이 글은 다음 Keras Example을 번역합니다.
https://keras.io/examples/structured_data/tabtransformer/
Keras documentation: Structured data learning with TabTransformer
Structured data learning with TabTransformer Author: Khalid Salama Date created: 2022/01/18 Last modified: 2022/01/18 Description: Using contextual embeddings for structured data classification. View in Colab • GitHub source Introduction This example dem
keras.io
Introduction
이 예제는 suvervised, semi-supervised로 활용할 수 있는 TabTransformer를 다룹니다. TabTransformer는 self-attention의 Transformer로 이루어지며, 범주형 특성을 임베딩하는 일반적인 층이 아닌 문맥을 고려할 수 있는 임베딩 층을 사용하여 더 높은 정확도를 달성할 수 있습니다.
이 예제는 TensorFlow 2.7 이상, TensorFlow Addons가 필요합니다.
pip install -U tensorflow-addonsSetup
import math
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_addons as tfa
import matplotlib.pyplot as pltPrepare the data
이 예제에서는 UC Irvine Machine Learning Repository에서 제공하는 United States Census Income Dataset을 사용합니다. 이 데이터셋은 한 사람이 연간 USD 50,000 이상 벌 가능성이 있는지 여부를 판단하는 이진 분류 문제입니다.
5 numerical feature, 9 categorical feature로 이루어진 48,842 데이터를 포함하고 있습니다.
먼저, 데이터셋을 로드합니다.
CSV_HEADER = [
"age",
"workclass",
"fnlwgt",
"education",
"education_num",
"marital_status",
"occupation",
"relationship",
"race",
"gender",
"capital_gain",
"capital_loss",
"hours_per_week",
"native_country",
"income_bracket",
]
train_data_url = (
"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
)
train_data = pd.read_csv(train_data_url, header=None, names=CSV_HEADER)
test_data_url = (
"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
)
test_data = pd.read_csv(test_data_url, header=None, names=CSV_HEADER)
print(f"Train dataset shape: {train_data.shape}") # (32561, 15)
print(f"Test dataset shape: {test_data.shape}") # (16282, 15)test_data의 첫 번째 행은 검증되지 않은 데이터이므로 제거하고, 레이블에 포함되어 있는 '.'을 제거합니다.
test_data = test_data[1:]
test_data.income_bracket = test_data.income_bracket.apply(
lambda value: value.replace(".", "")
)CSV 파일로 저장합니다.
train_data_file = "train_data.csv"
test_data_file = "test_data.csv"
train_data.to_csv(train_data_file, index=False, header=False)
test_data.to_csv(test_data_file, index=False, header=False)Define dataset metadata
다음은 input feature를 인코딩하고, 처리하기 유용하도록 데이터셋의 메타데이터를 정의합니다.
# NUMERICAL FEATURE 목록입니다
NUMERIC_FEATURE_NAMES = [
"age",
"education_num",
"capital_gain",
"capital_loss",
"hours_per_week",
]
# CATEGORICAL FEATURES, VOCABULARY를 모아놓은 DICT입니다
CATEGORICAL_FEATURES_WITH_VOCABULARY = {
"workclass": sorted(list(train_data["workclass"].unique())),
"education": sorted(list(train_data["education"].unique())),
"marital_status": sorted(list(train_data["marital_status"].unique())),
"occupation": sorted(list(train_data["occupation"].unique())),
"relationship": sorted(list(train_data["relationship"].unique())),
"race": sorted(list(train_data["race"].unique())),
"gender": sorted(list(train_data["gender"].unique())),
"native_country": sorted(list(train_data["native_country"].unique())),
}
# WEIGHT COLUMN 이름을 정의합니다
WEIGHT_COLUMN_NAME = "fnlwgt"
# CATEGORICAL FEATURE 이름 목록입니다
CATEGORICAL_FEATURE_NAMES = list(CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())
# INPUT FEATURE의 모든 목록입니다
FEATURE_NAMES = NUMERIC_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES
# CSV_HEADER에 있는 값이면 [0], 아니면 ['NA']로 채웁니다
COLUMN_DEFAULTS = [
[0.0] if feature_name in NUMERIC_FEATURE_NAMES + [WEIGHT_COLUMN_NAME] else ["NA"]
for feature_name in CSV_HEADER
]
# TARGET FEATURE 이름입니다
TARGET_FEATURE_NAME = "income_bracket"
# TARGET FEATURE LABEL 목록입니다
TARGET_LABELS = [" <=50K", " >50K"]Configure the hyperparameters
모델 구조와 트레이닝 옵션 관련 하이퍼파라미터를 정의합니다.
LEARNING_RATE = 0.001
WEIGHT_DECAY = 0.0001
DROPOUT_RATE = 0.2
BATCH_SIZE = 265
NUM_EPOCHS = 15
NUM_TRANSFORMER_BLOCKS = 3 # transformer block 갯수
NUM_HEADS = 4 # attention head 갯수
EMBEDDING_DIMS = 16 # 임베딩 차원
MLP_HIDDEN_UNITS_FACTORS = [
2,
1,
] # MLP hidden layer unit 갯수
NUM_MLP_BLOCKS = 2 # MLP block 갯수Implement data reading pipeline
파일을 읽고 처리하는 함수를 정의하고, 훈련 및 평가를 위해 feature와 label을 tf.data.Dataset으로 변환합니다.
target_label_lookup = layers.StringLookup(
vocabulary=TARGET_LABELS, mask_token=None, num_oov_indices=0
)
# target(label)을 StringLookup 함수에 통과시킵니다
def prepare_example(features, target):
target_index = target_label_lookup(target)
weights = features.pop(WEIGHT_COLUMN_NAME)
return features, target_index, weights
def get_dataset_from_csv(csv_file_path, batch_size=128, shuffle=False):
dataset = tf.data.experimental.make_csv_dataset(
csv_file_path,
batch_size=batch_size,
column_names=CSV_HEADER,
column_defaults=COLUMN_DEFAULTS,
label_name=TARGET_FEATURE_NAME,
num_epochs=1,
header=False,
na_value="?",
shuffle=shuffle,
).map(prepare_example, num_parallel_calls=tf.data.AUTOTUNE, deterministic=False)
return dataset.cache()Implement a training and evaluation procedure
def run_experiment(
model,
train_data_file,
test_data_file,
num_epochs,
learning_rate,
weight_decay,
batch_size,
):
optimizer = tfa.optimizers.AdamW(
learning_rate=learning_rate, weight_decay=weight_decay
)
model.compile(
optimizer=optimizer,
loss=keras.losses.BinaryCrossentropy(),
metrics=[keras.metrics.BinaryAccuracy(name="accuracy")],
)
train_dataset = get_dataset_from_csv(train_data_file, batch_size, shuffle=True)
validation_dataset = get_dataset_from_csv(test_data_file, batch_size)
print("Start training the model...")
history = model.fit(
train_dataset, epochs=num_epochs, validation_data=validation_dataset
)
print("Model training finished")
_, accuracy = model.evaluate(validation_dataset, verbose=0)
print(f"Validation accuracy: {round(accuracy * 100, 2)}%")
return historyCreate model inputs
Dictionary 형태로 model input을 구성합니다.
def create_model_inputs():
inputs = {}
for feature_name in FEATURE_NAMES:
if feature_name in NUMERIC_FEATURE_NAMES:
inputs[feature_name] = layers.Input(
name=feature_name, shape=(), dtype=tf.float32
)
else:
inputs[feature_name] = layers.Input(
name=feature_name, shape=(), dtype=tf.string
)
return inputsEncode features
encode_inputs method는 numerical_feature_list와 embedding_dims로 categorical feature를 임베딩한 encoded_categorical_feature_list를 반환합니다.
def encode_inputs(inputs, embedding_dims):
encoded_categorical_feature_list = []
numerical_feature_list = []
for feature_name in inputs:
if feature_name in CATEGORICAL_FEATURE_NAMES:
# categorical feature의 vocabulary를 받아옵니다.
vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
# vocabulary의 string value를 integer 형태로 변환하고,
# mask token은 사용하지 않기 떄문에 mask_token은 None으로
# num_oov_indices는 0으로 설정합니다.
lookup = layers.StringLookup(
vocabulary=vocabulary,
mask_token=None,
num_oov_indices=0,
output_mode="int",
)
# string input value를 interger 형태로 변환합니다.
encoded_feature = lookup(inputs[feature_name])
# Embedding Layer를 정의합니다.
embedding = layers.Embedding(
input_dim=len(vocabulary), output_dim=embedding_dims
)
# Embedding Layer에 통과시켜 임베딩된 value를 얻습니다.
encoded_categorical_feature = embedding(encoded_feature)
encoded_categorical_feature_list.append(encoded_categorical_feature)
else:
# numerical feature는 별도의 처리없이 다음과 같이 list에 담습니다.
numerical_feature = tf.expand_dims(inputs[feature_name], -1)
numerical_feature_list.append(numerical_feature)
return encoded_categorical_feature_list, numerical_feature_listImplement an MLP block
def create_mlp(hidden_units, dropout_rate, activation, normalization_layer, name=None):
mlp_layers = []
for units in hidden_units:
mlp_layers.append(normalization_layer),
mlp_layers.append(layers.Dense(units, activation=activation))
mlp_layers.append(layers.Dropout(dropout_rate))
return keras.Sequential(mlp_layers, name=name)Experiment 1: a baseline model
첫 번째 실험으로, 간단한 multi-layer feed-forward network를 만듭니다.
def create_baseline_model(
embedding_dims, num_mlp_blocks, mlp_hidden_units_factors, dropout_rate
):
# model inputs를 생성합니다.
inputs = create_model_inputs()
# categorical, numerical feature를 인코딩합니다.
encoded_categorical_feature_list, numerical_feature_list = encode_inputs(
inputs, embedding_dims
)
# 모든 feature를 합칩니다.
features = layers.concatenate(
encoded_categorical_feature_list + numerical_feature_list
)
# features 마지막 차원을 hidden_units 하이퍼파라미터로 사용합니다.
feedforward_units = [features.shape[-1]]
# Create several feedforwad layers with skip connections.
for layer_idx in range(num_mlp_blocks):
features = create_mlp(
hidden_units=feedforward_units,
dropout_rate=dropout_rate,
activation=keras.activations.gelu,
normalization_layer=layers.LayerNormalization(epsilon=1e-6),
name=f"feedforward_{layer_idx}",
)(features)
# Compute MLP hidden_units.
mlp_hidden_units = [
factor * features.shape[-1] for factor in mlp_hidden_units_factors
]
# Create final MLP.
features = create_mlp(
hidden_units=mlp_hidden_units,
dropout_rate=dropout_rate,
activation=keras.activations.selu,
normalization_layer=layers.BatchNormalization(),
name="MLP",
)(features)
# Add a sigmoid as a binary classifer.
outputs = layers.Dense(units=1, activation="sigmoid", name="sigmoid")(features)
model = keras.Model(inputs=inputs, outputs=outputs)
return model
baseline_model = create_baseline_model(
embedding_dims=EMBEDDING_DIMS,
num_mlp_blocks=NUM_MLP_BLOCKS,
mlp_hidden_units_factors=MLP_HIDDEN_UNITS_FACTORS,
dropout_rate=DROPOUT_RATE,
)
print("Total model weights:", baseline_model.count_params())
keras.utils.plot_model(baseline_model, show_shapes=True, rankdir="LR")훈련 및 평가를 수행합니다.
history = run_experiment(
model=baseline_model,
train_data_file=train_data_file,
test_data_file=test_data_file,
num_epochs=NUM_EPOCHS,
learning_rate=LEARNING_RATE,
weight_decay=WEIGHT_DECAY,
batch_size=BATCH_SIZE,
)Experiment 2: TabTransformer
Tabtransformer 구조는 다음과 같습니다.
- 모든 categorical feature는 동일한 embedding_dims로 category feature embedding됩니다. 각 categorical feature가 고유한 임베딩 벡터를 가지게 됩니다.
- categorical feature인 column에 대해 column embedding이 추가됩니다. 예제 모델은 각 column을 표현할 수 있는 Embedding Layer를 추가해서 1번의 categorical feature 임베딩 벡터와 더해줍니다.
- 임베딩된 categorical feature는 트랜스포머에 입력됩니다. 각 트랜스포머 블럭은 multi-head self-attention layer와 feed-forward layer로 구성됩니다.
- categorical feature의 contextual embedding을 담당하는 마지막 Transformer layer에서 numerical feature와 concat을 수행한 뒤, MLP block에 입력됩니다.
- 1번 실험과 다르게 softmax classifier가 사용됩니다.(?)
이 논문에서 column embedding에 대한 내용을 자세하게 다루며, 모델 구조를 볼 수 있습니다.
모델 구성 순서입니다.
categorical, numerical encoding&embedding → categorical column embedding →
categorical embedding vector + column embedding vector → (Multi-head attention → skip connection →
MLP block → skip connection) → concat with numerical features → MLP block → Classifier
def create_tabtransformer_classifier(
num_transformer_blocks,
num_heads,
embedding_dims,
mlp_hidden_units_factors,
dropout_rate,
use_column_embedding=False,
):
# model inputs를 생성합니다.
inputs = create_model_inputs()
# 각 feature를 인코딩합니다.
encoded_categorical_feature_list, numerical_feature_list = encode_inputs(
inputs, embedding_dims
)
# categorical feature는 Transformer에 입력하기 위해 stack 합니다.
# (None, 8, 16)이 됩니다.
encoded_categorical_features = tf.stack(encoded_categorical_feature_list, axis=1)
# (None, 5)가 됩니다.
numerical_features = layers.concatenate(numerical_feature_list)
# categorical feature embedding에 column embedding을 추가합니다.
if use_column_embedding:
num_columns = encoded_categorical_features.shape[1]
column_embedding = layers.Embedding(
input_dim=num_columns, output_dim=embedding_dims
)
column_indices = tf.range(start=0, limit=num_columns, delta=1)
# (None, 8, 16) + (8, 16)
encoded_categorical_features = encoded_categorical_features + column_embedding(
column_indices
)
# Create multiple layers of the Transformer block.
for block_idx in range(num_transformer_blocks):
# Create a multi-head attention layer.
attention_output = layers.MultiHeadAttention(
num_heads=num_heads,
key_dim=embedding_dims,
dropout=dropout_rate,
name=f"multihead_attention_{block_idx}",
)(encoded_categorical_features, encoded_categorical_features)
# Skip connection 1.
x = layers.Add(name=f"skip_connection1_{block_idx}")(
[attention_output, encoded_categorical_features]
)
# Layer normalization 1.
x = layers.LayerNormalization(name=f"layer_norm1_{block_idx}", epsilon=1e-6)(x)
# Feedforward.
feedforward_output = create_mlp(
hidden_units=[embedding_dims],
dropout_rate=dropout_rate,
activation=keras.activations.gelu,
normalization_layer=layers.LayerNormalization(epsilon=1e-6),
name=f"feedforward_{block_idx}",
)(x)
# Skip connection 2.
x = layers.Add(name=f"skip_connection2_{block_idx}")([feedforward_output, x])
# Layer normalization 2.
encoded_categorical_features = layers.LayerNormalization(
name=f"layer_norm2_{block_idx}", epsilon=1e-6
)(x)
# Flatten the "contextualized" embeddings of the categorical features.
categorical_features = layers.Flatten()(encoded_categorical_features)
# Apply layer normalization to the numerical features.
numerical_features = layers.LayerNormalization(epsilon=1e-6)(numerical_features)
# Prepare the input for the final MLP block.
features = layers.concatenate([categorical_features, numerical_features])
# Compute MLP hidden_units.
mlp_hidden_units = [
factor * features.shape[-1] for factor in mlp_hidden_units_factors
]
# Create final MLP.
features = create_mlp(
hidden_units=mlp_hidden_units,
dropout_rate=dropout_rate,
activation=keras.activations.selu,
normalization_layer=layers.BatchNormalization(),
name="MLP",
)(features)
# Add a sigmoid as a binary classifer.
outputs = layers.Dense(units=1, activation="sigmoid", name="sigmoid")(features)
model = keras.Model(inputs=inputs, outputs=outputs)
return model
tabtransformer_model = create_tabtransformer_classifier(
num_transformer_blocks=NUM_TRANSFORMER_BLOCKS,
num_heads=NUM_HEADS,
embedding_dims=EMBEDDING_DIMS,
mlp_hidden_units_factors=MLP_HIDDEN_UNITS_FACTORS,
dropout_rate=DROPOUT_RATE,
)
print("Total model weights:", tabtransformer_model.count_params())
keras.utils.plot_model(tabtransformer_model, show_shapes=True, rankdir="LR")훈련 및 평가를 진행합니다.
history = run_experiment(
model=tabtransformer_model,
train_data_file=train_data_file,
test_data_file=test_data_file,
num_epochs=NUM_EPOCHS,
learning_rate=LEARNING_RATE,
weight_decay=WEIGHT_DECAY,
batch_size=BATCH_SIZE,
)추가로 아래 예제 마지막 결론을 간단히 해석해보면
TabTransformer는 Embedding이 핵심 아이디어이기 때문에 unlabeled 데이터를 pre-train에 활용할 수 있다고 합니다. 아마 semi-supervised를 표현하는 것 같아 보입니다.
TabTransformer significantly outperforms MLP and recent deep networks for tabular data while matching the performance of tree-based ensemble models. TabTransformer can be learned in end-to-end supervised training using labeled examples. For a scenario where there are a few labeled examples and a large number of unlabeled examples, a pre-training procedure can be employed to train the Transformer layers using unlabeled data. This is followed by fine-tuning of the pre-trained Transformer layers along with the top MLP layer using the labeled data.
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