def outlierDetection(samples_x, samples_y_aggregation):
'''
'''
outliers = []
for samples_idx in range(0, len(samples_x)):
#sys.stderr.write("[%s] DEBUG: Evaluating %d of %d samples\n"
# \ % (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
diagnostic_regressor_gp = gp_create_model.createModel(\
samples_x[0:samples_idx] + samples_x[samples_idx + 1:],\
samples_y_aggregation[0:samples_idx] + samples_y_aggregation[samples_idx + 1:])
mu, sigma = gp_prediction.predict(samples_x[samples_idx],
diagnostic_regressor_gp['model'])
# 2.33 is the z-score for 98% confidence level
if abs(samples_y_aggregation[samples_idx] - mu) > (2.33 * sigma):
outliers.append({
"samples_idx":
samples_idx,
"expected_mu":
mu,
"expected_sigma":
sigma,
"difference":
abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)
})
outliers = None if len(outliers) == 0 else outliers
return outliers
def _outlierDetection_threaded(inputs):
'''
Detect the outlier
'''
[samples_idx, samples_x, samples_y_aggregation] = inputs
sys.stderr.write("[%s] DEBUG: Evaluating %dth of %d samples\n"\
% (os.path.basename(__file__), samples_idx + 1, len(samples_x)))
outlier = None
# Create a diagnostic regression model which removes the sample that we want to evaluate
diagnostic_regressor_gp = gp_create_model.createModel(\
samples_x[0:samples_idx] + samples_x[samples_idx + 1:],\
samples_y_aggregation[0:samples_idx] + samples_y_aggregation[samples_idx + 1:])
mu, sigma = gp_prediction.predict(samples_x[samples_idx],
diagnostic_regressor_gp['model'])
# 2.33 is the z-score for 98% confidence level
if abs(samples_y_aggregation[samples_idx] - mu) > (2.33 * sigma):
outlier = {
"samples_idx":
samples_idx,
"expected_mu":
mu,
"expected_sigma":
sigma,
"difference":
abs(samples_y_aggregation[samples_idx] - mu) - (2.33 * sigma)
}
return outlier
