Python plot_confusion_matrix示例

示例#1

nmse = normalized_mean_squared_error(truthMean_test, truthMean_pred)
mse = mean_squared_error(truthMean_test, truthMean_pred)
evs = explained_variance_score(truthMean_test, truthMean_pred)
r2 = r2_score(truthMean_test, truthMean_pred)
mae = mean_absolute_error(truthMean_test, truthMean_pred)
med_a_e = median_absolute_error(truthMean_test, truthMean_pred)

truthClass_test = [0 if t == 'no-clickbait' else 1 for t in truthClass_test]
truthClass_pred = [0 if t < 0.5 else 1 for t in truthMean_pred]


tn, fp, fn, tp = conf_matrix = confusion_matrix(truthClass_test, truthClass_pred).ravel()
print("(TN, FP, FN, TP) = {}".format((tn, fp, fn, tp)))

compute_diffs(truthClass_test, truthClass_pred, truthMean_test, truthMean_pred)
plot_confusion_matrix(truthClass_test, truthClass_pred, title='Random Forest confusion matrix')

accuracy = accuracy_score(truthClass_test, truthClass_pred)
auc = roc_auc_score(truthClass_test, truthClass_pred)
precision = precision_score(truthClass_test, truthClass_pred)
recall = recall_score(truthClass_test, truthClass_pred)
f1 = f1_score(truthClass_test, truthClass_pred)

print('----------------Regression metrics-------------------------\n')
print("Explained Variance Score is ", evs)
print("Mean Squared Error  is ", mse)
print("Normalized Mean Squared Error  is ", nmse)
print("Mean Absolute Error  is ", mae)
print("Median Absolute Error  is ", med_a_e)
print("R2 score is ", r2)

示例#2

evs = explained_variance_score(truthMean_test, truthMean_pred)
r2 = r2_score(truthMean_test, truthMean_pred)
mae = mean_absolute_error(truthMean_test, truthMean_pred)
med_a_e = median_absolute_error(truthMean_test, truthMean_pred)

truthClass_test = [0 if t == 'no-clickbait' else 1 for t in truthClass_test]
truthClass_pred = [0 if t < 0.5 else 1 for t in truthMean_pred]

compute_diffs(truthClass_test, truthClass_pred, truthMean_test, truthMean_pred)

tn, fp, fn, tp = conf_matrix = confusion_matrix(truthClass_test,
                                                truthClass_pred).ravel()
print("(TN, FP, FN, TP) = {}".format((tn, fp, fn, tp)))

plot_confusion_matrix(truthClass_test,
                      truthClass_pred,
                      title='Linear regression confusion matrix')

accuracy = accuracy_score(truthClass_test, truthClass_pred)
auc = roc_auc_score(truthClass_test, truthClass_pred)
precision = precision_score(truthClass_test, truthClass_pred)
recall = recall_score(truthClass_test, truthClass_pred)
f1 = f1_score(truthClass_test, truthClass_pred)

print('----------------Regression metrics-------------------------\n')
print("Explained Variance Score is ", evs)
print("Mean Squared Error  is ", mse)
print("Normalized Mean Squared Error  is ", nmse)
print("Mean Absolute Error  is ", mae)
print("Median Absolute Error  is ", med_a_e)
print("R2 score is ", r2)

示例#3

clf.fit(X_train, truthMean_train)
truthMean_pred = clf.predict(X_test)

nmse = normalized_mean_squared_error(truthMean_test, truthMean_pred)
mse = mean_squared_error(truthMean_test, truthMean_pred)
evs = explained_variance_score(truthMean_test, truthMean_pred)
r2 = r2_score(truthMean_test, truthMean_pred)
mae = mean_absolute_error(truthMean_test, truthMean_pred)
med_a_e = median_absolute_error(truthMean_test, truthMean_pred)

truthClass_test = [0 if t == 'no-clickbait' else 1 for t in truthClass_test]
truthClass_pred = [0 if t < 0.5 else 1 for t in truthMean_pred]

compute_diffs(truthClass_test, truthClass_pred, truthMean_test, truthMean_pred)
plot_confusion_matrix(truthClass_test,
                      truthClass_pred,
                      title='Adaboost confusion matrix')

tn, fp, fn, tp = conf_matrix = confusion_matrix(truthClass_test,
                                                truthClass_pred).ravel()
print("(TN, FP, FN, TP) = {}".format((tn, fp, fn, tp)))

accuracy = accuracy_score(truthClass_test, truthClass_pred)
auc = roc_auc_score(truthClass_test, truthClass_pred)
precision = precision_score(truthClass_test, truthClass_pred)
recall = recall_score(truthClass_test, truthClass_pred)
f1 = f1_score(truthClass_test, truthClass_pred)

print('----------------Regression metrics-------------------------\n')
print("Explained Variance Score is ", evs)
print("Mean Squared Error  is ", mse)

示例#4

文件： final_svr.py 项目： rpytel1/clickbait-challenge

mse = mean_squared_error(truthMean_test, truthMean_pred)
evs = explained_variance_score(truthMean_test, truthMean_pred)
r2 = r2_score(truthMean_test, truthMean_pred)
mae = mean_absolute_error(truthMean_test, truthMean_pred)
med_a_e = median_absolute_error(truthMean_test, truthMean_pred)

truthClass_test = [0 if t == 'no-clickbait' else 1 for t in truthClass_test]
truthClass_pred = [0 if t < 0.5 else 1 for t in truthMean_pred]

tn, fp, fn, tp = conf_matrix = confusion_matrix(truthClass_test,
                                                truthClass_pred).ravel()
print("(TN, FP, FN, TP) = {}".format((tn, fp, fn, tp)))

compute_diffs(truthClass_test, truthClass_pred, truthMean_test, truthMean_pred)
plot_confusion_matrix(truthClass_test,
                      truthClass_pred,
                      title='SVR confusion matrix')

accuracy = accuracy_score(truthClass_test, truthClass_pred)
auc = roc_auc_score(truthClass_test, truthClass_pred)
precision = precision_score(truthClass_test, truthClass_pred)
recall = recall_score(truthClass_test, truthClass_pred)
f1 = f1_score(truthClass_test, truthClass_pred)

print('----------------Regression metrics-------------------------\n')
print("Explained Variance Score is ", evs)
print("Mean Squared Error  is ", mse)
print("Normalized Mean Squared Error  is ", nmse)
print("Mean Absolute Error  is ", mae)
print("Median Absolute Error  is ", med_a_e)
print("R2 score is ", r2)