C-Dash: Fix anomaly detection for the news
[csit.git] / csit.infra.dash / app / cdash / news / layout.py
1 # Copyright (c) 2023 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
5 #
6 #     http://www.apache.org/licenses/LICENSE-2.0
7 #
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
13
14 """Plotly Dash HTML layout override.
15 """
16
17 import pandas as pd
18 import dash_bootstrap_components as dbc
19
20 from flask import Flask
21 from dash import dcc
22 from dash import html
23 from dash import callback_context
24 from dash import Input, Output, State
25
26 from ..utils.constants import Constants as C
27 from ..utils.utils import gen_new_url
28 from ..utils.anomalies import classify_anomalies
29 from ..utils.url_processing import url_decode
30 from .tables import table_summary
31
32
33 class Layout:
34     """The layout of the dash app and the callbacks.
35     """
36
37     def __init__(
38             self,
39             app: Flask,
40             data_stats: pd.DataFrame,
41             data_trending: pd.DataFrame,
42             html_layout_file: str
43         ) -> None:
44         """Initialization:
45         - save the input parameters,
46         - read and pre-process the data,
47         - prepare data for the control panel,
48         - read HTML layout file,
49         - read tooltips from the tooltip file.
50
51         :param app: Flask application running the dash application.
52         :param data_stats: Pandas dataframe with staistical data.
53         :param data_trending: Pandas dataframe with trending data.
54         :param html_layout_file: Path and name of the file specifying the HTML
55             layout of the dash application.
56         :type app: Flask
57         :type data_stats: pandas.DataFrame
58         :type data_trending: pandas.DataFrame
59         :type html_layout_file: str
60         """
61
62         # Inputs
63         self._app = app
64         self._html_layout_file = html_layout_file
65
66         # Prepare information for the control panel:
67         self._jobs = sorted(list(data_trending["job"].unique()))
68         d_job_info = {
69             "job": list(),
70             "dut": list(),
71             "ttype": list(),
72             "cadence": list(),
73             "tbed": list()
74         }
75         for job in self._jobs:
76             lst_job = job.split("-")
77             d_job_info["job"].append(job)
78             d_job_info["dut"].append(lst_job[1])
79             d_job_info["ttype"].append(lst_job[3])
80             d_job_info["cadence"].append(lst_job[4])
81             d_job_info["tbed"].append("-".join(lst_job[-2:]))
82         self.job_info = pd.DataFrame.from_dict(d_job_info)
83
84         # Pre-process the data:
85
86         def _create_test_name(test: str) -> str:
87             lst_tst = test.split(".")
88             suite = lst_tst[-2].replace("2n1l-", "").replace("1n1l-", "").\
89                 replace("2n-", "")
90             return f"{suite.split('-')[0]}-{lst_tst[-1]}"
91
92         def _get_rindex(array: list, itm: any) -> int:
93             return len(array) - 1 - array[::-1].index(itm)
94
95         tst_info = {
96             "job": list(),
97             "build": list(),
98             "start": list(),
99             "dut_type": list(),
100             "dut_version": list(),
101             "hosts": list(),
102             "failed": list(),
103             "regressions": list(),
104             "progressions": list()
105         }
106         for job in self._jobs:
107             # Create lists of failed tests:
108             df_job = data_trending.loc[(data_trending["job"] == job)]
109             last_build = str(max(pd.to_numeric(df_job["build"].unique())))
110             df_build = df_job.loc[(df_job["build"] == last_build)]
111             tst_info["job"].append(job)
112             tst_info["build"].append(last_build)
113             tst_info["start"].append(data_stats.loc[
114                 (data_stats["job"] == job) &
115                 (data_stats["build"] == last_build)
116             ]["start_time"].iloc[-1].strftime('%Y-%m-%d %H:%M'))
117             tst_info["dut_type"].append(df_build["dut_type"].iloc[-1])
118             tst_info["dut_version"].append(df_build["dut_version"].iloc[-1])
119             tst_info["hosts"].append(df_build["hosts"].iloc[-1])
120             failed_tests = df_build.loc[(df_build["passed"] == False)]\
121                 ["test_id"].to_list()
122             l_failed = list()
123             try:
124                 for tst in failed_tests:
125                     l_failed.append(_create_test_name(tst))
126             except KeyError:
127                 l_failed = list()
128             tst_info["failed"].append(sorted(l_failed))
129
130             # Create lists of regressions and progressions:
131             l_reg = list()
132             l_prog = list()
133
134             tests = df_job["test_id"].unique()
135             for test in tests:
136                 tst_data = df_job.loc[(
137                     (df_job["test_id"] == test) &
138                     (df_job["passed"] == True)
139                 )].sort_values(by="start_time", ignore_index=True)
140                 if "-ndrpdr" in test:
141                     tst_data = tst_data.dropna(
142                         subset=["result_pdr_lower_rate_value", ]
143                     )
144                     if tst_data.empty:
145                         continue
146                     x_axis = tst_data["start_time"].tolist()
147                     try:
148                         anomalies, _, _ = classify_anomalies({
149                             k: v for k, v in zip(
150                                 x_axis,
151                                 tst_data["result_ndr_lower_rate_value"].tolist()
152                             )
153                         })
154                     except ValueError:
155                         continue
156                     if "progression" in anomalies:
157                         l_prog.append((
158                             _create_test_name(test).replace("-ndrpdr", "-ndr"),
159                             x_axis[_get_rindex(anomalies, "progression")]
160                         ))
161                     if "regression" in anomalies:
162                         l_reg.append((
163                             _create_test_name(test).replace("-ndrpdr", "-ndr"),
164                             x_axis[_get_rindex(anomalies, "regression")]
165                         ))
166                     try:
167                         anomalies, _, _ = classify_anomalies({
168                             k: v for k, v in zip(
169                                 x_axis,
170                                 tst_data["result_pdr_lower_rate_value"].tolist()
171                             )
172                         })
173                     except ValueError:
174                         continue
175                     if "progression" in anomalies:
176                         l_prog.append((
177                             _create_test_name(test).replace("-ndrpdr", "-pdr"),
178                             x_axis[_get_rindex(anomalies, "progression")]
179                         ))
180                     if "regression" in anomalies:
181                         l_reg.append((
182                             _create_test_name(test).replace("-ndrpdr", "-pdr"),
183                             x_axis[_get_rindex(anomalies, "regression")]
184                         ))
185                 else:  # mrr
186                     tst_data = tst_data.dropna(
187                         subset=["result_receive_rate_rate_avg", ]
188                     )
189                     if tst_data.empty:
190                         continue
191                     x_axis = tst_data["start_time"].tolist()
192                     try:
193                         anomalies, _, _ = classify_anomalies({
194                             k: v for k, v in zip(
195                                 x_axis,
196                                 tst_data["result_receive_rate_rate_avg"].\
197                                     tolist()
198                             )
199                         })
200                     except ValueError:
201                         continue
202                     if "progression" in anomalies:
203                         l_prog.append((
204                             _create_test_name(test),
205                             x_axis[_get_rindex(anomalies, "progression")]
206                         ))
207                     if "regression" in anomalies:
208                         l_reg.append((
209                             _create_test_name(test),
210                             x_axis[_get_rindex(anomalies, "regression")]
211                         ))
212
213             tst_info["regressions"].append(
214                 sorted(l_reg, key=lambda k: k[1], reverse=True))
215             tst_info["progressions"].append(
216                 sorted(l_prog, key=lambda k: k[1], reverse=True))
217
218         self._data = pd.DataFrame.from_dict(tst_info)
219
220         # Read from files:
221         self._html_layout = str()
222
223         try:
224             with open(self._html_layout_file, "r") as file_read:
225                 self._html_layout = file_read.read()
226         except IOError as err:
227             raise RuntimeError(
228                 f"Not possible to open the file {self._html_layout_file}\n{err}"
229             )
230
231         self._default_period = C.NEWS_SHORT
232         self._default_active = (False, True, False)
233
234         # Callbacks:
235         if self._app is not None and hasattr(self, 'callbacks'):
236             self.callbacks(self._app)
237
238     @property
239     def html_layout(self) -> dict:
240         return self._html_layout
241
242     def add_content(self):
243         """Top level method which generated the web page.
244
245         It generates:
246         - Store for user input data,
247         - Navigation bar,
248         - Main area with control panel and ploting area.
249
250         If no HTML layout is provided, an error message is displayed instead.
251
252         :returns: The HTML div with the whole page.
253         :rtype: html.Div
254         """
255
256         if self.html_layout:
257             return html.Div(
258                 id="div-main",
259                 className="small",
260                 children=[
261                     dcc.Location(id="url", refresh=False),
262                     dbc.Row(
263                         id="row-navbar",
264                         class_name="g-0",
265                         children=[
266                             self._add_navbar()
267                         ]
268                     ),
269                     dbc.Row(
270                         id="row-main",
271                         class_name="g-0",
272                         children=[
273                             self._add_ctrl_col(),
274                             self._add_plotting_col()
275                         ]
276                     )
277                 ]
278             )
279         else:
280             return html.Div(
281                 id="div-main-error",
282                 children=[
283                     dbc.Alert(
284                         [
285                             "An Error Occured"
286                         ],
287                         color="danger"
288                     )
289                 ]
290             )
291
292     def _add_navbar(self):
293         """Add nav element with navigation panel. It is placed on the top.
294
295         :returns: Navigation bar.
296         :rtype: dbc.NavbarSimple
297         """
298
299         return dbc.NavbarSimple(
300             id="navbarsimple-main",
301             children=[
302                 dbc.NavItem(
303                     dbc.NavLink(
304                         C.NEWS_TITLE,
305                         disabled=True,
306                         external_link=True,
307                         href="#"
308                     )
309                 )
310             ],
311             brand=C.BRAND,
312             brand_href="/",
313             brand_external_link=True,
314             class_name="p-2",
315             fluid=True
316         )
317
318     def _add_ctrl_col(self) -> dbc.Col:
319         """Add column with control panel. It is placed on the left side.
320
321         :returns: Column with the control panel.
322         :rtype: dbc.Col
323         """
324         return dbc.Col([
325             html.Div(
326                 children=self._add_ctrl_panel(),
327                 className="sticky-top"
328             )
329         ])
330
331     def _add_plotting_col(self) -> dbc.Col:
332         """Add column with tables. It is placed on the right side.
333
334         :returns: Column with tables.
335         :rtype: dbc.Col
336         """
337         return dbc.Col(
338             id="col-plotting-area",
339             children=[
340                 dbc.Spinner(
341                     children=[
342                         dbc.Row(
343                             id="plotting-area",
344                             class_name="g-0 p-0",
345                             children=[
346                                 C.PLACEHOLDER
347                             ]
348                         )
349                     ]
350                 )
351             ],
352             width=9
353         )
354
355     def _add_ctrl_panel(self) -> list:
356         """Add control panel.
357
358         :returns: Control panel.
359         :rtype: list
360         """
361         return [
362             dbc.Row(
363                 class_name="g-0 p-1",
364                 children=[
365                     dbc.ButtonGroup(
366                         id="bg-time-period",
367                         children=[
368                             dbc.Button(
369                                 id="period-last",
370                                 children="Last Run",
371                                 className="me-1",
372                                 outline=True,
373                                 color="info"
374                             ),
375                             dbc.Button(
376                                 id="period-short",
377                                 children=f"Last {C.NEWS_SHORT} Runs",
378                                 className="me-1",
379                                 outline=True,
380                                 active=True,
381                                 color="info"
382                             ),
383                             dbc.Button(
384                                 id="period-long",
385                                 children="All Runs",
386                                 className="me-1",
387                                 outline=True,
388                                 color="info"
389                             )
390                         ]
391                     )
392                 ]
393             )
394         ]
395
396     def _get_plotting_area(
397             self,
398             period: int,
399             url: str
400         ) -> list:
401         """Generate the plotting area with all its content.
402
403         :param period: The time period for summary tables.
404         :param url: URL to be displayed in the modal window.
405         :type period: int
406         :type url: str
407         :returns: The content of the plotting area.
408         :rtype: list
409         """
410         return [
411             dbc.Row(
412                 id="row-table",
413                 class_name="g-0 p-1",
414                 children=table_summary(self._data, self._jobs, period)
415             ),
416             dbc.Row(
417                 [
418                     dbc.Col([html.Div(
419                         [
420                             dbc.Button(
421                                 id="plot-btn-url",
422                                 children="Show URL",
423                                 class_name="me-1",
424                                 color="info",
425                                 style={
426                                     "text-transform": "none",
427                                     "padding": "0rem 1rem"
428                                 }
429                             ),
430                             dbc.Modal(
431                                 [
432                                     dbc.ModalHeader(dbc.ModalTitle("URL")),
433                                     dbc.ModalBody(url)
434                                 ],
435                                 id="plot-mod-url",
436                                 size="xl",
437                                 is_open=False,
438                                 scrollable=True
439                             )
440                         ],
441                         className=\
442                             "d-grid gap-0 d-md-flex justify-content-md-end"
443                     )])
444                 ],
445                 class_name="g-0 p-0"
446             )
447         ]
448
449     def callbacks(self, app):
450         """Callbacks for the whole application.
451
452         :param app: The application.
453         :type app: Flask
454         """
455
456         @app.callback(
457             Output("plotting-area", "children"),
458             Output("period-last", "active"),
459             Output("period-short", "active"),
460             Output("period-long", "active"),
461             Input("url", "href"),
462             Input("period-last", "n_clicks"),
463             Input("period-short", "n_clicks"),
464             Input("period-long", "n_clicks")
465         )
466         def _update_application(href: str, *_) -> tuple:
467             """Update the application when the event is detected.
468
469             :returns: New values for web page elements.
470             :rtype: tuple
471             """
472
473             periods = {
474                 "period-last": C.NEWS_LAST,
475                 "period-short": C.NEWS_SHORT,
476                 "period-long": C.NEWS_LONG
477             }
478             actives = {
479                 "period-last": (True, False, False),
480                 "period-short": (False, True, False),
481                 "period-long": (False, False, True)
482             }
483
484             # Parse the url:
485             parsed_url = url_decode(href)
486             if parsed_url:
487                 url_params = parsed_url["params"]
488             else:
489                 url_params = None
490
491             trigger_id = callback_context.triggered[0]["prop_id"].split(".")[0]
492             if trigger_id == "url" and url_params:
493                 trigger_id = url_params.get("period", list())[0]
494
495             ret_val = [
496                 self._get_plotting_area(
497                     periods.get(trigger_id, self._default_period),
498                     gen_new_url(parsed_url, {"period": trigger_id})
499                 )
500             ]
501             ret_val.extend(actives.get(trigger_id, self._default_active))
502             return ret_val
503
504         @app.callback(
505             Output("plot-mod-url", "is_open"),
506             [Input("plot-btn-url", "n_clicks")],
507             [State("plot-mod-url", "is_open")],
508         )
509         def toggle_plot_mod_url(n, is_open):
510             """Toggle the modal window with url.
511             """
512             if n:
513                 return not is_open
514             return is_open