nannyml.drift.model_inputs.multivariate.data_reconstruction.results module
Contains the results of the data reconstruction drift calculation and provides plotting functionality.
- class nannyml.drift.model_inputs.multivariate.data_reconstruction.results.DataReconstructionDriftCalculatorResult(results_data: DataFrame, calculator: AbstractCalculator)[source]
Bases:
AbstractCalculatorResult
Contains the results of the data reconstruction 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 = 'drift', plot_reference: bool = False, *args, **kwargs) Optional[Figure] [source]
Renders plots for metrics returned by the multivariate data reconstruction calculator.
The different plot kinds that are available:
drift
plots the multivariate reconstruction error over the provided features per
Chunk
.
- Parameters:
kind (str, default=`drift`) – The kind of plot you want to have. Value can currently only be
drift
.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.DataReconstructionDriftCalculator( >>> feature_column_names=feature_column_names, >>> timestamp_column_name='timestamp' >>> ) >>> calc.fit(reference_df) >>> results = calc.calculate(analysis_df) >>> print(results.data) # access the numbers key start_index ... upper_threshold alert 0 [0:4999] 0 ... 1.511762 True 1 [5000:9999] 5000 ... 1.511762 True 2 [10000:14999] 10000 ... 1.511762 True 3 [15000:19999] 15000 ... 1.511762 True 4 [20000:24999] 20000 ... 1.511762 True 5 [25000:29999] 25000 ... 1.511762 True 6 [30000:34999] 30000 ... 1.511762 True 7 [35000:39999] 35000 ... 1.511762 True 8 [40000:44999] 40000 ... 1.511762 True 9 [45000:49999] 45000 ... 1.511762 True >>> fig = results.plot(plot_reference=True) >>> fig.show()