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deep_radiomics_nsga_svm.py
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deep_radiomics_nsga_svm.py
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import pickle
from tqdm import tqdm
import numpy as np
import matplotlib.pylab as plt
import pandas as pd
from collections import OrderedDict
from sklearn.metrics import make_scorer
from sklearn.model_selection import cross_validate
from platypus import NSGAII, Problem, Binary, Hypervolume, calculate, display
from platypus.core import nondominated
from radiomics_all_svm import specificity_loss_func, print_summary, read_data, validate, get_model
class SVM(Problem):
def __init__(self):
super(SVM, self).__init__(1, 2)
self.X, self.Y = read_data('deep_radiomics.csv')
self.types[:] = Binary(self.X.shape[1])
self.model = get_model()
self.directions[:] = Problem.MAXIMIZE
def evaluate(self, solution):
columns = solution.variables[:]
# Selecting the columns
X = self.X[:, columns[0]]
scores = {'AUC': 'roc_auc', 'ACC': 'accuracy', 'F1': 'f1', 'Sensitivity': 'recall',
'Precision': 'precision', 'Specificity': make_scorer(specificity_loss_func, greater_is_better=True)}
results = cross_validate(
self.model, X, self.Y, scoring=scores, cv=3, return_estimator=True, n_jobs=3)
solution.objectives[:] = [
np.mean(results['test_Sensitivity']), np.mean(results['test_Specificity'])]
if __name__ == "__main__":
experiment = 'deep_radiomics_nsga_svm'
X, Y = read_data('deep_radiomics.csv')
num_iter, generations_amount, pop = 5, 10, 10
hypervolumes = [[] for i in range(num_iter)]
hypervolumes_std = []
hypervolumes_mean = []
results = {'acc_mean': [],'acc_std': [], 'spec_mean': [], 'spec_std': [], 'sens_mean': [], 'sens_std': [], 'f1_score_mean': [], 'f1_score_std': [], 'auc_mean': [], 'auc_std': []}
for i in range(num_iter):
algorithm = NSGAII(SVM(), population_size=pop)
for j in tqdm(range(generations_amount), desc="Iteration " + str(i+1)):
algorithm.step()
# Defining structure to pass as parameter to class Hypervolume
nsga_results = OrderedDict()
nsga_results["NSGAII"] = {}
nsga_results["NSGAII"]["SVM"] = [algorithm.result]
# calculate the hypervolume indicator
hyp = Hypervolume(minimum=[0, 0, 0], maximum=[1, 1, 1])
hyp_result = calculate(nsga_results, hyp)
hypervolume = np.mean(list(hyp_result["NSGAII"]["SVM"]["Hypervolume"]))
hypervolumes[i].append(hypervolume)
nondominated_results=nondominated(algorithm.result)
# display(hipervolume, ndigits=3)
solution = nondominated_results[0]
features = solution.variables[0]
for s in nondominated_results:
if abs(s.objectives[0] - s.objectives[1]) < abs(solution.objectives[0] - solution.objectives[1]):
solution = s
features = s.variables[0]
model = get_model(probability=True)
result = validate(model, X[:, features], Y, plot = False)
results['acc_mean'].append(np.mean(result['acc']))
results['acc_std'].append(np.std(result['acc']))
results['spec_mean'].append(np.mean(result['spec']))
results['spec_std'].append(np.std(result['spec']))
results['sens_mean'].append(np.mean(result['sens']))
results['sens_std'].append(np.std(result['sens']))
results['f1_score_mean'].append(np.mean(result['f1_score']))
results['f1_score_std'].append(np.std(result['f1_score']))
results['auc_mean'].append(np.mean(result['auc']))
results['auc_std'].append(np.std(result['auc']))
gen_std = np.std(hypervolumes,axis=0)
gen_mean = np.mean(hypervolumes,axis=0)
dict_results = {'mean':gen_mean, 'std':gen_std}
outfile = open('results/' + experiment + '.pickle','wb')
pickle.dump(dict_results, outfile)
outfile.close()
df = pd.DataFrame(results)
df.to_csv('results/' + experiment + '.csv')