言語処理100本ノック 2020「58. 正則化パラメータの変更」

問題文

nlp100.github.io

問題の概要

学習時の「C」の値を調整することで、学習・予測結果が変わります。

import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score


X_train = pd.read_table('ch06/train.feature.txt', header=None)
X_valid = pd.read_table('ch06/valid.feature.txt', header=None)
X_test = pd.read_table('ch06/test.feature.txt', header=None)
y_train = pd.read_table('ch06/train.txt', header=None)[1]
y_valid = pd.read_table('ch06/valid.txt', header=None)[1]
y_test = pd.read_table('ch06/test.txt', header=None)[1]

C_candidate = [0.1, 1.0, 10, 100]
train_acc = []
valid_acc = []
test_acc = []

for c in C_candidate:
    clf = LogisticRegression(penalty='l2', solver='sag', random_state=0, C=c)
    clf.fit(X_train, y_train)
    train_acc.append(accuracy_score(y_train, clf.predict(X_train)))
    valid_acc.append(accuracy_score(y_valid, clf.predict(X_valid)))
    test_acc.append(accuracy_score(y_test, clf.predict(X_test)))

plt.plot(C_candidate, train_acc, label='train acc')
plt.plot(C_candidate, valid_acc, label='valid acc')
plt.plot(C_candidate, test_acc, label='test acc')
plt.legend()
plt.savefig('ch06/ans58.png')