class TwitterHawk(object):
def __init__(self, model_path):
# Load model
with open(model_path + '.model', 'rb') as fid:
self.clf = pickle.load(fid)
with open(model_path + '.dict', 'rb') as fid:
self.vec = pickle.load(fid)
self.feat_obj = FeaturesWrapper()
def predict(self, X, predict_type='label'):
# Data -> features
#feat_obj = FeaturesWrapper()
feats = self.feat_obj.extract_features(X)
if predict_type == 'label':
return self.predict_vectorized(feats)
elif predict_type == 'probs':
return self.predict_probs_vectorized(feats)
def predict_probs_vectorized(self, feats):
vectorized = self.vec.transform(feats)
return self.clf.decision_function(vectorized)
def predict_vectorized(self, feats):
vectorized = self.vec.transform(feats)
labels = self.clf.predict(vectorized)
labels = [reverse_labels_map[y] for y in labels]
return labels
def predict(X, clf, vec, feat_obj=None):
# Data -> features
if feat_obj == None:
feat_obj = FeaturesWrapper()
feats = feat_obj.extract_features(X)
return predict_vectorized(feats, clf, vec)
class TwitterHawk(object):
def __init__(self, model_path):
# Load model
with open(model_path+'.model', 'rb') as fid:
self.clf = pickle.load(fid)
with open(model_path+'.dict', 'rb') as fid:
self.vec = pickle.load(fid)
self.feat_obj = FeaturesWrapper()
def predict(self, X, predict_type='label'):
# Data -> features
#feat_obj = FeaturesWrapper()
feats = self.feat_obj.extract_features(X)
if predict_type == 'label':
return self.predict_vectorized(feats)
elif predict_type == 'probs':
return self.predict_probs_vectorized(feats)
def predict_probs_vectorized(self, feats):
vectorized = self.vec.transform(feats)
return self.clf.decision_function(vectorized)
def predict_vectorized(self, feats):
vectorized = self.vec.transform(feats)
labels = self.clf.predict(vectorized)
labels = [ reverse_labels_map[y] for y in labels ]
return labels
def predict(X, clf, vec, feat_obj=None, prob=False):
# Data -> features
if feat_obj == None:
feat_obj = FeaturesWrapper()
feats = feat_obj.extract_features(X)
if prob:
return predict_probs_vectorized(feats, clf, vec)
else:
return predict_vectorized(feats, clf, vec)
def predict(X, clf, vec, feat_obj=None, prob=False):
# Data -> features
if feat_obj == None:
feat_obj = FeaturesWrapper()
feats = feat_obj.extract_features(X)
if prob:
return predict_probs_vectorized(feats, clf, vec)
else:
return predict_vectorized(feats, clf, vec)
def extract_features(X, feat_obj=None):
# Data -> features
if feat_obj == None:
feat_obj = FeaturesWrapper()
feats = feat_obj.extract_features(X)
return feats
def extract_features(X, feat_obj=None):
# Data -> features
if feat_obj == None:
feat_obj = FeaturesWrapper()
feats = feat_obj.extract_features(X)
return feats
