gators.transformers.Transformer¶

class gators.transformers.Transformer[source]¶

Abstract gators transformer class.

Examples

A Gators transformer.

>>> import pandas as pd
>>> import databricks.koalas as ks
>>> import numpy as np
>>> from gators.transformers import Transformer
>>> class GetFirstColumn(Transformer):
...    def fit(self, X, y: Series = None):
...        return self
...    def transform(self, X: pd.DataFrame):
...        return X[[X.columns[0]]]
...    def transform_numpy(self, X: np.ndarray):
...        return X[:, 0].reshape(-1, 1)
>>> obj = GetFirstColumn()

The fit, transform, and fit_transform methods accept:

dask dataframes:

>>> import dask.dataframe as dd
>>> import pandas as pd
>>> import numpy as np
>>> X = dd.from_pandas(pd.DataFrame({'A':[1, 2], 'B':[3, 4]}), npartitions=1)

koalas dataframes:

>>> import databricks.koalas as ks
>>> import numpy as np
>>> X = ks.DataFrame({'A':[1, 2], 'B':[3, 4]})

and pandas dataframes:

>>> import pandas as pd
>>> import numpy as np
>>> X = pd.DataFrame({'A':[1, 2], 'B':[3, 4]})

The result is a transformed dataframe belonging to the same dataframe library.

>>> obj.fit_transform(X)
   A
0  1
1  2

Independly of the dataframe library used to fit the transformer, the tranform_numpy method only accepts NumPy arrays and returns a transformed NumPy array. Note that this transformer should only be used when the number of rows is small e.g. in real-time environment.

>>> X = pd.DataFrame({'A':[1, 2], 'B':[3, 4]})
>>> obj.transform_numpy(X.to_numpy())
array([[1],
       [2]])

abstract fit(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.transformers.transformer.Transformer[source]¶

Fit the transformer on the dataframe X.

Parameters

XDataFrame.: Input dataframe.
ySeries, default None.: Target values.

Returns

selfTransformer: Instance of itself.

abstract transform(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame][source]¶

Transform the dataframe X.

Parameters

XDataFrame: Input dataframe.

Returns

XDataFrame: Transformed dataframe.

abstract transform_numpy(X: Union[pd.Series, ks.Series, dd.Series], y: Union[pd.Series, ks.Series, dd.Series] = None)[source]¶

Transform the array X.

Parameters

Xnp.ndarray: Array.

Returns

Xnp.ndarray: Transformed array.

fit_transform(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame][source]¶

Fit and Transform the dataframe X.

Parameters

XDataFrame.: Input dataframe.
ySeries, default None.: Input target.

Returns

XDataFrame: Transformed dataframe.

static check_dataframe(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])[source]¶

Validate dataframe.

Parameters

XDataFrame: Dataframe.

static check_target(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])[source]¶

Validate target.

Parameters

XDataFrame: Dataframe.
ySeries: Target values.

static check_array(X: numpy.ndarray)[source]¶

Validate array.

Parameters

Xnp.ndarray: Array.

static check_dataframe_is_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])[source]¶

Check if dataframe is only numerics.

Parameters

XDataFrame: Dataframe.

check_datatype(dtype, accepted_dtypes)[source]¶

Check if dataframe is only numerics.

Parameters

XDataFrame: Dataframe.

static check_binary_target(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])[source]¶

Raise an error if the target is not binary.

Parameters

ySeries: Target values.

static check_multiclass_target(y: Union[pd.Series, ks.Series, dd.Series])[source]¶

Raise an error if the target is not discrete.

Parameters

ySeries: Target values.

static check_regression_target(y: Union[pd.Series, ks.Series, dd.Series])[source]¶

Raise an error if the target is not discrete.

Parameters

ySeries: Target values.

static check_dataframe_contains_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])[source]¶

Check if dataframe is only numerics.

Parameters

XDataFrame: Dataframe.

check_dataframe_with_objects(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])[source]¶

Check if dataframe contains object columns.

Parameters

XDataFrame: Dataframe.

check_array_is_numerics(X: numpy.ndarray)[source]¶

Check if array is only numerics.

Parameters

Xnp.ndarray: Array.

check_nans(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], columns: List[str])[source]¶

Raise an error if X contains NaN values.

Parameters

XDataFrame: Dataframe.
theta_vecList[float]: List of columns.

static get_column_names(inplace: bool, columns: List[str], suffix: str)[source]¶

Return the names of the modified columns.

Parameters

inplacebool: If True return columns. If False return columns__suffix.
columnsList[str]: List of columns.
suffixstr: Suffix used if inplace is False.

Returns

List[str]: List of column names.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict: Parameter names mapped to their values.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.

Transformers

gators.transformers.TransformerXY