import plotly.offline as ploff
import plotly.graph_objs as plgo
import plotly.exceptions as plerr
-import numpy as np
-import pandas as pd
from collections import OrderedDict
from datetime import datetime
-from utils import split_outliers, archive_input_data, execute_command, Worker
+from utils import archive_input_data, execute_command, \
+ classify_anomalies, Worker
# Command to build the html format of the report
return ret_code
-def _evaluate_results(trimmed_data, window=10):
- """Evaluates if the sample value is regress, normal or progress compared to
- previous data within the window.
- We use the intervals defined as:
- - regress: less than trimmed moving median - 3 * stdev
- - normal: between trimmed moving median - 3 * stdev and median + 3 * stdev
- - progress: more than trimmed moving median + 3 * stdev
- where stdev is trimmed moving standard deviation.
-
- :param trimmed_data: Full data set with the outliers replaced by nan.
- :param window: Window size used to calculate moving average and moving stdev.
- :type trimmed_data: pandas.Series
- :type window: int
- :returns: Evaluated results.
- :rtype: list
- """
-
- if len(trimmed_data) > 2:
- win_size = trimmed_data.size if trimmed_data.size < window else window
- results = [0.66, ]
- tmm = trimmed_data.rolling(window=win_size, min_periods=2).median()
- tmstd = trimmed_data.rolling(window=win_size, min_periods=2).std()
-
- first = True
- for build_nr, value in trimmed_data.iteritems():
- if first:
- first = False
- continue
- if (np.isnan(value)
- or np.isnan(tmm[build_nr])
- or np.isnan(tmstd[build_nr])):
- results.append(0.0)
- elif value < (tmm[build_nr] - 3 * tmstd[build_nr]):
- results.append(0.33)
- elif value > (tmm[build_nr] + 3 * tmstd[build_nr]):
- results.append(1.0)
- else:
- results.append(0.66)
- else:
- results = [0.0, ]
- try:
- tmm = np.median(trimmed_data)
- tmstd = np.std(trimmed_data)
- if trimmed_data.values[-1] < (tmm - 3 * tmstd):
- results.append(0.33)
- elif (tmm - 3 * tmstd) <= trimmed_data.values[-1] <= (
- tmm + 3 * tmstd):
- results.append(0.66)
- else:
- results.append(1.0)
- except TypeError:
- results.append(None)
- return results
-
-
-def _generate_trending_traces(in_data, build_info, moving_win_size=10,
+def _generate_trending_traces(in_data, job_name, build_info,
show_trend_line=True, name="", color=""):
"""Generate the trending traces:
- samples,
- - trimmed moving median (trending line)
- outliers, regress, progress
+ - average of normal samples (trending line)
:param in_data: Full data set.
+ :param job_name: The name of job which generated the data.
:param build_info: Information about the builds.
- :param moving_win_size: Window size.
:param show_trend_line: Show moving median (trending plot).
:param name: Name of the plot
:param color: Name of the color for the plot.
:type in_data: OrderedDict
+ :type job_name: str
:type build_info: dict
- :type moving_win_size: int
:type show_trend_line: bool
:type name: str
:type color: str
hover_text = list()
xaxis = list()
for idx in data_x:
- hover_text.append("vpp-ref: {0}<br>csit-ref: mrr-daily-build-{1}".
- format(build_info[str(idx)][1].rsplit('~', 1)[0],
- idx))
- date = build_info[str(idx)][0]
+ date = build_info[job_name][str(idx)][0]
+ hover_str = ("date: {0}<br>"
+ "value: {1:,}<br>"
+ "{2}-ref: {3}<br>"
+ "csit-ref: mrr-{4}-build-{5}")
+ if "dpdk" in job_name:
+ hover_text.append(hover_str.format(
+ date,
+ int(in_data[idx].avg),
+ "dpdk",
+ build_info[job_name][str(idx)][1].
+ rsplit('~', 1)[0],
+ "weekly",
+ idx))
+ elif "vpp" in job_name:
+ hover_text.append(hover_str.format(
+ date,
+ int(in_data[idx].avg),
+ "vpp",
+ build_info[job_name][str(idx)][1].
+ rsplit('~', 1)[0],
+ "daily",
+ idx))
+
xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
int(date[9:11]), int(date[12:])))
- data_pd = pd.Series(data_y, index=xaxis)
-
- t_data, outliers = split_outliers(data_pd, outlier_const=1.5,
- window=moving_win_size)
- results = _evaluate_results(t_data, window=moving_win_size)
-
- anomalies = pd.Series()
- anomalies_res = list()
- for idx, item in enumerate(data_pd.items()):
- item_pd = pd.Series([item[1], ], index=[item[0], ])
- if item[0] in outliers.keys():
- anomalies = anomalies.append(item_pd)
- anomalies_res.append(0.0)
- elif results[idx] in (0.33, 1.0):
- anomalies = anomalies.append(item_pd)
- anomalies_res.append(results[idx])
- anomalies_res.extend([0.0, 0.33, 0.66, 1.0])
+ data_pd = OrderedDict()
+ for key, value in zip(xaxis, data_y):
+ data_pd[key] = value
+
+ anomaly_classification, avgs = classify_anomalies(data_pd)
+
+ anomalies = OrderedDict()
+ anomalies_colors = list()
+ anomalies_avgs = list()
+ anomaly_color = {
+ "regression": 0.0,
+ "normal": 0.5,
+ "progression": 1.0
+ }
+ if anomaly_classification:
+ for idx, (key, value) in enumerate(data_pd.iteritems()):
+ if anomaly_classification[idx] in \
+ ("outlier", "regression", "progression"):
+ anomalies[key] = value
+ anomalies_colors.append(
+ anomaly_color[anomaly_classification[idx]])
+ anomalies_avgs.append(avgs[idx])
+ anomalies_colors.extend([0.0, 0.5, 1.0])
# Create traces
- color_scale = [[0.00, "grey"],
- [0.25, "grey"],
- [0.25, "red"],
- [0.50, "red"],
- [0.50, "white"],
- [0.75, "white"],
- [0.75, "green"],
- [1.00, "green"]]
trace_samples = plgo.Scatter(
x=xaxis,
- y=data_y,
+ y=[y.avg for y in data_y],
mode='markers',
line={
"width": 1
},
+ showlegend=True,
legendgroup=name,
- name="{name}-thput".format(name=name),
+ name="{name}".format(name=name),
marker={
"size": 5,
"color": color,
"symbol": "circle",
},
text=hover_text,
- hoverinfo="x+y+text+name"
+ hoverinfo="text"
)
traces = [trace_samples, ]
+ if show_trend_line:
+ trace_trend = plgo.Scatter(
+ x=xaxis,
+ y=avgs,
+ mode='lines',
+ line={
+ "shape": "linear",
+ "width": 1,
+ "color": color,
+ },
+ showlegend=False,
+ legendgroup=name,
+ name='{name}'.format(name=name),
+ text=["trend: {0:,}".format(int(avg)) for avg in avgs],
+ hoverinfo="text+name"
+ )
+ traces.append(trace_trend)
+
trace_anomalies = plgo.Scatter(
x=anomalies.keys(),
- y=anomalies.values,
+ y=anomalies_avgs,
mode='markers',
hoverinfo="none",
- showlegend=True,
+ showlegend=False,
legendgroup=name,
name="{name}-anomalies".format(name=name),
marker={
"size": 15,
"symbol": "circle-open",
- "color": anomalies_res,
- "colorscale": color_scale,
+ "color": anomalies_colors,
+ "colorscale": [[0.00, "red"],
+ [0.33, "red"],
+ [0.33, "white"],
+ [0.66, "white"],
+ [0.66, "green"],
+ [1.00, "green"]],
"showscale": True,
"line": {
"width": 2
"size": 14
},
"tickmode": 'array',
- "tickvals": [0.125, 0.375, 0.625, 0.875],
- "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
+ "tickvals": [0.167, 0.500, 0.833],
+ "ticktext": ["Regression", "Normal", "Progression"],
"ticks": "",
"ticklen": 0,
"tickangle": -90,
)
traces.append(trace_anomalies)
- if show_trend_line:
- data_trend = t_data.rolling(window=moving_win_size,
- min_periods=2).median()
- trace_trend = plgo.Scatter(
- x=data_trend.keys(),
- y=data_trend.tolist(),
- mode='lines',
- line={
- "shape": "spline",
- "width": 1,
- "color": color,
- },
- legendgroup=name,
- name='{name}-trend'.format(name=name)
- )
- traces.append(trace_trend)
-
- return traces, results[-1]
+ if anomaly_classification:
+ return traces, anomaly_classification[-1]
+ else:
+ return traces, None
def _generate_all_charts(spec, input_data):
logs.append(("INFO", " Generating the chart '{0}' ...".
format(graph.get("title", ""))))
- job_name = spec.cpta["data"].keys()[0]
+ job_name = graph["data"].keys()[0]
csv_tbl = list()
res = list()
return
chart_data = dict()
- for job in data:
- for index, bld in job.items():
+ for job, job_data in data.iteritems():
+ if job != job_name:
+ continue
+ for index, bld in job_data.items():
for test_name, test in bld.items():
if chart_data.get(test_name, None) is None:
chart_data[test_name] = OrderedDict()
try:
chart_data[test_name][int(index)] = \
- test["result"]["throughput"]
+ test["result"]["receive-rate"]
except (KeyError, TypeError):
pass
# Add items to the csv table:
for tst_name, tst_data in chart_data.items():
tst_lst = list()
- for bld in builds_lst:
+ for bld in builds_dict[job_name]:
itm = tst_data.get(int(bld), '')
+ if not isinstance(itm, str):
+ itm = itm.avg
tst_lst.append(str(itm))
csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
# Generate traces:
traces = list()
- win_size = 14
index = 0
for test_name, test_data in chart_data.items():
if not test_data:
test_name = test_name.split('.')[-1]
trace, rslt = _generate_trending_traces(
test_data,
+ job_name=job_name,
build_info=build_info,
- moving_win_size=win_size,
- name='-'.join(test_name.split('-')[3:-1]),
+ name='-'.join(test_name.split('-')[2:-1]),
color=COLORS[index])
traces.extend(trace)
res.append(rslt)
except plerr.PlotlyEmptyDataError:
logs.append(("WARNING", "No data for the plot. Skipped."))
- logging.info(" Done.")
-
data_out = {
+ "job_name": job_name,
"csv_table": csv_tbl,
"results": res,
"logs": logs
}
data_q.put(data_out)
- job_name = spec.cpta["data"].keys()[0]
-
- builds_lst = list()
- for build in spec.input["builds"][job_name]:
- status = build["status"]
- if status != "failed" and status != "not found":
- builds_lst.append(str(build["build"]))
-
- # Get "build ID": "date" dict:
- build_info = OrderedDict()
- for build in builds_lst:
- try:
- build_info[build] = (
- input_data.metadata(job_name, build)["generated"][:14],
- input_data.metadata(job_name, build)["version"]
+ builds_dict = dict()
+ for job in spec.input["builds"].keys():
+ if builds_dict.get(job, None) is None:
+ builds_dict[job] = list()
+ for build in spec.input["builds"][job]:
+ status = build["status"]
+ if status != "failed" and status != "not found":
+ builds_dict[job].append(str(build["build"]))
+
+ # Create "build ID": "date" dict:
+ build_info = dict()
+ for job_name, job_data in builds_dict.items():
+ if build_info.get(job_name, None) is None:
+ build_info[job_name] = OrderedDict()
+ for build in job_data:
+ build_info[job_name][build] = (
+ input_data.metadata(job_name, build).get("generated", ""),
+ input_data.metadata(job_name, build).get("version", "")
)
- except KeyError:
- build_info[build] = ("", "")
work_queue = multiprocessing.JoinableQueue()
manager = multiprocessing.Manager()
work_queue.put((chart, ))
work_queue.join()
- results = list()
+ anomaly_classifications = list()
# Create the header:
- csv_table = list()
- header = "Build Number:," + ",".join(builds_lst) + '\n'
- csv_table.append(header)
- build_dates = [x[0] for x in build_info.values()]
- header = "Build Date:," + ",".join(build_dates) + '\n'
- csv_table.append(header)
- vpp_versions = [x[1] for x in build_info.values()]
- header = "VPP Version:," + ",".join(vpp_versions) + '\n'
- csv_table.append(header)
+ csv_tables = dict()
+ for job_name in builds_dict.keys():
+ if csv_tables.get(job_name, None) is None:
+ csv_tables[job_name] = list()
+ header = "Build Number:," + ",".join(builds_dict[job_name]) + '\n'
+ csv_tables[job_name].append(header)
+ build_dates = [x[0] for x in build_info[job_name].values()]
+ header = "Build Date:," + ",".join(build_dates) + '\n'
+ csv_tables[job_name].append(header)
+ versions = [x[1] for x in build_info[job_name].values()]
+ header = "Version:," + ",".join(versions) + '\n'
+ csv_tables[job_name].append(header)
while not data_queue.empty():
result = data_queue.get()
- results.extend(result["results"])
- csv_table.extend(result["csv_table"])
+ anomaly_classifications.extend(result["results"])
+ csv_tables[result["job_name"]].extend(result["csv_table"])
for item in result["logs"]:
if item[0] == "INFO":
worker.join()
# Write the tables:
- file_name = spec.cpta["output-file"] + "-trending"
- with open("{0}.csv".format(file_name), 'w') as file_handler:
- file_handler.writelines(csv_table)
-
- txt_table = None
- with open("{0}.csv".format(file_name), 'rb') as csv_file:
- csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
- line_nr = 0
- for row in csv_content:
- if txt_table is None:
- txt_table = prettytable.PrettyTable(row)
- else:
- if line_nr > 1:
- for idx, item in enumerate(row):
- try:
- row[idx] = str(round(float(item) / 1000000, 2))
- except ValueError:
- pass
- try:
- txt_table.add_row(row)
- except Exception as err:
- logging.warning("Error occurred while generating TXT table:"
- "\n{0}".format(err))
- line_nr += 1
- txt_table.align["Build Number:"] = "l"
- with open("{0}.txt".format(file_name), "w") as txt_file:
- txt_file.write(str(txt_table))
+ for job_name, csv_table in csv_tables.items():
+ file_name = spec.cpta["output-file"] + "-" + job_name + "-trending"
+ with open("{0}.csv".format(file_name), 'w') as file_handler:
+ file_handler.writelines(csv_table)
+
+ txt_table = None
+ with open("{0}.csv".format(file_name), 'rb') as csv_file:
+ csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+ line_nr = 0
+ for row in csv_content:
+ if txt_table is None:
+ txt_table = prettytable.PrettyTable(row)
+ else:
+ if line_nr > 1:
+ for idx, item in enumerate(row):
+ try:
+ row[idx] = str(round(float(item) / 1000000, 2))
+ except ValueError:
+ pass
+ try:
+ txt_table.add_row(row)
+ except Exception as err:
+ logging.warning("Error occurred while generating TXT "
+ "table:\n{0}".format(err))
+ line_nr += 1
+ txt_table.align["Build Number:"] = "l"
+ with open("{0}.txt".format(file_name), "w") as txt_file:
+ txt_file.write(str(txt_table))
# Evaluate result:
- result = "PASS"
- for item in results:
- if item is None:
- result = "FAIL"
- break
- if item == 0.66 and result == "PASS":
- result = "PASS"
- elif item == 0.33 or item == 0.0:
- result = "FAIL"
-
- logging.info("Partial results: {0}".format(results))
+ if anomaly_classifications:
+ result = "PASS"
+ for classification in anomaly_classifications:
+ if classification == "regression" or classification == "outlier":
+ result = "FAIL"
+ break
+ else:
+ result = "FAIL"
+
+ logging.info("Partial results: {0}".format(anomaly_classifications))
logging.info("Result: {0}".format(result))
return result
Code Review / csit.git / blobdiff