tubular.dates.DatetimeInfoExtractor¶
- class tubular.dates.DatetimeInfoExtractor(columns, include=['timeofday', 'timeofmonth', 'timeofyear', 'dayofweek'], datetime_mappings={}, **kwargs)[source]¶
Bases:
tubular.base.BaseTransformerTransformer to extract various features from datetime var.
- Parameters
columns (str or list) – datetime columns to extract information from
include (list of str, default = ["timeofday", "timeofmonth", "timeofyear", "dayofweek"]) – Which datetime categorical information to extract
datetime_mappings (dict, default = {}) –
Optional argument to define custom mappings for datetime values. Keys of the dictionary must be contained in include All possible values of each feature must be included in the mappings, ie, a mapping for dayofweek must include all values 0-6; datetime_mappings = {“dayofweek”: {“week”: [0, 1, 2, 3, 4],
”weekend”: [5, 6]}}
The values for the mapping array must be iterable; datetime_mappings = {“timeofday”: {“am”: range(0, 12),
”pm”: range(12, 24)}}
- The required ranges for each mapping are:
timeofday: 0-23 timeofmonth: 1-31 timeofyear: 1-12 dayofweek: 0-6
- If in include but no mappings provided default values will be used as follows:
- timeofday_mapping = {
“night”: range(0, 6), # Midnight - 6am “morning”: range(6, 12), # 6am - Noon “afternoon”: range(12, 18), # Noon - 6pm “evening”: range(18, 24), # 6pm - Midnight
} timeofmonth_mapping = {
”start”: range(0, 11), “middle”: range(11, 21), “end”: range(21, 32),
} timeofyear_mapping = {
”spring”: range(3, 6), # Mar, Apr, May “summer”: range(6, 9), # Jun, Jul, Aug “autumn”: range(9, 12), # Sep, Oct, Nov “winter”: [12, 1, 2], # Dec, Jan, Feb
} dayofweek_mapping = {
”monday”: [0], “tuesday”: [1], “wednesday”: [2], “thursday”: [3], “friday”: [4], “saturday”: [5], “sunday”: [6],
}
- **kwargs
Arbitrary keyword arguments passed onto BaseTransformer.init method.
- include¶
Which datetime categorical information to extract
- Type
list of str, default = [“timeofday”, “timeofmonth”, “timeofyear”, “dayofweek”]
- datetime_mappings¶
Optional argument to define custom mappings for datetime values.
- Type
dict, default = {}
- __init__(columns, include=['timeofday', 'timeofmonth', 'timeofyear', 'dayofweek'], datetime_mappings={}, **kwargs) → None[source]¶
Initialize self. See help(type(self)) for accurate signature.
Methods
__init__(columns[, include, datetime_mappings])Initialize self.
check_is_fitted(attribute)Check if particular attributes are on the object.
check_weights_column(X, weights_column)Helper method for validating weights column in dataframe.
Method that returns the name of the current class when called.
Method to check that the columns attribute is set and all values are present in X.
Function to check or set columns attribute.
fit(X[, y])Base transformer fit method, checks X and y types.
fit_transform(X[, y])Fit to data, then transform it.
get_params([deep])Get parameters for this estimator.
set_params(**params)Set the parameters of this estimator.
transform(X)Transform - Extracts new features from datetime variables.
- check_is_fitted(attribute)¶
Check if particular attributes are on the object. This is useful to do before running transform to avoid trying to transform data without first running the fit method.
Wrapper for utils.validation.check_is_fitted function.
- Parameters
attributes (List) – List of str values giving names of attribute to check exist on self.
- static check_weights_column(X, weights_column)¶
Helper method for validating weights column in dataframe.
X (pd.DataFrame): df containing weight column weights_column (str): name of weight column
- classname()¶
Method that returns the name of the current class when called.
- columns_check(X)¶
Method to check that the columns attribute is set and all values are present in X.
- Parameters
X (pd.DataFrame) – Data to check columns are in.
- columns_set_or_check(X)¶
Function to check or set columns attribute.
If the columns attribute is None then set it to all columns in X. Otherwise run the columns_check method.
- Parameters
X (pd.DataFrame) – Data to check columns are in.
- fit(X, y=None)¶
Base transformer fit method, checks X and y types. Currently only pandas DataFrames are allowed for X and DataFrames or Series for y.
Fit calls the columns_set_or_check method which will set the columns attribute to all columns in X, if it is None.
- Parameters
X (pd.DataFrame) – Data to fit the transformer on.
y (None or pd.DataFrame or pd.Series, default = None) – Optional argument only required for the transformer to work with sklearn pipelines.
- fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns
X_new – Transformed array.
- Return type
ndarray array of shape (n_samples, n_features_new)
- get_params(deep=True)¶
Get parameters for this estimator.
- Parameters
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
params – Parameter names mapped to their values.
- Return type
dict
- 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
**params (dict) – Estimator parameters.
- Returns
self – Estimator instance.
- Return type
estimator instance