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Diffstat (limited to 'third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/apm_quality_assessment_boxplot.py')
-rw-r--r-- | third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/apm_quality_assessment_boxplot.py | 154 |
1 files changed, 154 insertions, 0 deletions
diff --git a/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/apm_quality_assessment_boxplot.py b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/apm_quality_assessment_boxplot.py new file mode 100644 index 0000000000..c425885b95 --- /dev/null +++ b/third_party/libwebrtc/modules/audio_processing/test/py_quality_assessment/apm_quality_assessment_boxplot.py @@ -0,0 +1,154 @@ +#!/usr/bin/env python +# Copyright (c) 2017 The WebRTC project authors. All Rights Reserved. +# +# Use of this source code is governed by a BSD-style license +# that can be found in the LICENSE file in the root of the source +# tree. An additional intellectual property rights grant can be found +# in the file PATENTS. All contributing project authors may +# be found in the AUTHORS file in the root of the source tree. +"""Shows boxplots of given score for different values of selected +parameters. Can be used to compare scores by audioproc_f flag. + +Usage: apm_quality_assessment_boxplot.py -o /path/to/output + -v polqa + -n /path/to/dir/with/apm_configs + -z audioproc_f_arg1 [arg2 ...] + +Arguments --config_names, --render_names, --echo_simulator_names, +--test_data_generators, --eval_scores can be used to filter the data +used for plotting. +""" + +import collections +import logging +import matplotlib.pyplot as plt +import os + +import quality_assessment.data_access as data_access +import quality_assessment.collect_data as collect_data + + +def InstanceArgumentsParser(): + """Arguments parser factory. + """ + parser = collect_data.InstanceArgumentsParser() + parser.description = ( + 'Shows boxplot of given score for different values of selected' + 'parameters. Can be used to compare scores by audioproc_f flag') + + parser.add_argument('-v', + '--eval_score', + required=True, + help=('Score name for constructing boxplots')) + + parser.add_argument( + '-n', + '--config_dir', + required=False, + help=('path to the folder with the configuration files'), + default='apm_configs') + + parser.add_argument('-z', + '--params_to_plot', + required=True, + nargs='+', + help=('audioproc_f parameter values' + 'by which to group scores (no leading dash)')) + + return parser + + +def FilterScoresByParams(data_frame, filter_params, score_name, config_dir): + """Filters data on the values of one or more parameters. + + Args: + data_frame: pandas.DataFrame of all used input data. + + filter_params: each config of the input data is assumed to have + exactly one parameter from `filter_params` defined. Every value + of the parameters in `filter_params` is a key in the returned + dict; the associated value is all cells of the data with that + value of the parameter. + + score_name: Name of score which value is boxplotted. Currently cannot do + more than one value. + + config_dir: path to dir with APM configs. + + Returns: dictionary, key is a param value, result is all scores for + that param value (see `filter_params` for explanation). + """ + results = collections.defaultdict(dict) + config_names = data_frame['apm_config'].drop_duplicates().values.tolist() + + for config_name in config_names: + config_json = data_access.AudioProcConfigFile.Load( + os.path.join(config_dir, config_name + '.json')) + data_with_config = data_frame[data_frame.apm_config == config_name] + data_cell_scores = data_with_config[data_with_config.eval_score_name == + score_name] + + # Exactly one of `params_to_plot` must match: + (matching_param, ) = [ + x for x in filter_params if '-' + x in config_json + ] + + # Add scores for every track to the result. + for capture_name in data_cell_scores.capture: + result_score = float(data_cell_scores[data_cell_scores.capture == + capture_name].score) + config_dict = results[config_json['-' + matching_param]] + if capture_name not in config_dict: + config_dict[capture_name] = {} + + config_dict[capture_name][matching_param] = result_score + + return results + + +def _FlattenToScoresList(config_param_score_dict): + """Extracts a list of scores from input data structure. + + Args: + config_param_score_dict: of the form {'capture_name': + {'param_name' : score_value,.. } ..} + + Returns: Plain list of all score value present in input data + structure + """ + result = [] + for capture_name in config_param_score_dict: + result += list(config_param_score_dict[capture_name].values()) + return result + + +def main(): + # Init. + # TODO(alessiob): INFO once debugged. + logging.basicConfig(level=logging.DEBUG) + parser = InstanceArgumentsParser() + args = parser.parse_args() + + # Get the scores. + src_path = collect_data.ConstructSrcPath(args) + logging.debug(src_path) + scores_data_frame = collect_data.FindScores(src_path, args) + + # Filter the data by `args.params_to_plot` + scores_filtered = FilterScoresByParams(scores_data_frame, + args.params_to_plot, + args.eval_score, args.config_dir) + + data_list = sorted(scores_filtered.items()) + data_values = [_FlattenToScoresList(x) for (_, x) in data_list] + data_labels = [x for (x, _) in data_list] + + _, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6)) + axes.boxplot(data_values, labels=data_labels) + axes.set_ylabel(args.eval_score) + axes.set_xlabel('/'.join(args.params_to_plot)) + plt.show() + + +if __name__ == "__main__": + main() |