import logging
import csv
import prettytable
+import pandas as pd
from string import replace
+from math import isnan
+from xml.etree import ElementTree as ET
from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers
+from utils import mean, stdev, relative_change, remove_outliers, split_outliers
def generate_tables(spec, data):
format(table.get("title", "")))
# Transform the data
- data = input_data.filter_data(table)
+ data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
try:
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
if tbl_dict[tst_name]["ref-data"]:
- item.append(round(mean(remove_outliers(
- tbl_dict[tst_name]["ref-data"], 1.5)) / 1000000, 2))
- item.append(round(stdev(remove_outliers(
- tbl_dict[tst_name]["ref-data"], 1.5)) / 1000000, 2))
+ 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))
else:
item.extend([None, None])
if tbl_dict[tst_name]["cmp-data"]:
- item.append(round(mean(remove_outliers(
- tbl_dict[tst_name]["cmp-data"], 1.5)) / 1000000, 2))
- item.append(round(stdev(remove_outliers(
- tbl_dict[tst_name]["cmp-data"], 1.5)) / 1000000, 2))
+ 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))
else:
item.extend([None, None])
if item[1] is not None and item[3] is not None:
table["output-file-ext"])
]
for file_name in tbl_names:
- logging.info(" Writing file: '{}'".format(file_name))
+ logging.info(" Writing file: '{0}'".format(file_name))
with open(file_name, "w") as file_handler:
file_handler.write(header_str)
for test in tbl_lst:
for i, txt_name in enumerate(tbl_names_txt):
txt_table = None
- logging.info(" Writing file: '{}'".format(txt_name))
+ logging.info(" Writing file: '{0}'".format(txt_name))
with open(tbl_names[i], 'rb') as csv_file:
csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
for row in csv_content:
txt_table = prettytable.PrettyTable(row)
else:
txt_table.add_row(row)
+ txt_table.align["Test case"] = "l"
with open(txt_name, "w") as txt_file:
txt_file.write(str(txt_table))
output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"],
table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(output_file))
+ logging.info(" Writing file: '{0}'".format(output_file))
with open(output_file, "w") as out_file:
out_file.write(header_str)
for i, line in enumerate(lines[1:]):
output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"],
table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(output_file))
+ logging.info(" Writing file: '{0}'".format(output_file))
with open(output_file, "w") as out_file:
out_file.write(header_str)
for i, line in enumerate(lines[-1:0:-1]):
output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"],
table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(output_file))
+ logging.info(" Writing file: '{0}'".format(output_file))
with open(output_file, "w") as out_file:
out_file.write(header_str)
for i, line in enumerate(lines[1:]):
output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"],
table["output-file-ext"])
- logging.info(" Writing file: '{}'".format(output_file))
+ logging.info(" Writing file: '{0}'".format(output_file))
with open(output_file, "w") as out_file:
out_file.write(header_str)
for i, line in enumerate(lines[-1:0:-1]):
if i == table["nr-of-tests-shown"]:
break
out_file.write(line)
+
+
+def table_performance_comparison_mrr(table, input_data):
+ """Generate the table(s) with algorithm: table_performance_comparison_mrr
+ specified in the specification file.
+
+ :param table: Table to generate.
+ :param input_data: Data to process.
+ :type table: pandas.Series
+ :type input_data: InputData
+ """
+
+ logging.info(" Generating the table {0} ...".
+ format(table.get("title", "")))
+
+ # Transform the data
+ 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_str = ",".join(header) + "\n"
+ except (AttributeError, KeyError) as err:
+ logging.error("The model is invalid, missing parameter: {0}".
+ format(err))
+ return
+
+ # Prepare data to the table:
+ tbl_dict = dict()
+ for job, builds in table["reference"]["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:
+ name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
+ "-".join(tst_data["name"].
+ split("-")[1:]))
+ tbl_dict[tst_name] = {"name": name,
+ "ref-data": list(),
+ "cmp-data": list()}
+ try:
+ tbl_dict[tst_name]["ref-data"].\
+ append(tst_data["result"]["throughput"])
+ except TypeError:
+ pass # No data in output.xml for this test
+
+ for job, builds in table["compare"]["data"].items():
+ for build in builds:
+ for tst_name, tst_data in data[job][str(build)].iteritems():
+ try:
+ tbl_dict[tst_name]["cmp-data"].\
+ append(tst_data["result"]["throughput"])
+ except KeyError:
+ pass
+ except TypeError:
+ tbl_dict.pop(tst_name, None)
+
+ tbl_lst = list()
+ for tst_name in tbl_dict.keys():
+ item = [tbl_dict[tst_name]["name"], ]
+ if tbl_dict[tst_name]["ref-data"]:
+ 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))
+ 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))
+ else:
+ item.extend([None, None])
+ if item[1] is not None and item[3] is not None and item[1] != 0:
+ item.append(int(relative_change(float(item[1]), float(item[3]))))
+ if len(item) == 6:
+ tbl_lst.append(item)
+
+ # Sort the table according to the relative change
+ tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+
+ # Generate tables:
+ # All tests in csv:
+ tbl_names = ["{0}-1t1c-full{1}".format(table["output-file"],
+ table["output-file-ext"]),
+ "{0}-2t2c-full{1}".format(table["output-file"],
+ table["output-file-ext"]),
+ "{0}-4t4c-full{1}".format(table["output-file"],
+ table["output-file-ext"])
+ ]
+ for file_name in tbl_names:
+ logging.info(" Writing file: '{0}'".format(file_name))
+ with open(file_name, "w") as file_handler:
+ file_handler.write(header_str)
+ for test in tbl_lst:
+ if file_name.split("-")[-2] in test[0]: # cores
+ test[0] = "-".join(test[0].split("-")[:-1])
+ file_handler.write(",".join([str(item) for item in test]) +
+ "\n")
+
+ # All tests in txt:
+ tbl_names_txt = ["{0}-1t1c-full.txt".format(table["output-file"]),
+ "{0}-2t2c-full.txt".format(table["output-file"]),
+ "{0}-4t4c-full.txt".format(table["output-file"])
+ ]
+
+ for i, txt_name in enumerate(tbl_names_txt):
+ txt_table = None
+ logging.info(" Writing file: '{0}'".format(txt_name))
+ with open(tbl_names[i], 'rb') as csv_file:
+ csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+ for row in csv_content:
+ if txt_table is None:
+ txt_table = prettytable.PrettyTable(row)
+ else:
+ txt_table.add_row(row)
+ txt_table.align["Test case"] = "l"
+ with open(txt_name, "w") as txt_file:
+ txt_file.write(str(txt_table))
+
+
+def table_performance_trending_dashboard(table, input_data):
+ """Generate the table(s) with algorithm: table_performance_comparison
+ specified in the specification file.
+
+ :param table: Table to generate.
+ :param input_data: Data to process.
+ :type table: pandas.Series
+ :type input_data: InputData
+ """
+
+ logging.info(" Generating the table {0} ...".
+ format(table.get("title", "")))
+
+ # Transform the data
+ data = input_data.filter_data(table, continue_on_error=True)
+
+ # Prepare the header of the tables
+ header = ["Test Case",
+ "Throughput Trend [Mpps]",
+ "Long Trend Compliance",
+ "Trend Compliance",
+ "Top Anomaly [Mpps]",
+ "Change [%]",
+ "Outliers [Number]"
+ ]
+ header_str = ",".join(header) + "\n"
+
+ # Prepare data to the table:
+ tbl_dict = dict()
+ for job, builds in table["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:
+ name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
+ "-".join(tst_data["name"].
+ split("-")[1:]))
+ tbl_dict[tst_name] = {"name": name,
+ "data": dict()}
+ try:
+ tbl_dict[tst_name]["data"][str(build)] = \
+ tst_data["result"]["throughput"]
+ except (TypeError, KeyError):
+ pass # No data in output.xml for this test
+
+ 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 = min(pd_data.size, table["window"])
+ # Test name:
+ name = tbl_dict[tst_name]["name"]
+
+ median = pd_data.rolling(window=win_size, min_periods=2).median()
+ median_idx = pd_data.size - table["long-trend-window"]
+ median_idx = 0 if median_idx < 0 else median_idx
+ max_median = max(median.values[median_idx:])
+ 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 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
+
+ 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 '-'
+ if not isnan(max_median):
+ if not isnan(sample_lst[index]):
+ long_trend_threshold = max_median * \
+ (table["long-trend-threshold"] / 100)
+ if sample_lst[index] < long_trend_threshold:
+ long_trend_classification = "failure"
+ else:
+ long_trend_classification = '-'
+ else:
+ long_trend_classification = "failure"
+ else:
+ long_trend_classification = '-'
+ tbl_lst.append([name,
+ trend,
+ long_trend_classification,
+ 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 long_trend_class in ("failure", '-'):
+ tbl_long = [item for item in tbl_lst if item[2] == long_trend_class]
+ for classification in \
+ ("failure", "regression", "progression", "normal"):
+ tbl_tmp = [item for item in tbl_long if item[3] == classification]
+ tbl_tmp.sort(key=lambda rel: rel[0])
+ tbl_sorted.extend(tbl_tmp)
+
+ file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
+
+ logging.info(" Writing file: '{0}'".format(file_name))
+ with open(file_name, "w") as file_handler:
+ file_handler.write(header_str)
+ for test in tbl_sorted:
+ file_handler.write(",".join([str(item) for item in test]) + '\n')
+
+ txt_file_name = "{0}.txt".format(table["output-file"])
+ txt_table = None
+ logging.info(" Writing file: '{0}'".format(txt_file_name))
+ with open(file_name, 'rb') as csv_file:
+ csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+ for row in csv_content:
+ if txt_table is None:
+ txt_table = prettytable.PrettyTable(row)
+ else:
+ txt_table.add_row(row)
+ txt_table.align["Test case"] = "l"
+ with open(txt_file_name, "w") as txt_file:
+ txt_file.write(str(txt_table))
+
+
+def table_performance_trending_dashboard_html(table, input_data):
+ """Generate the table(s) with algorithm:
+ table_performance_trending_dashboard_html specified in the specification
+ file.
+
+ :param table: Table to generate.
+ :param input_data: Data to process.
+ :type table: pandas.Series
+ :type input_data: InputData
+ """
+
+ logging.info(" Generating the table {0} ...".
+ format(table.get("title", "")))
+
+ try:
+ with open(table["input-file"], 'rb') as csv_file:
+ csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+ csv_lst = [item for item in csv_content]
+ except KeyError:
+ logging.warning("The input file is not defined.")
+ return
+ except csv.Error as err:
+ logging.warning("Not possible to process the file '{0}'.\n{1}".
+ format(table["input-file"], err))
+ return
+
+ # Table:
+ dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
+
+ # Table header:
+ tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7"))
+ for idx, item in enumerate(csv_lst[0]):
+ alignment = "left" if idx == 0 else "center"
+ th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
+ th.text = item
+
+ # Rows:
+ for r_idx, row in enumerate(csv_lst[1:]):
+ background = "#D4E4F7" if r_idx % 2 else "white"
+ tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
+
+ # Columns:
+ for c_idx, item in enumerate(row):
+ alignment = "left" if c_idx == 0 else "center"
+ td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
+ # Name:
+ url = "../trending/"
+ file_name = ""
+ anchor = "#"
+ feature = ""
+ if c_idx == 0:
+ if "memif" in item:
+ file_name = "container_memif.html"
+
+ elif "vhost" in item:
+ if "l2xcbase" in item or "l2bdbasemaclrn" in item:
+ file_name = "vm_vhost_l2.html"
+ elif "ip4base" in item:
+ file_name = "vm_vhost_ip4.html"
+
+ elif "ipsec" in item:
+ file_name = "ipsec.html"
+
+ elif "ethip4lispip" in item or "ethip4vxlan" in item:
+ file_name = "ip4_tunnels.html"
+
+ elif "ip4base" in item or "ip4scale" in item:
+ file_name = "ip4.html"
+ if "iacl" in item or "snat" in item or "cop" in item:
+ feature = "-features"
+
+ elif "ip6base" in item or "ip6scale" in item:
+ file_name = "ip6.html"
+
+ elif "l2xcbase" in item or "l2xcscale" in item \
+ or "l2bdbasemaclrn" in item or "l2bdscale" in item \
+ or "l2dbbasemaclrn" in item or "l2dbscale" in item:
+ file_name = "l2.html"
+ if "iacl" in item:
+ feature = "-features"
+
+ if "x520" in item:
+ anchor += "x520-"
+ elif "x710" in item:
+ anchor += "x710-"
+ elif "xl710" in item:
+ anchor += "xl710-"
+
+ if "64b" in item:
+ anchor += "64b-"
+ elif "78b" in item:
+ anchor += "78b"
+ elif "imix" in item:
+ anchor += "imix-"
+ elif "9000b" in item:
+ anchor += "9000b-"
+ elif "1518" in item:
+ anchor += "1518b-"
+
+ if "1t1c" in item:
+ anchor += "1t1c"
+ elif "2t2c" in item:
+ anchor += "2t2c"
+ elif "4t4c" in item:
+ anchor += "4t4c"
+
+ url = url + file_name + anchor + feature
+
+ ref = ET.SubElement(td, "a", attrib=dict(href=url))
+ ref.text = item
+
+ if c_idx == 3:
+ 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
+
+ try:
+ with open(table["output-file"], 'w') as html_file:
+ logging.info(" Writing file: '{0}'".
+ format(table["output-file"]))
+ html_file.write(".. raw:: html\n\n\t")
+ html_file.write(ET.tostring(dashboard))
+ html_file.write("\n\t<p><br><br></p>\n")
+ except KeyError:
+ logging.warning("The output file is not defined.")
+ return
Code Review / csit.git / blobdiff