- 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
- try:
- max_median = max([x for x in median.values[median_idx:]
- if not isnan(x)])
- except ValueError:
- max_median = None
- 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
- index = None
- for idx in range(first_idx, len(classification_lst)):
- if classification_lst[idx] == tmp_classification:
- if rel_change_lst[idx]:
- index = idx
- break
- if index is None:
- continue
- for idx in range(index+1, len(classification_lst)):
- if classification_lst[idx] == tmp_classification:
- if rel_change_lst[idx]:
- if (abs(rel_change_lst[idx]) >
- abs(rel_change_lst[index])):
- index = idx
-
- logging.debug("{}".format(name))
- logging.debug("sample_lst: {} - {}".
- format(len(sample_lst), sample_lst))
- logging.debug("median_lst: {} - {}".
- format(len(median_lst), median_lst))
- logging.debug("rel_change: {} - {}".
- format(len(rel_change_lst), rel_change_lst))
- logging.debug("classn_lst: {} - {}".
- format(len(classification_lst), classification_lst))
- logging.debug("index: {}".format(index))
- logging.debug("classifica: {}".format(classification))
-
- try:
- 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 max_median is not None:
- 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 = 'normal'
- 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])
- except IndexError as err:
- logging.error("{}".format(err))
- continue
-
- # Sort the table according to the classification