python - Python SKlearn污染必须为(0，0.5]错误

我是机器学习的新手，正在从事使用Python（3.6），Pandas，Numpy和SKlearn进行的项目。我已经完成了分类和重塑，但是在预测时它会抛出错误contamination must be in (0, 0.5]。

这是我尝试过的：

# Determine no of fraud cases in dataset
Fraud = data[data['Class'] == 1]
Valid = data[data['Class'] == 0]

# calculate percentages for Fraud & Valid
outlier_fraction = len(Fraud) / float(len(Valid))
print(outlier_fraction)

print('Fraud Cases : {}'.format(len(Fraud)))
print('Valid Cases : {}'.format(len(Valid)))
# Get all the columns from dataframe
columns = data.columns.tolist()

# Filter the columns to remove data we don't want
columns = [c for c in columns if c not in ["Class"] ]

# store the variables we want to predicting on
target = "Class"
X = data.drop(target, 1)
Y = data[target]

# Print the shapes of X & Y
print(X.shape)
print(Y.shape)

# define a random state
state = 1

# define the outlier detection method
classifiers = {
    "Isolation Forest": IsolationForest(max_samples=len(X),
                                       contamination=outlier_fraction,
                                       random_state=state),
    "Local Outlier Factor": LocalOutlierFactor(
    contamination = outlier_fraction)
}
# fit the model
n_outliers = len(Fraud)

for i, (clf_name, clf) in enumerate(classifiers.items()):

    # fit te data and tag outliers
    if clf_name == "Local Outlier Factor":
        y_pred = clf.fit_predict(X)
        scores_pred = clf.negative_outlier_factor_
    else:
        clf.fit(X)
        scores_pred = clf.decision_function(X)
        y_pred = clf.predict(X)

    # Reshape the prediction values to 0 for valid and 1 for fraudulent
    y_pred[y_pred == 1] = 0
    y_pred[y_pred == -1] = 1

    n_errors = (y_pred != Y).sum()

    # run classification metrics
    print('{}:{}'.format(clf_name, n_errors))
    print(accuracy_score(Y, y_pred ))
    print(classification_report(Y, y_pred ))

这是返回的内容：


  ValueError：污染度必须在（0，0.5]


如Traceback中所指出的，它会为y_pred = clf.predict(X)行引发此错误。

我是机器学习的新手，对**污染**不太了解，所以我在哪里做错了什么？

请帮帮我！

提前致谢！

最佳答案

ValueError：污染度必须在（0，0.5]


这意味着contamination必须严格大于0.0且小于或等于0.5。 （在括号表示法上，What does this square bracket and parenthesis bracket notation mean [first1,last1)?是一个很好的问题）正如您所评论的，print(outlier_fraction)输出0.0，问题出在所发布代码的前6行。

关于python - Python SKlearn污染必须为(0，0.5]错误，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/49663796/