{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "3689760c-41f8-4a33-9c96-3fd17803950e", "_uuid": "3e0ad409d438c7c68ea6a76700a1e964a357453f" }, "source": [ "https://www.kaggle.com/code/janiobachmann/credit-fraud-dealing-with-imbalanced-datasets\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "ae8dd7f3-80a7-4db9-a132-823b0e48c041", "_uuid": "c999e5f1ac81513263d83883008f2844209e9e07" }, "source": [ "## 데이터 센스 수집:\n", "\n", "가장 먼저 해야 할 일은 데이터에 대한 기본 감각 을 수집하는 것입니다. 거래 및 금액을 제외하고 다른 열이 무엇인지 알 수 없음을 기억하십시오(개인정보 보호상의 이유로). 우리가 아는 유일한 것은 알려지지 않은 열이 이미 크기가 조정되었다는 것입니다.\n", "\n", "

요약:

\n", "<울>\n", "
  • 거래 금액이 상대적으로 작습니다. 제작된 모든 마운트의 평균은 약 USD 88입니다.
    • \n", "
  • \"Null\" 값이 없으므로 값을 대체할 방법을 모색할 필요가 없습니다.
    • \n", "
  • 대부분의 거래는 시간의 비 사기(99.83%)인 반면, 사기 거래는 데이터 프레임에서 발생하는 시간(017%)이었습니다.
    • \n", "\n", "\n", "

    기능 기술:

    \n", "<울>\n", "
  • PCA 변환: 데이터 설명에 따르면 모든 기능이 PCA 변환(차원 축소 기법)을 거쳤습니다(시간 및 양 제외).
    • \n", "
  • 크기 조정: PCA 변환 기능을 구현하려면 미리 크기를 조정해야 합니다. (이 경우 모든 V 기능이 확장되었거나 최소한 데이터세트를 개발한 사람들이 그렇게 했다고 가정합니다.)
    • \n", "" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" }, "outputs": [ { "data": { "text/html": [ "
    \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    TimeV1V2V3V4V5V6V7V8V9...V21V22V23V24V25V26V27V28AmountClass
    00.0-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.0986980.363787...-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.021053149.620
    10.01.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102-0.255425...-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.0147242.690
    21.0-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676-1.514654...0.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.059752378.660
    31.0-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436-1.387024...-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.061458123.500
    42.0-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.2705330.817739...-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.21515369.990
    \n", "

    5 rows × 31 columns

    \n", "
    " ], "text/plain": [ " Time V1 V2 V3 V4 V5 V6 V7 \\\n", "0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n", "1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n", "2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n", "3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n", "4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n", "\n", " V8 V9 ... V21 V22 V23 V24 V25 \\\n", "0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 \n", "1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 \n", "2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 \n", "3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 \n", "4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 -0.206010 \n", "\n", " V26 V27 V28 Amount Class \n", "0 -0.189115 0.133558 -0.021053 149.62 0 \n", "1 0.125895 -0.008983 0.014724 2.69 0 \n", "2 -0.139097 -0.055353 -0.059752 378.66 0 \n", "3 -0.221929 0.062723 0.061458 123.50 0 \n", "4 0.502292 0.219422 0.215153 69.99 0 \n", "\n", "[5 rows x 31 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as mpatches\n", "import seaborn as sns\n", "import time\n", "\n", "# 차원축소\n", "from sklearn.manifold import TSNE\n", "from sklearn.decomposition import PCA, TruncatedSVD\n", "\n", "# 모델\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "import collections\n", "\n", "# Other Libraries\n", "from sklearn.model_selection import train_test_split, KFold, StratifiedKFold\n", "from sklearn.pipeline import make_pipeline\n", "from imblearn.pipeline import make_pipeline as imbalanced_make_pipeline\n", "\n", "from imblearn.over_sampling import SMOTE # 오버샘플링\n", "from imblearn.under_sampling import NearMiss # 언더샘플링\n", "\n", "from imblearn.metrics import classification_report_imbalanced\n", "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report\n", "from collections import Counter\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "df = pd.read_csv('./input/creditcard.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(284807, 31)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "_cell_guid": "376ce881-463a-4a09-9ac0-c63f85577eec", "_kg_hide-input": true, "_uuid": "93031e732e5aca3a2b4984799d6bf58d76e4b52d" }, "outputs": [ { "data": { "text/html": [ "
    \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    TimeV1V2V3V4V5V6V7V8V9...V21V22V23V24V25V26V27V28AmountClass
    count284807.0000002.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+05...2.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+052.848070e+05284807.000000284807.000000
    mean94813.8595753.918649e-155.682686e-16-8.761736e-152.811118e-15-1.552103e-152.040130e-15-1.698953e-15-1.893285e-16-3.147640e-15...1.473120e-168.042109e-165.282512e-164.456271e-151.426896e-151.701640e-15-3.662252e-16-1.217809e-1688.3496190.001727
    std47488.1459551.958696e+001.651309e+001.516255e+001.415869e+001.380247e+001.332271e+001.237094e+001.194353e+001.098632e+00...7.345240e-017.257016e-016.244603e-016.056471e-015.212781e-014.822270e-014.036325e-013.300833e-01250.1201090.041527
    min0.000000-5.640751e+01-7.271573e+01-4.832559e+01-5.683171e+00-1.137433e+02-2.616051e+01-4.355724e+01-7.321672e+01-1.343407e+01...-3.483038e+01-1.093314e+01-4.480774e+01-2.836627e+00-1.029540e+01-2.604551e+00-2.256568e+01-1.543008e+010.0000000.000000
    25%54201.500000-9.203734e-01-5.985499e-01-8.903648e-01-8.486401e-01-6.915971e-01-7.682956e-01-5.540759e-01-2.086297e-01-6.430976e-01...-2.283949e-01-5.423504e-01-1.618463e-01-3.545861e-01-3.171451e-01-3.269839e-01-7.083953e-02-5.295979e-025.6000000.000000
    50%84692.0000001.810880e-026.548556e-021.798463e-01-1.984653e-02-5.433583e-02-2.741871e-014.010308e-022.235804e-02-5.142873e-02...-2.945017e-026.781943e-03-1.119293e-024.097606e-021.659350e-02-5.213911e-021.342146e-031.124383e-0222.0000000.000000
    75%139320.5000001.315642e+008.037239e-011.027196e+007.433413e-016.119264e-013.985649e-015.704361e-013.273459e-015.971390e-01...1.863772e-015.285536e-011.476421e-014.395266e-013.507156e-012.409522e-019.104512e-027.827995e-0277.1650000.000000
    max172792.0000002.454930e+002.205773e+019.382558e+001.687534e+013.480167e+017.330163e+011.205895e+022.000721e+011.559499e+01...2.720284e+011.050309e+012.252841e+014.584549e+007.519589e+003.517346e+003.161220e+013.384781e+0125691.1600001.000000
    \n", "

    8 rows × 31 columns

    \n", "
    " ], "text/plain": [ " Time V1 V2 V3 V4 \\\n", "count 284807.000000 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 \n", "mean 94813.859575 3.918649e-15 5.682686e-16 -8.761736e-15 2.811118e-15 \n", "std 47488.145955 1.958696e+00 1.651309e+00 1.516255e+00 1.415869e+00 \n", "min 0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00 \n", "25% 54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01 \n", "50% 84692.000000 1.810880e-02 6.548556e-02 1.798463e-01 -1.984653e-02 \n", "75% 139320.500000 1.315642e+00 8.037239e-01 1.027196e+00 7.433413e-01 \n", "max 172792.000000 2.454930e+00 2.205773e+01 9.382558e+00 1.687534e+01 \n", "\n", " V5 V6 V7 V8 V9 \\\n", "count 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 \n", "mean -1.552103e-15 2.040130e-15 -1.698953e-15 -1.893285e-16 -3.147640e-15 \n", "std 1.380247e+00 1.332271e+00 1.237094e+00 1.194353e+00 1.098632e+00 \n", "min -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01 \n", "25% -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01 \n", "50% -5.433583e-02 -2.741871e-01 4.010308e-02 2.235804e-02 -5.142873e-02 \n", "75% 6.119264e-01 3.985649e-01 5.704361e-01 3.273459e-01 5.971390e-01 \n", "max 3.480167e+01 7.330163e+01 1.205895e+02 2.000721e+01 1.559499e+01 \n", "\n", " ... V21 V22 V23 V24 \\\n", "count ... 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 \n", "mean ... 1.473120e-16 8.042109e-16 5.282512e-16 4.456271e-15 \n", "std ... 7.345240e-01 7.257016e-01 6.244603e-01 6.056471e-01 \n", "min ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00 \n", "25% ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01 \n", "50% ... -2.945017e-02 6.781943e-03 -1.119293e-02 4.097606e-02 \n", "75% ... 1.863772e-01 5.285536e-01 1.476421e-01 4.395266e-01 \n", "max ... 2.720284e+01 1.050309e+01 2.252841e+01 4.584549e+00 \n", "\n", " V25 V26 V27 V28 Amount \\\n", "count 2.848070e+05 2.848070e+05 2.848070e+05 2.848070e+05 284807.000000 \n", "mean 1.426896e-15 1.701640e-15 -3.662252e-16 -1.217809e-16 88.349619 \n", "std 5.212781e-01 4.822270e-01 4.036325e-01 3.300833e-01 250.120109 \n", "min -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01 0.000000 \n", "25% -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02 5.600000 \n", "50% 1.659350e-02 -5.213911e-02 1.342146e-03 1.124383e-02 22.000000 \n", "75% 3.507156e-01 2.409522e-01 9.104512e-02 7.827995e-02 77.165000 \n", "max 7.519589e+00 3.517346e+00 3.161220e+01 3.384781e+01 25691.160000 \n", "\n", " Class \n", "count 284807.000000 \n", "mean 0.001727 \n", "std 0.041527 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 0.000000 \n", "75% 0.000000 \n", "max 1.000000 \n", "\n", "[8 rows x 31 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "_cell_guid": "03ddb929-5bc8-4af4-90cd-21dcbb57560d", "_kg_hide-input": true, "_uuid": "38bec67888aa534e9739e95ef9fac62d27a87021" }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Good No Null Values!\n", "df.isnull().sum().max()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "_cell_guid": "6a526b6c-8463-4f6f-92b0-e8a3a21cbb2e", "_kg_hide-input": true, "_uuid": "479a5f12d3dd68262316a17b4b7b3499e0a2cbe0" }, "outputs": [ { "data": { "text/plain": [ "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n", " 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n", " 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n", " 'Class'],\n", " dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "_cell_guid": "01c007fa-0fcc-4eea-84ff-0861a2f8c533", "_kg_hide-input": true, "_uuid": "f6b96ff34855e3bf7af1f6979342b01c473e4e07" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Frauds 99.83 % of the dataset\n", "Frauds 0.17 % of the dataset\n" ] } ], "source": [ "# The classes are heavily skewed we need to solve this issue later.\n", "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100, 2), '% of the dataset')\n", "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100, 2), '% of the dataset')" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "558c9b60-3f52-4da5-92fa-9fc4acbdbb3a", "_uuid": "c2bb0945a312508e908386fc87adc227f0afe0e0" }, "source": [ "**Note:** Notice how imbalanced is our original dataset! Most of the transactions are non-fraud. If we use this dataframe as the base for our predictive models and analysis we might get a lot of errors and our algorithms will probably overfit since it will \"assume\" that most transactions are not fraud. But we don't want our model to assume, we want our model to detect patterns that give signs of fraud!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "_cell_guid": "657bc987-4b15-4cfa-b290-c39a2632e2ac", "_kg_hide-input": true, "_uuid": "337caaf6ed3f65beedb24a74eebb22d97ff52ba4" }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Class Distributions \\n (0: No Fraud || 1: Fraud)')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "colors = [\"#0101DF\", \"#DF0101\"]\n", "\n", "sns.countplot('Class', data=df, palette=colors)\n", "plt.title('Class Distributions \\n (0: No Fraud || 1: Fraud)', fontsize=14)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "3c9973d0-83bd-4b09-860e-c1f507f88310", "_uuid": "6894af2afdbfd5cd670d00b66f10ae49f1cab421" }, "source": [ "**Distributions:** By seeing the distributions we can have an idea how skewed are these features, we can also see further distributions of the other features. There are techniques that can help the distributions be less skewed which will be implemented in this notebook in the future." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "_cell_guid": "cee315f2-325f-42b6-a640-736f10c272cc", "_kg_hide-input": true, "_uuid": "cfa51792bf6f8a6b318ae1bffcff4e922b1d1917" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, 2, figsize=(18,4))\n", "\n", "amount_val = df['Amount'].values\n", "time_val = df['Time'].values\n", "\n", "sns.distplot(amount_val, ax=ax[0], color='r')\n", "ax[0].set_title('Distribution of Transaction Amount', fontsize=14)\n", "ax[0].set_xlim([min(amount_val), max(amount_val)])\n", "\n", "sns.distplot(time_val, ax=ax[1], color='b')\n", "ax[1].set_title('Distribution of Transaction Time', fontsize=14)\n", "ax[1].set_xlim([min(time_val), max(time_val)])\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "72fdda5e-7f82-488d-a433-6157d6180bb8", "_uuid": "c5d6781e61c0ee84e72d26e8465bfd98ef91f3b9" }, "source": [ "

    확장 및 배포

    \n", "\n", "커널의 이 단계에서는 먼저 시간 및 금액 으로 구성된 열의 크기를 조정할 것입니다. 시간과 양은 다른 열과 같이 조정되어야 합니다. 반면에 사기 및 비 사기 사례의 동일한 양을 가지기 위해 데이터 프레임의 하위 샘플도 생성해야 하므로 거래가 사기인지 여부를 결정하는 패턴을 알고리즘이 더 잘 이해할 수 있습니다.\n", "\n", "

    하위 샘플이란 무엇입니까?

    \n", "이 시나리오에서 하위 샘플은 사기 및 비 사기 거래 비율이 50/50인 데이터 프레임이 됩니다. 즉, 하위 샘플에는 동일한 양의 사기 및 비 사기 거래가 있습니다.\n", "\n", "

    하위 샘플을 만드는 이유는 무엇입니까?

    \n", "이 노트북의 시작 부분에서 우리는 원본 데이터 프레임의 불균형이 심한 것을 보았습니다! 원본 데이터 프레임을 사용하면 다음 문제가 발생합니다.\n", "<울>\n", "
  • 과적합: 우리의 분류 모델은 대부분의 경우 사기가 없다고 가정합니다! 우리가 모델에서 원하는 것은 사기가 발생했을 때 확실하게 하는 것입니다.
    • \n", "
  • 잘못된 상관관계: \"V\" 기능이 무엇을 의미하는지 모르지만 이러한 각 기능이 불균형 데이터 프레임에서는 클래스와 기능 간의 진정한 상관 관계를 볼 수 없습니다.
    • \n", "\n", "\n", "

    요약:

    \n", "<울>\n", "
  • 조정된 양 및 조정된 시간 은 조정된 값이 있는 열입니다.
    • \n", "
  • 데이터 세트에는 사기 사례가 492건 있으므로 새로운 하위 데이터 프레임을 생성하기 위해 사기가 아닌 사례 492건을 무작위로 얻을 수 있습니다.
    • \n", "
  • 492개의 사기 및 비 사기 사례를 연결하여 새로운 하위 샘플을 만듭니다.
    • \n", "" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "_cell_guid": "d5d64bf0-2fbb-4096-a265-f68887bf2fde", "_kg_hide-input": true, "_uuid": "1501ec379c9b5c39c3857ba0febd0aedee9c30d5" }, "outputs": [], "source": [ "# 대부분의 데이터가 이미 크기가 조정되었으므로 나머지 열(amount 및 time)의 크기를 조정해야 합니다.\n", "from sklearn.preprocessing import StandardScaler, RobustScaler\n", "\n", "# RobustScaler는 이상치에 덜 취약합니다.\n", "std_scaler = StandardScaler()\n", "rob_scaler = RobustScaler()\n", "\n", "df['scaled_amount'] = rob_scaler.fit_transform(df['Amount'].values.reshape(-1,1)) # 특이치를 염두해둔 RobustScaler\n", "df['scaled_time'] = rob_scaler.fit_transform(df['Time'].values.reshape(-1,1))\n", "\n", "df.drop(['Time','Amount'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "_cell_guid": "cdb9bb1e-9fab-4fd1-a409-468ba8bc36ee", "_kg_hide-input": true, "_uuid": "a33d701247ab45d849c5e94735346a738a6c6970" }, "outputs": [ { "data": { "text/html": [ "
    \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    scaled_amountscaled_timeV1V2V3V4V5V6V7V8...V20V21V22V23V24V25V26V27V28Class
    01.783274-0.994983-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.098698...0.251412-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.0210530
    1-0.269825-0.9949831.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102...-0.069083-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.0147240
    24.983721-0.994972-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676...0.5249800.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.0597520
    31.418291-0.994972-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436...-0.208038-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.0614580
    40.670579-0.994960-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.270533...0.408542-0.0094310.798278-0.1374580.141267-0.2060100.5022920.2194220.2151530
    \n", "

    5 rows × 31 columns

    \n", "
    " ], "text/plain": [ " scaled_amount scaled_time V1 V2 V3 V4 \\\n", "0 1.783274 -0.994983 -1.359807 -0.072781 2.536347 1.378155 \n", "1 -0.269825 -0.994983 1.191857 0.266151 0.166480 0.448154 \n", "2 4.983721 -0.994972 -1.358354 -1.340163 1.773209 0.379780 \n", "3 1.418291 -0.994972 -0.966272 -0.185226 1.792993 -0.863291 \n", "4 0.670579 -0.994960 -1.158233 0.877737 1.548718 0.403034 \n", "\n", " V5 V6 V7 V8 ... V20 V21 V22 \\\n", "0 -0.338321 0.462388 0.239599 0.098698 ... 0.251412 -0.018307 0.277838 \n", "1 0.060018 -0.082361 -0.078803 0.085102 ... -0.069083 -0.225775 -0.638672 \n", "2 -0.503198 1.800499 0.791461 0.247676 ... 0.524980 0.247998 0.771679 \n", "3 -0.010309 1.247203 0.237609 0.377436 ... -0.208038 -0.108300 0.005274 \n", "4 -0.407193 0.095921 0.592941 -0.270533 ... 0.408542 -0.009431 0.798278 \n", "\n", " V23 V24 V25 V26 V27 V28 Class \n", "0 -0.110474 0.066928 0.128539 -0.189115 0.133558 -0.021053 0 \n", "1 0.101288 -0.339846 0.167170 0.125895 -0.008983 0.014724 0 \n", "2 0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 0 \n", "3 -0.190321 -1.175575 0.647376 -0.221929 0.062723 0.061458 0 \n", "4 -0.137458 0.141267 -0.206010 0.502292 0.219422 0.215153 0 \n", "\n", "[5 rows x 31 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaled_amount = df['scaled_amount']\n", "scaled_time = df['scaled_time']\n", "\n", "df.drop(['scaled_amount', 'scaled_time'], axis=1, inplace=True)\n", "df.insert(0, 'scaled_amount', scaled_amount) # 맨뒤 변수를 첫번째로 옯기기\n", "df.insert(1, 'scaled_time', scaled_time)# 맨뒤 변수를 두번째로 옯기기\n", "\n", "# Amount and Time are Scaled!\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a59c8c8d-a4bc-4671-aa2f-9f959c142cde", "_uuid": "5119c4ea9e0b9031dbc5937b56323da224985024" }, "source": [ "### 데이터 분할(Original DataFrame)\n", "\n", " Random UnderSampling 기술을 진행하기 전에 원본 데이터 프레임을 분리해야 합니다. 왜? 테스트 목적을 위해 Random UnderSampling 또는 OverSampling 기술을 구현할 때 데이터를 분할하지만 우리는 이러한 기술 중 하나에 의해 생성된 테스트 세트가 아닌 원래 테스트 세트에서 모델을 테스트하기를 원한다는 것을 기억하십시오. 주요 목표는 모델을 언더샘플링 및 오버샘플링(모델이 패턴을 감지할 수 있도록)한 데이터 프레임으로 모델을 맞추고 원래 테스트 세트에서 테스트합니다." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "_cell_guid": "c6c962cc-6f38-4a00-bcd7-ce9d91db954c", "_kg_hide-input": true, "_uuid": "9f7b5d920703b3a3c8c0f62bc6042e4615bc8324" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Frauds 99.83 % of the dataset\n", "Frauds 0.17 % of the dataset\n", "Train: [ 30473 30496 31002 ... 284804 284805 284806] Test: [ 0 1 2 ... 57017 57018 57019]\n", "Train: [ 0 1 2 ... 284804 284805 284806] Test: [ 30473 30496 31002 ... 113964 113965 113966]\n", "Train: [ 0 1 2 ... 284804 284805 284806] Test: [ 81609 82400 83053 ... 170946 170947 170948]\n", "Train: [ 0 1 2 ... 284804 284805 284806] Test: [150654 150660 150661 ... 227866 227867 227868]\n", "Train: [ 0 1 2 ... 227866 227867 227868] Test: [212516 212644 213092 ... 284804 284805 284806]\n", "(227846, 30) (56961, 30) (227846,) (56961,)\n", "----------------------------------------------------------------------------------------------------\n", "Label Distributions: \n", "\n", "[0.99827076 0.00172924]\n", "[0.99827952 0.00172048]\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split, StratifiedShuffleSplit\n", "\n", "print('No Frauds', round(df['Class'].value_counts()[0]/len(df) * 100,2), '% of the dataset')\n", "print('Frauds', round(df['Class'].value_counts()[1]/len(df) * 100,2), '% of the dataset')\n", "\n", "X = df.drop('Class', axis=1)\n", "y = df['Class']\n", "\n", "skf = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n", "\n", "for train_index, test_index in skf.split(X, y):\n", " print(\"Train:\", train_index, \"Test:\", test_index)\n", " original_Xtrain, original_Xtest = X.iloc[train_index], X.iloc[test_index]\n", " original_ytrain, original_ytest = y.iloc[train_index], y.iloc[test_index]\n", "\n", "original_Xtrain = original_Xtrain.values\n", "original_Xtest = original_Xtest.values\n", "original_ytrain = original_ytrain.values\n", "original_ytest = original_ytest.values\n", "print(original_Xtrain.shape, original_Xtest.shape, original_ytrain.shape, original_ytest.shape)\n", "\n", "\n", "train_unique_label, train_counts_label = np.unique(original_ytrain, return_counts=True) # array([0, 1]), array([227452, 394])\n", "test_unique_label, test_counts_label = np.unique(original_ytest, return_counts=True) # array([0, 1]), array([56863, 98])\n", "print('-' * 100)\n", "\n", "# 나눈 데이터셋(train/test)의 0,1 비율\n", "print('Label Distributions: \\n')\n", "print(train_counts_label/ len(original_ytrain))\n", "print(test_counts_label/ len(original_ytest))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "956d34b9-e562-4b70-a2f8-fbe060273a83", "_uuid": "cc554c4ffec656cb38d01c034f2cd338e1cb4565" }, "source": [ "## 무작위 언더샘플링:\n", "\n", "\n", "프로젝트의 이 단계에서 우리는 기본적으로 보다 균형 잡힌 데이터 세트 를 갖고 모델이 과적합되는 것을 피하기 위해 데이터를 제거하는 것으로 구성된 *\"Random Under Sampling\"*을 구현할 것입니다.\n", "\n", "#### 단계:\n", "<울>\n", "
  • 먼저 해야 할 일은 클래스가 얼마나 불균형인지 확인하는 것입니다(클래스 열에서 \"value_counts()\"를 사용하여 각 레이블의 양을 결정)
    • \n", "
  • 사기 거래 (Fraud = \"1\")로 간주되는 인스턴스 수를 결정하면 비사기 거래를 사기 거래와 동일한 금액으로 가져와야 합니다. (50/50 비율을 원한다고 가정) 이는 492건의 사기 및 492건의 비사기 거래에 해당합니다.
    • \n", "
  • 이 기술을 구현한 후 클래스와 관련하여 비율이 50/50인 데이터 프레임의 하위 샘플이 있습니다. 그런 다음 구현할 다음 단계는 이 스크립트를 실행할 때마다 모델이 특정 정확도를 유지할 수 있는지 확인하기 위해 데이터를 섞는 것입니다.
    • \n", "\n", "\n", "**참고:** \"Random Under-Sampling\"의 주요 문제는 정보 손실이 많기 때문에 분류 모델이 원하는 만큼 정확하지 않을 위험이 있다는 것입니다. b> (비사기 거래 284,315건에서 비사기 거래 492건)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "_cell_guid": "f0acfc44-eb2a-4356-ad03-d0c12807acd7", "_kg_hide-input": true, "_uuid": "e3a2b89752681164f14c8273452fc66734d7f41b" }, "outputs": [ { "data": { "text/html": [ "
    \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    scaled_amountscaled_timeV1V2V3V4V5V6V7V8...V20V21V22V23V24V25V26V27V28Class
    441030.207643-0.5039531.1871080.197527-0.0052610.8669550.096189-0.2360650.148301-0.054294...-0.0402800.0544200.100704-0.225825-0.3412030.739539-0.3069050.0033050.0090110
    234633-0.2851950.7443811.2613242.726800-5.4350195.3427591.447043-1.442584-0.8987020.123062...0.3133320.209086-0.425938-0.154440-0.0188200.6322340.1929220.4681810.2804861
    269782-0.1992590.9290640.0305750.7320280.089890-0.8027830.598855-0.5386190.8586170.018576...-0.060654-0.248125-0.6193630.018734-0.524455-0.4842920.1531810.2355300.0807220
    30314-0.201076-0.573620-2.0444893.368306-3.9371115.623120-3.079232-1.253474-5.7788801.707428...1.1120281.4835940.834311-0.1484860.001669-0.0389960.3895261.3002360.5499401
    93424-0.293440-0.238255-1.3480422.522821-0.7824324.083047-0.662280-0.598776-1.943552-0.329579...0.3488961.079871-0.352026-0.2183580.125866-0.0741800.1791160.6125800.2342061
    ..................................................................
    183106-0.3074130.4812790.2244142.994499-3.4324583.9865193.7602330.1656401.099378-0.654557...-0.2008460.491337-0.984223-0.421979-1.0480580.7264120.2686250.2836890.4191021
    1015094.164047-0.197782-1.739334-1.3046550.3141030.053740-0.0586960.0712600.694862-0.313270...-1.463994-0.665172-0.632078-0.421176-0.400774-0.001640-0.4951620.0316330.0662801
    199905-0.1303710.5697791.981899-0.165623-1.4686460.7956980.125458-0.326336-0.1383160.016419...-0.274902-0.433873-0.9101550.2546150.463135-0.011024-0.5210540.021107-0.0190860
    832974.152868-0.292708-8.257111-4.814461-5.3653071.204230-3.347420-1.331601-1.9678931.295438...-1.2339870.436390-0.077553-3.091624-0.390201-0.288689-0.3400040.039819-1.0079001
    131272-0.304618-0.060527-0.1143611.0361291.9844053.128243-0.7403441.548619-1.701284-2.203842...0.732852-1.0329351.196428-0.1128570.2547190.6966680.4823700.1299690.2239241
    \n", "

    984 rows × 31 columns

    \n", "
    " ], "text/plain": [ " scaled_amount scaled_time V1 V2 V3 V4 \\\n", "44103 0.207643 -0.503953 1.187108 0.197527 -0.005261 0.866955 \n", "234633 -0.285195 0.744381 1.261324 2.726800 -5.435019 5.342759 \n", "269782 -0.199259 0.929064 0.030575 0.732028 0.089890 -0.802783 \n", "30314 -0.201076 -0.573620 -2.044489 3.368306 -3.937111 5.623120 \n", "93424 -0.293440 -0.238255 -1.348042 2.522821 -0.782432 4.083047 \n", "... ... ... ... ... ... ... \n", "183106 -0.307413 0.481279 0.224414 2.994499 -3.432458 3.986519 \n", "101509 4.164047 -0.197782 -1.739334 -1.304655 0.314103 0.053740 \n", "199905 -0.130371 0.569779 1.981899 -0.165623 -1.468646 0.795698 \n", "83297 4.152868 -0.292708 -8.257111 -4.814461 -5.365307 1.204230 \n", "131272 -0.304618 -0.060527 -0.114361 1.036129 1.984405 3.128243 \n", "\n", " V5 V6 V7 V8 ... V20 V21 \\\n", "44103 0.096189 -0.236065 0.148301 -0.054294 ... -0.040280 0.054420 \n", "234633 1.447043 -1.442584 -0.898702 0.123062 ... 0.313332 0.209086 \n", "269782 0.598855 -0.538619 0.858617 0.018576 ... -0.060654 -0.248125 \n", "30314 -3.079232 -1.253474 -5.778880 1.707428 ... 1.112028 1.483594 \n", "93424 -0.662280 -0.598776 -1.943552 -0.329579 ... 0.348896 1.079871 \n", "... ... ... ... ... ... ... ... \n", "183106 3.760233 0.165640 1.099378 -0.654557 ... -0.200846 0.491337 \n", "101509 -0.058696 0.071260 0.694862 -0.313270 ... -1.463994 -0.665172 \n", "199905 0.125458 -0.326336 -0.138316 0.016419 ... -0.274902 -0.433873 \n", "83297 -3.347420 -1.331601 -1.967893 1.295438 ... -1.233987 0.436390 \n", "131272 -0.740344 1.548619 -1.701284 -2.203842 ... 0.732852 -1.032935 \n", "\n", " V22 V23 V24 V25 V26 V27 V28 \\\n", "44103 0.100704 -0.225825 -0.341203 0.739539 -0.306905 0.003305 0.009011 \n", "234633 -0.425938 -0.154440 -0.018820 0.632234 0.192922 0.468181 0.280486 \n", "269782 -0.619363 0.018734 -0.524455 -0.484292 0.153181 0.235530 0.080722 \n", "30314 0.834311 -0.148486 0.001669 -0.038996 0.389526 1.300236 0.549940 \n", "93424 -0.352026 -0.218358 0.125866 -0.074180 0.179116 0.612580 0.234206 \n", "... ... ... ... ... ... ... ... \n", "183106 -0.984223 -0.421979 -1.048058 0.726412 0.268625 0.283689 0.419102 \n", "101509 -0.632078 -0.421176 -0.400774 -0.001640 -0.495162 0.031633 0.066280 \n", "199905 -0.910155 0.254615 0.463135 -0.011024 -0.521054 0.021107 -0.019086 \n", "83297 -0.077553 -3.091624 -0.390201 -0.288689 -0.340004 0.039819 -1.007900 \n", "131272 1.196428 -0.112857 0.254719 0.696668 0.482370 0.129969 0.223924 \n", "\n", " Class \n", "44103 0 \n", "234633 1 \n", "269782 0 \n", "30314 1 \n", "93424 1 \n", "... ... \n", "183106 1 \n", "101509 1 \n", "199905 0 \n", "83297 1 \n", "131272 1 \n", "\n", "[984 rows x 31 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 우리의 클래스는 매우 편향되어 있으므로 클래스의 정규 분포를 가지려면 동등하게 만들어야 합니다.\n", "# 서브샘플을 만들기 전에 데이터를 섞을 수 있습니다.\n", "df = df.sample(frac=1) # 데이터 섞기\n", "\n", "# amount of fraud classes 492 rows.\n", "fraud_df = df.loc[df['Class'] == 1]\n", "non_fraud_df = df.loc[df['Class'] == 0][:492]\n", "\n", "normal_distributed_df = pd.concat([fraud_df, non_fraud_df])\n", "new_df = normal_distributed_df.sample(frac=1, random_state=42)\n", "new_df" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "77198464-c0f8-4694-ac0b-4b29b94d0da3", "_uuid": "b6818122806657e7accb8be1f4bf17086bb9b149" }, "source": [ "## 동등하게 분배 및 상관:\n", "\n", "이제 데이터 프레임의 균형이 올바르게 조정되었으므로 분석 및 데이터 사전 처리를 더 진행할 수 있습니다." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "_cell_guid": "73454100-dc69-49fd-b1b2-f72e326bca5d", "_kg_hide-input": true, "_uuid": "68b42e92df59f10fbd3ba700389796c4506af604" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Distribution of the Classes in the subsample dataset\n", "0 0.5\n", "1 0.5\n", "Name: Class, dtype: float64\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print('Distribution of the Classes in the subsample dataset')\n", "print(new_df['Class'].value_counts()/len(new_df))\n", "\n", "sns.countplot('Class', data=new_df)\n", "plt.title('Equally Distributed Classes', fontsize=14)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "0abc31ee-a78e-43af-822f-f06772d00c1c", "_uuid": "88477bac6687f110e9d64ec22837c250d85d2a2b" }, "source": [ "

    상관 행렬

    \n", "상관 행렬은 데이터 이해의 핵심입니다. 특정 거래가 사기인지 여부에 큰 영향을 미치는 기능이 있는지 알고 싶습니다. 그러나 어떤 기능이 사기 거래와 관련하여 높은 양의 상관 관계 또는 음의 상관 관계가 있는지 확인하려면 올바른 데이터 프레임(하위 샘플)을 사용하는 것이 중요합니다.\n", "\n", "### 요약 및 설명:\n", "<울>\n", "
  • 음의 상관 관계: V17, V14, V12 및 V10은 음의 상관 관계가 있습니다. 이 값이 낮을수록 최종 결과가 사기 거래가 될 가능성이 높아집니다.
    • \n", "
  • 양의 상관 관계: V2, V4, V11 및 V19는 양의 상관 관계가 있습니다. 이 값이 높을수록 최종 결과가 사기 거래가 될 가능성이 높아집니다.
    • \n", "
  • BoxPlots: 사기 및 비 사기 거래에서 이러한 기능의 분포를 더 잘 이해하기 위해 상자 그림을 사용할 것입니다.
    • \n", "\n", "\n", "\n", "**참고: ** 상관 행렬의 하위 샘플을 사용해야 합니다. 그렇지 않으면 상관 행렬이 클래스 간의 높은 불균형의 영향을 받습니다. 이것은 원본 데이터 프레임의 높은 클래스 불균형으로 인해 발생합니다." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "_cell_guid": "9f353623-9435-4bb2-b854-b4a201ec7dd9", "_kg_hide-input": true, "_uuid": "e2f417c5d7c633a1e3cdfaa78acd6bd77a38400e" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, (ax1, ax2) = plt.subplots(2, 1, figsize=(24,20))\n", "\n", "# 전체 데이터프레임\n", "corr = df.corr()\n", "sns.heatmap(corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax1)\n", "ax1.set_title(\"Imbalanced Correlation Matrix \\n (don't use for reference)\", fontsize=14)\n", "\n", "# 일부 데이터프레임\n", "sub_sample_corr = new_df.corr()\n", "sns.heatmap(sub_sample_corr, cmap='coolwarm_r', annot_kws={'size':20}, ax=ax2)\n", "ax2.set_title('SubSample Correlation Matrix \\n (use for reference)', fontsize=14)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "_cell_guid": "2f02c21f-daa3-4251-a8e9-acad09a5ce0f", "_kg_hide-input": true, "_uuid": "318d0e7e0443f99139be21c00a7abc663be26385" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n", "\n", "# 음의 상곤관계\n", "sns.boxplot(x=\"Class\", y=\"V17\", data=new_df, palette=colors, ax=axes[0])\n", "axes[0].set_title('V17 vs Class Negative Correlation')\n", "\n", "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df, palette=colors, ax=axes[1])\n", "axes[1].set_title('V14 vs Class Negative Correlation')\n", "\n", "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, palette=colors, ax=axes[2])\n", "axes[2].set_title('V12 vs Class Negative Correlation')\n", "\n", "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, palette=colors, ax=axes[3])\n", "axes[3].set_title('V10 vs Class Negative Correlation')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "b457b10e-c17c-4cb2-9719-6d4128377c9f", "_kg_hide-input": true, "_uuid": "7bfc46c028f8602ee949de83629082633aa47b2c" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, axes = plt.subplots(ncols=4, figsize=(20,4))\n", "\n", "# 양의 상곤관계\n", "sns.boxplot(x=\"Class\", y=\"V11\", data=new_df, palette=colors, ax=axes[0])\n", "axes[0].set_title('V11 vs Class Positive Correlation')\n", "\n", "sns.boxplot(x=\"Class\", y=\"V4\", data=new_df, palette=colors, ax=axes[1])\n", "axes[1].set_title('V4 vs Class Positive Correlation')\n", "\n", "sns.boxplot(x=\"Class\", y=\"V2\", data=new_df, palette=colors, ax=axes[2])\n", "axes[2].set_title('V2 vs Class Positive Correlation')\n", "\n", "sns.boxplot(x=\"Class\", y=\"V19\", data=new_df, palette=colors, ax=axes[3])\n", "axes[3].set_title('V19 vs Class Positive Correlation')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "93e56c89-185e-40d2-9ccc-29b123feb5a6", "_uuid": "a721282c0f44ec8030bbad6d0220091bde8cbec8" }, "source": [ "## 이상 감지:\n", "\n", "\n", "\n", "\n", "이 섹션의 주요 목표는 클래스와 높은 상관 관계가 있는 기능에서 \"극단적인 이상값\"을 제거하는 것입니다. 이것은 우리 모델의 정확도에 긍정적인 영향을 미칠 것입니다.

    \n", "\n", "\n", "### 사분위수 범위 방법:\n", "<울>\n", "
  • 사분위수 범위(IQR): 75번째 백분위수와 25번째 백분위수 간의 차이로 이를 계산합니다. 우리의 목표는 75번째 및 25번째 백분위수를 초과하는 임계값을 생성하여 일부 인스턴스가 이 임계값을 초과하는 경우 해당 인스턴스가 삭제되도록 하는 것입니다.
    • \n", "
  • Boxplots: 25번째 백분위수와 75번째 백분위수(사각형의 양쪽 끝)를 쉽게 볼 수 있을 뿐만 아니라 극단적인 이상값(하위 및 상위 극단 너머에 있는 점)도 쉽게 볼 수 있습니다.
    • \n", "\n", "\n", "### 이상치 제거 트레이드오프:\n", "우리는 이상치를 제거하기 위한 임계값을 어디까지 원하는지 주의해야 합니다. 숫자(예: 1.5)에 (사분위수 범위)를 곱하여 임계값을 결정합니다. 이 임계값이 높을수록 더 적은 이상값을 감지하고(더 높은 숫자로 곱하기: 3), 이 임계값이 낮을수록 더 많은 이상값을 감지합니다.

    \n", "\n", "**상충관계: **\n", "임계값이 낮을수록 더 많은 이상값이 제거되지만 우리는 이상값보다 \"극단적인 이상값\"에 더 집중하고 싶습니다. 왜요? 정보 손실의 위험이 있어 모델의 정확도가 낮아질 수 있기 때문입니다. 이 임계값을 사용하여 분류 모델의 정확도에 어떤 영향을 미치는지 확인할 수 있습니다.\n", "\n", "\n", "### 요약:\n", "<울>\n", "
  • 분포 시각화: 먼저 일부 이상값을 제거하는 데 사용할 기능의 분포를 시각화하는 것으로 시작합니다. V14는 V12 및 V10에 비해 가우스 분포가 있는 유일한 기능입니다.
    • \n", "
  • 임계값 결정: iqr을 곱하는 데 사용할 숫자를 결정한 후(낮은 이상값이 더 많이 제거됨) q25 - 임계값 (하한 극한 임계값) 및 q75 + 임계값 추가 (상한 극한 임계값).
    • \n", "
  • 조건부 ​​삭제: 마지막으로 \"임계값\"이 두 극단 모두에서 초과되면 인스턴스가 제거된다는 조건부 삭제를 생성합니다.
    • \n", "
  • 상자 그림 표현: 상자 그림을 통해 \"극단적인 이상값\"의 수가 상당히 감소했음을 시각화합니다.
    • \n", "\n", "\n", "**참고:** 이상치 감소를 구현한 후 정확도가 3% 이상 향상되었습니다! 일부 이상값은 모델의 정확도를 왜곡할 수 있지만 극단적인 정보 손실을 피해야 합니다. 그렇지 않으면 모델이 과소적합될 위험이 있습니다.\n", "\n", "\n", "**참고**: 사분위수 범위 방법에 대한 추가 정보: 통계 사용 방법 데이터의 이상값 식별 by Jason Brownless(Machine Learning Mastery 블로그)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "_cell_guid": "9c690dfa-8fed-44e5-99f5-ff4eb6f87f16", "_kg_hide-input": true, "_uuid": "b6963900379db5b0d4adf92f8c7f959164e9119f" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy.stats import norm\n", "\n", "f, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(20, 6))\n", "\n", "v14_fraud_dist = new_df['V14'].loc[new_df['Class'] == 1].values\n", "sns.distplot(v14_fraud_dist, ax=ax1, fit=norm, color='#FB8861')\n", "ax1.set_title('V14 Distribution \\n (Fraud Transactions)', fontsize=14)\n", "\n", "v12_fraud_dist = new_df['V12'].loc[new_df['Class'] == 1].values\n", "sns.distplot(v12_fraud_dist, ax=ax2, fit=norm, color='#56F9BB')\n", "ax2.set_title('V12 Distribution \\n (Fraud Transactions)', fontsize=14)\n", "\n", "v10_fraud_dist = new_df['V10'].loc[new_df['Class'] == 1].values\n", "sns.distplot(v10_fraud_dist, ax=ax3, fit=norm, color='#C5B3F9')\n", "ax3.set_title('V10 Distribution \\n (Fraud Transactions)', fontsize=14)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "_cell_guid": "2e19fe33-f85a-4ffd-8e4a-807d0e0fb992", "_kg_hide-input": true, "_uuid": "21e43406e62a9561fba2f065ce15a8d87a1bf389" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Quartile 25: -9.692722964972386 | Quartile 75: -4.282820849486865\n", "Quartile 25: -8.67303320439115 | Quartile 75: -2.893030568676315\n", "Number of Instances after outliers removal: 947\n" ] } ], "source": [ "# # -----> V14 이상치 제거(레이블과 상관관계가 가장 높은 음수)\n", "v14_fraud = new_df['V14'].loc[new_df['Class'] == 1].values\n", "q1, q3 = np.percentile(v14_fraud, 25), np.percentile(v14_fraud, 75)\n", "print('Quartile 25: {} | Quartile 75: {}'.format(q1, q3))\n", "\n", "v14_iqr = q3-q1\n", "v14_lower, v14_upper = q1-1.5*v14_iqr, q3+1.5*v14_iqr\n", "outliers = [x for x in v14_fraud if x < v14_lower or x > v14_upper]\n", "new_df = new_df.drop(new_df[(new_df['V14'] > v14_upper) | (new_df['V14'] < v14_lower)].index)\n", "\n", "# -----> V12 사기 거래에서 이상값 제거\n", "v12_fraud = new_df['V12'].loc[new_df['Class'] == 1].values\n", "q1, q3 = np.percentile(v12_fraud, 25), np.percentile(v12_fraud, 75)\n", "print('Quartile 25: {} | Quartile 75: {}'.format(q1, q3))\n", "\n", "v12_iqr = q3-q1\n", "v12_lower, v12_upper = q1-1.5*v12_iqr, q3+1.5*v12_iqr\n", "outliers = [x for x in v12_fraud if x < v12_lower or x > v12_upper]\n", "new_df = new_df.drop(new_df[(new_df['V12'] > v12_upper) | (new_df['V12'] < v12_lower)].index)\n", "\n", "\n", "# Removing outliers V10 Feature\n", "v10_fraud = new_df['V10'].loc[new_df['Class'] == 1].values\n", "q1, q3 = np.percentile(v10_fraud, 25), np.percentile(v10_fraud, 75)\n", "\n", "v10_iqr = q3-q1\n", "v10_lower, v10_upper = q1-1.5*v10_iqr, q3+1.5*v10_iqr\n", "outliers = [x for x in v10_fraud if x < v10_lower or x > v10_upper]\n", "new_df = new_df.drop(new_df[(new_df['V10'] > v10_upper) | (new_df['V10'] < v10_lower)].index)\n", "\n", "print('Number of Instances after outliers removal: {}'.format(len(new_df)))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "_cell_guid": "66e44398-7c91-4cce-9778-4512cb838973", "_kg_hide-input": true, "_uuid": "ac80d9cfb07f1865094a8d460ae801750e93d694" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f,(ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20,6))\n", "\n", "colors = ['#B3F9C5', '#f9c5b3']\n", "# Boxplots with outliers removed\n", "# Feature V14\n", "sns.boxplot(x=\"Class\", y=\"V14\", data=new_df,ax=ax1, palette=colors)\n", "ax1.set_title(\"V14 Feature \\n Reduction of outliers\", fontsize=14)\n", "ax1.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.5), xytext=(0, -12),\n", " arrowprops=dict(facecolor='black'),\n", " fontsize=14)\n", "\n", "# Feature 12\n", "sns.boxplot(x=\"Class\", y=\"V12\", data=new_df, ax=ax2, palette=colors)\n", "ax2.set_title(\"V12 Feature \\n Reduction of outliers\", fontsize=14)\n", "ax2.annotate('Fewer extreme \\n outliers', xy=(0.98, -17.3), xytext=(0, -12),\n", " arrowprops=dict(facecolor='black'),\n", " fontsize=14)\n", "\n", "# Feature V10\n", "sns.boxplot(x=\"Class\", y=\"V10\", data=new_df, ax=ax3, palette=colors)\n", "ax3.set_title(\"V10 Feature \\n Reduction of outliers\", fontsize=14)\n", "ax3.annotate('Fewer extreme \\n outliers', xy=(0.95, -16.5), xytext=(0, -12),\n", " arrowprops=dict(facecolor='black'),\n", " fontsize=14)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "74903f3b-dc6b-40ba-abc8-86c3df5ca46e", "_uuid": "0b365b10bd363c23068accc448509ced879f1670" }, "source": [ "

    차원 축소 및 클러스터링:

    \n", "\n", "\n", "

    t-SNE 이해하기:

    \n", "이 알고리즘을 이해하려면 다음 용어를 이해해야 합니다.
    \n", "<울>\n", "
  • 유클리드 거리
    • \n", "
  • 조건부 확률
    • \n", "
  • 정규 및 T-분포도
    • \n", "\n", "\n", "**참고:** 간단한 교육용 비디오를 보려면 다음을 참조하십시오. StatQuest: t-SNE, 명확하게 설명 조슈아 스타머\n", "\n", "\n", "

    요약:

    \n", "<울>\n", "
  • t-SNE 알고리즘은 데이터 세트에서 사기 및 비 사기 사례를 매우 정확하게 클러스터링할 수 있습니다.
    • \n", "
  • 하위 샘플은 매우 작지만 t-SNE 알고리즘은 모든 시나리오에서 클러스터를 매우 정확하게 감지할 수 있습니다(t-SNE를 실행하기 전에 데이터 세트를 섞습니다)
    • \n", "
  • 이것은 추가 예측 모델이 사기 사례와 비 사기 사례를 구분하는 데 꽤 잘 수행할 것임을 시사합니다.
    • \n", "" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "_cell_guid": "f83cde6b-90d0-4e9d-ac63-fb69780431b2", "_kg_hide-input": true, "_uuid": "af3027e7df67b75c92c88d597003632e285c9bff" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "T-SNE took 4.1 s\n", "PCA took 0.089 s\n", "Truncated SVD took 0.0037 s\n" ] } ], "source": [ "# New_df is from the random undersample data (fewer instances)\n", "X = new_df.drop('Class', axis=1)\n", "y = new_df['Class']\n", "\n", "# T-SNE Implementation\n", "t0 = time.time()\n", "X_reduced_tsne = TSNE(n_components=2, random_state=42).fit_transform(X.values)\n", "t1 = time.time()\n", "print(\"T-SNE took {:.2} s\".format(t1 - t0))\n", "\n", "# PCA Implementation\n", "t0 = time.time()\n", "X_reduced_pca = PCA(n_components=2, random_state=42).fit_transform(X.values)\n", "t1 = time.time()\n", "print(\"PCA took {:.2} s\".format(t1 - t0))\n", "\n", "# TruncatedSVD\n", "t0 = time.time()\n", "X_reduced_svd = TruncatedSVD(n_components=2, algorithm='randomized', random_state=42).fit_transform(X.values)\n", "t1 = time.time()\n", "print(\"Truncated SVD took {:.2} s\".format(t1 - t0))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "_cell_guid": "07015ae5-f7ac-4d64-8f41-1e4b7c9dd2ac", "_kg_hide-input": true, "_uuid": "084f2a7421c2212082491d2a90e65d65c52b434a" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24,6))\n", "f.suptitle('Clusters using Dimensionality Reduction', fontsize=14)\n", "\n", "blue_patch = mpatches.Patch(color='#0A0AFF', label='No Fraud')\n", "red_patch = mpatches.Patch(color='#AF0000', label='Fraud')\n", "\n", "# t-SNE scatter plot\n", "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n", "ax1.scatter(X_reduced_tsne[:,0], X_reduced_tsne[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n", "ax1.set_title('t-SNE', fontsize=14)\n", "\n", "ax1.grid(True)\n", "ax1.legend(handles=[blue_patch, red_patch])\n", "\n", "# PCA scatter plot\n", "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n", "ax2.scatter(X_reduced_pca[:,0], X_reduced_pca[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n", "ax2.set_title('PCA', fontsize=14)\n", "\n", "ax2.grid(True)\n", "ax2.legend(handles=[blue_patch, red_patch])\n", "\n", "# TruncatedSVD scatter plot\n", "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 0), cmap='coolwarm', label='No Fraud', linewidths=2)\n", "ax3.scatter(X_reduced_svd[:,0], X_reduced_svd[:,1], c=(y == 1), cmap='coolwarm', label='Fraud', linewidths=2)\n", "ax3.set_title('Truncated SVD', fontsize=14)\n", "\n", "ax3.grid(True)\n", "ax3.legend(handles=[blue_patch, red_patch])\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "cb2c480a-090a-4cfb-b12e-3b74c325826c", "_uuid": "1b63bfd92008043cc1a336f924c835e73792f6d8" }, "source": [ "

    분류자(언더샘플링):

    \n", "\n", "이 섹션에서는 4가지 유형의 분류기를 교육하고 어떤 분류기가 사기 거래를 탐지하는 데 더 효과적인지 결정할 것입니다. 데이터를 훈련 세트와 테스트 세트로 분할하고 레이블에서 기능을 분리하기 전에.\n", "\n", "## 요약:\n", "<울>\n", "
  • 로지스틱 회귀 분류기는 대부분의 경우 다른 세 분류기보다 정확합니다. (우리는 로지스틱 회귀 분석을 추가로 분석할 것입니다)
    • \n", "
  • GridSearchCV 는 분류자에 대한 최상의 예측 점수를 제공하는 매개변수를 결정하는 데 사용됩니다.
    • \n", "
  • Logistic Regression은 ROC(수신 운영 특성 점수)가 가장 높기 때문에 LogisticRegression이 사기 및 사기가 아닌 거래를 매우 정확하게 구분합니다.
    • \n", "\n", "\n", "## 학습 곡선:\n", "<울>\n", "
  • 훈련 점수와 교차 검증 점수 사이의 차이가 클수록 모델이 과적합(높은 분산)일 가능성이 높아집니다.
    • \n", "
  • 훈련 세트와 교차 검증 세트 모두에서 점수가 낮은 경우 이는 우리 모델이 과소적합(높은 편향)
    • 임을 나타냅니다.\n", "
  • 로지스틱 회귀 분류기는 훈련 세트와 교차 검증 세트 모두에서 최고 점수를 보여줍니다.
    • \n", "" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "_cell_guid": "85ce8738-7599-4b06-a722-5c0ed073599b", "_kg_hide-input": true, "_uuid": "e3751d88766a982119e522e27a9c0c647f20af85" }, "outputs": [], "source": [ "# 교차 검증 전 언더샘플링(과적합이 발생하기 쉬움)\n", "X = new_df.drop('Class', axis=1)\n", "y = new_df['Class']" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "_cell_guid": "288a65b7-8b86-44b1-973d-38dbcfe82bbb", "_kg_hide-input": true, "_uuid": "fb0a479efaa7147d6702c2c24083f1118621863f" }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "_cell_guid": "bccd5685-a979-451e-85b3-1cb968523540", "_kg_hide-input": true, "_uuid": "28f5178089d2d133b9e7478c1c7dc7a1f98aabee" }, "outputs": [], "source": [ "X_train = X_train.values\n", "X_test = X_test.values\n", "y_train = y_train.values\n", "y_test = y_test.values" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(757, 30) (190, 30) (757,) (190,)\n" ] } ], "source": [ "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "_cell_guid": "7810d0b9-b4e5-4b7f-909b-c127365b167c", "_kg_hide-input": true, "_uuid": "8dd4ea07fd60973fccabc2d46af28a09b0de9178" }, "outputs": [], "source": [ "classifiers = {\n", " \"LogisiticRegression\": LogisticRegression(),\n", " \"KNearest\": KNeighborsClassifier(),\n", " \"Support Vector Classifier\": SVC(),\n", " \"DecisionTreeClassifier\": DecisionTreeClassifier()\n", "}" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "_cell_guid": "eb37c0f6-9cfe-48b6-92d3-475d5e6767a6", "_kg_hide-input": true, "_uuid": "fe129af379caccc5428cf1836e6c96bd32e68feb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classifiers: LogisticRegression Has a training score of 93.0 % accuracy score\n", "Classifiers: KNeighborsClassifier Has a training score of 92.0 % accuracy score\n", "Classifiers: SVC Has a training score of 93.0 % accuracy score\n", "Classifiers: DecisionTreeClassifier Has a training score of 89.0 % accuracy score\n" ] } ], "source": [ "from sklearn.model_selection import cross_val_score\n", "\n", "for key, classifier in classifiers.items():\n", " classifier.fit(X_train, y_train)\n", " training_score = cross_val_score(classifier, X_train, y_train, cv=5)\n", " print(\"Classifiers: \", classifier.__class__.__name__, \"Has a training score of\", round(training_score.mean(), 2) * 100, \"% accuracy score\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "_cell_guid": "a1c35773-f4c7-4caf-9911-532784c9eae0", "_kg_hide-input": true, "_uuid": "d15b1ab16737358806e34c48dc57aa238cf0cfd2" }, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV\n", "\n", "# Logistic Regression \n", "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n", "grid_log_reg = GridSearchCV(LogisticRegression(), log_reg_params)\n", "grid_log_reg.fit(X_train, y_train)\n", "\n", "log_reg = grid_log_reg.best_estimator_\n", "\n", "# KNN\n", "knears_params = {\"n_neighbors\": list(range(2,5,1)), 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute']}\n", "grid_knears = GridSearchCV(KNeighborsClassifier(), knears_params)\n", "grid_knears.fit(X_train, y_train)\n", "\n", "knears_neighbors = grid_knears.best_estimator_\n", "\n", "# Support Vector Classifier\n", "svc_params = {'C': [0.5, 0.7, 0.9, 1], 'kernel': ['rbf', 'poly', 'sigmoid', 'linear']}\n", "grid_svc = GridSearchCV(SVC(), svc_params)\n", "grid_svc.fit(X_train, y_train)\n", "\n", "svc = grid_svc.best_estimator_\n", "\n", "# DecisionTree Classifier\n", "tree_params = {\"criterion\": [\"gini\", \"entropy\"], \"max_depth\": list(range(2,4,1)), \n", " \"min_samples_leaf\": list(range(5,7,1))}\n", "grid_tree = GridSearchCV(DecisionTreeClassifier(), tree_params)\n", "grid_tree.fit(X_train, y_train)\n", "\n", "tree_clf = grid_tree.best_estimator_" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=0.1)" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_log_reg.best_estimator_" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "_cell_guid": "7f327bcd-335f-4e49-af07-fc4214dbcbdc", "_kg_hide-input": true, "_uuid": "1b2108bf377b924ed8a6efe580d9e162a132cd9e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression Cross Validation Score: 93.13%\n", "Knears Neighbors Cross Validation Score 92.34%\n", "Support Vector Classifier Cross Validation Score 92.99%\n", "DecisionTree Classifier Cross Validation Score 91.41%\n" ] } ], "source": [ "log_reg_score = cross_val_score(log_reg, X_train, y_train, cv=5)\n", "print('Logistic Regression Cross Validation Score: ', round(log_reg_score.mean() * 100, 2).astype(str) + '%')\n", "\n", "knears_score = cross_val_score(knears_neighbors, X_train, y_train, cv=5)\n", "print('Knears Neighbors Cross Validation Score', round(knears_score.mean() * 100, 2).astype(str) + '%')\n", "\n", "svc_score = cross_val_score(svc, X_train, y_train, cv=5)\n", "print('Support Vector Classifier Cross Validation Score', round(svc_score.mean() * 100, 2).astype(str) + '%')\n", "\n", "tree_score = cross_val_score(tree_clf, X_train, y_train, cv=5)\n", "print('DecisionTree Classifier Cross Validation Score', round(tree_score.mean() * 100, 2).astype(str) + '%')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "_cell_guid": "38e430ef-0160-47a1-9b6f-11ff62c5ecc0", "_kg_hide-input": true, "_uuid": "eeb5736b279bb8fa3804689a175394f216ec4f72" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train: [ 51670 51702 52463 ... 284804 284805 284806] Test: [ 0 1 2 ... 56970 56971 56972]\n", "Train: [ 0 1 2 ... 284804 284805 284806] Test: [ 51670 51702 52463 ... 113936 113937 113938]\n", "Train: [ 0 1 2 ... 284804 284805 284806] Test: [104565 104758 104781 ... 170890 170891 170892]\n", "Train: [ 0 1 2 ... 284804 284805 284806] Test: [167814 168370 168487 ... 227846 227847 227848]\n", "Train: [ 0 1 2 ... 227846 227847 227848] Test: [224612 225025 226641 ... 284804 284805 284806]\n", "NearMiss Label Distribution: Counter({0: 492, 1: 492})\n" ] } ], "source": [ "undersample_X = df.drop('Class', axis=1)\n", "undersample_y = df['Class']\n", "\n", "for train_index, test_index in skf.split(undersample_X, undersample_y):\n", " print(\"Train:\", train_index, \"Test:\", test_index)\n", " undersample_Xtrain, undersample_Xtest = undersample_X.iloc[train_index], undersample_X.iloc[test_index]\n", " undersample_ytrain, undersample_ytest = undersample_y.iloc[train_index], undersample_y.iloc[test_index]\n", " \n", "undersample_Xtrain = undersample_Xtrain.values\n", "undersample_Xtest = undersample_Xtest.values\n", "undersample_ytrain = undersample_ytrain.values\n", "undersample_ytest = undersample_ytest.values \n", "\n", "undersample_accuracy = []\n", "undersample_precision = []\n", "undersample_recall = []\n", "undersample_f1 = []\n", "undersample_auc = []\n", "\n", "# NearMiss 기법 구현\n", "# NearMiss의 분포(이 변수를 사용하지 않을 레이블이 어떻게 분포하는지 확인하기 위해)\n", "X_nearmiss, y_nearmiss = NearMiss().fit_resample(undersample_X.values, undersample_y.values)\n", "print('NearMiss Label Distribution: {}'.format(Counter(y_nearmiss)))\n", "# Cross Validating the right way\n", "\n", "# \n", "for train, test in skf.split(undersample_Xtrain, undersample_ytrain):\n", " undersample_pipeline = imbalanced_make_pipeline(NearMiss(sampling_strategy='majority'), log_reg) # SMOTE happens during Cross Validation not before..\n", " undersample_model = undersample_pipeline.fit(undersample_Xtrain[train], undersample_ytrain[train])\n", " undersample_prediction = undersample_model.predict(undersample_Xtrain[test])\n", " \n", " undersample_accuracy.append(undersample_pipeline.score(original_Xtrain[test], original_ytrain[test]))\n", " undersample_precision.append(precision_score(original_ytrain[test], undersample_prediction))\n", " undersample_recall.append(recall_score(original_ytrain[test], undersample_prediction))\n", " undersample_f1.append(f1_score(original_ytrain[test], undersample_prediction))\n", " undersample_auc.append(roc_auc_score(original_ytrain[test], undersample_prediction))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "_cell_guid": "bb72803c-3ea3-40cd-8ac3-399540ab7f5a", "_kg_hide-input": true, "_uuid": "a12fb2f7e104931bb78e1bd6cfc5a516c970708b", "code_folding": [ 0 ] }, "outputs": [], "source": [ "# Let's Plot LogisticRegression Learning Curve\n", "from sklearn.model_selection import ShuffleSplit\n", "from sklearn.model_selection import learning_curve\n", "\n", "def plot_learning_curve(estimator1, estimator2, estimator3, estimator4, X, y, ylim=None, cv=None,\n", " n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n", " f, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2, figsize=(20,14), sharey=True)\n", " if ylim is not None:\n", " plt.ylim(*ylim)\n", " # First Estimator\n", " train_sizes, train_scores, test_scores = learning_curve(\n", " estimator1, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n", " train_scores_mean = np.mean(train_scores, axis=1)\n", " train_scores_std = np.std(train_scores, axis=1)\n", " test_scores_mean = np.mean(test_scores, axis=1)\n", " test_scores_std = np.std(test_scores, axis=1)\n", " ax1.fill_between(train_sizes, train_scores_mean - train_scores_std,\n", " train_scores_mean + train_scores_std, alpha=0.1,\n", " color=\"#ff9124\")\n", " ax1.fill_between(train_sizes, test_scores_mean - test_scores_std,\n", " test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n", " ax1.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n", " label=\"Training score\")\n", " ax1.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n", " label=\"Cross-validation score\")\n", " ax1.set_title(\"Logistic Regression Learning Curve\", fontsize=14)\n", " ax1.set_xlabel('Training size (m)')\n", " ax1.set_ylabel('Score')\n", " ax1.grid(True)\n", " ax1.legend(loc=\"best\")\n", " \n", " # Second Estimator \n", " train_sizes, train_scores, test_scores = learning_curve(\n", " estimator2, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n", " train_scores_mean = np.mean(train_scores, axis=1)\n", " train_scores_std = np.std(train_scores, axis=1)\n", " test_scores_mean = np.mean(test_scores, axis=1)\n", " test_scores_std = np.std(test_scores, axis=1)\n", " ax2.fill_between(train_sizes, train_scores_mean - train_scores_std,\n", " train_scores_mean + train_scores_std, alpha=0.1,\n", " color=\"#ff9124\")\n", " ax2.fill_between(train_sizes, test_scores_mean - test_scores_std,\n", " test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n", " ax2.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n", " label=\"Training score\")\n", " ax2.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n", " label=\"Cross-validation score\")\n", " ax2.set_title(\"Knears Neighbors Learning Curve\", fontsize=14)\n", " ax2.set_xlabel('Training size (m)')\n", " ax2.set_ylabel('Score')\n", " ax2.grid(True)\n", " ax2.legend(loc=\"best\")\n", " \n", " # Third Estimator\n", " train_sizes, train_scores, test_scores = learning_curve(\n", " estimator3, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n", " train_scores_mean = np.mean(train_scores, axis=1)\n", " train_scores_std = np.std(train_scores, axis=1)\n", " test_scores_mean = np.mean(test_scores, axis=1)\n", " test_scores_std = np.std(test_scores, axis=1)\n", " ax3.fill_between(train_sizes, train_scores_mean - train_scores_std,\n", " train_scores_mean + train_scores_std, alpha=0.1,\n", " color=\"#ff9124\")\n", " ax3.fill_between(train_sizes, test_scores_mean - test_scores_std,\n", " test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n", " ax3.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n", " label=\"Training score\")\n", " ax3.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n", " label=\"Cross-validation score\")\n", " ax3.set_title(\"Support Vector Classifier \\n Learning Curve\", fontsize=14)\n", " ax3.set_xlabel('Training size (m)')\n", " ax3.set_ylabel('Score')\n", " ax3.grid(True)\n", " ax3.legend(loc=\"best\")\n", " \n", " # Fourth Estimator\n", " train_sizes, train_scores, test_scores = learning_curve(\n", " estimator4, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n", " train_scores_mean = np.mean(train_scores, axis=1)\n", " train_scores_std = np.std(train_scores, axis=1)\n", " test_scores_mean = np.mean(test_scores, axis=1)\n", " test_scores_std = np.std(test_scores, axis=1)\n", " ax4.fill_between(train_sizes, train_scores_mean - train_scores_std,\n", " train_scores_mean + train_scores_std, alpha=0.1,\n", " color=\"#ff9124\")\n", " ax4.fill_between(train_sizes, test_scores_mean - test_scores_std,\n", " test_scores_mean + test_scores_std, alpha=0.1, color=\"#2492ff\")\n", " ax4.plot(train_sizes, train_scores_mean, 'o-', color=\"#ff9124\",\n", " label=\"Training score\")\n", " ax4.plot(train_sizes, test_scores_mean, 'o-', color=\"#2492ff\",\n", " label=\"Cross-validation score\")\n", " ax4.set_title(\"Decision Tree Classifier \\n Learning Curve\", fontsize=14)\n", " ax4.set_xlabel('Training size (m)')\n", " ax4.set_ylabel('Score')\n", " ax4.grid(True)\n", " ax4.legend(loc=\"best\")\n", " return plt" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "_cell_guid": "5b8302aa-0207-455f-8c1a-78ff3e9b5141", "_kg_hide-input": true, "_uuid": "15b262baa0c61c288a5453031b4d7f80f5a7a5ab" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=42)\n", "plot_learning_curve(log_reg, knears_neighbors, svc, tree_clf, X_train, y_train, (0.87, 1.01), cv=cv, n_jobs=4)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "_cell_guid": "780e485a-ea64-48a0-ad97-a7516b047f32", "_kg_hide-input": true, "_uuid": "fdd59bf2c7a8e61cfb401142570643e8a29cf86b" }, "outputs": [], "source": [ "from sklearn.metrics import roc_curve\n", "from sklearn.model_selection import cross_val_predict\n", "\n", "log_reg_pred = cross_val_predict(log_reg, X_train, y_train, cv=5, method=\"decision_function\")\n", "\n", "knears_pred = cross_val_predict(knears_neighbors, X_train, y_train, cv=5)\n", "\n", "svc_pred = cross_val_predict(svc, X_train, y_train, cv=5, method=\"decision_function\")\n", "\n", "tree_pred = cross_val_predict(tree_clf, X_train, y_train, cv=5)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "_cell_guid": "57c211c6-e88f-4634-b321-4949df08815d", "_kg_hide-input": true, "_uuid": "cb2e4715e91e36f2029ef2a5c241991ff162cd9f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression: 0.9649541875647959\n", "KNears Neighbors: 0.9196426069657317\n", "Support Vector Classifier: 0.9598755919190787\n", "Decision Tree Classifier: 0.9115448737706296\n" ] } ], "source": [ "from sklearn.metrics import roc_auc_score\n", "\n", "print('Logistic Regression: ', roc_auc_score(y_train, log_reg_pred))\n", "print('KNears Neighbors: ', roc_auc_score(y_train, knears_pred))\n", "print('Support Vector Classifier: ', roc_auc_score(y_train, svc_pred))\n", "print('Decision Tree Classifier: ', roc_auc_score(y_train, tree_pred))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "_cell_guid": "89b0b9b6-ef82-4b69-9517-e89a79696dbb", "_kg_hide-input": true, "_uuid": "9d57aad23f3f72f3c45bf80b089a65acbce2a9ab" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "log_fpr, log_tpr, log_thresold = roc_curve(y_train, log_reg_pred)\n", "knear_fpr, knear_tpr, knear_threshold = roc_curve(y_train, knears_pred)\n", "svc_fpr, svc_tpr, svc_threshold = roc_curve(y_train, svc_pred)\n", "tree_fpr, tree_tpr, tree_threshold = roc_curve(y_train, tree_pred)\n", "\n", "\n", "def graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr):\n", " plt.figure(figsize=(16,8))\n", " plt.title('ROC Curve \\n Top 4 Classifiers', fontsize=18)\n", " plt.plot(log_fpr, log_tpr, label='Logistic Regression Classifier Score: {:.4f}'.format(roc_auc_score(y_train, log_reg_pred)))\n", " plt.plot(knear_fpr, knear_tpr, label='KNears Neighbors Classifier Score: {:.4f}'.format(roc_auc_score(y_train, knears_pred)))\n", " plt.plot(svc_fpr, svc_tpr, label='Support Vector Classifier Score: {:.4f}'.format(roc_auc_score(y_train, svc_pred)))\n", " plt.plot(tree_fpr, tree_tpr, label='Decision Tree Classifier Score: {:.4f}'.format(roc_auc_score(y_train, tree_pred)))\n", " plt.plot([0, 1], [0, 1], 'k--')\n", " plt.axis([-0.01, 1, 0, 1])\n", " plt.xlabel('False Positive Rate', fontsize=16)\n", " plt.ylabel('True Positive Rate', fontsize=16)\n", " plt.annotate('Minimum ROC Score of 50% \\n (This is the minimum score to get)', xy=(0.5, 0.5), xytext=(0.6, 0.3),\n", " arrowprops=dict(facecolor='#6E726D', shrink=0.05),\n", " )\n", " plt.legend()\n", " \n", "graph_roc_curve_multiple(log_fpr, log_tpr, knear_fpr, knear_tpr, svc_fpr, svc_tpr, tree_fpr, tree_tpr)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "f56e6936-314c-42d4-8ea2-0cb2386ad382", "_uuid": "d6e62d64e9d9aa70223576a1df91a008aa6c2664" }, "source": [ "## A Deeper Look into LogisticRegression:\n", "\n", "In this section we will ive a deeper look into the logistic regression classifier.\n", "\n", "\n", "### Terms:\n", "
      \n", "
    • True Positives: Correctly Classified Fraud Transactions
      • \n", "
    • False Positives: Incorrectly Classified Fraud Transactions
      • \n", "
    • True Negative: Correctly Classified Non-Fraud Transactions
      • \n", "
    • False Negative: Incorrectly Classified Non-Fraud Transactions
      • \n", "
    • Precision: True Positives/(True Positives + False Positives)
      • \n", "
    • Recall: True Positives/(True Positives + False Negatives)
      • \n", "
    • Precision as the name says, says how precise (how sure) is our model in detecting fraud transactions while recall is the amount of fraud cases our model is able to detect.
      • \n", "
    • Precision/Recall Tradeoff: The more precise (selective) our model is, the less cases it will detect. Example: Assuming that our model has a precision of 95%, Let's say there are only 5 fraud cases in which the model is 95% precise or more that these are fraud cases. Then let's say there are 5 more cases that our model considers 90% to be a fraud case, if we lower the precision there are more cases that our model will be able to detect.
      • \n", "
    \n", "\n", "### Summary:\n", "
      \n", "
    • Precision starts to descend between 0.90 and 0.92 nevertheless, our precision score is still pretty high and still we have a descent recall score.
      • \n", "\n", "
    " ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "_cell_guid": "b4eaea18-ec79-4cb2-9a92-8d70a7f593bf", "_kg_hide-input": true, "_uuid": "0daaa7137ab61d6fd88e5fcc0849acc94c693df0" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def logistic_roc_curve(log_fpr, log_tpr):\n", " plt.figure(figsize=(12,8))\n", " plt.title('Logistic Regression ROC Curve', fontsize=16)\n", " plt.plot(log_fpr, log_tpr, 'b-', linewidth=2)\n", " plt.plot([0, 1], [0, 1], 'r--')\n", " plt.xlabel('False Positive Rate', fontsize=16)\n", " plt.ylabel('True Positive Rate', fontsize=16)\n", " plt.axis([-0.01,1,0,1])\n", " \n", " \n", "logistic_roc_curve(log_fpr, log_tpr)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "_cell_guid": "d59c7621-5adb-4339-9a04-54630f679665", "_uuid": "f6d54ac036fa499104d269dd52d704c71629c1b0" }, "outputs": [], "source": [ "from sklearn.metrics import precision_recall_curve\n", "\n", "precision, recall, threshold = precision_recall_curve(y_train, log_reg_pred)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "_cell_guid": "06d9e8b8-3ba5-4c1e-8480-af5a8e04f851", "_kg_hide-input": true, "_uuid": "b19df81d0a5178a260d7518f9cca804646839c01" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---------------------------------------------------------------------------------------------------------------------------------------\n", "Overfitting: \n", "\n", "Recall Score: 0.88\n", "Precision Score: 0.83\n", "F1 Score: 0.85\n", "Accuracy Score: 0.86\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", "How it should be:\n", "\n", "Accuracy Score: 0.76\n", "Precision Score: 0.00\n", "Recall Score: 0.22\n", "F1 Score: 0.00\n", "---------------------------------------------------------------------------------------------------------------------------------------\n" ] } ], "source": [ "from sklearn.metrics import recall_score, precision_score, f1_score, accuracy_score\n", "y_pred = log_reg.predict(X_train)\n", "\n", "# Overfitting Case\n", "print('---' * 45)\n", "print('Overfitting: \\n')\n", "print('Recall Score: {:.2f}'.format(recall_score(y_train, y_pred)))\n", "print('Precision Score: {:.2f}'.format(precision_score(y_train, y_pred)))\n", "print('F1 Score: {:.2f}'.format(f1_score(y_train, y_pred)))\n", "print('Accuracy Score: {:.2f}'.format(accuracy_score(y_train, y_pred)))\n", "print('---' * 45)\n", "\n", "# How it should look like\n", "print('---' * 45)\n", "print('How it should be:\\n')\n", "print(\"Accuracy Score: {:.2f}\".format(np.mean(undersample_accuracy)))\n", "print(\"Precision Score: {:.2f}\".format(np.mean(undersample_precision)))\n", "print(\"Recall Score: {:.2f}\".format(np.mean(undersample_recall)))\n", "print(\"F1 Score: {:.2f}\".format(np.mean(undersample_f1)))\n", "print('---' * 45)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "_cell_guid": "89d88863-536f-4eea-a0cd-d2c77f407dd6", "_kg_hide-input": true, "_uuid": "f041ab92c183d2aa29569fc048ee6af4e6ee81f0" }, "outputs": [], "source": [ "undersample_y_score = log_reg.decision_function(original_Xtest)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "_cell_guid": "3f54604f-396c-421e-ae93-305ad0103591", "_kg_hide-input": true, "_uuid": "c501d9226855a510a136bbf06794c702497e5b28" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average precision-recall score: 0.07\n" ] } ], "source": [ "from sklearn.metrics import average_precision_score\n", "\n", "undersample_average_precision = average_precision_score(original_ytest, undersample_y_score)\n", "\n", "print('Average precision-recall score: {0:0.2f}'.format(\n", " undersample_average_precision))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "_cell_guid": "2442a51e-0263-48a3-99f9-06f47bdc04f1", "_kg_hide-input": true, "_uuid": "2edd5461ff5253f12955ac02106c323f7aabe49f" }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.07')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import precision_recall_curve\n", "import matplotlib.pyplot as plt\n", "\n", "fig = plt.figure(figsize=(12,6))\n", "\n", "precision, recall, _ = precision_recall_curve(original_ytest, undersample_y_score)\n", "\n", "plt.step(recall, precision, color='#004a93', alpha=0.2,\n", " where='post')\n", "plt.fill_between(recall, precision, step='post', alpha=0.2,\n", " color='#48a6ff')\n", "\n", "plt.xlabel('Recall')\n", "plt.ylabel('Precision')\n", "plt.ylim([0.0, 1.05])\n", "plt.xlim([0.0, 1.0])\n", "plt.title('UnderSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n", " undersample_average_precision), fontsize=16)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "e70e913a-173b-401b-96ee-93ddda7374c0", "_uuid": "d901eb00581cc890075a93d292935304e5b63355" }, "source": [ "### SMOTE 기법(오버샘플링):\n", "\n", "\n", "SMOTE는 Synthetic Minority Over-sampling Technique의 약자입니다. Random UnderSampling과 달리 SMOTE는 클래스의 균등한 균형을 유지하기 위해 새로운 합성 포인트를 생성합니다. 이것은 \"클래스 불균형 문제\"를 해결하기 위한 또 다른 대안입니다.

    \n", "\n", "\n", " SMOTE에 대한 이해: \n", "<울>\n", "
  • 클래스 불균형 해결: SMOTE는 소수 클래스와 다수 클래스 간의 동등한 균형에 도달하기 위해 소수 클래스에서 종합 점수를 만듭니다.
    • \n", "
  • 합성 포인트의 위치: SMOTE는 소수 클래스의 가장 가까운 이웃 사이의 거리를 선택하고 이 거리 사이에서 합성 포인트를 생성합니다.
    • \n", "
  • 최종 효과: 임의의 언더샘플링과 달리 행을 삭제할 필요가 없기 때문에 더 많은 정보가 유지됩니다.
    • \n", "
  • 정확도 || 시간 절충: SMOTE가 임의의 언더샘플링보다 더 정확할 가능성이 높지만 앞서 언급한 것처럼 제거된 행이 없기 때문에 훈련하는 데 더 많은 시간이 걸립니다.
    • \n", "\n", "\n", "\n", "### 교차 검증 과적합 실수:\n", "## 교차 검증 중 과적합:\n", "우리의 언더샘플 분석에서 저는 여러분 모두와 공유하고 싶은 일반적인 실수를 보여주고 싶습니다. 간단합니다. 데이터를 언더샘플링하거나 오버샘플링하려면 교차 검증 전에 수행하지 않아야 합니다. 교차 유효성 검사를 구현하기 전에 유효성 검사 세트에 직접 영향을 미치기 때문에 \"데이터 누출\" 문제가 발생하기 때문입니다. 다음 섹션에서 놀라운 정밀도와 재현율 점수를 볼 수 있지만 실제로는 데이터가 과적합됩니다!\n", "### 잘못된 방법:\n", "
    \n", "\n", "앞서 언급했듯이 우리의 경우 소수 클래스(\"사기\")를 얻고 교차 검증 전에 합성 포인트를 생성하면 교차 검증 프로세스의 \"검증 세트\"에 특정 영향을 미칩니다. 교차 검증이 작동하는 방식을 기억합시다. 데이터를 5개의 배치로 분할한다고 가정하면 데이터 세트의 4/5가 훈련 세트가 되고 1/5이 검증 세트가 됩니다. 테스트 세트는 건들면 안 됩니다! 교차 검증 \"중\" 합성 데이터 포인트(아래와 같이 이전 아님):
    \n", "\n", "\n", "### 옳은 길:\n", "
    \n", "위에서 볼 수 있듯이 SMOTE는 교차 검증 프로세스의 \"이전\"이 아니라 \"중\" 교차 검증에 발생합니다. 검증 세트에 영향을 주지 않고 훈련 세트에 대해서만 합성 데이터가 생성됩니다.\n", "\n", "\n", "\n", "\n", "**참조**:\n", "<울>\n", "
  • \n", "불균형 데이터 처리: 언더샘플링, 오버샘플링 및 적절한 교차 검증
    • \n", "\n", "
  • 멍청한 놈을 위한 SMOTE 설명
    • \n", "
  • 기계 학습 - 오버샘플링 및 언더샘플링 - Python/ Scikit/ Scikit-Imblearn
    • \n", "" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "_cell_guid": "cc175ddc-ddd7-4087-ae1f-dd6fac664d58", "_kg_hide-input": true, "_uuid": "96f8d3f4160d65f12af4c7106739c4ad46d1e76b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Length of X (train): 227846 | Length of y (train): 227846\n", "Length of X (test): 56961 | Length of y (test): 56961\n", "---------------------------------------------------------------------------------------------------------------------------------------\n", "\n", "accuracy: 0.9414743009119715\n", "precision: 0.06098042241564661\n", "recall: 0.9137293086660175\n", "f1: 0.1125194816171553\n", "---------------------------------------------------------------------------------------------------------------------------------------\n" ] } ], "source": [ "from imblearn.over_sampling import SMOTE\n", "from sklearn.model_selection import train_test_split, RandomizedSearchCV\n", "\n", "print('Length of X (train): {} | Length of y (train): {}'.format(len(original_Xtrain), len(original_ytrain)))\n", "print('Length of X (test): {} | Length of y (test): {}'.format(len(original_Xtest), len(original_ytest)))\n", "\n", "# List to append the score and then find the average\n", "accuracy_lst = []\n", "precision_lst = []\n", "recall_lst = []\n", "f1_lst = []\n", "auc_lst = []\n", "\n", "# 모델\n", "log_reg_sm = LogisticRegression()\n", "log_reg_params = {\"penalty\": ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}\n", "rand_log_reg = RandomizedSearchCV(LogisticRegression(), log_reg_params, n_iter=4)\n", "\n", "\n", "for train, test in skf.split(original_Xtrain, original_ytrain):\n", " pipeline = imbalanced_make_pipeline(SMOTE(sampling_strategy='minority'), rand_log_reg) # SMOTE happens during Cross Validation not before..\n", " model = pipeline.fit(original_Xtrain[train], original_ytrain[train])\n", " best_est = rand_log_reg.best_estimator_\n", " prediction = best_est.predict(original_Xtrain[test])\n", " \n", " accuracy_lst.append(pipeline.score(original_Xtrain[test], original_ytrain[test]))\n", " precision_lst.append(precision_score(original_ytrain[test], prediction))\n", " recall_lst.append(recall_score(original_ytrain[test], prediction))\n", " f1_lst.append(f1_score(original_ytrain[test], prediction))\n", " auc_lst.append(roc_auc_score(original_ytrain[test], prediction))\n", " \n", "print('---' * 45)\n", "print('')\n", "print(\"accuracy: {}\".format(np.mean(accuracy_lst)))\n", "print(\"precision: {}\".format(np.mean(precision_lst)))\n", "print(\"recall: {}\".format(np.mean(recall_lst)))\n", "print(\"f1: {}\".format(np.mean(f1_lst)))\n", "print('---' * 45)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "_cell_guid": "41dd6215-2927-4de3-999a-724272aea2b6", "_kg_hide-input": true, "_uuid": "d109652d1e170d0f9938d64f29aa33d93c941cdc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " No Fraud 1.00 0.99 0.99 56863\n", " Fraud 0.11 0.86 0.20 98\n", "\n", " accuracy 0.99 56961\n", " macro avg 0.56 0.92 0.60 56961\n", "weighted avg 1.00 0.99 0.99 56961\n", "\n" ] } ], "source": [ "labels = ['No Fraud', 'Fraud']\n", "smote_prediction = best_est.predict(original_Xtest)\n", "print(classification_report(original_ytest, smote_prediction, target_names=labels))" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "_cell_guid": "77bed8fa-1117-4bc0-a740-bd1bd97012a4", "_kg_hide-input": true, "_uuid": "f9213b24dd2fb3eb04f9b59c3b715dcb167664b5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average precision-recall score: 0.75\n" ] } ], "source": [ "y_score = best_est.decision_function(original_Xtest)\n", "average_precision = average_precision_score(original_ytest, y_score)\n", "\n", "print('Average precision-recall score: {0:0.2f}'.format(average_precision))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "_cell_guid": "54e926f4-2a5d-4bb1-b74c-8cd79da7b6e5", "_kg_hide-input": true, "_uuid": "7be0445ac80df7ca252ec350b026d6275669aea6" }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'OverSampling Precision-Recall curve: \\n Average Precision-Recall Score =0.75')" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,6))\n", "\n", "precision, recall, _ = precision_recall_curve(original_ytest, y_score)\n", "\n", "plt.step(recall, precision, color='r', alpha=0.2,\n", " where='post')\n", "plt.fill_between(recall, precision, step='post', alpha=0.2,\n", " color='#F59B00')\n", "\n", "plt.xlabel('Recall')\n", "plt.ylabel('Precision')\n", "plt.ylim([0.0, 1.05])\n", "plt.xlim([0.0, 1.0])\n", "plt.title('OverSampling Precision-Recall curve: \\n Average Precision-Recall Score ={0:0.2f}'.format(\n", " average_precision), fontsize=16)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "_cell_guid": "d5c6fe5b-f086-4151-aba5-3c758677be0f", "_kg_hide-input": true, "_uuid": "787ec6bb25c3dc379c12a57619f5cc3e41afa42e" }, "outputs": [], "source": [ "# SMOTE Technique (OverSampling) After splitting and Cross Validating\n", "sm = SMOTE(sampling_strategy='minority', random_state=42)\n", "\n", "Xsm_train, ysm_train = sm.fit_resample(original_Xtrain, original_ytrain)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "_cell_guid": "7af62152-e7e3-45c8-9a56-69467ede59a6", "_kg_hide-input": true, "_uuid": "a25f7cc327bbaeae985cb0d2f9a0c8e2c2009aa3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fitting oversample data took :4.309025764465332 sec\n" ] } ], "source": [ "# We Improve the score by 2% points approximately \n", "# Implement GridSearchCV and the other models.\n", "\n", "# Logistic Regression\n", "t0 = time.time()\n", "log_reg_sm = grid_log_reg.best_estimator_\n", "log_reg_sm.fit(Xsm_train, ysm_train)\n", "t1 = time.time()\n", "print(\"Fitting oversample data took :{} sec\".format(t1 - t0))" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "a250e819-cdd4-43f5-b0a4-eb8f232199a0", "_uuid": "feb07b601c9ec79be1fe96cbbadf4ac838f7f7a8" }, "source": [ "# Test Data with Logistic Regression:\n", "\n", "## Confusion Matrix:\n", "**Positive/Negative:** Type of Class (label) [\"No\", \"Yes\"]\n", "**True/False:** Correctly or Incorrectly classified by the model.

    \n", "\n", "**True Negatives (Top-Left Square):** This is the number of **correctly** classifications of the \"No\" (No Fraud Detected) class.

    \n", "\n", "**False Negatives (Top-Right Square):** This is the number of **incorrectly** classifications of the \"No\"(No Fraud Detected) class.

    \n", "\n", "**False Positives (Bottom-Left Square):** This is the number of **incorrectly** classifications of the \"Yes\" (Fraud Detected) class

    \n", "\n", "**True Positives (Bottom-Right Square):** This is the number of **correctly** classifications of the \"Yes\" (Fraud Detected) class.\n", "\n", "\n", "### Summary: \n", "
      \n", "
    • Random UnderSampling: We will evaluate the final performance of the classification models in the random undersampling subset. Keep in mind that this is not the data from the original dataframe.
      • \n", "
    • Classification Models: The models that performed the best were logistic regression and support vector classifier (SVM)
      • \n", "
    " ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "_cell_guid": "13a7d31c-2586-4946-aaa3-60090cd5680b", "_kg_hide-input": true, "_uuid": "d0e37500506d1b942431ac5bfabedcfea30275ce" }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "y_pred_log_reg = log_reg_sm.predict(X_test)\n", "y_pred_knear = knears_neighbors.predict(X_test)\n", "y_pred_svc = svc.predict(X_test)\n", "y_pred_tree = tree_clf.predict(X_test)\n", "\n", "\n", "log_reg_cf = confusion_matrix(y_test, y_pred_log_reg)\n", "kneighbors_cf = confusion_matrix(y_test, y_pred_knear)\n", "svc_cf = confusion_matrix(y_test, y_pred_svc)\n", "tree_cf = confusion_matrix(y_test, y_pred_tree)\n", "\n", "fig, ax = plt.subplots(2, 2,figsize=(22,12))\n", "\n", "\n", "sns.heatmap(log_reg_cf, ax=ax[0][0], annot=True, cmap=plt.cm.copper)\n", "ax[0, 0].set_title(\"Logistic Regression \\n Confusion Matrix\", fontsize=14)\n", "ax[0, 0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n", "ax[0, 0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n", "\n", "sns.heatmap(kneighbors_cf, ax=ax[0][1], annot=True, cmap=plt.cm.copper)\n", "ax[0][1].set_title(\"KNearsNeighbors \\n Confusion Matrix\", fontsize=14)\n", "ax[0][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n", "ax[0][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n", "\n", "sns.heatmap(svc_cf, ax=ax[1][0], annot=True, cmap=plt.cm.copper)\n", "ax[1][0].set_title(\"Suppor Vector Classifier \\n Confusion Matrix\", fontsize=14)\n", "ax[1][0].set_xticklabels(['', ''], fontsize=14, rotation=90)\n", "ax[1][0].set_yticklabels(['', ''], fontsize=14, rotation=360)\n", "\n", "sns.heatmap(tree_cf, ax=ax[1][1], annot=True, cmap=plt.cm.copper)\n", "ax[1][1].set_title(\"DecisionTree Classifier \\n Confusion Matrix\", fontsize=14)\n", "ax[1][1].set_xticklabels(['', ''], fontsize=14, rotation=90)\n", "ax[1][1].set_yticklabels(['', ''], fontsize=14, rotation=360)\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "_cell_guid": "bd4529fd-f38a-4dd1-8b63-467a15a2167d", "_kg_hide-input": true, "_uuid": "1380d639d3b9087ec767ed6db391fc4b8c01e765" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression:\n", " precision recall f1-score support\n", "\n", " 0 0.97 1.00 0.98 89\n", " 1 1.00 0.97 0.98 101\n", "\n", " accuracy 0.98 190\n", " macro avg 0.98 0.99 0.98 190\n", "weighted avg 0.98 0.98 0.98 190\n", "\n", "KNears Neighbors:\n", " precision recall f1-score support\n", "\n", " 0 0.93 0.99 0.96 89\n", " 1 0.99 0.93 0.96 101\n", "\n", " accuracy 0.96 190\n", " macro avg 0.96 0.96 0.96 190\n", "weighted avg 0.96 0.96 0.96 190\n", "\n", "Support Vector Classifier:\n", " precision recall f1-score support\n", "\n", " 0 0.93 0.98 0.95 89\n", " 1 0.98 0.93 0.95 101\n", "\n", " accuracy 0.95 190\n", " macro avg 0.95 0.95 0.95 190\n", "weighted avg 0.95 0.95 0.95 190\n", "\n", "Support Vector Classifier:\n", " precision recall f1-score support\n", "\n", " 0 0.93 0.94 0.94 89\n", " 1 0.95 0.94 0.95 101\n", "\n", " accuracy 0.94 190\n", " macro avg 0.94 0.94 0.94 190\n", "weighted avg 0.94 0.94 0.94 190\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "\n", "print('Logistic Regression:')\n", "print(classification_report(y_test, y_pred_log_reg))\n", "\n", "print('KNears Neighbors:')\n", "print(classification_report(y_test, y_pred_knear))\n", "\n", "print('Support Vector Classifier:')\n", "print(classification_report(y_test, y_pred_svc))\n", "\n", "print('Support Vector Classifier:')\n", "print(classification_report(y_test, y_pred_tree))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "_cell_guid": "9103c5ed-df9d-4441-91dc-1104a51f06ff", "_kg_hide-input": true, "_uuid": "49c94105ad280d1ca16271daf7f9395041016c5c" }, "outputs": [ { "data": { "text/html": [ "
    \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    TechniqueScore
    0Random UnderSampling0.984211
    1Oversampling (SMOTE)0.987922
    \n", "
    " ], "text/plain": [ " Technique Score\n", "0 Random UnderSampling 0.984211\n", "1 Oversampling (SMOTE) 0.987922" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Final Score in the test set of logistic regression\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Logistic Regression with Under-Sampling\n", "y_pred = log_reg.predict(X_test)\n", "undersample_score = accuracy_score(y_test, y_pred)\n", "\n", "# Logistic Regression with SMOTE Technique (Better accuracy with SMOTE t)\n", "y_pred_sm = best_est.predict(original_Xtest)\n", "oversample_score = accuracy_score(original_ytest, y_pred_sm)\n", "\n", "d = {'Technique': ['Random UnderSampling', 'Oversampling (SMOTE)'], 'Score': [undersample_score, oversample_score]}\n", "final_df = pd.DataFrame(data=d)\n", "\n", "# Move column\n", "score = final_df['Score']\n", "final_df.drop('Score', axis=1, inplace=True)\n", "final_df.insert(1, 'Score', score)\n", "\n", "# Note how high is accuracy score it can be misleading! \n", "final_df" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5ec3ca7c-fae2-4e0a-991b-bb689a8c4456", "_uuid": "d709eac19181e9b7026e2f9e9f780a207bc8c19a" }, "source": [ "## 신경망 테스트 랜덤 언더샘플링 데이터 대 오버샘플링(SMOTE):\n", "\n", "이 섹션에서는 우리가 (undersample 또는 oversample(SMOTE))에서 구현한 두 가지 로지스틱 회귀 모델 중 사기 및 비 사기 탐지에 더 나은 정확도를 갖는 간단한 신경망(하나의 은닉 레이어 포함)을 구현합니다. 업무.

    \n", "\n", "### 우리의 주요 목표:\n", "우리의 주요 목표는 무작위 언더샘플링 및 오버샘플링 데이터 프레임 모두에서 간단한 신경망이 어떻게 작동하는지 탐구하고 비 사기 및 사기 사례를 모두 정확하게 예측할 수 있는지 확인하는 것입니다. 사기에만 집중하지 않는 이유는 무엇입니까? 당신이 카드 소지자이고 상품을 구매한 후 은행의 알고리즘이 당신의 구매가 사기라고 생각했기 때문에 카드가 차단되었다고 상상해 보십시오. 그렇기 때문에 부정행위 적발에만 중점을 두는 것이 아니라 부정행위가 아닌 거래를 정확하게 분류하는 것도 강조해야 합니다.\n", "\n", "\n", "### 혼란 매트릭스:\n", "다음은 혼동 행렬이 작동하는 방식입니다.\n", "<울>\n", "
  • 상단 왼쪽 사각형: 사기 거래가 없는 당사 모델에 따라 정확하게 분류된 금액입니다.
    • \n", "
  • 오른쪽 상단 사각형: 거래를 사기 사례로 잘못 분류했지만 실제 레이블은 사기가 아닙니다 .
    • \n", "
  • 왼쪽 아래 사각형: 거래 금액이 부정확하게 분류되어 사기 사례가 아닌 것으로 표시되지만 실제 레이블은 사기 입니다.
    • \n", "
  • 오른쪽 하단 사각형: 사기 거래 모델에 따라 정확하게 분류된 금액입니다.
    • \n", "\n", "\n", "### 요약(Keras || 무작위 언더샘플링):\n", "<울>\n", "
  • 데이터 세트: 테스트의 이 마지막 단계에서 우리는 이 모델을 무작위 언더샘플링된 하위 집합 및 오버샘플링된 데이터세트(SMOTE) 에 맞출 것입니다. 원본 데이터 프레임 테스트 데이터
    • 를 사용하여 최종 결과를 예측하기 위해\n", "
  • 신경망 구조: 이전에 언급했듯이 이것은 하나의 입력 레이어(여기서 노드의 수는 기능의 수와 동일)와 바이어스 노드, 32개의 은닉 레이어로 구성된 간단한 모델입니다. 노드 및 두 개의 가능한 결과 0 또는 1로 구성된 하나의 출력 노드(사기 또는 사기 없음).
    • \n", "
  • 기타 특성: 학습률은 0.001, 우리가 사용할 옵티마이저는 AdamOptimizer, 이 시나리오에서 사용되는 활성화 함수는 \"Relu\"이며 최종 출력에 대해 사용할 것입니다. 희소 범주형 교차 엔트로피, 인스턴스 사례가 사기 또는 사기가 아닐 확률을 제공합니다(예측은 둘 중 가장 높은 확률을 선택합니다.)
    • \n", "" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "_cell_guid": "e774e22e-8ce0-4c2e-99fa-9f3c6a915b6d", "_kg_hide-input": true, "_uuid": "35be99c61da4054c952e1955a5e809d003966975", "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_3\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", " dense_9 (Dense) (None, 30) 930 \n", " \n", " dense_10 (Dense) (None, 32) 992 \n", " \n", " dense_11 (Dense) (None, 2) 66 \n", " \n", "=================================================================\n", "Total params: 1,988\n", "Trainable params: 1,988\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "Epoch 1/20\n", "25/25 - 1s - loss: 0.5406 - accuracy: 0.6711 - val_loss: 0.4605 - val_accuracy: 0.7697 - 501ms/epoch - 20ms/step\n", "Epoch 2/20\n", "25/25 - 0s - loss: 0.3604 - accuracy: 0.8678 - val_loss: 0.3604 - val_accuracy: 0.8684 - 62ms/epoch - 2ms/step\n", "Epoch 3/20\n", "25/25 - 0s - loss: 0.2719 - accuracy: 0.9091 - val_loss: 0.3106 - val_accuracy: 0.8816 - 53ms/epoch - 2ms/step\n", "Epoch 4/20\n", "25/25 - 0s - loss: 0.2263 - accuracy: 0.9256 - val_loss: 0.2890 - val_accuracy: 0.8947 - 54ms/epoch - 2ms/step\n", "Epoch 5/20\n", "25/25 - 0s - loss: 0.1958 - accuracy: 0.9289 - val_loss: 0.2771 - val_accuracy: 0.8947 - 53ms/epoch - 2ms/step\n", "Epoch 6/20\n", "25/25 - 0s - loss: 0.1749 - accuracy: 0.9355 - val_loss: 0.2669 - val_accuracy: 0.8947 - 58ms/epoch - 2ms/step\n", "Epoch 7/20\n", "25/25 - 0s - loss: 0.1567 - accuracy: 0.9355 - val_loss: 0.2688 - val_accuracy: 0.8947 - 56ms/epoch - 2ms/step\n", "Epoch 8/20\n", "25/25 - 0s - loss: 0.1456 - accuracy: 0.9438 - val_loss: 0.2690 - val_accuracy: 0.9013 - 62ms/epoch - 2ms/step\n", "Epoch 9/20\n", "25/25 - 0s - loss: 0.1372 - accuracy: 0.9438 - val_loss: 0.2999 - val_accuracy: 0.9079 - 56ms/epoch - 2ms/step\n", "Epoch 10/20\n", "25/25 - 0s - loss: 0.1261 - accuracy: 0.9554 - val_loss: 0.2824 - val_accuracy: 0.9013 - 57ms/epoch - 2ms/step\n", "Epoch 11/20\n", "25/25 - 0s - loss: 0.1173 - accuracy: 0.9587 - val_loss: 0.2966 - val_accuracy: 0.9013 - 58ms/epoch - 2ms/step\n", "Epoch 12/20\n", "25/25 - 0s - loss: 0.1110 - accuracy: 0.9603 - val_loss: 0.3015 - val_accuracy: 0.9013 - 63ms/epoch - 3ms/step\n", "Epoch 13/20\n", "25/25 - 0s - loss: 0.1047 - accuracy: 0.9620 - val_loss: 0.3176 - val_accuracy: 0.9013 - 56ms/epoch - 2ms/step\n", "Epoch 14/20\n", "25/25 - 0s - loss: 0.1001 - accuracy: 0.9603 - val_loss: 0.3315 - val_accuracy: 0.9079 - 64ms/epoch - 3ms/step\n", "Epoch 15/20\n", "25/25 - 0s - loss: 0.0954 - accuracy: 0.9636 - val_loss: 0.3504 - val_accuracy: 0.9079 - 57ms/epoch - 2ms/step\n", "Epoch 16/20\n", "25/25 - 0s - loss: 0.0914 - accuracy: 0.9603 - val_loss: 0.3603 - val_accuracy: 0.9079 - 59ms/epoch - 2ms/step\n", "Epoch 17/20\n", "25/25 - 0s - loss: 0.0855 - accuracy: 0.9653 - val_loss: 0.3710 - val_accuracy: 0.9211 - 55ms/epoch - 2ms/step\n", "Epoch 18/20\n", "25/25 - 0s - loss: 0.0818 - accuracy: 0.9669 - val_loss: 0.3831 - val_accuracy: 0.9211 - 67ms/epoch - 3ms/step\n", "Epoch 19/20\n", "25/25 - 0s - loss: 0.0787 - accuracy: 0.9669 - val_loss: 0.4084 - val_accuracy: 0.9211 - 58ms/epoch - 2ms/step\n", "Epoch 20/20\n", "25/25 - 0s - loss: 0.0744 - accuracy: 0.9686 - val_loss: 0.4227 - val_accuracy: 0.9211 - 54ms/epoch - 2ms/step\n" ] } ], "source": [ "import keras\n", "from keras import backend as K\n", "from keras.models import Sequential\n", "from keras.layers import Activation\n", "from keras.layers.core import Dense\n", "from tensorflow.keras.optimizers import Adam\n", "from keras.metrics import categorical_crossentropy\n", "\n", "n_inputs = X_train.shape[1]\n", "\n", "undersample_model = Sequential([\n", " Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n", " Dense(32, activation='relu'),\n", " Dense(2, activation='softmax')\n", "])\n", "\n", "undersample_model.summary()\n", "\n", "undersample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", "undersample_model.fit(X_train, y_train, validation_split=0.2, batch_size=25, epochs=20, shuffle=True, verbose=2)\n", "undersample_predictions = undersample_model.predict(original_Xtest, batch_size=200, verbose=0)\n", "undersample_fraud_predictions = undersample_model.predict(original_Xtest, batch_size=200, verbose=0).argmax(axis=-1)\n" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "_cell_guid": "44216511-5fa7-404f-9b40-2c72f30c1ca7", "_kg_hide-input": true, "_uuid": "b0681d10d7f3e68a6b91864670b7aa04cacd362f" }, "outputs": [], "source": [ "import itertools\n", "\n", "# Create a confusion matrix\n", "def plot_confusion_matrix(cm, classes,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", " print(\"Normalized confusion matrix\")\n", " else:\n", " print('Confusion matrix, without normalization')\n", "\n", " print(cm)\n", "\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title, fontsize=14)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " plt.xticks(tick_marks, classes, rotation=45)\n", " plt.yticks(tick_marks, classes)\n", "\n", " fmt = '.2f' if normalize else 'd'\n", " thresh = cm.max() / 2.\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, format(cm[i, j], fmt),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "_cell_guid": "ee16d514-2134-4cfd-b4d8-1ddb183960f0", "_kg_hide-input": true, "_uuid": "003c84c96d49bebdb5f09970102d89e3db5ff2f1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[55140 1723]\n", " [ 7 91]]\n", "Confusion matrix, without normalization\n", "[[56863 0]\n", " [ 0 98]]\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "undersample_cm = confusion_matrix(original_ytest, undersample_fraud_predictions)\n", "actual_cm = confusion_matrix(original_ytest, original_ytest)\n", "labels = ['No Fraud', 'Fraud']\n", "\n", "fig = plt.figure(figsize=(16,8))\n", "\n", "fig.add_subplot(221)\n", "plot_confusion_matrix(undersample_cm, labels, title=\"Random UnderSample \\n Confusion Matrix\", cmap=plt.cm.Reds)\n", "\n", "fig.add_subplot(222)\n", "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "9d7cd385-9270-426e-b537-c80501dce889", "_uuid": "be2b0e76445ecb745b6e49e69993dd1de7839eb4" }, "source": [ "### Keras || OverSampling (SMOTE):\n" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "_cell_guid": "e7c29164-751a-4ccd-b517-527debf38fdf", "_kg_hide-input": true, "_uuid": "7130856ed8a6f87fe86b72c5142ff27ccf4eef1a", "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/20\n", "1214/1214 - 2s - loss: 0.0622 - accuracy: 0.9786 - val_loss: 0.0212 - val_accuracy: 0.9957 - 2s/epoch - 2ms/step\n", "Epoch 2/20\n", "1214/1214 - 2s - loss: 0.0133 - accuracy: 0.9970 - val_loss: 0.0063 - val_accuracy: 0.9998 - 2s/epoch - 1ms/step\n", "Epoch 3/20\n", "1214/1214 - 2s - loss: 0.0074 - accuracy: 0.9985 - val_loss: 0.0044 - val_accuracy: 0.9999 - 2s/epoch - 1ms/step\n", "Epoch 4/20\n", "1214/1214 - 2s - loss: 0.0053 - accuracy: 0.9990 - val_loss: 0.0045 - val_accuracy: 0.9998 - 2s/epoch - 2ms/step\n", "Epoch 5/20\n", "1214/1214 - 2s - loss: 0.0042 - accuracy: 0.9992 - val_loss: 0.0021 - val_accuracy: 1.0000 - 2s/epoch - 1ms/step\n", "Epoch 6/20\n", "1214/1214 - 2s - loss: 0.0032 - accuracy: 0.9994 - val_loss: 0.0015 - val_accuracy: 0.9998 - 2s/epoch - 1ms/step\n", "Epoch 7/20\n", "1214/1214 - 2s - loss: 0.0030 - accuracy: 0.9994 - val_loss: 7.7747e-04 - val_accuracy: 1.0000 - 2s/epoch - 1ms/step\n", "Epoch 8/20\n", "1214/1214 - 2s - loss: 0.0023 - accuracy: 0.9996 - val_loss: 0.0036 - val_accuracy: 1.0000 - 2s/epoch - 2ms/step\n", "Epoch 9/20\n", "1214/1214 - 2s - loss: 0.0021 - accuracy: 0.9996 - val_loss: 0.0012 - val_accuracy: 1.0000 - 2s/epoch - 2ms/step\n", "Epoch 10/20\n", "1214/1214 - 2s - loss: 0.0020 - accuracy: 0.9996 - val_loss: 8.3022e-04 - val_accuracy: 1.0000 - 2s/epoch - 2ms/step\n", "Epoch 11/20\n", "1214/1214 - 2s - loss: 0.0016 - accuracy: 0.9997 - val_loss: 8.5525e-04 - val_accuracy: 0.9999 - 2s/epoch - 2ms/step\n", "Epoch 12/20\n", "1214/1214 - 2s - loss: 0.0017 - accuracy: 0.9997 - val_loss: 3.7124e-04 - val_accuracy: 1.0000 - 2s/epoch - 1ms/step\n", "Epoch 13/20\n", "1214/1214 - 2s - loss: 0.0014 - accuracy: 0.9997 - val_loss: 4.3511e-04 - val_accuracy: 1.0000 - 2s/epoch - 1ms/step\n", "Epoch 14/20\n", "1214/1214 - 2s - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0011 - val_accuracy: 0.9999 - 2s/epoch - 1ms/step\n", "Epoch 15/20\n", "1214/1214 - 2s - loss: 0.0014 - accuracy: 0.9997 - val_loss: 6.5888e-04 - val_accuracy: 1.0000 - 2s/epoch - 1ms/step\n", "Epoch 16/20\n", "1214/1214 - 2s - loss: 0.0014 - accuracy: 0.9997 - val_loss: 3.6779e-04 - val_accuracy: 1.0000 - 2s/epoch - 1ms/step\n", "Epoch 17/20\n", "1214/1214 - 1s - loss: 0.0010 - accuracy: 0.9998 - val_loss: 6.5732e-04 - val_accuracy: 1.0000 - 1s/epoch - 1ms/step\n", "Epoch 18/20\n", "1214/1214 - 2s - loss: 0.0010 - accuracy: 0.9998 - val_loss: 0.0032 - val_accuracy: 0.9994 - 2s/epoch - 1ms/step\n", "Epoch 19/20\n", "1214/1214 - 2s - loss: 0.0011 - accuracy: 0.9998 - val_loss: 0.0016 - val_accuracy: 0.9994 - 2s/epoch - 1ms/step\n", "Epoch 20/20\n", "1214/1214 - 2s - loss: 8.1615e-04 - accuracy: 0.9998 - val_loss: 0.0011 - val_accuracy: 0.9999 - 2s/epoch - 1ms/step\n" ] } ], "source": [ "n_inputs = Xsm_train.shape[1]\n", "\n", "oversample_model = Sequential([\n", " Dense(n_inputs, input_shape=(n_inputs, ), activation='relu'),\n", " Dense(32, activation='relu'),\n", " Dense(2, activation='softmax')\n", "])\n", "\n", "oversample_model.compile(Adam(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n", "oversample_model.fit(Xsm_train, ysm_train, validation_split=0.2, batch_size=300, epochs=20, shuffle=True, verbose=2)\n", "oversample_predictions = oversample_model.predict(original_Xtest, batch_size=200, verbose=0)\n", "oversample_fraud_predictions = oversample_model.predict(original_Xtest, batch_size=200, verbose=0).argmax(axis=-1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "_cell_guid": "9a58d39f-9149-4279-bce3-fc8372e55f93", "_kg_hide-input": true, "_uuid": "a18452b7051e4905f32b27940f006d0bc4bc2d5e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion matrix, without normalization\n", "[[56853 10]\n", " [ 30 68]]\n", "Confusion matrix, without normalization\n", "[[56863 0]\n", " [ 0 98]]\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "oversample_smote = confusion_matrix(original_ytest, oversample_fraud_predictions)\n", "actual_cm = confusion_matrix(original_ytest, original_ytest)\n", "labels = ['No Fraud', 'Fraud']\n", "\n", "fig = plt.figure(figsize=(16,8))\n", "\n", "fig.add_subplot(221)\n", "plot_confusion_matrix(oversample_smote, labels, title=\"OverSample (SMOTE) \\n Confusion Matrix\", cmap=plt.cm.Oranges)\n", "\n", "fig.add_subplot(222)\n", "plot_confusion_matrix(actual_cm, labels, title=\"Confusion Matrix \\n (with 100% accuracy)\", cmap=plt.cm.Greens)" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "ee0932c1-e9dc-4135-93af-1972d07d3d0f", "_uuid": "1b03ca4b3e37985bea49686abd466fdd9a7d84d3" }, "source": [ "### 결론:\n", "불균형 데이터 세트에 SMOTE를 구현하면 레이블의 불균형을 해결하는 데 도움이 되었습니다(사기 거래보다 사기가 더 없음). 그럼에도 불구하고 오버샘플링된 데이터세트의 신경망이 언더샘플링된 데이터세트를 사용하는 우리 모델보다 부정확한 사기 거래를 덜 예측하는 경우가 있다는 점을 여전히 언급해야 합니다. 그러나 이상값 제거는 무작위 언더샘플 데이터 세트에서만 구현되었으며 오버샘플링된 데이터 세트에서는 구현되지 않았음을 기억하십시오. 또한, 우리의 언더샘플 데이터에서 우리 모델은 다수의 사례에 대해 사기가 아닌 거래를 올바르게 감지할 수 없으며 대신 이러한 비 사기 거래를 사기 사례로 잘못 분류합니다. 우리 모델이 해당 거래를 사기 거래로 분류했기 때문에 정기적으로 구매하는 사람들이 카드를 차단했다고 상상해 보세요. 이는 금융 기관에 큰 불이익이 될 것입니다. 고객 불만 및 고객 불만이 증가할 것입니다. 이 분석의 다음 단계는 과표본 데이터 세트에서 이상값을 제거하고 테스트 세트의 정확도가 향상되는지 확인하는 것입니다.

    \n", "\n", "**참고:** 마지막으로 두 가지 유형의 데이터 프레임에서 데이터 셔플링을 구현했기 때문에 예측과 정확도가 변경될 수 있습니다. 가장 중요한 것은 우리 모델이 사기 및 사기 거래를 올바르게 분류할 수 있는지 확인하는 것입니다. 나는 더 많은 업데이트를 가져올 것입니다, 계속 지켜봐주세요!" ] }, { "cell_type": "code", "execution_count": null, "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": "3.9.7" }, "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()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }