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 1/5] 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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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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przemekolender <56126908+przemekolender@users.noreply.github.com> Date: Tue, 11 May 2021 14:13:13 +0200 Subject: [PATCH 3/5] del dir --- .../Olender_Pawlak/FeatureEngineering.ipynb | 524 ------------------ .../Olender_Pawlak/FeatureEngineering.pdf | Bin 113421 -> 0 bytes 2 files changed, 524 deletions(-) delete mode 100644 Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.ipynb delete mode 100644 Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.pdf diff --git a/Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.ipynb b/Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.ipynb deleted file mode 100644 index 136c7ad36..000000000 --- a/Projekty/Projekt1/Olender_Pawlak/FeatureEngineering.ipynb +++ /dev/null @@ -1,524 +0,0 @@ -{ - "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 deleted file mode 100644 index 71c8e30d64b19a3505bf8f4820c60a1620b68c7e..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 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 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-0,0 +1,545 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "seven-career", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "moving-induction", + "metadata": {}, + "outputs": [], + "source": [ + "religion_unlab = pd.read_csv('AllBooks_baseline_DTM_Unlabelled.csv')\n", + "religion_lab = pd.read_csv('AllBooks_baseline_DTM_Labelled.csv')\n", + "\n", + "print(religion_lab.shape)\n", + "religion_lab.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "banned-southwest", + "metadata": {}, + "outputs": [], + "source": [ + "religion_unlab.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "authorized-report", + "metadata": {}, + "outputs": [], + "source": [ + "religion_lab['label'] = \"\"\n", + "\n", + "for i in range(len(religion_lab)):\n", + " religion_lab['label'][i] = religion_lab['Unnamed: 0'][i].split('_')[0]\n", + " \n", + "\n", + "labels = religion_lab['label'].value_counts()\n", + "labels = pd.DataFrame(labels).reset_index()\n", + "labels.columns = ['label', 'count']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colored-inclusion", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize = (9, 6))\n", + "sns.barplot(data = labels, y = 'label', x = 'count', color = 'dodgerblue')\n", + "fig.suptitle('Żródła tekstów', fontsize=18)\n", + "plt.xlabel('Liczba tesktów')\n", + "plt.ylabel(\"Księga\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ecological-frame", + "metadata": {}, + "outputs": [], + "source": [ + "most_pop_words = religion_unlab.sum().sort_values(ascending=False).head(30)\n", + "most_pop_words = pd.DataFrame(most_pop_words).reset_index()\n", + "most_pop_words.columns = ['word', 'count']\n", + "\n", + "fig = plt.figure(figsize = (9, 6))\n", + "sns.barplot(data = most_pop_words, x = 'word', y = 'count', color = 'dodgerblue')\n", + "plt.xticks(rotation=60)\n", + "fig.suptitle('Najpopularniejsze słowa', fontsize=18)\n", + "plt.xlabel('')\n", + "plt.ylabel('Liczba wystąpień we wszystkich tekstach')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "atomic-staff", + "metadata": {}, + "outputs": [], + "source": [ + "religion_lab_stacked = religion_lab.drop('Unnamed: 0', axis = 1).groupby('label').sum().transpose()\n", + "\n", + "fig, ax = plt.subplots(4, 2, figsize = (15, 24))\n", + "i = 0\n", + "\n", + "for c in religion_lab_stacked.columns:\n", + " df = pd.DataFrame(religion_lab_stacked[c].sort_values(ascending = False)).reset_index().head(10)\n", + " df.columns = ['word', 'count']\n", + " sns.barplot(data = df, x = 'word', y = 'count', color = 'dodgerblue', ax = ax[int(np.floor(i/2)%4), int(i%2)])\n", + " ax[int(np.floor(i/2)%4), int(i%2)].set_title(c)\n", + " ax[int(np.floor(i/2)%4), int(i%2)].set_xlabel('')\n", + " ax[int(np.floor(i/2)%4), int(i%2)].set_xticklabels(ax[int(np.floor(i/2)%4), int(i%2)].get_xticklabels(), rotation=45)\n", + "\n", + " \n", + " i+=1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cellular-flour", + "metadata": {}, + "outputs": [], + "source": [ + "lengths = religion_unlab.transpose().sum()\n", + "\n", + "fig = plt.figure(figsize = (9, 6))\n", + "sns.histplot(lengths)\n", + "fig.suptitle('Rozkład liczby słów w tekstach', fontsize=18)\n", + "plt.ylabel('Liczba wystąpień')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "appreciated-cookie", + "metadata": {}, + "outputs": [], + "source": [ + "religion_unlab.columns = religion_unlab.columns[1:].insert(0, 'foolishness')\n", + "religion_unlab.columns\n", + "df = religion_unlab.copy(deep=True)\n", + "\n", + "for i in range(len(df)):\n", + " for c in df.columns:\n", + " df[c][i] = df[c][i] * len(c)\n", + "\n", + "lengths2 = df.transpose().sum()\n", + "\n", + "fig = plt.figure(figsize = (9, 6))\n", + "sns.histplot(lengths2)\n", + "fig.suptitle('Rozkład liczby znaków w teksatch', fontsize=18)\n", + "plt.ylabel('Liczba wystąpień')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "rocky-editor", + "metadata": {}, + "source": [ + "## Druga część" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "physical-vault", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"AllBooks_baseline_DTM_Unlabelled.csv\")\n", + "print(f\"Shape of data: {df.shape}.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "individual-dryer", + "metadata": {}, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "banned-johnston", + "metadata": {}, + "source": [ + "Mamy 8266 słów (kolumn), 590 rekordów. Wszystkie wartości są dodatnimi wartościami. Nie mamy braków w danych." + ] + }, + { + "cell_type": "markdown", + "id": "happy-administrator", + "metadata": {}, + "source": [ + "Na początek sprawdźmy czy mamy w naszej ramce danych tzw. skrótowce, czyli słówka typu \"don't\",\"aren't\", isn't\" itp." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "virgin-compression", + "metadata": {}, + "outputs": [], + "source": [ + "df.rename(columns = {\"# foolishness\":\"foolishness\"}, inplace = True)\n", + "for i in df.columns:\n", + " if \"'\" in i: print(i)" + ] + }, + { + "cell_type": "markdown", + "id": "usual-binding", + "metadata": {}, + "source": [ + "Wniosek: nie mamy skrótowców, więc możemy pominąć punkt ich rozwijania. " + ] + }, + { + "cell_type": "markdown", + "id": "private-seminar", + "metadata": {}, + "source": [ + "Z naszych słów wyciągnijmy korzeń. Może się zdarzyć, że mamy jednocześnie 2 różne formy tego samego wyrazu, np. 'play', 'playing', 'plays'. Dla naszego zadania jest to oczywiście jedno i to samo słowo. Zrobimy to ponownie wykorzystując bibliotekę Spacy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "convenient-sleeve", + "metadata": {}, + "outputs": [], + "source": [ + "import en_core_web_sm\n", + "nlp = en_core_web_sm.load()\n", + "\n", + "listToStr = ' '.join([str(elem) for elem in df.columns])\n", + "doc = nlp(listToStr)\n", + "\n", + "i=0\n", + "tokenDict = {}\n", + "for token in doc:\n", + " if (str(token) != str(token.lemma_)): \n", + " tokenDict[str(token)] = token.lemma_\n", + " \n", + "print(tokenDict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "found-probability", + "metadata": {}, + "outputs": [], + "source": [ + "df.rename(columns = tokenDict, inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "finnish-moderator", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Kolumny unikalne: {len(df.columns.unique())}.\")\n", + "print(f\"Wszystkie kolumny: {len(df.columns)}.\")\n", + "print(\"Przyklad recznie znaleziony zduplikowanych kolumn\")\n", + "df[\"oppose\"]" + ] + }, + { + "cell_type": "markdown", + "id": "unnecessary-breeding", + "metadata": {}, + "source": [ + "Pozbadzmy sie duplikujacych kolumn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "medium-scoop", + "metadata": {}, + "outputs": [], + "source": [ + "df = df.sum(axis=1, level=0)\n", + "print(df.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "alpine-pocket", + "metadata": {}, + "source": [ + "Sprawdźmy teraz czy mamy słówka zaliczane do grupy 'najpopularniejszych słówek języka'. W języku angielskim są to słówka typu “the”, “is”, “in”, “for”, “where”, “when”, “to”, “at” etc. Ponownie wykorzystamy bibliotekę spaCy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "colored-costume", + "metadata": {}, + "outputs": [], + "source": [ + "from spacy.lang.en import English\n", + "from spacy.lang.en.stop_words import STOP_WORDS\n", + "\n", + "\n", + "nlp = English()\n", + "stopwords = []\n", + "for i in df.columns:\n", + " lexeme = nlp.vocab[i]\n", + " if lexeme.is_stop == True: stopwords.append(i)\n", + "print(stopwords)\n", + "print(len(stopwords))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "attempted-trust", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"{len(stopwords)} słów z naszej ramki zostało zklasyfikowane jako słowa o niskiej wartości dla całościowego znaczenia tekstu. Spośród ponad 6000 wszystkich słów, stanowią one niewielki procent więc możemy je usunąć.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "architectural-submission", + "metadata": {}, + "outputs": [], + "source": [ + "df = df.drop(columns = stopwords)\n", + "print(df.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "prerequisite-morgan", + "metadata": {}, + "source": [ + "Teraz sprawdźmy najczęściej pojawiające się słówka" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "silent-sweet", + "metadata": {}, + "outputs": [], + "source": [ + "most_pop_words = df.sum().sort_values(ascending=False).head(30)\n", + "most_pop_words = pd.DataFrame(most_pop_words).reset_index()\n", + "most_pop_words.columns = ['word', 'count']\n", + "\n", + "fig = plt.figure(figsize = (9, 6))\n", + "sns.barplot(data = most_pop_words, x = 'word', y = 'count', color = 'dodgerblue')\n", + "plt.xticks(rotation=60)\n", + "fig.suptitle('Most popular words in whole dataset', fontsize=18)\n", + "plt.show()\n", + "\n", + "print(f\"Średnia liczba wystąpień jednego słowa: {np.mean(most_pop_words['count']).round(2)}\")\n", + "print(f\"Odchylenie standardowe liczby wystąpień jednego słowa: {np.std(most_pop_words['count']).round(2)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "paperback-vacation", + "metadata": {}, + "outputs": [], + "source": [ + "d = {'word': df.columns}\n", + "word_len = pd.DataFrame(data = d)\n", + "word_len['nchars'] = word_len['word'].apply(lambda x: len(x))\n", + "word_len['occurences'] = word_len['word'].apply(lambda x: df[x].sum())\n", + "\n", + "\n", + "fig = plt.figure(figsize = (9, 6))\n", + "word_len['nchars'].plot(kind = 'hist', title = 'Rozkład długości wyrazów ze wszystkich tekstów', bins = 25\n", + " , xlabel = \"Liczba liter\", ylabel = 'Liczba słów o danej długości')\n", + "plt.show()\n", + "print(f\"Średnia długość słowa: {np.mean(word_len['nchars']).round(2)}\")\n", + "print(f\"Odchylenie standardowe: {np.std(word_len['nchars']).round(2)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fifteen-rider", + "metadata": {}, + "outputs": [], + "source": [ + "# Sprawdźmy te słowa, które sa bardzo długie albo krótkie\n", + "short_words = word_len.loc[word_len['nchars'] == 2]\n", + "short_words = short_words['word'].to_numpy()\n", + "\n", + "long_words = word_len.loc[word_len['nchars'] >= 17]\n", + "long_words = long_words['word'].to_numpy()\n", + "\n", + "print(word_len.loc[word_len['nchars'] == 2])\n", + "print(word_len.loc[word_len['nchars'] >= 17])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "obvious-scheduling", + "metadata": {}, + "outputs": [], + "source": [ + "# Ponieważ liczby wystąpień tych słów są dużo niższe niż odchylenie standardowe, również usuniemy je z ramki danych\n", + "\n", + "df = df.drop(columns = (short_words), axis = 1)\n", + "df = df.drop(columns = (long_words), axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mechanical-drawing", + "metadata": {}, + "outputs": [], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "id": "structured-touch", + "metadata": {}, + "source": [ + "Oceńmy teraz czy nasze słowa są nacechowane pozytywnie czy negatywnie." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "after-mexico", + "metadata": {}, + "outputs": [], + "source": [ + "from textblob import TextBlob\n", + "\n", + "def polarity(text):\n", + " return TextBlob(text).sentiment.polarity\n", + "\n", + "def sentiment(x):\n", + " if x<0:\n", + " return 'neg'\n", + " elif x==0:\n", + " return 'neu'\n", + " else:\n", + " return 'pos'\n", + " \n", + "def subjectivity(text):\n", + " return TextBlob(text).sentiment.subjectivity\n", + " \n", + "word_len['polarity_score']=word_len['word'].\\\n", + " apply(lambda x : polarity(x))\n", + "\n", + "word_len['polarity']=word_len['polarity_score'].\\\n", + " map(lambda x: sentiment(x))\n", + "\n", + "word_len['subjectivity']=word_len['word'].\\\n", + " map(lambda x: subjectivity(x))\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize = (15, 6))\n", + "print(\"Nacechowanie emocjonalne słów:\")\n", + "word_len['polarity_score'].hist(ax = axs[0])\n", + "word_len['polarity'].hist(ax = axs[1])\n", + "\n", + "plt.show()\n", + "\n", + "plot_sub = word_len['subjectivity'].hist()\n", + "plot_sub.set_title(\"Obiektywność\")" + ] + }, + { + "cell_type": "markdown", + "id": "affecting-breach", + "metadata": {}, + "source": [ + "Wniosek: większość słów z naszej bazy ma neutralne nacechowanie emocjonalne. Z pozostałych nielicznych słów, większość jest nacechowana pozytywnie. Słowa są również raczej obiektywne." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "funny-apollo", + "metadata": {}, + "outputs": [], + "source": [ + "from wordcloud import WordCloud, STOPWORDS\n", + "stopwords = set(STOPWORDS)\n", + "\n", + "def show_wordcloud(data):\n", + " wordcloud = WordCloud(\n", + " background_color='white',\n", + " stopwords=stopwords,\n", + " max_words=100,\n", + " max_font_size=30,\n", + " scale=3,\n", + " random_state=1)\n", + " \n", + " wordcloud=wordcloud.generate(str(data))\n", + "\n", + " fig = plt.figure(1, figsize=(12, 12))\n", + " plt.axis('off')\n", + "\n", + " plt.imshow(wordcloud)\n", + " plt.show()\n", + "\n", + "show_wordcloud(religion_unlab.sum().sort_values(ascending=False).to_dict())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", 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religion_lab['label'][i] = religion_lab['Unnamed: 0'][i].split('_')[0]\n", + "#for i in range(len(religion_lab)):\n", + "# religion_lab['label'][i] = religion_lab['Unnamed: 0'][i].split('_')[0]\n", " \n", "\n", "labels = religion_lab['label'].value_counts()\n", @@ -90,6 +90,36 @@ "plt.show()" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "funny-apollo", + "metadata": {}, + "outputs": [], + "source": [ + "from wordcloud import WordCloud, STOPWORDS\n", + "stopwords = set(STOPWORDS)\n", + "\n", + "def show_wordcloud(data):\n", + " wordcloud = WordCloud(\n", + " background_color='white',\n", + " stopwords=stopwords,\n", + " max_words=100,\n", + " max_font_size=30,\n", + " scale=3,\n", + " random_state=1)\n", + " \n", + " wordcloud=wordcloud.generate(str(data))\n", + "\n", + " fig = plt.figure(1, figsize=(12, 12))\n", + " plt.axis('off')\n", + "\n", + " plt.imshow(wordcloud)\n", + " plt.show()\n", + "\n", + "show_wordcloud(religion_unlab.sum().sort_values(ascending=False).to_dict())" + ] + }, { "cell_type": "code", "execution_count": null, @@ -117,40 +147,122 @@ { "cell_type": "code", "execution_count": null, - "id": "cellular-flour", + "id": "august-video", "metadata": {}, "outputs": [], "source": [ - "lengths = religion_unlab.transpose().sum()\n", - "\n", - "fig = plt.figure(figsize = (9, 6))\n", - "sns.histplot(lengths)\n", - "fig.suptitle('Rozkład liczby słów w tekstach', fontsize=18)\n", - "plt.ylabel('Liczba wystąpień')\n", - "plt.show()" + "import re\n", + "from textstat import lexicon_count \n", + "from textstat import flesch_reading_ease \n", + "from textstat import flesch_kincaid_grade \n", + "from textstat import sentence_count \n", + "from textstat import lexicon_count " ] }, { "cell_type": "code", "execution_count": null, - "id": "appreciated-cookie", + "id": "advisory-bidder", "metadata": {}, "outputs": [], "source": [ - "religion_unlab.columns = religion_unlab.columns[1:].insert(0, 'foolishness')\n", - "religion_unlab.columns\n", - "df = religion_unlab.copy(deep=True)\n", + "file = open('Complete_data .txt', 'r')\n", + "file_content = file.read()\n", + "file.close()\n", "\n", - "for i in range(len(df)):\n", - " for c in df.columns:\n", - " df[c][i] = df[c][i] * len(c)\n", + "content_list = re.split('\\d+\\.\\d+', file_content)\n", "\n", - "lengths2 = df.transpose().sum()\n", + "text = []\n", + "for i in range(len(content_list)):\n", + " stripped = content_list[i].strip()\n", + " if stripped != '':\n", + " text.append(content_list[i])\n", + " \n", + "substrings_to_drop = ['\\n', ' \\n', '\\n ', ' \\n', '§', '§ ']\n", + " \n", + "for i in range(len(corpus)):\n", + " for j in substrings_to_drop:\n", + " corpus[i] = corpus[i].replace(j, '') \n", + " \n", + "data = pd.DataFrame(corpus).reset_index()\n", + "data.columns = ['index', 'text']\n", "\n", - "fig = plt.figure(figsize = (9, 6))\n", - "sns.histplot(lengths2)\n", - "fig.suptitle('Rozkład liczby znaków w teksatch', fontsize=18)\n", - "plt.ylabel('Liczba wystąpień')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "earlier-canon", + "metadata": {}, + "outputs": [], + "source": [ + "#characters\n", + "data['len'] = data['text'].str.len()\n", + "#words\n", + "data['words'] = data['text'].apply(lambda x : lexicon_count(x, removepunct=True))\n", + "#average sentence length\n", + "data['avg_sen'] = data['text'].str.split().apply(lambda x : [len(i) for i in x]).map(lambda x: np.mean(x))\n", + "#reading ease\n", + "data['reading_ease'] = data['text'].apply(lambda x : flesch_reading_ease(x))\n", + "#flesch_kincaid_grade\n", + "data['grade'] = data['text'].apply(lambda x : flesch_kincaid_grade(x))\n", + "#sentences\n", + "data['sentences'] = data['text'].apply(lambda x : sentence_count(x))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "living-envelope", + "metadata": {}, + "outputs": [], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "formed-sight", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (15, 5))\n", + "sns.histplot(data = data, x = 'len', ax = ax1)\n", + "sns.histplot(data['words'], ax = ax2)\n", + "ax1.set_title('Rozkład liczby znaków w tekście')\n", + "ax2.set_title('Rozkład liczby słów w tekście')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fitted-cloud", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (15, 5))\n", + "sns.histplot(np.log1p(data['sentences']), ax = ax1)\n", + "sns.histplot(data = data, x = 'avg_sen', ax = ax2)\n", + "ax1.set_title('Rozkład liczby zdać w tekstach (skala logarytmiczna)')\n", + "ax2.set_title('Rozkład średniej iczby zdań w tekstach')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "contrary-twelve", + "metadata": {}, + "outputs": [], + "source": [ + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (15, 5))\n", + "sns.histplot(data = data, x = 'reading_ease', ax = ax1)\n", + "sns.histplot(data['grade'], ax = ax2)\n", + "ax1.set_title('Flesh reading ease')\n", + "ax2.set_title('Flesh-Kincaid grade level')\n", "plt.show()" ] }, @@ -489,36 +601,6 @@ "source": [ "Wniosek: większość słów z naszej bazy ma neutralne nacechowanie emocjonalne. Z pozostałych nielicznych słów, większość jest nacechowana pozytywnie. Słowa są również raczej obiektywne." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "funny-apollo", - "metadata": {}, - "outputs": [], - "source": [ - "from wordcloud import WordCloud, STOPWORDS\n", - "stopwords = set(STOPWORDS)\n", - "\n", - "def show_wordcloud(data):\n", - " wordcloud = WordCloud(\n", - " background_color='white',\n", - " stopwords=stopwords,\n", - " max_words=100,\n", - " max_font_size=30,\n", - " scale=3,\n", - " random_state=1)\n", - " \n", - " wordcloud=wordcloud.generate(str(data))\n", - "\n", - " fig = plt.figure(1, figsize=(12, 12))\n", - " plt.axis('off')\n", - "\n", - " plt.imshow(wordcloud)\n", - " plt.show()\n", - "\n", - "show_wordcloud(religion_unlab.sum().sort_values(ascending=False).to_dict())" - ] } ], "metadata": { diff --git a/Projekty/Projket2/Grupa3/Olender_Pawlak/EDA.pdf b/Projekty/Projket2/Grupa3/Olender_Pawlak/EDA.pdf index 599afa23e83d5b6471cb7121c35684bc1341fb93..cdecb5154ac719ca4568d25a9825acb1f945ec92 100644 GIT binary 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