#coding:utf-8
import numpy as np
from scipy import io
from sklearn.externals import joblib
from logreg import LogisticRegression
if __name__ == "__main__":
X_train = io.loadmat("X_train")["X_train"]
X_train = X_train.tocsr() #疎行列の種類の変更(tfidfVectorizerで出力されるものと同じものにする)
y_train = np.load("y_train.npy")
clf = LogisticRegression("logreg")
y_predict = clf.predict(X_train)
probs = clf.predict_proba(X_train)
labels = ["-1", "+1"]
print("正解ラベル\t予測ラベル\t予測確率(+1)")
for (train, pred, prob) in zip(y_train, y_predict, probs):
print("%s\t%s\t%s" % (labels[int(train)], labels[int(pred)], prob))
for threshold in threshold_list:
y_predict = clf.predict(X_train, threshold)
precision_list.append(precision_score(y_train, y_predict))
recall_list.append(recall_score(y_train, y_predict))
plt.plot(threshold_list, precision_list, label="precision", color="red")
plt.plot(threshold_list, recall_list, label="recall", color="blue")
plt.xlabel("threshold")
plt.ylabel("rate")
plt.xlim(0.0, 1.0)
plt.ylim(0, 1)
plt.title("logistic_regresssion")
plt.legend(loc=3)
plt.show()
#precision-recall curveをプロット
from sklearn.metrics import precision_recall_curve
precision, recall, thresholds = precision_recall_curve(
y_train, clf.predict_proba(X_train))
print(len(thresholds))
plt.clf()
plt.plot(recall, precision, label='Precision-Recall curve')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.title('Precision-Recall curve')
plt.legend(loc="lower left")
plt.show()
