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__main__.py
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__main__.py
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import json
from argparse import ArgumentParser
import midi
import glob
import copy
import os
import numpy as np
import pretty_midi
from pprint import pprint
import pickle
from mgeval import core, utils
from sklearn.model_selection import LeaveOneOut
parser = ArgumentParser()
parser.add_argument('--set1dir', required=True, type=str,
help='Path (absolute) to the first dataset (folder)')
parser.add_argument('--set2dir', required=True, type=str,
help='Path (absolute) to the second dataset (folder)')
parser.add_argument('--outfile', required=True, type=str,
help='File (pickle) where the analysis will be stored')
parser.add_argument('--num-bar', required=False, type=int, default=None,
help='Number of bars to account for during processing')
args = parser.parse_args()
set1 = glob.glob(os.path.join(args.set1dir, '*'))
set2 = glob.glob(os.path.join(args.set2dir, '*'))
print('Evaluation sets (sample and baseline):')
print(set1)
print(set2)
if not any(set1):
print("Error: sample set it empty")
exit()
if not any(set2):
print("Error: baseline set it empty")
exit()
# Initialize Evaluation Set
num_samples = min(len(set2), len(set1))
print(num_samples)
evalset = {
'total_used_pitch': np.zeros((num_samples, 1))
, 'pitch_range': np.zeros((num_samples, 1))
, 'avg_pitch_shift': np.zeros((num_samples, 1))
, 'avg_IOI': np.zeros((num_samples, 1))
, 'total_used_note': np.zeros((num_samples, 1))
, 'bar_used_pitch': np.zeros((num_samples, args.num_bar, 1))
, 'bar_used_note': np.zeros((num_samples, args.num_bar, 1))
, 'total_pitch_class_histogram': np.zeros((num_samples, 12))
, 'bar_pitch_class_histogram': np.zeros((num_samples, args.num_bar, 12))
, 'note_length_hist': np.zeros((num_samples, 12))
, 'pitch_class_transition_matrix': np.zeros((num_samples, 12, 12))
, 'note_length_transition_matrix': np.zeros((num_samples, 12, 12))
}
bar_metrics = [ 'bar_used_pitch', 'bar_used_note', 'bar_pitch_class_histogram' ]
for metric in bar_metrics:
print(args.num_bar)
if not args.num_bar:
evalset.pop(metric)
# print(evalset)
metrics_list = evalset.keys()
single_arg_metrics = (
[ 'total_used_pitch'
, 'avg_IOI'
, 'total_pitch_class_histogram'
, 'pitch_range'
])
set1_eval = copy.deepcopy(evalset)
set2_eval = copy.deepcopy(evalset)
sets = [ (set1, set1_eval), (set2, set2_eval) ]
# Extract Fetures
for _set, _set_eval in sets:
for i in range(0, num_samples):
feature = core.extract_feature(_set[i])
for metric in metrics_list:
evaluator = getattr(core.metrics(), metric)
if metric in single_arg_metrics:
tmp = evaluator(feature)
elif metric in bar_metrics:
# print(metric)
tmp = evaluator(feature, 0, args.num_bar)
# print(tmp.shape)
else:
tmp = evaluator(feature, 0)
_set_eval[metric][i] = tmp
loo = LeaveOneOut()
loo.get_n_splits(np.arange(num_samples))
set1_intra = np.zeros((num_samples, len(metrics_list), num_samples - 1))
set2_intra = np.zeros((num_samples, len(metrics_list), num_samples - 1))
# Calculate Intra-set Metrics
for i, metric in enumerate(metrics_list):
for train_index, test_index in loo.split(np.arange(num_samples)):
set1_intra[test_index[0]][i] = utils.c_dist(
set1_eval[metrics_list[i]][test_index], set1_eval[metrics_list[i]][train_index])
set2_intra[test_index[0]][i] = utils.c_dist(
set2_eval[metrics_list[i]][test_index], set2_eval[metrics_list[i]][train_index])
loo = LeaveOneOut()
loo.get_n_splits(np.arange(num_samples))
sets_inter = np.zeros((num_samples, len(metrics_list), num_samples))
# Calculate Inter-set Metrics
for i, metric in enumerate(metrics_list):
for train_index, test_index in loo.split(np.arange(num_samples)):
sets_inter[test_index[0]][i] = utils.c_dist(set1_eval[metric][test_index], set2_eval[metric])
plot_set1_intra = np.transpose(
set1_intra, (1, 0, 2)).reshape(len(metrics_list), -1)
plot_set2_intra = np.transpose(
set2_intra, (1, 0, 2)).reshape(len(metrics_list), -1)
plot_sets_inter = np.transpose(
sets_inter, (1, 0, 2)).reshape(len(metrics_list), -1)
output = {}
for i, metric in enumerate(metrics_list):
# print('calculating kl of: {}'.format(metric))
mean = np.mean(set1_eval[metric], axis=0).tolist()
std = np.std(set1_eval[metric], axis=0).tolist()
print(metric)
pprint(plot_set1_intra[i])
pprint(plot_set2_intra[i])
pprint(plot_sets_inter[i])
kl1 = utils.kl_dist(plot_set1_intra[i], plot_sets_inter[i])
ol1 = utils.overlap_area(plot_set1_intra[i], plot_sets_inter[i])
kl2 = utils.kl_dist(plot_set2_intra[i], plot_sets_inter[i])
ol2 = utils.overlap_area(plot_set2_intra[i], plot_sets_inter[i])
print(kl1)
print(kl2)
output[metric] = [mean, std, kl1, ol1, kl2, ol2]
# Save output
if os.path.exists(args.outfile):
os.remove(args.outfile)
output_file = open(args.outfile, 'w')
json.dump(output, output_file)
output_file.close()
print('Saved output to file: ' + args.outfile)