# Author: Niels Nuyttens <niels@nannyml.com>
#
# License: Apache Software License 2.0
"""Contains the results of the data reconstruction drift calculation and provides plotting functionality."""
from typing import Optional
import pandas as pd
import plotly.graph_objects as go
from nannyml.base import AbstractCalculator, AbstractCalculatorResult
from nannyml.exceptions import InvalidArgumentsException
from nannyml.plots._step_plot import _step_plot
[docs]class DataReconstructionDriftCalculatorResult(AbstractCalculatorResult):
"""Contains the results of the data reconstruction drift calculation and provides plotting functionality."""
def __init__(self, results_data: pd.DataFrame, calculator: AbstractCalculator):
super().__init__(results_data)
from . import DataReconstructionDriftCalculator
if not isinstance(calculator, DataReconstructionDriftCalculator):
raise RuntimeError(
f"{calculator.__class__.__name__} is not an instance of type " f"DataReconstructionDriftCalculator"
)
self.calculator = calculator
@property
def calculator_name(self) -> str:
return "multivariate_data_reconstruction_feature_drift"
[docs] def plot(self, kind: str = 'drift', plot_reference: bool = False, *args, **kwargs) -> Optional[go.Figure]:
"""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 :class:`~nannyml.chunk.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: :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:`~plotly.graph_objs._figure.Figure.show` method.
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()
"""
if kind == 'drift':
return _plot_drift(self.data, self.calculator, plot_reference)
else:
raise InvalidArgumentsException(f"unknown plot kind '{kind}'. " f"Please provide one of: ['drift'].")
# @property
# def plots(self) -> Dict[str, go.Figure]:
# return {'multivariate_feature_drift': _plot_drift(self.data)}
def _plot_drift(data: pd.DataFrame, calculator, plot_reference: bool) -> go.Figure:
plot_period_separator = plot_reference
data['period'] = 'analysis'
if plot_reference:
reference_results = calculator.previous_reference_results.copy()
reference_results['period'] = 'reference'
data = pd.concat([reference_results, data], ignore_index=True)
fig = _step_plot(
table=data,
metric_column_name='reconstruction_error',
chunk_column_name='key',
drift_column_name='alert',
lower_threshold_column_name='lower_threshold',
upper_threshold_column_name='upper_threshold',
hover_labels=['Chunk', 'Reconstruction error', 'Target data'],
title='Data Reconstruction Drift',
y_axis_title='Reconstruction Error',
v_line_separating_analysis_period=plot_period_separator,
sampling_error_column_name='sampling_error',
lower_confidence_column_name='lower_confidence_bound',
upper_confidence_column_name='upper_confidence_bound',
plot_confidence_for_reference=True,
)
return fig