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
- results = [0.0, ] * win_size
- median = in_data.rolling(window=win_size).median()
- stdev_t = trimmed_data.rolling(window=win_size, min_periods=2).std()
- m_vals = median.values
- s_vals = stdev_t.values
- d_vals = in_data.values
- for day in range(win_size, in_data.size):
- if np.isnan(m_vals[day - 1]) or np.isnan(s_vals[day - 1]):
+ if len(trimmed_data) > 2:
+ win_size = trimmed_data.size if trimmed_data.size < window else window
+ results = [0.66, ]
+ 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 trimmed_data.iteritems():
+ if first:
+ first = False
+ continue
+ if (np.isnan(value)
+ or np.isnan(tmm[build_nr])
+ or np.isnan(tmstd[build_nr])):
results.append(0.0)
- elif d_vals[day] < (m_vals[day - 1] - 3 * s_vals[day - 1]):
+ elif value < (tmm[build_nr] - 2 * tmstd[build_nr]):
results.append(0.33)
- elif (m_vals[day - 1] - 3 * s_vals[day - 1]) <= d_vals[day] <= \
- (m_vals[day - 1] + 3 * s_vals[day - 1]):
- results.append(0.66)
- else:
+ 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).
in_data = _select_data(in_data, period,
fill_missing=fill_missing,
use_first=use_first)
- try:
- data_x = ["{0}/{1}".format(key, build_info[str(key)][1].split("~")[-1])
- for key in in_data.keys()]
- except KeyError:
- data_x = [key for key in in_data.keys()]
- # hover_text = ["vpp-build: {0}".format(x[1].split("~")[-1])
- # for x in build_info.values()]
- # data_x = [key for key in in_data.keys()]
-
- data_y = [val for val in in_data.values()]
- data_pd = pd.Series(data_y, index=data_x)
- t_data, outliers = find_outliers(data_pd)
+ data_x = list(in_data.keys())
+ data_y = list(in_data.values())
+
+ hover_text = list()
+ for idx in data_x:
+ hover_text.append("vpp-build: {0}".
+ format(build_info[str(idx)][1].split("~")[-1]))
+
+ data_pd = pd.Series(data_y, index=data_x)
- 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()
for idx, item in enumerate(in_data.items()):
- item_pd = pd.Series([item[1], ],
- index=["{0}/{1}".
- format(item[0],
- build_info[str(item[0])][1].split("~")[-1]),
- ])
- #item_pd = pd.Series([item[1], ], index=[item[0], ])
+ item_pd = pd.Series([item[1], ], index=[item[0], ])
if item[0] in outliers.keys():
anomalies = anomalies.append(item_pd)
anomalies_res.append(0.0)
"color": color,
"symbol": "circle",
},
- # text=hover_text,
- # hoverinfo="x+y+text+name"
+ text=hover_text,
+ hoverinfo="x+y+text+name"
)
traces = [trace_samples, ]
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]
builds_lst.append(str(build["build"]))
# Get "build ID": "date" dict:
- build_info = dict()
+ build_info = OrderedDict()
for build in builds_lst:
try:
build_info[build] = (
)
except KeyError:
build_info[build] = ("", "")
+ logging.info("{}: {}, {}".format(build,
+ build_info[build][0],
+ build_info[build][1]))
# Create the header:
csv_table = list()
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"]:
# Generate traces:
traces = list()
- win_size = 10 if period == 1 else 5 if period < 20 else 3
+ win_size = 14 if period == 1 else 5 if period < 20 else 3
idx = 0
for test_name, test_data in chart_data.items():
if not test_data:
Code Review / csit.git / blobdiff