Python sklearn.impute() Examples

The following are 2 code examples of sklearn.impute(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn , or try the search function .
Example #1
Source File: simple_imputer.py    From lale with Apache License 2.0 5 votes vote down vote up 
def __init__(self, missing_values=None, strategy='mean', fill_value=None, verbose=0, copy=True):
        self._hyperparams = {
            'missing_values': missing_values,
            'strategy': strategy,
            'fill_value': fill_value,
            'verbose': verbose,
            'copy': copy}
        self._wrapped_model = sklearn.impute.SimpleImputer(**self._hyperparams)
Example #2
Source File: classical.py    From netharn with Apache License 2.0 4 votes vote down vote up 
def _make_est_func(self):
        import sklearn
        from sklearn import multiclass  # NOQA
        from sklearn import ensemble  # NOQA
        from sklearn import neural_network  # NOQA
        from sklearn import svm  # NOQA
        from sklearn import preprocessing  # NOQA
        from sklearn import pipeline  # NOQA
        from functools import partial

        wrap_type = self.wrap_type
        est_type = self.est_type

        multiclass_wrapper = {
            None: ub.identity,
            'OVR': sklearn.multiclass.OneVsRestClassifier,
            'OVO': sklearn.multiclass.OneVsOneClassifier,
        }[wrap_type]
        est_class = {
            'RF': sklearn.ensemble.RandomForestClassifier,
            'SVC': sklearn.svm.SVC,
            'Logit': partial(sklearn.linear_model.LogisticRegression, solver='lbfgs'),
            'MLP': sklearn.neural_network.MLPClassifier,
        }[est_type]

        est_kw = self.est_kw
        try:
            from sklearn.impute import SimpleImputer
            Imputer = SimpleImputer
            import numpy as np
            NAN = np.nan
        except Exception:
            from sklearn.preprocessing import Imputer
            NAN = 'NaN'
        if est_type == 'MLP':
            def make_estimator():
                pipe = sklearn.pipeline.Pipeline([
                    ('inputer', Imputer(
                        missing_values=NAN, strategy='mean')),
                    # ('scale', sklearn.preprocessing.StandardScaler),
                    ('est', est_class(**est_kw)),
                ])
                return multiclass_wrapper(pipe)
        elif est_type == 'Logit':
            def make_estimator():
                pipe = sklearn.pipeline.Pipeline([
                    ('inputer', Imputer(
                        missing_values=NAN, strategy='mean')),
                    ('est', est_class(**est_kw)),
                ])
                return multiclass_wrapper(pipe)
        else:
            def make_estimator():
                return multiclass_wrapper(est_class(**est_kw))

        return make_estimator