import pandas as pd
from collections import OrderedDict
-from utils import find_outliers, archive_input_data, execute_command
+from utils import split_outliers, archive_input_data, execute_command
# Command to build the html format of the report
return OrderedDict(sorted(data_dict.items(), key=lambda t: t[0]))
-def _evaluate_results(in_data, trimmed_data, window=10):
+def _evaluate_results(trimmed_data, window=10):
"""Evaluates if the sample value is regress, normal or progress compared to
previous data within the window.
We use the intervals defined as:
- - regress: less than median - 3 * stdev
- - normal: between median - 3 * stdev and median + 3 * stdev
- - progress: more than median + 3 * stdev
+ - regress: less than trimmed moving median - 3 * stdev
+ - normal: between trimmed moving median - 3 * stdev and median + 3 * stdev
+ - progress: more than trimmed moving median + 3 * stdev
+ where stdev is trimmed moving standard deviation.
- :param in_data: Full data set.
- :param trimmed_data: Full data set without the outliers.
- :param window: Window size used to calculate moving median and moving stdev.
- :type in_data: pandas.Series
+ :param trimmed_data: Full data set with the outliers replaced by nan.
+ :param window: Window size used to calculate moving average and moving stdev.
:type trimmed_data: pandas.Series
:type window: int
:returns: Evaluated results.
:rtype: list
"""
- if len(in_data) > 2:
- win_size = in_data.size if in_data.size < window else window
+ if len(trimmed_data) > 2:
+ win_size = trimmed_data.size if trimmed_data.size < window else window
results = [0.66, ]
- median = in_data.rolling(window=win_size, min_periods=2).median()
- stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std()
+ tmm = trimmed_data.rolling(window=win_size, min_periods=2).median()
+ tmstd = trimmed_data.rolling(window=win_size, min_periods=2).std()
first = True
- for build_nr, value in in_data.iteritems():
+ for build_nr, value in trimmed_data.iteritems():
if first:
first = False
continue
- if np.isnan(trimmed_data[build_nr]) \
- or np.isnan(median[build_nr]) \
- or np.isnan(stdev_t[build_nr]) \
- or np.isnan(value):
+ if (np.isnan(value)
+ or np.isnan(tmm[build_nr])
+ or np.isnan(tmstd[build_nr])):
results.append(0.0)
- elif value < (median[build_nr] - 3 * stdev_t[build_nr]):
+ elif value < (tmm[build_nr] - 2 * tmstd[build_nr]):
results.append(0.33)
- elif value > (median[build_nr] + 3 * stdev_t[build_nr]):
+ elif value > (tmm[build_nr] + 2 * tmstd[build_nr]):
results.append(1.0)
else:
results.append(0.66)
else:
results = [0.0, ]
try:
- median = np.median(in_data)
- stdev = np.std(in_data)
- if in_data.values[-1] < (median - 3 * stdev):
+ tmm = np.median(trimmed_data)
+ tmstd = np.std(trimmed_data)
+ if trimmed_data.values[-1] < (tmm - 2 * tmstd):
results.append(0.33)
- elif (median - 3 * stdev) <= in_data.values[-1] <= (
- median + 3 * stdev):
+ elif (tmm - 2 * tmstd) <= trimmed_data.values[-1] <= (
+ tmm + 2 * tmstd):
results.append(0.66)
else:
results.append(1.0)
def _generate_trending_traces(in_data, build_info, period, moving_win_size=10,
fill_missing=True, use_first=False,
- show_moving_median=True, name="", color=""):
+ show_trend_line=True, name="", color=""):
"""Generate the trending traces:
- samples,
- - moving median (trending plot)
+ - trimmed moving median (trending line)
- outliers, regress, progress
:param in_data: Full data set.
:param period: Sampling period.
:param moving_win_size: Window size.
:param fill_missing: If the chosen sample is missing in the full set, its
- nearest neighbour is used.
+ nearest neighbour is used.
:param use_first: Use the first sample even though it is not chosen.
- :param show_moving_median: Show moving median (trending plot).
+ :param show_trend_line: Show moving median (trending plot).
:param name: Name of the plot
:param color: Name of the color for the plot.
:type in_data: OrderedDict
:type moving_win_size: int
:type fill_missing: bool
:type use_first: bool
- :type show_moving_median: bool
+ :type show_trend_line: bool
:type name: str
:type color: str
:returns: Generated traces (list) and the evaluated result (float).
fill_missing=fill_missing,
use_first=use_first)
- data_x = [key for key in in_data.keys()]
- data_y = [val for val in in_data.values()]
+ data_x = list(in_data.keys())
+ data_y = list(in_data.values())
hover_text = list()
for idx in data_x:
data_pd = pd.Series(data_y, index=data_x)
- t_data, outliers = find_outliers(data_pd, outlier_const=1.5)
- results = _evaluate_results(data_pd, t_data, window=moving_win_size)
+ t_data, outliers = split_outliers(data_pd, outlier_const=1.5,
+ window=moving_win_size)
+ results = _evaluate_results(t_data, window=moving_win_size)
anomalies = pd.Series()
anomalies_res = list()
y=anomalies.values,
mode='markers',
hoverinfo="none",
- showlegend=False,
+ showlegend=True,
legendgroup=name,
- name="{name}: outliers".format(name=name),
+ name="{name}-anomalies".format(name=name),
marker={
"size": 15,
"symbol": "circle-open",
)
traces.append(trace_anomalies)
- if show_moving_median:
- data_mean_y = pd.Series(data_y).rolling(
- window=moving_win_size, min_periods=2).median()
- trace_median = plgo.Scatter(
- x=data_x,
- y=data_mean_y,
+ if show_trend_line:
+ data_trend = t_data.rolling(window=moving_win_size,
+ min_periods=2).median()
+ trace_trend = plgo.Scatter(
+ x=data_trend.keys(),
+ y=data_trend.tolist(),
mode='lines',
line={
"shape": "spline",
},
name='{name}-trend'.format(name=name)
)
- traces.append(trace_median)
+ traces.append(trace_trend)
return traces, results[-1]
tst_lst = list()
for build in builds_lst:
item = tst_data.get(int(build), '')
- tst_lst.append(str(item) if item else '')
+ tst_lst.append(str(item))
+ # tst_lst.append(str(item) if item else '')
csv_table.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
for period in chart["periods"]:
Code Review / csit.git / blobdiff