def draw_activation_pattern(ax,
model,
spectrogram,
feature_layer_name='reshape_1'):
""" Draws a t-sne of a layer's feature vector to a matplotlib plot.
Args:
ax: A matplotlib axis to plot to.
model: The neural network used to make predictions.
spectrogram: A spectrogram of an audio fragment.
feature_layer_name: The name of the layer whose output vector will be visualized.
"""
feature_layer = model.get_layer(feature_layer_name).output
input_placeholder = model.input
trimmed_network = backend.function([input_placeholder], [feature_layer])
spectrogram = [spectrogram]
output_batch = trimmed_network(spectrogram)[0]
for output in output_batch:
squeezed = output
embedding = TSNE(n_components=1).fit_transform(squeezed)
ys = embedding.flatten()
ys = (ys - ys.min()) / (ys.max() - ys.min())
nr_frames = ys.shape[0]
colors = ys
xs = np.arange(0, nr_frames)
ax.scatter(xs, ys, c=colors)
def tsne_f(embeddings,y):
'''
Visualize t-SNE embeddings of the embeddings
'''
plt.clf()
X_embedded = TSNE(n_components=2, perplexity = 20, metric = 'cityblock').fit_transform(embeddings)
X_embedded_flattened = X_embedded.flatten('F')
fashion_scatter(np.array(X_embedded), y)
return X_embedded,X_embedded_flattened
def tsne(signalruns):
event_list = list()
count = 0
for signalrun in signalruns:
signalrun_doc = schema.Signalrun.find_one(signalrun=signalrun['id'])
event_docs = schema.Event.find(signalrun=signalrun_doc)
events = [(doc.start_time, doc.stop_time) for doc in event_docs]
event_list.append({
'eid': count,
'events': events,
'start': signalrun_doc.signal.start_time,
'end': signalrun_doc.signal.stop_time,
'signalrun_id': str(signalrun_doc.id)
})
count += 1
dist_matrix = np.empty((count, count))
for i in range(count):
for j in range(i + 1):
if i == j:
dist_matrix[i, j] = 0
else:
dist_matrix[i, j] = event_overlap_distance(
event_list[i]['events'], event_list[j]['events'],
min(event_list[i]['start'], event_list[j]['start']),
max(event_list[i]['end'], event_list[j]['end']))
dist_matrix[j, i] = dist_matrix[i, j]
def func_dist(a, b):
return dist_matrix[int(a[0]), int(b[0])]
X = np.arange(count)
X = X.reshape(-1, 1).astype(int)
X_embedded = TSNE(n_components=1, metric=func_dist).fit_transform(X)
X_embedded = X_embedded.flatten()
X_sorted = [{'i': i, 'v': v} for i, v in enumerate(X_embedded)]
X_sorted = sorted(X_sorted, key=lambda x: x['v'])
for i, d in enumerate(X_sorted):
event_list[d['i']]['order'] = i
signalruns_ordered = [None] * len(event_list)
for i in range(len(event_list)):
signalruns_ordered[event_list[i]['order']] = signalruns[i]
return signalruns_ordered
def tsne_n1(x):
value = TSNE(n_components=1).fit_transform(x)
arr = value.flatten()
return arr
if part.__contains__('WSAS'):
phases = ['Base', 'Anes']
if part.__contains__('MCD'):
phases = ['eyesclosed1', 'emergencefirst5']
label_part = label[data['ID'] == part]
X_part = X[data['ID'] == part].copy()
# drop columns with nan
X_part = X_part[:, ~np.isnan(X_part).any(axis=0)]
X_2 = TSNE(n_components=2).fit_transform(X_part)
X_1 = TSNE(n_components=1).fit_transform(X_part)
data_one = pd.DataFrame(
np.transpose(np.vstack((X_1.flatten(), label_part))))
data_one.columns = ['conn_1D', 'label']
oneD.append(data_one)
mean_base = np.mean(X_1[label_part == 0])
mean_anes = np.mean(X_1[label_part == 1])
dist_tmp = max(mean_base, mean_anes) - min(mean_base, mean_anes)
Dist.append(dist_tmp)
meanA.append(mean_anes)
meanB.append(mean_base)
fig = plt.figure(figsize=(10, 10))
scatter = plt.scatter(X_2[:, 0], X_2[:, 1], marker='o', c=label_part)
plt.scatter(X_2[:, 0], X_2[:, 1], marker='o', c=label_part)
plt.legend(handles=scatter.legend_elements()[0], labels=phases)
plt.title(part)
label = np.zeros(len(data['Phase']))
label[np.where(data['Phase'] == 'Anes')] = 1
#label[np.where(data['Phase']=='Sedoff')]=2
#label[np.where(data['Phase']=='Sedon1')]=3
#label[np.where(data['Phase']=='eyesclosed1')]=4
#label[np.where(data['Phase']=='emergencefirst5')]=5
#plt.plot(label)
#plt.show()
#phases = ['Base', 'Anes','Sedoff', 'Sedon1','eyesclosed1', 'emergencefirst5']
#phases = ['Base', 'Anes','Sedoff', 'Sedon1']
phases = ['Base', 'Anes']
participants = np.unique(data['ID'])
len(participants)
X_1 = TSNE(n_components=1).fit_transform(X)
data_one = pd.DataFrame(
np.transpose(np.vstack((X_1.flatten(), label, outcome))))
data_one.columns = ['conn_1D', 'label', 'outcome']
data_one.index = data.index
ax = sns.displot(data=data_one,
x="conn_1D",
hue="label",
kind="kde",
col="outcome",
fill=True)
ax.set_titles("{col_name} participants")
plt.show()
dist_matrix = np.empty((count, count))
for i in range(count):
for j in range(i + 1):
if i == j:
dist_matrix[i, j] = 0
else:
dist_matrix[i, j] = event_overlap_distance(
event_list[i]['events'], event_list[j]['events'],
min(event_list[i]['start'], event_list[j]['start']),
max(event_list[i]['end'], event_list[j]['end']))
dist_matrix[j, i] = dist_matrix[i, j]
def func_dist(a, b):
return dist_matrix[int(a[0]), int(b[0])]
X = np.arange(count)
X = X.reshape(-1, 1).astype(int)
X_embedded = TSNE(n_components=1, metric=func_dist).fit_transform(X)
X_embedded = X_embedded.flatten()
X_sorted = [{'i': i, 'v': v} for i, v in enumerate(X_embedded)]
X_sorted = sorted(X_sorted, key=lambda x: x['v'])
for i, d in enumerate(X_sorted):
event_list[d['i']]['order'] = i
for d in event_list:
print(d)
fig = plt.figure(figsize=(10, 10))
ax = fig.gca(projection='3d')
scatter = ax.scatter(data_3[:, 0],
data_3[:, 1],
data_3[:, 2],
marker='o',
c=label)
ax.scatter(data_3[:, 0], data_3[:, 1], data_3[:, 2], marker='o', c=label)
ax.plot(data_3[:, 0], data_3[:, 1], data_3[:, 2], linewidth=0.6, color='k')
ax.set_title(p_id)
pdf.savefig()
plt.close()
data_1 = TSNE(n_components=1).fit_transform(data)
data_one = pd.DataFrame(np.transpose(np.vstack((data_1.flatten(), label))))
data_one.columns = ['conn_1D', 'label']
oneD.append(data_one)
mean_base = np.mean(data_1[label == 0])
mean_anes = np.mean(data_1[label == 1])
dist_tmp = max(mean_base, mean_anes) - min(mean_base, mean_anes)
Dist.append(dist_tmp)
meanA.append(mean_anes)
meanB.append(mean_base)
for i, p in enumerate(P_IDS):
data = oneD[i]
mean_anes = meanA[i]
mean_base = meanB[i]
a = sns.displot(data=data, x="conn_1D", hue="label", kind="kde", fill=True)
