def main(args):
artists_filename = args.i_path
chunk_filename = args.i_chunk
global output_path
output_path = args.output_path
if output_path[-1] != '/':
output_path += '/'
global artists
print('LOADING PKL...', end='')
artists = load_data(filename=artists_filename)
print('DONE')
global chunk
print('LOADING CHUNKS...')
chunk = load_data(filename=chunk_filename)
print('DONE')
print('COMPUTE RANKING of selection ', chunk_filename)
chunk_level_ranking = compute_ranking_master()
output_filename= os.path.basename(chunk_filename)
output_filename += '_OUT.pkl'
output_path += output_filename
save_data(chunk_level_ranking, filename=output_path)
def main(args):
input_pkl= args.i_path
input_ranking = args.i_ranking
output_path = args.output_path
if output_path[-1] != '/':
output_path += '/'
global heatmap_metric
global ranking_metric
global peak_thresh
heatmap_metric = args.heatmap_metric
ranking_metric = args.ranking_metric
peak_thresh=args.peak_thresh
print('LOADING PKL ARTISTS...', end='')
artists = load_data(filename=input_pkl)
print('DONE')
if input_pkl != 'NO':
# update artists file
print('LOADING PKL RANKING...', end='')
ranking = load_data(filename=input_ranking)
print('DONE')
for k, v in ranking.items():
arr = np.array(v)
artists[k].my_similar_artists = arr[:, 0]
artists[k].my_similar_artists_distances = arr[:, 1]
ranking_pathname = output_path + 'ranking.txt'
print_rankings_verbose(artists=artists, filename=ranking_pathname,output_path=output_path,heatmap_metric=heatmap_metric,
ranking_metric=ranking_metric, peak_thresh=peak_thresh)
compute_ranking_score(artists=artists, ranking_metric=ranking_metric, heatmap_metric=heatmap_metric)
def main(args):
input_folder = args.i_path
threshold = args.threshold
mode = args.mode
print('LOADING PKL...')
artists = load_data(filename=input_folder)
print('PREPROCESSING ', d[mode])
X, y = gen_dataset(artists=artists, mode=mode)
X, y = remove_outlier(X=X, y=y, thresh=threshold)
X = normalize(X=X)
print('TSNE')
X = tsne(X=X, lr=1000)
artists = optimize_artists_dictionary(artists)
artists = attach_tsne_to_art_dict(artists=artists, X=X, y=y)
tsne_min = np.amin(X, axis=0)
tsne_max = np.amax(X, axis=0)
print('[TSNE-1 - TSNE-2]')
print('min values')
print(np.amin(X, axis=0))
print('max values')
print(np.amax(X, axis=0))
print('mean values')
print(np.mean(X, axis=0))
print('variance values')
print(np.var(X, axis=0))
artists = clean_similar_artists(artists=artists)
def main(args):
input_path = args.input_pkl
output_path = args.output_path
global metric
metric = args.metric
global artists
artists = load_data(input_path)
chunk_filename = args.input_chunk
print('LOADING CHUNKS...')
chunk = load_data(filename=chunk_filename)
print('DONE')
d = build_matrix_master(chunk=chunk)
save_data(filename=output_path, dict=d)
def main(args):
n_chunks = args.n_chunks
chunk_folder = args.chunk_folder
if chunk_folder[-1] != '/':
chunk_folder += '/'
#group all chunk level ranking in a single ranking file
dictionary = dict()
for i in range(n_chunks):
chunk_filename = 'chunk_' + str(i) + '_OUT.pkl'
chunk_pathname = chunk_folder + chunk_filename
chunk_out = load_data(filename=chunk_pathname)
for k, v in chunk_out.items():
dictionary[k] = v
del chunk_out
print('chunk ', str(i), 'Memory (GB) : ',
getCurrentMemoryUsage() / (2**20))
final_pathname = chunk_folder + 'merged_OUT.pkl'
print('before gc Memory (GB) : ', getCurrentMemoryUsage() / (2**20))
gc.collect()
print('after gc Memory (GB) : ', getCurrentMemoryUsage() / (2**20))
df = pd.DataFrame.from_dict(dictionary)
save_data(dict=df, filename=final_pathname)
print('chunk ', str(i), 'Memory (GB) : ',
getCurrentMemoryUsage() / (2**20))
def main(args):
input_folder = args.i_path
threshold = args.threshold
mode = args.mode
intervals = args.resolution
print('LOADING PKL...')
artists = load_data(filename=input_folder)
print('PREPROCESSING ', d[mode])
X, y = gen_dataset(artists=artists, mode=mode)
X, y = remove_outlier(X=X, y=y, thresh=threshold)
X = normalize(X=X)
for lr in [10, 100, 500, 1000]:
print('TSNE with learning rate =', lr)
X_emb = tsne(X, lr=lr)
print('[TSNE-1 - TSNE-2]')
print('min values')
print(np.amin(X_emb, axis=0))
print('max values')
print(np.amax(X_emb, axis=0))
print('mean values')
print(np.mean(X_emb, axis=0))
print('variance values')
print(np.var(X_emb, axis=0))
#artists = optimize_artists_dictionary(artists)
#artists = attach_tsne_to_art_dict(artists=artists, X=X, y=y)
tsne_min = np.amin(X, axis=0)
tsne_max = np.amax(X, axis=0)
def main(args):
input_filename = args.i_path
output_folder = args.o_folder
type = args.type
if output_folder[-1] != '/':
output_folder += '/'
artists = load_data(filename=input_filename)
if type == 'only_tsne':
out_patname = output_folder + 'dataset_tsne.xlsx'
columns = [
'SONG_ID', 'SONG_NAME', 'ARTIST_ID', 'ARTIST_NAME', 'TSNE_1',
'TSNE_2', 'SIMILAR_ARTISTS'
]
n_out = 0
data = []
for a in artists.values():
for s in a.song_list.values():
try:
row = [
s.id, s.name, a.id, a.name, s.tsne[0], s.tsne[1],
a.similar_artists
]
data.append(row)
except:
n_out += 1
df = pd.DataFrame(data=data, columns=columns)
writer = pd.ExcelWriter(out_patname, engine='xlsxwriter')
# Convert the dataframe to an XlsxWriter Excel object.
df.to_excel(writer, sheet_name='Sheet1')
# Close the Pandas Excel writer and output the Excel file.
writer.save()
def main(args):
distances_filename = args.distances
note = args.note
distances = load_data(filename=distances_filename)
max_length_ranking = build_max_length_ranking(distances=distances)
output_path = os.path.dirname(distances_filename)
basename = 'max_length_ranking_'+note+'.pkl'
final_pathname = os.path.join(output_path,basename)
save_data(filename=final_pathname, dict=max_length_ranking)
def main(args):
input_folder = args.i_path
global output_path
output_path = args.o_path
if output_path[-1] != '/':
output_path += '/'
global artists
print('LOADING PKL...')
artists = load_data(filename=input_folder)
print('PREPROCESSING')
X, y = gen_dataset(artists=artists, mode=3)
for t in np.arange(1, 3, 0.2):
A, b = remove_outlier(X=X, y=y, thresh=t)
def main(args):
n_chunks = args.n_chunks
chunk_folder = args.chunk_folder
if chunk_folder[-1] != '/':
chunk_folder += '/'
#group all chunk level ranking in a single ranking file
ranking = dict()
for i in range(n_chunks):
chunk_filename = 'chunk_' + str(i) + '.pkl_OUT.pkl'
chunk_pathname = chunk_folder+chunk_filename
chunk_out = load_data(filename=chunk_pathname)
for k,v in chunk_out.items():
ranking[k]=v
final_pathname= chunk_folder+'merged_OUT.pkl'
save_data(ranking,filename=final_pathname)
def main(args):
input_folder = args.i_path
threshold = args.threshold
output_pkl = args.output_pkl
global output_path
output_path = args.o_path
if output_path[-1] != '/':
output_path += '/'
mode = args.mode
global artists
print('LOADING PKL...')
artists = load_data(filename=input_folder)
print('PREPROCESSING ', d[mode])
X, y = gen_dataset(artists=artists, mode=mode)
X, y = remove_outlier(X=X, y=y, thresh=threshold)
X = normalize(X=X)
print('TSNE')
X = tsne(X=X, lr=1000)
artists = optimize_artists_dictionary(artists)
artists = attach_tsne_to_art_dict(artists=artists, X=X, y=y)
min = np.amin(X, axis=0)
max = np.amax(X, axis=0)
dimension = 20
print('[TSNE-1 - TSNE-2]')
print('min values')
print(np.amin(X, axis=0))
print('max values')
print(np.amax(X, axis=0))
print('mean values')
print(np.mean(X, axis=0))
print('variance values')
print(np.var(X, axis=0))
artists = clean_similar_artists(artists=artists)
print('GENERATE HEATMAPS')
gen_heatmaps_master(dimension=dimension, min=min, max=max)
print('SAVING DATA')
save_data(artists, filename=output_pkl)
print('PLOT HEATMAPS in ', output_path)
plot_heatmaps_master(dimension=dimension, min=min, max=max)
def main(args):
input_folder = args.i_path
if args.o_path[-1] == '/':
output_filename = args.o_path + args.o_name
else:
output_filename = args.o_path + '/' + args.o_name
artists = load_data(filename=input_folder)
print('PREPROCESSING')
X , y = prepare_dataset(artists=artists, remove_outliers=True, mode=3, local_outlier=False,print_stats=args.stats, print_outlier_percentage_p_feature=True, outlier_trheshold=args.outlier_trheshold)
'''
X = tsne(X)
artists = optimize_artists_dictionary(artists)
artists = attach_tsne_to_art_dict(artists=artists, X=X, y=y)
tsne_min = np.amin(X, axis=0)
tsne_max = np.amax(X, axis=0)
if args.verbosity > 0:
print('[TSNE-1 - TSNE-2]')
print('min values')
print(np.amin(X, axis=0))
print('max values')
print(np.amax(X, axis=0))
print('mean values')
print(np.mean(X, axis=0))
print('variance values')
print(np.var(X, axis=0))
artists = clean_similar_artists(artists=artists)
save_data(artists, filename=output_filename)
'''
return
def main(args):
input_pkl = args.i_path
n_chunks = args.n_chunks
output_folder = args.o_path
if output_folder[-1] != '/':
output_folder += '/'
print('LOADING PKL ', input_pkl)
artists = load_data(filename=input_pkl)
clean_list = dict()
#remove from lists those artists that don't have an heatmap
for k, v in artists.items():
if v.tsne_heatmap is not None:
clean_list[k] = k
divide_dict_in_N_parts(artists=clean_list,
n=n_chunks,
save_to_pkl=True,
output_path=output_folder)
print(str(len(clean_list)), ' over ', str(len(artists)),
' artists, those with no heatmap were not added to lists')
def main(args):
input_folder = args.i_path
threshold = args.threshold
artists = load_data(filename=input_folder)
X, y = gen_dataset(artists=artists)
if PRINT_DISTRIBUTION:
#PRINT VALUES BEFORE OUTLIER REMOTION
feat_names = get_features_dict()
x = np.array(X)
for i in range(x.shape[1]):
ax = plt.hist(x[:, i], bins=200)
filename = args.o_path + '/BEFORE/' + feat_names[i] + '.png'
title = feat_names[i] + 'BEFORE outlier remotion'
plt.title(title)
plt.savefig(filename)
plt.close('all')
X, y = remove_outlier(X=X,
y=y,
thresh=threshold,
verbose=False,
save_histogram=True)
#X = normalize(X=X)
#X, y = remove_outliers_lof(data=X, y=y)
if PRINT_DISTRIBUTION:
# PRINT VALUES AFTER OUTLIER REMOTION
x = np.array(X)
for i in range(x.shape[1]):
ax = plt.hist(x[:, i], bins=200)
filename = args.o_path + '/AFTER/' + feat_names[i] + '.png'
title = feat_names[i] + 'AFTER outlier remotion'
plt.title(title)
plt.savefig(filename)
plt.close('all')
def main(args):
input_path = args.input_pkl
output_path = args.output_path
if output_path[-1] != '/':
output_path += '/'
output_names = output_path + 'names.pkl'
output_heatmaps = output_path + 'heatmaps.pkl'
output_gt = output_path + 'ground_truth.pkl'
artists = load_data(filename=input_path)
names = dict()
heatmaps = dict()
ground_truth = dict()
for id_, artist in artists.items():
names[id_] = artist.id
heatmaps[id_] = artist.tsne_heatmap
ground_truth[id_] = artist.similar_artists
save_data(filename=output_heatmaps, dict=heatmaps)
save_data(filename=output_names, dict=names)
save_data(filename=output_gt, dict=ground_truth)
help='path to pkl name file')
parser.add_argument('--ranking',
'-r',
required=False,
type=str,
default='./.pkl',
help='path to ranking file')
parser.add_argument('--output_folder',
'-o',
required=False,
type=str,
default='./OUTPUT',
help='output folder')
args = parser.parse_args()
names = load_data(filename=args.names)
heatmaps = load_data(filename=args.heatmaps)
ground_truth = load_data(filename=args.ground_truth)
distances = load_data(filename=args.distances)
ranking = load_data(filename=args.ranking)
output_folder = args.output_folder
print_histograms(gt_distances=distance_vs_gt_position(
ground_truth=ground_truth, distances=distances),
folder=output_folder)
'''
names = load_data(filename='/home/gigi/PycharmProjects/TESI_BIS/OUTPUT/names.pkl')
heatmaps = load_data(filename='/home/gigi/PycharmProjects/TESI_BIS/OUTPUT/heatmaps.pkl')
ground_truth = load_data(filename='/home/gigi/PycharmProjects/TESI_BIS/OUTPUT/ground_truth.pkl')
distances = load_data(filename='/home/gigi/PycharmProjects/TESI_BIS/OUTPUT/distances_cc_peak_2.pkl')
ranking = load_data(filename=)'''
import sys
# insert at 1, 0 is the script path (or '' in REPL)
from primary.heatmap import compute_heatmap_distance
import pandas as pd
sys.path.insert(1, '/home/crottondi/PIRISI_TESI/TESI_BIS/')
import argparse
from primary.data_io import save_data, load_data
import primary.rbo as rbo
import numpy as np
if __name__ == '__main__':
artists = load_data(
filename='/home/gigi/PycharmProjects/TESI_BIS/PKL/artists_subset_hm.pkl'
)
max = 0
max_id = ''
for id_, a_ in artists.items():
if len(artists[id_].song_list) > max:
max_id = id_
max = len(artists[id_].song_list)
print(max_id, max)
