Merge branch 'ci/fix-long-term-logs' into 'main' (abe55b66) · Commits · IVAS Codec Public Collaboration / IVAS Codec

ci/process_long_term_logs.py

+139 −71

Original line number	Diff line number	Diff line
		#!/usr/bin/env python3

		import os
		import pandas as pd
		import argparse
		import plotly.express as px
		import re
		import plotly.graph_objects as go
		from plotly.subplots import make_subplots

		@@ -23,15 +25,18 @@ def read_csv_files(root_dir):


		def parse_csv_data(csv_data):
		"""keep 'testcase', 'format', 'MLD', 'MAX_ABS_DIFF' and add
		'date' column."""
		cols_to_keep = ["testcase", "format", "MLD", "MAX_ABS_DIFF"]
		"""keep 'testcase', 'format', 'MLD', 'MAX_ABS_DIFF', 'MIN_ODG', 'MIN_SSNR' and add
		'date' and 'job' column."""
		cols_to_keep = ["testcase", "format", "MLD", "MAX_ABS_DIFF", "MIN_ODG", "MIN_SSNR"]
		parsed_data = {}
		for key, df in csv_data.items():
		tmp = key.split("-")
		job = "-".join(tmp[4:-4])
		cols = [col for col in cols_to_keep if col in df.columns]
		date = os.path.basename(os.path.dirname(key))
		new_df = df[cols].copy()
		new_df["date"] = date
		new_df["job"] = job
		parsed_data[key] = new_df

		# concatenate all dataframe in the dictionary
		@@ -39,38 +44,67 @@ def parse_csv_data(csv_data):
		return concat_df


		def plot_data(df, output_filename):
		"""plot max values for 'MLD' and 'MAX_ABS_DIFF' data and save
		def plot_data(df, args):
		"""plot max values for measure and data and save
		to html file."""

		measure = args.measure
		days = args.days

		# Convert 'date' to datetime
		df["date"] = pd.to_datetime(df["date"], errors="coerce")
		df["MLD"] = pd.to_numeric(df["MLD"], errors="coerce")
		df["MAX_ABS_DIFF"] = pd.to_numeric(df["MAX_ABS_DIFF"], errors="coerce")
		df[measure] = pd.to_numeric(df[measure], errors="coerce")

		# Filter out rows older than "days"
		cutoff = df["date"].max() - pd.Timedelta(days=days)
		df = df[df["date"] > cutoff].reset_index(drop=True)

		# Drop rows with NaT and NaN
		clean_df = df.dropna(subset=["date", "MLD", "MAX_ABS_DIFF"])
		df = df.dropna(subset=["date", measure])

		# Filter test cases based on include/reject/match arguments
		if args.include:
		mask = pd.Series(False, index=df.index)
		for tag in args.include:
		mask \|= df["testcase"].str.contains(tag, case=False, na=False)
		df = df[mask]
		if args.reject:
		mask = pd.Series(False, index=df.index)
		for tag in args.reject:
		mask \|= df["testcase"].str.contains(tag, case=False, na=False)
		df = df[~mask]
		if args.match:
		pattern = re.compile(args.match, re.IGNORECASE)
		df = df[df["testcase"].str.contains(pattern, na=False)]

		# Filter jobs based on job-include/job-reject/job-match arguments
		if args.job_include:
		mask = pd.Series(False, index=df.index)
		for tag in args.job_include:
		mask \|= df["job"].str.contains(tag, case=False, na=False)
		df = df[mask]
		if args.job_reject:
		mask = pd.Series(False, index=df.index)
		for tag in args.job_reject:
		mask \|= df["job"].str.contains(tag, case=False, na=False)
		df = df[~mask]
		if args.job_match:
		pattern = re.compile(args.job_match, re.IGNORECASE)
		df = df[df["job"].str.contains(pattern, na=False)]

		# Group by 'format' and 'date' to get rows with max 'MLD' per group
		max_mld = (
		clean_df.groupby(["format", "date"])
		.apply(lambda x: x.loc[x["MLD"].idxmax()])
		.reset_index(drop=True)
		)

		# Group by 'format' and 'date' to get rows with max 'MAX_ABS_DIFF' per
		# group
		max_abs_diff = (
		clean_df.groupby(["format", "date"])
		.apply(lambda x: x.loc[x["MAX_ABS_DIFF"].idxmax()])
		.reset_index(drop=True)
		)
		# Group by 'format' and 'date' to get rows with max 'MLD' per group
		idx = df.groupby(["format", "date"])[measure].idxmax()
		max = df.loc[idx].reset_index(drop=True)
		idx = df.groupby(["format", "date"])[measure].idxmin()
		min = df.loc[idx].reset_index(drop=True)
		mean = df.groupby(["format", "date"])[measure].mean().to_frame("mean").reset_index()

		formats = sorted(clean_df["format"].unique())
		formats = sorted(df["format"].unique())

		fig = make_subplots(
		rows=5,
		cols=2,
		specs=[[{"secondary_y": True}] * 2] * 5,
		subplot_titles=[f"{i}" for i in formats],
		shared_xaxes="columns",
		)
		@@ -79,64 +113,65 @@ def plot_data(df, output_filename):
		row = i // 2 + 1
		col = i % 2 + 1

		data_mld = max_mld[max_mld["format"] == fmt].sort_values("date")
		data_diff = max_abs_diff[max_abs_diff["format"]
		== fmt].sort_values("date")
		if "MIN" in measure:
		data = min[min["format"] == fmt].sort_values("date")
		maxmin_str = "Min"
		else:
		data = max[max["format"] == fmt].sort_values("date")
		maxmin_str = "Max"

		# Add max 'MLD' to primary y-axis
		# Add max measure to plots
		fig.add_trace(
		go.Scatter(
		x=data_mld["date"],
		y=data_mld["MLD"],
		x=data["date"],
		y=data[measure],
		mode="lines+markers",
		name=f" {fmt} - Max MLD",
		name=f"{maxmin_str} {measure}",
		hovertext=[
		f"Testcase: {tc}<br>MLD: {mld:.4f}<br>MAX_ABS_DIFF:"
		f"{abs_diff}<br>Format:"
		f" {format}<br>Date: {date.date()}"
		for tc, mld, abs_diff, format, date in zip(
		data_mld["testcase"],
		data_mld["MLD"],
		data_mld["MAX_ABS_DIFF"],
		data_mld["format"],
		data_mld["date"],
		f"Testcase: {tc}<br>{maxmin_str} {measure}: {value:.4f}"
		f"<br>Job: {job}"
		f"<br>Date: {date.date()}"
		for job, tc, value, date in zip(
		data["job"],
		data["testcase"],
		data[measure],
		data["date"],
		)
		],
		hoverinfo="text",
		marker_color="red",
		showlegend=(i == 0),
		),
		row=row,
		col=col,
		secondary_y=False,
		)

		# Add max 'MAX_ABS_DIFF' to secondary y-axis
		data = mean[mean["format"] == fmt].sort_values("date")

		# Add mean measure to plots
		fig.add_trace(
		go.Scatter(
		x=data_diff["date"],
		y=data_diff["MAX_ABS_DIFF"],
		x=data["date"],
		y=data["mean"],
		mode="lines+markers",
		name=f"{fmt} - Max MAX_ABS_DIFF",
		name=f"Mean {measure}",
		hovertext=[
		f"Testcase: {tc}<br>MLD: {mld:.4f}<br>MAX_ABS_DIFF:"
		f" {abs_diff:.4f}<br>Format:"
		f" {format}<br>Date: {date.date()}"
		for tc, mld, abs_diff, format, date in zip(
		data_diff["testcase"],
		data_diff["MLD"],
		data_diff["MAX_ABS_DIFF"],
		data_diff["format"],
		data_diff["date"],
		f"Mean {measure}: {value:.4f}" f"<br>Date: {date.date()}"
		for value, date in zip(
		data["mean"],
		data["date"],
		)
		],
		hoverinfo="text",
		marker_color="blue",
		showlegend=(i == 0),
		),
		row=row,
		col=col,
		secondary_y=True,
		)

		fig.update_layout(
		title_text="Long-term regression: max MLD and max MAX_ABS_DIFF",
		title_text=f"History: {measure}",
		legend=dict(x=1, y=1, orientation="v"),
		hovermode="x unified",
		)
		@@ -144,21 +179,8 @@ def plot_data(df, output_filename):
		fig.update_xaxes(automargin=True)
		fig.update_yaxes(automargin=True)

		# Update y-axes titles per subplot
		for i in range(10):
		yaxis_num = i * 2 + 1
		yaxis2_num = yaxis_num + 1
		fig["layout"][f"yaxis{yaxis_num}"].update(
		title="Max MLD", titlefont=dict(color="blue"), tickfont=dict(color="blue")
		)
		fig["layout"][f"yaxis{yaxis2_num}"].update(
		title="Max MAX_ABS_DIFF",
		titlefont=dict(color="green"),
		tickfont=dict(color="green"),
		)

		# Save to html
		fig.write_html(output_filename)
		fig.write_html(args.output_filename)


		if __name__ == "__main__":
		@@ -173,8 +195,54 @@ if __name__ == "__main__":
		type=str,
		help="Filename of the generated plot. e.g" ". long_term_regression.html",
		)
		parser.add_argument(
		"--days",
		type=int,
		help="Number of days in history. Default: 30",
		default=30,
		)
		parser.add_argument(
		"--measure",
		type=str,
		help="Measure for analysis: MLD, MAX_ABS_DIFF, MIN_ODG, MIN_SSNR, default: MLD",
		default="MLD",
		)
		parser.add_argument(
		"--include",
		nargs="+",
		type=str,
		help="List of tags to include in testcases",
		)
		parser.add_argument(
		"--reject",
		nargs="+",
		type=str,
		help="List of tags to reject in testcases",
		)
		parser.add_argument(
		"--match",
		type=str,
		help="Regex pattern for selecting testcases",
		)
		parser.add_argument(
		"--job-include",
		nargs="+",
		type=str,
		help="List of tags to include in jobs",
		)
		parser.add_argument(
		"--job-reject",
		nargs="+",
		type=str,
		help="List of tags to reject in jobs",
		)
		parser.add_argument(
		"--job-match",
		type=str,
		help="Regex pattern for selecting jobs",
		)
		args = parser.parse_args()

		csv_data = read_csv_files(args.root_dir)
		data = parse_csv_data(csv_data)
		plot_data(data, args.output_filename)
		plot_data(data, args)

scripts/detect_regressions.py

0 → 100644

+194 −0

Original line number	Diff line number	Diff line
		#!/usr/bin/env python3

		import argparse
		from pathlib import Path
		import pandas as pd
		import plotly.graph_objects as go
		from plotly.subplots import make_subplots


		def main(args):

		logs_dir = args.logs_dir
		output_filename = args.output_filename
		measure = args.measure
		days = args.days

		input_path = Path(logs_dir)
		logs = [f for f in input_path.iterdir() if f.is_dir()]

		# Build dict of scores
		formatdict = {}
		sha = {}
		logdict = {}
		for log in logs:
		date = log.name
		logdict[date] = {}
		formatdict[date] = {}
		for logfile in log.glob("*.csv"):
		tmp = logfile.name.split("-")
		job = "-".join(tmp[3:-4])
		sha[date] = tmp[-1].split(".")[0]
		data = pd.read_csv(logfile, usecols=["testcase", measure, "format"])
		logdict[date][job] = {}
		formatdict[date][job] = {}

		for testcase, value, format in zip(
		data["testcase"], data[measure], data["format"]
		):
		formatdict[date][job][testcase] = format
		logdict[date][job][testcase] = value

		# Restructure dict
		csv_rows = []
		formats = []
		for date, jobs in logdict.items():
		for job, testcases in jobs.items():
		for testcase, value in testcases.items():
		csv_rows.append(
		(job, testcase, formatdict[date][job][testcase], date, value)
		)

		result = pd.DataFrame(
		csv_rows, columns=["job", "testcase", "format", "date", "value"]
		)
		result = result.pivot(
		index=["job", "testcase", "format"], columns="date", values="value"
		).reset_index()

		# Keep only tests for which results exist in any of the days
		if days == -1:
		rng = result.columns[3:] # Whole history
		else:
		rng = result.columns[-days:]
		result = result.dropna(subset=rng)
		result = result.reset_index(drop=True)

		ratio = result.copy()
		ratio = ratio.reset_index()
		dates = result.iloc[:, 3:].columns

		# Calculate ratios
		ratio[dates[0]] = 1.0 # Set first ratio to 1.0
		for prevdate, currdate in zip(dates[0:-1], dates[1:]):
		ratio[currdate] = result[currdate] / result[prevdate]

		values = result.iloc[:, 3:]
		date = values.columns

		formats = result["format"].dropna().unique().tolist()

		plotdata = pd.DataFrame(0.0, index=formats, columns=dates[-(days) : -1])
		plottext = pd.DataFrame("", index=formats, columns=dates[-(days) : -1])

		all_indices = []

		for i in range(days):
		currdate = dates[-(days - i)] # Make robust for shorter history
		prevdate = dates[-(days - i + 1)]

		idx = ratio.groupby("format")[currdate].idxmax()
		all_indices.extend(idx.tolist())

		# Store worst case per format for plotting
		for f in formats:
		plotdata.loc[f, currdate] = ratio.iloc[idx[f]][currdate]
		plottext.loc[f, currdate] = (
		f"Job: {result.iloc[idx[f]]['job']}<br>Testcase: {result.iloc[idx[f]]['testcase']} <br>Max {measure} ratio: {ratio.iloc[idx[f]][currdate]:.2f}<br>Date: {currdate}"
		)

		fig = make_subplots(
		rows=5,
		cols=2,
		subplot_titles=[f"{i}" for i in formats],
		shared_xaxes="columns",
		)

		for i, fmt in enumerate(formats):
		row = i // 2 + 1
		col = i % 2 + 1

		fig.add_trace(
		go.Scatter(
		x=pd.to_datetime(plotdata.columns),
		y=plotdata.loc[fmt],
		mode="lines+markers",
		name=f"Max {measure}",
		hovertext=plottext.loc[fmt],
		hoverinfo="text",
		showlegend=False,
		),
		row=row,
		col=col,
		)

		fig.update_layout(
		title_text=f"Regression detection: Max {measure} ratio",
		legend=dict(x=1, y=1, orientation="v"),
		hovermode="x unified",
		)

		fig.update_xaxes(automargin=True)
		fig.update_yaxes(automargin=True)

		# Save to html
		fig.write_html(output_filename)

		# Write CSV-file
		if args.csv:
		output = result.iloc[all_indices].copy()
		cols = ["job","testcase","format"]
		cols.extend(date[-days:].tolist())
		output = output.loc[:,cols]
		values = output.iloc[:, 3:]
		last_date = values.columns[-1]
		output.insert(3, "min_date", values.idxmin(axis=1))
		output.insert(4, "min_sha", output["min_date"].map(sha))
		output.insert(5, "curr_value", output[last_date])
		output.insert(6, "min_value", values.min(axis=1))
		output.insert(7, "diff", output["curr_value"] - output["min_value"])
		output.insert(8, "ratio", output["curr_value"] / output["min_value"])
		output.loc[output["min_value"] == 0, "ratio"] = (
		1 # Set ratio to 1 for denominator 0
		)
		output["min_sha"] = (
		"'" + output["min_sha"]
		) # Add apostrophy to prevent Excel reading this as a number
		output.sort_values(
		by=["format", "ratio"], ascending=[True, False], inplace=True
		)
		output.to_csv(args.csv, sep=";", index=False)


		if __name__ == "__main__":
		parser = argparse.ArgumentParser(description="logs dir")
		parser.add_argument(
		"logs_dir",
		type=str,
		help="Logs dir, e.g. logs",
		)
		parser.add_argument(
		"output_filename",
		type=str,
		help="Output html file. e.g mld.html",
		)
		parser.add_argument(
		"--measure",
		type=str,
		help="Measure for summary, one of MLD MIN_SSNR MAX_ABS_DIFF MIN_ODG, (default: MLD)",
		default="MLD",
		)
		parser.add_argument(
		"--days",
		type=int,
		help="Number of days in history, (default: whole history)",
		default=-1,
		)
		parser.add_argument(
		"--csv",
		type=str,
		help="CSV output file",
		)

		args = parser.parse_args()
		main(args)

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