 在 TensorFlow.org 上檢視
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 在 Google Colab 中執行
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 在 GitHub 上檢視原始碼
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 下載筆記本
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TFL 預製聚合函數模型是快速又輕鬆的方式，可建構 TFL keras.Model 執行個體，以學習複雜的聚合函數。本指南概述建構 TFL 預製聚合函數模型並進行訓練/測試所需的步驟。
設定
安裝 TF Lattice 套件
pip install --pre -U tensorflow tf-keras tensorflow-lattice pydot graphviz
匯入必要套件
import tensorflow as tf
import collections
import logging
import numpy as np
import pandas as pd
import sys
import tensorflow_lattice as tfl
logging.disable(sys.maxsize)
# Use Keras 2.
version_fn = getattr(tf.keras, "version", None)
if version_fn and version_fn().startswith("3."):
import tf_keras as keras
else:
keras = tf.keras
下載 Puzzles 資料集
train_dataframe = pd.read_csv(
'https://raw.githubusercontent.com/wbakst/puzzles_data/master/train.csv')
train_dataframe.head()
test_dataframe = pd.read_csv(
'https://raw.githubusercontent.com/wbakst/puzzles_data/master/test.csv')
test_dataframe.head()
擷取並轉換特徵和標籤
# Features:
# - star_rating rating out of 5 stars (1-5)
# - word_count number of words in the review
# - is_amazon 1 = reviewed on amazon; 0 = reviewed on artifact website
# - includes_photo if the review includes a photo of the puzzle
# - num_helpful number of people that found this review helpful
# - num_reviews total number of reviews for this puzzle (we construct)
#
# This ordering of feature names will be the exact same order that we construct
# our model to expect.
feature_names = [
'star_rating', 'word_count', 'is_amazon', 'includes_photo', 'num_helpful',
'num_reviews'
]
def extract_features(dataframe, label_name):
# First we extract flattened features.
flattened_features = {
feature_name: dataframe[feature_name].values.astype(float)
for feature_name in feature_names[:-1]
}
# Construct mapping from puzzle name to feature.
star_rating = collections.defaultdict(list)
word_count = collections.defaultdict(list)
is_amazon = collections.defaultdict(list)
includes_photo = collections.defaultdict(list)
num_helpful = collections.defaultdict(list)
labels = {}
# Extract each review.
for i in range(len(dataframe)):
row = dataframe.iloc[i]
puzzle_name = row['puzzle_name']
star_rating[puzzle_name].append(float(row['star_rating']))
word_count[puzzle_name].append(float(row['word_count']))
is_amazon[puzzle_name].append(float(row['is_amazon']))
includes_photo[puzzle_name].append(float(row['includes_photo']))
num_helpful[puzzle_name].append(float(row['num_helpful']))
labels[puzzle_name] = float(row[label_name])
# Organize data into list of list of features.
names = list(star_rating.keys())
star_rating = [star_rating[name] for name in names]
word_count = [word_count[name] for name in names]
is_amazon = [is_amazon[name] for name in names]
includes_photo = [includes_photo[name] for name in names]
num_helpful = [num_helpful[name] for name in names]
num_reviews = [[len(ratings)] * len(ratings) for ratings in star_rating]
labels = [labels[name] for name in names]
# Flatten num_reviews
flattened_features['num_reviews'] = [len(reviews) for reviews in num_reviews]
# Convert data into ragged tensors.
star_rating = tf.ragged.constant(star_rating)
word_count = tf.ragged.constant(word_count)
is_amazon = tf.ragged.constant(is_amazon)
includes_photo = tf.ragged.constant(includes_photo)
num_helpful = tf.ragged.constant(num_helpful)
num_reviews = tf.ragged.constant(num_reviews)
labels = tf.constant(labels)
# Now we can return our extracted data.
return (star_rating, word_count, is_amazon, includes_photo, num_helpful,
num_reviews), labels, flattened_features
train_xs, train_ys, flattened_features = extract_features(train_dataframe, 'Sales12-18MonthsAgo')
test_xs, test_ys, _ = extract_features(test_dataframe, 'SalesLastSixMonths')
2024-03-23 11:15:20.462626: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:282] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
# Let's define our label minimum and maximum.
min_label, max_label = float(np.min(train_ys)), float(np.max(train_ys))
min_label, max_label = float(np.min(train_ys)), float(np.max(train_ys))
設定本指南中用於訓練的預設值
LEARNING_RATE = 0.1
BATCH_SIZE = 128
NUM_EPOCHS = 500
MIDDLE_DIM = 3
MIDDLE_LATTICE_SIZE = 2
MIDDLE_KEYPOINTS = 16
OUTPUT_KEYPOINTS = 8
特徵配置
特徵校正和每個特徵的配置是使用 tfl.configs.FeatureConfig 設定的。特徵配置包括單調性約束、每個特徵的正規化 (請參閱 tfl.configs.RegularizerConfig)，以及格狀模型的格狀大小。
請注意，對於任何我們希望模型識別的特徵，我們都必須完整指定特徵配置。否則，模型將無法得知存在此類特徵。對於聚合模型，這些特徵將自動被考量並適當地處理為參差不齊的特徵。
計算分位數
雖然 tfl.configs.FeatureConfig 中 pwl_calibration_input_keypoints 的預設設定為「分位數」，但對於預製模型，我們必須手動定義輸入關鍵點。為此，我們先定義自己的輔助函式來計算分位數。
def compute_quantiles(features,
num_keypoints=10,
clip_min=None,
clip_max=None,
missing_value=None):
# Clip min and max if desired.
if clip_min is not None:
features = np.maximum(features, clip_min)
features = np.append(features, clip_min)
if clip_max is not None:
features = np.minimum(features, clip_max)
features = np.append(features, clip_max)
# Make features unique.
unique_features = np.unique(features)
# Remove missing values if specified.
if missing_value is not None:
unique_features = np.delete(unique_features,
np.where(unique_features == missing_value))
# Compute and return quantiles over unique non-missing feature values.
return np.quantile(
unique_features,
np.linspace(0., 1., num=num_keypoints),
interpolation='nearest').astype(float)
定義我們的特徵配置
現在我們可以計算分位數了，我們為每個希望模型接受為輸入的特徵定義一個特徵配置。
# Feature configs are used to specify how each feature is calibrated and used.
feature_configs = [
tfl.configs.FeatureConfig(
name='star_rating',
lattice_size=2,
monotonicity='increasing',
pwl_calibration_num_keypoints=5,
pwl_calibration_input_keypoints=compute_quantiles(
flattened_features['star_rating'], num_keypoints=5),
),
tfl.configs.FeatureConfig(
name='word_count',
lattice_size=2,
monotonicity='increasing',
pwl_calibration_num_keypoints=5,
pwl_calibration_input_keypoints=compute_quantiles(
flattened_features['word_count'], num_keypoints=5),
),
tfl.configs.FeatureConfig(
name='is_amazon',
lattice_size=2,
num_buckets=2,
),
tfl.configs.FeatureConfig(
name='includes_photo',
lattice_size=2,
num_buckets=2,
),
tfl.configs.FeatureConfig(
name='num_helpful',
lattice_size=2,
monotonicity='increasing',
pwl_calibration_num_keypoints=5,
pwl_calibration_input_keypoints=compute_quantiles(
flattened_features['num_helpful'], num_keypoints=5),
# Larger num_helpful indicating more trust in star_rating.
reflects_trust_in=[
tfl.configs.TrustConfig(
feature_name="star_rating", trust_type="trapezoid"),
],
),
tfl.configs.FeatureConfig(
name='num_reviews',
lattice_size=2,
monotonicity='increasing',
pwl_calibration_num_keypoints=5,
pwl_calibration_input_keypoints=compute_quantiles(
flattened_features['num_reviews'], num_keypoints=5),
)
]
/tmpfs/tmp/ipykernel_9889/285458577.py:8: DeprecationWarning: the `interpolation=` argument to quantile was renamed to `method=`, which has additional options. Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22) pwl_calibration_input_keypoints=compute_quantiles( /tmpfs/tmp/ipykernel_9889/285458577.py:16: DeprecationWarning: the `interpolation=` argument to quantile was renamed to `method=`, which has additional options. Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22) pwl_calibration_input_keypoints=compute_quantiles( /tmpfs/tmp/ipykernel_9889/285458577.py:34: DeprecationWarning: the `interpolation=` argument to quantile was renamed to `method=`, which has additional options. Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22) pwl_calibration_input_keypoints=compute_quantiles( /tmpfs/tmp/ipykernel_9889/285458577.py:47: DeprecationWarning: the `interpolation=` argument to quantile was renamed to `method=`, which has additional options. Users of the modes 'nearest', 'lower', 'higher', or 'midpoint' are encouraged to review the method they used. (Deprecated NumPy 1.22) pwl_calibration_input_keypoints=compute_quantiles(
聚合函數模型
若要建構 TFL 預製模型，請先從 tfl.configs 建構模型配置。聚合函數模型是使用 tfl.configs.AggregateFunctionConfig 建構的。它會套用分段線性校正和類別校正，然後對參差不齊輸入的每個維度套用格狀模型。接著，它會對每個維度的輸出套用聚合層。之後，可以選擇性地套用輸出分段線性校正。
# Model config defines the model structure for the aggregate function model.
aggregate_function_model_config = tfl.configs.AggregateFunctionConfig(
feature_configs=feature_configs,
middle_dimension=MIDDLE_DIM,
middle_lattice_size=MIDDLE_LATTICE_SIZE,
middle_calibration=True,
middle_calibration_num_keypoints=MIDDLE_KEYPOINTS,
middle_monotonicity='increasing',
output_min=min_label,
output_max=max_label,
output_calibration=True,
output_calibration_num_keypoints=OUTPUT_KEYPOINTS,
output_initialization=np.linspace(
min_label, max_label, num=OUTPUT_KEYPOINTS))
# An AggregateFunction premade model constructed from the given model config.
aggregate_function_model = tfl.premade.AggregateFunction(
aggregate_function_model_config)
# Let's plot our model.
keras.utils.plot_model(
aggregate_function_model, show_layer_names=False, rankdir='LR')
每個聚合層的輸出都是參差不齊輸入上校正格狀的平均輸出。以下是第一個聚合層內部使用的模型
aggregation_layers = [
layer for layer in aggregate_function_model.layers
if isinstance(layer, tfl.layers.Aggregation)
]
keras.utils.plot_model(
aggregation_layers[0].model, show_layer_names=False, rankdir='LR')
現在，與任何其他 keras.Model 一樣，我們將模型編譯並擬合到我們的資料。
aggregate_function_model.compile(
loss='mae',
optimizer=keras.optimizers.Adam(LEARNING_RATE))
aggregate_function_model.fit(
train_xs, train_ys, epochs=NUM_EPOCHS, batch_size=BATCH_SIZE, verbose=False)
<tf_keras.src.callbacks.History at 0x7ff66c544700>
訓練模型後，我們可以在測試集上評估它。
print('Test Set Evaluation...')
print(aggregate_function_model.evaluate(test_xs, test_ys))
Test Set Evaluation... 7/7 [==============================] - 2s 3ms/step - loss: 47.7068 47.70683670043945