def get_label_probs(self, c2_data, vector=None):
if vector is None:
vector = vectorize_with_sparse_features(self.sparse_features,
self.total_feature_count,
c2_data)
label_results = {}
label_scores = {}
best_choice = None
best_score = 0
for label in self.decision_trees.keys():
label_results[label] = {1: 0.0, 0: 0.0}
for pair in self.decision_trees[label]:
model = pair["model"]
probs = model.predict_probs(vector)[0]
label_results[label][0] += 0.0 if 0 not in probs else probs[0]
label_results[label][1] += 0.0 if 1 not in probs else probs[1]
#print label_results[label]
score = label_results[label][1] - label_results[label][0]
score = float(score) / float(len(self.decision_trees[label]))
label_scores[label] = score
if score > best_score:
best_choice = label
best_score = score
return best_choice, label_results, label_scores
def get_label_probs(self, c2_data, vector=None):
if vector is None:
vector = vectorize_with_sparse_features(self.sparse_features, self.total_feature_count, c2_data)
label_results = {}
label_scores = {}
best_choice = None
best_score = 0
for label in self.decision_trees.keys():
label_results[label] = {1: 0.0, 0: 0.0}
for pair in self.decision_trees[label]:
model = pair["model"]
probs = model.predict_probs(vector)[0]
label_results[label][0] += 0.0 if 0 not in probs else probs[0]
label_results[label][1] += 0.0 if 1 not in probs else probs[1]
#print label_results[label]
score = label_results[label][1] - label_results[label][0]
score = float(score) / float(len(self.decision_trees[label]))
label_scores[label] = score
if score > best_score:
best_choice = label
best_score = score
return best_choice, label_results, label_scores
def get_label_scores(self, c2_data, vector=None):
if vector is None:
vector = vectorize_with_sparse_features(self.sparse_features, self.total_feature_count, c2_data)
label_results = {}
best_choice = None
best_score = 0
for label in self.decision_trees.keys():
label_results[label] = []
for pair in self.decision_trees[label]:
model = pair["model"]
label_results[label].append(float(model.predict(vector)[0]))
#print label_results[label]
score = sum(label_results[label])
if score > best_score:
best_choice = label
best_score = score
return best_choice, label_results
def get_label_scores(self, c2_data, vector=None):
if vector is None:
vector = vectorize_with_sparse_features(self.sparse_features,
self.total_feature_count,
c2_data)
label_results = {}
best_choice = None
best_score = 0
for label in self.decision_trees.keys():
label_results[label] = []
for pair in self.decision_trees[label]:
model = pair["model"]
label_results[label].append(float(model.predict(vector)[0]))
#print label_results[label]
score = sum(label_results[label])
if score > best_score:
best_choice = label
best_score = score
return best_choice, label_results
