def find_genes_CX(drug, model, gdsc_expr, gdsc_dr, test_tcga_expr):
print('obtaining masked data...')
# masked_data, list_of_baselines = get_masked_data_for_CXPlain(model, gdsc_expr, pathway_matrix)
masked_data, list_of_baselines = get_masked_data_for_CXPlain(model, gdsc_expr, pathway_matrix, subtract_mean=True)
lb = np.concatenate(list_of_baselines).reshape(len(gdsc_expr), -1)
lb = pd.DataFrame(lb, index=gdsc_expr.index, columns=pathway_names)
lb.to_csv(res_dir + drug + '/baselines.csv')
# print(lb.shape)
# exit()
print('obtained masked data...')
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.keras.backend.clear_session()
tf.random.set_seed(SEED)
from tensorflow.python.keras.losses import mean_squared_error as loss
from cxplain import CXPlain
from cxplain.backend.model_builders.custom_mlp import CustomMLPModelBuilder
# from cxplain.backend.masking.zero_masking import FastZeroMasking
n_pathways = len(pathway_names)
model_builder = CustomMLPModelBuilder(num_layers=2, num_units=512, batch_size=16, learning_rate=0.001, n_feature_groups=n_pathways)
# masking_operation = FastZeroMasking()
print(gdsc_expr.values.shape, gdsc_dr.values.shape)
print("Fitting CXPlain model")
explainer = CXPlain(model, model_builder, None, loss, num_models=3)
explainer.fit(gdsc_expr.values, gdsc_dr.values, masked_data=masked_data)
print("Attributing using CXPlain")
attr,_ = explainer.explain_groups(test_tcga_expr.values)
print('attr')
attr = pd.DataFrame(attr, index=test_tcga_expr.index, columns=pathway_names)
borda = get_ranked_list(attr, k=n_pathways)
attr_mean = list(np.abs(attr).mean(axis=0).nlargest(n_pathways).index)
out = pd.DataFrame(columns=['borda', 'mean'])
out['borda'] = borda
out['mean'] = attr_mean
out.to_csv(res_dir + drug + '/pathways.csv', index=False)
if not os.path.exists(res_dir + drug + '/explainer/'):
os.mkdir(res_dir + drug + '/explainer/')
explainer.save(res_dir + drug + '/explainer/', custom_model_saver=None)
def find_genes_CX(drug, model, gdsc_expr, gdsc_dr, test_tcga_expr):
print('obtaining precalculating omegas...')
loss = torch.nn.MSELoss(reduction='none')
# idx = gdsc_expr.index[:10]
# gdsc_expr = gdsc_expr.loc[idx]
# gdsc_dr = gdsc_dr[idx]
# omegas, lb = precalculate_omegas(model, gdsc_expr, gdsc_dr, pathway_matrix, loss)
# omegas, lb = precalculate_omegas_scaled(model, gdsc_expr, gdsc_dr, pathway_matrix, loss)
# omegas, lb = precalculate_omegas(model, gdsc_expr, gdsc_dr, pathway_matrix, loss,mode='scaled-difference')
# omegas, lb = precalculate_omegas(model, gdsc_expr, gdsc_dr, pathway_matrix, loss, mode='delta-of-delta')
omegas, lb = precalculate_omegas(model,
gdsc_expr,
gdsc_dr,
pathway_matrix,
loss,
mode='delta-scaled')
if lb is not None:
lb = pd.DataFrame(lb, index=gdsc_expr.index, columns=pathway_names)
lb.to_csv(res_dir + drug + '/baselines.csv')
print('obtained masked data...')
import tensorflow as tf
tf.compat.v1.disable_v2_behavior()
tf.keras.backend.clear_session()
tf.random.set_seed(SEED)
from tensorflow.python.keras.losses import mean_squared_error as loss
from cxplain import CXPlain
from cxplain.backend.model_builders.custom_mlp_precalc import CustomMLPModelBuilder
# from cxplain.backend.masking.zero_masking import FastZeroMasking
n_pathways = len(pathway_names)
model_builder = CustomMLPModelBuilder(num_layers=2,
num_units=512,
batch_size=16,
learning_rate=0.001,
n_feature_groups=n_pathways)
# masking_operation = FastZeroMasking()
print(gdsc_expr.values.shape, gdsc_dr.values.shape)
print("Fitting CXPlain model")
explainer = CXPlain(model, model_builder, None, loss, num_models=3)
explainer.fit_precalc(gdsc_expr.values, gdsc_dr.values, omega=omegas)
print("Attributing using CXPlain")
attr, _ = explainer.explain_groups(test_tcga_expr.values)
print('attr')
attr = pd.DataFrame(attr,
index=test_tcga_expr.index,
columns=pathway_names)
borda = get_ranked_list(attr, k=n_pathways)
attr_mean = list(np.abs(attr).mean(axis=0).nlargest(n_pathways).index)
out = pd.DataFrame(columns=['borda', 'mean'])
out['borda'] = borda
out['mean'] = attr_mean
out.to_csv(res_dir + drug + '/pathways.csv', index=False)
if not os.path.exists(res_dir + drug + '/explainer/'):
os.mkdir(res_dir + drug + '/explainer/')
explainer.save(res_dir + drug + '/explainer/', custom_model_saver=None)
return (x, y_pred, masked_outs)
model_builder = CustomMLPModelBuilder(num_layers=2,
num_units=32,
batch_size=32,
learning_rate=0.001,
n_feature_groups=10)
# masking_operation = ZeroMasking()
# masking_operation = FastZeroMasking()
k = get_masked_data_for_CXPlain(model, x[:200])
explainer = CXPlain(model, model_builder, None,
loss) #,downsample_factors=(5,))
explainer.fit(x[:200], y[:200], masked_data=k)
attributions = explainer.explain_groups(x[200:])
attr = pd.DataFrame(attributions, index=range(200, 300))
# plt.plot(range(50), np.abs(attr).mean(axis=0), label='attr')
# #plt.plot(range(50), np.abs(mult).mean(axis=0), label='mult')
# plt.show()
print(attr.shape)
print(attr.loc[200])
df = pd.DataFrame(x[200:], index=range(200, 300))
idx = df.loc[np.abs(df[20]) < 0.1].loc[np.abs(df[40]) > 1].index[0]
# plt.plot(range(50), np.abs(attr.loc[idx]), label='attr')
# plt.legend()
# plt.show()