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:
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:
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]",
- "Anomaly [Mpps]",
+ header = ["Test Case",
+ "Throughput Trend [Mpps]",
+ "Trend Compliance",
+ "Top Anomaly [Mpps]",
"Change [%]",
- "Classification"]
+ "Outliers [Number]"
+ ]
header_str = ",".join(header) + "\n"
# Prepare data to the table:
"-".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:
- sample_lst = tbl_dict[tst_name]["data"]
- pd_data = pd.Series(sample_lst)
+
+ 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"]
- # Trend list:
- trend_lst = list(pd_data.rolling(window=win_size, min_periods=2).
- median())
- # Stdevs list:
- t_data, _ = find_outliers(pd_data)
- t_data_lst = list(t_data)
- stdev_lst = list(t_data.rolling(window=win_size, min_periods=2).
- std())
+ median = pd_data.rolling(window=win_size, min_periods=2).median()
+ trimmed_data, _ = find_outliers(pd_data, outlier_const=1.5)
+ stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
rel_change_lst = [None, ]
classification_lst = [None, ]
- for idx in range(1, len(trend_lst)):
+ 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(sample_lst[idx]) \
- and not isnan(trend_lst[idx])\
- and trend_lst[idx] != 0:
- rel_change_lst.append(
- int(relative_change(float(trend_lst[idx]),
- float(sample_lst[idx]))))
+ 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(t_data_lst[idx]) or isnan(stdev_lst[idx]):
+ 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 sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]):
+ elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
classification_lst.append("regression")
- elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]):
+ 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(sample_lst) - 1
+ last_idx = len(classification_lst) - 1
first_idx = last_idx - int(table["evaluated-window"])
if first_idx < 0:
first_idx = 0
- if "regression" in classification_lst[first_idx:]:
+ 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 "outlier" in classification_lst[first_idx:]:
- classification = "outlier"
elif "progression" in classification_lst[first_idx:]:
classification = "progression"
- elif "normal" in classification_lst[first_idx:]:
+ else:
classification = "normal"
+
+ if classification == "normal":
+ index = len(classification_lst) - 1
else:
- classification = None
-
- idx = len(classification_lst) - 1
- while idx:
- if classification_lst[idx] == classification:
- break
- idx -= 1
-
- trend = round(float(trend_lst[-2]) / 1000000, 2) \
- if not isnan(trend_lst[-2]) else ''
- sample = round(float(sample_lst[idx]) / 1000000, 2) \
- if not isnan(sample_lst[idx]) else ''
- rel_change = rel_change_lst[idx] \
- if rel_change_lst[idx] is not None 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,
- sample,
- rel_change,
- 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 classification in ("regression", "progression", "outlier", "normal"):
- tbl_tmp = [item for item in tbl_lst if item[4] == classification]
+ 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)
dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
# Table header:
- tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#6699ff"))
+ tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7"))
for idx, item in enumerate(csv_lst[0]):
- alignment = "left" if idx == 0 else "right"
+ alignment = "left" if idx == 0 else "center"
th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
th.text = item
for c_idx, item in enumerate(row):
alignment = "left" if c_idx == 0 else "center"
td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
- if c_idx == 4:
+ # 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 == 2:
if item == "regression":
td.set("bgcolor", "#eca1a6")
- elif item == "outlier":
+ elif item == "failure":
td.set("bgcolor", "#d6cbd3")
elif item == "progression":
td.set("bgcolor", "#bdcebe")
- td.text = item
+ if c_idx > 0:
+ td.text = item
try:
with open(table["output-file"], 'w') as html_file:
Code Review / csit.git / blobdiff