gators.feature_generation_dt.DeltaTime¶
-
class
gators.feature_generation_dt.
DeltaTime
(columns_a: List[str], columns_b: List[str])[source]¶ Create new columns based on the time difference in sec. between two columns.
- Parameters
- theta_vecList[float]
List of columns.
Examples
Imports and initialization:
>>> from gators.feature_generation_dt import DeltaTime >>> obj = DeltaTime(columns_a=['C'], columns_b=['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', '2020-01-02T00', None], ... 'B': [0, 1, 0], ... 'C': ['2020-01-15T23', '2020-01-03T05', None]}), npartitions=1) >>> X[['A', 'C']] = X[['A', 'C']].astype('datetime64[ns]')
koalas dataframes:
>>> import databricks.koalas as ks >>> X = ks.DataFrame({ ... 'A': ['2020-01-01T23', '2020-01-02T00', None], ... 'B': [0, 1, 0], ... 'C': ['2020-01-15T23', '2020-01-03T05', None]}) >>> X[['A', 'C']] = X[['A', 'C']].astype('datetime64[ns]')
and pandas dataframes:
>>> import pandas as pd >>> X = pd.DataFrame({ ... 'A': ['2020-01-01T23', '2020-01-02T00', None], ... 'B': [0, 1, 0], ... 'C': ['2020-01-15T23', '2020-01-03T05', None]}) >>> X[['A', 'C']] = X[['A', 'C']].astype('datetime64[ns]')
The result is a transformed dataframe belonging to the same dataframe library.
>>> obj.fit_transform(X) A B C C__A__Deltatime[s] 0 2020-01-01 23:00:00 0 2020-01-15 23:00:00 1209600.0 1 2020-01-02 00:00:00 1 2020-01-03 05:00:00 104400.0 2 NaT 0 NaT NaN
>>> X = pd.DataFrame({ ... 'A': ['2020-01-01T23', '2020-01-02T00', None], ... 'B': [0, 1, 0], ... 'C': ['2020-01-15T23', '2020-01-03T05', None]}) >>> X[['A', 'C']] = X[['A', 'C']].astype('datetime64[ns]') >>> _ = obj.fit(X) >>> obj.transform_numpy(X.to_numpy()) array([[Timestamp('2020-01-01 23:00:00'), 0, Timestamp('2020-01-15 23:00:00'), 1209600.0], [Timestamp('2020-01-02 00:00:00'), 1, Timestamp('2020-01-03 05:00:00'), 104400.0], [NaT, 0, NaT, nan]], dtype=object)
-
fit
(X: Union[pd.DataFrame, ks.DataFrame, dd.DataFrame], y: Union[pd.Series, ks.Series, dd.Series] = None) → gators.feature_generation_dt.delta_time.DeltaTime[source]¶ Fit the transformer on the dataframe X.
- Parameters
- Xpd.DataFrame
Input dataframe.
- ySeries, default None.
Target values.
- Returns
- selfDeltaTime
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
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.