def evaluate_AUC(prices, model, score_thresholds=None,
window_size=def_prediction_params['window_size'],
future_window=def_annotation_params['future_window'],
drop_threshold=def_annotation_params['drop_threshold'],
drop_window=def_annotation_params['drop_window'],
percentage=def_annotation_params['use_percentage']):
if score_thresholds==None:
score_thresholds = np.linspace(0.0, 1.0, 101)
recalls = []
precisions = []
for threshold in score_thresholds:
ir_measures = irutils.calculate_IR(**counts_pn(prices, model,
divide_threshold=threshold,
window_size=window_size,
future_window=future_window,
drop_threshold=drop_threshold,
drop_window=drop_window,
percentage=percentage
)
)
recalls.append(ir_measures['recall'])
precisions.append(ir_measures['precision'])
recalls = np.array(recalls)
precisions = np.array(precisions)
evaluation_results = {'score_thresholds': score_thresholds,
'recalls': recalls,
'precisions': precisions,
'AUC': irutils.calculate_AUC(recalls, precisions)}
return evaluation_results
def evaluate(prices, model,
divide_threshold=def_prediction_params['divide_threshold'],
window_size=def_prediction_params['window_size'],
future_window=def_annotation_params['future_window'],
drop_threshold=def_annotation_params['drop_threshold'],
drop_window=def_annotation_params['drop_window'],
percentage=def_annotation_params['use_percentage']):
ir_counts = counts_pn(prices, model,
divide_threshold=divide_threshold,
window_size=window_size,
future_window=future_window,
drop_threshold=drop_threshold,
drop_window=drop_window,
percentage=percentage)
print "tp = ", ir_counts['tp'], "fp = ", ir_counts['fp'], "fn = ", ir_counts['fn'], "tn = ", ir_counts['tn']
ir_measures = irutils.calculate_IR(**ir_counts)
print "recall = ", ir_measures['recall']
print "precision = ", ir_measures['precision']
print "F-score = ", ir_measures['fscore']
