#Train VAE
DTCRU.Train_VAE(accuracy_min=0.9, Load_Prev_Data=True)
#Create Repertoire Dendrogram
color_dict = {
'Control': 'limegreen',
'9H10': 'red',
'RT': 'darkorange',
'Combo': 'magenta'
}
DTCRU.Repertoire_Dendrogram(n_jobs=40,
distance_metric='KL',
log_scale=True,
dendrogram_radius=0.28,
repertoire_radius=0.35,
Load_Prev_Data=True,
gridsize=60,
color_dict=color_dict,
lw=4,
gaussian_sigma=1.0,
vmax=0.001)
import matplotlib.pyplot as plt
plt.figure()
plt.scatter(0,
0,
s=5000,
edgecolors='magenta',
facecolors='none',
linewidths=8)
from DeepTCR.DeepTCR import DeepTCR_U
# Instantiate training object
DTCRU = DeepTCR_U('Rep_Dendrogram',device='/gpu:2')
#Load Data from directories
DTCRU.Get_Data(directory='../../Data/Rudqvist',Load_Prev_Data=False,aggregate_by_aa=True,
aa_column_beta=1,count_column=2,v_beta_column=7,d_beta_column=14,j_beta_column=21)
#Train VAE
DTCRU.Train_VAE(accuracy_min=0.9)
color_dict = {'Control':'limegreen','9H10':'red','RT':'darkorange','Combo':'magenta'}
DTCRU.Repertoire_Dendrogram(n_jobs=40,distance_metric='KL',
dendrogram_radius=0.28,repertoire_radius=0.35,Load_Prev_Data=True,gridsize=6,
color_dict=color_dict)
