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# Using the diabetes dataset as an example
from sklearn import datasets

df = datasets.load_diabetes(as_frame=True)['data']
X = df[['bmi', 'age', 's1']].values # some predictors
y = df['s6'].values # the outcome variable
c = df['sex'].values # a confound - does not matter which

# Splitting into a training and a test set
from sklearn.model_selection import train_test_split

train_ind, test_ind = train_test_split(np.arange(0, len(y)), test_size=0.2)
train_X = X[train_ind, :]
train_y = y[train_ind]
train_C = c[train_ind]

test_X = X[test_ind, :]
test_y = y[test_ind]
test_C = c[test_ind]

# Fitting Residualize to remove the confound
from confounds import Residualize

resid = Residualize()
resid.fit(train_X, train_C)
deconf_train_X = resid.transform(train_X, train_C)

TypeError: check_is_fitted() takes from 1 to 2 positional arguments but 3 were given
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/var/folders/m0/mddm8pfx1vs3q52qvgx4mpxw0000gp/T/ipykernel_27134/3595471338.py in <module>
      1 resid = Residualize()
      2 resid.fit(train_X, train_C)
----> 3 deconf_train_X = resid.transform(train_X, train_C)

/opt/anaconda3/envs/brain_shadows/lib/python3.9/site-packages/confounds/base.py in transform(self, X, y)
    186         """Placeholder to pass sklearn conventions"""
    187 
--> 188         return self._transform(X, y)
    189 
    190 

/opt/anaconda3/envs/brain_shadows/lib/python3.9/site-packages/confounds/base.py in _transform(self, test_features, test_confounds)
    192         """Actual deconfounding of the test features"""
    193 
--> 194         check_is_fitted(self, 'model_', 'n_features_')
    195         test_features = check_array(test_features, accept_sparse=True)
    196 

TypeError: check_is_fitted() takes from 1 to 2 positional arguments but 3 were given

from sklearn.base import clone

class SklearnWrapper():

    def __init__(self,
                 deconfounder,
                 estimator):

        self.deconfounder = deconfounder
        self.estimator = estimator

    def fit(self,
            input_data,
            target_data,
            confounders,
            sample_weight=None):

        # clone input arguments
        deconfounder = clone(self.deconfounder)
        estimator = clone(self.estimator)

        # Deconfound input data
        deconf_input = deconfounder.fit_transform(input_data, confounders)
        self.deconfounder_ = deconfounder

        # Fit deconfounded input data
        estimator.fit(deconf_input, target_data, sample_weight)
        self.estimator_ = estimator

        return self

    def predict(self,
                input_data,
                confounders):

        deconf_input = self.deconfounder_.transform(input_data, confounders)

        return self.estimator_.predict(deconf_input)