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: pandas.core.frame.DataFrame, metrics: List[nannyml.performance_estimation.confidence_based.metrics.Metric], y_pred: str, y_pred_proba: Union[str, Dict[str, str]], y_true: str, chunker: nannyml.chunk.Chunker, problem_type: nannyml._typing.ProblemType, timestamp_column_name: Optional[str] = None)[source]
Bases:
nannyml.base.PerMetricResult
[nannyml.performance_estimation.confidence_based.metrics.Metric
],nannyml.plots.blueprints.comparisons.ResultCompareMixin
Contains results for CBPE estimation and adds filtering and plotting functionality.
- 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[nannyml._typing.Key] [source]
Creates a list of keys where each Key is a namedtuple(‘Key’, ‘properties display_names’)
- plot(kind: str = 'performance', *args, **kwargs) plotly.graph_objs._figure.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') –
- 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 >>> >>> reference_df, analysis_df, target_df = nml.load_synthetic_binary_classification_dataset() >>> >>> estimator = nml.CBPE( >>> y_true='work_home_actual', >>> y_pred='y_pred', >>> y_pred_proba='y_pred_proba', >>> timestamp_column_name='timestamp', >>> metrics=['f1', 'roc_auc'] >>> ) >>> >>> estimator.fit(reference_df) >>> >>> results = estimator.estimate(analysis_df) >>> results.plot().show()