Scale the source price data to the range [0,1] using MinMaxScaler: #scale data using MinMaxScalerfromsklearn.preprocessingimportMinMaxScaler scaler=MinMaxScaler(feature_range=(0,1)) scaled_data=scaler.fit_transform(data) The first 80% of the data will be used for training. #training size is 8...
# scale train and test data to [-1, 1] def scale(train, test): # fit scaler scaler = MinMaxScaler(feature_range=(-1, 1)) scaler = scaler.fit(train) # transform train train = train.reshape(train.shape[0], train.shape[1]) train_scaled = scaler.transform(train) # transform test ...
This means you can use the normalized data to train your model. This is done by calling the transform() function. Apply the scale to data going forward. This means you can prepare new data in the future on which you want to make predictions. The default scale for the Min...
preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from sklearn.preprocessing import StandardScaler import xgboost as xgb from sklearn.metrics import accuracy_score import warnings warnings.filterwarnings("ignore") context = mx.cpu(); model_ctx=mx.cpu() mx.random.seed(...
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535) """ from tensorflow.python.keras.models import Model from tensorflow.python.keras.layers import Dense, Concatenate from tensorflow.keras.models import...
#scaler = preprocessing.MinMaxScaler().fit(X) X = scaler.transform(X) print("standard X sample:", X[:3]) black_verify = scaler.transform(black_verify) print(black_verify) white_verify = scaler.transform(white_verify) print(white_verify) unknown_verify = scaler.transform(unknown_verify) pri...
scaler = preprocessing.StandardScaler().fit(X) #scaler = preprocessing.MinMaxScaler().fit(X) X = scaler.transform(X) print("standard X sample:", X[:3]) black_verify = scaler.transform(black_verify) print(black_verify) white_verify = scaler.transform(white_verify) ...
apply(lambda x: x.count(" ")).max() vocab_size_tit = ad_title_table.wordId.nunique() from sklearn.preprocessing import LabelEncoder, MinMaxScaler from sklearn.model_selection import train_test_split from deepctr.models import DeepFM, FiBiNET from deepctr.inputs import SparseFeat, DenseFeat,...
preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error from sklearn.preprocessing import StandardScaler import xgboost as xgb from sklearn.metrics import accuracy_score import warnings warnings.filterwarnings("ignore") context = mx.cpu(); model_ctx=mx.cpu() mx.random.seed(...
random_normal(loc=0, scale=1, shape=(self.batch_size, self.n_latent), ctx=model_ctx) z = mu + F.exp(0.5*lv)*eps y = self.decoder(z) self.output = y KL = 0.5*F.sum(1+lv-mu*mu-F.exp(lv),axis=1) logloss = F.sum(x*F.log(y+self.soft_zero)+ (1-x)*F.log(1-y...