# 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 __future__ import annotations
import copy
from typing import List, Optional
import pandas as pd
import plotly.graph_objects as go
from nannyml.base import AbstractCalculatorResult
from nannyml.exceptions import InvalidArgumentsException
from nannyml.plots.blueprints.metrics import plot_metric
from nannyml.usage_logging import UsageEvent, log_usage
[docs]class Result(AbstractCalculatorResult):
"""Contains the results of the data reconstruction drift calculation and provides 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,
):
super().__init__(results_data)
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
self.metrics = ['reconstruction_error']
def _filter(self, period: str, metrics: Optional[List[str]] = None, *args, **kwargs) -> Result:
if metrics is None:
metrics = self.metrics
data = pd.concat([self.data.loc[:, (['chunk'])], self.data.loc[:, (metrics,)]], axis=1)
if period != 'all':
data = data.loc[data[('chunk', 'period')] == period, :]
data = data.reset_index(drop=True)
result = copy.deepcopy(self)
result.data = data
return result
[docs] @log_usage(UsageEvent.MULTIVAR_DRIFT_PLOT, metadata_from_kwargs=['kind'])
def plot(self, kind: str = 'drift', *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()
>>>
>>> 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(
>>> column_names=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_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'].")