Пример #1
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def get_space_p_in_p():
    space = HyperSpace()
    with space.as_default():
        p1 = Pipeline([SimpleImputer(name='imputer1'), StandardScaler(name='scaler1')], name='p1')
        p2 = Pipeline([SimpleImputer(name='imputer2'), StandardScaler(name='scaler2')], name='p2')
        input = HyperInput(name='input1')
        p3 = Pipeline([p1, p2], name='p3')(input)
        space.set_inputs(input)
    return space
Пример #2
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def get_space_column_transformer():
    space = HyperSpace()
    with space.as_default():
        input = HyperInput(name='input1')
        p1 = Pipeline([SimpleImputer(name='imputer1'), StandardScaler(name='scaler1')], columns=['a', 'b', 'c'],
                      name='p1')(input)
        p2 = Pipeline([SimpleImputer(name='imputer2'), StandardScaler(name='scaler2')], columns=['c', 'd'], name='p2')(
            input)
        p3 = ColumnTransformer()([p1, p2])
        space.set_inputs(input)
    return space
Пример #3
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def numeric_pipeline_complex(impute_strategy=None, seq_no=0):
    if impute_strategy is None:
        impute_strategy = Choice(
            ['mean', 'median', 'constant', 'most_frequent'])
    elif isinstance(impute_strategy, list):
        impute_strategy = Choice(impute_strategy)
    # reduce_skewness_kurtosis = SkewnessKurtosisTransformer(transform_fn=Choice([np.log, np.log10, np.log1p]))
    # reduce_skewness_kurtosis_optional = Optional(reduce_skewness_kurtosis, keep_link=True,
    #                                             name=f'numeric_reduce_skewness_kurtosis_optional_{seq_no}')

    imputer = SimpleImputer(missing_values=np.nan,
                            strategy=impute_strategy,
                            name=f'numeric_imputer_{seq_no}')
    scaler_options = ModuleChoice([
        StandardScaler(name=f'numeric_standard_scaler_{seq_no}'),
        MinMaxScaler(name=f'numeric_minmax_scaler_{seq_no}'),
        MaxAbsScaler(name=f'numeric_maxabs_scaler_{seq_no}'),
        RobustScaler(name=f'numeric_robust_scaler_{seq_no}')
    ],
                                  name=f'numeric_or_scaler_{seq_no}')
    scaler_optional = Optional(scaler_options,
                               keep_link=True,
                               name=f'numeric_scaler_optional_{seq_no}')

    pipeline = Pipeline([imputer, scaler_optional],
                        name=f'numeric_pipeline_complex_{seq_no}',
                        columns=column_number_exclude_timedelta)
    return pipeline
Пример #4
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def categorical_pipeline_complex(impute_strategy=None,
                                 svd_components=3,
                                 seq_no=0):
    if impute_strategy is None:
        impute_strategy = Choice(['constant', 'most_frequent'])
    elif isinstance(impute_strategy, list):
        impute_strategy = Choice(impute_strategy)
    if isinstance(svd_components, list):
        svd_components = Choice(svd_components)

    def onehot_svd():
        onehot = OneHotEncoder(name=f'categorical_onehot_{seq_no}')
        optional_svd = Optional(TruncatedSVD(n_components=svd_components,
                                             name=f'categorical_svd_{seq_no}'),
                                name=f'categorical_optional_svd_{seq_no}',
                                keep_link=True)(onehot)
        return optional_svd

    imputer = SimpleImputer(missing_values=np.nan,
                            strategy=impute_strategy,
                            name=f'categorical_imputer_{seq_no}')
    label_encoder = MultiLabelEncoder(
        name=f'categorical_label_encoder_{seq_no}')
    onehot = onehot_svd()
    le_or_onehot_pca = ModuleChoice(
        [label_encoder, onehot], name=f'categorical_le_or_onehot_pca_{seq_no}')
    pipeline = Pipeline([imputer, le_or_onehot_pca],
                        name=f'categorical_pipeline_complex_{seq_no}',
                        columns=column_object_category_bool)
    return pipeline
Пример #5
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def numeric_pipeline_simple(impute_strategy='mean', seq_no=0):
    pipeline = Pipeline(
        [
            SimpleImputer(missing_values=np.nan,
                          strategy=impute_strategy,
                          name=f'numeric_imputer_{seq_no}'),
            StandardScaler(name=f'numeric_standard_scaler_{seq_no}')
        ],
        columns=column_number_exclude_timedelta,
        name=f'numeric_pipeline_simple_{seq_no}',
    )
    return pipeline
Пример #6
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def categorical_pipeline_simple(impute_strategy='constant', seq_no=0):
    pipeline = Pipeline(
        [
            SimpleImputer(missing_values=np.nan,
                          strategy=impute_strategy,
                          name=f'categorical_imputer_{seq_no}'),
            MultiLabelEncoder(name=f'categorical_label_encoder_{seq_no}')
        ],
        columns=column_object_category_bool,
        name=f'categorical_pipeline_simple_{seq_no}',
    )
    return pipeline
Пример #7
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def get_space_2outputs():
    space = HyperSpace()
    with space.as_default():
        Pipeline([tow_outputs()])
    return space
Пример #8
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def get_space_2inputs():
    space = HyperSpace()
    with space.as_default():
        Pipeline([tow_inputs(), StandardScaler()])
    return space
Пример #9
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def get_space():
    space = HyperSpace()
    with space.as_default():
        Pipeline([SimpleImputer(), StandardScaler()])
    return space