lightgbm || ValueError: Series.dtypes must be int, float or bool

Question

Dataframe 已填充 na 个值。

数据集的架构没有文档中指定的对象数据类型。

df.info()

输出：

<class 'pandas.core.frame.DataFrame'>
Int64Index: 429 entries, 351 to 559
Data columns (total 11 columns):
 #   Column             Non-Null Count  Dtype   
---  ------             --------------  -----   
 0   Gender             429 non-null    category
 1   Married            429 non-null    category
 2   Dependents         429 non-null    category
 3   Education          429 non-null    category
 4   Self_Employed      429 non-null    category
 5   ApplicantIncome    429 non-null    int64   
 6   CoapplicantIncome  429 non-null    float64 
 7   LoanAmount         429 non-null    float64 
 8   Loan_Amount_Term   429 non-null    float64 
 9   Credit_History     429 non-null    float64 
 10  Property_Area      429 non-null    category
dtypes: category(6), float64(4), int64(1)
memory usage: 23.3 KB

我有以下代码...................................... ..................................................... ..................................................... ..................................................... ..................................................... ..................................................... .....................

我正在尝试使用 lightgbm 对数据集进行分类


import lightgbm as lgb

train_data=lgb.Dataset(x_train,label=y_train,categorical_feature=cat_cols)


#define parameters
params = {'learning_rate':0.001}


model= lgb.train(params, train_data, 100,categorical_feature=cat_cols)

出现以下错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-178-aaa91a2d8719> in <module>
      6 
      7 
----> 8 model= lgb.train(params, train_data, 100,categorical_feature=cat_cols)

~\Anaconda3\lib\site-packages\lightgbm\engine.py in train(params, train_set, num_boost_round, valid_sets, valid_names, fobj, feval, init_model, feature_name, categorical_feature, early_stopping_rounds, evals_result, verbose_eval, learning_rates, keep_training_booster, callbacks)
    229     # construct booster
    230     try:
--> 231         booster = Booster(params=params, train_set=train_set)
    232         if is_valid_contain_train:
    233             booster.set_train_data_name(train_data_name)

~\Anaconda3\lib\site-packages\lightgbm\basic.py in __init__(self, params, train_set, model_file, model_str, silent)
   1981                     break
   1982             # construct booster object
-> 1983             train_set.construct()
   1984             # copy the parameters from train_set
   1985             params.update(train_set.get_params())

~\Anaconda3\lib\site-packages\lightgbm\basic.py in construct(self)
   1319             else:
   1320                 # create train
-> 1321                 self._lazy_init(self.data, label=self.label,
   1322                                 weight=self.weight, group=self.group,
   1323                                 init_score=self.init_score, predictor=self._predictor,

~\Anaconda3\lib\site-packages\lightgbm\basic.py in _lazy_init(self, data, label, reference, weight, group, init_score, predictor, silent, feature_name, categorical_feature, params)
   1133                 raise TypeError('Cannot initialize Dataset from {}'.format(type(data).__name__))
   1134         if label is not None:
-> 1135             self.set_label(label)
   1136         if self.get_label() is None:
   1137             raise ValueError("Label should not be None")

~\Anaconda3\lib\site-packages\lightgbm\basic.py in set_label(self, label)
   1648         self.label = label
   1649         if self.handle is not None:
-> 1650             label = list_to_1d_numpy(_label_from_pandas(label), name='label')
   1651             self.set_field('label', label)
   1652             self.label = self.get_field('label')  # original values can be modified at cpp side

~\Anaconda3\lib\site-packages\lightgbm\basic.py in list_to_1d_numpy(data, dtype, name)
     88     elif isinstance(data, Series):
     89         if _get_bad_pandas_dtypes([data.dtypes]):
---> 90             raise ValueError('Series.dtypes must be int, float or bool')
     91         return np.array(data, dtype=dtype, copy=False)  # SparseArray should be supported as well
     92     else:

ValueError: Series.dtypes must be int, float or bool

Answer 1

有人帮助过你吗？如果不是：答案在于转换变量。

转到此 link:GitHub Discussion lightGBM

LightGBM 的创建者曾经遇到过同样的问题。在上面的 Link 中，他们 (STRIKER) 告诉你，你应该：使用 astype("category") (pandas/scikit) 转换你的变量，并且你应该对它们进行 labelEncode，因为你需要一个 INT ！特征列中的值，尤其是 INT32。

然而，labelEncoding 和 astype('category') 通常应该做同样的事情： Encoding

Antoher 很有用 link 是关于分类特征的高级文档：Categorical feature light gbm homepage 他们告诉你他们不能处理数据中的对象（字符串）数据类型。

如果你仍然对这个解释感到不舒服，这里是我从 kaggle space_race_set 中提取的代码片段。如果您仍然遇到问题。问吧。

cat_feats = ['Company Name', 'Night_and_Day', 'Rocket Type', 'Rocket Mission Type', 'State', 'Country']
labelencoder = LabelEncoder()

for col in cat_feats:
    train_df[col] = labelencoder.fit_transform(train_df[col])

for col in cat_feats:
    train_df[col] = train_df[col].astype('int')
    

y = train_df[["Status Mission"]]
X = train_df.drop(["Status Mission"], axis=1)

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 42)    

train_data = lgb.Dataset(X_train, 
                         label=y_train, 
                         categorical_feature=['Company Name', 'Night_and_Day', 'Rocket Type', 'Rocket Mission Type', 'State', 'Country'], 
                         free_raw_data=False)
test_data = lgb.Dataset(X_test, 
                        label=y_test, 
                        categorical_feature=['Company Name', 'Night_and_Day', 'Rocket Type', 'Rocket Mission Type', 'State', 'Country'], 
                        free_raw_data=False)

Answer 2

我遇到了同样的问题。我的 y_train 是 int64 dtype。这解决了我的问题：

model_LGB.fit(
    X = X_train,
    y = y_train.astype('int32'))

lightgbm || ValueError: Series.dtypes must be int, float or bool

lightgbm || ValueError: Series.dtypes must be int, float or bool

python-3.x

scikit-learn

jupyter-notebook

lightgbm