X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fgenerator_tables.py;h=29e1006950a5fdc7e3054b994d7ff2547f291dcc;hp=f55c41ecf725670da24d04726992fe76a7161916;hb=2387340f7050112311cd231f7d30d06731da4836;hpb=79bcdf2e3321ca46c07e6b49a77ce1cf5b513956

diff --git a/resources/tools/presentation/generator_tables.py b/resources/tools/presentation/generator_tables.py
index f55c41ecf7..29e1006950 100644
--- a/resources/tools/presentation/generator_tables.py
+++ b/resources/tools/presentation/generator_tables.py
@@ -18,11 +18,14 @@
 import logging
 import csv
 import prettytable
+import pandas as pd
 
 from string import replace
+from math import isnan
+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, find_outliers
 
 
 def generate_tables(spec, data):
@@ -401,17 +404,17 @@ def table_performance_comparison(table, input_data):
     for tst_name in tbl_dict.keys():
         item = [tbl_dict[tst_name]["name"], ]
         if tbl_dict[tst_name]["ref-data"]:
-            item.append(round(mean(remove_outliers(
-                tbl_dict[tst_name]["ref-data"], 2)) / 1000000, 2))
-            item.append(round(stdev(remove_outliers(
-                tbl_dict[tst_name]["ref-data"], 2)) / 1000000, 2))
+            data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
+                                     table["outlier-const"])
+            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"]:
-            item.append(round(mean(remove_outliers(
-                tbl_dict[tst_name]["cmp-data"], 2)) / 1000000, 2))
-            item.append(round(stdev(remove_outliers(
-                tbl_dict[tst_name]["cmp-data"], 2)) / 1000000, 2))
+            data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
+                                     table["outlier-const"])
+            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:
@@ -438,7 +441,7 @@ 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 +462,7 @@ 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:
@@ -481,7 +484,7 @@ 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:]):
@@ -491,7 +494,7 @@ 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]):
@@ -508,7 +511,7 @@ 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:]):
@@ -518,10 +521,346 @@ 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)
+
+    # 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"],
+                                     table["outlier-const"])
+            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"],
+                                     table["outlier-const"])
+            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:
+            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)
+
+    # Prepare the header of the tables
+    header = ["Test case",
+              "Thput trend [Mpps]",
+              "Anomaly [Mpps]",
+              "Change [%]",
+              "Classification"]
+    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": list()}
+                try:
+                    tbl_dict[tst_name]["data"]. \
+                        append(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:
+            sample_lst = tbl_dict[tst_name]["data"]
+            pd_data = pd.Series(sample_lst)
+            win_size = pd_data.size \
+                if pd_data.size < table["window"] else table["window"]
+            # Test name:
+            name = tbl_dict[tst_name]["name"]
+
+            # Trend list:
+            trend_lst = list(pd_data.rolling(window=win_size, min_periods=2).
+                             median())
+            # Stdevs list:
+            t_data, _ = find_outliers(pd_data)
+            t_data_lst = list(t_data)
+            stdev_lst = list(t_data.rolling(window=win_size, min_periods=2).
+                             std())
+
+            rel_change_lst = [None, ]
+            classification_lst = [None, ]
+            for idx in range(1, len(trend_lst)):
+                # Relative changes list:
+                if not isnan(sample_lst[idx]) \
+                        and not isnan(trend_lst[idx])\
+                        and trend_lst[idx] != 0:
+                    rel_change_lst.append(
+                        int(relative_change(float(trend_lst[idx]),
+                                            float(sample_lst[idx]))))
+                else:
+                    rel_change_lst.append(None)
+                # Classification list:
+                if isnan(t_data_lst[idx]) or isnan(stdev_lst[idx]):
+                    classification_lst.append("outlier")
+                elif sample_lst[idx] < (trend_lst[idx] - 3*stdev_lst[idx]):
+                    classification_lst.append("regression")
+                elif sample_lst[idx] > (trend_lst[idx] + 3*stdev_lst[idx]):
+                    classification_lst.append("progression")
+                else:
+                    classification_lst.append("normal")
+
+            last_idx = len(sample_lst) - 1
+            first_idx = last_idx - int(table["evaluated-window"])
+            if first_idx < 0:
+                first_idx = 0
+
+            if "regression" in classification_lst[first_idx:]:
+                classification = "regression"
+            elif "outlier" in classification_lst[first_idx:]:
+                classification = "outlier"
+            elif "progression" in classification_lst[first_idx:]:
+                classification = "progression"
+            elif "normal" in classification_lst[first_idx:]:
+                classification = "normal"
+            else:
+                classification = None
+
+            idx = len(classification_lst) - 1
+            while idx:
+                if classification_lst[idx] == classification:
+                    break
+                idx -= 1
+
+            trend = round(float(trend_lst[-2]) / 1000000, 2) \
+                if not isnan(trend_lst[-2]) else ''
+            sample = round(float(sample_lst[idx]) / 1000000, 2) \
+                if not isnan(sample_lst[idx]) else ''
+            rel_change = rel_change_lst[idx] \
+                if rel_change_lst[idx] is not None else ''
+            tbl_lst.append([name,
+                            trend,
+                            sample,
+                            rel_change,
+                            classification])
+
+    # Sort the table according to the classification
+    tbl_sorted = list()
+    for classification in ("regression", "progression", "outlier", "normal"):
+        tbl_tmp = [item for item in tbl_lst if item[4] == classification]
+        tbl_tmp.sort(key=lambda rel: rel[0])
+        tbl_sorted.extend(tbl_tmp)
+
+    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="#6699ff"))
+    for idx, item in enumerate(csv_lst[0]):
+        alignment = "left" if idx == 0 else "right"
+        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))
+            if c_idx == 4:
+                if item == "regression":
+                    td.set("bgcolor", "#eca1a6")
+                elif item == "outlier":
+                    td.set("bgcolor", "#d6cbd3")
+                elif item == "progression":
+                    td.set("bgcolor", "#bdcebe")
+            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