Source code for nannyml.performance_calculation.result

#  Author:   Niels Nuyttens  <>
#  License: Apache Software License 2.0

"""Contains the results of the realized performance calculation and provides filtering and plotting functionality."""
from __future__ import annotations

import copy
from typing import Dict, List, Optional, Union, cast

import pandas as pd
import plotly.graph_objects as go

from nannyml._typing import Key, ProblemType, Self
from nannyml.base import PerMetricResult
from nannyml.exceptions import InvalidArgumentsException
from nannyml.performance_calculation import SUPPORTED_METRIC_FILTER_VALUES
from nannyml.performance_calculation.metrics.base import Metric
from nannyml.plots.blueprints.comparisons import ResultCompareMixin
from nannyml.plots.blueprints.metrics import plot_metrics
from nannyml.usage_logging import UsageEvent, log_usage

[docs]class Result(PerMetricResult[Metric], ResultCompareMixin): """Wraps performance calculation results and provides filtering and plotting functionality.""" metrics: List[Metric] def __init__( self, results_data: pd.DataFrame, problem_type: ProblemType, y_pred: str, y_pred_proba: Optional[Union[str, Dict[str, str]]], y_true: str, metrics: List[Metric], timestamp_column_name: Optional[str] = None, reference_data: Optional[pd.DataFrame] = None, analysis_data: Optional[pd.DataFrame] = None, ): """Creates a new Result instance. Parameters ---------- results_data: pd.DataFrame Results data returned by a CBPE estimator. problem_type: ProblemType Determines which method to use. Allowed values are: - 'regression' - 'classification_binary' - 'classification_multiclass' 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). metrics: List[nannyml.performance_calculation.metrics.base.Metric] List of metrics to evaluate. 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. reference_data: pd.DataFrame, default=None The reference data used for fitting. Must have target data available. analysis_data: pd.DataFrame, default=None The data on which NannyML calculates the perfomance. """ super().__init__(results_data, metrics) self.problem_type = problem_type self.y_true = y_true self.y_pred_proba = y_pred_proba self.y_pred = y_pred self.timestamp_column_name = timestamp_column_name self.reference_data = reference_data self.analysis_data = analysis_data
[docs] def keys(self) -> List[Key]: """ Creates a list of keys where each Key is a `namedtuple('Key', 'properties display_names')` """ return [ Key( properties=(component[1],), display_names=( f'realized {component[0]}', component[0],, ), ) for metric in self.metrics for component in cast(Metric, metric).components ]
[docs] @log_usage(UsageEvent.PERFORMANCE_PLOT, metadata_from_kwargs=['kind']) def plot( self, kind: str = 'performance', *args, **kwargs, ) -> go.Figure: """Render realized performance metrics. This function will return a :class:`plotly.graph_objects.Figure` object. Parameters ---------- kind: str, default='performance' The kind of plot to render. Only the 'performance' plot is currently available. Raises ------ InvalidArgumentsException: when an unknown plot ``kind`` is provided. Returns ------- fig: :class:`plotly.graph_objs._figure.Figure` A :class:`~plotly.graph_objs._figure.Figure` object containing the requested drift plot. Can be saved to disk using the :meth:`~plotly.graph_objs._figure.Figure.write_image` method or shown rendered on screen using the :meth:`` method. Examples -------- >>> import nannyml as nml >>> from IPython.display import display >>> reference_df, analysis_df, analysis_target_df = nml.load_synthetic_car_loan_dataset() >>> analysis_df = analysis_df.merge(analysis_target_df, left_index=True, right_index=True) >>> display(reference_df.head(3)) >>> calc = nml.PerformanceCalculator( ... y_pred_proba='y_pred_proba', ... y_pred='y_pred', ... y_true='repaid', ... timestamp_column_name='timestamp', ... problem_type='classification_binary', ... metrics=['roc_auc', 'f1', 'precision', 'recall', 'specificity', 'accuracy'], ... chunk_size=5000) >>> >>> results = calc.calculate(analysis_df) >>> display(results.filter(period='analysis').to_df()) >>> display(results.filter(period='reference').to_df()) >>> figure = results.plot() >>> """ if kind == 'performance': return plot_metrics( result=self, title='Realized performance', subplot_title_format='Realized <b>{display_names[1]}</b>', subplot_y_axis_title_format='{display_names[1]}', ) else: raise InvalidArgumentsException(f"unknown plot kind '{kind}'. " f"Please provide on of: ['performance'].")
def _filter(self, period: str, metrics: Optional[List[str]] = None, *args, **kwargs) -> Self: """Filter the results based on the specified period and metrics.""" if metrics is None: filtered_metrics = self.metrics else: filtered_metrics = [] for name in metrics: if name not in SUPPORTED_METRIC_FILTER_VALUES: raise InvalidArgumentsException(f"invalid metric '{name}'") m = self._get_metric_by_name(name) if m: filtered_metrics = filtered_metrics + [m] else: raise InvalidArgumentsException(f"no '{name}' in result, did you calculate it?") metric_column_names = [name for metric in filtered_metrics for name in metric.column_names] res = super()._filter(period, metric_column_names, *args, **kwargs) res.metrics = filtered_metrics return res def _get_metric_by_name(self, name: str) -> Optional[Metric]: for metric in self.metrics: # If we match the metric by name, return the metric # E.g. matching the name 'confusion_matrix' if name == return metric # If we match one of the metric component names # E.g. matching the name 'true_positive' with the confusion matrix metric elif name in metric.column_names: # Only retain the component whose column name was given to filter on res = copy.deepcopy(metric) res.components = list(filter(lambda c: c[1] == name, metric.components)) return res else: continue return None