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Python preprocessing.QuantileTransformer方法代码示例

本文整理汇总了Python中sklearn.preprocessing.QuantileTransformer方法的典型用法代码示例。如果您正苦于以下问题:Python preprocessing.QuantileTransformer方法的具体用法?Python preprocessing.QuantileTransformer怎么用?Python preprocessing.QuantileTransformer使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块sklearn.preprocessing的用法示例。

在下文中一共展示了preprocessing.QuantileTransformer方法的13个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: fit

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def fit(self, X, y=None):
        """Compute the lower and upper quantile cutoffs, columns to transform, and each column's quantiles.

        Parameters
        ----------
        X : array-like, shape [n_samples, n_features]
            The data array to transform. Must be numeric, non-sparse, and two-dimensional.

        Returns
        -------
        self : QuantileExtremeValueTransformer
        """
        super().fit(X)
        X = check_array(X)
        self.quantile_transformer_ = QuantileTransformer(random_state=0, copy=True)
        self.quantile_transformer_.fit(X)
        return self 
开发者ID:aws,项目名称:sagemaker-scikit-learn-extension,代码行数:19,代码来源:base.py


示例2: do_feature_engineering

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def do_feature_engineering(list_text):
    df = pd.DataFrame(list_text, columns=["col2"])

    feature_list = []
    feature = get_length_related_features_col2(df)
    feature_list.append(feature)
    feature = get_col2_re_features(df)
    feature_list.append(feature)
    index = feature_list[0].index

    for feature_dataset in feature_list[1:]:
        pd.testing.assert_index_equal(index, feature_dataset.index)

    data = pd.concat(feature_list, axis=1)
    qt = QuantileTransformer(random_state=2019)
    for col in data.columns:
        data[col] = qt.fit_transform(data[[col]])
    return data 
开发者ID:GuidoPaul,项目名称:CAIL2019,代码行数:20,代码来源:processing.py


示例3: fit

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def fit(self, X):
        """ Fit the quantile mapping model.

        Parameters
        ----------
        X : array-like, shape  [n_samples, n_features]
            Training data.
        """
        X = ensure_samples_features(X)

        qt_kws = self.qt_kwargs.copy()

        if "n_quantiles" not in qt_kws:
            qt_kws["n_quantiles"] = len(X)

        # maybe detrend the input datasets
        if self.detrend:
            x_to_cdf = LinearTrendTransformer(**self.lt_kwargs).fit_transform(X)
        else:
            x_to_cdf = X

        # calculate the cdfs for X
        # TODO: replace this transformer with something that uses robust
        # empirical cdf plotting positions
        self.x_cdf_fit_ = QuantileTransformer(**qt_kws).fit(x_to_cdf)

        return self 
开发者ID:jhamman,项目名称:scikit-downscale,代码行数:29,代码来源:utils.py


示例4: _transform_function

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def _transform_function(self, x, idx=None):
        """Applies single column quantile transform from ``sklearn.preprocessing.QuantileTransformer``.

        Uses ``quantile_transformer_.quantiles_`` calculated during ``fit`` if given an index, otherwise the quantiles
        will be calculated from input ``x``.
        """
        if idx:
            return self.quantile_transformer_._transform_col(  # pylint: disable=protected-access
                x, self.quantile_transformer_.quantiles_[:, idx], False
            )
        return quantile_transform_nonrandom(x) 
开发者ID:aws,项目名称:sagemaker-scikit-learn-extension,代码行数:13,代码来源:base.py


示例5: __init__

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def __init__(self, n_quantiles=1000, output_distribution='uniform', ignore_implicit_zeros=False, subsample=100000, random_state=None, copy=True):
        self._hyperparams = {
            'n_quantiles': n_quantiles,
            'output_distribution': output_distribution,
            'ignore_implicit_zeros': ignore_implicit_zeros,
            'subsample': subsample,
            'random_state': random_state,
            'copy': copy}
        self._wrapped_model = SKLModel(**self._hyperparams) 
开发者ID:IBM,项目名称:lale,代码行数:11,代码来源:quantile_transformer.py


示例6: __init__

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def __init__(self, options):
        self.handle_options(options)

        out_params = convert_params(
            options.get('params', {}),
            bools=['copy'],
            ints=['n_quantiles'],
            strs=['output_distribution']
        )
        self.estimator = _QuantileTransformer(**out_params)
        self.columns = None 
开发者ID:splunk,项目名称:mltk-algo-contrib,代码行数:13,代码来源:QuantileTransformer.py


示例7: quantile_transform

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def quantile_transform(train_targets, non_train_targets):
    transformer = QuantileTransformer(output_distribution="normal", n_quantiles=100)
    train_targets[train_targets.columns] = transformer.fit_transform(train_targets.values)
    non_train_targets[train_targets.columns] = transformer.transform(non_train_targets.values)
    return train_targets, non_train_targets, transformer 
开发者ID:HunterMcGushion,项目名称:hyperparameter_hunter,代码行数:7,代码来源:target_transform_inverse_example.py


示例8: bad_quantile_transform

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def bad_quantile_transform(train_targets, non_train_targets):
    transformer = QuantileTransformer(output_distribution="normal", n_quantiles=100)
    train_targets[train_targets.columns] = transformer.fit_transform(train_targets.values)
    non_train_targets[train_targets.columns] = transformer.transform(non_train_targets.values)
    return train_targets, non_train_targets, "i am the wrong type for an inversion result" 
开发者ID:HunterMcGushion,项目名称:hyperparameter_hunter,代码行数:7,代码来源:test_support.py


示例9: my_quantile_transform

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def my_quantile_transform(train_targets, non_train_targets):
    transformer = QuantileTransformer(output_distribution="uniform")
    train_targets[train_targets.columns] = transformer.fit_transform(train_targets.values)
    non_train_targets[train_targets.columns] = transformer.transform(non_train_targets.values)
    return train_targets, non_train_targets 
开发者ID:HunterMcGushion,项目名称:hyperparameter_hunter,代码行数:7,代码来源:test_intra_cv_target_transform.py


示例10: test_basic

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def test_basic(self, output_distribution):
        rs = da.random.RandomState(0)
        a = dpp.QuantileTransformer(output_distribution=output_distribution)
        b = spp.QuantileTransformer(output_distribution=output_distribution)

        X = rs.uniform(size=(1000, 3), chunks=50)
        a.fit(X)
        b.fit(X)
        assert_estimator_equal(a, b, atol=0.02)

        # set the quantiles, so that from here out, we're exact
        a.quantiles_ = b.quantiles_
        assert_eq_ar(a.transform(X), b.transform(X), atol=1e-7)
        assert_eq_ar(X, a.inverse_transform(a.transform(X))) 
开发者ID:dask,项目名称:dask-ml,代码行数:16,代码来源:test_data.py


示例11: test_types

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def test_types(self, type_, kwargs):
        X = np.random.uniform(size=(1000, 3))
        dX = type_(X, **kwargs)
        qt = spp.QuantileTransformer()
        qt.fit(X)
        dqt = dpp.QuantileTransformer()
        dqt.fit(dX) 
开发者ID:dask,项目名称:dask-ml,代码行数:9,代码来源:test_data.py


示例12: test_fit_transform_frame

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def test_fit_transform_frame(self):
        df = pd.DataFrame(np.random.randn(1000, 3))
        ddf = dd.from_pandas(df, 2)

        a = spp.QuantileTransformer()
        b = dpp.QuantileTransformer()

        expected = a.fit_transform(df)
        result = b.fit_transform(ddf)
        assert_eq_ar(result, expected, rtol=1e-3, atol=1e-3) 
开发者ID:dask,项目名称:dask-ml,代码行数:12,代码来源:test_data.py


示例13: preprocess_train

# 需要导入模块: from sklearn import preprocessing [as 别名]
# 或者: from sklearn.preprocessing import QuantileTransformer [as 别名]
def preprocess_train(self, X, feature_types, vect_max_features):
        transformer_list = []
        if len(feature_types['language']) > 0:
            pipeline = Pipeline(steps=[
                ("preparator", NlpDataPreprocessor(nlp_cols=feature_types['language'])),
                ("vectorizer",
                 TfidfVectorizer(ngram_range=self.params['proc.ngram_range'], sublinear_tf=True, max_features=vect_max_features, tokenizer=self.tokenize))
            ])
            transformer_list.append(('vect', pipeline))
        if len(feature_types['onehot']) > 0:
            pipeline = Pipeline(steps=[
                ('generator', OheFeaturesGenerator(cats_cols=feature_types['onehot'])),
            ])
            transformer_list.append(('cats', pipeline))
        if len(feature_types['continuous']) > 0:
            pipeline = Pipeline(steps=[
                ('generator', NumericDataPreprocessor(cont_cols=feature_types['continuous'])),
                ('imputer', SimpleImputer(strategy=self.params['proc.impute_strategy'])),
                ('scaler', StandardScaler())
            ])
            transformer_list.append(('cont', pipeline))
        if len(feature_types['skewed']) > 0:
            pipeline = Pipeline(steps=[
                ('generator', NumericDataPreprocessor(cont_cols=feature_types['skewed'])),
                ('imputer', SimpleImputer(strategy=self.params['proc.impute_strategy'])),
                ('quantile', QuantileTransformer(output_distribution='normal')),  # Or output_distribution = 'uniform'
            ])
            transformer_list.append(('skew', pipeline))
        self.pipeline = FeatureUnion(transformer_list=transformer_list)
        self.pipeline.fit(X) 
开发者ID:awslabs,项目名称:autogluon,代码行数:32,代码来源:lr_model.py



注:本文中的sklearn.preprocessing.QuantileTransformer方法示例整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。