gators.encoders.FrequencyEncoder¶
-
class
gators.encoders.
FrequencyEncoder
(inplace=True)[source]¶ Encode the categorical columns as integer columns based on the category count.
- Parameters
- inplacebool, default to True.
If True, replace in-place the categorical values by numerical ones. If False, keep the categorical columns and create new encoded columns.
Examples
Imports and initialization:
>>> from gators.encoders import FrequencyEncoder >>> obj = FrequencyEncoder()
The fit, transform, and fit_transform methods accept:
dask dataframes:
>>> import dask.dataframe as dd >>> import pandas as pd >>> X = dd.from_pandas(pd.DataFrame({'A': ['a', 'a', 'b'], 'B': ['c', 'd', 'd']}), npartitions=1)
koalas dataframes:
>>> import databricks.koalas as ks >>> X = ks.DataFrame({'A': ['a', 'a', 'b'], 'B': ['c', 'd', 'd']})
and pandas dataframes:
>>> import pandas as pd >>> X = pd.DataFrame({'A': ['a', 'a', 'b'], 'B': ['c', 'd', 'd']})
The result is a transformed dataframe belonging to the same dataframe library.
>>> obj.fit_transform(X) A B 0 2.0 1.0 1 2.0 2.0 2 1.0 2.0
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.
>>> obj.transform_numpy(X.to_numpy()) array([[2., 1.], [2., 2.], [1., 2.]])
-
fit
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.encoders.frequency_encoder.FrequencyEncoder[source]¶ Fit the transformer on the dataframe X.
- Parameters
- XDataFrame.
Input dataframe.
- ySeries, default None.
Target values.
- Returns
- FrequencyEncoder: Instance of itself.
-
generate_mapping
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]) → Dict[str, Dict[str, float]][source]¶ Generate the mapping to perform the encoding.
- Parameters
- XDataFrame
Input dataframe.
- Returns
- Dict[str, Dict[str, float]]
Mapping.
-
static
check_array
(X: numpy.ndarray)¶ Validate array.
- Parameters
- Xnp.ndarray
Array.
-
check_array_is_numerics
(X: numpy.ndarray)¶ Check if array is only numerics.
- Parameters
- Xnp.ndarray
Array.
-
static
check_binary_target
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶ Raise an error if the target is not binary.
- Parameters
- ySeries
Target values.
-
static
check_dataframe
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Validate dataframe.
- Parameters
- XDataFrame
Dataframe.
-
static
check_dataframe_contains_numerics
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
-
static
check_dataframe_is_numerics
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
-
check_dataframe_with_objects
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe contains object columns.
- Parameters
- XDataFrame
Dataframe.
-
check_datatype
(dtype, accepted_dtypes)¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
-
static
check_multiclass_target
(y: Union[pd.Series, ks.Series, dd.Series])¶ Raise an error if the target is not discrete.
- Parameters
- ySeries
Target values.
-
check_nans
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], columns: List[str])¶ Raise an error if X contains NaN values.
- Parameters
- XDataFrame
Dataframe.
- theta_vecList[float]
List of columns.
-
static
check_regression_target
(y: Union[pd.Series, ks.Series, dd.Series])¶ Raise an error if the target is not discrete.
- Parameters
- ySeries
Target values.
-
static
check_target
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series])¶ Validate target.
- Parameters
- XDataFrame
Dataframe.
- ySeries
Target values.
-
static
decompose_mapping
(mapping: Dict[str, Dict[str, float]]) → Tuple[List[str], numpy.ndarray, numpy.ndarray]¶ Decompose the mapping.
- Parameters
- mappingDict[str, Dict[str, float]]
The dictionary keys are the categorical columns, the keys are the mapping itself for the assocaited column.
- Returns
- columnsList[float]
List of columns.
- values_vecnp.ndarray
Values to encode.
- encoded_values_vecnp.ndarray
Values used to encode.
-
display_mapping
(cmap: Union[str, colormap], k=5, decimal=2, title='')¶ Display the encoder mapping in a jupyter notebook. Parameters ———- cmap : Union[str, ‘colormap’]
Matplotlib colormap.
- kint, default 5.
Number of mappings displayed.
- decimalsint, default 2.
Number of decimal places to use.
- titlestr, default ‘’.
Plot title.
-
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]¶ Fit and Transform the dataframe X.
- Parameters
- XDataFrame.
Input dataframe.
- ySeries, default None.
Input target.
- Returns
- XDataFrame
Transformed dataframe.
-
static
get_column_names
(inplace: bool, columns: List[str], suffix: str)¶ 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.
-
transform
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]) → Union[pd.DataFrame, ks.DataFrame, dd.DataFrame]¶ Transform the dataframe X.
- Parameters
- XDataFrame.
Input dataframe.
- Returns
- ——-
- XDataFrame
Transformed dataframe.
-
transform_numpy
(X: numpy.ndarray) → numpy.ndarray¶ Transform the array X.
- Parameters
- Xnp.ndarray
Input array.
- Returns
- ——-
- Xnp.ndarray
Encoded array.