# Copyright (c) 2024 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. """The comparison tables. """ import pandas as pd from numpy import mean, std, percentile from copy import deepcopy from ..utils.constants import Constants as C from ..utils.utils import relative_change_stdev def select_comp_data( data: pd.DataFrame, selected: dict, normalize: bool=False, remove_outliers: bool=False, raw_data: bool=False ) -> pd.DataFrame: """Select data for a comparison table. :param data: Data to be filtered for the comparison table. :param selected: A dictionary with parameters and their values selected by the user. :param normalize: If True, the data is normalized to CPU frequency Constants.NORM_FREQUENCY. :param remove_outliers: If True the outliers are removed before generating the table. :param raw_data: If True, returns data as it is in parquets without any processing. It is used for "download raw data" feature. :type data: pandas.DataFrame :type selected: dict :type normalize: bool :type remove_outliers: bool :type raw_data: bool :returns: A data frame with selected data. :rtype: pandas.DataFrame """ def _calculate_statistics( data_in: pd.DataFrame, ttype: str, drv: str, norm_factor: float, remove_outliers: bool=False ) -> pd.DataFrame: """Calculates mean value and standard deviation for provided data. :param data_in: Input data for calculations. :param ttype: The test type. :param drv: The driver. :param norm_factor: The data normalization factor. :param remove_outliers: If True the outliers are removed before generating the table. :type data_in: pandas.DataFrame :type ttype: str :type drv: str :type norm_factor: float :type remove_outliers: bool :returns: A pandas dataframe with: test name, mean value, standard deviation and unit. :rtype: pandas.DataFrame """ d_data = { "name": list(), "mean": list(), "stdev": list(), "unit": list() } for itm in data_in["test_id"].unique().tolist(): itm_lst = itm.split(".") test = itm_lst[-1].rsplit("-", 1)[0] if "hoststack" in itm: test_type = f"hoststack-{ttype}" else: test_type = ttype df = data_in.loc[(data_in["test_id"] == itm)] l_df = df[C.VALUE_ITER[test_type]].to_list() if len(l_df) and isinstance(l_df[0], list): tmp_df = list() for l_itm in l_df: tmp_df.extend(l_itm) l_df = tmp_df try: if remove_outliers: q1 = percentile(l_df, 25, method=C.COMP_PERCENTILE_METHOD) q3 = percentile(l_df, 75, method=C.COMP_PERCENTILE_METHOD) irq = q3 - q1 lif = q1 - C.COMP_OUTLIER_TYPE * irq uif = q3 + C.COMP_OUTLIER_TYPE * irq l_df = [i for i in l_df if i >= lif and i <= uif] mean_val = mean(l_df) std_val = std(l_df) except (TypeError, ValueError): continue d_data["name"].append(f"{test.replace(f'{drv}-', '')}-{ttype}") d_data["mean"].append(int(mean_val * norm_factor)) d_data["stdev"].append(int(std_val * norm_factor)) d_data["unit"].append(df[C.UNIT[test_type]].to_list()[0]) return pd.DataFrame(d_data) lst_df = list() for itm in selected: if itm["ttype"] in ("NDR", "PDR", "Latency"): test_type = "ndrpdr" elif itm["ttype"] in ("CPS", "RPS", "BPS"): test_type = "hoststack" else: test_type = itm["ttype"].lower() dutver = itm["dutver"].split("-", 1) # 0 -> release, 1 -> dut version tmp_df = pd.DataFrame(data.loc[( (data["passed"] == True) & (data["dut_type"] == itm["dut"]) & (data["dut_version"] == dutver[1]) & (data["test_type"] == test_type) & (data["release"] == dutver[0]) )]) drv = "" if itm["driver"] == "dpdk" else itm["driver"].replace("_", "-") core = str() if itm["dut"] == "trex" else itm["core"].lower() ttype = "ndrpdr" if itm["ttype"] in ("NDR", "PDR", "Latency") \ else itm["ttype"].lower() tmp_df = tmp_df[ (tmp_df.job.str.endswith(itm["tbed"])) & (tmp_df.test_id.str.contains( ( f"^.*[.|-]{itm['nic']}.*{itm['frmsize'].lower()}-" f"{core}-{drv}.*-{ttype}$" ), regex=True )) ] if itm["driver"] == "dpdk": for drv in C.DRIVERS: tmp_df.drop( tmp_df[tmp_df.test_id.str.contains(f"-{drv}-")].index, inplace=True ) # Change the data type from ndrpdr to one of ("NDR", "PDR", "Latency") if test_type == "ndrpdr": tmp_df = tmp_df.assign(test_type=itm["ttype"].lower()) if not tmp_df.empty: if normalize: if itm["ttype"] == "Latency": norm_factor = C.FREQUENCY[itm["tbed"]] / C.NORM_FREQUENCY else: norm_factor = C.NORM_FREQUENCY / C.FREQUENCY[itm["tbed"]] else: norm_factor = 1.0 if not raw_data: tmp_df = _calculate_statistics( tmp_df, itm["ttype"].lower(), itm["driver"], norm_factor, remove_outliers=remove_outliers ) lst_df.append(tmp_df) if len(lst_df) == 1: df = lst_df[0] elif len(lst_df) > 1: df = pd.concat( lst_df, ignore_index=True, copy=False ) else: df = pd.DataFrame() return df def comparison_table( data: pd.DataFrame, selected: dict, normalize: bool, format: str="html", remove_outliers: bool=False, raw_data: bool=False ) -> tuple: """Generate a comparison table. :param data: Iterative data for the comparison table. :param selected: A dictionary with parameters and their values selected by the user. :param normalize: If True, the data is normalized to CPU frequency Constants.NORM_FREQUENCY. :param format: The output format of the table: - html: To be displayed on html page, the values are shown in millions of the unit. - csv: To be downloaded as a CSV file the values are stored in base units. :param remove_outliers: If True the outliers are removed before generating the table. :param raw_data: If True, returns data as it is in parquets without any processing. It is used for "download raw data" feature. :type data: pandas.DataFrame :type selected: dict :type normalize: bool :type format: str :type remove_outliers: bool :type raw_data: bool :returns: A tuple with the tabe title and the comparison table. :rtype: tuple[str, pandas.DataFrame] """ def _create_selection(sel: dict) -> list: """Transform the complex dictionary with user selection to list of simple items. :param sel: A complex dictionary with user selection. :type sel: dict :returns: A list of simple items. :rtype: list """ l_infra = sel["infra"].split("-") selection = list() for core in sel["core"]: for fsize in sel["frmsize"]: for ttype in sel["ttype"]: selection.append({ "dut": sel["dut"], "dutver": sel["dutver"], "tbed": f"{l_infra[0]}-{l_infra[1]}", "nic": l_infra[2], "driver": l_infra[-1].replace("_", "-"), "core": core, "frmsize": fsize, "ttype": ttype }) return selection # Select reference data r_sel = deepcopy(selected["reference"]["selection"]) r_selection = _create_selection(r_sel) r_data = select_comp_data( data, r_selection, normalize, remove_outliers, raw_data ) # Select compare data c_sel = deepcopy(selected["reference"]["selection"]) c_params = selected["compare"] if c_params["parameter"] in ("core", "frmsize", "ttype"): c_sel[c_params["parameter"]] = [c_params["value"], ] else: c_sel[c_params["parameter"]] = c_params["value"] c_selection = _create_selection(c_sel) c_data = select_comp_data( data, c_selection, normalize, remove_outliers, raw_data ) if raw_data: r_data["ref/cmp"] = "reference" c_data["ref/cmp"] = "compare" return str(), pd.concat([r_data, c_data], ignore_index=True, copy=False) if r_data.empty or c_data.empty: return str(), pd.DataFrame() if format == "html" and "Latency" not in r_sel["ttype"]: unit_factor, s_unit_factor = (1e6, "M") else: unit_factor, s_unit_factor = (1, str()) # Create Table title and titles of columns with data params = list(r_sel) params.remove(c_params["parameter"]) lst_title = list() for param in params: value = r_sel[param] if isinstance(value, list): lst_title.append("|".join(value)) else: lst_title.append(value) title = "Comparison for: " + "-".join(lst_title) r_name = r_sel[c_params["parameter"]] if isinstance(r_name, list): r_name = "|".join(r_name) c_name = c_params["value"] l_name, l_r_mean, l_r_std, l_c_mean, l_c_std, l_rc_mean, l_rc_std, unit = \ list(), list(), list(), list(), list(), list(), list(), set() for _, row in r_data.iterrows(): if c_params["parameter"] in ("core", "frmsize", "ttype"): l_cmp = row["name"].split("-") if c_params["parameter"] == "core": c_row = c_data[ (c_data.name.str.contains(l_cmp[0])) & (c_data.name.str.contains("-".join(l_cmp[2:]))) ] elif c_params["parameter"] == "frmsize": c_row = c_data[c_data.name.str.contains("-".join(l_cmp[1:]))] elif c_params["parameter"] == "ttype": regex = r"^" + f"{'-'.join(l_cmp[:-1])}" + r"-.{3}$" c_row = c_data[c_data.name.str.contains(regex, regex=True)] else: c_row = c_data[c_data["name"] == row["name"]] if not c_row.empty: r_mean = row["mean"] r_std = row["stdev"] c_mean = c_row["mean"].values[0] c_std = c_row["stdev"].values[0] if r_mean == 0.0 or c_mean == 0.0: continue unit.add(f"{s_unit_factor}{row['unit']}") l_name.append(row["name"]) l_r_mean.append(r_mean / unit_factor) l_r_std.append(r_std / unit_factor) l_c_mean.append(c_mean / unit_factor) l_c_std.append(c_std / unit_factor) delta, d_stdev = relative_change_stdev(r_mean, c_mean, r_std, c_std) l_rc_mean.append(delta) l_rc_std.append(d_stdev) s_unit = "|".join(unit) df_cmp = pd.DataFrame.from_dict({ "Test Name": l_name, f"{r_name} Mean [{s_unit}]": l_r_mean, f"{r_name} Stdev [{s_unit}]": l_r_std, f"{c_name} Mean [{s_unit}]": l_c_mean, f"{c_name} Stdev [{s_unit}]": l_c_std, "Relative Change Mean [%]": l_rc_mean, "Relative Change Stdev [%]": l_rc_std }) df_cmp.sort_values( by="Relative Change Mean [%]", ascending=False, inplace=True ) return (title, df_cmp)