imblearn.oversampling SMOTENC 值错误
imblearn.oversampling SMOTENC ValueError
这是我第一次使用 SMOTENC 对我的分类数据进行上采样。但是,我一直在出错。你能告诉我应该在 SMOTENC 中为 categorical_features 传递什么吗?
from imblearn.over_sampling import SMOTENC
x=df.drop("A",1)
y=df["A"]
smote_nc = SMOTENC(categorical_features=['A','B','C','D','E','F','G','H'], random_state=0)
X_resampled, y_resampled = smote_nc.fit_resample(x, y)
错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-26-f6c9d8a17967> in <module>
---> 14 X_resampled, y_resampled = smote_nc.fit_resample(x, y)
15 #sm = SMOTE(random_state=100)
16 #ros = RandomOverSampler()
~/.local/lib/python3.5/site-packages/imblearn/base.py in fit_resample(self, X, y)
82 self.sampling_strategy, y, self._sampling_type)
83
---> 84 output = self._fit_resample(X, y)
85
86 if binarize_y:
~/.local/lib/python3.5/site-packages/imblearn/over_sampling/_smote.py in _fit_resample(self, X, y)
986 def _fit_resample(self, X, y):
987 self.n_features_ = X.shape[1]
--> 988 self._validate_estimator()
989
990 # compute the median of the standard deviation of the minority class
~/.local/lib/python3.5/site-packages/imblearn/over_sampling/_smote.py in _validate_estimator(self)
979 raise ValueError(
980 'Some of the categorical indices are out of range. Indices'
--> 981 ' should be between 0 and {}'.format(self.n_features_))
982 self.categorical_features_ = categorical_features
983 self.continuous_features_ = np.setdiff1d(np.arange(self.n_features_),
ValueError: Some of the categorical indices are out of range. Indices should be between 0 and 7
根据文档:
categorical_features : ndarray, shape (n_cat_features,) or (n_features,)
Specified which features are categorical. Can either be:
- array of indices specifying the categorical features;
- mask array of shape (n_features, ) and ``bool`` dtype for which
``True`` indicates the categorical features.
所以,只需替换行
smote_nc = SMOTENC(categorical_features=['A','B','C','D','E','F','G','H'], random_state=0)
与行
smote_nc = SMOTENC(categorical_features=[df.dtypes==object], random_state=0)
试试这个:
categorical_feature_mask = df.dtypes == object
smt=SMOTENC(categorical_features=categorical_feature_mask,random_state=99)
这是我第一次使用 SMOTENC 对我的分类数据进行上采样。但是,我一直在出错。你能告诉我应该在 SMOTENC 中为 categorical_features 传递什么吗?
from imblearn.over_sampling import SMOTENC
x=df.drop("A",1)
y=df["A"]
smote_nc = SMOTENC(categorical_features=['A','B','C','D','E','F','G','H'], random_state=0)
X_resampled, y_resampled = smote_nc.fit_resample(x, y)
错误:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-26-f6c9d8a17967> in <module>
---> 14 X_resampled, y_resampled = smote_nc.fit_resample(x, y)
15 #sm = SMOTE(random_state=100)
16 #ros = RandomOverSampler()
~/.local/lib/python3.5/site-packages/imblearn/base.py in fit_resample(self, X, y)
82 self.sampling_strategy, y, self._sampling_type)
83
---> 84 output = self._fit_resample(X, y)
85
86 if binarize_y:
~/.local/lib/python3.5/site-packages/imblearn/over_sampling/_smote.py in _fit_resample(self, X, y)
986 def _fit_resample(self, X, y):
987 self.n_features_ = X.shape[1]
--> 988 self._validate_estimator()
989
990 # compute the median of the standard deviation of the minority class
~/.local/lib/python3.5/site-packages/imblearn/over_sampling/_smote.py in _validate_estimator(self)
979 raise ValueError(
980 'Some of the categorical indices are out of range. Indices'
--> 981 ' should be between 0 and {}'.format(self.n_features_))
982 self.categorical_features_ = categorical_features
983 self.continuous_features_ = np.setdiff1d(np.arange(self.n_features_),
ValueError: Some of the categorical indices are out of range. Indices should be between 0 and 7
根据文档:
categorical_features : ndarray, shape (n_cat_features,) or (n_features,)
Specified which features are categorical. Can either be:
- array of indices specifying the categorical features;
- mask array of shape (n_features, ) and ``bool`` dtype for which
``True`` indicates the categorical features.
所以,只需替换行
smote_nc = SMOTENC(categorical_features=['A','B','C','D','E','F','G','H'], random_state=0)
与行
smote_nc = SMOTENC(categorical_features=[df.dtypes==object], random_state=0)
试试这个:
categorical_feature_mask = df.dtypes == object
smt=SMOTENC(categorical_features=categorical_feature_mask,random_state=99)