class MYModel:
def __init__(self):
self.classifier = NaiveBayes(0.8)
# self.classifier = LogitRegression()
def trainBatch(self, train_data):
self.classifier.train(train_data)
print('... Pickling the model')
DataHandling.store_data(Constants.model_path + 'classifier_NB.pickle', self.classifier)
def crossfoldValidation(self, train_data, number_of_folds):
return self.classifier.classification_CV(train_data, number_of_folds)
def trainOnline(self, row):
"""
update the model on only one instance
:param row: columns: "Output","OsrItem","Publisher","Osfamily","ItemSrc","UserID","UserClicksAd"
:type row: pd.Series
:return:
"""
pass
def predict(self, data, load_classifier=False):
if load_classifier:
self.classifier = DataHandling.load_data(Constants.model_path + 'classifier_NB.pickle')
return self.classifier.classification(data)
def predict_prob(self, data, load_classifier=False):
if load_classifier:
self.classifier = DataHandling.load_data(Constants.model_path + 'classifier_NB.pickle')
return self.classifier.predict_prob(data)
def series_predict_prob(self, data):
return self.classifier.series_predict_prob(data)
"""
select all item from term Vectors and shuffle them
"""
# publisher = str(int(row["Publisher"]))
# items = list(self.__termVectors[publisher].keys())
# shuffle(items)
# return np.array(items, dtype=float)
def __init__(self):
self.classifier = NaiveBayes(0.8)
