gators.feature_generation_dt.OrdinalDayOfMonth¶
-
class
gators.feature_generation_dt.OrdinalDayOfMonth(columns: List[str], date_format: str = 'ymd')[source]¶ Create new columns based on the day of the month.
- Parameters
- theta_vecList[float]
List of columns.
Examples
Imports and initialization:
>>> from gators.feature_generation_dt import OrdinalDayOfMonth >>> obj = OrdinalDayOfMonth(columns=['A'])
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': ['2020-01-01T23', '2020-12-15T18', None], 'B': [0, 1, 0]}), npartitions=1) >>> X['A'] = X['A'].astype('datetime64[ns]')
koalas dataframes:
>>> import databricks.koalas as ks >>> X = ks.DataFrame({'A': ['2020-01-01T23', '2020-12-15T18', None], 'B': [0, 1, 0]}) >>> X['A'] = X['A'].astype('datetime64[ns]')
and pandas dataframes:
>>> import pandas as pd >>> X = pd.DataFrame({'A': ['2020-01-01T23', '2020-12-15T18', None], 'B': [0, 1, 0]}) >>> X['A'] = X['A'].astype('datetime64[ns]')
The result is a transformed dataframe belonging to the same dataframe library.
>>> obj.fit_transform(X) A B A__day_of_month 0 2020-01-01 23:00:00 0 1.0 1 2020-12-15 18:00:00 1 15.0 2 NaT 0 NaN
>>> X = pd.DataFrame({'A': ['2020-01-01T23', '2020-12-15T18', None], 'B': [0, 1, 0]}) >>> X['A'] = X['A'].astype('datetime64[ns]') >>> _ = obj.fit(X) >>> obj.transform_numpy(X.to_numpy()) array([[Timestamp('2020-01-01 23:00:00'), 0, 1.0], [Timestamp('2020-12-15 18:00:00'), 1, 15.0], [NaT, 0, nan]], dtype=object)
-
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
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(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.transformers.transformer.Transformer¶ Fit the transformer on the dataframe X.
- Parameters
- XDataFrame
Input dataframe.
- ySeries, default None.
Target values.
- Returns
- selfTransformer
Instance of itself.
-
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.
-
static
get_cyclic_column_names(columns: List[str], pattern: str)¶ Get the column names.
- Parameters
- theta_vecList[float]
List of datetime features.
- pattern: str
Pattern.
-
static
get_idx(date_format: str) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]¶ [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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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.