def __init__(self,
normalize=False,
balance=False,
tweet_threshold=0,
score=False,
dump_model=True):
"""
Import or train the regression model
"""
self.model = RegressionModel()
if not self.model.load():
training_set, testing_set = RegressionModel.load_datasets(
balance=balance, viral_threshold=tweet_threshold)
if ViralityPrediction.CLASSIFICATION == True:
training_set = self.model.normaliseFeats(training_set)
testing_set = self.model.normaliseFeats(testing_set)
self.model.trainClassifier(training_set, normalize=normalize)
if score:
self.model.scoreClassifier(testing_set)
else:
self.model.trainRegression(training_set, normalize=normalize)
if score:
self.model.scoreRegression(testing_set)
if dump_model:
self.model.dump()
def loadData(self): ''' initialises the data and loads it from hdfs and prepares the data. ''' self.training_data, self.test_data = RegressionModel.load_datasets(balance= True, viral_threshold=50000) self.viral_threshold = 50000 # picking a subset of training data as training with full data takes a lot of time. self.X_train = self.training_data[:30000, :-1] self.Y_train = self.training_data[:30000,-1] self.X_test = self.test_data[:, :-1] self.Y_test = self.test_data[:, -1] self.test_median = np.median(self.Y_test) self.train_median = np.median(self.Y_train) self.train_median = self.viral_threshold self.test_median = self.viral_threshold print "Test Median" print self.test_median print "Training Median" print self.train_median
def loadData(self):
'''
initialises the data and loads it from hdfs
and prepares the data.
'''
self.training_data, self.test_data = RegressionModel.load_datasets(
balance=True, viral_threshold=50000)
self.viral_threshold = 50000
# picking a subset of training data as training with full data takes a lot of time.
self.X_train = self.training_data[:30000, :-1]
self.Y_train = self.training_data[:30000, -1]
self.X_test = self.test_data[:, :-1]
self.Y_test = self.test_data[:, -1]
self.test_median = np.median(self.Y_test)
self.train_median = np.median(self.Y_train)
self.train_median = self.viral_threshold
self.test_median = self.viral_threshold
print "Test Median"
print self.test_median
print "Training Median"
print self.train_median
def __init__(self, normalize=False, balance=False, tweet_threshold=0, score=False, dump_model=True):
"""
Import or train the regression model
"""
self.model = RegressionModel()
if not self.model.load():
training_set, testing_set = RegressionModel.load_datasets(
balance=balance, viral_threshold=tweet_threshold)
if ViralityPrediction.CLASSIFICATION == True:
training_set = self.model.normaliseFeats(training_set)
testing_set = self.model.normaliseFeats(testing_set)
self.model.trainClassifier(training_set, normalize=normalize)
if score:
self.model.scoreClassifier(testing_set)
else:
self.model.trainRegression(training_set, normalize=normalize)
if score:
self.model.scoreRegression(testing_set)
if dump_model:
self.model.dump()
