def output_evaluation_statistics(test, predictions):
logger.info("Statistics with probability cutoff at 0.5")
# binary predictions with some cutoff for these evaluations
cutoff = 0.5
predictions_binary = np.copy(predictions)
predictions_binary[predictions_binary >= cutoff] = 1
predictions_binary[predictions_binary < cutoff] = 0
evaluation.print_model_statistics(test.y, predictions_binary)
evaluation.print_confusion_matrix(test.y, predictions_binary)
precision1 = precision_at(test.y, predictions, 0.01)
logger.debug("Precision at 1%: {} (probability cutoff {})".format(
round(precision1[0], 2), precision1[1]))
precision10 = precision_at(test.y, predictions, 0.1)
logger.debug("Precision at 10%: {} (probability cutoff {})".format(
round(precision10[0], 2), precision10[1]))
def output_evaluation_statistics(test, predictions):
logger.info("Statistics with probability cutoff at 0.5")
# binary predictions with some cutoff for these evaluations
cutoff = 0.5
predictions_binary = np.copy(predictions)
predictions_binary[predictions_binary >= cutoff] = 1
predictions_binary[predictions_binary < cutoff] = 0
evaluation.print_model_statistics(test.y, predictions_binary)
evaluation.print_confusion_matrix(test.y, predictions_binary)
precision1 = precision_at(test.y, predictions, 0.01)
logger.debug("Precision at 1%: {} (probability cutoff {})".format(
round(precision1[0], 2), precision1[1]))
precision10 = precision_at(test.y, predictions, 0.1)
logger.debug("Precision at 10%: {} (probability cutoff {})".format(
round(precision10[0], 2), precision10[1]))
def print_evaluation(self):
y_pred = self.clf.predict(self.X)
evaluation.print_confusion_matrix(self.y, y_pred)
evaluation.print_precision_recall(self.y, y_pred)
print("Accuracy: ",
evaluation.get_classifier_accuracy(self.y, y_pred) * 100)