| #!/usr/bin/env python |
| """Tool to filter, organize, compare and display benchmarking results. Usefull |
| for smaller datasets. It works great with a few dozen runs it is not designed to |
| deal with hundreds. |
| Requires the pandas library to be installed.""" |
| from __future__ import print_function |
| |
| import pandas as pd |
| from scipy import stats |
| import sys |
| import os.path |
| import re |
| import numbers |
| import argparse |
| |
| GEOMEAN_ROW = 'Geomean difference' |
| |
| def read_lit_json(filename): |
| import json |
| jsondata = json.load(open(filename)) |
| columns = [] |
| columnindexes = {} |
| names = set() |
| info_columns = ['hash'] |
| # Pass1: Figure out metrics (= the column index) |
| if 'tests' not in jsondata: |
| print("%s: Could not find toplevel 'tests' key") |
| sys.exit(1) |
| for test in jsondata['tests']: |
| name = test.get("name") |
| if name is None: |
| sys.stderr.write("Error: Found unnamed test\n" % name) |
| sys.exit(1) |
| if name in names: |
| sys.stderr.write("Error: Multiple tests with name '%s'\n" % name) |
| sys.exit(1) |
| if "metrics" not in test: |
| print("Warning: '%s' has no metrics, skipping!" % test['name']) |
| continue |
| names.add(name) |
| for name in test["metrics"].keys(): |
| if name not in columnindexes: |
| columnindexes[name] = len(columns) |
| columns.append(name) |
| for name in test.keys(): |
| if name not in columnindexes and name in info_columns: |
| columnindexes[name] = len(columns) |
| columns.append(name) |
| |
| # Pass2 actual data construction |
| nan = float('NaN') |
| data = [] |
| testnames = [] |
| for test in jsondata['tests']: |
| if "metrics" not in test: |
| continue |
| name = test['name'] |
| if 'shortname' in test: |
| name = test['shortname'] |
| testnames.append(name) |
| |
| datarow = [nan] * len(columns) |
| for (metricname, value) in test['metrics'].items(): |
| datarow[columnindexes[metricname]] = value |
| for (name, value) in test.items(): |
| index = columnindexes.get(name) |
| if index is not None: |
| datarow[index] = test[name] |
| data.append(datarow) |
| index = pd.Index(testnames, name='Program') |
| return pd.DataFrame(data=data, index=index, columns=columns) |
| |
| def read_report_simple_csv(filename): |
| return pd.read_csv(filename, na_values=['*'], index_col=0, header=0) |
| |
| def read(name): |
| if name.endswith(".json"): |
| return read_lit_json(name) |
| if name.endswith(".csv"): |
| return read_report_simple_csv(name) |
| raise Exception("Cannot determine file format"); |
| |
| def readmulti(filenames): |
| # Read datasets |
| datasetnames = [] |
| datasets = [] |
| prev_index = None |
| for filename in filenames: |
| data = read(filename) |
| name = os.path.basename(filename) |
| # drop .json/.csv suffix; TODO: Should we rather do this in the printing |
| # logic? |
| for ext in ['.csv', '.json']: |
| if name.endswith(ext): |
| name = name[:-len(ext)] |
| datasets.append(data) |
| suffix = "" |
| count = 0 |
| while True: |
| if name+suffix not in datasetnames: |
| break |
| suffix = str(count) |
| count +=1 |
| |
| datasetnames.append(name+suffix) |
| # Warn if index names are different |
| if prev_index is not None and prev_index.name != data.index.name: |
| sys.stderr.write("Warning: Mismatched index names: '%s' vs '%s'\n" |
| % (prev_index.name, data.index.name)) |
| prev_index = data.index |
| # Merge datasets |
| d = pd.concat(datasets, axis=0, names=['run'], keys=datasetnames) |
| return d |
| |
| def get_values(values): |
| # Create data view without diff column. |
| if 'diff' in values.columns: |
| values = values[[c for c in values.columns if c != 'diff']] |
| has_two_runs = len(values.columns) == 2 |
| if has_two_runs: |
| return (values.iloc[:,0], values.iloc[:,1]) |
| else: |
| return (values.min(axis=1), values.max(axis=1)) |
| |
| def add_diff_column(metric, values, absolute_diff=False): |
| values0, values1 = get_values(values[metric]) |
| values0.fillna(0.0, inplace=True) |
| values1.fillna(0.0, inplace=True) |
| # Quotient or absolute difference? |
| if absolute_diff: |
| values[(metric, 'diff')] = values1 - values0 |
| else: |
| values[(metric, 'diff')] = (values1 / values0) - 1.0 |
| return values |
| |
| def add_geomean_row(metrics, data, dataout): |
| """ |
| Normalize values1 over values0, compute geomean difference and add a |
| summary row to dataout. |
| """ |
| gm = pd.DataFrame(index=[GEOMEAN_ROW], columns=dataout.columns, |
| dtype='float64') |
| for metric in metrics: |
| values0, values1 = get_values(data[metric]) |
| # Avoid infinite values in the diff and instead use NaN, as otherwise |
| # the computation of the geometric mean will fail. |
| values0 = values0.replace({0: float('NaN')}) |
| relative = values1 / values0 |
| gm_diff = stats.gmean(relative.dropna()) - 1.0 |
| gm[(metric, 'diff')] = gm_diff |
| gm.Program = GEOMEAN_ROW |
| return pd.concat([dataout, gm]) |
| |
| def filter_failed(data, key='Exec'): |
| return data.loc[data[key] == "pass"] |
| |
| def filter_short(data, threshold, key='Exec_Time'): |
| return data.loc[data[key] >= threshold] |
| |
| def filter_same_hash(data, key='hash'): |
| assert key in data.columns |
| assert data.index.get_level_values(0).nunique() > 1 |
| |
| return data.groupby(level=1).filter(lambda x: x[key].nunique() != 1) |
| |
| def filter_blacklist(data, blacklist): |
| return data.loc[~(data.index.get_level_values(1).isin(blacklist))] |
| |
| def print_filter_stats(reason, before, after): |
| n_before = len(before.groupby(level=1)) |
| n_after = len(after.groupby(level=1)) |
| n_filtered = n_before - n_after |
| if n_filtered != 0: |
| print("%s: %s (filtered out)" % (reason, n_filtered)) |
| |
| # Truncate a string to a maximum length by keeping a prefix, a suffix and ... |
| # in the middle |
| def truncate(string, prefix_len, suffix_len): |
| return re.sub("^(.{%d}).*(.{%d})$" % (prefix_len, suffix_len), |
| "\g<1>...\g<2>", string) |
| |
| # Search for common prefixes and suffixes in a list of names and return |
| # a (prefix,suffix) tuple that specifies how many characters can be dropped |
| # for the prefix/suffix. The numbers will be small enough that no name will |
| # become shorter than min_len characters. |
| def determine_common_prefix_suffix(names, min_len=8): |
| if len(names) <= 1: |
| return (0,0) |
| name0 = names[0] |
| prefix = name0 |
| prefix_len = len(name0) |
| suffix = name0 |
| suffix_len = len(name0) |
| shortest_name = len(name0) |
| for name in names: |
| if len(name) < shortest_name: |
| shortest_name = len(name) |
| while prefix_len > 0 and name[:prefix_len] != prefix: |
| prefix_len -= 1 |
| prefix = name0[:prefix_len] |
| while suffix_len > 0 and name[-suffix_len:] != suffix: |
| suffix_len -= 1 |
| suffix = name0[-suffix_len:] |
| |
| if suffix[0] != '.' and suffix[0] != '_': |
| suffix_len = 0 |
| suffix_len = max(0, min(shortest_name - prefix_len - min_len, suffix_len)) |
| prefix_len = max(0, min(shortest_name - suffix_len, prefix_len)) |
| return (prefix_len, suffix_len) |
| |
| def format_relative_diff(value): |
| if not isinstance(value, numbers.Integral): |
| return "%4.1f%%" % (value * 100.) |
| else: |
| return "%-5d" % value |
| |
| def print_result(d, limit_output=True, shorten_names=True, minimal_names=False, |
| show_diff_column=True, sortkey='diff', sort_by_abs=True, |
| absolute_diff=False): |
| metrics = d.columns.levels[0] |
| if sort_by_abs: |
| d = d.sort_values(by=(metrics[0], sortkey), key=pd.Series.abs, ascending=False) |
| else: |
| d = d.sort_values(by=(metrics[0], sortkey), ascending=False) |
| |
| # Ensure that the columns are grouped by metric (rather than having the |
| # diffs at the end of the line). |
| d = d.reindex(columns=d.columns.levels[0], level=0) |
| |
| if not show_diff_column: |
| # Remove all diff columns (using level=1 since level 0 is the metric). |
| d.drop(labels='diff', level=1, axis=1, inplace=True) |
| dataout = d |
| if limit_output: |
| # Take 15 topmost elements |
| dataout = dataout.head(15) |
| |
| formatters = dict() |
| if not absolute_diff: |
| for m in metrics: |
| formatters[(m, 'diff')] = format_relative_diff |
| # Turn index into a column so we can format it... |
| formatted_program = dataout.index.to_series() |
| if shorten_names: |
| def format_name(name, common_prefix, common_suffix): |
| name = name[common_prefix:] |
| if common_suffix > 0: |
| name = name[:-common_suffix] |
| return "%-45s" % truncate(name, 10, 30) |
| |
| |
| def strip_name_fully(name): |
| name = name.split('/')[-1] |
| if name.endswith('.test'): |
| name = name[:-5] |
| return name |
| |
| # The to_string formatters argument appears to be ignored for |
| # dtype=object, so transform the program column manually. |
| if minimal_names: |
| formatted_program = formatted_program.map(strip_name_fully) |
| else: |
| drop_prefix, drop_suffix = determine_common_prefix_suffix(formatted_program) |
| formatted_program = formatted_program.map(lambda name: format_name(name, drop_prefix, drop_suffix)) |
| dataout.insert(0, 'Program', formatted_program) |
| # Add the geometric mean row after we have formatted the program names |
| # as it will otherwise interfere with common prefix/suffix computation. |
| if show_diff_column and not absolute_diff: |
| # geometric mean only makes sense for relative differences. |
| dataout = add_geomean_row(metrics, d, dataout) |
| |
| def float_format(x): |
| if x == '': |
| return '' |
| return "%6.2f" % (x,) |
| |
| pd.set_option("display.max_colwidth", 0) |
| pd.set_option('display.width', 0) |
| # Print an empty value instead of NaN (for the geomean row). |
| out = dataout.to_string(index=False, justify='left', na_rep='', |
| float_format=float_format, formatters=formatters) |
| print(out) |
| print(d.describe()) |
| |
| def main(): |
| parser = argparse.ArgumentParser(prog='compare.py') |
| parser.add_argument('-a', '--all', action='store_true') |
| parser.add_argument('-f', '--full', action='store_true') |
| parser.add_argument('-m', '--metric', action='append', dest='metrics', |
| default=[]) |
| parser.add_argument('--nodiff', action='store_false', dest='show_diff', |
| default=None) |
| parser.add_argument('--diff', action='store_true', dest='show_diff') |
| parser.add_argument('--absolute-diff', action='store_true', |
| help='Use an absolute instead of a relative difference') |
| parser.add_argument('--filter-short', nargs='?', |
| dest='filter_short', default=None, |
| help="Filter benchmarks with execution times less than N seconds (default 1.0s)") |
| parser.add_argument('--no-filter-failed', action='store_false', |
| dest='filter_failed', default=True) |
| parser.add_argument('--filter-hash', action='store_true', |
| dest='filter_hash', default=False) |
| parser.add_argument('--filter-blacklist', |
| dest='filter_blacklist', default=None) |
| parser.add_argument('--merge-average', action='store_const', |
| dest='merge_function', const=pd.DataFrame.mean, |
| default=pd.DataFrame.min) |
| parser.add_argument('--merge-min', action='store_const', |
| dest='merge_function', const=pd.DataFrame.min) |
| parser.add_argument('--merge-max', action='store_const', |
| dest='merge_function', const=pd.DataFrame.max) |
| parser.add_argument('--lhs-name', default="lhs", |
| help="Name used to describe left side in 'vs' mode") |
| parser.add_argument('--rhs-name', default="rhs", |
| help="Name used to describe right side in 'vs' mode") |
| parser.add_argument('files', metavar='FILE', nargs='+', help="To compare two groups of results, put 'vs' between them") |
| parser.add_argument('--minimal-names', action='store_true', |
| dest='minimal_names', default=False) |
| parser.add_argument('--no-abs-sort', action='store_true', |
| dest='no_abs_sort', default=False, help="Don't use abs() when sorting results") |
| config = parser.parse_args() |
| |
| if config.show_diff is None: |
| config.show_diff = len(config.files) > 1 |
| |
| # If only --filter-short is provided, i.e. its optional argument is |
| # omitted, we default to threshold of 1 second to filter out apps and |
| # results with a execution time less than that. |
| filter_short_threshold = 1.0 |
| |
| # If the optional argument to --filter-short is omitted, we need to take |
| # care of this case and command line: |
| # --filter-short FILE [FILE ...] |
| # I.e., we need to recognise that FILE is not the optional argument to |
| # --filter-short. The way we do this, is to try converting the option value |
| # to a float, and if that fails, we insert it back into the files list (in |
| # the first position). |
| if config.filter_short is not None: |
| try: |
| filter_short_threshold = float(config.filter_short) |
| except: |
| config.files.insert(0, config.filter_short) |
| |
| # Read inputs |
| files = config.files |
| if "vs" in files: |
| split = files.index("vs") |
| lhs = files[0:split] |
| rhs = files[split+1:] |
| |
| # Filter minimum of lhs and rhs |
| lhs_d = readmulti(lhs) |
| lhs_merged = lhs_d.groupby(level=1).apply(config.merge_function) |
| rhs_d = readmulti(rhs) |
| rhs_merged = rhs_d.groupby(level=1).apply(config.merge_function) |
| |
| # Combine to new dataframe |
| data = pd.concat([lhs_merged, rhs_merged], names=['l/r'], |
| keys=[config.lhs_name, config.rhs_name]) |
| else: |
| data = readmulti(files) |
| |
| # Decide which metric to display / what is our "main" metric |
| metrics = config.metrics |
| if len(metrics) == 0: |
| defaults = [ 'Exec_Time', 'exec_time', 'Value', 'Runtime' ] |
| for defkey in defaults: |
| if defkey in data.columns: |
| metrics = [defkey] |
| break |
| if len(metrics) == 0: |
| sys.stderr.write("No default metric found and none specified\n") |
| sys.stderr.write("Available metrics:\n") |
| for column in data.columns: |
| sys.stderr.write("\t%s\n" % column) |
| sys.exit(1) |
| for metric in metrics: |
| problem = False |
| if metric not in data.columns: |
| sys.stderr.write("Unknown metric '%s'\n" % metric) |
| problem = True |
| if problem: |
| sys.exit(1) |
| |
| # Filter data |
| proggroup = data.groupby(level=1) |
| initial_size = len(proggroup.indices) |
| print("Tests: %s" % (initial_size,)) |
| if config.filter_failed and hasattr(data, 'Exec'): |
| newdata = filter_failed(data) |
| print_filter_stats("Failed", data, newdata) |
| newdata = newdata.drop('Exec', 1) |
| data = newdata |
| if config.filter_short: |
| newdata = filter_short(data, filter_short_threshold, metric) |
| print_filter_stats("Short Running", data, newdata) |
| data = newdata |
| if config.filter_hash and 'hash' in data.columns and \ |
| data.index.get_level_values(0).nunique() > 1: |
| newdata = filter_same_hash(data) |
| print_filter_stats("Same hash", data, newdata) |
| data = newdata |
| if config.filter_blacklist: |
| blacklist = open(config.filter_blacklist).readlines() |
| blacklist = [line.strip() for line in blacklist] |
| newdata = filter_blacklist(data, blacklist) |
| print_filter_stats("In Blacklist", data, newdata) |
| data = newdata |
| final_size = len(data.groupby(level=1)) |
| if final_size != initial_size: |
| print("Remaining: %d" % (final_size,)) |
| |
| # Reduce / add columns |
| print("Metric: %s" % (",".join(metrics),)) |
| if len(metrics) > 0: |
| data = data[metrics] |
| |
| data = data.unstack(level=0) |
| |
| for metric in data.columns.levels[0]: |
| data = add_diff_column(metric, data, absolute_diff=config.absolute_diff) |
| |
| sortkey = 'diff' |
| # TODO: should we still be sorting by diff even if the diff is hidden? |
| if len(config.files) == 1: |
| sortkey = data.columns.levels[1][0] |
| |
| # Print data |
| print("") |
| shorten_names = not config.full |
| limit_output = (not config.all) and (not config.full) |
| print_result(data, limit_output, shorten_names, config.minimal_names, |
| config.show_diff, sortkey, config.no_abs_sort, |
| config.absolute_diff) |
| |
| |
| if __name__ == "__main__": |
| main() |
