def over(C,solver,class_weight):
clf = LogisticRegression(C=C, solver=solver, class_weight=class_weight)
clf.fit(log['train_feature'], log['train_category'])
train_sc=ai_def.estimation(clf, log['train_feature'])
valid_sc=ai_def.estimation(clf, log['valid_feature'])
test_sc=ai_def.estimation(clf, log['test_feature'])
train_accuracy = accuracy_score(log['train_category'],train_sc)
valid_accuracy = accuracy_score(log['valid_category'],valid_sc)
test_accuracy = accuracy_score(log['test_category'],test_sc)
result=[]
result.append([C,train_accuracy, valid_accuracy, test_accuracy])
return result
""" 55. 混同行列の作成 52で学習したロジスティック回帰モデルの混同行列 （confusion matrix）を， 学習データおよび評価データ上で作成せよ """ from sklearn.metrics import confusion_matrix import ai_def log=ai_def.Lg() clf=ai_def.load_model() train_score=ai_def.estimation(clf,log['train_feature']) train_matrix = confusion_matrix(log['train_category'],train_score) print(train_matrix) test_score=ai_def.estimation(clf,log['test_feature']) test_matrix = confusion_matrix(log['test_category'],test_score) print(test_matrix)
"""
import ai_def
import numpy as np
from tqdm import tqdm
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from matplotlib import pyplot as plt
log=ai_def.Lg()
result=[]
for C in tqdm(np.logspace(-5,4,10,base=10)):
lg=LogisticRegression(random_state=123, max_iter=10000, C=C)
clf=lg.fit(log['train_feature'], log['train_category'])
train_sc=ai_def.estimation(clf, log['train_feature'])
valid_sc=ai_def.estimation(clf, log['valid_feature'])
test_sc=ai_def.estimation(clf, log['test_feature'])
train_accuracy = accuracy_score(log['train_category'],train_sc)
valid_accuracy = accuracy_score(log['valid_category'],valid_sc)
test_accuracy = accuracy_score(log['test_category'],test_sc)
result.append([C,train_accuracy, valid_accuracy, test_accuracy])
result=np.array(result).T
plt.plot(result[0],result[1], label='train')
plt.plot(result[0],result[2], label='valid')
plt.plot(result[0],result[3], label='test')
plt.ylim(0,1.1)
plt.ylabel('Accuracy')