def __init__(self, features, labels, standardize=False):
self.standardize = standardize
self.means = None
self.stdevs = None
if standardize:
self.features = self._standardize(features)
else:
self.features = features
self.labels = labels
self.feature_cols = features.column_names()
labeled_feature_vector = XFrame(features)
label_col = 'label' # TODO what if there is a feature with this name ?
feature_cols = self.feature_cols # need local reference
labeled_feature_vector[label_col] = labels
def build_labeled_features(row):
label = row[label_col]
features =[row[col] for col in feature_cols]
return LabeledPoint(label, features)
self.labeled_feature_vector = labeled_feature_vector.apply(build_labeled_features)
def _base_evaluate(self, data, labels):
"""
Evaluate the performance of the classifier.
Use the data to make predictions, then test the effectiveness of
the predictions against the labels.
The data must be a collection of items (XArray of SenseVector).
Returns
-------
out : A list of:
- overall correct prediction proportion
- true positive proportion
- true negative proportion
- false positive proportion
- false negative proportion
"""
results = XFrame()
predictions = self._base_predict(data)
results['predicted'] = predictions
results['actual'] = labels
# print results
def evaluate(row):
prediction = row['predicted']
actual = row['actual']
return {'correct': 1 if prediction == actual else 0,
'true_pos': 1 if prediction == 1 and actual == 1 else 0,
'true_neg': 1 if prediction == 0 and actual == 0 else 0,
'false_pos': 1 if prediction == 1 and actual == 0 else 0,
'false_neg': 1 if prediction == 0 and actual == 1 else 0,
'positive': 1 if actual == 1 else 0,
'negative': 1 if actual == 0 else 0
}
score = results.apply(evaluate)
def sum_item(item):
return score.apply(lambda x: x[item]).sum()
all_scores = float(len(labels))
correct = float(sum_item('correct'))
tp = float(sum_item('true_pos'))
tn = float(sum_item('true_neg'))
fp = float(sum_item('false_pos'))
fn = float(sum_item('false_neg'))
pos = float(sum_item('positive'))
neg = float(sum_item('negative'))
# precision = true pos / (true pos + false pos)
# recall = true pos / (true pos + false neg)
# true pos rate = true pos / positive
# false pos rate = false pos / negative
result = {}
result['correct'] = correct
result['true_pos'] = tp
result['true_neg'] = tn
result['false_pos'] = fp
result['false_neg'] = fn
result['all'] = all_scores
result['accuracy'] = correct / all_scores if all_scores > 0 else float('nan')
result['precision'] = tp / (tp + fp) if (tp + fp) > 0 else float('nan')
result['recall'] = tp / (tp + fn) if (tp + fn) > 0 else float('nan')
result['tpr'] = tp / pos if pos > 0 else float('nan')
result['fpr'] = fp / neg if neg > 0 else float('nan')
return result
