def run(run_name=DEFAULT_RUN_NAME, trials=5, patient_count=2000,
num_effects=[1, 2, 4, 8], observed_variables=[50], per_effect=[5],
effect_mag=[1], sim_model=1, systematic_bias=0.1, input_variance=0.1,
missing_data=0):
i = 0
patients_list = []
for effects in num_effects:
for ov in observed_variables:
# for pe in per_effect:
for mag in effect_mag:
for trial in range(trials):
patients = sim.create_patients(patient_count=patient_count,
observed_variables=ov,
systematic_bias=systematic_bias,
input_variance=input_variance,
effects=effects,
per_effect=per_effect,
effect_mag=mag,
trial=trial,
sim_model=sim_model,
missing_data=missing_data)
file_name = ('./data/' + run_name + '/patients/' +
str(sim_model) + '_' + str(effects) + '_' +
str(per_effect) + '_' + str(mag) + '_' +
str(ov) + '_' + str(trial) + '.p')
pkl.dump(patients, open(file_name, "wb"),
protocol=2)
del patients
print(i)
i += 1
def testDaRun(self):
p = 100
hn = 2
patients = sim.create_patients(patient_count=p,
observed_variables=100,
systematic_bias=0.1,
input_variance=0.1,
effects=4,
per_effect=5,
effect_mag=5,
trial=1,
sim_model=1,
missing_data=0)
X = patients[:, :-1]
y = patients[:, -1]
dAs = {}
dAs[p] = {}
dAs[p][hn] = trainer.train_da(X,
learning_rate=0.1,
coruption_rate=0.2,
batch_size=10,
training_epochs=1000,
n_hidden=hn,
missing_data=None)
self.assertTrue(str(dAs[p][hn].trained_cost)[:7] == str(3.78843))
scores = classifier.classify(X, y, dAs)
print(scores)
da_rfc_scores = [
1.0, 1.0, 1.0, 0.90000000000000002, 1.0, 1.0, 0.96000000000000008,
1.0, 0.78000000000000003, 1.0
]
rfc_score_name = 'da_' + str(p) + '_' + str(hn) + '_rfc'
self.assertTrue(scores[rfc_score_name] == da_rfc_scores)
def testDaRun(self):
p = 100
hn = 2
patients = sim.create_patients(patient_count=p,
observed_variables=100,
systematic_bias=0.1,
input_variance=0.1,
effects=4,
per_effect=5,
effect_mag=5,
trial=1,
sim_model=1,
missing_data=0)
X = patients[:, :-1]
y = patients[:, -1]
dAs = {}
dAs[p] = {}
dAs[p][hn] = trainer.train_da(X,
learning_rate=0.1,
coruption_rate=0.2,
batch_size=10,
training_epochs=1000,
n_hidden=hn,
missing_data=None)
self.assertTrue(str(dAs[p][hn].trained_cost)[:7] == str(3.78843))
scores = classifier.classify(X, y, dAs)
print(scores)
da_rfc_scores = [1.0, 1.0, 1.0, 0.90000000000000002, 1.0, 1.0,
0.96000000000000008, 1.0, 0.78000000000000003, 1.0]
rfc_score_name = 'da_' + str(p) + '_' + str(hn) + '_rfc'
self.assertTrue(scores[rfc_score_name] == da_rfc_scores)
