from numpy import nan
from sagemaker_sklearn_extension.externals import Header
from sagemaker_sklearn_extension.impute import RobustImputer
from sagemaker_sklearn_extension.preprocessing import RobustLabelEncoder
from sagemaker_sklearn_extension.preprocessing import RobustStandardScaler
from sagemaker_sklearn_extension.preprocessing import ThresholdOneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

# Given a list of column names and target column name, Header can return the index
# for given column name
HEADER = Header(
    column_names=[
        'Unnamed: 0', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9',
        'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19',
        'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Class',
        'amt'
    ],
    target_column_name='Class'
)


def build_feature_transform():
    """ Returns the model definition representing feature processing."""

    # These features can be parsed as numeric.

    numeric = HEADER.as_feature_indices(
        [
            'Unnamed: 0', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9',
            'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18',
            'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27',
            'V28', 'amt'
        ]
    )

    # These features contain a relatively small number of unique items.

    categorical = HEADER.as_feature_indices(['amt'])

    numeric_processors = Pipeline(
        steps=[
            (
                'robustimputer',
                RobustImputer(strategy='constant', fill_values=nan)
            )
        ]
    )

    categorical_processors = Pipeline(
        steps=[
            ('thresholdonehotencoder', ThresholdOneHotEncoder(threshold=635))
        ]
    )

    column_transformer = ColumnTransformer(
        transformers=[
            ('numeric_processing', numeric_processors, numeric
            ), ('categorical_processing', categorical_processors, categorical)
        ]
    )

    return Pipeline(
        steps=[
            ('column_transformer', column_transformer
            ), ('robuststandardscaler', RobustStandardScaler())
        ]
    )


def build_label_transform():
    """Returns the model definition representing feature processing."""

    return RobustLabelEncoder(
        labels=['0'], fill_label_value='1', include_unseen_class=True
    )

c8829ec3d200456bf1aaac6cb3df5649f885ac62
<mask token>


def build_feature_transform():
    """ Returns the model definition representing feature processing."""
    numeric = HEADER.as_feature_indices(['Unnamed: 0', 'V1', 'V2', 'V3',
        'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13',
        'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22',
        'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'amt'])
    categorical = HEADER.as_feature_indices(['amt'])
    numeric_processors = Pipeline(steps=[('robustimputer', RobustImputer(
        strategy='constant', fill_values=nan))])
    categorical_processors = Pipeline(steps=[('thresholdonehotencoder',
        ThresholdOneHotEncoder(threshold=635))])
    column_transformer = ColumnTransformer(transformers=[(
        'numeric_processing', numeric_processors, numeric), (
        'categorical_processing', categorical_processors, categorical)])
    return Pipeline(steps=[('column_transformer', column_transformer), (
        'robuststandardscaler', RobustStandardScaler())])


<mask token>

<mask token>


def build_feature_transform():
    """ Returns the model definition representing feature processing."""
    numeric = HEADER.as_feature_indices(['Unnamed: 0', 'V1', 'V2', 'V3',
        'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13',
        'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22',
        'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'amt'])
    categorical = HEADER.as_feature_indices(['amt'])
    numeric_processors = Pipeline(steps=[('robustimputer', RobustImputer(
        strategy='constant', fill_values=nan))])
    categorical_processors = Pipeline(steps=[('thresholdonehotencoder',
        ThresholdOneHotEncoder(threshold=635))])
    column_transformer = ColumnTransformer(transformers=[(
        'numeric_processing', numeric_processors, numeric), (
        'categorical_processing', categorical_processors, categorical)])
    return Pipeline(steps=[('column_transformer', column_transformer), (
        'robuststandardscaler', RobustStandardScaler())])


def build_label_transform():
    """Returns the model definition representing feature processing."""
    return RobustLabelEncoder(labels=['0'], fill_label_value='1',
        include_unseen_class=True)

<mask token>
HEADER = Header(column_names=['Unnamed: 0', 'V1', 'V2', 'V3', 'V4', 'V5',
    'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16',
    'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26',
    'V27', 'V28', 'Class', 'amt'], target_column_name='Class')


def build_feature_transform():
    """ Returns the model definition representing feature processing."""
    numeric = HEADER.as_feature_indices(['Unnamed: 0', 'V1', 'V2', 'V3',
        'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13',
        'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22',
        'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'amt'])
    categorical = HEADER.as_feature_indices(['amt'])
    numeric_processors = Pipeline(steps=[('robustimputer', RobustImputer(
        strategy='constant', fill_values=nan))])
    categorical_processors = Pipeline(steps=[('thresholdonehotencoder',
        ThresholdOneHotEncoder(threshold=635))])
    column_transformer = ColumnTransformer(transformers=[(
        'numeric_processing', numeric_processors, numeric), (
        'categorical_processing', categorical_processors, categorical)])
    return Pipeline(steps=[('column_transformer', column_transformer), (
        'robuststandardscaler', RobustStandardScaler())])


def build_label_transform():
    """Returns the model definition representing feature processing."""
    return RobustLabelEncoder(labels=['0'], fill_label_value='1',
        include_unseen_class=True)

from numpy import nan
from sagemaker_sklearn_extension.externals import Header
from sagemaker_sklearn_extension.impute import RobustImputer
from sagemaker_sklearn_extension.preprocessing import RobustLabelEncoder
from sagemaker_sklearn_extension.preprocessing import RobustStandardScaler
from sagemaker_sklearn_extension.preprocessing import ThresholdOneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
HEADER = Header(column_names=['Unnamed: 0', 'V1', 'V2', 'V3', 'V4', 'V5',
    'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16',
    'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26',
    'V27', 'V28', 'Class', 'amt'], target_column_name='Class')


def build_feature_transform():
    """ Returns the model definition representing feature processing."""
    numeric = HEADER.as_feature_indices(['Unnamed: 0', 'V1', 'V2', 'V3',
        'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13',
        'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22',
        'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'amt'])
    categorical = HEADER.as_feature_indices(['amt'])
    numeric_processors = Pipeline(steps=[('robustimputer', RobustImputer(
        strategy='constant', fill_values=nan))])
    categorical_processors = Pipeline(steps=[('thresholdonehotencoder',
        ThresholdOneHotEncoder(threshold=635))])
    column_transformer = ColumnTransformer(transformers=[(
        'numeric_processing', numeric_processors, numeric), (
        'categorical_processing', categorical_processors, categorical)])
    return Pipeline(steps=[('column_transformer', column_transformer), (
        'robuststandardscaler', RobustStandardScaler())])


def build_label_transform():
    """Returns the model definition representing feature processing."""
    return RobustLabelEncoder(labels=['0'], fill_label_value='1',
        include_unseen_class=True)

from numpy import nan
from sagemaker_sklearn_extension.externals import Header
from sagemaker_sklearn_extension.impute import RobustImputer
from sagemaker_sklearn_extension.preprocessing import RobustLabelEncoder
from sagemaker_sklearn_extension.preprocessing import RobustStandardScaler
from sagemaker_sklearn_extension.preprocessing import ThresholdOneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline

# Given a list of column names and target column name, Header can return the index
# for given column name
HEADER = Header(
    column_names=[
        'Unnamed: 0', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9',
        'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19',
        'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Class',
        'amt'
    ],
    target_column_name='Class'
)


def build_feature_transform():
    """ Returns the model definition representing feature processing."""

    # These features can be parsed as numeric.

    numeric = HEADER.as_feature_indices(
        [
            'Unnamed: 0', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9',
            'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18',
            'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27',
            'V28', 'amt'
        ]
    )

    # These features contain a relatively small number of unique items.

    categorical = HEADER.as_feature_indices(['amt'])

    numeric_processors = Pipeline(
        steps=[
            (
                'robustimputer',
                RobustImputer(strategy='constant', fill_values=nan)
            )
        ]
    )

    categorical_processors = Pipeline(
        steps=[
            ('thresholdonehotencoder', ThresholdOneHotEncoder(threshold=635))
        ]
    )

    column_transformer = ColumnTransformer(
        transformers=[
            ('numeric_processing', numeric_processors, numeric
            ), ('categorical_processing', categorical_processors, categorical)
        ]
    )

    return Pipeline(
        steps=[
            ('column_transformer', column_transformer
            ), ('robuststandardscaler', RobustStandardScaler())
        ]
    )


def build_label_transform():
    """Returns the model definition representing feature processing."""

    return RobustLabelEncoder(
        labels=['0'], fill_label_value='1', include_unseen_class=True
    )
