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abhi1868sharma/xdeepfm-3

Created December 16, 2019 11:33
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xdeepfm-3
# encoding function
def encoding(data,feat,encoder):
data[feat] = encoder.fit_transform(data[feat])
# encoding for categorical features
[encoding(data,feat,LabelEncoder()) for feat in sparse_features]
# Using normalization for dense feature
mms = MinMaxScaler(feature_range=(0,1))
data[dense_features] = mms.fit_transform(data[dense_features])
# creating a 4 bit embedding for every sparse feature
sparse_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4) \
for i,feat in enumerate(sparse_features)]
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