from xml.etree import ElementTree as ET
from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers,\
- split_outliers, classify_anomalies, convert_csv_to_pretty_txt
+from utils import mean, stdev, relative_change, classify_anomalies, \
+ convert_csv_to_pretty_txt
def generate_tables(spec, data):
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])
+ item.append(round(mean(hist_data) / 1000000, 2))
+ item.append(round(stdev(hist_data) / 1000000, 2))
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"],
- 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])
+ data_t = tbl_dict[tst_name]["ref-data"]
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
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])
+ data_t = tbl_dict[tst_name]["cmp-data"]
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if item[-4] is not None and item[-2] is not None and item[-4] != 0:
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])
+ data_t = tbl_dict[tst_name]["ref-data"]
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
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])
+ data_t = tbl_dict[tst_name]["cmp-data"]
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if item[1] is not None and item[3] is not None and item[1] != 0:
def table_performance_trending_dashboard(table, input_data):
- """Generate the table(s) with algorithm: table_performance_comparison
+ """Generate the table(s) with algorithm:
+ table_performance_trending_dashboard
specified in the specification file.
:param table: Table to generate.
"Short-Term Change [%]",
"Long-Term Change [%]",
"Regressions [#]",
- "Progressions [#]",
- "Outliers [#]"
+ "Progressions [#]"
]
header_str = ",".join(header) + "\n"
tbl_lst = list()
for tst_name in tbl_dict.keys():
- if len(tbl_dict[tst_name]["data"]) < 3:
+ if len(tbl_dict[tst_name]["data"]) < 2:
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]
+ data_t = pd.Series(tbl_dict[tst_name]["data"])
+
+ classification_lst, avgs = classify_anomalies(data_t)
+
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]
+ max_long_avg = max(
+ [x for x in avgs[-long_win_size:-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
+ max_long_avg = nan
+ last_avg = avgs[-1]
+ avg_week_ago = avgs[max(-win_size, -len(avgs))]
- if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0:
+ if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
rel_change_last = nan
else:
rel_change_last = round(
- ((last_median_t - median_t_14) / median_t_14) * 100, 2)
+ ((last_avg - avg_week_ago) / avg_week_ago) * 100, 2)
- if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
+ if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
rel_change_long = nan
else:
rel_change_long = round(
- ((last_median_t - max_median) / max_median) * 100, 2)
-
- # Classification list:
- classification_lst = classify_anomalies(data_t, window=14)
+ ((last_avg - max_long_avg) / max_long_avg) * 100, 2)
if classification_lst:
if isnan(rel_change_last) and isnan(rel_change_long):
continue
tbl_lst.append(
[tbl_dict[tst_name]["name"],
- '-' if isnan(last_median_t) else
- round(last_median_t / 1000000, 2),
+ '-' if isnan(last_avg) else
+ round(last_avg / 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")])
+ classification_lst[-win_size:].count("regression"),
+ classification_lst[-win_size:].count("progression")])
tbl_lst.sort(key=lambda rel: rel[0])
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)
+ tbl_out = [item for item in tbl_reg if item[5] == nrp]
+ 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"])
# 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]
Code Review / csit.git / blobdiff