CSIT-1110: Integrate anomaly detection into PAL

+ Keep the original detection,
+ add the new one as subdirectory
  (both in source and in rendered tree).
- The new detection is not rebased over "Add dpdk mrr tests to trending".

New detection features:
+ Do not remove (nor detect) outliers.
+ Trend line shows the constant average within a group.
+ Anomaly circles are placed at the changed average.
+ Small bias against too similar averages.
+ Should be ready for moving the detection library out to pip.

Change-Id: I7ab1a92b79eeeed53ba65a071b1305e927816a89
Signed-off-by: Vratko Polak <vrpolak@cisco.com>

1 files changed, 448 insertions, 0 deletions

diff --git a/resources/tools/presentation/new/generator_CPTA.py b/resources/tools/presentation/new/generator_CPTA.py
new file mode 100644
index 0000000000..1b4115f1f6
--- /dev/null
+++ b/resources/tools/presentation/new/generator_CPTA.py
@@ -0,0 +1,448 @@
+# Copyright (c) 2018 Cisco and/or its affiliates.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at:
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Generation of Continuous Performance Trending and Analysis.
+"""
+
+import multiprocessing
+import os
+import logging
+import csv
+import prettytable
+import plotly.offline as ploff
+import plotly.graph_objs as plgo
+import plotly.exceptions as plerr
+import pandas as pd
+
+from collections import OrderedDict
+from datetime import datetime
+
+from utils import archive_input_data, execute_command,\
+    classify_anomalies, Worker
+
+
+# Command to build the html format of the report
+HTML_BUILDER = 'sphinx-build -v -c conf_cpta -a ' \
+               '-b html -E ' \
+               '-t html ' \
+               '-D version="{date}" ' \
+               '{working_dir} ' \
+               '{build_dir}/'
+
+# .css file for the html format of the report
+THEME_OVERRIDES = """/* override table width restrictions */
+.wy-nav-content {
+    max-width: 1200px !important;
+}
+"""
+
+COLORS = ["SkyBlue", "Olive", "Purple", "Coral", "Indigo", "Pink",
+          "Chocolate", "Brown", "Magenta", "Cyan", "Orange", "Black",
+          "Violet", "Blue", "Yellow"]
+
+
+def generate_cpta(spec, data):
+    """Generate all formats and versions of the Continuous Performance Trending
+    and Analysis.
+
+    :param spec: Specification read from the specification file.
+    :param data: Full data set.
+    :type spec: Specification
+    :type data: InputData
+    """
+
+    logging.info("Generating the Continuous Performance Trending and Analysis "
+                 "...")
+
+    ret_code = _generate_all_charts(spec, data)
+
+    cmd = HTML_BUILDER.format(
+        date=datetime.utcnow().strftime('%m/%d/%Y %H:%M UTC'),
+        working_dir=spec.environment["paths"]["DIR[WORKING,SRC]"],
+        build_dir=spec.environment["paths"]["DIR[BUILD,HTML]"])
+    execute_command(cmd)
+
+    with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE]"], "w") as \
+            css_file:
+        css_file.write(THEME_OVERRIDES)
+
+    with open(spec.environment["paths"]["DIR[CSS_PATCH_FILE2]"], "w") as \
+            css_file:
+        css_file.write(THEME_OVERRIDES)
+
+    archive_input_data(spec)
+
+    logging.info("Done.")
+
+    return ret_code
+
+
+def _generate_trending_traces(in_data, build_info, moving_win_size=10,
+                              show_trend_line=True, name="", color=""):
+    """Generate the trending traces:
+     - samples,
+     - trimmed moving median (trending line)
+     - outliers, regress, progress
+
+    :param in_data: Full data set.
+    :param build_info: Information about the builds.
+    :param moving_win_size: Window size.
+    :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 build_info: dict
+    :type moving_win_size: int
+    :type show_trend_line: bool
+    :type name: str
+    :type color: str
+    :returns: Generated traces (list) and the evaluated result.
+    :rtype: tuple(traces, result)
+    """
+
+    data_x = list(in_data.keys())
+    data_y = list(in_data.values())
+
+    hover_text = list()
+    xaxis = list()
+    for idx in data_x:
+        hover_text.append("vpp-ref: {0}<br>csit-ref: mrr-daily-build-{1}".
+                          format(build_info[str(idx)][1].rsplit('~', 1)[0],
+                                 idx))
+        date = build_info[str(idx)][0]
+        xaxis.append(datetime(int(date[0:4]), int(date[4:6]), int(date[6:8]),
+                              int(date[9:11]), int(date[12:])))
+
+    data_pd = pd.Series(data_y, index=xaxis)
+
+    anomaly_classification, avgs = classify_anomalies(data_pd)
+
+    anomalies = pd.Series()
+    anomalies_colors = list()
+    anomalies_avgs = list()
+    anomaly_color = {
+        "outlier": 0.0,
+        "regression": 0.33,
+        "normal": 0.66,
+        "progression": 1.0
+    }
+    if anomaly_classification:
+        for idx, item in enumerate(data_pd.items()):
+            if anomaly_classification[idx] in \
+                    ("outlier", "regression", "progression"):
+                anomalies = anomalies.append(pd.Series([item[1], ],
+                                                       index=[item[0], ]))
+                anomalies_colors.append(
+                    anomaly_color[anomaly_classification[idx]])
+                anomalies_avgs.append(avgs[idx])
+        anomalies_colors.extend([0.0, 0.33, 0.66, 1.0])
+
+    # Create traces
+
+    trace_samples = plgo.Scatter(
+        x=xaxis,
+        y=data_y,
+        mode='markers',
+        line={
+            "width": 1
+        },
+        legendgroup=name,
+        name="{name}-thput".format(name=name),
+        marker={
+            "size": 5,
+            "color": color,
+            "symbol": "circle",
+        },
+        text=hover_text,
+        hoverinfo="x+y+text+name"
+    )
+    traces = [trace_samples, ]
+
+    if show_trend_line:
+        trace_trend = plgo.Scatter(
+            x=xaxis,
+            y=avgs,
+            mode='lines',
+            line={
+                "shape": "linear",
+                "width": 1,
+                "color": color,
+            },
+            legendgroup=name,
+            name='{name}-trend'.format(name=name)
+        )
+        traces.append(trace_trend)
+
+    trace_anomalies = plgo.Scatter(
+        x=anomalies.keys(),
+        y=anomalies_avgs,
+        mode='markers',
+        hoverinfo="none",
+        showlegend=True,
+        legendgroup=name,
+        name="{name}-anomalies".format(name=name),
+        marker={
+            "size": 15,
+            "symbol": "circle-open",
+            "color": anomalies_colors,
+            "colorscale": [[0.00, "grey"],
+                           [0.25, "grey"],
+                           [0.25, "red"],
+                           [0.50, "red"],
+                           [0.50, "white"],
+                           [0.75, "white"],
+                           [0.75, "green"],
+                           [1.00, "green"]],
+            "showscale": True,
+            "line": {
+                "width": 2
+            },
+            "colorbar": {
+                "y": 0.5,
+                "len": 0.8,
+                "title": "Circles Marking Data Classification",
+                "titleside": 'right',
+                "titlefont": {
+                    "size": 14
+                },
+                "tickmode": 'array',
+                "tickvals": [0.125, 0.375, 0.625, 0.875],
+                "ticktext": ["Outlier", "Regression", "Normal", "Progression"],
+                "ticks": "",
+                "ticklen": 0,
+                "tickangle": -90,
+                "thickness": 10
+            }
+        }
+    )
+    traces.append(trace_anomalies)
+
+    return traces, anomaly_classification[-1]
+
+
+def _generate_all_charts(spec, input_data):
+    """Generate all charts specified in the specification file.
+
+    :param spec: Specification.
+    :param input_data: Full data set.
+    :type spec: Specification
+    :type input_data: InputData
+    """
+
+    def _generate_chart(_, data_q, graph):
+        """Generates the chart.
+        """
+
+        logs = list()
+
+        logging.info("  Generating the chart '{0}' ...".
+                     format(graph.get("title", "")))
+        logs.append(("INFO", "  Generating the chart '{0}' ...".
+                     format(graph.get("title", ""))))
+
+        job_name = spec.cpta["data"].keys()[0]
+
+        csv_tbl = list()
+        res = list()
+
+        # Transform the data
+        logs.append(("INFO", "    Creating the data set for the {0} '{1}'.".
+                     format(graph.get("type", ""), graph.get("title", ""))))
+        data = input_data.filter_data(graph, continue_on_error=True)
+        if data is None:
+            logging.error("No data.")
+            return
+
+        chart_data = dict()
+        for job in data:
+            for index, bld in job.items():
+                for test_name, test in bld.items():
+                    if chart_data.get(test_name, None) is None:
+                        chart_data[test_name] = OrderedDict()
+                    try:
+                        chart_data[test_name][int(index)] = \
+                            test["result"]["throughput"]
+                    except (KeyError, TypeError):
+                        pass
+
+        # Add items to the csv table:
+        for tst_name, tst_data in chart_data.items():
+            tst_lst = list()
+            for bld in builds_lst:
+                itm = tst_data.get(int(bld), '')
+                tst_lst.append(str(itm))
+            csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
+        # Generate traces:
+        traces = list()
+        win_size = 14
+        index = 0
+        for test_name, test_data in chart_data.items():
+            if not test_data:
+                logs.append(("WARNING", "No data for the test '{0}'".
+                             format(test_name)))
+                continue
+            test_name = test_name.split('.')[-1]
+            trace, rslt = _generate_trending_traces(
+                test_data,
+                build_info=build_info,
+                moving_win_size=win_size,
+                name='-'.join(test_name.split('-')[3:-1]),
+                color=COLORS[index])
+            traces.extend(trace)
+            res.append(rslt)
+            index += 1
+
+        if traces:
+            # Generate the chart:
+            graph["layout"]["xaxis"]["title"] = \
+                graph["layout"]["xaxis"]["title"].format(job=job_name)
+            name_file = "{0}-{1}{2}".format(spec.cpta["output-file"],
+                                            graph["output-file-name"],
+                                            spec.cpta["output-file-type"])
+
+            logs.append(("INFO", "    Writing the file '{0}' ...".
+                         format(name_file)))
+            plpl = plgo.Figure(data=traces, layout=graph["layout"])
+            try:
+                ploff.plot(plpl, show_link=False, auto_open=False,
+                           filename=name_file)
+            except plerr.PlotlyEmptyDataError:
+                logs.append(("WARNING", "No data for the plot. Skipped."))
+
+        data_out = {
+            "csv_table": csv_tbl,
+            "results": res,
+            "logs": logs
+        }
+        data_q.put(data_out)
+
+    job_name = spec.cpta["data"].keys()[0]
+
+    builds_lst = list()
+    for build in spec.input["builds"][job_name]:
+        status = build["status"]
+        if status != "failed" and status != "not found":
+            builds_lst.append(str(build["build"]))
+
+    # Get "build ID": "date" dict:
+    build_info = OrderedDict()
+    for build in builds_lst:
+        try:
+            build_info[build] = (
+                input_data.metadata(job_name, build)["generated"][:14],
+                input_data.metadata(job_name, build)["version"]
+            )
+        except KeyError:
+            build_info[build] = ("", "")
+
+    work_queue = multiprocessing.JoinableQueue()
+    manager = multiprocessing.Manager()
+    data_queue = manager.Queue()
+    cpus = multiprocessing.cpu_count()
+
+    workers = list()
+    for cpu in range(cpus):
+        worker = Worker(work_queue,
+                        data_queue,
+                        _generate_chart)
+        worker.daemon = True
+        worker.start()
+        workers.append(worker)
+        os.system("taskset -p -c {0} {1} > /dev/null 2>&1".
+                  format(cpu, worker.pid))
+
+    for chart in spec.cpta["plots"]:
+        work_queue.put((chart, ))
+    work_queue.join()
+
+    anomaly_classifications = list()
+
+    # Create the header:
+    csv_table = list()
+    header = "Build Number:," + ",".join(builds_lst) + '\n'
+    csv_table.append(header)
+    build_dates = [x[0] for x in build_info.values()]
+    header = "Build Date:," + ",".join(build_dates) + '\n'
+    csv_table.append(header)
+    vpp_versions = [x[1] for x in build_info.values()]
+    header = "VPP Version:," + ",".join(vpp_versions) + '\n'
+    csv_table.append(header)
+
+    while not data_queue.empty():
+        result = data_queue.get()
+
+        anomaly_classifications.extend(result["results"])
+        csv_table.extend(result["csv_table"])
+
+        for item in result["logs"]:
+            if item[0] == "INFO":
+                logging.info(item[1])
+            elif item[0] == "ERROR":
+                logging.error(item[1])
+            elif item[0] == "DEBUG":
+                logging.debug(item[1])
+            elif item[0] == "CRITICAL":
+                logging.critical(item[1])
+            elif item[0] == "WARNING":
+                logging.warning(item[1])
+
+    del data_queue
+
+    # Terminate all workers
+    for worker in workers:
+        worker.terminate()
+        worker.join()
+
+    # Write the tables:
+    file_name = spec.cpta["output-file"] + "-trending"
+    with open("{0}.csv".format(file_name), 'w') as file_handler:
+        file_handler.writelines(csv_table)
+
+    txt_table = None
+    with open("{0}.csv".format(file_name), 'rb') as csv_file:
+        csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+        line_nr = 0
+        for row in csv_content:
+            if txt_table is None:
+                txt_table = prettytable.PrettyTable(row)
+            else:
+                if line_nr > 1:
+                    for idx, item in enumerate(row):
+                        try:
+                            row[idx] = str(round(float(item) / 1000000, 2))
+                        except ValueError:
+                            pass
+                try:
+                    txt_table.add_row(row)
+                except Exception as err:
+                    logging.warning("Error occurred while generating TXT table:"
+                                    "\n{0}".format(err))
+            line_nr += 1
+        txt_table.align["Build Number:"] = "l"
+    with open("{0}.txt".format(file_name), "w") as txt_file:
+        txt_file.write(str(txt_table))
+
+    # Evaluate result:
+    if anomaly_classifications:
+        result = "PASS"
+        for classification in anomaly_classifications:
+            if classification == "regression" or classification == "outlier":
+                result = "FAIL"
+                break
+    else:
+        result = "FAIL"
+
+    logging.info("Partial results: {0}".format(anomaly_classifications))
+    logging.info("Result: {0}".format(result))
+
+    return result