path_model_cell_out = tfm_data.def_check_create_path(
path_model, n_out_cell_type)
print(n_out_cell_type)
# DESIGN P with dense
# ### StandarScaler normalization - Fully connected - 100 dense
print('Metabolic and Signaling with StandardScaler normalization - ' +
str(df_weight_both.shape[0]) + ' gene - dense100')
ss_dense_model, ss_dense_metric, ss_dense_hp = tfm_kt.TFM_KerasTunerExperiment(
X_train_=array_train_X_ss,
y_train_=array_train_y_ss,
X_test_=array_test_X_ss,
y_test_=array_test_y_ss,
df_w_=pd.DataFrame(),
bio_='dense_',
design_name_='met_sig_SScaler_1_layer_100',
directory_='met_sig_kt_ss_dense100',
project_name_='kt_ss_dense_100_experiment_',
second_layer=False,
path_=path_hyperband_,
epochs_=epochs_default,
batch_size_=batch_size_default).build()
# DESIGN A with 1-LAYER and 2-LAYER
# ## with 1-LAYER
# ### StandardScaler normalization - Pathways connection
print('Metabolic and Signaling with StandardScaler normalization - ' +
str(df_weight_both.shape[0]) + ' gene - pathways' +
str(len(df_weight_metabolic_signaling.columns)))
ss_a1_model, ss_a1_metric, ss_a1_hp = tfm_kt.TFM_KerasTunerExperiment(
array_test_y_ss.append(np.array(y_test_ss))
# ORIGINAL DATASET (9437 genes)
# DESIGN P1 with 1-LAYER
### StandardScaler normalization - Fully connected - 100 dense
print('Default with StandardScaler normalization - ' +
str(df_paper.shape[1] - 1) + ' gene - dense100')
ss_p1_model, _, ss_p1_hp = tfm_kt.TFM_KerasTunerExperiment(
X_train_=array_train_X_ss,
y_train_=array_train_y_ss,
X_test_=array_test_X_ss,
y_test_=array_test_y_ss,
df_w_=pd.DataFrame(),
bio_='dense_',
design_name_='default_SScaler_1_layer_100_p1',
directory_='kt_ss_p1_default',
project_name_='kt_ss_p1_dense100_experiment_',
second_layer=False,
path_=path_hyperband_,
epochs_=epochs_default,
batch_size_=batch_size_default).build()
# DESIGN P2 with 1-LAYER - signaling
### StandardScaler normalization - Fully (100 dense) + Partially (92 signaling pathway) connected - dense+pathway192
print('Signaling with StandardScaler normalization - ' +
str(df_paper.shape[1] - 1) + ' gene - pathways' +
str(len(df_weight_paper_signaling_dense_pathway.columns)))
ss_p2_sig_model, _, ss_p2_sig_hp = tfm_kt.TFM_KerasTunerExperiment(
X_train_=array_train_X_ss,