if __name__ == "__main__":
from q51 import load
from q52 import read_features
X_test, y_test = read_features("test.feature.txt")
model = load("model.dat")
# 予測カテゴリ
y_pred = model.predict(X_test)
# カテゴリごとの予測確率
proba = model.predict_proba(X_test)
print(f"正解\t予想\t{model.classes_}")
for true, pred, p in zip(y_test, y_pred, proba):
print(f"{true}\t{pred}\t{p}")
if __name__ == "__main__":
from sklearn.metrics import accuracy_score
from q51 import load
from q52 import read_features
X_train, y_train = read_features("train.feature.txt")
X_test, y_test = read_features("test.feature.txt")
model = load("model.dat")
y_pred = model.predict(X_train)
print("学習データ")
print(accuracy_score(y_true=y_train, y_pred=y_pred))
y_pred = model.predict(X_test)
print("評価データ")
print(accuracy_score(y_true=y_test, y_pred=y_pred))
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
def predict_accuracy(model, X, y):
y_pred = model.predict(X)
score = accuracy_score(y_true=y, y_pred=y_pred)
print(score)
return score
if __name__ == "__main__":
from q52 import read_features
from sklearn.linear_model import LogisticRegression
X_train, y_train = read_features("train.feature.txt")
X_test, y_test = read_features("test.feature.txt")
X_valid, y_valid = read_features("valid.feature.txt")
acc_train = []
acc_test = []
acc_valid = []
C = [0.001, 0.01, 0.1, 1, 10, 100]
for c in C:
model = LogisticRegression(max_iter=10000, C=c)
model.fit(X_train, y_train)
acc_train.append(predict_accuracy(model, X_train, y_train))
acc_test.append(predict_accuracy(model, X_test, y_test))
acc_valid.append(predict_accuracy(model, X_valid, y_valid))