def setCFtype(self, cftype):
self.CFtype = cftype
foldername, filename = "Models", self.CFtype + "_CFModel.pkl"
if (self.fitModeAtInitialize == True):
if (AlgoBase.checkPathExists(self, foldername, filename) == True):
self.preds_df = AlgoBase.loadModel(self, foldername, filename)
else:
self.fit(similarity="cosine", CFtype="user")
self.fit(similarity="cosine", CFtype="item")
def __init__(self, dsReader, fitModeAtInitialize=True):
AlgoBase.__init__(self, dsReader, fitModeAtInitialize)
self.RECOMMENDER_NAME = "SVD Recommender"
foldername, filename = "Models", "SVDModel.pkl"
if (self.fitModeAtInitialize == True):
if (AlgoBase.checkPathExists(self, foldername, filename) == True):
self.preds_df = AlgoBase.loadModel(self, foldername, filename)
else:
self.fit()
def __init__(self, dsReader, fitModeAtInitialize=True):
AlgoBase.__init__(self, dsReader, fitModeAtInitialize)
self.RECOMMENDER_NAME = "TF=IDF Recommender"
foldername, filename = "Models", "TfidfModel.pkl"
if(self.fitModeAtInitialize == True):
if(AlgoBase.checkPathExists(self, foldername, filename) == True):
self.similarityM = AlgoBase.loadModel(self, foldername, filename)
else:
self.fit()
def __init__(self, dsReader, fitModeAtInitialize=True):
AlgoBase.__init__(self, dsReader, fitModeAtInitialize)
self.RECOMMENDER_NAME = "Embeddings Recommender"
# Define N and P - number of users and books respectively
self.N = len(self.RatingsM["User-ID"].unique().tolist())
self.P = len(self.RatingsM["ISBN"].unique().tolist())
foldername, filename = "Models", "EMBModel.h5"
self.filepath = os.path.dirname(os.path.abspath(__file__))
self.filepath = self.filepath + "/" + foldername + "/" + filename
if (self.fitModeAtInitialize == True):
if (AlgoBase.checkPathExists(self, foldername, filename) == True):
#self.model = AlgoBase.loadModel(self, foldername, filename)
self.model = tf.keras.models.load_model(self.filepath)
else:
self.fit()