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# This example shows how to use the MITIE Python API to train a
# text_categorizer that produces smaller model files. However, these
# smaller files are dependent on some feature extractor file which must be
# provided when the model is loaded, as you will see in this example.
# For a comparison of what a non-pure version of the API looks like you can
# read the and examples.
import sys, os
# Make sure you put the mitielib folder into the python search path. There are
# a lot of ways to do this, here we do it programmatically with the following
# two statements:
parent = os.path.dirname(os.path.realpath(__file__))
sys.path.append(parent + '/../../mitielib')
from mitie import *
fe_filename= "../../MITIE-models/english/total_word_feature_extractor.dat"
trainer = text_categorizer_trainer(fe_filename)
# Don't forget to add the training data. Here we have only two examples, but for real
# uses you need to have thousands. You could also pass whole sentences in to the tokenize() function
# to get the tokens.
# The trainer can take advantage of a multi-core CPU. So set the number of threads
# equal to the number of processing cores for maximum training speed.
trainer.num_threads = 4
# This function does the work of training. Note that it can take a long time to run
# when using larger training datasets. So be patient.
cat = trainer.train()
# Now that training is done we can save the categorizer object to disk like so.
# In pure_model mode we do not include a copy of the feature extractor.
# Now to load load the 'pure model' from disk we also pass the feature extractor filename
cat2 = text_categorizer("new_text_categorizer_pure_model.dat",fe_filename)
text1 = "I am so happy"
pred, conf = cat2(tokenize(text1))
print ("predict sentiment of text '{0}' to be {1} with confidence {2}".format(text1,pred,conf))