nannyml.drift.univariate.result module
Contains the results of the univariate drift calculator and provides plotting functionality.
- class nannyml.drift.univariate.result.Result(results_data: DataFrame, column_names: List[str], categorical_column_names: List[str], continuous_column_names: List[str], categorical_method_names: List[str], continuous_method_names: List[str], timestamp_column_name: Optional[str], chunker: Chunker, analysis_data: Optional[DataFrame] = None, reference_data: Optional[DataFrame] = None)[source]
Bases:
PerMetricPerColumnResult
[Method
],ResultCompareMixin
Class wrapping the results of the univariate drift calculator and providing plotting functionality.
- Parameters:
results_data (pd.DataFrame) – Results data returned by a DataReconstructionDriftCalculator.
column_names (List[str]) – A list of column names indicating which columns contain feature values.
categorical_column_names (List[str]) – Subset of categorical features to be included in calculation.
continuous_column_names (List[str]) – Subset of continuous features to be included in calculation.
categorical_method_names (List[str]) –
A list of method names that will be performed on categorical columns. Supported methods for categorical variables:
jensen_shannon
chi2
hellinger
l_infinity
continuous_method_names (List[str]) –
A list of method names that will be performed on continuous columns. Supported methods for continuous variables:
jensen_shannon
kolmogorov_smirnov
hellinger
wasserstein
timestamp_column_name (Optional[str], default=None) – The name of the column containing the timestamp of the model prediction. If not given, plots will not use a time-based x-axis but will use the index of the chunks instead.
chunker (Chunker) – The Chunker used to split the data sets into a lists of chunks.
analysis_data (pd.DataFrame, default= None) – Portion of data that NannyML will use to calculate the observed drift.
reference_data (pd.DataFrame, default = None) – Portion of data that NannyML will use to fit its drift methods.
- keys() List[Key] [source]
Creates a list of keys for continuos and categorial columns where each Key is a namedtuple(‘Key’, ‘properties display_names’)
- plot(kind: str = 'drift', *args, **kwargs) Figure [source]
Renders plots for metrics returned by the univariate distance drift calculator.
For any feature you can render the statistic value or p-values as a step plot, or create a distribution plot.
- Parameters:
kind (str, default='drift') –
The kind of plot you want to have. Allowed values are:
- Returns:
fig – A
Figure
object containing the requested drift plot.Can be saved to disk using the
write_image()
method or shown rendered on screen using theshow()
method.- Return type:
plotly.graph_objs._figure.Figure
Examples
>>> import nannyml as nml >>> reference, analysis, _ = nml.load_synthetic_car_price_dataset() >>> column_names = [col for col in reference.columns if col not in ['timestamp', 'y_pred', 'y_true']] >>> calc = nml.UnivariateDriftCalculator( ... column_names=column_names, ... timestamp_column_name='timestamp', ... continuous_methods=['kolmogorov_smirnov', 'jensen_shannon', 'wasserstein'], ... categorical_methods=['chi2', 'jensen_shannon', 'l_infinity'], ... ).fit(reference) >>> res = calc.calculate(analysis) >>> res = res.filter(period='analysis') >>> for column_name in res.continuous_column_names: ... for method in res.continuous_method_names: ... res.plot(kind='drift', column_name=column_name, method=method).show()