.sort_values(axis=0, ascending=False, by='xgb')\n", + " #print('Top 10 features:')\n", + " #print(importance.iloc[:, 1:])\n", + " print(importance.reset_index().drop('index', axis = 1))\n", + " \n", + " print(f'xgboost accuarcy: {model_xgb.score(X_test,y_test)}')\n", + " \n", + " modelLR = LogisticRegression(random_state=1, max_iter=100000)\n", + " modelLR.fit(X_train, y_train)\n", + " y_hat = modelLR.predict(X_test)\n", + " print(f'Logistic Regression accuracy: {accuracy_score(y_test, y_hat)}')\n", + " \n", + " model_rf = RandomForestClassifier(n_estimators=188,\n", + " max_depth=5,\n", + " min_samples_split = 7, \n", + " max_features = len(X_train.columns),\n", + " random_state=0,\n", + " n_jobs = 15)\n", + " model_rf.fit(X_train, y_train)\n", + " print(f'RandomForestClassifier accuracy: {model_rf.score(X_test,y_test)}')\n", + " \n", + " model1 = DecisionTreeClassifier(random_state=1)\n", + " clf = BaggingClassifier(base_estimator=model_rf,\n", + " n_estimators=100, random_state=0)\n", + " clf.fit(X_train, y_train)\n", + " print(f'DecisionTreeClassifier accuracy: {clf.score(X_test,y_test)}')\n", + " \n", + " return importance['col'].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = grades_df_new.drop(['G3', 'result'], axis = 1)\n", + "Y = grades_df_new['result']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n", + "#X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.3, random_state=42)\n", + "\n", + "#print(X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cols = modelScores(X, X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = grades_df_new[cols]\n", + "Y = grades_df_new['result']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n", + "#X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.3, random_state=42)\n", + "\n", + "#print(X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape)\n", + "modelScores(X, X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = grades_df_new.drop(['G3', 'result'], axis = 1)\n", + "Y = grades_df_new['result']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n", + "#X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.3, random_state=42)\n", + "\n", + "#print(X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wyniki gorsze niż dla prostej regresji liniowej.\n", + "\n", + "Stworzylismy prosty model opierający sięn a G1 i G2, spróbujmy teraz bez tych atrybutów." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "grades_df_new = grades_df_new.drop(columns = ['G1', 'G2'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Przewidywanie przedziału oceny za pomocą wszystkich kolumn:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = grades_df_new.drop(['G3', 'result'], axis = 1)\n", + "Y = grades_df_new['result']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n", + "#X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.3, random_state=42)\n", + "\n", + "#print(X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cols = modelScores(X, X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Przewidywanie przedziału oceny za pomocą najważnijeszych kolumn:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = grades_df_new[cols]\n", + "Y = grades_df_new['result']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n", + "#X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.3, random_state=42)\n", + "\n", + "#print(X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape)\n", + "modelScores(X, X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Przewidywanie dokładnej oceny za pomocą wszystkich kolumn:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = grades_df_new.drop(['G3', 'result'], axis = 1)\n", + "Y = grades_df_new['G3']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n", + "#X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.3, random_state=42)\n", + "\n", + "#print(X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape)\n", + "\n", + "cols = modelScores(X, X_train, X_test, y_train, y_test)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Przewidywanie dokładnej oceny za pomocą najważnieszych kolumn:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "X = grades_df_new[cols]\n", + "Y = grades_df_new['G3']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=42)\n", + "#X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=0.3, random_state=42)\n", + "\n", + "#print(X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape)\n", + "modelScores(X, X_train, X_test, y_train, y_test)" + ] + } + ], + "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": 5 +} diff --git a/Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.pdf b/Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.pdf new file mode 100644 index 0000000000000000000000000000000000000000..71c8e30d64b19a3505bf8f4820c60a1620b68c7e GIT binary patch literal 113421 zcmce;byS?ovOY|ZK!D&9f_re+L4vzmaCeu%B?NbO4el<%Ew}}OySuyd8?yI4Th6)P zz4wp%t*n_vzin03)m6{aJ#R>51%+uCXjx!MH%<<3U>WHN=n1U#%wV~==!8uz91QK~ zge`O(3M9ZU!qnHf2Fcwp@v>gx!iRGR@ z5`TiPm~UTjjhfd%C{#4Nio~{UqHTAQ#g>TDl>7gXb+4OC5FVH-I0k7|;1ZBCgaf z$cT(oC{_+xEzrc@+1t?@mJVI_2Ym)^mY)G(F@DWmY{^CDtI^hh#1bQo!xXUFem!(; z7W&@t@ure6Slx|rwf-`D{s5*~r-^8P*gc~XG-W?#jaI4I?W&(r@;eig&nb+mkh#rm z>|VXlUN@uV+ikqO6yC#6DR@FXM8Xx0cWPE0&IbS(`W6&I*a<{_Jv6>M$)Fyy;zEh z92$zdQ!AKSMLL==K%vOKhG-Yn7bat5vLiRD+P~ zj}J>#UvLE``P5e@9Kb}SGJB}nZC2p@A>gGWJD<)d%qN 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