329 lines
15 KiB
Plaintext
329 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"tags": []
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},
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"source": [
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"# Feature Engineering\n",
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"# Klassifikation\n",
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"# Regression\n",
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"# Validierung und mehr\n",
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"## Sampling und Resampling\n",
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"## Validierungstechniken"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-15T20:53:02.218161Z",
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"start_time": "2020-04-15T20:53:02.079407Z"
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}
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},
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"outputs": [],
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"source": [
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"## preparaton\n",
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"\n",
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"## import libraries\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns; sns.set()\n",
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"%matplotlib inline\n",
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"\n",
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"## load data\n",
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"datapath = '../3_data'\n",
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"from os import chdir; chdir(datapath)\n",
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"data = pd.read_csv('bank_data_prep.csv')\n",
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"data.shape ## zur Kontrolle\n",
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"\n",
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"## features - target - split\n",
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"X = data.drop('y', axis=1)\n",
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"y = data['y']"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Holdout Validierung"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.8773958016428354\n"
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]
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}
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],
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"source": [
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"## repetition from classification and regression\n",
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"\n",
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"## train - test - split\n",
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"from sklearn.model_selection import train_test_split\n",
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"X_train, X_test, y_train, y_test, = train_test_split(\n",
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" X,\n",
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" y,\n",
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" train_size=2/3,\n",
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" random_state=1234)\n",
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"\n",
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"## train and score\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"model = RandomForestClassifier()\n",
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"model.fit(X_train, y_train)\n",
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"print(model.score(X_test, y_test))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Kreuzvalidierung"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"ref: https://scikit-learn.org/stable/modules/cross_validation.html"
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]
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},
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{
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"cell_type": "raw",
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"metadata": {
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"tags": []
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},
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"source": [
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"## \"manually\" with sklearn.model_selection.KFold\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.model_selection import KFold\n",
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"kf = KFold(n_splits=5, shuffle=True, random_state=1234)\n",
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"\n",
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"scores = []\n",
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"for train, test in kf.split(X):\n",
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" X_train = X.iloc[train,:]\n",
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" X_test = X.iloc[test,:]\n",
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" y_train = y[train]\n",
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" y_test = y[test]\n",
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" model = RandomForestClassifier()\n",
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" model.fit(X_train, y_train)\n",
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" score = model.score(X_test, y_test)\n",
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" scores.append(score)\n",
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" print(score)\n",
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"\n",
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"## results\n",
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"print(scores)\n",
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"print('mean:', np.mean(scores))\n",
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"print('std: ', np.std(scores))\n",
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"sns.boxplot(x=scores);"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-14T21:29:33.905316Z",
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"start_time": "2020-04-14T21:29:32.438063Z"
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}
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},
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"outputs": [],
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"source": [
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.model_selection import cross_val_score\n",
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"\n",
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"model = RandomForestClassifier(random_state=1234)\n",
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"\n",
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"## cross validation\n",
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"kfold = 10 ## default: 5\n",
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"scores = cross_val_score(model, X, y, cv=kfold)\n",
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"\n",
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"## combine call\n",
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"#scores = cross_val_score(RandomForestClassifier(random_state=1234), X, y, cv=kfold)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-14T21:29:33.920862Z",
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"start_time": "2020-04-14T21:29:33.909541Z"
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},
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"mean: 0.8855983772819472\n",
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"std: 0.011729681375092045\n"
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]
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},
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{
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"data": {
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"image/png": 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vW9x3331l8eLF9e2Dlk022aRsv/325dZbb33R8uPGjSsbbbRR3UX73HPP1X/XX3992WabbervDfbHP/6x/PKXvyxnn312WX/99ft334fYTdgSv7/llluudpvrWjvGHO6///6y7bbblnYZinFvttlm5YEHHigzZ86MaK1vwTz44IN1d1+47bbbyite8YpVxh1vXfT09PT/HYzEcY/E+Y63FRcuXFh39w705je/udxyyy39873DDjuUl7/85f2Xx/081nXvvfeWkTjmZcuW1V3hb3jDG0q7ZMf96KOP1uu76aab9v8slm3NYbyVE2MauL6xY8fW26G1vnbPdbvGvawD5nuojM0s/NRTT9WvW2211So/32KLLfovGyieDL/61a+Ws846q96g8SQQy1555ZWlt/fF3XLBBRfU944G3vni/cSJEyeWDTbYYK22ua61Y8whnmxi3B/84AfrbTBp0qT6/urAJ5xuG/dHP/rRcuedd5aPf/zjZcyYMfX9zhNOOKG8//3vr5fHOAdvL9Y7YcKE8uSTT5aROu5nn322jj0ekK6++up63EfM8+mnn16fmLpx3PEkEcu0juFpefzxx/uPV4r1vvKVr3zR9kLMdxwfMtLGPHv27Dr/v/jFL8pXvvKVejxXHN8Tc90a+1DLjjt+/swzz9TrHX+/rTGFeAL9d+uLJ+xOmOvWdRjucc/ugPkeKqk9D60D1wa+Qg7xxB43ymDxKiuqMg4cioNKvvvd75ZXvepV5cQTT6zFOVCU2t13310vG7zNwdv7d9tc19ox5rgzLVq0qDz//PPli1/8YrnkkkvK5ptvXo466qj6xzgchmLcMa54YowH32uuuaacccYZ9eCyH/7whx0x163rMNzjjlBsreu8886rB5LFtuLgqzlz5nTluOPBNg4QjYPFIpziAfTnP/95uemmm8ry5cvrOpYuXbra7YVuvG+vzZhnzZpVv2644Ybloosuqk8oDz30UD2wMm6P4ZAd90EHHVQWLFhQ/zbjMSn+Js8555z6KjvGtTbra/dct2vcszpgvjtiz0Pstmvtimn9d4gbKm6cwX72s5/VKo87z8Ybb1x/dtlll5X999+/PnAeffTR/cted9119dVW7NoavM3Y3mBr2ua61o4xR8lGWMT611tvvfqzHXfcsR7F+73vfa+egdCN4z755JPrg+uRRx7Zvzs3XnWff/75dQ/Lv5vrl73sZWU4tGPc8Sr25ptvrnua4tVsuPjii8vb3/72cu2119ajvbtx3GeeeWYNpsMPP7w+8caT7jHHHFNmzJjRv83B89160B2O+W7HmA855JB61P7AXeFvetOb6s9+85vf1DNxOm3cr3/96+sTX4wroinmJv6m44XM+PHjV1nfQAPX1+65bl2H4R73IR0w3x2x56G1eyZ25QwU38cxCIPFbtjY7dq6o4XYtRc/i/eTBp4+Ezfk+973vhetI3Z1Rf0NnqA1bXNda8eYW+/FtcIhxG7ReE988CmA3TLu2G0bxR0RNFCcLhXzG/9irgdvL+Y9LhuuXXztGHeIB5dWOIR48HnNa17TtfMd4gH2wgsvrMer/OlPf6pvycQrtte+9rX18tXNd+v7br1v/39jDgOfSEL8bcdbc8PxNux/Mu5wwAEH1OOzfve739XQPfTQQ+sr8a233nqt1tfuuW7XuDthvjsiHiZPnlzvOAM/ayAOEIpXxPE+zmDxBxN3qoG7hGL3T5zvG1XXEiUXu3X32muvF61j6tSp9Yl24AFzDz/8cH1QXd0217V2jPn3v/99fcUy8PzjOLc43kd74xvfWIbDuh53PMjGE2IcGDhQfB+hFHewWG/coQZGVutAs/g7GKnjjlel8ZkB8XstsRs8DrTq1vkO8So8XpHH+GOcsRv/xhtvLHvvvXe9PNYb6x/4dl4cVBoHJcb1GYljjrCIz4CIvRIt8fvxWNCpcx3RFMftxGNQPPHFbvo4yDvGGJ/ZEAcER0ANXF8sG7/XWl+757pd476wA+Z7yGRPz7jgggvqeaxxitLA82TjPN44Jz3OX12yZEld9umnn67LximHsWz8O/7445t99923WbhwYf86r7vuumaHHXZoVq5cudptnnbaac0BBxzQzJw5s/9zHuLc2+Ey3GNetGhR/XyDj3zkI83f/va35r777qu3we67797885//7Npxf+Mb36inSsXY4/MO4mt8/+1vf7te3tfXV8+hj9MaY57j8xHidhh4mttIHPcTTzxRT/H79Kc/3cyaNau588476+l98TkfS5cu7dpxT58+vX5/yy231NOrTz311Gbvvfdu5s2bVy+PscUYP/nJT9bfb537H+fij9Qxx/057vdnnXVWvTyWi8ez+LuPv/9OHHecqhiPPXHKcfz9xjxNnTq1ufTSS/vXN2PGjHrKY3zuQevzDuLzFFqnOXbCXLdj3H/rkPkeCul4iBs4zmvdc8896zmwxx57bP85r/E1zoG95ppr+pePc/bjDhYTFr9z0kkn9S/fcvnll9cP41iTxYsXN1/4whfqA2z8iyfS1p1xOLRjzI8++mj9/IdYR3zITPyRx4erDKd1Pe5Y33e+8536mRYxpve85z31w3QG3onmzJlTxx3bizthfN7FcD6Btmvc8SE0xxxzTH1winPf4zaIqOjmcccD8nnnnVf/zmNMsezDDz+8yjYfeeSROu4dd9yx2WeffeqT75peRIyUMceHnx122GF1e7Ge+NChgZ9/0Injvv3225sPf/jD9YnywAMPXO3nFkQM77fffnWZI444on6QVifNdbvG3QnzPRR64n/avfcDAOge/o+xAIAU8QAApIgHACBFPAAAKeIBAEgRDwBAingAAFLEAwCQIh4AgBTxAACkiAcAoGT8H5+yNqVYiMM3AAAAAElFTkSuQmCC",
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"## results\n",
|
|
"print('mean:', np.mean(scores))\n",
|
|
"print('std: ', np.std(scores))\n",
|
|
"sns.boxplot(x=scores);"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Bootstrap Validierung"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {
|
|
"scrolled": true
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.6321205588285577\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(1 - 1 / np.exp(1))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"0.631855\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"## experimental\n",
|
|
"pop_size = 1000000\n",
|
|
"pop = range(pop_size)\n",
|
|
"smpl = np.random.choice(pop, pop_size, replace=True)\n",
|
|
"smpl_size = len(np.unique(smpl))\n",
|
|
"print(smpl_size / pop_size)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"source": [
|
|
"### Train - Eval - Test - Split"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "teaching",
|
|
"language": "python",
|
|
"name": "teaching"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.13.0"
|
|
},
|
|
"toc": {
|
|
"base_numbering": "4.2",
|
|
"nav_menu": {
|
|
"height": "189px",
|
|
"width": "303.333px"
|
|
},
|
|
"number_sections": true,
|
|
"sideBar": true,
|
|
"skip_h1_title": false,
|
|
"title_cell": "4.2 Validierung und mehr - Validierungstechniken",
|
|
"title_sidebar": "Contents",
|
|
"toc_cell": true,
|
|
"toc_position": {
|
|
"height": "calc(100% - 180px)",
|
|
"left": "10px",
|
|
"top": "150px",
|
|
"width": "230.667px"
|
|
},
|
|
"toc_section_display": true,
|
|
"toc_window_display": true
|
|
},
|
|
"toc-autonumbering": true,
|
|
"varInspector": {
|
|
"cols": {
|
|
"lenName": 16,
|
|
"lenType": 16,
|
|
"lenVar": 40
|
|
},
|
|
"kernels_config": {
|
|
"python": {
|
|
"delete_cmd_postfix": "",
|
|
"delete_cmd_prefix": "del ",
|
|
"library": "var_list.py",
|
|
"varRefreshCmd": "print(var_dic_list())"
|
|
},
|
|
"r": {
|
|
"delete_cmd_postfix": ") ",
|
|
"delete_cmd_prefix": "rm(",
|
|
"library": "var_list.r",
|
|
"varRefreshCmd": "cat(var_dic_list()) "
|
|
}
|
|
},
|
|
"position": {
|
|
"height": "298.85px",
|
|
"left": "782px",
|
|
"right": "20px",
|
|
"top": "120px",
|
|
"width": "350px"
|
|
},
|
|
"types_to_exclude": [
|
|
"module",
|
|
"function",
|
|
"builtin_function_or_method",
|
|
"instance",
|
|
"_Feature"
|
|
],
|
|
"window_display": false
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|