from string import replace
from math import isnan
+from numpy import nan
from xml.etree import ElementTree as ET
from errors import PresentationError
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:
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:
# Prepare the header of the tables
header = ["Test Case",
- "Throughput Trend [Mpps]",
- "Long Trend Compliance",
- "Trend Compliance",
- "Top Anomaly [Mpps]",
- "Change [%]",
- "Outliers [Number]"
+ "Trend [Mpps]",
+ "Short-Term Change [%]",
+ "Long-Term Change [%]",
+ "Regressions [#]",
+ "Progressions [#]",
+ "Outliers [#]"
]
header_str = ",".join(header) + "\n"
if len(tbl_dict[tst_name]["data"]) > 2:
pd_data = pd.Series(tbl_dict[tst_name]["data"])
+ 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:]
+ 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"]
- 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
+ logging.info("{}".format(name))
+ logging.info("pd_data : {}".format(pd_data))
+ logging.info("data_t : {}".format(data_t))
+ logging.info("median_t : {}".format(median_t))
+ logging.info("last_median_t : {}".format(last_median_t))
+ logging.info("median_t_14 : {}".format(median_t_14))
+ logging.info("max_median : {}".format(max_median))
+
+ # Classification list:
+ classification_lst = list()
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]) \
+ 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[build_nr] - 3 * stdev_t[build_nr]):
+ elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]):
classification_lst.append("regression")
- elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
+ elif value > (median_t[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
+ if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0:
+ rel_change_last = nan
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"
+ rel_change_last = round(
+ (last_median_t - median_t_14) / median_t_14, 2)
+
+ if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
+ rel_change_long = nan
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
+ rel_change_long = round(
+ (last_median_t - max_median) / max_median, 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 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)
+ 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[5] == nro]
+ tbl_sorted.extend(tbl_out)
file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
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
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