gators.feature_generation_dt._BaseDatetimeFeature¶
-
class
gators.feature_generation_dt._BaseDatetimeFeature(columns: List[str], date_format: str, column_names: List[str])[source]¶ Base datetime transformer class.
- Parameters
- theta_vecList[float]
List of columns.
- column_namesList[str], default None.
List of column names.
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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.
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static
get_cyclic_column_names(columns: List[str], pattern: str)[source]¶ Get the column names.
- Parameters
- theta_vecList[float]
List of datetime features.
- pattern: str
Pattern.
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static
get_idx(date_format: str) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray][source]¶ [summary]
- Parameters
- date_formatstr
Datetime format
- Returns
- idx_day_boundsnp.ndarray
Start and end indices of the day.
- idx_monthnp.ndarray
Start and end indices of the month.
- idx_year_boundsnp.ndarray
Start and end indices of the year.
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static
check_array(X: numpy.ndarray)¶ Validate array.
- Parameters
- Xnp.ndarray
Array.
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check_array_is_numerics(X: numpy.ndarray)¶ Check if array is only numerics.
- Parameters
- Xnp.ndarray
Array.
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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.
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static
check_dataframe(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Validate dataframe.
- Parameters
- XDataFrame
Dataframe.
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static
check_dataframe_contains_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
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static
check_dataframe_is_numerics(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
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check_dataframe_with_objects(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame])¶ Check if dataframe contains object columns.
- Parameters
- XDataFrame
Dataframe.
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check_datatype(dtype, accepted_dtypes)¶ Check if dataframe is only numerics.
- Parameters
- XDataFrame
Dataframe.
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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.
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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.
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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.
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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.
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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.
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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.
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abstract
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.
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abstract
transform_numpy(X: Union[pd.Series, ks.Series, dd.Series], y: Union[pd.Series, ks.Series, dd.Series] = None)¶ Transform the array X.
- Parameters
- Xnp.ndarray
Array.
- Returns
- Xnp.ndarray
Transformed array.