from string import replace
from math import isnan
+from collections import OrderedDict
+from numpy import nan
+from xml.etree import ElementTree as ET
from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers, find_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:
- 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"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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"],
- table["outlier-const"])
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
+ 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
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.
+ 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.
+ 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:
+ 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.
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
- header = ["Test case",
- "Thput trend [Mpps]",
- "Change [Mpps]",
- "Change [%]",
- "Anomaly"]
+ header = [" Test Case",
+ "Trend [Mpps]",
+ " Short-Term Change [%]",
+ " Long-Term Change [%]",
+ " Regressions [#]",
+ " Progressions [#]",
+ " Outliers [#]"
+ ]
header_str = ",".join(header) + "\n"
# Prepare data to the table:
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": list()}
+ "data": dict()}
try:
- tbl_dict[tst_name]["data"]. \
- append(tst_data["result"]["throughput"])
+ 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 = pd_data.size \
- if pd_data.size < table["window"] else table["window"]
+ last_key = pd_data.keys()[-1]
+ win_size = min(pd_data.size, table["window"])
+ win_first_idx = pd_data.size - win_size
+ key_14 = pd_data.keys()[win_first_idx]
+ long_win_size = min(pd_data.size, table["long-trend-window"])
+
+ data_t, _ = split_outliers(pd_data, outlier_const=1.5,
+ window=win_size)
+
+ median_t = data_t.rolling(window=win_size, min_periods=2).median()
+ stdev_t = data_t.rolling(window=win_size, min_periods=2).std()
+ median_first_idx = pd_data.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
+
# Test name:
name = tbl_dict[tst_name]["name"]
- # Throughput trend:
- trend = list(pd_data.rolling(window=win_size, min_periods=2).
- median())[-2]
- # Anomaly:
- t_data, _ = find_outliers(pd_data)
- last = list(t_data)[-1]
- t_stdev = list(t_data.rolling(window=win_size, min_periods=2).
- std())[-2]
- if isnan(last):
- anomaly = "outlier"
- last = list(pd_data)[-1]
- elif last < (trend - 3 * t_stdev):
- anomaly = "regression"
- elif last > (trend + 3 * t_stdev):
- anomaly = "progression"
- else:
- anomaly = "normal"
- if not isnan(last) and not isnan(trend) and trend != 0:
- # Change:
- change = round(float(last - trend) / 1000000, 2)
- # Relative change:
- rel_change = int(relative_change(float(trend), float(last)))
-
- tbl_lst.append([name,
- round(float(trend) / 1000000, 2),
- change,
- rel_change,
- anomaly])
+ # Classification list:
+ classification_lst = list()
+ for build_nr, value in pd_data.iteritems():
+
+ if isnan(data_t[build_nr]) \
+ or isnan(median_t[build_nr]) \
+ or isnan(stdev_t[build_nr]) \
+ or isnan(value):
+ classification_lst.append("outlier")
+ elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]):
+ classification_lst.append("regression")
+ elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]):
+ classification_lst.append("progression")
+ else:
+ classification_lst.append("normal")
- # Sort the table according to the relative change
- tbl_lst.sort(key=lambda rel: rel[-2], reverse=True)
+ 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)
- file_name = "{0}.{1}".format(table["output-file"], table["output-file-ext"])
+ 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)
+
+ logging.info("rel_change_last : {}".format(rel_change_last))
+ logging.info("rel_change_long : {}".format(rel_change_long))
+
+ tbl_lst.append(
+ [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])
+
+ tbl_sorted = list()
+ 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"])
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 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.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:
+ colors = {"regression": ("#ffcccc", "#ff9999"),
+ "progression": ("#c6ecc6", "#9fdf9f"),
+ "outlier": ("#e6e6e6", "#cccccc"),
+ "normal": ("#e9f1fb", "#d4e4f7")}
+ for r_idx, row in enumerate(csv_lst[1:]):
+ 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:
+ 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 > 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