# Author: Niels Nuyttens <niels@nannyml.com>
# Author: Nikolaos Perrakis <nikos@nannyml.com>
#
# License: Apache Software License 2.0
"""Contains the results of the univariate statistical drift calculation and provides plotting functionality."""
from __future__ import annotations
import warnings
from typing import List, Optional
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
import pandas as pd
import plotly.graph_objects as go
from nannyml._typing import Key
from nannyml.base import PerColumnResult
from nannyml.chunk import Chunker
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(PerColumnResult, ResultCompareMixin):
"""Missing Values Result Class.
Contains calculation results and provides plotting functionality.
"""
def __init__(
self,
results_data: pd.DataFrame,
column_names: List[str],
data_quality_metric: str,
timestamp_column_name: Optional[str],
chunker: Chunker,
):
"""Initialize Missing Values Result Class."""
super().__init__(results_data, column_names)
self.timestamp_column_name = timestamp_column_name
self.data_quality_metric = data_quality_metric
self.chunker = chunker
[docs] def keys(self) -> List[Key]: # noqa: D102
return [
Key(
properties=(column_name,),
display_names=(column_name, f"{self.data_quality_metric.replace('_', ' ').title()}"),
)
for column_name in self.column_names
]
[docs] @log_usage(UsageEvent.DQ_CALC_MISSING_VALUES_PLOT)
def plot(
self,
*args,
**kwargs,
) -> go.Figure:
"""Plot Missing Values results.
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, analysis, _ = nml.load_synthetic_car_price_dataset()
>>> column_names = [col for col in reference.columns if col not in ['timestamp', 'y_pred', 'y_true']]
>>> calc = nml.MissingValuesCalculator(
... column_names=column_names,
... timestamp_column_name='timestamp',
... ).fit(reference)
>>> res = calc.calculate(analysis)
>>> for column_name in res.column_names:
... res = res.filter(period='analysis', column_name=column_name).plot().show()
"""
return plot_metrics(
self,
title='Data Quality ',
subplot_title_format='{display_names[1]} for <b>{display_names[0]}</b>',
subplot_y_axis_title_format='{display_names[1]}',
)