def sent_class(sentence):
id = 1 # features needs an ID passing in at moment - maybe redundant?
f = features.features_dict(str(id), sentence)
fseries = features.features_series(f)
width = len(fseries)
fseries = fseries[
1:width -
1] # All but the first and last item (strip ID and null class off)
# Get a classification prediction from the Model, based on supplied features
sentence_class = loaded_model.predict([fseries])[0].strip()
return sentence_class
def sentenceForestClass(sentence):
with open(MODEL_LOC, 'rb') as f:
rf = pickle.load(f, encoding='latin1')
id = hashtext(
sentence
) #features needs an ID passing in at moment - maybe redundant?
fseries = features.features_series(features.features_dict(id, sentence))
width = len(fseries)
fseries = fseries[
1:width -
1] #All but the first and last item (strip ID and null class off)
#Get a classification prediction from the Model, based on supplied features
sentenceClass = rf.predict([fseries])[0].strip()
return sentenceClass
def sentence_rf_class(sentence):
"""
Pass in a sentence, with unique ID and pass back a classification code
Use a pre-built Random Forest model to determine classification based on
features extracted from the sentence.
"""
# Load a pre-built Random Forest Model
with open(RF_MODEL_LOCATION, 'rb') as f:
rf = pickle.load(f)
id = hashtext(sentence) #features needs an ID passing in at moment - maybe redundant?
fseries = features.features_series(features.features_dict(id,sentence))
width = len(fseries)
fseries = fseries[1:width-1] #All but the first and last item (strip ID and null class off)
#Get a classification prediction from the Model, based on supplied features
sentence_class = rf.predict([fseries])[0].strip()
return sentence_class