-# Copyright (c) 2017 Cisco and/or its affiliates.
+# Copyright (c) 2018 Cisco and/or its affiliates.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at:
import pandas as pd
from string import replace
-from math import isnan
+from collections import OrderedDict
+from numpy import nan, isnan
from xml.etree import ElementTree as ET
from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers, split_outliers
+from utils import mean, stdev, relative_change, remove_outliers,\
+ split_outliers, classify_anomalies
def generate_tables(spec, data):
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
# Prepare the header of the tables
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
data = input_data.merge_data(data)
data.sort_index(inplace=True)
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
suites = input_data.filter_data(table, data_set="suites")
suites = input_data.merge_data(suites)
return None
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table)
# Prepare the header of the tables
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
- header = ["Test case",
- "{0} Throughput [Mpps]".format(table["reference"]["title"]),
- "{0} stdev [Mpps]".format(table["reference"]["title"]),
- "{0} Throughput [Mpps]".format(table["compare"]["title"]),
- "{0} stdev [Mpps]".format(table["compare"]["title"]),
- "Change [%]"]
+ header = ["Test case", ]
+
+ history = table.get("history", None)
+ if history:
+ for item in history:
+ header.extend(
+ ["{0} Throughput [Mpps]".format(item["title"]),
+ "{0} Stdev [Mpps]".format(item["title"])])
+ header.extend(
+ ["{0} Throughput [Mpps]".format(table["reference"]["title"]),
+ "{0} Stdev [Mpps]".format(table["reference"]["title"]),
+ "{0} Throughput [Mpps]".format(table["compare"]["title"]),
+ "{0} Stdev [Mpps]".format(table["compare"]["title"]),
+ "Change [%]"])
header_str = ",".join(header) + "\n"
except (AttributeError, KeyError) as err:
logging.error("The model is invalid, missing parameter: {0}".
pass
except TypeError:
tbl_dict.pop(tst_name, None)
+ if history:
+ for item in history:
+ for job, builds in item["data"].items():
+ for build in builds:
+ for tst_name, tst_data in data[job][str(build)].iteritems():
+ if tbl_dict.get(tst_name, None) is None:
+ continue
+ if tbl_dict[tst_name].get("history", None) is None:
+ tbl_dict[tst_name]["history"] = OrderedDict()
+ if tbl_dict[tst_name]["history"].get(item["title"],
+ None) is None:
+ tbl_dict[tst_name]["history"][item["title"]] = \
+ list()
+ try:
+ tbl_dict[tst_name]["history"][item["title"]].\
+ append(tst_data["throughput"]["value"])
+ except (TypeError, KeyError):
+ pass
tbl_lst = list()
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
+ if history:
+ if tbl_dict[tst_name].get("history", None) is not None:
+ for hist_data in tbl_dict[tst_name]["history"].values():
+ if hist_data:
+ data_t = remove_outliers(
+ hist_data, outlier_const=table["outlier-const"])
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ else:
+ item.extend([None, None])
+ else:
+ item.extend([None, None])
+ else:
+ item.extend([None, None])
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- outlier_constant=table["outlier-const"])
+ outlier_const=table["outlier-const"])
# TODO: Specify window size.
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ else:
+ item.extend([None, None])
else:
item.extend([None, None])
if tbl_dict[tst_name]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- outlier_constant=table["outlier-const"])
+ outlier_const=table["outlier-const"])
# TODO: Specify window size.
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ else:
+ item.extend([None, None])
else:
item.extend([None, None])
- if item[1] is not None and item[3] is not None:
- item.append(int(relative_change(float(item[1]), float(item[3]))))
- if len(item) == 6:
+ if item[-4] is not None and item[-2] is not None and item[-4] != 0:
+ item.append(int(relative_change(float(item[-4]), float(item[-2]))))
+ if len(item) == len(header):
tbl_lst.append(item)
# Sort the table according to the relative change
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
outlier_const=table["outlier-const"])
# TODO: Specify window size.
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ else:
+ item.extend([None, None])
else:
item.extend([None, None])
if tbl_dict[tst_name]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
outlier_const=table["outlier-const"])
# TODO: Specify window size.
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
+ else:
+ item.extend([None, None])
else:
item.extend([None, None])
if item[1] is not None and item[3] is not None and item[1] != 0:
format(table.get("title", "")))
# Transform the data
+ logging.info(" Creating the data set for the {0} '{1}'.".
+ format(table.get("type", ""), table.get("title", "")))
data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
header = ["Test Case",
- "Throughput Trend [Mpps]",
- "Trend Compliance",
- "Top Anomaly [Mpps]",
- "Change [%]",
- "Outliers [Number]"
+ "Trend [Mpps]",
+ "Short-Term Change [%]",
+ "Long-Term Change [%]",
+ "Regressions [#]",
+ "Progressions [#]",
+ "Outliers [#]"
]
header_str = ",".join(header) + "\n"
for job, builds in table["data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
+ if tst_name.lower() in table["ignore-list"]:
+ continue
if tbl_dict.get(tst_name, None) is None:
name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
"-".join(tst_data["name"].
split("-")[1:]))
tbl_dict[tst_name] = {"name": name,
- "data": dict()}
+ "data": OrderedDict()}
try:
tbl_dict[tst_name]["data"][str(build)] = \
tst_data["result"]["throughput"]
tbl_lst = list()
for tst_name in tbl_dict.keys():
- if len(tbl_dict[tst_name]["data"]) > 2:
-
- pd_data = pd.Series(tbl_dict[tst_name]["data"])
- win_size = pd_data.size \
- if pd_data.size < table["window"] else table["window"]
- # Test name:
- name = tbl_dict[tst_name]["name"]
-
- median = pd_data.rolling(window=win_size, min_periods=2).median()
- trimmed_data, _ = split_outliers(pd_data, outlier_const=1.5,
- window=win_size)
- stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
-
- rel_change_lst = [None, ]
- classification_lst = [None, ]
- median_lst = [None, ]
- sample_lst = [None, ]
- first = True
- for build_nr, value in pd_data.iteritems():
- if first:
- first = False
- continue
- # Relative changes list:
- if not isnan(value) \
- and not isnan(median[build_nr]) \
- and median[build_nr] != 0:
- rel_change_lst.append(round(
- relative_change(float(median[build_nr]), float(value)),
- 2))
- else:
- rel_change_lst.append(None)
-
- # Classification list:
- if isnan(trimmed_data[build_nr]) \
- or isnan(median[build_nr]) \
- or isnan(stdev_t[build_nr]) \
- or isnan(value):
- classification_lst.append("outlier")
- elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
- classification_lst.append("regression")
- elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
- classification_lst.append("progression")
- else:
- classification_lst.append("normal")
- sample_lst.append(value)
- median_lst.append(median[build_nr])
-
- last_idx = len(classification_lst) - 1
- first_idx = last_idx - int(table["evaluated-window"])
- if first_idx < 0:
- first_idx = 0
-
- nr_outliers = 0
- consecutive_outliers = 0
- failure = False
- for item in classification_lst[first_idx:]:
- if item == "outlier":
- nr_outliers += 1
- consecutive_outliers += 1
- if consecutive_outliers == 3:
- failure = True
- else:
- consecutive_outliers = 0
-
- if failure:
- classification = "failure"
- elif "regression" in classification_lst[first_idx:]:
- classification = "regression"
- elif "progression" in classification_lst[first_idx:]:
- classification = "progression"
- else:
- classification = "normal"
+ if len(tbl_dict[tst_name]["data"]) < 3:
+ continue
+
+ pd_data = pd.Series(tbl_dict[tst_name]["data"])
+ data_t, _ = split_outliers(pd_data, outlier_const=1.5,
+ window=table["window"])
+ last_key = data_t.keys()[-1]
+ win_size = min(data_t.size, table["window"])
+ win_first_idx = data_t.size - win_size
+ key_14 = data_t.keys()[win_first_idx]
+ long_win_size = min(data_t.size, table["long-trend-window"])
+ median_t = data_t.rolling(window=win_size, min_periods=2).median()
+ median_first_idx = median_t.size - long_win_size
+ try:
+ max_median = max(
+ [x for x in median_t.values[median_first_idx:-win_size]
+ if not isnan(x)])
+ except ValueError:
+ max_median = nan
+ try:
+ last_median_t = median_t[last_key]
+ except KeyError:
+ last_median_t = nan
+ try:
+ median_t_14 = median_t[key_14]
+ except KeyError:
+ median_t_14 = nan
+
+ if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0:
+ rel_change_last = nan
+ else:
+ rel_change_last = round(
+ ((last_median_t - median_t_14) / median_t_14) * 100, 2)
+
+ if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
+ rel_change_long = nan
+ else:
+ rel_change_long = round(
+ ((last_median_t - max_median) / max_median) * 100, 2)
+
+ # Classification list:
+ classification_lst = classify_anomalies(data_t, window=14)
+
+ if classification_lst:
+ tbl_lst.append(
+ [tbl_dict[tst_name]["name"],
+ '-' if isnan(last_median_t) else
+ round(last_median_t / 1000000, 2),
+ '-' if isnan(rel_change_last) else rel_change_last,
+ '-' if isnan(rel_change_long) else rel_change_long,
+ classification_lst[win_first_idx:].count("regression"),
+ classification_lst[win_first_idx:].count("progression"),
+ classification_lst[win_first_idx:].count("outlier")])
+
+ tbl_lst.sort(key=lambda rel: rel[0])
- if classification == "normal":
- index = len(classification_lst) - 1
- else:
- tmp_classification = "outlier" if classification == "failure" \
- else classification
- for idx in range(first_idx, len(classification_lst)):
- if classification_lst[idx] == tmp_classification:
- index = idx
- break
- for idx in range(index+1, len(classification_lst)):
- if classification_lst[idx] == tmp_classification:
- if rel_change_lst[idx] > rel_change_lst[index]:
- index = idx
-
- # if "regression" in classification_lst[first_idx:]:
- # classification = "regression"
- # elif "outlier" in classification_lst[first_idx:]:
- # classification = "outlier"
- # elif "progression" in classification_lst[first_idx:]:
- # classification = "progression"
- # elif "normal" in classification_lst[first_idx:]:
- # classification = "normal"
- # else:
- # classification = None
- #
- # nr_outliers = 0
- # consecutive_outliers = 0
- # failure = False
- # for item in classification_lst[first_idx:]:
- # if item == "outlier":
- # nr_outliers += 1
- # consecutive_outliers += 1
- # if consecutive_outliers == 3:
- # failure = True
- # else:
- # consecutive_outliers = 0
- #
- # idx = len(classification_lst) - 1
- # while idx:
- # if classification_lst[idx] == classification:
- # break
- # idx -= 1
- #
- # if failure:
- # classification = "failure"
- # elif classification == "outlier":
- # classification = "normal"
-
- trend = round(float(median_lst[-1]) / 1000000, 2) \
- if not isnan(median_lst[-1]) else ''
- sample = round(float(sample_lst[index]) / 1000000, 2) \
- if not isnan(sample_lst[index]) else ''
- rel_change = rel_change_lst[index] \
- if rel_change_lst[index] is not None else ''
- tbl_lst.append([name,
- trend,
- classification,
- '-' if classification == "normal" else sample,
- '-' if classification == "normal" else rel_change,
- nr_outliers])
-
- # Sort the table according to the classification
tbl_sorted = list()
- for classification in ("failure", "regression", "progression", "normal"):
- tbl_tmp = [item for item in tbl_lst if item[2] == classification]
- tbl_tmp.sort(key=lambda rel: rel[0])
- tbl_sorted.extend(tbl_tmp)
+ for nrr in range(table["window"], -1, -1):
+ tbl_reg = [item for item in tbl_lst if item[4] == nrr]
+ for nrp in range(table["window"], -1, -1):
+ tbl_pro = [item for item in tbl_reg if item[5] == nrp]
+ for nro in range(table["window"], -1, -1):
+ tbl_out = [item for item in tbl_pro if item[6] == nro]
+ tbl_out.sort(key=lambda rel: rel[2])
+ tbl_sorted.extend(tbl_out)
file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
th.text = item
# Rows:
+ colors = {"regression": ("#ffcccc", "#ff9999"),
+ "progression": ("#c6ecc6", "#9fdf9f"),
+ "outlier": ("#e6e6e6", "#cccccc"),
+ "normal": ("#e9f1fb", "#d4e4f7")}
for r_idx, row in enumerate(csv_lst[1:]):
- background = "#D4E4F7" if r_idx % 2 else "white"
+ if int(row[4]):
+ color = "regression"
+ elif int(row[5]):
+ color = "progression"
+ elif int(row[6]):
+ color = "outlier"
+ else:
+ color = "normal"
+ background = colors[color][r_idx % 2]
tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
# Columns:
if "64b" in item:
anchor += "64b-"
elif "78b" in item:
- anchor += "78b"
+ anchor += "78b-"
elif "imix" in item:
anchor += "imix-"
elif "9000b" in item:
ref = ET.SubElement(td, "a", attrib=dict(href=url))
ref.text = item
- if c_idx == 2:
- if item == "regression":
- td.set("bgcolor", "#eca1a6")
- elif item == "failure":
- td.set("bgcolor", "#d6cbd3")
- elif item == "progression":
- td.set("bgcolor", "#bdcebe")
if c_idx > 0:
td.text = item
Code Review / csit.git / blobdiff