Python SKlearn适合方法不起作用 [英] Python SKlearn fit method not working

问题描述

我正在使用Python(3.6)和Sklearn进行一个项目.我已经完成了分类，但是当我尝试将其应用于重塑以便与sklearn的fit方法一起使用时，它会返回错误.

I'm working on a project using Python(3.6) and Sklearn.I have done classifications but when I try to apply it for reshaping in order to use it with fit method of sklearn it returns an error.

这是我尝试过的:

# 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(
    n_neighbors = 20,
    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 ))

然后返回以下错误:

ValueError: could not convert string to float: '301.48 Change: $0.00'
and it's pointed to  `clf.fit(X)` line.

我配置错了什么?

推荐答案

我们可以基于数据集的唯一性将数据集转换为数字数据值，还可以从数据集中删除不必要的列.

We can convert out dataset to numeric data values on the base of their uniqueness and you can also drop un-necessary columns form the dataset.

这是您可以尝试的方法:

Here's how you can try that:

df_full = pd.read_excel('input/samp.xlsx', sheet_name=0,)
df_full = df_full[df_full.filter(regex='^(?!Unnamed)').columns]
df_full.drop(['paymentdetails',], 1, inplace=True)
df_full.drop(['timestamp'], 1, inplace=True)
# Handle non numaric data
def handle_non_numaric_data(df_full):
    columns = df_full.columns.values

    for column in columns:
        text_digit_vals = {}
        def convert_to_int(val):
            return text_digit_vals[val]

        if df_full[column].dtype != np.int64 and df_full[column].dtype != np.float64:
            column_contents = df_full[column].values.tolist()
            unique_elements = set(column_contents)
            x = 0
            for unique in unique_elements:
                if unique not in text_digit_vals:
                    text_digit_vals[unique] = x
                    x+=1

            df_full[column] = list(map(convert_to_int, df_full[column]))

    return df_full

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