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dimensionKnockOutExperiments.py
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dimensionKnockOutExperiments.py
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import torch
import numpy as np
from torch.nn import functional as F
from sklearn import metrics
import pandas as pd
from exprVAEwithAdditionalFeatures import ExprOmiVAE
import shapExplainerHelper as ShapHelper
import generalHelperFunctions as GeneralHelper
def nullingDimensions(sample_id,expr_df,diseaseCode,chosenTissue,dimension,device='cpu'):
"""
Tests four different methods to perturb the dimension in the network.
:param diseaseCode: Required to measure the change in accuracy.
:param chosenTissue: Required to measure the change in accuracy.
:param dimension: Chosen dimension to perturb
"""
input_path = 'DataSources/GDC-PANCAN_'
vae_model=ExprOmiVAE(input_path,expr_df)
def runThroughModel(vae_mod,exprTensor,disease_code,dim_number):
with torch.no_grad():
_, recon_data, mean, log_var, pred_y = vae_mod(exprTensor)
#print("mean is")
#print(mean[:, dim_number])
pred_y_softmax = F.softmax(pred_y, dim=1)
_, predicted = torch.max(pred_y_softmax, 1)
correct_vals = (predicted == disease_code).sum().item()
accuracy_tumour = correct_vals / len(pred_y) * 100
return accuracy_tumour,predicted
def getAccuracyOfModel(disease_code, vae_mod, norm_expr_tensor, tumour_expr_tensor,dim_number):
accuracy,predictedTumour=runThroughModel(vae_mod,tumour_expr_tensor,disease_code,dim_number)
print('Cancer tissue accuracy is: {}'.format(accuracy))
accuracy,predictedNormal = runThroughModel(vae_mod, norm_expr_tensor, 0,dim_number)
print('Normal tissue accuracy is: {}'.format(accuracy))
"""
print("Other metrics")
output_y_normal = np.zeros(predictedNormal.shape)
output_y_tumour = np.zeros(predictedTumour.shape)
output_y_tumour[output_y_tumour == 0] = disease_code
output_y = np.concatenate([output_y_normal, output_y_tumour])
unique, counts = np.unique(predictedTumour, return_counts=True)
print("Predictions for tumour")
print(np.asarray((unique, counts)).T)
unique, counts = np.unique(predictedNormal, return_counts=True)
print("Predictions for normal tissue")
print(np.asarray((unique, counts)).T)
prediction = torch.cat((predictedNormal, predictedTumour), 0)
precision = metrics.precision_score(output_y, prediction, average='weighted')
print('Precision: {}'.format(precision))
recall = metrics.recall_score(output_y, prediction, average='weighted')
print('Recall: {}'.format(recall))
f1Score = metrics.f1_score(output_y, prediction, average='weighted')
print('f1Score: {}'.format(f1Score))
"""
def changeWeightandBias(weightZero=False, weightOne=False, weightReverse=False, changeOutput=True, dim_number=0, vae_mod=vae_model,
disease_code=1, norm_expr_tensor=0, tumour_expr_tensor=0):
vae_mod.load_state_dict(torch.load('DataSources/vae_saved_model(original).pt', map_location=torch.device('cpu')))
vae_mod.eval()
with torch.no_grad():
if weightZero == True:
vae_mod.e_fc4_mean[0].weight[dim_number] = 0.
#vae_mod.e_fc4_mean[0].weight[dim_number, :] = 0.
elif weightOne == True:
vae_mod.e_fc4_mean[0].weight[dim_number, :] = 1.
vae_mod.e_fc4_mean[0].bias[dim_number] = 200.
elif weightReverse == True:
vae_mod.e_fc4_mean[0].weight[dim_number, :] = (vae_mod.e_fc4_mean[0].weight[dim_number, :]) * -1
# this is for changing the output of the dimension
elif changeOutput == True:
vae_mod.load_state_dict(
torch.load('DataSources/vae_saved_model(original).pt',
map_location=torch.device('cpu')))
vae_mod.eval()
getAccuracyOfModel(disease_code, vae_mod, norm_expr_tensor, tumour_expr_tensor,dim_number)
def extract_relevantExpr(expr_df, condition):
expr_df_T = expr_df.T
relevantExpr = expr_df_T[condition].T
# tumour_expr = tumour_expr.sample(n=100, axis=1)
relevantExpr_tensor=GeneralHelper.addToTensor(relevantExpr,device)
return relevantExpr_tensor
def nullingDimensions(disease_code, disease_string, dim_number):
phenotype = pd.read_csv('DataSources/GDC-PANCAN.basic_phenotype.tsv', sep='\t', header=0, index_col=0)
phenotype = phenotype.T[sample_id]
phenotype = phenotype.T
conditionone = phenotype['sample_type'] == "Primary Tumor"
conditiontwo = phenotype['project_id'] == disease_string
conditionthree = phenotype['sample_type'] == "Solid Tissue Normal"
conditionaltumour = np.logical_and(conditionone, conditiontwo)
conditionalnormal = np.logical_and(conditionthree, conditiontwo)
norm_expr_tensor = extract_relevantExpr(expr_df, conditionalnormal)
tumour_expr_tensor = extract_relevantExpr(expr_df, conditionaltumour)
#saved pickle file of omiVAE we are using
vae_model.load_state_dict(torch.load('DataSources/vae_saved_model(original).pt', map_location=torch.device('cpu')))
vae_model.eval()
print("normal before")
getAccuracyOfModel(disease_code, vae_model, norm_expr_tensor=norm_expr_tensor,
tumour_expr_tensor=tumour_expr_tensor,dim_number=dim_number)
#print("weight of model before")
#print(vae_model.e_fc4_mean[0].weight[dim_number, :])
print("weight set to zero")
changeWeightandBias(weightZero=True, dim_number=dim_number, disease_code=disease_code,
norm_expr_tensor=norm_expr_tensor, tumour_expr_tensor=tumour_expr_tensor)
print("weight set to 1")
changeWeightandBias(weightOne=True, dim_number=dim_number, vae_mod=vae_model, disease_code=disease_code,
norm_expr_tensor=norm_expr_tensor, tumour_expr_tensor=tumour_expr_tensor)
print("weight reversed")
changeWeightandBias(weightReverse=True, dim_number=dim_number, vae_mod=vae_model, disease_code=disease_code,
norm_expr_tensor=norm_expr_tensor, tumour_expr_tensor=tumour_expr_tensor)
print("change output of model")
from exprVAEwithAdditionalFeatures import ExprOmiVAE
input_path="DataSources/GDC-PANCAN_"
vae_model_two = ExprOmiVAE(input_path=input_path, expr_df=expr_df, latent_dim=128, knockingOut=True, dimension_number=dim_number)
vae_model_two.load_state_dict(
torch.load('DataSources/vae_saved_model(original).pt',
map_location=torch.device('cpu')))
vae_model_two.eval()
changeWeightandBias(changeOutput=True, dim_number=dim_number, vae_mod=vae_model_two, disease_code=disease_code,
norm_expr_tensor=norm_expr_tensor, tumour_expr_tensor=tumour_expr_tensor)
nullingDimensions(disease_code=diseaseCode, disease_string=chosenTissue, dim_number=dimension)