nannyml.performance_estimation.confidence_based.results module
Module containing CBPE estimation results and plotting implementations.
- class nannyml.performance_estimation.confidence_based.results.Result(results_data: DataFrame, metrics: List[Metric], y_pred: str, y_pred_proba: Union[str, Dict[str, str]], y_true: str, chunker: Chunker, problem_type: ProblemType, timestamp_column_name: Optional[str] = None)[source]
Bases:
PerMetricResult
[Metric
],ResultCompareMixin
Contains results for CBPE estimation and adds filtering and plotting functionality.
Initialize CBPE results class.
- Parameters:
results_data (pd.DataFrame) – Results data returned by a CBPE estimator.
metrics (List[nannyml.performance_estimation.confidence_based.metrics.Metric]) – List of metrics to evaluate.
y_pred (str) – The name of the column containing your model predictions.
y_pred_proba (Union[str, Dict[str, str]]) –
- Name(s) of the column(s) containing your model output.
For binary classification, pass a single string refering to the model output column.
For multiclass classification, pass a dictionary that maps a class string to the column name containing model outputs for that class.
y_true (str) – The name of the column containing target values (that are provided in reference data during fitting).
chunker (Chunker) – The Chunker used to split the data sets into a lists of chunks.
problem_type (ProblemType) – Determines which CBPE implementation to use. Allowed problem type values are ‘classification_binary’ and ‘classification_multiclass’.
timestamp_column_name (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.
- keys() List[Key] [source]
Creates a list of keys where each Key is a namedtuple(‘Key’, ‘properties display_names’).
- plot(kind: str = 'performance', *args, **kwargs) Figure [source]
Render plots based on CBPE estimation results.
This function will return a
plotly.graph_objects.Figure
object. The following kinds of plots are available:- Parameters:
kind (str, default='performance') – What kind of plot to create. Only performance type is available.
- Raises:
InvalidArgumentsException – when an unknown plot
kind
is provided.:- 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 >>> from IPython.display import display >>> reference_df = nml.load_synthetic_car_loan_dataset()[0] >>> analysis_df = nml.load_synthetic_car_loan_dataset()[1] >>> display(reference_df.head(3)) >>> estimator = nml.CBPE( ... y_pred_proba='y_pred_proba', ... y_pred='y_pred', ... y_true='repaid', ... timestamp_column_name='timestamp', ... metrics=['roc_auc', 'accuracy', 'f1'], ... chunk_size=5000, ... problem_type='classification_binary', >>> ) >>> estimator.fit(reference_df) >>> results = estimator.estimate(analysis_df) >>> display(results.filter(period='analysis').to_df()) >>> metric_fig = results.plot() >>> metric_fig.show()