Source code for nannyml.drift.multivariate.data_reconstruction.result

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

"""Contains the results of the data reconstruction drift calculation and provides plotting functionality."""
from __future__ import annotations

from collections import namedtuple
from typing import List, Optional

import pandas as pd
import plotly.graph_objects as go

from nannyml._typing import Key
from nannyml.base import PerMetricResult
from nannyml.exceptions import InvalidArgumentsException
from nannyml.plots.blueprints.comparisons import ResultCompareMixin
from nannyml.plots.blueprints.metrics import plot_metric
from nannyml.usage_logging import UsageEvent, log_usage

Metric = namedtuple("Metric", "display_name column_name")

[docs]class Result(PerMetricResult[Metric], ResultCompareMixin): """Class wrapping the results of the data reconstruction drift calculator and providing plotting functionality.""" def __init__( self, results_data: pd.DataFrame, column_names: List[str], categorical_column_names: List[str], continuous_column_names: List[str], timestamp_column_name: Optional[str] = None, ): """ Parameters ---------- results_data: pd.DataFrame Results data returned by a DataReconstructionDriftCalculator. column_names: List[str] A list of column names indicating which columns contain feature values. categorical_column_names : List[str] Subset of categorical features to be included in calculation. continuous_column_names : List[str] Subset of continuous features to be included in calculation. timestamp_column_name: Optional[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. """ metric = Metric(display_name='Reconstruction error', column_name='reconstruction_error') super().__init__(results_data, [metric]) self.column_names = column_names self.categorical_column_names = categorical_column_names self.continuous_column_names = continuous_column_names self.timestamp_column_name = timestamp_column_name
[docs] def keys(self) -> List[Key]: """ Creates a list of keys where each Key is a `namedtuple('Key', 'properties display_names')` """ return [Key(properties=('reconstruction_error',), display_names=('Reconstruction error',))]
[docs] @log_usage(UsageEvent.MULTIVAR_DRIFT_PLOT, metadata_from_kwargs=['kind']) def plot(self, kind: str = 'drift', *args, **kwargs) -> go.Figure: """Renders plots for metrics returned by the multivariate data reconstruction calculator. Parameters ---------- kind: str, default='drift' The kind of plot you want to have. Value can currently only be 'drift'. 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 >>> # Load synthetic data >>> reference, analysis, _ = nml.load_synthetic_car_loan_dataset() >>> non_feature_columns = ['timestamp', 'y_pred_proba', 'y_pred', 'repaid'] >>> feature_column_names = [ ... col for col in reference.columns ... if col not in non_feature_columns >>> ] >>> calc = nml.DataReconstructionDriftCalculator( ... column_names=feature_column_names, ... timestamp_column_name='timestamp', ... chunk_size=5000 >>> ) >>> >>> results = calc.calculate(analysis) >>> figure = results.plot() >>> """ if kind == 'drift': return plot_metric( self, title='Multivariate drift (PCA reconstruction error)', metric_display_name='Data reconstruction drift', metric_column_name='reconstruction_error', ) else: raise InvalidArgumentsException(f"unknown plot kind '{kind}'. " f"Please provide one of: ['drift'].")