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feat(report): overview section for quickly navigating reports
aggregate alerts per feature (ordered from descending) click the feature name to directly switch to that feature closes #202
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# Copyright (c) 2022 ING Wholesale Banking Advanced Analytics | ||
# | ||
# Permission is hereby granted, free of charge, to any person obtaining a copy of | ||
# this software and associated documentation files (the "Software"), to deal in | ||
# the Software without restriction, including without limitation the rights to | ||
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of | ||
# the Software, and to permit persons to whom the Software is furnished to do so, | ||
# subject to the following conditions: | ||
# | ||
# The above copyright notice and this permission notice shall be included in all | ||
# copies or substantial portions of the Software. | ||
# | ||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS | ||
# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR | ||
# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER | ||
# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN | ||
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. | ||
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from typing import Optional | ||
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import numpy as np | ||
import pandas as pd | ||
from tqdm import tqdm | ||
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from ..base import Module | ||
from ..resources import templates_env | ||
from ..utils import filter_metrics | ||
from ..visualization.utils import _prune | ||
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class OverviewSectionGenerator(Module): | ||
"""This module takes the time-series data of already computed statistics, plots the data and | ||
combines all the plots into a list which is stored together with the section name in a dictionary | ||
which later will be used for the report generation. | ||
""" | ||
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_input_keys = ("read_key", "dynamic_bounds", "store_key") | ||
_output_keys = ("store_key",) | ||
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def __init__( | ||
self, | ||
read_key, | ||
store_key, | ||
section_name, | ||
features=None, | ||
ignore_features=None, | ||
last_n=0, | ||
skip_first_n=0, | ||
skip_last_n=0, | ||
static_bounds=None, | ||
dynamic_bounds=None, | ||
prefix="traffic_light_", | ||
suffices=["_red_high", "_yellow_high", "_yellow_low", "_red_low"], | ||
ignore_stat_endswith=None, | ||
skip_empty_plots=True, | ||
description="", | ||
show_stats=None, | ||
): | ||
"""Initialize an instance of SectionGenerator. | ||
:param str read_key: key of input data to read from the datastore and use for plotting | ||
:param str store_key: key for output data to be stored in the datastore | ||
:param str section_name: key of output data to store in the datastore | ||
:param list features: list of features to pick up from input data (optional) | ||
:param list ignore_features: ignore list of features, if present (optional) | ||
:param int last_n: plot statistic data for last 'n' periods (optional) | ||
:param int skip_first_n: when plotting data skip first 'n' periods. last_n takes precedence (optional) | ||
:param int skip_last_n: in plot skip last 'n' periods. last_n takes precedence (optional) | ||
:param str static_bounds: key to static traffic light bounds key in datastore (optional) | ||
:param str dynamic_bounds: key to dynamic traffic light bounds key in datastore (optional) | ||
:param str prefix: dynamic traffic light prefix. default is ``'traffic_light_'`` (optional) | ||
:param str suffices: dynamic traffic light suffices. (optional) | ||
:param list ignore_stat_endswith: ignore stats ending with any of list of suffices. (optional) | ||
:param bool skip_empty_plots: if false, also show empty plots in report with only nans or zeroes (optional) | ||
:param str description: description of the section. default is empty (optional) | ||
:param list show_stats: list of statistic name patterns to show in the report. If None, show all (optional) | ||
:param bool plot_overview: heatmap overview of traffic lights (features x time) | ||
:param bool plot_metrics: individual plot per feature | ||
""" | ||
super().__init__() | ||
self.read_key = read_key | ||
self.store_key = store_key | ||
self.dynamic_bounds = dynamic_bounds | ||
self.static_bounds = static_bounds | ||
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self.features = features or [] | ||
self.ignore_features = ignore_features or [] | ||
self.section_name = section_name | ||
self.last_n = last_n | ||
self.skip_first_n = skip_first_n | ||
self.skip_last_n = skip_last_n | ||
self.prefix = prefix | ||
self.suffices = suffices | ||
self.ignore_stat_endswith = ignore_stat_endswith or [] | ||
self.skip_empty_plots = skip_empty_plots | ||
self.description = description | ||
self.show_stats = show_stats | ||
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def get_description(self): | ||
return self.section_name | ||
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def transform( | ||
self, | ||
data_obj: dict, | ||
dynamic_bounds: Optional[dict] = None, | ||
sections: Optional[list] = None, | ||
): | ||
assert isinstance(data_obj, dict) | ||
if dynamic_bounds is None: | ||
dynamic_bounds = {} | ||
assert isinstance(dynamic_bounds, dict) | ||
if sections is None: | ||
sections = [] | ||
assert isinstance(sections, list) | ||
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features = self.get_features(list(data_obj.keys())) | ||
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self.logger.info( | ||
f'Generating section "{self.section_name}". skip empty plots: {self.skip_empty_plots}' | ||
) | ||
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values = {} | ||
for feature in tqdm(features, ncols=100): | ||
df = data_obj.get(feature, pd.DataFrame()) | ||
fdbounds = dynamic_bounds.get(feature, pd.DataFrame(index=df.index)) | ||
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assert all(df.index == fdbounds.index) | ||
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# prepare date labels | ||
df.drop( | ||
columns=["histogram", "reference_histogram"], | ||
inplace=True, | ||
errors="ignore", | ||
) | ||
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metrics = filter_metrics( | ||
df.columns, self.ignore_stat_endswith, self.show_stats | ||
) | ||
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values[feature] = _get_metrics( | ||
metrics, | ||
df, | ||
self.last_n, | ||
self.skip_first_n, | ||
self.skip_last_n, | ||
self.skip_empty_plots, | ||
) | ||
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plots = [_plot_metrics(values)] | ||
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# filter out potential empty plots (from skip empty plots) | ||
if self.skip_empty_plots: | ||
plots = [e for e in plots if len(e["plot"])] | ||
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plots = sorted(plots, key=lambda plot: plot["name"]) | ||
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sections.append( | ||
{ | ||
"section_title": self.section_name, | ||
"section_description": self.description, | ||
"plots": plots, | ||
} | ||
) | ||
return sections | ||
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def _plot_metrics( | ||
values, | ||
): | ||
# sort features by n_red, n_yellow, n_green | ||
values = { | ||
k: v | ||
for k, v in sorted( | ||
values.items(), | ||
key=lambda x: ( | ||
x[1][2] / x[1]["total"] if x[1]["total"] > 0 else 0, | ||
x[1][1] / x[1]["total"] if x[1]["total"] > 0 else 0, | ||
x[1][0] / x[1]["total"] if x[1]["total"] > 0 else 0, | ||
), | ||
reverse=True, | ||
) | ||
} | ||
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plot = templates_env( | ||
"aggregated-overview.html", | ||
values=values, | ||
) | ||
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return { | ||
"name": "Alert frequency per Feature", | ||
"description": "", | ||
"plot": plot, | ||
"full_width": True, | ||
} | ||
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def _get_metrics( | ||
metrics, | ||
df, | ||
last_n, | ||
skip_first_n, | ||
skip_last_n, | ||
skip_empty, | ||
): | ||
values = [] | ||
nonempty_metrics = [] | ||
for metric in metrics: | ||
value = _prune(df[metric], last_n, skip_first_n, skip_last_n) | ||
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if not skip_empty or np.sum(value) > 0: | ||
values.append(value) | ||
nonempty_metrics.append(metric) | ||
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empty = {0: 0, 1: 0, 2: 0} | ||
if len(values) > 0: | ||
values = np.stack(values) | ||
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keys, counts = np.unique(values, return_counts=True) | ||
counts = dict(zip(keys, counts)) | ||
empty.update(counts) | ||
empty["total"] = empty[0] + empty[1] + empty[2] | ||
empty["n_zero"] = sum(empty[i] == 0 for i in range(3)) | ||
return empty |
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<table width="95%" style="margin-bottom: 30px;"> | ||
{% for feature, vals in values.items() %} | ||
<tr> | ||
<td width="20%" style="text-align:center;"><a class="table-item" href="#" data-feature="{{ feature }}">{{ feature }}</a></td> | ||
<td width="80%"> | ||
{% for i in [2, 1, 0] %} | ||
{% if vals[i] > 0 %} | ||
<div style="width: calc(6% + {{ vals[i] }} / {{ vals['total'] }} * {{ 82 + 6 * vals['n_zero'] }}%)" class="{% if i == 2%}red{%elif i == 1%}yellow{%else%}green{%endif%}"> | ||
{{ vals[i] }} ({{ "%d" % (vals[i] / (vals["total"] * 0.01))}}%) | ||
</div> | ||
{% else %} | ||
<div style="width: 0%" class="white"> </div> | ||
{% endif %} | ||
{% endfor %} | ||
</td> | ||
</tr> | ||
{% endfor %} | ||
</table> |
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