1 # Copyright (c) 2024 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
14 """The comparison tables.
19 from numpy import mean, std, percentile
20 from copy import deepcopy
22 from ..utils.constants import Constants as C
23 from ..utils.utils import relative_change_stdev
29 normalize: bool=False,
30 remove_outliers: bool=False
32 """Select data for a comparison table.
34 :param data: Data to be filtered for the comparison table.
35 :param selected: A dictionary with parameters and their values selected by
37 :param normalize: If True, the data is normalized to CPU frequency
38 Constants.NORM_FREQUENCY.
39 :param remove_outliers: If True the outliers are removed before
41 :type data: pandas.DataFrame
44 :type remove_outliers: bool
45 :returns: A data frame with selected data.
46 :rtype: pandas.DataFrame
49 def _calculate_statistics(
50 data_in: pd.DataFrame,
54 remove_outliers: bool=False
56 """Calculates mean value and standard deviation for provided data.
58 :param data_in: Input data for calculations.
59 :param ttype: The test type.
60 :param drv: The driver.
61 :param norm_factor: The data normalization factor.
62 :param remove_outliers: If True the outliers are removed before
64 :type data_in: pandas.DataFrame
67 :type norm_factor: float
68 :type remove_outliers: bool
69 :returns: A pandas dataframe with: test name, mean value, standard
71 :rtype: pandas.DataFrame
79 for itm in data_in["test_id"].unique().tolist():
80 itm_lst = itm.split(".")
81 test = itm_lst[-1].rsplit("-", 1)[0]
82 if "hoststack" in itm:
83 test_type = f"hoststack-{ttype}"
86 df = data_in.loc[(data_in["test_id"] == itm)]
87 l_df = df[C.VALUE_ITER[test_type]].to_list()
88 if len(l_df) and isinstance(l_df[0], list):
95 q1 = percentile(l_df, 25, method=C.COMP_PERCENTILE_METHOD)
96 q3 = percentile(l_df, 75, method=C.COMP_PERCENTILE_METHOD)
98 lif = q1 - C.COMP_OUTLIER_TYPE * irq
99 uif = q3 + C.COMP_OUTLIER_TYPE * irq
100 l_df = [i for i in l_df if i >= lif and i <= uif]
103 mean_val = mean(l_df)
105 except (TypeError, ValueError):
107 d_data["name"].append(f"{test.replace(f'{drv}-', '')}-{ttype}")
108 d_data["mean"].append(int(mean_val * norm_factor))
109 d_data["stdev"].append(int(std_val * norm_factor))
110 d_data["unit"].append(df[C.UNIT[test_type]].to_list()[0])
111 return pd.DataFrame(d_data)
115 if itm["ttype"] in ("NDR", "PDR", "Latency"):
117 elif itm["ttype"] in ("CPS", "RPS", "BPS"):
118 test_type = "hoststack"
120 test_type = itm["ttype"].lower()
122 dutver = itm["dutver"].split("-", 1) # 0 -> release, 1 -> dut version
123 tmp_df = pd.DataFrame(data.loc[(
124 (data["passed"] == True) &
125 (data["dut_type"] == itm["dut"]) &
126 (data["dut_version"] == dutver[1]) &
127 (data["test_type"] == test_type) &
128 (data["release"] == dutver[0])
131 drv = "" if itm["driver"] == "dpdk" else itm["driver"].replace("_", "-")
132 core = str() if itm["dut"] == "trex" else itm["core"].lower()
133 ttype = "ndrpdr" if itm["ttype"] in ("NDR", "PDR", "Latency") \
134 else itm["ttype"].lower()
136 (tmp_df.job.str.endswith(itm["tbed"])) &
137 (tmp_df.test_id.str.contains(
139 f"^.*[.|-]{itm['nic']}.*{itm['frmsize'].lower()}-"
140 f"{core}-{drv}.*-{ttype}$"
145 if itm["driver"] == "dpdk":
146 for drv in C.DRIVERS:
148 tmp_df[tmp_df.test_id.str.contains(f"-{drv}-")].index,
152 # Change the data type from ndrpdr to one of ("NDR", "PDR", "Latency")
153 if test_type == "ndrpdr":
154 tmp_df = tmp_df.assign(test_type=itm["ttype"].lower())
158 if itm["ttype"] == "Latency":
159 norm_factor = C.FREQUENCY[itm["tbed"]] / C.NORM_FREQUENCY
161 norm_factor = C.NORM_FREQUENCY / C.FREQUENCY[itm["tbed"]]
164 tmp_df = _calculate_statistics(
166 itm["ttype"].lower(),
169 remove_outliers=remove_outliers
172 lst_df.append(tmp_df)
176 elif len(lst_df) > 1:
188 def comparison_table(
193 remove_outliers: bool=False
195 """Generate a comparison table.
197 :param data: Iterative data for the comparison table.
198 :param selected: A dictionary with parameters and their values selected by
200 :param normalize: If True, the data is normalized to CPU frequency
201 Constants.NORM_FREQUENCY.
202 :param format: The output format of the table:
203 - html: To be displayed on html page, the values are shown in millions
205 - csv: To be downloaded as a CSV file the values are stored in base
207 :param remove_outliers: If True the outliers are removed before
208 generating the table.
209 :type data: pandas.DataFrame
211 :type normalize: bool
213 :type remove_outliers: bool
214 :returns: A tuple with the tabe title and the comparison table.
215 :rtype: tuple[str, pandas.DataFrame]
218 def _create_selection(sel: dict) -> list:
219 """Transform the complex dictionary with user selection to list
222 :param sel: A complex dictionary with user selection.
224 :returns: A list of simple items.
227 l_infra = sel["infra"].split("-")
229 for core in sel["core"]:
230 for fsize in sel["frmsize"]:
231 for ttype in sel["ttype"]:
234 "dutver": sel["dutver"],
235 "tbed": f"{l_infra[0]}-{l_infra[1]}",
237 "driver": l_infra[-1].replace("_", "-"),
244 r_sel = deepcopy(selected["reference"]["selection"])
245 c_params = selected["compare"]
246 r_selection = _create_selection(r_sel)
248 if format == "html" and "Latency" not in r_sel["ttype"]:
249 unit_factor, s_unit_factor = (1e6, "M")
251 unit_factor, s_unit_factor = (1, str())
253 # Create Table title and titles of columns with data
255 params.remove(c_params["parameter"])
259 if isinstance(value, list):
260 lst_title.append("|".join(value))
262 lst_title.append(value)
263 title = "Comparison for: " + "-".join(lst_title)
264 r_name = r_sel[c_params["parameter"]]
265 if isinstance(r_name, list):
266 r_name = "|".join(r_name)
267 c_name = c_params["value"]
269 # Select reference data
270 r_data = select_comp_data(data, r_selection, normalize, remove_outliers)
272 # Select compare data
273 c_sel = deepcopy(selected["reference"]["selection"])
274 if c_params["parameter"] in ("core", "frmsize", "ttype"):
275 c_sel[c_params["parameter"]] = [c_params["value"], ]
277 c_sel[c_params["parameter"]] = c_params["value"]
279 c_selection = _create_selection(c_sel)
280 c_data = select_comp_data(data, c_selection, normalize, remove_outliers)
282 if r_data.empty or c_data.empty:
283 return str(), pd.DataFrame()
285 l_name, l_r_mean, l_r_std, l_c_mean, l_c_std, l_rc_mean, l_rc_std, unit = \
286 list(), list(), list(), list(), list(), list(), list(), set()
287 for _, row in r_data.iterrows():
288 if c_params["parameter"] in ("core", "frmsize", "ttype"):
289 l_cmp = row["name"].split("-")
290 if c_params["parameter"] == "core":
292 (c_data.name.str.contains(l_cmp[0])) &
293 (c_data.name.str.contains("-".join(l_cmp[2:])))
295 elif c_params["parameter"] == "frmsize":
296 c_row = c_data[c_data.name.str.contains("-".join(l_cmp[1:]))]
297 elif c_params["parameter"] == "ttype":
298 regex = r"^" + f"{'-'.join(l_cmp[:-1])}" + r"-.{3}$"
299 c_row = c_data[c_data.name.str.contains(regex, regex=True)]
301 c_row = c_data[c_data["name"] == row["name"]]
303 unit.add(f"{s_unit_factor}{row['unit']}")
306 c_mean = c_row["mean"].values[0]
307 c_std = c_row["stdev"].values[0]
308 l_name.append(row["name"])
309 l_r_mean.append(r_mean / unit_factor)
310 l_r_std.append(r_std / unit_factor)
311 l_c_mean.append(c_mean / unit_factor)
312 l_c_std.append(c_std / unit_factor)
313 delta, d_stdev = relative_change_stdev(r_mean, c_mean, r_std, c_std)
314 l_rc_mean.append(delta)
315 l_rc_std.append(d_stdev)
317 s_unit = "|".join(unit)
318 df_cmp = pd.DataFrame.from_dict({
320 f"{r_name} Mean [{s_unit}]": l_r_mean,
321 f"{r_name} Stdev [{s_unit}]": l_r_std,
322 f"{c_name} Mean [{s_unit}]": l_c_mean,
323 f"{c_name} Stdev [{s_unit}]": l_c_std,
324 "Relative Change Mean [%]": l_rc_mean,
325 "Relative Change Stdev [%]": l_rc_std
328 by="Relative Change Mean [%]",
333 return (title, df_cmp)