X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=db79396857734478613dfa5dbfc4aff842507bcd;hp=443c11c9f6169f5f4cc22be9760accf331993ba1;hb=d5d53957f4686398727469e0f5b1774a5b6560fe;hpb=cfa3d9b1808826d1855bcf6c556cd26190e6f43a

diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index 443c11c9f6..db79396857 100644
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -18,11 +18,16 @@
 import logging
 import csv
 import prettytable
+import pandas as pd
 
 from string import replace
+from math import isnan
+from collections import OrderedDict
+from numpy import nan
+from xml.etree import ElementTree as ET
 
 from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers
+from utils import mean, stdev, relative_change, remove_outliers, split_outliers
 
 
 def generate_tables(spec, data):
@@ -352,16 +357,24 @@ def table_performance_comparison(table, input_data):
                  format(table.get("title", "")))
 
     # Transform the data
-    data = input_data.filter_data(table)
+    data = input_data.filter_data(table, continue_on_error=True)
 
     # Prepare the header of the tables
     try:
-        header = ["Test case",
-                  "{0} Throughput [Mpps]".format(table["reference"]["title"]),
-                  "{0} stdev [Mpps]".format(table["reference"]["title"]),
-                  "{0} Throughput [Mpps]".format(table["compare"]["title"]),
-                  "{0} stdev [Mpps]".format(table["compare"]["title"]),
-                  "Change [%]"]
+        header = ["Test case", ]
+
+        history = table.get("history", None)
+        if history:
+            for item in history:
+                header.extend(
+                    ["{0} Throughput [Mpps]".format(item["title"]),
+                     "{0} Stdev [Mpps]".format(item["title"])])
+        header.extend(
+            ["{0} Throughput [Mpps]".format(table["reference"]["title"]),
+             "{0} Stdev [Mpps]".format(table["reference"]["title"]),
+             "{0} Throughput [Mpps]".format(table["compare"]["title"]),
+             "{0} Stdev [Mpps]".format(table["compare"]["title"]),
+             "Change [%]"])
         header_str = ",".join(header) + "\n"
     except (AttributeError, KeyError) as err:
         logging.error("The model is invalid, missing parameter: {0}".
@@ -396,27 +409,66 @@ def table_performance_comparison(table, input_data):
                     pass
                 except TypeError:
                     tbl_dict.pop(tst_name, None)
+    if history:
+        for item in history:
+            for job, builds in item["data"].items():
+                for build in builds:
+                    for tst_name, tst_data in data[job][str(build)].iteritems():
+                        if tbl_dict.get(tst_name, None) is None:
+                            continue
+                        if tbl_dict[tst_name].get("history", None) is None:
+                            tbl_dict[tst_name]["history"] = OrderedDict()
+                        if tbl_dict[tst_name]["history"].get(item["title"],
+                                                             None) is None:
+                            tbl_dict[tst_name]["history"][item["title"]] = \
+                                list()
+                        try:
+                            tbl_dict[tst_name]["history"][item["title"]].\
+                                append(tst_data["throughput"]["value"])
+                        except (TypeError, KeyError):
+                            pass
 
     tbl_lst = list()
     for tst_name in tbl_dict.keys():
         item = [tbl_dict[tst_name]["name"], ]
+        if history:
+            for hist_list in tbl_dict[tst_name]["history"].values():
+                for hist_data in hist_list:
+                    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])
+                    else:
+                        item.extend([None, None])
         if tbl_dict[tst_name]["ref-data"]:
-            item.append(round(mean(remove_outliers(
-                tbl_dict[tst_name]["ref-data"], 1.5)) / 1000000, 2))
-            item.append(round(stdev(remove_outliers(
-                tbl_dict[tst_name]["ref-data"], 1.5)) / 1000000, 2))
+            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])
         else:
             item.extend([None, None])
         if tbl_dict[tst_name]["cmp-data"]:
-            item.append(round(mean(remove_outliers(
-                tbl_dict[tst_name]["cmp-data"], 1.5)) / 1000000, 2))
-            item.append(round(stdev(remove_outliers(
-                tbl_dict[tst_name]["cmp-data"], 1.5)) / 1000000, 2))
+            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])
         else:
             item.extend([None, None])
-        if item[1] is not None and item[3] is not None:
-            item.append(int(relative_change(float(item[1]), float(item[3]))))
-        if len(item) == 6:
+        if item[-5] is not None and item[-3] is not None and item[-5] != 0:
+            item.append(int(relative_change(float(item[-5]), float(item[-3]))))
+        if len(item) == len(header):
             tbl_lst.append(item)
 
     # Sort the table according to the relative change
@@ -438,7 +490,7 @@ def table_performance_comparison(table, input_data):
                                                table["output-file-ext"])
                  ]
     for file_name in tbl_names:
-        logging.info("      Writing file: '{}'".format(file_name))
+        logging.info("      Writing file: '{0}'".format(file_name))
         with open(file_name, "w") as file_handler:
             file_handler.write(header_str)
             for test in tbl_lst:
@@ -459,7 +511,7 @@ def table_performance_comparison(table, input_data):
 
     for i, txt_name in enumerate(tbl_names_txt):
         txt_table = None
-        logging.info("      Writing file: '{}'".format(txt_name))
+        logging.info("      Writing file: '{0}'".format(txt_name))
         with open(tbl_names[i], 'rb') as csv_file:
             csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
             for row in csv_content:
@@ -467,6 +519,7 @@ def table_performance_comparison(table, input_data):
                     txt_table = prettytable.PrettyTable(row)
                 else:
                     txt_table.add_row(row)
+            txt_table.align["Test case"] = "l"
         with open(txt_name, "w") as txt_file:
             txt_file.write(str(txt_table))
 
@@ -480,7 +533,7 @@ def table_performance_comparison(table, input_data):
 
     output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"],
                                                table["output-file-ext"])
-    logging.info("      Writing file: '{}'".format(output_file))
+    logging.info("      Writing file: '{0}'".format(output_file))
     with open(output_file, "w") as out_file:
         out_file.write(header_str)
         for i, line in enumerate(lines[1:]):
@@ -490,7 +543,7 @@ def table_performance_comparison(table, input_data):
 
     output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"],
                                                   table["output-file-ext"])
-    logging.info("      Writing file: '{}'".format(output_file))
+    logging.info("      Writing file: '{0}'".format(output_file))
     with open(output_file, "w") as out_file:
         out_file.write(header_str)
         for i, line in enumerate(lines[-1:0:-1]):
@@ -507,7 +560,7 @@ def table_performance_comparison(table, input_data):
 
     output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"],
                                                table["output-file-ext"])
-    logging.info("      Writing file: '{}'".format(output_file))
+    logging.info("      Writing file: '{0}'".format(output_file))
     with open(output_file, "w") as out_file:
         out_file.write(header_str)
         for i, line in enumerate(lines[1:]):
@@ -517,10 +570,429 @@ def table_performance_comparison(table, input_data):
 
     output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"],
                                                   table["output-file-ext"])
-    logging.info("      Writing file: '{}'".format(output_file))
+    logging.info("      Writing file: '{0}'".format(output_file))
     with open(output_file, "w") as out_file:
         out_file.write(header_str)
         for i, line in enumerate(lines[-1:0:-1]):
             if i == table["nr-of-tests-shown"]:
                 break
             out_file.write(line)
+
+
+def table_performance_comparison_mrr(table, input_data):
+    """Generate the table(s) with algorithm: table_performance_comparison_mrr
+    specified in the specification file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    # Transform the data
+    data = input_data.filter_data(table, continue_on_error=True)
+
+    # Prepare the header of the tables
+    try:
+        header = ["Test case",
+                  "{0} Throughput [Mpps]".format(table["reference"]["title"]),
+                  "{0} stdev [Mpps]".format(table["reference"]["title"]),
+                  "{0} Throughput [Mpps]".format(table["compare"]["title"]),
+                  "{0} stdev [Mpps]".format(table["compare"]["title"]),
+                  "Change [%]"]
+        header_str = ",".join(header) + "\n"
+    except (AttributeError, KeyError) as err:
+        logging.error("The model is invalid, missing parameter: {0}".
+                      format(err))
+        return
+
+    # Prepare data to the table:
+    tbl_dict = dict()
+    for job, builds in table["reference"]["data"].items():
+        for build in builds:
+            for tst_name, tst_data in data[job][str(build)].iteritems():
+                if tbl_dict.get(tst_name, None) is None:
+                    name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
+                                            "-".join(tst_data["name"].
+                                                     split("-")[1:]))
+                    tbl_dict[tst_name] = {"name": name,
+                                          "ref-data": list(),
+                                          "cmp-data": list()}
+                try:
+                    tbl_dict[tst_name]["ref-data"].\
+                        append(tst_data["result"]["throughput"])
+                except TypeError:
+                    pass  # No data in output.xml for this test
+
+    for job, builds in table["compare"]["data"].items():
+        for build in builds:
+            for tst_name, tst_data in data[job][str(build)].iteritems():
+                try:
+                    tbl_dict[tst_name]["cmp-data"].\
+                        append(tst_data["result"]["throughput"])
+                except KeyError:
+                    pass
+                except TypeError:
+                    tbl_dict.pop(tst_name, None)
+
+    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])
+        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])
+        else:
+            item.extend([None, None])
+        if item[1] is not None and item[3] is not None and item[1] != 0:
+            item.append(int(relative_change(float(item[1]), float(item[3]))))
+        if len(item) == 6:
+            tbl_lst.append(item)
+
+    # Sort the table according to the relative change
+    tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+
+    # Generate tables:
+    # All tests in csv:
+    tbl_names = ["{0}-1t1c-full{1}".format(table["output-file"],
+                                           table["output-file-ext"]),
+                 "{0}-2t2c-full{1}".format(table["output-file"],
+                                           table["output-file-ext"]),
+                 "{0}-4t4c-full{1}".format(table["output-file"],
+                                           table["output-file-ext"])
+                 ]
+    for file_name in tbl_names:
+        logging.info("      Writing file: '{0}'".format(file_name))
+        with open(file_name, "w") as file_handler:
+            file_handler.write(header_str)
+            for test in tbl_lst:
+                if file_name.split("-")[-2] in test[0]:  # cores
+                    test[0] = "-".join(test[0].split("-")[:-1])
+                    file_handler.write(",".join([str(item) for item in test]) +
+                                       "\n")
+
+    # All tests in txt:
+    tbl_names_txt = ["{0}-1t1c-full.txt".format(table["output-file"]),
+                     "{0}-2t2c-full.txt".format(table["output-file"]),
+                     "{0}-4t4c-full.txt".format(table["output-file"])
+                     ]
+
+    for i, txt_name in enumerate(tbl_names_txt):
+        txt_table = None
+        logging.info("      Writing file: '{0}'".format(txt_name))
+        with open(tbl_names[i], 'rb') as csv_file:
+            csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+            for row in csv_content:
+                if txt_table is None:
+                    txt_table = prettytable.PrettyTable(row)
+                else:
+                    txt_table.add_row(row)
+            txt_table.align["Test case"] = "l"
+        with open(txt_name, "w") as txt_file:
+            txt_file.write(str(txt_table))
+
+
+def table_performance_trending_dashboard(table, input_data):
+    """Generate the table(s) with algorithm: table_performance_comparison
+    specified in the specification file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    # Transform the data
+    data = input_data.filter_data(table, continue_on_error=True)
+
+    # Prepare the header of the tables
+    header = ["Test Case",
+              "Trend [Mpps]",
+              "Short-Term Change [%]",
+              "Long-Term Change [%]",
+              "Regressions [#]",
+              "Progressions [#]",
+              "Outliers [#]"
+              ]
+    header_str = ",".join(header) + "\n"
+
+    # Prepare data to the table:
+    tbl_dict = dict()
+    for job, builds in table["data"].items():
+        for build in builds:
+            for tst_name, tst_data in data[job][str(build)].iteritems():
+                if tbl_dict.get(tst_name, None) is None:
+                    name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
+                                            "-".join(tst_data["name"].
+                                                     split("-")[1:]))
+                    tbl_dict[tst_name] = {"name": name,
+                                          "data": dict()}
+                try:
+                    tbl_dict[tst_name]["data"][str(build)] =  \
+                        tst_data["result"]["throughput"]
+                except (TypeError, KeyError):
+                    pass  # No data in output.xml for this test
+
+    tbl_lst = list()
+    for tst_name in tbl_dict.keys():
+        if len(tbl_dict[tst_name]["data"]) > 2:
+
+            pd_data = pd.Series(tbl_dict[tst_name]["data"])
+            last_key = pd_data.keys()[-1]
+            win_size = min(pd_data.size, table["window"])
+            win_first_idx = pd_data.size - win_size
+            key_14 = pd_data.keys()[win_first_idx]
+            long_win_size = min(pd_data.size, table["long-trend-window"])
+
+            data_t, _ = split_outliers(pd_data, outlier_const=1.5,
+                                       window=win_size)
+
+            median_t = data_t.rolling(window=win_size, min_periods=2).median()
+            stdev_t = data_t.rolling(window=win_size, min_periods=2).std()
+            median_first_idx = pd_data.size - long_win_size
+            try:
+                max_median = max([x for x in median_t.values[median_first_idx:]
+                                  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
+
+            # Test name:
+            name = tbl_dict[tst_name]["name"]
+
+            logging.info("{}".format(name))
+            logging.info("pd_data : {}".format(pd_data))
+            logging.info("data_t : {}".format(data_t))
+            logging.info("median_t : {}".format(median_t))
+            logging.info("last_median_t : {}".format(last_median_t))
+            logging.info("median_t_14 : {}".format(median_t_14))
+            logging.info("max_median : {}".format(max_median))
+
+            # Classification list:
+            classification_lst = list()
+            for build_nr, value in pd_data.iteritems():
+
+                if isnan(data_t[build_nr]) \
+                        or isnan(median_t[build_nr]) \
+                        or isnan(stdev_t[build_nr]) \
+                        or isnan(value):
+                    classification_lst.append("outlier")
+                elif value < (median_t[build_nr] - 2 * stdev_t[build_nr]):
+                    classification_lst.append("regression")
+                elif value > (median_t[build_nr] + 2 * stdev_t[build_nr]):
+                    classification_lst.append("progression")
+                else:
+                    classification_lst.append("normal")
+
+            if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0:
+                rel_change_last = nan
+            else:
+                rel_change_last = round(
+                    ((last_median_t - median_t_14) / median_t_14) * 100, 2)
+
+            if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
+                rel_change_long = nan
+            else:
+                rel_change_long = round(
+                    ((last_median_t - max_median) / max_median) * 100, 2)
+
+            logging.info("rel_change_last : {}".format(rel_change_last))
+            logging.info("rel_change_long : {}".format(rel_change_long))
+
+            tbl_lst.append(
+                [name,
+                 '-' if isnan(last_median_t) else
+                 round(last_median_t / 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")])
+
+    tbl_lst.sort(key=lambda rel: rel[0])
+
+    tbl_sorted = list()
+    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[5] == nro]
+                tbl_sorted.extend(tbl_out)
+
+    file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
+
+    logging.info("      Writing file: '{0}'".format(file_name))
+    with open(file_name, "w") as file_handler:
+        file_handler.write(header_str)
+        for test in tbl_sorted:
+            file_handler.write(",".join([str(item) for item in test]) + '\n')
+
+    txt_file_name = "{0}.txt".format(table["output-file"])
+    txt_table = None
+    logging.info("      Writing file: '{0}'".format(txt_file_name))
+    with open(file_name, 'rb') as csv_file:
+        csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+        for row in csv_content:
+            if txt_table is None:
+                txt_table = prettytable.PrettyTable(row)
+            else:
+                txt_table.add_row(row)
+        txt_table.align["Test case"] = "l"
+    with open(txt_file_name, "w") as txt_file:
+        txt_file.write(str(txt_table))
+
+
+def table_performance_trending_dashboard_html(table, input_data):
+    """Generate the table(s) with algorithm:
+    table_performance_trending_dashboard_html specified in the specification
+    file.
+
+    :param table: Table to generate.
+    :param input_data: Data to process.
+    :type table: pandas.Series
+    :type input_data: InputData
+    """
+
+    logging.info("  Generating the table {0} ...".
+                 format(table.get("title", "")))
+
+    try:
+        with open(table["input-file"], 'rb') as csv_file:
+            csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+            csv_lst = [item for item in csv_content]
+    except KeyError:
+        logging.warning("The input file is not defined.")
+        return
+    except csv.Error as err:
+        logging.warning("Not possible to process the file '{0}'.\n{1}".
+                        format(table["input-file"], err))
+        return
+
+    # Table:
+    dashboard = ET.Element("table", attrib=dict(width="100%", border='0'))
+
+    # Table header:
+    tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7"))
+    for idx, item in enumerate(csv_lst[0]):
+        alignment = "left" if idx == 0 else "center"
+        th = ET.SubElement(tr, "th", attrib=dict(align=alignment))
+        th.text = item
+
+    # Rows:
+    for r_idx, row in enumerate(csv_lst[1:]):
+        background = "#D4E4F7" if r_idx % 2 else "white"
+        tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background))
+
+        # Columns:
+        for c_idx, item in enumerate(row):
+            alignment = "left" if c_idx == 0 else "center"
+            td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
+            # Name:
+            url = "../trending/"
+            file_name = ""
+            anchor = "#"
+            feature = ""
+            if c_idx == 0:
+                if "memif" in item:
+                    file_name = "container_memif.html"
+
+                elif "vhost" in item:
+                    if "l2xcbase" in item or "l2bdbasemaclrn" in item:
+                        file_name = "vm_vhost_l2.html"
+                    elif "ip4base" in item:
+                        file_name = "vm_vhost_ip4.html"
+
+                elif "ipsec" in item:
+                    file_name = "ipsec.html"
+
+                elif "ethip4lispip" in item or "ethip4vxlan" in item:
+                    file_name = "ip4_tunnels.html"
+
+                elif "ip4base" in item or "ip4scale" in item:
+                    file_name = "ip4.html"
+                    if "iacl" in item or "snat" in item or "cop" in item:
+                        feature = "-features"
+
+                elif "ip6base" in item or "ip6scale" in item:
+                    file_name = "ip6.html"
+
+                elif "l2xcbase" in item or "l2xcscale" in item \
+                        or "l2bdbasemaclrn" in item or "l2bdscale" in item \
+                        or "l2dbbasemaclrn" in item or "l2dbscale" in item:
+                    file_name = "l2.html"
+                    if "iacl" in item:
+                        feature = "-features"
+
+                if "x520" in item:
+                    anchor += "x520-"
+                elif "x710" in item:
+                    anchor += "x710-"
+                elif "xl710" in item:
+                    anchor += "xl710-"
+
+                if "64b" in item:
+                    anchor += "64b-"
+                elif "78b" in item:
+                    anchor += "78b"
+                elif "imix" in item:
+                    anchor += "imix-"
+                elif "9000b" in item:
+                    anchor += "9000b-"
+                elif "1518" in item:
+                    anchor += "1518b-"
+
+                if "1t1c" in item:
+                    anchor += "1t1c"
+                elif "2t2c" in item:
+                    anchor += "2t2c"
+                elif "4t4c" in item:
+                    anchor += "4t4c"
+
+                url = url + file_name + anchor + feature
+
+                ref = ET.SubElement(td, "a", attrib=dict(href=url))
+                ref.text = item
+
+            if c_idx > 0:
+                td.text = item
+
+    try:
+        with open(table["output-file"], 'w') as html_file:
+            logging.info("      Writing file: '{0}'".
+                         format(table["output-file"]))
+            html_file.write(".. raw:: html\n\n\t")
+            html_file.write(ET.tostring(dashboard))
+            html_file.write("\n\t<p><br><br></p>\n")
+    except KeyError:
+        logging.warning("The output file is not defined.")
+        return