model_Sam.py

from medacy.data import Dataset
from medacy.ner.pipelines import ClinicalPipeline
from medacy.ner.model import Model
from medacy.pipeline_components import MetaMap

import logging,sys

# print logs
logging.basicConfig(stream=sys.stdout,level=logging.DEBUG) #set level=logging.DEBUG for more information

#entity types
entities = ['Symptom', 'Form', 'Route', 'Frequency', 'Duration', 'Dosage', 'Strength', 'Drug']

training_dataset = Dataset('/home/mahendrand/VE/Data/Sam_Henry/Train')

#set metamap path
metamap = MetaMap(metamap_path="/home/share/programs/metamap/2016/public_mm/bin/metamap", convert_ascii=True)
training_dataset.metamap(metamap)


pipeline = ClinicalPipeline(metamap=metamap, entities=entities)
model = Model(pipeline, n_jobs=1) #distribute documents between 30 processes during training and prediction

model.fit(training_dataset)

#cross validation
# model.cross_validate(num_folds = 5, training_dataset = training_dataset, prediction_directory=True, groundtruth_directory=True)

#location to store the clinical model
model.dump('/home/mahendrand/VE/SMM4H/medaCy/medacy/clinical_model.pickle')

#run on a separate testing dataset
testing_dataset = Dataset('/home/mahendrand/VE/Data/Sam_Henry/Test')

# location to store the predictions
model.predict(testing_dataset)