StudyDesignOptimization/generate_test_results.py at main · CMUSchwartzLab/StudyDesignOptimization · GitHub

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import seaborn as sns
from liquid_biopsyopt import *

path_to_train_matrix = '/projects/schwartzlabscratch/DesignOpt/test_results/adjustpaired3/allX3.txt'
liquid_bio_source_dir = '/projects/schwartzlabscratch/LiquidBioData/test_data'
ground_truth_dir = '/projects/schwartzlabscratch/LiquidBiopsyData/test_data/results'
output_dir = '/projects/schwartzlabscratch/DesignOpt/final_liquid_biopsy_results'

def loadTestPoints(X_final):
  X_withscore = np.loadtxt(X_final)
  X = X_withscore[:-1]
  return X

def generate_and_save_plot(X, fname):
  coverage = X[:,0]
  num_samples = X[:,6]
  score = X[:,7]
  plot = sns.scatterplot(x = coverage, y = num_samples, hue =score)
  plot.set_title("Coverage vs Number of Samples colored by Overall Score")
  plot.set_xlabel("Coverage")
  plot.set_ylabel("Number of Samples")
  figure = plot.get_figure()
  figure.savefig(output_dir + '/'+fname+ '.png')

X = loadTestPoints(path_to_train_matrix)
lamb = 0
points = doLiquidBiopsySimulationPipeline(X, lamb,0, output_dir, liquid_bio_source_dir, ground_truth_dir)
points = np.array(points)
#points = loss_function_matrix(X)
points = points.reshape(-1,1)
costs = cost_function(X)
costs = costs.reshape(-1,1)
total_loss = points + lamb*costs
total_loss = np.array(total_loss)
scores = np.reshape(total_loss, (len(total_loss), 1))
final_X = np.hstack((X, scores))
np.savetxt(final_X, output_dir +'/final_array.txt')


#sample_dir = '/projects/schwartzlabscratch/DesignOpt/test_results/072324realruntwo/0'
#ground_truth_dir = '/projects/schwartzlabscratch/LiquidBiopsyData/training_data/results'

#print(getscoresfromcalls(sample_dir, ground_truth_dir))