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aCoY4?s}cBTy#O;Xn>E_DwHwg0#{U7tsu`UC literal 0 HcmV?d00001 diff --git a/src/multidim/notebooks/05_mds_outliers.ipynb b/src/multidim/notebooks/05_mds_outliers.ipynb new file mode 100644 index 0000000..1daa095 --- /dev/null +++ b/src/multidim/notebooks/05_mds_outliers.ipynb @@ -0,0 +1,515 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d8927acb", + "metadata": {}, + "source": [ + "# Analiza Wielowymiarowa - zajecia 05 - Skalowanie wielowymiarowe i obserwacje nietypowe" + ] + }, + { + "cell_type": "markdown", + "id": "e285e16f", + "metadata": {}, + "source": [ + "## Skalowanie wielowymiarowe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b64e6d7f-c1c6-4248-8d3e-d51600c69de1", + "metadata": {}, + "outputs": [], + "source": [ + "from multidim.utils import resolve_stata, load_stata\n", + "\n", + "STATA_PATH, STATA_TYPE = resolve_stata(version = 18, stype = \"se\")\n", + "# make sure they are proper ones\n", + "STATA_PATH, STATA_TYPE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1bf9afd9-feeb-41c4-ae20-d620098eb810", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#load_stata(STATA_PATH, STATA_TYPE)\n", + "load_stata(\"D:\\Stata18\", \"se\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "086775bb", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import scipy\n", + "import sklearn\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import scipy as sp\n", + "from scipy.stats import chi2\n", + "from matplotlib import pyplot as plt\n", + "from sklearn.cluster import AgglomerativeClustering\n", + "from multidim.funs import plot_dendrogram\n", + "from multidim.datasets import load_boston" + ] + }, + { + "cell_type": "markdown", + "id": "a8aecafd", + "metadata": {}, + "source": [ + "### Przykład 1." + ] + }, + { + "cell_type": "markdown", + "id": "02066827", + "metadata": {}, + "source": [ + "Dane i przyklad zostaly pozyczone z podrecznika Stata MV" + ] + }, + { + "cell_type": "markdown", + "id": "584ddf05", + "metadata": {}, + "source": [ + "Zaladowanie danych z internetu. Jezeli nie dziala, zbior danych jest w materialach do zajec\n", + "\n", + "Dane dotycza charakterystyk odzywczych platkow sniadanowych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa7313eb", + "metadata": {}, + "outputs": [], + "source": [ + "%%stata \n", + "use https://www.stata-press.com/data/r17/cerealnut\n", + "des " + ] + }, + { + "cell_type": "markdown", + "id": "69e4edb1", + "metadata": {}, + "source": [ + "Podstawowy opis statystyczny cech charakterystycznych płatkow sniadaniowych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6be565a9", + "metadata": {}, + "outputs": [], + "source": [ + "%stata summarize calories-K, sep(4)" + ] + }, + { + "cell_type": "raw", + "id": "91eb0aef", + "metadata": {}, + "source": [ + "Zastepujemy odstepy w nazwach znakiem podreslenia dla czytelnosci wykresow" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "573323e2", + "metadata": {}, + "outputs": [], + "source": [ + "%stata replace brand = subinstr(brand,\" \",\"_\",.)" + ] + }, + { + "cell_type": "markdown", + "id": "4bdf01e8", + "metadata": {}, + "source": [ + "Polecenie mds wykonuje skalowanie wielowymiarowe na podstawie podanych charakterystyk\n", + "opcja \"id\" jest niezbedna, identyfikuje obserwacje" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2207f143", + "metadata": {}, + "outputs": [], + "source": [ + "%stata mds calories-K, id(brand)" + ] + }, + { + "cell_type": "markdown", + "id": "780c7b17", + "metadata": {}, + "source": [ + "Dwie pierwsze wartosci wlasne wyjasniaja 99,7% niepodobienstwa " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "77d1c505", + "metadata": {}, + "outputs": [], + "source": [ + "%stata mds calories-K, id(brand) std" + ] + }, + { + "cell_type": "markdown", + "id": "c19b33df", + "metadata": {}, + "source": [ + "opcja \"std\" przed wykonaniem obliczen standaryzuje wartosci charakterystyk" + ] + }, + { + "cell_type": "markdown", + "id": "cb365671", + "metadata": {}, + "source": [ + "## Obserwacje nietypowe" + ] + }, + { + "cell_type": "markdown", + "id": "33034ef9", + "metadata": {}, + "source": [ + "Wczytanie danych do pakietu Stata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3deb87d3", + "metadata": {}, + "outputs": [], + "source": [ + "%stata load boston " + ] + }, + { + "cell_type": "markdown", + "id": "325fb836", + "metadata": {}, + "source": [ + "Wczytanie danych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85e79dce", + "metadata": {}, + "outputs": [], + "source": [ + "boston = load_boston()\n", + "y = boston['MEDV'] \n", + "df = boston[['CRIM', 'ZN', 'INDUS', 'CHAS','NOX','RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT' ]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "721defa2", + "metadata": {}, + "outputs": [], + "source": [ + "print(boston)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef1fb479", + "metadata": {}, + "outputs": [], + "source": [ + "#df.describe()\n", + "df_1 = df[['TAX', 'B']]\n", + "df_2 = df[['CRIM', 'ZN', 'INDUS', 'RM', 'AGE', 'DIS', 'RAD', 'PTRATIO','LSTAT']]\n", + "df_3 = df[['CHAS', 'NOX']]\n", + "\n", + "ax = sns.boxplot(data=df_2, orient=\"h\", palette=\"Set2\")\n", + "\n", + "ax = sns.boxplot(x=df[\"CRIM\"])\n", + "ax.set_xlabel('Crime rate per capita')\n" + ] + }, + { + "cell_type": "markdown", + "id": "5005b530", + "metadata": {}, + "source": [ + "Wizualizacja danych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d393131", + "metadata": {}, + "outputs": [], + "source": [ + "#Wykres rozrzutu\n", + "ax = sns.scatterplot(x=\"LSTAT\", y=\"CRIM\", data=df)" + ] + }, + { + "cell_type": "markdown", + "id": "0819a2d4", + "metadata": {}, + "source": [ + "Odległość Mahalanobisa - funckcja zwracająca obserwacje odstające" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14bd9807", + "metadata": {}, + "outputs": [], + "source": [ + "def mahalanobis_method(df):\n", + " #M-Distance\n", + " x_minus_mu = df - np.mean(df, axis=0)\n", + " cov = np.cov(df.values.T) #Covariance\n", + " inv_covmat = sp.linalg.inv(cov) #Inverse covariance\n", + " left_term = np.dot(x_minus_mu, inv_covmat) \n", + " mahal = np.dot(left_term, x_minus_mu.T)\n", + " md = np.sqrt(mahal.diagonal())\n", + " \n", + " #Flag as outlier\n", + " outlier = []\n", + " #Cut-off point\n", + " C = np.sqrt(chi2.ppf((1-0.001), df=df.shape[1])) #degrees of freedom = number of variables\n", + " for index, value in enumerate(md):\n", + " if value > C:\n", + " outlier.append(index)\n", + " else:\n", + " continue\n", + " return outlier, md" + ] + }, + { + "cell_type": "markdown", + "id": "46d1b04c", + "metadata": {}, + "source": [ + "Obserwacje odstające według odległości Mahalanobisa: 2 wymiarowe" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e1e452f", + "metadata": {}, + "outputs": [], + "source": [ + "df_bivariate = df[['LSTAT', 'CRIM']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd711c1f", + "metadata": {}, + "outputs": [], + "source": [ + "outliers_mahal_bi, md_bi = mahalanobis_method(df=df_bivariate)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "232e0200", + "metadata": {}, + "outputs": [], + "source": [ + "outliers_mahal_bi" + ] + }, + { + "cell_type": "markdown", + "id": "832e8fe8", + "metadata": {}, + "source": [ + "Obserwacje odstające według odległości Mahalanobisa: względem wszystkich zmiennych w zbiorze" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5e2f332", + "metadata": {}, + "outputs": [], + "source": [ + "outliers_mahal, md = mahalanobis_method(df=df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa926fc0", + "metadata": {}, + "outputs": [], + "source": [ + "outliers_mahal" + ] + }, + { + "cell_type": "markdown", + "id": "d56794dc", + "metadata": {}, + "source": [ + "### Winsoryzacja" + ] + }, + { + "cell_type": "markdown", + "id": "bc68873c", + "metadata": {}, + "source": [ + "Stworzenie kopii danych" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2f657f64", + "metadata": {}, + "outputs": [], + "source": [ + "df_win = df.copy(deep=True)" + ] + }, + { + "cell_type": "markdown", + "id": "9c19abb3", + "metadata": {}, + "source": [ + "Winsoryzacja prawego ogona rozkładu " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28e6d114", + "metadata": {}, + "outputs": [], + "source": [ + "df_win['CRIM_wins_95%'] = sp.stats.mstats.winsorize(df['CRIM'], limits=(0, 0.05))\n", + "df_win['CRIM_wins_925%'] = sp.stats.mstats.winsorize(df['CRIM'], limits=(0, 0.075))" + ] + }, + { + "cell_type": "markdown", + "id": "0714427a", + "metadata": {}, + "source": [ + "Tu będzie opis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ccb6502", + "metadata": {}, + "outputs": [], + "source": [ + "df_win.describe()" + ] + }, + { + "cell_type": "markdown", + "id": "863a476b", + "metadata": {}, + "source": [ + "Wykres rozkładu zmiennej Crime rate per capita by town" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23c1b673", + "metadata": {}, + "outputs": [], + "source": [ + "sns.distplot(df['CRIM'])" + ] + }, + { + "cell_type": "markdown", + "id": "0cb07301", + "metadata": {}, + "source": [ + "Wykresy po winsoryzacji " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "678bd5eb", + "metadata": {}, + "outputs": [], + "source": [ + "sns.distplot(df_win['CRIM_wins_95%'])\n", + "sns.distplot(df_win['CRIM_wins_925%'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d9bd4fd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.4" + }, + "vscode": { + "interpreter": { + "hash": "a8685faa0ed749449a0f1a8710c4e7cd8c1c7833bc8ac4d1844d25fbee35609f" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}