def main():
"""" Preprocesses, extracts, learns, tests"""
# process flags
do_retrain, do_rebuildValidation, do_test = False, False, False
for arg in sys.argv[1:]:
if ("--retrain" in arg):
if ("yes" in arg):
do_retrain = True
if ("--rebuildValidation" in arg):
if ("yes" in arg):
do_rebuildValidation = True
if ("--test" in arg):
if ("yes" in arg):
do_test = True
# preprocessing
do = DataOrganizer()
# __________________________________ TRAINING ________________________ #
# use BoG to convert to frequency vector
fe = FeatureExtractor(FeatureExtractor.ModelType.BagOfClusters)
clf = 0
clf_file = ""
# get the latest trained model
filenames = os.listdir("models/")
if len(filenames) > 0:
clf_file = "models/" + filenames[-1]
else:
clf_file = None
# get sets of tweets as training data
# trainData0, trainData1, validation0, validation1 \
# = do.organizeTrainWithValidation("data/trainValidate/", do_rebuildValidation)
trainData0, trainData1 = do.organizeTrain("data/train/")
if do_retrain or not clf_file:
# split training set into validation and training set
X0, X1 = fe.extractTrainFeatureVectors((trainData0, trainData1))
clf = learn(X0, X1)
millis = int(round(time.time() * 1000))
clf_file = "trainedModel" + str(millis)
print "Saving model to file..."
joblib.dump(clf, "models/" + clf_file, compress=1)
else:
print "Using trained model and BoG..."
fe.bog = BagOfWords()
fe.bog.getLatestBoG()
clf = joblib.load(clf_file)
# we're either validating or testing based on the passed flag
# ____________________________________VALIDATION__________________________#
if not do_test:
# feed in the validation sets as one set
validationData = do.organizeTest("data/validation/")
validationFeatures, validationLabels = fe.extractTestFeatureVectors(
validationData)
test("Validation", clf, validationFeatures, validationLabels)
else:
# ____________________________________TESTING _______________________ #
# extract test features and test
print "Using testing"
testData, testLabels = do.organizeTest("data/test/")
testFeatures = fe.extractTestFeatureVectors(testData)
test("Testing, Global Protests With Background Subtraction", clf,
testFeatures, testLabels)
