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task3_sound_event_detection_in_real_life_audio.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# DCASE 2016::Sound Event Detection in Real-life Audio / Baseline System
from src.ui import *
from src.general import *
from src.files import *
from src.features import *
from src.sound_event_detection import *
from src.dataset import *
from src.evaluation import *
import numpy
import csv
import warnings
import argparse
import textwrap
import math
from sklearn import mixture
__version_info__ = ('1', '0', '1')
__version__ = '.'.join(__version_info__)
def main(argv):
numpy.random.seed(123456) # let's make randomization predictable
parser = argparse.ArgumentParser(
prefix_chars='-+',
formatter_class=argparse.RawDescriptionHelpFormatter,
description=textwrap.dedent('''\
DCASE 2016
Task 3: Sound Event Detection in Real-life Audio
Baseline System
---------------------------------------------
Tampere University of Technology / Audio Research Group
Author: Toni Heittola ( toni.heittola@tut.fi )
System description
This is an baseline implementation for the D-CASE 2016, task 3 - Sound event detection in real life audio.
The system has binary classifier for each included sound event class. The GMM classifier is trained with
the positive and negative examples from the mixture signals, and classification is done between these
two models as likelihood ratio. Acoustic features are MFCC+Delta+Acceleration (MFCC0 omitted).
'''))
parser.add_argument("-development", help="Use the system in the development mode", action='store_true',
default=False, dest='development')
parser.add_argument("-challenge", help="Use the system in the challenge mode", action='store_true',
default=False, dest='challenge')
parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + __version__)
args = parser.parse_args()
# Load parameters from config file
parameter_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
os.path.splitext(os.path.basename(__file__))[0]+'.yaml')
params = load_parameters(parameter_file)
params = process_parameters(params)
make_folders(params)
title("DCASE 2016::Sound Event Detection in Real-life Audio / Baseline System")
# Check if mode is defined
if not (args.development or args.challenge):
args.development = True
args.challenge = False
dataset_evaluation_mode = 'folds'
if args.development and not args.challenge:
print "Running system in development mode"
dataset_evaluation_mode = 'folds'
elif not args.development and args.challenge:
print "Running system in challenge mode"
dataset_evaluation_mode = 'full'
# Get dataset container class
dataset = eval(params['general']['development_dataset'])(data_path=params['path']['data'])
# Fetch data over internet and setup the data
# ==================================================
if params['flow']['initialize']:
dataset.fetch()
# Extract features for all audio files in the dataset
# ==================================================
if params['flow']['extract_features']:
section_header('Feature extraction [Development data]')
# Collect files from evaluation sets
files = []
for fold in dataset.folds(mode=dataset_evaluation_mode):
for item_id, item in enumerate(dataset.train(fold)):
if item['file'] not in files:
files.append(item['file'])
for item_id, item in enumerate(dataset.test(fold)):
if item['file'] not in files:
files.append(item['file'])
# Go through files and make sure all features are extracted
do_feature_extraction(files=files,
dataset=dataset,
feature_path=params['path']['features'],
params=params['features'],
overwrite=params['general']['overwrite'])
foot()
# Prepare feature normalizers
# ==================================================
if params['flow']['feature_normalizer']:
section_header('Feature normalizer [Development data]')
do_feature_normalization(dataset=dataset,
feature_normalizer_path=params['path']['feature_normalizers'],
feature_path=params['path']['features'],
dataset_evaluation_mode=dataset_evaluation_mode,
overwrite=params['general']['overwrite'])
foot()
# System training
# ==================================================
if params['flow']['train_system']:
section_header('System training [Development data]')
do_system_training(dataset=dataset,
model_path=params['path']['models'],
feature_normalizer_path=params['path']['feature_normalizers'],
feature_path=params['path']['features'],
hop_length_seconds=params['features']['hop_length_seconds'],
classifier_params=params['classifier']['parameters'],
dataset_evaluation_mode=dataset_evaluation_mode,
classifier_method=params['classifier']['method'],
overwrite=params['general']['overwrite']
)
foot()
# System evaluation in development mode
if args.development and not args.challenge:
# System testing
# ==================================================
if params['flow']['test_system']:
section_header('System testing [Development data]')
do_system_testing(dataset=dataset,
result_path=params['path']['results'],
feature_path=params['path']['features'],
model_path=params['path']['models'],
feature_params=params['features'],
detector_params=params['detector'],
dataset_evaluation_mode=dataset_evaluation_mode,
classifier_method=params['classifier']['method'],
overwrite=params['general']['overwrite']
)
foot()
# System evaluation
# ==================================================
if params['flow']['evaluate_system']:
section_header('System evaluation [Development data]')
do_system_evaluation(dataset=dataset,
dataset_evaluation_mode=dataset_evaluation_mode,
result_path=params['path']['results'])
foot()
# System evaluation with challenge data
elif not args.development and args.challenge:
# Fetch data over internet and setup the data
challenge_dataset = eval(params['general']['challenge_dataset'])()
if params['flow']['initialize']:
challenge_dataset.fetch()
# System testing
if params['flow']['test_system']:
section_header('System testing [Challenge data]')
do_system_testing(dataset=challenge_dataset,
result_path=params['path']['challenge_results'],
feature_path=params['path']['features'],
model_path=params['path']['models'],
feature_params=params['features'],
detector_params=params['detector'],
dataset_evaluation_mode=dataset_evaluation_mode,
classifier_method=params['classifier']['method'],
overwrite=True
)
foot()
print " "
print "Your results for the challenge data are stored at ["+params['path']['challenge_results']+"]"
print " "
def process_parameters(params):
"""Parameter post-processing.
Parameters
----------
params : dict
parameters in dict
Returns
-------
params : dict
processed parameters
"""
params['features']['mfcc']['win_length'] = int(params['features']['win_length_seconds'] * params['features']['fs'])
params['features']['mfcc']['hop_length'] = int(params['features']['hop_length_seconds'] * params['features']['fs'])
# Copy parameters for current classifier method
params['classifier']['parameters'] = params['classifier_parameters'][params['classifier']['method']]
# Hash
params['features']['hash'] = get_parameter_hash(params['features'])
params['classifier']['hash'] = get_parameter_hash(params['classifier'])
params['detector']['hash'] = get_parameter_hash(params['detector'])
# Paths
params['path']['data'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), params['path']['data'])
params['path']['base'] = os.path.join(os.path.dirname(os.path.realpath(__file__)), params['path']['base'])
# Features
params['path']['features_'] = params['path']['features']
params['path']['features'] = os.path.join(params['path']['base'],
params['path']['features'],
params['features']['hash'])
# Feature normalizers
params['path']['feature_normalizers_'] = params['path']['feature_normalizers']
params['path']['feature_normalizers'] = os.path.join(params['path']['base'],
params['path']['feature_normalizers'],
params['features']['hash'])
# Models
# Save parameters into folders to help manual browsing of files.
params['path']['models_'] = params['path']['models']
params['path']['models'] = os.path.join(params['path']['base'],
params['path']['models'],
params['features']['hash'],
params['classifier']['hash'])
# Results
params['path']['results_'] = params['path']['results']
params['path']['results'] = os.path.join(params['path']['base'],
params['path']['results'],
params['features']['hash'],
params['classifier']['hash'],
params['detector']['hash'])
return params
def make_folders(params, parameter_filename='parameters.yaml'):
"""Create all needed folders, and saves parameters in yaml-file for easier manual browsing of data.
Parameters
----------
params : dict
parameters in dict
parameter_filename : str
filename to save parameters used to generate the folder name
Returns
-------
nothing
"""
# Check that target path exists, create if not
check_path(params['path']['features'])
check_path(params['path']['feature_normalizers'])
check_path(params['path']['models'])
check_path(params['path']['results'])
# Save parameters into folders to help manual browsing of files.
# Features
feature_parameter_filename = os.path.join(params['path']['features'], parameter_filename)
if not os.path.isfile(feature_parameter_filename):
save_parameters(feature_parameter_filename, params['features'])
# Feature normalizers
feature_normalizer_parameter_filename = os.path.join(params['path']['feature_normalizers'], parameter_filename)
if not os.path.isfile(feature_normalizer_parameter_filename):
save_parameters(feature_normalizer_parameter_filename, params['features'])
# Models
model_features_parameter_filename = os.path.join(params['path']['base'],
params['path']['models_'],
params['features']['hash'],
parameter_filename)
if not os.path.isfile(model_features_parameter_filename):
save_parameters(model_features_parameter_filename, params['features'])
model_models_parameter_filename = os.path.join(params['path']['base'],
params['path']['models_'],
params['features']['hash'],
params['classifier']['hash'],
parameter_filename)
if not os.path.isfile(model_models_parameter_filename):
save_parameters(model_models_parameter_filename, params['classifier'])
# Results
# Save parameters into folders to help manual browsing of files.
result_features_parameter_filename = os.path.join(params['path']['base'],
params['path']['results_'],
params['features']['hash'],
parameter_filename)
if not os.path.isfile(result_features_parameter_filename):
save_parameters(result_features_parameter_filename, params['features'])
result_models_parameter_filename = os.path.join(params['path']['base'],
params['path']['results_'],
params['features']['hash'],
params['classifier']['hash'],
parameter_filename)
if not os.path.isfile(result_models_parameter_filename):
save_parameters(result_models_parameter_filename, params['classifier'])
result_detector_parameter_filename = os.path.join(params['path']['base'],
params['path']['results_'],
params['features']['hash'],
params['classifier']['hash'],
params['detector']['hash'],
parameter_filename)
if not os.path.isfile(result_detector_parameter_filename):
save_parameters(result_detector_parameter_filename, params['detector'])
def get_feature_filename(audio_file, path, extension='cpickle'):
"""Get feature filename
Parameters
----------
audio_file : str
audio file name from which the features are extracted
path : str
feature path
extension : str
file extension
(Default value='cpickle')
Returns
-------
feature_filename : str
full feature filename
"""
return os.path.join(path, 'sequence_' + os.path.splitext(audio_file)[0] + '.' + extension)
def get_feature_normalizer_filename(fold, scene_label, path, extension='cpickle'):
"""Get normalizer filename
Parameters
----------
fold : int >= 0
evaluation fold number
scene_label : str
scene label
path : str
normalizer path
extension : str
file extension
(Default value='cpickle')
Returns
-------
normalizer_filename : str
full normalizer filename
"""
return os.path.join(path, 'scale_fold' + str(fold) + '_' + str(scene_label) + '.' + extension)
def get_model_filename(fold, scene_label, path, extension='cpickle'):
"""Get model filename
Parameters
----------
fold : int >= 0
evaluation fold number
scene_label : str
scene label
path : str
model path
extension : str
file extension
(Default value='cpickle')
Returns
-------
model_filename : str
full model filename
"""
return os.path.join(path, 'model_fold' + str(fold) + '_' + str(scene_label) + '.' + extension)
def get_result_filename(fold, scene_label, path, extension='txt'):
"""Get result filename
Parameters
----------
fold : int >= 0
evaluation fold number
scene_label : str
scene label
path : str
result path
extension : str
file extension
(Default value='cpickle')
Returns
-------
result_filename : str
full result filename
"""
if fold == 0:
return os.path.join(path, 'results_' + str(scene_label) + '.' + extension)
else:
return os.path.join(path, 'results_fold' + str(fold) + '_' + str(scene_label) + '.' + extension)
def do_feature_extraction(files, dataset, feature_path, params, overwrite=False):
"""Feature extraction
Parameters
----------
files : list
file list
dataset : class
dataset class
feature_path : str
path where the features are saved
params : dict
parameter dict
overwrite : bool
overwrite existing feature files
(Default value=False)
Returns
-------
nothing
Raises
-------
IOError
Audio file not found.
"""
for file_id, audio_filename in enumerate(files):
# Get feature filename
current_feature_file = get_feature_filename(audio_file=os.path.split(audio_filename)[1], path=feature_path)
progress(title_text='Extracting [sequences]',
percentage=(float(file_id) / len(files)),
note=os.path.split(audio_filename)[1])
if not os.path.isfile(current_feature_file) or overwrite:
# Load audio
if os.path.isfile(dataset.relative_to_absolute_path(audio_filename)):
y, fs = load_audio(filename=dataset.relative_to_absolute_path(audio_filename), mono=True, fs=params['fs'])
else:
raise IOError("Audio file not found [%s]" % audio_filename)
# Extract features
feature_data = feature_extraction(y=y,
fs=fs,
include_mfcc0=params['include_mfcc0'],
include_delta=params['include_delta'],
include_acceleration=params['include_acceleration'],
mfcc_params=params['mfcc'],
delta_params=params['mfcc_delta'],
acceleration_params=params['mfcc_acceleration'])
# Save
save_data(current_feature_file, feature_data)
def do_feature_normalization(dataset, feature_normalizer_path, feature_path, dataset_evaluation_mode='folds', overwrite=False):
"""Feature normalization
Calculated normalization factors for each evaluation fold based on the training material available.
Parameters
----------
dataset : class
dataset class
feature_normalizer_path : str
path where the feature normalizers are saved.
feature_path : str
path where the features are saved.
dataset_evaluation_mode : str ['folds', 'full']
evaluation mode, 'full' all material available is considered to belong to one fold.
(Default value='folds')
overwrite : bool
overwrite existing normalizers
(Default value=False)
Returns
-------
nothing
Raises
-------
IOError
Feature file not found.
"""
for fold in dataset.folds(mode=dataset_evaluation_mode):
for scene_id, scene_label in enumerate(dataset.scene_labels):
current_normalizer_file = get_feature_normalizer_filename(fold=fold, scene_label=scene_label, path=feature_normalizer_path)
if not os.path.isfile(current_normalizer_file) or overwrite:
# Collect sequence files from scene class
files = []
for item_id, item in enumerate(dataset.train(fold, scene_label=scene_label)):
if item['file'] not in files:
files.append(item['file'])
file_count = len(files)
# Initialize statistics
normalizer = FeatureNormalizer()
for file_id, audio_filename in enumerate(files):
progress(title_text='Collecting data',
fold=fold,
percentage=(float(file_id) / file_count),
note=os.path.split(audio_filename)[1])
# Load features
feature_filename = get_feature_filename(audio_file=os.path.split(audio_filename)[1], path=feature_path)
if os.path.isfile(feature_filename):
feature_data = load_data(feature_filename)['stat']
else:
raise IOError("Feature file not found [%s]" % audio_filename)
# Accumulate statistics
normalizer.accumulate(feature_data)
# Calculate normalization factors
normalizer.finalize()
# Save
save_data(current_normalizer_file, normalizer)
def do_system_training(dataset, model_path, feature_normalizer_path, feature_path, hop_length_seconds, classifier_params,
dataset_evaluation_mode='folds', classifier_method='gmm', overwrite=False):
"""System training
Train a model pair for each sound event class, one for activity and one for inactivity.
model container format:
{
'normalizer': normalizer class
'models' :
{
'mouse click' :
{
'positive': mixture.GMM class,
'negative': mixture.GMM class
}
'keyboard typing' :
{
'positive': mixture.GMM class,
'negative': mixture.GMM class
}
...
}
}
Parameters
----------
dataset : class
dataset class
model_path : str
path where the models are saved.
feature_normalizer_path : str
path where the feature normalizers are saved.
feature_path : str
path where the features are saved.
hop_length_seconds : float > 0
feature frame hop length in seconds
classifier_params : dict
parameter dict
dataset_evaluation_mode : str ['folds', 'full']
evaluation mode, 'full' all material available is considered to belong to one fold.
(Default value='folds')
classifier_method : str ['gmm']
classifier method, currently only GMM supported
(Default value='gmm')
overwrite : bool
overwrite existing models
(Default value=False)
Returns
-------
nothing
Raises
-------
ValueError
classifier_method is unknown.
IOError
Feature normalizer not found.
Feature file not found.
"""
if classifier_method != 'gmm':
raise ValueError("Unknown classifier method ["+classifier_method+"]")
for fold in dataset.folds(mode=dataset_evaluation_mode):
for scene_id, scene_label in enumerate(dataset.scene_labels):
current_model_file = get_model_filename(fold=fold, scene_label=scene_label, path=model_path)
if not os.path.isfile(current_model_file) or overwrite:
# Load normalizer
feature_normalizer_filename = get_feature_normalizer_filename(fold=fold, scene_label=scene_label, path=feature_normalizer_path)
if os.path.isfile(feature_normalizer_filename):
normalizer = load_data(feature_normalizer_filename)
else:
raise IOError("Feature normalizer not found [%s]" % feature_normalizer_filename)
# Initialize model container
model_container = {'normalizer': normalizer, 'models': {}}
# Restructure training data in to structure[files][events]
ann = {}
for item_id, item in enumerate(dataset.train(fold=fold, scene_label=scene_label)):
filename = os.path.split(item['file'])[1]
if filename not in ann:
ann[filename] = {}
if item['event_label'] not in ann[filename]:
ann[filename][item['event_label']] = []
ann[filename][item['event_label']].append((item['event_onset'], item['event_offset']))
# Collect training examples
data_positive = {}
data_negative = {}
file_count = len(ann)
for item_id, audio_filename in enumerate(ann):
progress(title_text='Collecting data',
fold=fold,
percentage=(float(item_id) / file_count),
note=scene_label+" / "+os.path.split(audio_filename)[1])
# Load features
feature_filename = get_feature_filename(audio_file=audio_filename, path=feature_path)
if os.path.isfile(feature_filename):
feature_data = load_data(feature_filename)['feat']
else:
raise IOError("Feature file not found [%s]" % feature_filename)
# Normalize features
feature_data = model_container['normalizer'].normalize(feature_data)
for event_label in ann[audio_filename]:
positive_mask = numpy.zeros((feature_data.shape[0]), dtype=bool)
for event in ann[audio_filename][event_label]:
start_frame = int(math.floor(event[0] / hop_length_seconds))
stop_frame = int(math.ceil(event[1] / hop_length_seconds))
if stop_frame > feature_data.shape[0]:
stop_frame = feature_data.shape[0]
positive_mask[start_frame:stop_frame] = True
# Store positive examples
if event_label not in data_positive:
data_positive[event_label] = feature_data[positive_mask, :]
else:
data_positive[event_label] = numpy.vstack((data_positive[event_label], feature_data[positive_mask, :]))
# Store negative examples
if event_label not in data_negative:
data_negative[event_label] = feature_data[~positive_mask, :]
else:
data_negative[event_label] = numpy.vstack((data_negative[event_label], feature_data[~positive_mask, :]))
# Train models for each class
for event_label in data_positive:
progress(title_text='Train models',
fold=fold,
note=scene_label+" / "+event_label)
if classifier_method == 'gmm':
model_container['models'][event_label] = {}
model_container['models'][event_label]['positive'] = mixture.GMM(**classifier_params).fit(data_positive[event_label])
model_container['models'][event_label]['negative'] = mixture.GMM(**classifier_params).fit(data_negative[event_label])
else:
raise ValueError("Unknown classifier method ["+classifier_method+"]")
# Save models
save_data(current_model_file, model_container)
def do_system_testing(dataset, result_path, feature_path, model_path, feature_params, detector_params,
dataset_evaluation_mode='folds', classifier_method='gmm', overwrite=False):
"""System testing.
If extracted features are not found from disk, they are extracted but not saved.
Parameters
----------
dataset : class
dataset class
result_path : str
path where the results are saved.
feature_path : str
path where the features are saved.
model_path : str
path where the models are saved.
feature_params : dict
parameter dict
dataset_evaluation_mode : str ['folds', 'full']
evaluation mode, 'full' all material available is considered to belong to one fold.
(Default value='folds')
classifier_method : str ['gmm']
classifier method, currently only GMM supported
(Default value='gmm')
overwrite : bool
overwrite existing models
(Default value=False)
Returns
-------
nothing
Raises
-------
ValueError
classifier_method is unknown.
IOError
Model file not found.
Audio file not found.
"""
if classifier_method != 'gmm':
raise ValueError("Unknown classifier method ["+classifier_method+"]")
for fold in dataset.folds(mode=dataset_evaluation_mode):
for scene_id, scene_label in enumerate(dataset.scene_labels):
current_result_file = get_result_filename(fold=fold, scene_label=scene_label, path=result_path)
if not os.path.isfile(current_result_file) or overwrite:
results = []
# Load class model container
model_filename = get_model_filename(fold=fold, scene_label=scene_label, path=model_path)
if os.path.isfile(model_filename):
model_container = load_data(model_filename)
else:
raise IOError("Model file not found [%s]" % model_filename)
file_count = len(dataset.test(fold, scene_label=scene_label))
for file_id, item in enumerate(dataset.test(fold=fold, scene_label=scene_label)):
progress(title_text='Testing',
fold=fold,
percentage=(float(file_id) / file_count),
note=scene_label+" / "+os.path.split(item['file'])[1])
# Load features
feature_filename = get_feature_filename(audio_file=item['file'], path=feature_path)
if os.path.isfile(feature_filename):
feature_data = load_data(feature_filename)['feat']
else:
# Load audio
if os.path.isfile(dataset.relative_to_absolute_path(item['file'])):
y, fs = load_audio(filename=item['file'], mono=True, fs=feature_params['fs'])
else:
raise IOError("Audio file not found [%s]" % item['file'])
# Extract features
feature_data = feature_extraction(y=y,
fs=fs,
include_mfcc0=feature_params['include_mfcc0'],
include_delta=feature_params['include_delta'],
include_acceleration=feature_params['include_acceleration'],
mfcc_params=feature_params['mfcc'],
delta_params=feature_params['mfcc_delta'],
acceleration_params=feature_params['mfcc_acceleration'],
statistics=False)['feat']
# Normalize features
feature_data = model_container['normalizer'].normalize(feature_data)
current_results = event_detection(feature_data=feature_data,
model_container=model_container,
hop_length_seconds=feature_params['hop_length_seconds'],
smoothing_window_length_seconds=detector_params['smoothing_window_length'],
decision_threshold=detector_params['decision_threshold'],
minimum_event_length=detector_params['minimum_event_length'],
minimum_event_gap=detector_params['minimum_event_gap'])
# Store the result
for event in current_results:
results.append((dataset.absolute_to_relative(item['file']), event[0], event[1], event[2] ))
# Save testing results
with open(current_result_file, 'wt') as f:
writer = csv.writer(f, delimiter='\t')
for result_item in results:
writer.writerow(result_item)
def do_system_evaluation(dataset, result_path, dataset_evaluation_mode='folds'):
"""System evaluation. Testing outputs are collected and evaluated. Evaluation results are printed.
Parameters
----------
dataset : class
dataset class
result_path : str
path where the results are saved.
dataset_evaluation_mode : str ['folds', 'full']
evaluation mode, 'full' all material available is considered to belong to one fold.
(Default value='folds')
Returns
-------
nothing
Raises
-------
IOError
Result file not found
"""
# Set warnings off, sklearn metrics will trigger warning for classes without
# predicted samples in F1-scoring. This is just to keep printing clean.
warnings.simplefilter("ignore")
overall_metrics_per_scene = {}
for scene_id, scene_label in enumerate(dataset.scene_labels):
if scene_label not in overall_metrics_per_scene:
overall_metrics_per_scene[scene_label] = {}
dcase2016_segment_based_metric = DCASE2016_EventDetection_SegmentBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))
dcase2016_event_based_metric = DCASE2016_EventDetection_EventBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))
for fold in dataset.folds(mode=dataset_evaluation_mode):
results = []
result_filename = get_result_filename(fold=fold, scene_label=scene_label, path=result_path)
if os.path.isfile(result_filename):
with open(result_filename, 'rt') as f:
for row in csv.reader(f, delimiter='\t'):
results.append(row)
else:
raise IOError("Result file not found [%s]" % result_filename)
for file_id, item in enumerate(dataset.test(fold, scene_label=scene_label)):
current_file_results = []
for result_line in results:
if len(result_line) != 0 and result_line[0] == dataset.absolute_to_relative(item['file']):
current_file_results.append(
{'file': result_line[0],
'event_onset': float(result_line[1]),
'event_offset': float(result_line[2]),
'event_label': result_line[3].rstrip()
}
)
meta = dataset.file_meta(dataset.absolute_to_relative(item['file']))
dcase2016_segment_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)
dcase2016_event_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)
overall_metrics_per_scene[scene_label]['segment_based_metrics'] = dcase2016_segment_based_metric.results()
overall_metrics_per_scene[scene_label]['event_based_metrics'] = dcase2016_event_based_metric.results()
print " Evaluation over %d folds" % dataset.fold_count
print " "
print " Results per scene "
print " {:18s} | {:5s} | | {:39s} ".format('', 'Main', 'Secondary metrics')
print " {:18s} | {:5s} | | {:38s} | {:14s} | {:14s} | {:14s} ".format('', '', 'Seg/Overall','Seg/Class', 'Event/Overall','Event/Class')
print " {:18s} | {:5s} | | {:6s} : {:5s} : {:5s} : {:5s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} |".format('Scene', 'ER', 'F1', 'ER', 'ER/S', 'ER/D', 'ER/I', 'F1', 'ER', 'F1', 'ER', 'F1', 'ER')
print " -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+"
averages = {
'segment_based_metrics': {
'overall': {
'ER': [],
'F': [],
},
'class_wise_average': {
'ER': [],
'F': [],
}
},
'event_based_metrics': {
'overall': {
'ER': [],
'F': [],
},
'class_wise_average': {
'ER': [],
'F': [],
}
},
}
for scene_id, scene_label in enumerate(dataset.scene_labels):
print " {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:5.2f} : {:5.2f} : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |".format(scene_label,
overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],
overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'] * 100,
overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],
overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['S'],
overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['D'],
overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['I'],
overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F']*100,
overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'],
overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F']*100,
overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'],
overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F']*100,
overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'],
)
averages['segment_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'])
averages['segment_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'])
averages['segment_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'])
averages['segment_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F'])
averages['event_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'])
averages['event_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F'])
averages['event_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'])
averages['event_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F'])
print " -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+"
print " {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:21s} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |".format('Average',
numpy.mean(averages['segment_based_metrics']['overall']['ER']),
numpy.mean(averages['segment_based_metrics']['overall']['F'])*100,
numpy.mean(averages['segment_based_metrics']['overall']['ER']),
' ',
numpy.mean(averages['segment_based_metrics']['class_wise_average']['F'])*100,
numpy.mean(averages['segment_based_metrics']['class_wise_average']['ER']),
numpy.mean(averages['event_based_metrics']['overall']['F'])*100,
numpy.mean(averages['event_based_metrics']['overall']['ER']),
numpy.mean(averages['event_based_metrics']['class_wise_average']['F'])*100,
numpy.mean(averages['event_based_metrics']['class_wise_average']['ER']),
)