- 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 len(tbl_dict[tst_name]["data"]) < 3:
+ continue
+
+ pd_data = pd.Series(tbl_dict[tst_name]["data"])
+ data_t, _ = split_outliers(pd_data, outlier_const=1.5,
+ window=table["window"])
+ last_key = data_t.keys()[-1]
+ win_size = min(data_t.size, table["window"])
+ win_first_idx = data_t.size - win_size
+ key_14 = data_t.keys()[win_first_idx]
+ long_win_size = min(data_t.size, table["long-trend-window"])
+ median_t = data_t.rolling(window=win_size, min_periods=2).median()
+ median_first_idx = median_t.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