text stringlengths 1 93.6k |
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return d_path + o_filename + ".npz"
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def transformCriteoAdData(X_cat, X_int, y, days, data_split, randomize, total_per_file):
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# Transforms Criteo Kaggle or terabyte data by applying log transformation
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# on dense features and converting everything to appropriate tensors.
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#
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# Inputs:
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# X_cat (ndarray): array of integers corresponding to preprocessed
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# categorical features
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# X_int (ndarray): array of integers corresponding to dense features
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# y (ndarray): array of bool corresponding to labels
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# data_split(str): flag for splitting dataset into training/validation/test
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# sets
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# randomize (str): determines randomization scheme
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# "none": no randomization
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# "day": randomizes each day"s data (only works if split = True)
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# "total": randomizes total dataset
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#
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# Outputs:
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# if split:
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# X_cat_train (tensor): sparse features for training set
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# X_int_train (tensor): dense features for training set
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# y_train (tensor): labels for training set
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# X_cat_val (tensor): sparse features for validation set
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# X_int_val (tensor): dense features for validation set
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# y_val (tensor): labels for validation set
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# X_cat_test (tensor): sparse features for test set
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# X_int_test (tensor): dense features for test set
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# y_test (tensor): labels for test set
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# else:
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# X_cat (tensor): sparse features
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# X_int (tensor): dense features
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# y (tensor): label
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# define initial set of indices
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indices = np.arange(len(y))
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# create offset per file
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offset_per_file = np.array([0] + [x for x in total_per_file])
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for i in range(days):
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offset_per_file[i + 1] += offset_per_file[i]
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# split dataset
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if data_split == 'train':
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indices = np.array_split(indices, offset_per_file[1:-1])
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# randomize train data (per day)
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if randomize == "day": # or randomize == "total":
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for i in range(len(indices) - 1):
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indices[i] = np.random.permutation(indices[i])
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print("Randomized indices per day ...")
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train_indices = np.concatenate(indices[:-1])
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test_indices = indices[-1]
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test_indices, val_indices = np.array_split(test_indices, 2)
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print("Defined training and testing indices...")
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# randomize train data (across days)
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if randomize == "total":
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train_indices = np.random.permutation(train_indices)
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print("Randomized indices across days ...")
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# indices = np.concatenate((train_indices, test_indices))
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# create training, validation, and test sets
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X_cat_train = X_cat[train_indices]
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X_int_train = X_int[train_indices]
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y_train = y[train_indices]
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X_cat_val = X_cat[val_indices]
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X_int_val = X_int[val_indices]
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y_val = y[val_indices]
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X_cat_test = X_cat[test_indices]
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X_int_test = X_int[test_indices]
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y_test = y[test_indices]
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print("Split data according to indices...")
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X_cat_train = X_cat_train.astype(np.long)
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X_int_train = np.log(X_int_train.astype(np.float32) + 1)
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y_train = y_train.astype(np.float32)
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X_cat_val = X_cat_val.astype(np.long)
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X_int_val = np.log(X_int_val.astype(np.float32) + 1)
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y_val = y_val.astype(np.float32)
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X_cat_test = X_cat_test.astype(np.long)
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X_int_test = np.log(X_int_test.astype(np.float32) + 1)
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y_test = y_test.astype(np.float32)
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print("Converted to tensors...done!")
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return (
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X_cat_train,
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X_int_train,
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y_train,
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X_cat_val,
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