[five_p_loader, three_p_loader, ref_loader, alt_loader, strand_loader],
]
# set y label
y_label = np.log(
sample_df['non_syn_counts'].values /
(panels.loc[panels['Panel'] == 'Agilent_kit']['cds'].values[0] / 1e6) +
1)[:, np.newaxis]
y_strat = np.argmax(samples['histology'], axis=-1)
losses = [Losses.QuantileLoss()]
metrics = [Metrics.QuantileLoss()]
encoders = [
InstanceModels.PassThrough(shape=(1, )),
InstanceModels.VariantPositionBin(24, 100),
InstanceModels.VariantSequence(6,
4,
2, [16, 16, 8, 8],
fusion_dimension=32)
]
all_weights = [
pickle.load(
open(
cwd / 'figures' / 'tmb' / 'tcga' / 'VICC_01_R2' / 'results' /
'run_naive.pkl', 'rb')),
pickle.load(
open(
cwd / 'figures' / 'tmb' / 'tcga' / 'VICC_01_R2' / 'results' /
'run_position.pkl', 'rb')),
y,
tf.gather(tf.constant(y_weights, dtype=tf.float32), x)
))
ds_valid = tf.data.Dataset.from_tensor_slices((idx_valid, y_label[idx_valid]))
ds_valid = ds_valid.batch(len(idx_valid), drop_remainder=False)
ds_valid = ds_valid.map(lambda x, y: ((pos_loader(x, ragged_output=True),
bin_loader(x, ragged_output=True),
chr_loader(x, ragged_output=True),
),
y,
tf.gather(tf.constant(y_weights, dtype=tf.float32), x)
))
while True:
position_encoder = InstanceModels.VariantPositionBin(24, 100)
mil = RaggedModels.MIL(instance_encoders=[position_encoder.model], output_dim=2, pooling='sum', mil_hidden=(64, 32, 16, 8), output_type='anlulogits')
mil.model.compile(loss=losses,
metrics=[Metrics.CrossEntropy(), Metrics.Accuracy()],
weighted_metrics=[Metrics.CrossEntropy(), Metrics.Accuracy()],
optimizer=tf.keras.optimizers.Adam(learning_rate=0.005,
clipvalue=10000))
mil.model.fit(ds_train,
steps_per_epoch=20,
validation_data=ds_valid,
epochs=10000,
callbacks=callbacks)
eval = mil.model.evaluate(ds_valid)
