4.6 LightGBM || 유방암 예측

import lightgbm print(lightgbm.__version__) # 2.1.0

LightGBM 적용 – 위스콘신 Breast Cancer Prediction

# LightGBM의 파이썬 패키지인 lightgbm에서 LGBMClassifier 임포트 from lightgbm import LGBMClassifier import pandas as pd import numpy as np from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split dataset = load_breast_cancer() ftr = dataset.data target = dataset.target # 전체 데이터 중 80%는 학습용 데이터, 20%는 테스트용 데이터 추출 X_train, X_test, y_train, y_test=train_test_split(ftr, target, test_size=0.2, random_state=156 ) # 앞서 XGBoost와 동일하게 n_estimators는 400 설정. lgbm_wrapper = LGBMClassifier(n_estimators=400) # LightGBM도 XGBoost와 동일하게 조기 중단 수행 가능. evals = [(X_test, y_test)] lgbm_wrapper.fit(X_train, y_train, early_stopping_rounds=100, eval_metric="logloss", eval_set=evals, verbose=True) preds = lgbm_wrapper.predict(X_test) pred_proba = lgbm_wrapper.predict_proba(X_test)[:, 1] [1] valid_0's binary_logloss: 0.565079 Training until validation scores don't improve for 100 rounds [2] valid_0's binary_logloss: 0.507451 [3] valid_0's binary_logloss: 0.458489 [4] valid_0's binary_logloss: 0.417481 [5] valid_0's binary_logloss: 0.385507 [6] valid_0's binary_logloss: 0.355773 [7] valid_0's binary_logloss: 0.329587 [8] valid_0's binary_logloss: 0.308478 [9] valid_0's binary_logloss: 0.285395 [10] valid_0's binary_logloss: 0.267055 [11] valid_0's binary_logloss: 0.252013 [12] valid_0's binary_logloss: 0.237018 [13] valid_0's binary_logloss: 0.224756 [14] valid_0's binary_logloss: 0.213383 [15] valid_0's binary_logloss: 0.203058 [16] valid_0's binary_logloss: 0.194015 [17] valid_0's binary_logloss: 0.186412 [18] valid_0's binary_logloss: 0.179108 [19] valid_0's binary_logloss: 0.174004 [20] valid_0's binary_logloss: 0.167155 [21] valid_0's binary_logloss: 0.162494 [22] valid_0's binary_logloss: 0.156886 [23] valid_0's binary_logloss: 0.152855 [24] valid_0's binary_logloss: 0.151113 [25] valid_0's binary_logloss: 0.148395 [26] valid_0's binary_logloss: 0.145869 [27] valid_0's binary_logloss: 0.143036 [28] valid_0's binary_logloss: 0.14033 [29] valid_0's binary_logloss: 0.139609 [30] valid_0's binary_logloss: 0.136109 [31] valid_0's binary_logloss: 0.134867 [32] valid_0's binary_logloss: 0.134729 [33] valid_0's binary_logloss: 0.1311 [34] valid_0's binary_logloss: 0.131143 [35] valid_0's binary_logloss: 0.129435 [36] valid_0's binary_logloss: 0.128474 [37] valid_0's binary_logloss: 0.126683 [38] valid_0's binary_logloss: 0.126112 [39] valid_0's binary_logloss: 0.122831 [40] valid_0's binary_logloss: 0.123162 [41] valid_0's binary_logloss: 0.125592 [42] valid_0's binary_logloss: 0.128293 [43] valid_0's binary_logloss: 0.128123 [44] valid_0's binary_logloss: 0.12789 [45] valid_0's binary_logloss: 0.122818 [46] valid_0's binary_logloss: 0.12496 [47] valid_0's binary_logloss: 0.125578 [48] valid_0's binary_logloss: 0.127381 [49] valid_0's binary_logloss: 0.128349 [50] valid_0's binary_logloss: 0.127004 [51] valid_0's binary_logloss: 0.130288 [52] valid_0's binary_logloss: 0.131362 [53] valid_0's binary_logloss: 0.133363 [54] valid_0's binary_logloss: 0.1332 [55] valid_0's binary_logloss: 0.134543 [56] valid_0's binary_logloss: 0.130803 [57] valid_0's binary_logloss: 0.130306 [58] valid_0's binary_logloss: 0.132514 [59] valid_0's binary_logloss: 0.133278 [60] valid_0's binary_logloss: 0.134804 [61] valid_0's binary_logloss: 0.136888 [62] valid_0's binary_logloss: 0.138745 [63] valid_0's binary_logloss: 0.140497 [64] valid_0's binary_logloss: 0.141368 [65] valid_0's binary_logloss: 0.140764 [66] valid_0's binary_logloss: 0.14348 [67] valid_0's binary_logloss: 0.143418 [68] valid_0's binary_logloss: 0.143682 [69] valid_0's binary_logloss: 0.145076 [70] valid_0's binary_logloss: 0.14686 [71] valid_0's binary_logloss: 0.148051 [72] valid_0's binary_logloss: 0.147664 [73] valid_0's binary_logloss: 0.149478 [74] valid_0's binary_logloss: 0.14708 [75] valid_0's binary_logloss: 0.14545 [76] valid_0's binary_logloss: 0.148767 [77] valid_0's binary_logloss: 0.149959 [78] valid_0's binary_logloss: 0.146083 [79] valid_0's binary_logloss: 0.14638 [80] valid_0's binary_logloss: 0.148461 [81] valid_0's binary_logloss: 0.15091 [82] valid_0's binary_logloss: 0.153011 [83] valid_0's binary_logloss: 0.154807 [84] valid_0's binary_logloss: 0.156501 [85] valid_0's binary_logloss: 0.158586 [86] valid_0's binary_logloss: 0.159819 [87] valid_0's binary_logloss: 0.161745 [88] valid_0's binary_logloss: 0.162829 [89] valid_0's binary_logloss: 0.159142 [90] valid_0's binary_logloss: 0.156765 [91] valid_0's binary_logloss: 0.158625 [92] valid_0's binary_logloss: 0.156832 [93] valid_0's binary_logloss: 0.154616 [94] valid_0's binary_logloss: 0.154263 [95] valid_0's binary_logloss: 0.157156 [96] valid_0's binary_logloss: 0.158617 [97] valid_0's binary_logloss: 0.157495 [98] valid_0's binary_logloss: 0.159413 [99] valid_0's binary_logloss: 0.15847 [100] valid_0's binary_logloss: 0.160746 [101] valid_0's binary_logloss: 0.16217 [102] valid_0's binary_logloss: 0.165293 [103] valid_0's binary_logloss: 0.164749 [104] valid_0's binary_logloss: 0.167097 [105] valid_0's binary_logloss: 0.167697 [106] valid_0's binary_logloss: 0.169462 [107] valid_0's binary_logloss: 0.169947 [108] valid_0's binary_logloss: 0.171 [109] valid_0's binary_logloss: 0.16907 [110] valid_0's binary_logloss: 0.169521 [111] valid_0's binary_logloss: 0.167719 [112] valid_0's binary_logloss: 0.166648 [113] valid_0's binary_logloss: 0.169053 [114] valid_0's binary_logloss: 0.169613 [115] valid_0's binary_logloss: 0.170059 [116] valid_0's binary_logloss: 0.1723 [117] valid_0's binary_logloss: 0.174733 [118] valid_0's binary_logloss: 0.173526 [119] valid_0's binary_logloss: 0.1751 [120] valid_0's binary_logloss: 0.178254 [121] valid_0's binary_logloss: 0.182968 [122] valid_0's binary_logloss: 0.179017 [123] valid_0's binary_logloss: 0.178326 [124] valid_0's binary_logloss: 0.177149 [125] valid_0's binary_logloss: 0.179171 [126] valid_0's binary_logloss: 0.180948 [127] valid_0's binary_logloss: 0.183861 [128] valid_0's binary_logloss: 0.187579 [129] valid_0's binary_logloss: 0.188122 [130] valid_0's binary_logloss: 0.1857 [131] valid_0's binary_logloss: 0.187442 [132] valid_0's binary_logloss: 0.188578 [133] valid_0's binary_logloss: 0.189729 [134] valid_0's binary_logloss: 0.187313 [135] valid_0's binary_logloss: 0.189279 [136] valid_0's binary_logloss: 0.191068 [137] valid_0's binary_logloss: 0.192414 [138] valid_0's binary_logloss: 0.191255 [139] valid_0's binary_logloss: 0.193453 [140] valid_0's binary_logloss: 0.196969 [141] valid_0's binary_logloss: 0.196378 [142] valid_0's binary_logloss: 0.196367 [143] valid_0's binary_logloss: 0.19869 [144] valid_0's binary_logloss: 0.200352 [145] valid_0's binary_logloss: 0.19712 Early stopping, best iteration is: [45] valid_0's binary_logloss: 0.122818

from sklearn.metrics import confusion_matrix, accuracy_score from sklearn.metrics import precision_score, recall_score from sklearn.metrics import f1_score, roc_auc_score # 수정된 get_clf_eval() 함수 def get_clf_eval(y_test, pred=None, pred_proba=None): confusion = confusion_matrix( y_test, pred) accuracy = accuracy_score(y_test , pred) precision = precision_score(y_test , pred) recall = recall_score(y_test , pred) f1 = f1_score(y_test,pred) # ROC-AUC 추가 roc_auc = roc_auc_score(y_test, pred_proba) print('오차 행렬') print(confusion) # ROC-AUC print 추가 print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f},\ F1: {3:.4f}, AUC:{4:.4f}'.format(accuracy, precision, recall, f1, roc_auc))

get_clf_eval(y_test, preds, pred_proba) 오차 행렬 [[33 4] [ 1 76]] 정확도: 0.9561, 정밀도: 0.9500, 재현율: 0.9870, F1: 0.9682, AUC:0.9905

from lightgbm import plot_importance import matplotlib.pyplot as plt %matplotlib inline fig, ax = plt.subplots(figsize=(10, 12)) # 사이킷런 래퍼 클래스를 입력해도 무방. plot_importance(lgbm_wrapper, ax=ax)

print(dataset.feature_names) ['mean radius' 'mean texture' 'mean perimeter' 'mean area' 'mean smoothness' 'mean compactness' 'mean concavity' 'mean concave points' 'mean symmetry' 'mean fractal dimension' 'radius error' 'texture error' 'perimeter error' 'area error' 'smoothness error' 'compactness error' 'concavity error' 'concave points error' 'symmetry error' 'fractal dimension error' 'worst radius' 'worst texture' 'worst perimeter' 'worst area' 'worst smoothness' 'worst compactness' 'worst concavity' 'worst concave points' 'worst symmetry' 'worst fractal dimension']