nannyml.drift.model_inputs.univariate.statistical.results module
Contains the results of the univariate statistical drift calculation and provides plotting functionality.
- class nannyml.drift.model_inputs.univariate.statistical.results.UnivariateStatisticalDriftCalculatorResult(results_data: DataFrame, calculator)[source]
Bases:
AbstractCalculatorResult
Contains the results of the univariate statistical drift calculation and provides plotting functionality.
Creates a new
AbstractCalculatorResult
instance.- Parameters:
results_data (pd.DataFrame) – The data returned by the Calculator.
- property calculator_name: str
- plot(kind: str = 'feature', metric: str = 'statistic', feature_column_name: Optional[str] = None, plot_reference: bool = False, *args, **kwargs) Optional[Figure] [source]
Renders plots for metrics returned by the univariate statistical drift calculator.
For any feature you can render the statistic value or p-values as a step plot, or create a distribution plot. Select a plot using the
kind
parameter:feature_drift
plots drift per
Chunk
for a single feature of a chunked data set.
feature_distribution
plots feature distribution per
Chunk
. Joyplot for continuous features, stacked bar charts for categorical features.
- Parameters:
kind (str, default=`feature_drift`) – The kind of plot you want to have. Allowed values are feature_drift` and
feature_distribution
.metric (str, default=``statistic``) – The metric to plot. Allowed values are
statistic
andp_value
. Not applicable when plotting distributions.feature_column_name (str) – Column name identifying a feature according to the preset model metadata. The function will raise an exception when no feature using that column name was found in the metadata. Either
feature_column_name
orfeature_label
should be specified.plot_reference (bool, default=False) – Indicates whether to include the reference period in the plot or not. Defaults to
False
.
- 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_df, analysis_df, _ = nml.load_synthetic_binary_classification_dataset() >>> >>> feature_column_names = [col for col in reference_df.columns >>> if col not in ['y_pred', 'y_pred_proba', 'work_home_actual', 'timestamp']] >>> calc = nml.UnivariateStatisticalDriftCalculator( >>> feature_column_names=feature_column_names, >>> timestamp_column_name='timestamp' >>> ) >>> calc.fit(reference_df) >>> results = calc.calculate(analysis_df) >>> print(results.data) # check the numbers key start_index ... identifier_alert identifier_threshold 0 [0:4999] 0 ... True 0.05 1 [5000:9999] 5000 ... True 0.05 2 [10000:14999] 10000 ... True 0.05 3 [15000:19999] 15000 ... True 0.05 4 [20000:24999] 20000 ... True 0.05 5 [25000:29999] 25000 ... True 0.05 6 [30000:34999] 30000 ... True 0.05 7 [35000:39999] 35000 ... True 0.05 8 [40000:44999] 40000 ... True 0.05 9 [45000:49999] 45000 ... True 0.05 >>> for feature in calc.feature_column_names: >>> fig = results.plot(kind='feature_drift', metric='statistic', plot_reference=True, >>> feature_column_name=feature) >>> fig.show()