interpreTS.utils package#

Submodules#

interpreTS.utils.data_conversion.convert_to_time_series(data)[source]#

Convert input data to a TimeSeriesData object.

Parameters:

data (pd.DataFrame, pd.Series, or np.ndarray) – The data to be converted into a TimeSeriesData object.

Returns:

An instance of TimeSeriesData wrapping the input data.

Return type:

TimeSeriesData

Raises:

TypeError – If the input data is not of type pd.DataFrame, pd.Series, or np.ndarray.
ValueError – If the input data is empty or has invalid dimensions.

Examples

>>> import pandas as pd
>>> data = pd.Series([1, 2, 3, 4, 5])
>>> ts_data = convert_to_time_series(data)

interpreTS.utils.data_validation.validate_time_series_data(data, feature_name=None, validation_requirements=None, **kwargs)[source]#

Validate the input time series data against dynamically provided requirements.

Parameters:

data (pd.Series, pd.DataFrame, or np.ndarray) – The time series data to be validated.
feature_name (str, optional) – The name of the feature to validate.
validation_requirements (dict, optional) – A dictionary specifying the validation requirements for each feature.
**kwargs (dict) – Additional validation parameters (overrides validation_requirements).

Returns:

True if the data is valid; raises an error otherwise.

Return type:

bool

Raises: