Python中的for循环逻辑回归

原文标题 ：For loop Logistic regression in Python

我想在数据框上构建一个 for 循环，目标是创建一个具有每只股票准确度分数的 df。

一只股票的模型工作正常，但 for 循环没有做任何事情。下面是 df 的输出，这不是完整的 df。

Date        Close   ticker  rating  price   returns direction   long direction 
2021-02-06  21.8   AD.AS    1       21.8    -0.02   -1          1
2021-02-06  21.8   AD.AS    1       21.8    -0.02   -1          1
2021-02-06  21.8   APPL     1       153     -0.02   -1          1
2021-02-06  21.8   APPL     1       153     -0.02   -1          1

stock_df['ticker'].unique()
array(['CSCO', 'IBM', 'AMZN', 'AD.AS'], dtype=object)

下面是代码，for循环抛出错误：

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').

以下是我现在拥有的代码：

#for loop test
#Split data into training and test sets
stock_df = stock_df.dropna()

result = {}
# loop on every type

for ticker in stock_df['ticker'].unique():
    # slice
    stock_slice = stock_df[stock_df['ticker'] == ticker]

    X = stock_df_slice.drop(['long direction', 'BuyFlag','SellFlag', 'Date', 'ticker'], axis=1)
    y = stock_df_slice['BuyFlag']

    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.25, random_state = 5, shuffle=False)

    #Creatig duplicate for back testing
    X_test_2 = X_test

    #building logistic regression model on training data
    result[ticker]=model1 = LogisticRegression(random_state=0, multi_class='ovr', penalty='none', solver='newton-cg', class_weight={0:0.6, 1:0.4}).fit(X_train, y_train)
    result[ticker]=preds_buy = model1.predict(X_test)

    #Accuracy statistics
    print('Accuracy Score:', metrics.accuracy_score(y_test, preds_buy))  

    #Create classification report
    class_report=classification_report(y_test, preds_buy)
    print(class_report)

# build dataframe with all your results
final_df = pd.DataFrame(result)