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compute_metrics.py
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compute_metrics.py
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"""
Created on Thu Dec 14 10:12:13 2023
@author: Shulei Ji
"""
import muspy
import numpy as np
from muspy.metrics.metrics import pitch_range,n_pitch_classes_used,polyphony
import os
import pickle
import matplotlib.pyplot as plt
def compute_object_metrics(path):
midi_files=os.listdir(path)
PR = [];NPC = [];POLY = []
cnt = 0
for midi in midi_files:
midi_file_path = os.path.join(path, midi)
music = muspy.read_midi(midi_file_path)
PR.append(pitch_range(music))
NPC.append(n_pitch_classes_used(music))
POLY.append(polyphony(music))
cnt += 1
return PR, NPC, POLY
def metrics_per_song(data):
data=data.tolist()
note_density_list=[]
note_pitch_list = []
bar_pitch = []
bar_pitch_range=[]
bar_NPC = []
bar_POLY=[]
bar_beat=[]
bar_beat_real = []
note_length_list=[]
note_velocity_list=[]
tempo_list=[]
chord_list=[]
beat_list=[]
type_list=[]
for i in range(len(data)):
type_list.append(data[i][3])
if data[i][3]==3:
if Duration_dict[data[i][5]]!=0 and Velocity_dict[data[i][6]]!=0:
duration_index=Duration_dict[data[i][5]].rindex('_')
note_length=int(Duration_dict[data[i][5]][duration_index+1:])/480
velocity_index=Velocity_dict[data[i][6]].rindex('_')
note_velocity=int(Velocity_dict[data[i][6]][velocity_index+1:])
pitch_index = Pitch_dict[data[i][4]].rindex('_')
note_pitch = int(Pitch_dict[data[i][4]][pitch_index + 1:])
note_length_list.append(note_length)
note_velocity_list.append(note_velocity)
note_pitch_list.append(note_pitch)
bar_pitch.append(note_pitch)
if len(bar_beat)==0:
bar_beat_real.append(0)
else:
bar_beat_real.append(bar_beat[-1])
if data[i][3]==2:
if data[i][2]!=1:
if Tempo_dict[data[i][0]]!=0:
if Tempo_dict[data[i][0]]=='CONTI':
if len(tempo_list)==0:
continue
tempo_list.append(tempo_list[-1])
else:
tempo_index=Tempo_dict[data[i][0]].rindex('_')
tempo=int(Tempo_dict[data[i][0]][tempo_index+1:])
tempo_list.append(tempo)
if chord_dict[data[i][1]] != 0:
if chord_dict[data[i][1]]=='CONTI':
if len(chord_list)==0:
continue
chord_list.append(chord_list[-1])
else:
chord_index = chord_dict[data[i][1]].rindex('_')
chord = chord_dict[data[i][1]][chord_index + 1:]
chord_list.append(chord)
if data[i][2] > 1:
beat_index = beat_dict[data[i][2]].rindex('_')
beat = int(beat_dict[data[i][2]][beat_index + 1:])
beat_list.append(beat)
bar_beat.append(beat)
else:
if len(bar_pitch)>0:
bar_pitch_range.append(max(bar_pitch)-min(bar_pitch))
bar_pitch_temp = set([i % 12 for i in bar_pitch])
bar_NPC.append(len(bar_pitch_temp))
if len(bar_beat)>0:
bar_POLY.append(len(bar_pitch)/len(set(bar_beat_real)))
bar_pitch=[]
bar_beat=[]
bar_beat_real=[]
if len(bar_pitch) > 0:
bar_pitch_range.append(max(bar_pitch) - min(bar_pitch))
bar_pitch_temp = set([i % 12 for i in bar_pitch])
bar_NPC.append(len(bar_pitch_temp))
assert len(note_length_list)==len(note_velocity_list)
return type_list,note_pitch_list,note_density_list,note_length_list,note_velocity_list,tempo_list,chord_list,beat_list,\
np.mean(bar_pitch_range),np.mean(bar_NPC),np.mean(bar_POLY)
def metrics_all_song(data,index):
data_stat = {"note_density":{1:[],2:[],3:[],4:[]},
"note_length":{1:[],2:[],3:[],4:[]},
"note_velocity":{1:[],2:[],3:[],4:[]},
"note_pitch":{1:[],2:[],3:[],4:[]},
"tempo":{1:[],2:[],3:[],4:[]},
"chord":{1:[],2:[],3:[],4:[]},
"beat":{1:[],2:[],3:[],4:[]},
"type":{1:[],2:[],3:[],4:[]}}
bar_pitch_range=[]
bar_NPC=[]
bar_POLY=[]
for i in range(len(data)):
type_list,note_pitch_list,note_density_list,note_length_list,note_velocity_list,tempo_list,chord_list,beat_list,\
BPR,BNPC,BPOLY=metrics_per_song(data[i])
bar_pitch_range.append(BPR)
bar_NPC.append(BNPC)
bar_POLY.append(BPOLY)
if i in index['cls_1_idx']:
data_stat["note_pitch"][1].extend(note_pitch_list)
data_stat["note_density"][1].extend(note_density_list)
data_stat["note_length"][1].extend(note_length_list)
data_stat["note_velocity"][1].extend(note_velocity_list)
data_stat["tempo"][1].extend(tempo_list)
data_stat["chord"][1].extend(chord_list)
data_stat["beat"][1].extend(beat_list)
data_stat["type"][1].extend(type_list)
elif i in index['cls_2_idx']:
data_stat["note_pitch"][2].extend(note_pitch_list)
data_stat["note_density"][2].extend(note_density_list)
data_stat["note_length"][2].extend(note_length_list)
data_stat["note_velocity"][2].extend(note_velocity_list)
data_stat["tempo"][2].extend(tempo_list)
data_stat["chord"][2].extend(chord_list)
data_stat["beat"][2].extend(beat_list)
data_stat["type"][2].extend(type_list)
elif i in index['cls_3_idx']:
data_stat["note_pitch"][3].extend(note_pitch_list)
data_stat["note_density"][3].extend(note_density_list)
data_stat["note_length"][3].extend(note_length_list)
data_stat["note_velocity"][3].extend(note_velocity_list)
data_stat["tempo"][3].extend(tempo_list)
data_stat["chord"][3].extend(chord_list)
data_stat["beat"][3].extend(beat_list)
data_stat["type"][3].extend(type_list)
elif i in index['cls_4_idx']:
data_stat["note_pitch"][4].extend(note_pitch_list)
data_stat["note_density"][4].extend(note_density_list)
data_stat["note_length"][4].extend(note_length_list)
data_stat["note_velocity"][4].extend(note_velocity_list)
data_stat["tempo"][4].extend(tempo_list)
data_stat["chord"][4].extend(chord_list)
data_stat["beat"][4].extend(beat_list)
data_stat["type"][4].extend(type_list)
return data_stat,bar_pitch_range,bar_NPC,bar_POLY
np.set_printoptions(threshold=np.inf)
def violin_plot(data,index):
data_stat,_,_,_=metrics_all_song(data,index)
chord_type_dict = {'N': 0, 'M7': 1, 'M': 2, 'm': 3, 'm7': 4, 'o': 5, 'sus2': 6, '7': 7, 'sus4': 8, '+': 9,
'/o7': 10, 'o7': 11}
plt.figure(figsize=(8,4.5))
plt.subplot(2,3,5)
plt.violinplot([data_stat["note_length"][1], data_stat["note_length"][2],
data_stat["note_length"][3], data_stat["note_length"][4]],
showextrema=True, showmedians=True,points=1000,widths=0.8)
for i, d in enumerate([data_stat["note_length"][1], data_stat["note_length"][2],
data_stat["note_length"][3], data_stat["note_length"][4]]):
min_value, quantile1, median, quantile3, max_value = np.percentile(d, [0, 25, 50, 75, 100])
plt.scatter(i + 1, median, color='white', zorder=8,s=6)
plt.vlines(i + 1, quantile1, quantile3, lw=6, zorder=3)
plt.xticks(ticks=[1,2,3,4],labels=["Q1","Q2","Q3","Q4"])
plt.ylabel("Duration")
plt.subplot(2,3,6)
plt.violinplot([data_stat["note_velocity"][1], data_stat["note_velocity"][2],
data_stat["note_velocity"][3], data_stat["note_velocity"][4]],
showextrema=True, showmedians=True,points=1000,widths=0.8)
for i, d in enumerate([data_stat["note_velocity"][1], data_stat["note_velocity"][2],
data_stat["note_velocity"][3], data_stat["note_velocity"][4]]):
min_value, quantile1, median, quantile3, max_value = np.percentile(d, [0, 25, 50, 75, 100])
# print(median)
plt.scatter(i + 1, median, color='white', zorder=8,s=6)
plt.vlines(i + 1, quantile1, quantile3, lw=6, zorder=3)
plt.xticks(ticks=[1, 2, 3, 4], labels=["Q1", "Q2", "Q3", "Q4"])
plt.ylabel("Velocity")
plt.subplot(2, 3, 1)
plt.violinplot([data_stat["tempo"][1], data_stat["tempo"][2],
data_stat["tempo"][3], data_stat["tempo"][4]],
showextrema=True, showmedians=True, points=1000,widths=0.8)
for i, d in enumerate([data_stat["tempo"][1], data_stat["tempo"][2],
data_stat["tempo"][3], data_stat["tempo"][4]]):
min_value, quantile1, median, quantile3, max_value = np.percentile(d, [0, 25, 50, 75, 100])
plt.scatter(i + 1, median, color='white', zorder=8,s=6)
plt.vlines(i + 1, quantile1, quantile3, lw=6, zorder=3)
plt.xticks(ticks=[1, 2, 3, 4], labels=["Q1", "Q2", "Q3", "Q4"])
plt.ylabel("Tempo")
plt.subplot(2, 3, 2)
def chordType2int(data):
new_data = []
for j in data:
new_data.append(chord_type_dict[j])
return new_data
plt.violinplot([chordType2int(data_stat["chord"][1]), chordType2int(data_stat["chord"][2]),
chordType2int(data_stat["chord"][3]), chordType2int(data_stat["chord"][4])],
showextrema=True, showmedians=True, points=1000, widths=0.8)
for i, d in enumerate([chordType2int(data_stat["chord"][1]), chordType2int(data_stat["chord"][2]),
chordType2int(data_stat["chord"][3]), chordType2int(data_stat["chord"][4])]):
min_value, quantile1, median, quantile3, max_value = np.percentile(d, [0, 25, 50, 75, 100])
plt.scatter(i + 1, median, color='white', zorder=8, s=6)
plt.vlines(i + 1, quantile1, quantile3, lw=6, zorder=3)
plt.xticks(ticks=[1, 2, 3, 4], labels=["Q1", "Q2", "Q3", "Q4"])
plt.yticks(ticks=[0,1,2,3,4,5,6,7,8,9,10,11],
labels=['N','M7', 'M', 'm', 'm7', 'o', 'sus2', '7', 'sus4', '+', '/o7', 'o7'])
plt.ylabel("Chord")
plt.subplot(2, 3, 4)
#
plt.violinplot([data_stat["note_pitch"][1], data_stat["note_pitch"][2],
data_stat["note_pitch"][3], data_stat["note_pitch"][4]],
showextrema=True, showmedians=True, points=1000, widths=0.8)
for i, d in enumerate([data_stat["note_pitch"][1], data_stat["note_pitch"][2],
data_stat["note_pitch"][3], data_stat["note_pitch"][4]]):
min_value, quantile1, median, quantile3, max_value = np.percentile(d, [0, 25, 50, 75, 100])
plt.scatter(i + 1, median, color='white', zorder=8, s=6)
plt.vlines(i + 1, quantile1, quantile3, lw=6, zorder=3)
plt.xticks(ticks=[1, 2, 3, 4], labels=["Q1", "Q2", "Q3", "Q4"])
plt.ylabel("Pitch")
plt.subplot(2, 3, 3)
plt.violinplot([data_stat["beat"][1], data_stat["beat"][2],
data_stat["beat"][3], data_stat["beat"][4]],
showextrema=True, showmedians=True, points=1000, widths=0.8)
for i, d in enumerate([data_stat["beat"][1], data_stat["beat"][2],
data_stat["beat"][3], data_stat["beat"][4]]):
min_value, quantile1, median, quantile3, max_value = np.percentile(d, [0, 25, 50, 75, 100])
plt.scatter(i + 1, median, color='white', zorder=8, s=6)
plt.vlines(i + 1, quantile1, quantile3, lw=6, zorder=3)
plt.xticks(ticks=[1, 2, 3, 4], labels=["Q1", "Q2", "Q3", "Q4"])
plt.ylabel("Beat")
plt.tight_layout()
if __name__=="__main__":
fr = open('./data/co-representation/dictionary.pkl', 'rb')
data = pickle.load(fr)
Pitch_dict = data[1]['pitch']
Duration_dict = data[1]['duration']
Velocity_dict = data[1]['velocity']
Tempo_dict = data[1]['tempo']
chord_dict = data[1]['chord']
beat_dict = data[1]['bar-beat']
data_file=open("generate_midi/MusER_TRANS_CA_ER_emopia_data.data",'rb')
data=pickle.load(data_file)
index_file=open("generate_midi/MusER_TRANS_CA_ER_emopia_index.data",'rb')
index=pickle.load(index_file)
violin_plot(data,index)
eps = plt.gcf()
eps.savefig('plot/MusER_TRANS_CA_ER_emopia.pdf', dpi=600, format='pdf', bbox_inches='tight')
plt.show()
_,BPR,BNPC,BPOLY=metrics_all_song(data,index)
PR, NPC, POLY=compute_object_metrics("generate_midi/MusER_TRANS_CA_ER_emopia/")
print("PR=",PR)
print("BPR=",BPR)
print("NPC=",NPC)
print("BNPC=",BNPC)
print("POLY=",POLY)
print("BPOLY=",BPOLY)
precision=2
print("PR: ",round(np.mean(PR),precision),"±",round(np.std(PR),precision),\
", BPR: ",round(np.mean(BPR),precision),"±",round(np.std(BPR),precision),\
", NPC: ",round(np.mean(NPC),precision),"±",round(np.std(NPC),precision),\
", BNPC: ",round(np.mean(BNPC),precision),"±",round(np.std(BNPC),precision),\
", POLY: ",round(np.mean(POLY),precision),"±",round(np.std(POLY),precision),\
", BPOLY: ",round(np.mean(BPOLY),precision),"±",round(np.std(BPOLY),precision))