gators.feature_generation.ElementaryArithmetics¶
-
class
gators.feature_generation.
ElementaryArithmetics
(columns_a: List[str], columns_b: List[str], operator: str, column_names: List[str] = None, coef: float = 1.0)[source]¶ Create new columns based on elementary arithmetics.
The data should be composed of numerical columns only. Use gators.encoders to replace the categorical columns by numerical ones before using ElementaryArithmetics.
- Parameters
- columns_aList[str]
List of columns.
- columns_bList[str]
List of columns.
- operatorstr
Arithmetic perator. The possible values are:
‘+’ for addition
‘*’ for multiplication
‘/’ for division
- column_namesList[str], default None.
List of new column names.
- coeffloat, default 1.
Coefficient value for the addition.
X[new] = X[column_a] + coef * X[column_b]
- dtypetype, default np.float64.
Numerical datatype of the output data.
Examples
Imports and initialization:
>>> from gators.feature_generation import ClusterStatistics >>> obj = ElementaryArithmetics( ... columns_a=['A'], columns_b=['B'], operator='+', coef=0.1)
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': [1, 1., 1.], 'B': [1., 2., 3.]}), npartitions=1)
koalas dataframes:
>>> import databricks.koalas as ks >>> X = ks.DataFrame({'A': [1, 1., 1.], 'B': [1., 2., 3.]})
and pandas dataframes:
>>> import pandas as pd >>> X = pd.DataFrame({'A': [1, 1., 1.], 'B': [1., 2., 3.]})
The result is a transformed dataframe belonging to the same dataframe library.
>>> obj.fit_transform(X) A B A+0.1xB 0 1.0 1.0 1.1 1 1.0 2.0 1.2 2 1.0 3.0 1.3
>>> X = pd.DataFrame({'A': [1, 1., 1.], 'B': [1., 2., 3.]}) >>> _ = obj.fit(X) >>> obj.transform_numpy(X.to_numpy()) array([[1. , 1. , 1.1], [1. , 2. , 1.2], [1. , 3. , 1.3]])
-
fit
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.feature_generation.elementary_arithmethics.ElementaryArithmetics[source]¶ Fit the transformer on the dataframe X.
- Parameters
- XDataFrame.
Input dataframe.
- ySeries, default None.
Target values.
- Returns
- ElementaryArithmetics
Instance of itself.
-
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.
-
transform_numpy
(X: numpy.ndarray) → numpy.ndarray[source]¶ Transform the array X.
- Parameters
- Xnp.ndarray
Input array.
- Returns
- Xnp.ndarray
Transformed array.
-
static
get_idx_columns
(columns: List[str], selected_columns: List[str]) → numpy.ndarray[source]¶ Get the indices of the columns used for the combination.
- Parameters
- theta_vecList[float]
List of columns.
- selected_theta_vecList[float]
List of columns.
- Returns:
- np.ndarray
Array of indices.
-
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.
-
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.