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Commit eab3f218 authored by Paul Best's avatar Paul Best
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clean

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with 168 additions and 431 deletions
.gitignore
+ 2
− 1
View file @ eab3f218
...@@ -6,7 +6,8 @@ ...@@ -6,7 +6,8 @@
*__pycache__ *__pycache__
*log *log
humpback2/annots humpback2/annots
gibbon
new_specie/*/
otter/pone.0112562.s003.xlsx otter/pone.0112562.s003.xlsx
zebra_finch/Library_notes.pdf zebra_finch/Library_notes.pdf
annot_distrib.pdf annot_distrib.pdf
......
plot_annot_distrib.py
+ 1
− 0
View file @ eab3f218
...@@ -11,6 +11,7 @@ info = { ...@@ -11,6 +11,7 @@ info = {
'zebra_finch': ['zebra finch', 'elie2018zebra', 'bird'], 'zebra_finch': ['zebra finch', 'elie2018zebra', 'bird'],
'otter': ['otter', '', ''], 'otter': ['otter', '', ''],
'humpback': ['humpback whale', 'malige2021use', 'cetacean'], 'humpback': ['humpback whale', 'malige2021use', 'cetacean'],
'humpback2':['humpback whale', 'malige2021use', 'cetacean'],
'dolphin': ['bottlenose dolphin', 'sayigh2022sarasota', 'cetacean'] 'dolphin': ['bottlenose dolphin', 'sayigh2022sarasota', 'cetacean']
} }
......
plot_clusters.py 100644 → 100755
+ 10
− 7
View file @ eab3f218
...@@ -5,7 +5,7 @@ import models, utils as u ...@@ -5,7 +5,7 @@ import models, utils as u
species = np.loadtxt('good_species.txt', dtype=str) species = np.loadtxt('good_species.txt', dtype=str)
fig, ax = plt.subplots(nrows=len(species), figsize=(7, 10)) fig, ax = plt.subplots(nrows=len(species), figsize=(7, 10), dpi=200)
for i, specie in enumerate(species): for i, specie in enumerate(species):
meta = models.meta[specie] meta = models.meta[specie]
frontend = models.frontend['pcenMel'](meta['sr'], meta['nfft'], meta['sampleDur'], 128) frontend = models.frontend['pcenMel'](meta['sr'], meta['nfft'], meta['sampleDur'], 128)
...@@ -14,15 +14,18 @@ for i, specie in enumerate(species): ...@@ -14,15 +14,18 @@ for i, specie in enumerate(species):
df = pd.read_csv(f'{specie}/{specie}.csv') df = pd.read_csv(f'{specie}/{specie}.csv')
clusters = hdbscan.HDBSCAN(min_cluster_size=10, min_samples=3, cluster_selection_epsilon=0.05, core_dist_n_jobs=-1, cluster_selection_method='leaf').fit_predict(X) clusters = hdbscan.HDBSCAN(min_cluster_size=10, min_samples=3, cluster_selection_epsilon=0.05, core_dist_n_jobs=-1, cluster_selection_method='leaf').fit_predict(X)
df.loc[idxs, 'cluster'] = clusters.astype(int) df.loc[idxs, 'cluster'] = clusters.astype(int)
for j, cluster in enumerate(np.random.choice(np.arange(df.cluster.max()), 5)): for j, cluster in enumerate(np.random.choice(np.arange(df.cluster.max()), 4)):
for k, (x, name) in enumerate(torch.utils.data.DataLoader(u.Dataset(df[df.cluster==cluster].sample(10), f'{specie}/audio/', meta['sr'], meta['sampleDur']), batch_size=1)): for k, (x, name) in enumerate(torch.utils.data.DataLoader(u.Dataset(df[df.cluster==cluster].sample(8), f'{specie}/audio/', meta['sr'], meta['sampleDur']), batch_size=1)):
ax[i].imshow(frontend(x).squeeze().numpy(), extent=[k, k+1, j, j+1], origin='lower', aspect='auto') spec = frontend(x).squeeze().numpy()
ax[i].imshow(spec, extent=[k, k+1, j, j+1], origin='lower', aspect='auto', cmap='Greys', vmin=np.quantile(spec, .2), vmax=np.quantile(spec, .98))
ax[i].set_xticks([]) ax[i].set_xticks([])
ax[i].set_yticks([]) ax[i].set_yticks([])
# ax[i].grid(color='w', xdata=np.arange(1, 10), ydata=np.arange(1, 5)) # ax[i].grid(color='w', xdata=np.arange(1, 10), ydata=np.arange(1, 5))
ax[i].set_ylabel(specie.replace('_', ' ')) ax[i].set_ylabel(specie.replace('_', ' '))
ax[i].set_xlim(0, 10) ax[i].set_xlim(0, 8)
ax[i].set_ylim(0, 5) ax[i].set_ylim(0, 4)
ax[i].vlines(np.arange(1, 8), 0, 4, linewidths=1, color='black')
ax[i].hlines(np.arange(1, 4), 0, 8, linewidths=1, color='black')
plt.subplots_adjust(wspace=0.1)
plt.tight_layout() plt.tight_layout()
plt.savefig('clusters.pdf') plt.savefig('clusters.pdf')
\ No newline at end of file
plot_hdbscan_HP.py deleted 100755 → 0
+ 0
− 44
View file @ fe3ac67b
import matplotlib.pyplot as plt
import pandas as pd, numpy as np
frontends = ['biosound', 'vggish', '256_logMel128', '256_logSTFT', '256_Mel128', '256_pcenMel128', '512_pcenMel128', '128_pcenMel128', '64_pcenMel128', '32_pcenMel128', '16_pcenMel128']
fn = open('hdbscan_HP.txt', 'r')
out = []
for l in fn.readlines():
l = l[:-1].split(' ')
if len(l)==2:
specie, frontend = l[0], l[1]
else:
out.append({'specie':specie, 'frontend':frontend, 'nmi':float(l[0]), 'mcs':int(l[1]), 'ms': l[2], 'eps': float(l[3]), 'al': l[4]})
df = pd.DataFrame().from_dict(out)
df.ms = df.ms.replace('None', 0).astype(int)
df.to_csv('hdbscan_HP3.csv', index=False)
best = df.loc[df.groupby(["specie", 'frontend']).nmi.idxmax()]
res = [(conf, (grp.set_index(['specie', 'frontend']).nmi / best.set_index(['specie', 'frontend']).nmi).sum()) for conf, grp in df.groupby(['mcs', 'ms', 'eps', 'al'])]
conf = res[np.argmax([r[1] for r in res])]
print('best HP', conf)
conf = conf[0]
fig, ax = plt.subplots(ncols = 2, figsize=(10, 5), sharex=True, sharey=True)
for s, grp in df.groupby('specie'):
ax[0].scatter([grp[grp.frontend==f].nmi.max() for f in frontends], np.arange(len(frontends)))
ax[0].grid()
ax[1].grid()
ax[0].set_yticks(np.arange(len(frontends)))
ax[0].set_yticklabels(frontends)
for s, grp in df[(( df.mcs==conf[0] )&( df.ms==conf[1] )&( df.eps==conf[2] )&( df.al==conf[3] ))].groupby('specie'):
ax[1].scatter([grp[grp.frontend==f].nmi.max() for f in frontends], np.arange(len(frontends)), label=s)
ax[1].legend(bbox_to_anchor=(1,1))
plt.tight_layout()
ax[0].set_title('Best HDBSCAN settings')
ax[1].set_title('Shared HDBSCAN settings')
ax[0].set_xlabel('NMI')
ax[1].set_xlabel('NMI')
plt.tight_layout()
plt.savefig('NMIs_hdbscan.pdf')
plot_main_results.py 0 → 100755
+ 31
− 0
View file @ eab3f218
import matplotlib.pyplot as plt
import pandas as pd, numpy as np
all_frontends = [['256_logMel128', '256_logMel128_noprcptl', 'spec32', 'biosound'][::-1],
['256_logMel128', 'allbut_AE_256_logMel128', 'openl3', 'wav2vec2', 'crepe'][::-1],
['256_logMel128', '256_Mel128', '256_pcenMel128', '256_logSTFT']]
all_frontendNames = [['AE prcptl', 'AE MSE', 'Spectro.', 'PAFs'][::-1],
['AE', 'gen. AE', 'OpenL3', 'Wav2Vec2', 'CREPE'][::-1],
['log-Mel', 'Mel', 'PCEN-Mel', 'log-STFT']]
all_plotNames = ['handcraft', 'deepembed', 'frontends']
for frontends, frontendNames, plotName in zip(all_frontends, all_frontendNames, all_plotNames):
df = pd.read_csv('hdbscan_HP.csv')
df.loc[df.ms.isna(), 'ms'] = 0
best = df.loc[df.groupby(["specie", 'frontend']).nmi.idxmax()]
res = [(conf, (grp.set_index(['specie', 'frontend']).nmi / best.set_index(['specie', 'frontend']).nmi).sum()) for conf, grp in df.groupby(['ncomp', 'mcs', 'ms', 'eps', 'al'])]
conf = res[np.argmax([r[1] for r in res])]
print('best HP', conf)
conf = conf[0]
df = df[((df.ncomp == conf[0])&(df.mcs == conf[1] )&( df.ms==conf[2] )&( df.eps==conf[3] )&(df.al==conf[4] ))]
df.specie = df.specie.replace('humpback2', 'humpback\n(small)')
species = df.specie.unique()
out = pd.DataFrame({fn:[df[((df.specie==s)&(df.frontend==f))].iloc[0].nmi for s in species] for f, fn in zip(frontends, frontendNames)}, index=[s.replace('_','\n') for s in species])
out.plot.bar(figsize=(9, 3), rot=0)
plt.legend(bbox_to_anchor=(1,1))
plt.ylabel('NMI')
plt.grid(axis='y')
plt.tight_layout()
plt.savefig(f'NMIs_hdbscan_barplot_{plotName}.pdf')
plot_prec_rec.py 100644 → 100755
+ 22
− 7
View file @ eab3f218
...@@ -10,30 +10,43 @@ info = { ...@@ -10,30 +10,43 @@ info = {
'cassin_vireo': ['cassin vireo', 'arriaga2015bird', 'bird'], 'cassin_vireo': ['cassin vireo', 'arriaga2015bird', 'bird'],
'black-headed_grosbeaks': ['black-headed grosbeaks', 'arriaga2015bird', 'bird'], 'black-headed_grosbeaks': ['black-headed grosbeaks', 'arriaga2015bird', 'bird'],
'zebra_finch': ['zebra finch', 'elie2018zebra', 'bird'], 'zebra_finch': ['zebra finch', 'elie2018zebra', 'bird'],
'otter': ['otter', '', ''],
'humpback': ['humpback whale', 'malige2021use', 'cetacean'], 'humpback': ['humpback whale', 'malige2021use', 'cetacean'],
'humpback2': ['humpback whale (small)', 'malige2021use', 'cetacean'],
'dolphin': ['bottlenose dolphin', 'sayigh2022sarasota', 'cetacean'] 'dolphin': ['bottlenose dolphin', 'sayigh2022sarasota', 'cetacean']
} }
out = "\\textbf{Specie and source} & \\textbf{\# labels} & \\textbf{\# clusters} & \\textbf{\# discr. clusters} & \\textbf{\% clustered vocs} & \\textbf{\# missed labels} \\\\ \hline \n" #out = "\\textbf{Specie and source} & \\textbf{\# labels} & \\textbf{\# clusters} & \\textbf{\% discr. clusters} & \\textbf{\% clustered vocs} & \\textbf{\# missed labels} \\\\ \hline \n"
out = ""
for specie in species: for specie in species:
dic = np.load(f'{specie}/encodings//encodings_{specie}_256_pcenMel128_sparrow_encoder_decod2_BN_nomaxPool.npy', allow_pickle=True).item() dic = np.load(f'{specie}/encodings//encodings_{specie}_256_logMel128_sparrow_encoder_decod2_BN_nomaxPool.npy', allow_pickle=True).item()
idxs, X = dic['idxs'], dic['umap'] idxs, X = dic['idxs'], dic['umap8']
df = pd.read_csv(f'{specie}/{specie}.csv') df = pd.read_csv(f'{specie}/{specie}.csv')
clusters = hdbscan.HDBSCAN(min_cluster_size=10, min_samples=3, cluster_selection_epsilon=0.05, core_dist_n_jobs=-1, cluster_selection_method='leaf').fit_predict(X) clusters = hdbscan.HDBSCAN(min_cluster_size=10, min_samples=3, cluster_selection_epsilon=0.1, core_dist_n_jobs=-1, cluster_selection_method='leaf' if not 'humpbacjjk' in specie else 'eom').fit_predict(X)
df.loc[idxs, 'cluster'] = clusters.astype(int) df.loc[idxs, 'cluster'] = clusters.astype(int)
dic = np.load(f'{specie}/encodings/encodings_spec32.npy', allow_pickle=True).item()
idxs, X = dic['idxs'], dic['umap8']
clusters = hdbscan.HDBSCAN(min_cluster_size=10, min_samples=3, cluster_selection_epsilon=0.1, core_dist_n_jobs=-1, cluster_selection_method='leaf' if not 'humpbacjjk' in specie else 'eom').fit_predict(X)
df.loc[idxs, 'cluster2'] = clusters.astype(int)
mask = ~df.loc[idxs].label.isna() mask = ~df.loc[idxs].label.isna()
print(specie) print(specie)
labelled = df[~df.label.isna()] labelled = df[~df.label.isna()]
goodClusters, missedLabels = [], [] goodClusters, goodClusters2, missedLabels, missedLabels2 = [], [], [], []
for l, grp in labelled.groupby('label'): for l, grp in labelled.groupby('label'):
precisions = grp.groupby('cluster').fn.count() / labelled.groupby('cluster').fn.count() precisions = grp.groupby('cluster').fn.count() / labelled.groupby('cluster').fn.count()
best = precisions.idxmax() best = precisions.idxmax()
goodClusters.extend(precisions[precisions > 0.9].index) goodClusters.extend(precisions[precisions > 0.9].index)
if not (precisions > .9).any(): if not (precisions > .9).any():
missedLabels.append(l) missedLabels.append(l)
precisions = grp.groupby('cluster2').fn.count() / labelled.groupby('cluster2').fn.count()
goodClusters2.extend(precisions[precisions > 0.9].index)
if not (precisions > .9).any():
missedLabels2.append(l)
# print(f'Best precision for {l} is for cluster {best} with {(df.cluster==best).sum()} points, \ # print(f'Best precision for {l} is for cluster {best} with {(df.cluster==best).sum()} points, \
# with precision {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.cluster==best).sum():.2f}\ # with precision {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.cluster==best).sum():.2f}\
# and recall {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.label==l).sum():.2f}') # and recall {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.label==l).sum():.2f}')
...@@ -43,7 +56,9 @@ for specie in species: ...@@ -43,7 +56,9 @@ for specie in species:
print(f'{len(goodClusters)/df.label.nunique():.1f} cluster per label in avg)') print(f'{len(goodClusters)/df.label.nunique():.1f} cluster per label in avg)')
print(f'{len(missedLabels)} over {df.label.nunique()} missed labels') print(f'{len(missedLabels)} over {df.label.nunique()} missed labels')
out += f"{info[specie][0]} \cite{{{info[specie][1]}}} & {df.label.nunique()} & {df.cluster.nunique()-1} & {len(goodClusters)} & {df.cluster.isin(goodClusters).sum()/len(df)*100:.0f} & {len(missedLabels)} \\\\ \hline \n" out += f"{info[specie][0]} \cite{{{info[specie][1]}}} & {df.label.nunique()} & {df.cluster.nunique()-1} & {df.cluster2.nunique()-1} & {len(goodClusters)/labelled.cluster.nunique()*100:.0f} &"
out += f"{len(goodClusters2)/labelled.cluster2.nunique()*100:.0f} & {df.cluster.isin(goodClusters).sum()/len(df[df.cluster.isin(labelled.cluster.unique())])*100:.0f} & "
out += f"{df.cluster2.isin(goodClusters2).sum()/len(df[df.cluster2.isin(labelled.cluster2.unique())])*100:.0f} & {len(missedLabels)} & {len(missedLabels2)} \\\\ \hline \n"
f = open('cluster_distrib.tex', 'w') f = open('cluster_distrib.tex', 'w')
f.write(out) f.write(out)
......
plot_projections.py 100644 → 100755
+ 17
− 12
View file @ eab3f218
from tqdm import tqdm
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np, pandas as pd import numpy as np, pandas as pd
species = np.loadtxt('good_species.txt', dtype=str) fig, ax = plt.subplots(ncols=4, nrows=2, figsize=(13, 7))
fig, ax = plt.subplots(ncols=4, nrows=2, figsize=(20, 10))
non_zero_min = lambda arr: np.min(arr[arr!=0]) non_zero_min = lambda arr: np.min(arr[arr!=0])
for i, specie in enumerate(species): for i, specie in tqdm(enumerate(['bengalese_finch1', 'bengalese_finch2', 'california_thrashers', 'cassin_vireo', 'black-headed_grosbeaks', 'humpback', 'humpback2', 'dolphin'])):
dic = np.load(f'{specie}/encodings//encodings_{specie}_256_pcenMel128_sparrow_encoder_decod2_BN_nomaxPool.npy', allow_pickle=True).item() dic = np.load(f'{specie}/encodings//encodings_{specie}_256_logMel128_sparrow_encoder_decod2_BN_nomaxPool.npy', allow_pickle=True).item()
df = pd.read_csv(f'{specie}/{specie}.csv') df = pd.read_csv(f'{specie}/{specie}.csv')
df.loc[dic['idxs'], 'umap_x'] = dic['umap'][:,0] df.loc[dic['idxs'], 'umap_x'] = dic['umap'][:,0]
df.loc[dic['idxs'], 'umap_y'] = dic['umap'][:,1] df.loc[dic['idxs'], 'umap_y'] = dic['umap'][:,1]
ax[i//4,i%4].scatter(df[df.label.isna()].umap_x, df[df.label.isna()].umap_y, s=2, color='grey') ax[i//4,i%4].scatter(df[df.label.isna()].umap_x, df[df.label.isna()].umap_y, s=1, color='grey')
for label, grp in df[~df.label.isna()].groupby('label'): for label, grp in df[~df.label.isna()].groupby('label'):
grp = grp.sample(min(len(grp), 1000)) grp = grp.sample(min(len(grp), 1000))
ax[i//4,i%4].scatter(grp.umap_x, grp.umap_y, s=2) ax[i//4,i%4].scatter(grp.umap_x, grp.umap_y, s=1)
ax[i//4,i%4].set_title(specie.replace('_', ' ')) ax[i//4,i%4].set_title(specie.replace('_', ' ') if specie != 'humpback2' else 'humpback (small)')
sampSize = 100 # Hopkins statistic
sampSize = len(df)//15
X = df.sample(sampSize)[['umap_x', 'umap_y']].to_numpy() X = df.sample(sampSize)[['umap_x', 'umap_y']].to_numpy()
Y = np.vstack([np.random.normal(np.mean(X[:,0]), np.std(X[:,0]), sampSize), np.random.normal(np.mean(X[:,1]), np.std(X[:,1]), sampSize)]).T Y = np.vstack([np.random.normal(np.mean(X[:,0]), np.std(X[:,0]), sampSize), np.random.normal(np.mean(X[:,1]), np.std(X[:,1]), sampSize)]).T
U = np.sum([min(np.sqrt(np.sum((y - df[['umap_x', 'umap_y']].to_numpy())**2, axis=1))) for y in Y]) U = np.sum([min(np.sqrt(np.sum((y - df[['umap_x', 'umap_y']].to_numpy())**2, axis=1))) for y in Y])
W = np.sum([non_zero_min(np.sqrt(np.sum((x - df[['umap_x', 'umap_y']].to_numpy())**2, axis=1))) for x in X]) W = np.sum([non_zero_min(np.sqrt(np.sum((x - df[['umap_x', 'umap_y']].to_numpy())**2, axis=1))) for x in X])
hopkins = U / (U + W) hopkins = U / (U + W)
ax[i//4, i%4].text(ax[i//4, i%4].get_xlim()[0] + .5, ax[i//4, i%4].get_ylim()[0] + .5, f'{hopkins:.2f}', fontsize=15) ax[i//4, i%4].text(ax[i//4, i%4].get_xlim()[0] + .5, ax[i//4, i%4].get_ylim()[0] + .5, f'{hopkins:.2f}', fontsize=10)
ax[i//4, i%4].set_xticks([])
ax[i//4, i%4].set_yticks([])
#ax[1,3].set_xticks([])
#ax[1,3].set_yticks([])
#ax[1,3].set_frame_on(False)
plt.tight_layout() plt.tight_layout()
plt.savefig('projections.pdf') plt.savefig('projections.pdf')
......
plot_results_hdbcsan.py deleted 100755 → 0
+ 0
− 63
View file @ fe3ac67b
import hdbscan
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from sklearn import metrics
from tqdm import tqdm
import os
species = np.loadtxt('good_species.txt', dtype=str)
frontends = ['biosound'] #['vggish', '256_logMel128', '256_logSTFT', '256_Mel128', '32_pcenMel128', '64_pcenMel128', '128_pcenMel128', '256_pcenMel128', '512_pcenMel128']
file = open('hdbscan_HP2.txt', 'w')
plt.figure()
for specie in ['humpback', 'dolphin', 'black-headed_grosbeaks', 'california_thrashers']: #species:
df = pd.read_csv(f'{specie}/{specie}.csv')
nmis = []
for i, frontend in tqdm(enumerate(frontends), desc=specie, total=len(frontends)):
file.write(specie+' '+frontend+'\n')
fn = f'{specie}/encodings/encodings_' + (f'{specie}_{frontend}_sparrow_encoder_decod2_BN_nomaxPool.npy' if not frontend in ['vggish', 'biosound'] else frontend+'.npy')
if not os.path.isfile(fn):
nmis.append(None)
print('not found')
continue
dic = np.load(fn, allow_pickle=True).item()
idxs, X = dic['idxs'], dic['umap']
# clusters = hdbscan.HDBSCAN(min_cluster_size=max(10, len(df)//100), core_dist_n_jobs=-1, cluster_selection_method='leaf').fit_predict(X)
# df.loc[idxs, 'cluster'] = clusters.astype(int)
# mask = ~df.loc[idxs].label.isna()
# clusters, labels = clusters[mask], df.loc[idxs[mask]].label
# nmis.append(metrics.normalized_mutual_info_score(labels, clusters))
# df.drop('cluster', axis=1, inplace=True)
# continue
nmi = 0
for mcs in [10, 20, 50, 100, 150, 200]:
for ms in [None, 3, 5, 10, 20, 30]:
for eps in [0.0, 0.01, 0.02, 0.05, 0.1]:
for al in ['leaf', 'eom']:
clusters = hdbscan.HDBSCAN(min_cluster_size=mcs, min_samples=ms, cluster_selection_epsilon=eps, \
core_dist_n_jobs=-1, cluster_selection_method=al).fit_predict(X)
df.loc[idxs, 'cluster'] = clusters.astype(int)
mask = ~df.loc[idxs].label.isna()
clusters, labels = clusters[mask], df.loc[idxs[mask]].label
tnmi = metrics.normalized_mutual_info_score(labels, clusters)
file.write(f'{tnmi} {mcs} {ms} {eps} {al}\n')
if tnmi > nmi :
nmi
df.drop('cluster', axis=1, inplace=True)
nmis.append(nmi)
plt.scatter(nmis, np.arange(len(frontends)), label=specie)
file.close()
plt.yticks(range(len(frontends)), frontends)
plt.ylabel('archi')
plt.xlabel('NMI with expert labels')
plt.grid()
plt.tight_layout()
plt.legend()
plt.savefig('NMIs_hdbscan.pdf')
plt.close()
print_annot.py
+ 9
− 7
View file @ eab3f218
import os, argparse import os, shutil, argparse
from tqdm import tqdm from tqdm import tqdm
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import pandas as pd import pandas as pd, numpy as np
import models, utils as u import models, utils as u
import torch import torch
...@@ -14,19 +14,21 @@ args = parser.parse_args() ...@@ -14,19 +14,21 @@ args = parser.parse_args()
meta = models.meta[args.specie] meta = models.meta[args.specie]
df = pd.read_csv(f'{args.specie}/{args.specie}.csv') df = pd.read_csv(f'{args.specie}/{args.specie}.csv')
frontend = models.frontend[args.frontend](meta['sr'], meta['nfft'], meta['sampleDur'], args.nMel) frontend = models.frontend[args.frontend](meta['sr'], meta['nfft'], meta['sampleDur'], args.nMel)
os.system(f'rm -R {args.specie}/annot_pngs/*') shutil.rmtree(f'{args.specie}/annot_pngs', ignore_errors=True)
for label, grp in df.groupby('label'): for label, grp in df.groupby('label'):
os.system(f'mkdir -p "{args.specie}/annot_pngs/{label}"') os.makedirs(f'{args.specie}/annot_pngs/{label}', exist_ok=True)
loader = torch.utils.data.DataLoader(u.Dataset(grp, args.specie+'/audio/', meta['sr'], meta['sampleDur']),\ loader = torch.utils.data.DataLoader(u.Dataset(grp, args.specie+'/audio/', meta['sr'], meta['sampleDur']),\
batch_size=1, num_workers=4, pin_memory=True) batch_size=1, num_workers=4, pin_memory=True)
for x, idx in tqdm(loader, desc=args.specie + ' ' + label, leave=False): for x, idx in tqdm(loader, desc=args.specie + ' ' + label, leave=False):
x = frontend(x).squeeze().detach() x = frontend(x).squeeze().detach()
assert not torch.isnan(x).any(), "Found a NaN in spectrogram... :/" assert not torch.isnan(x).any(), "Found a NaN in spectrogram... :/"
plt.figure() plt.figure()
plt.imshow(x, origin='lower', aspect='auto') plt.imshow(x, origin='lower', aspect='auto', cmap='Greys', vmin=np.quantile(x, .7))
plt.subplots_adjust(top=1, bottom=0, left=0, right=1)
row = df.loc[idx.item()] row = df.loc[idx.item()]
plt.savefig(f'{args.specie}/annot_pngs/{label}/{row.fn.split(".")[0]}_{row.pos:.2f}.png') #plt.savefig(f'{args.specie}/annot_pngs/{label}/{row.fn.split(".")[0]}_{row.pos:.2f}.png')
# plt.savefig(f'{args.specie}/annot_pngs/{label}/{idx.item()}') plt.savefig(f'{args.specie}/annot_pngs/{label}/{idx.item()}')
plt.close() plt.close()
......
print_reconstr.py 0 → 100755
+ 48
− 0
View file @ eab3f218
import os, shutil, argparse
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd, numpy as np
import models, utils as u
import torch
parser = argparse.ArgumentParser()
parser.add_argument("specie", type=str)
parser.add_argument("-bottleneck", type=int, default=16)
parser.add_argument("-frontend", type=str, default='logMel')
parser.add_argument("-encoder", type=str, default='sparrow_encoder')
parser.add_argument("-prcptl", type=int, default=1)
parser.add_argument("-nMel", type=int, default=128)
args = parser.parse_args()
modelname = f'{args.specie}_{args.bottleneck}_{args.frontend}{args.nMel if "Mel" in args.frontend else ""}_{args.encoder}_decod2_BN_nomaxPool{"_noprcptl" if args.prcptl==0 else ""}.stdc'
print(modelname)
gpu = torch.device(f'cuda')
meta = models.meta[args.specie]
frontend = models.frontend[args.frontend](meta['sr'], meta['nfft'], meta['sampleDur'], args.nMel)
encoder = models.__dict__[args.encoder](*((args.bottleneck // 16, (4, 4)) if args.nMel == 128 else (args.bottleneck // 8, (2, 4))))
decoder = models.sparrow_decoder(args.bottleneck, (4, 4) if args.nMel == 128 else (2, 4))
model = torch.nn.Sequential(frontend, encoder, decoder).to(gpu)
model.load_state_dict(torch.load(f'{args.specie}/weights/{modelname}'))
model.eval()
df = pd.read_csv(f'{args.specie}/{args.specie}.csv')
shutil.rmtree(f'{args.specie}/reconstruct_pngs', ignore_errors=True)
for label, grp in df.groupby('label'):
os.makedirs(f'{args.specie}/reconstruct_pngs/{label}', exist_ok=True)
loader = torch.utils.data.DataLoader(u.Dataset(grp, args.specie+'/audio/', meta['sr'], meta['sampleDur']),\
batch_size=1, num_workers=4, pin_memory=True)
with torch.no_grad():
for x, idx in tqdm(loader, desc=args.specie + ' ' + label, leave=False):
x = model(x.to(gpu)).squeeze().detach().cpu()
assert not torch.isnan(x).any(), "Found a NaN in spectrogram... :/"
plt.imshow(x, origin='lower', aspect='auto') #, cmap='Greys', vmin=np.quantile(x, .7))
plt.subplots_adjust(top=1, bottom=0, left=0, right=1)
row = df.loc[idx.item()]
plt.savefig(f'{args.specie}/reconstruct_pngs/{label}/{idx.item()}')
plt.close()
run_baseline.py
+ 1
− 1
View file @ eab3f218
...@@ -43,7 +43,7 @@ def process(idx): ...@@ -43,7 +43,7 @@ def process(idx):
return [sound.__dict__[f] for f in feats] return [sound.__dict__[f] for f in feats]
res = p_tqdm.p_map(process, df.index, num_cpus=14) res = p_tqdm.p_map(process, df.index, num_cpus=10)
for i, mr in zip(df.index, res): for i, mr in zip(df.index, res):
for f, r in zip(feats, mr): for f, r in zip(feats, mr):
......
run_hearbaseline.py
+ 8
− 50
View file @ eab3f218
from sklearn import metrics from sklearn import metrics
import matplotlib.pyplot as plt
#import umap, hdbscan
from tqdm import tqdm from tqdm import tqdm
import argparse, os import argparse, os
import models, utils as u import models, utils as u
import pandas as pd, numpy as np, torch import pandas as pd, numpy as np, torch
from hearbaseline import wav2vec2 as hear from hearbaseline import wav2vec2, torchopenl3, torchcrepe, vggish
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("specie", type=str) parser.add_argument("specie", type=str)
...@@ -17,57 +15,17 @@ df = pd.read_csv(f'{args.specie}/{args.specie}.csv') ...@@ -17,57 +15,17 @@ df = pd.read_csv(f'{args.specie}/{args.specie}.csv')
meta = models.meta[args.specie] meta = models.meta[args.specie]
batch_size = 32 batch_size = 32
if True : #not os.path.isfile(f'{args.specie}/encodings/encodings_wave2vec2.npy'):
gpu = torch.device(f'cuda:{args.cuda}') gpu = torch.device(f'cuda:{args.cuda}')
model = hear.load_model().to(gpu) for module, name in zip([torchopenl3, wav2vec2, torchcrepe, vggish], ['openl3', 'wav2vec2', 'crepe', 'vggish']):
model = module.load_model().to(gpu)
loader = torch.utils.data.DataLoader(u.Dataset(df, f'{args.specie}/audio/', model.sample_rate, meta['sampleDur']), batch_size=batch_size, num_workers=8, collate_fn=u.collate_fn) loader = torch.utils.data.DataLoader(u.Dataset(df, f'{args.specie}/audio/', model.sample_rate, meta['sampleDur']), batch_size=batch_size, num_workers=8, collate_fn=u.collate_fn)
with torch.inference_mode(): with torch.inference_mode():
encodings, idxs = [], [] encodings, idxs = [], []
for x, idx in tqdm(loader, desc='test '+args.specie, leave=False): for x, idx in tqdm(loader, desc='test '+args.specie, leave=False):
encoding = hear.get_scene_embeddings(x.to(gpu), model=model) encoding = module.get_scene_embeddings(x.to(gpu), model=model)
idxs.extend(idx.numpy()) idxs.extend(idx.numpy())
encodings.extend(encoding.view(len(x), -1).cpu().numpy()) encodings.extend(encoding.view(len(x), -1).cpu().numpy())
idxs, encodings = np.array(idxs), np.stack(encodings) idxs, encodings = np.array(idxs), np.stack(encodings)
# X = umap.UMAP(n_jobs=-1, n_components=8).fit_transform(encodings) # X = umap.UMAP(n_jobs=-1, n_components=8).fit_transform(encodings)
np.save(f'{args.specie}/encodings/encodings_wav2vec2.npy', {'idxs':idxs, 'encodings':encodings}) #, 'umap8':X}) np.save(f'{args.specie}/encodings/encodings_{name}.npy', {'idxs':idxs, 'encodings':encodings}) #, 'umap8':X})
exit()
else:
dic = np.load(f'{args.specie}/encodings/encodings_wave2vec2.npy', allow_pickle=True).item()
idxs, encodings, X = dic['idxs'], dic['encodings'], dic['umap8']
clusters = hdbscan.HDBSCAN(min_cluster_size=10, min_samples=3, cluster_selection_epsilon=0.1, core_dist_n_jobs=-1, cluster_selection_method='leaf').fit_predict(X)
df.loc[idxs, 'cluster'] = clusters.astype(int)
mask = ~df.loc[idxs].label.isna()
clusters, labels = clusters[mask], df.loc[idxs[mask]].label
print('NMI', metrics.normalized_mutual_info_score(labels, clusters))
exit()
#print('Found clusters : \n', pd.Series(clusters).value_counts())
plt.figure(figsize=(20, 10))
plt.scatter(X[clusters==-1,0], X[clusters==-1,1], s=2, alpha=.2, color='Grey')
plt.scatter(X[clusters!=-1,0], X[clusters!=-1,1], s=2, c=clusters[clusters!=-1], cmap='tab20')
plt.tight_layout()
plt.savefig(f'{args.specie}/projections/vggish_projection_clusters.png')
plt.figure(figsize=(20, 10))
plt.scatter(X[~mask,0], X[~mask,1], s=2, alpha=.2, color='Grey')
for l, grp in df.groupby('label'):
plt.scatter(X[df.loc[idxs].label==l, 0], X[df.loc[idxs].label==l, 1], s=4, label=l)
plt.legend()
plt.tight_layout()
plt.savefig(f'{args.specie}/projections/vggish_projection_labels.png')
clusters, labels = clusters[mask], df.loc[idxs[mask]].label
print('Silhouette', metrics.silhouette_score(encodings[mask], clusters))
print('NMI', metrics.normalized_mutual_info_score(labels, clusters))
print('Homogeneity', metrics.homogeneity_score(labels, clusters))
print('Completeness', metrics.completeness_score(labels, clusters))
print('V-Measure', metrics.v_measure_score(labels, clusters))
labelled = df[~df.label.isna()]
for l, grp in labelled.groupby('label'):
best = (grp.groupby('cluster').fn.count() / labelled.groupby('cluster').fn.count()).idxmax()
print(f'Best precision for {l} is for cluster {best} with {(df.cluster==best).sum()} points, \
with precision {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.cluster==best).sum():.2f} and recall {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.label==l).sum():.2f}')
run_openl3.py deleted 100755 → 0
+ 0
− 74
View file @ fe3ac67b
from sklearn import metrics
import matplotlib.pyplot as plt
import umap, hdbscan
from tqdm import tqdm
import argparse, os
import models, utils as u
import pandas as pd, numpy as np, torch
import torchopenl3 as openl3
parser = argparse.ArgumentParser()
parser.add_argument("specie", type=str)
parser.add_argument("-cuda", type=int, default=0)
args = parser.parse_args()
df = pd.read_csv(f'{args.specie}/{args.specie}.csv')
meta = models.meta[args.specie]
batch_size = 64
if True : #not os.path.isfile(f'{args.specie}/encodings/encodings_openl3.npy'):
gpu = torch.device(f'cuda:{args.cuda}')
frontend = models.frontend['logMel_vggish'](meta['sr'], meta['nfft'], meta['sampleDur'], 64)
loader = torch.utils.data.DataLoader(u.Dataset(df, f'{args.specie}/audio/', meta['sr'], meta['sampleDur']), batch_size=batch_size, num_workers=8, collate_fn=u.collate_fn)
model = openl3.models.load_audio_embedding_model(input_repr="mel128", content_type="music", embedding_size=512).to(gpu)
with torch.no_grad():
encodings, idxs = [], []
for x, idx in tqdm(loader, desc='test '+args.specie, leave=False):
encoding = openl3.get_audio_embedding(x.to(gpu), meta['sr'], model=model, center=False, batch_size=batch_size, verbose=False)[0]
idxs.extend(idx.numpy())
encodings.extend(encoding.mean(axis=1).cpu().numpy())
idxs, encodings = np.array(idxs), np.stack(encodings)
X = umap.UMAP(n_jobs=-1, n_components=8).fit_transform(encodings)
np.save(f'{args.specie}/encodings/encodings_openl3.npy', {'idxs':idxs, 'encodings':encodings, 'umap8':X})
else:
dic = np.load(f'{args.specie}/encodings/encodings_openl3.npy', allow_pickle=True).item()
idxs, encodings, X = dic['idxs'], dic['encodings'], dic['umap8']
clusters = hdbscan.HDBSCAN(min_cluster_size=10, min_samples=3, cluster_selection_epsilon=0.1, core_dist_n_jobs=-1, cluster_selection_method='leaf').fit_predict(X)
df.loc[idxs, 'cluster'] = clusters.astype(int)
mask = ~df.loc[idxs].label.isna()
clusters, labels = clusters[mask], df.loc[idxs[mask]].label
print('NMI', metrics.normalized_mutual_info_score(labels, clusters))
exit()
#print('Found clusters : \n', pd.Series(clusters).value_counts())
plt.figure(figsize=(20, 10))
plt.scatter(X[clusters==-1,0], X[clusters==-1,1], s=2, alpha=.2, color='Grey')
plt.scatter(X[clusters!=-1,0], X[clusters!=-1,1], s=2, c=clusters[clusters!=-1], cmap='tab20')
plt.tight_layout()
plt.savefig(f'{args.specie}/projections/vggish_projection_clusters.png')
plt.figure(figsize=(20, 10))
plt.scatter(X[~mask,0], X[~mask,1], s=2, alpha=.2, color='Grey')
for l, grp in df.groupby('label'):
plt.scatter(X[df.loc[idxs].label==l, 0], X[df.loc[idxs].label==l, 1], s=4, label=l)
plt.legend()
plt.tight_layout()
plt.savefig(f'{args.specie}/projections/vggish_projection_labels.png')
clusters, labels = clusters[mask], df.loc[idxs[mask]].label
print('Silhouette', metrics.silhouette_score(encodings[mask], clusters))
print('NMI', metrics.normalized_mutual_info_score(labels, clusters))
print('Homogeneity', metrics.homogeneity_score(labels, clusters))
print('Completeness', metrics.completeness_score(labels, clusters))
print('V-Measure', metrics.v_measure_score(labels, clusters))
labelled = df[~df.label.isna()]
for l, grp in labelled.groupby('label'):
best = (grp.groupby('cluster').fn.count() / labelled.groupby('cluster').fn.count()).idxmax()
print(f'Best precision for {l} is for cluster {best} with {(df.cluster==best).sum()} points, \
with precision {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.cluster==best).sum():.2f} and recall {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.label==l).sum():.2f}')
run_vggish.py deleted 100755 → 0
+ 0
− 74
View file @ fe3ac67b
from sklearn import metrics
import matplotlib.pyplot as plt
import umap, hdbscan
from tqdm import tqdm
import argparse, os
import models, utils as u
import pandas as pd, numpy as np, torch
torch.multiprocessing.set_sharing_strategy('file_system')
parser = argparse.ArgumentParser()
parser.add_argument("specie", type=str)
args = parser.parse_args()
df = pd.read_csv(f'{args.specie}/{args.specie}.csv')
meta = models.meta[args.specie]
if not os.path.isfile(f'{args.specie}/encodings/encodings_vggish.npy'):
gpu = torch.device('cuda')
frontend = models.frontend['logMel_vggish'](meta['sr'], meta['nfft'], meta['sampleDur'], 64)
vggish = torch.hub.load('harritaylor/torchvggish', 'vggish')
# vggish.preprocess = False
vggish.postprocess = False
model = torch.nn.Sequential(frontend, vggish).to(gpu)
model.eval()
loader = torch.utils.data.DataLoader(u.Dataset(df, f'{args.specie}/audio/', 16000, 1), batch_size=1, shuffle=True, num_workers=8, collate_fn=u.collate_fn)
with torch.no_grad():
encodings, idxs = [], []
for x, idx in tqdm(loader, desc='test '+args.specie, leave=False):
# encoding = model(x.to(gpu))
encoding = vggish(x.numpy().squeeze(0), fs=16000)
idxs.extend(idx.numpy())
encodings.extend(encoding.cpu().numpy())
idxs, encodings = np.array(idxs), np.stack(encodings)
X = umap.UMAP(n_jobs=-1).fit_transform(encodings)
np.save(f'{args.specie}/encodings/encodings_vggish.npy', {'idxs':idxs, 'encodings':encodings, 'umap':X})
else:
dic = np.load(f'{args.specie}/encodings/encodings_vggish.npy', allow_pickle=True).item()
idxs, encodings, X = dic['idxs'], dic['encodings'], dic['umap']
clusters = hdbscan.HDBSCAN(min_cluster_size=50, min_samples=5, cluster_selection_epsilon=0.05, core_dist_n_jobs=-1, cluster_selection_method='leaf').fit_predict(X)
df.loc[idxs, 'cluster'] = clusters.astype(int)
mask = ~df.loc[idxs].label.isna()
#print('Found clusters : \n', pd.Series(clusters).value_counts())
plt.figure(figsize=(20, 10))
plt.scatter(X[clusters==-1,0], X[clusters==-1,1], s=2, alpha=.2, color='Grey')
plt.scatter(X[clusters!=-1,0], X[clusters!=-1,1], s=2, c=clusters[clusters!=-1], cmap='tab20')
plt.tight_layout()
plt.savefig(f'{args.specie}/projections/vggish_projection_clusters.png')
plt.figure(figsize=(20, 10))
plt.scatter(X[~mask,0], X[~mask,1], s=2, alpha=.2, color='Grey')
for l, grp in df.groupby('label'):
plt.scatter(X[df.loc[idxs].label==l, 0], X[df.loc[idxs].label==l, 1], s=4, label=l)
plt.legend()
plt.tight_layout()
plt.savefig(f'{args.specie}/projections/vggish_projection_labels.png')
clusters, labels = clusters[mask], df.loc[idxs[mask]].label
print('Silhouette', metrics.silhouette_score(encodings[mask], clusters))
print('NMI', metrics.normalized_mutual_info_score(labels, clusters))
print('Homogeneity', metrics.homogeneity_score(labels, clusters))
print('Completeness', metrics.completeness_score(labels, clusters))
print('V-Measure', metrics.v_measure_score(labels, clusters))
labelled = df[~df.label.isna()]
for l, grp in labelled.groupby('label'):
best = (grp.groupby('cluster').fn.count() / labelled.groupby('cluster').fn.count()).idxmax()
print(f'Best precision for {l} is for cluster {best} with {(df.cluster==best).sum()} points, \
with precision {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.cluster==best).sum():.2f} and recall {((labelled.cluster==best)&(labelled.label==l)).sum()/(labelled.label==l).sum():.2f}')
test_AE.py
+ 1
− 1
View file @ eab3f218
...@@ -56,7 +56,7 @@ else: ...@@ -56,7 +56,7 @@ else:
if X is None: if X is None:
X = umap.UMAP(n_jobs=-1, n_components=8).fit_transform(encodings) X = umap.UMAP(n_jobs=-1, n_components=8).fit_transform(encodings)
print('/!\ no UMAP') print('/!\ no UMAP')
clusters = hdbscan.HDBSCAN(min_cluster_size=10, min_samples=3, cluster_selection_epsilon=.1, cluster_selection_method='leaf', core_dist_n_jobs=-1).fit_predict(X) clusters = hdbscan.HDBSCAN(min_cluster_size=10, min_samples=3, cluster_selection_epsilon=.1, cluster_selection_method='leaf' if not 'humpback' in args.specie else 'eom', core_dist_n_jobs=-1).fit_predict(X)
df.loc[idxs, 'cluster'] = clusters.astype(int) df.loc[idxs, 'cluster'] = clusters.astype(int)
mask = ~df.loc[idxs].label.isna() mask = ~df.loc[idxs].label.isna()
......
train_AE.py
+ 18
− 90
View file @ eab3f218
import umap, hdbscan
from torchvision.utils import make_grid from torchvision.utils import make_grid
from torch.utils.tensorboard import SummaryWriter from torch.utils.tensorboard import SummaryWriter
import torch import torch
from sklearn import metrics, cluster
import numpy as np, pandas as pd import numpy as np, pandas as pd
from scipy.stats import linregress
import utils as u, models import utils as u, models
from tqdm import tqdm from tqdm import tqdm
import os, argparse, warnings import os, argparse, warnings
...@@ -13,9 +10,10 @@ warnings.filterwarnings("error") ...@@ -13,9 +10,10 @@ warnings.filterwarnings("error")
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("specie", type=str) parser.add_argument("specie", type=str)
parser.add_argument("-bottleneck", type=int, default=16) parser.add_argument("-bottleneck", type=int, default=256)
parser.add_argument("-frontend", type=str, default='logMel') parser.add_argument("-frontend", type=str, default='logMel')
parser.add_argument("-encoder", type=str, default='sparrow_encoder') parser.add_argument("-encoder", type=str, default='sparrow_encoder')
parser.add_argument("-prcptl", type=int, default=1)
parser.add_argument("-nMel", type=int, default=128) parser.add_argument("-nMel", type=int, default=128)
parser.add_argument("-lr", type=float, default=3e-3) parser.add_argument("-lr", type=float, default=3e-3)
parser.add_argument("-lr_decay", type=float, default=1e-2) parser.add_argument("-lr_decay", type=float, default=1e-2)
...@@ -26,10 +24,10 @@ args = parser.parse_args() ...@@ -26,10 +24,10 @@ args = parser.parse_args()
df = pd.read_csv(f'{args.specie}/{args.specie}.csv') df = pd.read_csv(f'{args.specie}/{args.specie}.csv')
print(f'{len(df)} available vocs') print(f'{len(df)} available vocs')
modelname = f'{args.specie}_{args.bottleneck}_{args.frontend}{args.nMel if "Mel" in args.frontend else ""}_{args.encoder}_decod2_BN_nomaxPool.stdc' modelname = f'{args.specie}_{args.bottleneck}_{args.frontend}{args.nMel if "Mel" in args.frontend else ""}_{args.encoder}_decod2_BN_nomaxPool{"_noprcptl" if args.prcptl==0 else ""}.stdc'
gpu = torch.device(f'cuda:{args.cuda}') gpu = torch.device(f'cuda:{args.cuda}')
writer = SummaryWriter(f'runs2/{modelname}') writer = SummaryWriter(f'runs/{modelname}')
os.system(f'cp *.py runs2/{modelname}') os.system(f'cp *.py runs/{modelname}')
vgg16 = models.vgg16 vgg16 = models.vgg16
vgg16.eval().to(gpu) vgg16.eval().to(gpu)
meta = models.meta[args.specie] meta = models.meta[args.specie]
...@@ -56,8 +54,11 @@ for epoch in range(100_000//len(loader)): ...@@ -56,8 +54,11 @@ for epoch in range(100_000//len(loader)):
x = encoder(label) x = encoder(label)
pred = decoder(x) pred = decoder(x)
assert not torch.isnan(pred).any(), "found a NaN :'(" assert not torch.isnan(pred).any(), "found a NaN :'("
if args.prcptl == 1:
predd = vgg16(pred.expand(pred.shape[0], 3, *pred.shape[2:])) predd = vgg16(pred.expand(pred.shape[0], 3, *pred.shape[2:]))
labell = vgg16(label.expand(label.shape[0], 3, *label.shape[2:])) labell = vgg16(label.expand(label.shape[0], 3, *label.shape[2:]))
else:
predd, labell = pred, label
score = MSE(predd, labell) score = MSE(predd, labell)
score.backward() score.backward()
...@@ -69,10 +70,14 @@ for epoch in range(100_000//len(loader)): ...@@ -69,10 +70,14 @@ for epoch in range(100_000//len(loader)):
print('Early stop') print('Early stop')
torch.save(model.state_dict(), f'{args.specie}/weights/{modelname}') torch.save(model.state_dict(), f'{args.specie}/weights/{modelname}')
exit() exit()
step += 1 step += 1
continue
# TEST ROUTINE if step % 500 == 0: # scheduler
if step % 500 == 0: scheduler.step()
# Plot images
if step % 100 == 0 :
# Plot reconstructions # Plot reconstructions
images = [(e-e.min())/(e.max()-e.min()) for e in label[:8]] images = [(e-e.min())/(e.max()-e.min()) for e in label[:8]]
grid = make_grid(images) grid = make_grid(images)
...@@ -81,80 +86,3 @@ for epoch in range(100_000//len(loader)): ...@@ -81,80 +86,3 @@ for epoch in range(100_000//len(loader)):
images = [(e-e.min())/(e.max()-e.min()) for e in pred[:8]] images = [(e-e.min())/(e.max()-e.min()) for e in pred[:8]]
grid = make_grid(images) grid = make_grid(images)
writer.add_image('reconstruct', grid, step) writer.add_image('reconstruct', grid, step)
\ No newline at end of file
torch.save(model.state_dict(), f'{args.specie}/{modelname}')
scheduler.step()
# Actual test
model[1:].eval()
with torch.no_grad():
encodings, idxs = [], []
for x, idx in tqdm(loader, desc='test '+str(step), leave=False):
encoding = model[:2](x.to(gpu))
idxs.extend(idx)
encodings.extend(encoding.cpu().detach())
idxs, encodings = np.array(idxs), np.stack(encodings)
print('Computing UMAP...', end='')
try:
X = umap.UMAP(n_jobs=-1).fit_transform(encodings)
except UserWarning:
pass
print('\rRunning HDBSCAN...', end='')
clusters = hdbscan.HDBSCAN(min_cluster_size=len(df)//100, min_samples=5, core_dist_n_jobs=-1, cluster_selection_method='leaf').fit_predict(X)
# df.loc[idxs, 'cluster'] = clusters.astype(int)
mask = ~df.loc[idxs].label.isna()
clusters, labels = clusters[mask], df.loc[idxs[mask]].label
writer.add_scalar('NMI HDBSCAN', metrics.normalized_mutual_info_score(labels, clusters), step)
try:
writer.add_scalar('ARI HDBSCAN', metrics.adjusted_rand_score(labels, clusters), step)
except:
pass
writer.add_scalar('Homogeneity HDBSCAN', metrics.homogeneity_score(labels, clusters), step)
writer.add_scalar('Completeness HDBSCAN', metrics.completeness_score(labels, clusters), step)
writer.add_scalar('V-Measure HDBSCAN', metrics.v_measure_score(labels, clusters), step)
# print('\rComputing HDBSCAN precision and recall distributions', end='')
# labelled = df[~df.label.isna()]
# precs, recs = [], []
# for l, grp in labelled.groupby('label'):
# best = (grp.groupby('cluster').fn.count() / labelled.groupby('cluster').fn.count()).idxmax()
# precs.append((grp.cluster==best).sum()/(labelled.cluster==best).sum())
# recs.append((grp.cluster==best).sum()/len(grp))
# writer.add_histogram('HDBSCAN Precisions ', np.array(precs), step)
# writer.add_histogram('HDBSCAN Recalls ', np.array(recs), step)
# df.drop('cluster', axis=1, inplace=True)
# print('\rRunning elbow method for K-Means...', end='')
# ks = (5*1.2**np.arange(20)).astype(int)
# distorsions = [cluster.KMeans(n_clusters=k).fit(encodings).inertia_ for k in ks]
# print('\rEstimating elbow...', end='')
# errors = [linregress(ks[:i], distorsions[:i]).stderr + linregress(ks[i+1:], distorsions[i+1:]).stderr for i in range(2, len(ks)-2)]
# k = ks[np.argmin(errors)]
# writer.add_scalar('Chosen K', k, step)
# clusters = cluster.KMeans(n_clusters=k).fit_predict(encodings)
# df.loc[idxs, 'cluster'] = clusters.astype(int)
# writer.add_scalar('Silhouette', metrics.silhouette_score(encodings, clusters), step)
# clusters, labels = clusters[mask], df.loc[idxs[mask]].label
# writer.add_scalar('NMI K-Means', metrics.normalized_mutual_info_score(labels, clusters), step)
# try:
# writer.add_scalar('ARI K-Means', metrics.adjusted_rand_score(labels, clusters), step)
# except:
# pass
# writer.add_scalar('Homogeneity K-Means', metrics.homogeneity_score(labels, clusters), step)
# writer.add_scalar('Completeness K-Means', metrics.completeness_score(labels, clusters), step)
# writer.add_scalar('V-Measure K-Means', metrics.v_measure_score(labels, clusters), step)
# print('\rComputing K-Means precision and recall distributions', end='')
# labelled = df[~df.label.isna()]
# precs, recs = [], []
# for l, grp in labelled.groupby('label'):
# best = (grp.groupby('cluster').fn.count() / labelled.groupby('cluster').fn.count()).idxmax()
# precs.append((grp.cluster==best).sum()/(labelled.cluster==best).sum())
# recs.append((grp.cluster==best).sum()/len(grp))
# writer.add_histogram('K-Means Precisions ', np.array(precs), step)
# writer.add_histogram('K-Means Recalls ', np.array(recs), step)
# df.drop('cluster', axis=1, inplace=True)
print('\r', end='')
model[1:].train()
step += 1
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