tensorflow线性模型以及Wide deep learning

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原文地址

什么是线性模型

相信大多数人，刚开始接触机器学习的时候，就会接触到线性模型。来看一个简单的例子：通过人的年龄、受教育年数、工作年限等信息，可以预测出一个人的基本收入水平，预测方法就是对前面的限定特征赋予不同的权值，最后计算出工资；此外，线性模型也可以用于分类，例如逻辑回归就是一种典型的线性分类器。

相对于其他的复杂模型来说，线性模型主要有以下几个优点：

• 训练速度快
• 大量特征集的时候工作的很好
• 方便解释和debug，参数调节比较方便

tf.learn关于线性模型的一些API

• FeatureColumn
• sparse_column 用于解决类别特征的稀疏问题，对于类别型的特征，一般使用的One hot方法，会导致矩阵稀疏的问题。

eye_color = tf.contrib.layers.sparse_column_with_keys(
  column_name="eye_color", keys=["blue", "brown", "green"])
  education = tf.contrib.layers.sparse_column_with_hash_bucket(\
    "education", hash_bucket_size=1000)#不知道所有的可能值的时候用这个接口

• Feature Crosses 可以用来合并不同的特征

sport = tf.contrib.layers.sparse_column_with_hash_bucket(\
    "sport", hash_bucket_size=1000)
city = tf.contrib.layers.sparse_column_with_hash_bucket(\
    "city", hash_bucket_size=1000)
sport_x_city = tf.contrib.layers.crossed_column(
    [sport, city], hash_bucket_size=int(1e4))

• Continuous columns 用于连续的变量特征
  

  age = tf.contrib.layers.real_valued_column(“age”)

• Bucketization 将连续的变量变成类别标签
  age_buckets = tf.contrib.layers.bucketized_column(age, boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])

tf.contrib.learn.LinearClassifier和LinearRegressor

这两个一个用于分类，一个用于回归，使用步骤如下

• 创建对象实例，在构造函数中传入featureColumns
• 用fit训练模型
• 用evaluate评估

下面是一段示例代码：

e = tf.contrib.learn.LinearClassifier(feature_columns=[
  native_country, education, occupation, workclass, marital_status,
  race, age_buckets, education_x_occupation, age_buckets_x_race_x_occupation],
  model_dir=YOUR_MODEL_DIRECTORY)
e.fit(input_fn=input_fn_train, steps=200)

# Evaluate for one step (one pass through the test data).
results = e.evaluate(input_fn=input_fn_test, steps=1)


# Print the stats for the evaluation.
for key in sorted(results):
    print "%s: %s" % (key, results[key])

Wide and deep learning

最近刚看了这篇论文，打算专门写一章来详细讲解，这个训练模型的出现是为了结合memorization和generalization。下面推荐几篇文章:

• research blog
• Wide & Deep Learning for Recommender Systems

模型结构如下：

数据描述

下面我们用具体的示例来演示如何使用线性模型：通过统计数据，从一个人的年龄、性别、教育背景、职业来判断这个人的年收入是否超过50000元，如果超过就为1，否则输出0.下面是我从官网截取的数据描述：

• Listing of attributes: >50K, <=50K.
• age: continuous.
• workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
• fnlwgt: continuous.
• education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
• education-num: continuous.
• marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
• occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, * * Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
• relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
• race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
• sex: Female, Male.
• capital-gain: continuous.
• capital-loss: continuous.
• hours-per-week: continuous.
• native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.
  数据源

代码实现

注意事项，请在linux上运行该段代码！window会出现下面的错误：

AttributeError: ‘NoneType’ object has no attribute ‘bucketize’

如果确实想运行在windows上，请将model_type,修改为deep:

flags.DEFINE_string(“model_type”,”deep”,”valid model types:{‘wide’,’deep’, ‘wide_n_deep’”)
follow this issue

import tempfile
import tensorflow as tf

from six.moves import urllib

import pandas as pd

flags = tf.app.flags
FLAGS = flags.FLAGS

flags.DEFINE_string("model_dir","","Base directory for output models.")
flags.DEFINE_string("model_type","wide_n_deep","valid model types:{'wide','deep', 'wide_n_deep'")
flags.DEFINE_integer("train_steps",200,"Number of training steps.")
flags.DEFINE_string("train_data","", "Path to the training data.")
flags.DEFINE_string("test_data", "", "path to the test data")

COLUMNS = ["age", "workclass", "fnlwgt", "education", "education_num",
           "marital_status", "occupation", "relationship", "race", "gender",
           "capital_gain", "capital_loss", "hours_per_week", "native_country",
           "income_bracket"]

LABEL_COLUMN = "label"

CATEGORICAL_COLUMNS = ["workclass", "education", "marital_status", "occupation",
                       "relationship", "race", "gender", "native_country"]

CONTINUOUS_COLUMNS = ["age", "education_num", "capital_gain", "capital_loss",
                      "hours_per_week"]

# download test and train data
def maybe_download():
    if FLAGS.train_data:
        train_data_file = FLAGS.train_data
    else:
        train_file = tempfile.NamedTemporaryFile(delete=False)
        urllib.request.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.data", train_file.name)
        train_file_name = train_file.name
        train_file.close()
        print("Training data is downloaded to %s" % train_file_name)

    if FLAGS.test_data:
        test_file_name = FLAGS.test_data
    else:
        test_file = tempfile.NamedTemporaryFile(delete=False)
        urllib.request.urlretrieve("http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.test",
                                   test_file.name)  # pylint: disable=line-too-long
        test_file_name = test_file.name
        test_file.close()
        print("Test data is downloaded to %s" % test_file_name)

    return train_file_name, test_file_name

# build the estimator
def build_estimator(model_dir):
    # 离散分类别的
    gender = tf.contrib.layers.sparse_column_with_keys(column_name="gender", keys=["female","male"])
    education = tf.contrib.layers.sparse_column_with_hash_bucket("education", hash_bucket_size = 1000)
    relationship = tf.contrib.layers.sparse_column_with_hash_bucket("relationship", hash_bucket_size = 100)
    workclass = tf.contrib.layers.sparse_column_with_hash_bucket("workclass", hash_bucket_size=100)
    occupation = tf.contrib.layers.sparse_column_with_hash_bucket("occupation", hash_bucket_size=1000)
    native_country = tf.contrib.layers.sparse_column_with_hash_bucket( "native_country", hash_bucket_size=1000)

    # Continuous base columns.
    age = tf.contrib.layers.real_valued_column("age")
    education_num = tf.contrib.layers.real_valued_column("education_num")
    capital_gain = tf.contrib.layers.real_valued_column("capital_gain")
    capital_loss = tf.contrib.layers.real_valued_column("capital_loss")
    hours_per_week = tf.contrib.layers.real_valued_column("hours_per_week")
    #类别转换
    age_buckets = tf.contrib.layers.bucketized_column(age, boundaries= [18,25, 30, 35, 40, 45, 50, 55, 60, 65])

    wide_columns = [gender, native_country,education, occupation, workclass, relationship, age_buckets,
                    tf.contrib.layers.crossed_column([education, occupation], hash_bucket_size=int(1e4)),
                    tf.contrib.layers.crossed_column([age_buckets, education, occupation], hash_bucket_size=int(1e6)),
                    tf.contrib.layers.crossed_column([native_country, occupation],hash_bucket_size=int(1e4))]

    #embedding_column用来表示类别型的变量
    deep_columns = [tf.contrib.layers.embedding_column(workclass, dimension=8),
                    tf.contrib.layers.embedding_column(education, dimension=8),
                    tf.contrib.layers.embedding_column(gender, dimension=8),
                    tf.contrib.layers.embedding_column(relationship, dimension=8),
                    tf.contrib.layers.embedding_column(native_country,dimension=8),
                    tf.contrib.layers.embedding_column(occupation, dimension=8),
                    age,education_num,capital_gain,capital_loss,hours_per_week,]

    if FLAGS.model_type =="wide":
        m = tf.contrib.learn.LinearClassifier(model_dir=model_dir,feature_columns=wide_columns)
    elif FLAGS.model_type == "deep":
        m = tf.contrib.learn.DNNClassifier(model_dir=model_dir, feature_columns=deep_columns, hidden_units=[100,50])
    else:
        m = tf.contrib.learn.DNNLinearCombinedClassifier(model_dir=model_dir, linear_feature_columns=wide_columns, dnn_feature_columns = deep_columns, dnn_hidden_units=[100,50])

    return m

def input_fn(df):
    continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS}
    categorical_cols = {k: tf.SparseTensor(indices=[[i,0] for i in range( df[k].size)], values = df[k].values, shape=[df[k].size,1]) for k in CATEGORICAL_COLUMNS}#原文例子为dense_shape
    feature_cols = dict(continuous_cols)
    feature_cols.update(categorical_cols)
    label = tf.constant(df[LABEL_COLUMN].values)

    return feature_cols, label


def train_and_eval():
    train_file_name, test_file_name = maybe_download()
    df_train = pd.read_csv(
        tf.gfile.Open(train_file_name),
        names=COLUMNS,
        skipinitialspace=True,
        engine="python"
    )
    df_test = pd.read_csv(
        tf.gfile.Open(test_file_name),
        names=COLUMNS,
        skipinitialspace=True,
        skiprows=1,
        engine="python"
    )

    # drop Not a number elements
    df_train = df_train.dropna(how='any',axis=0)
    df_test = df_test.dropna(how='any', axis=0)

    #convert >50 to 1
    df_train[LABEL_COLUMN] = (
        df_train["income_bracket"].apply(lambda x: ">50" in x).astype(int)
    )
    df_test[LABEL_COLUMN] = (
        df_test["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)

    model_dir = tempfile.mkdtemp() if not FLAGS.model_dir else FLAGS.model_dir
    print("model dir = %s" % model_dir)

    m = build_estimator(model_dir)
    print (FLAGS.train_steps)
    m.fit(input_fn=lambda: input_fn(df_train),
          steps=FLAGS.train_steps)
    results = m.evaluate(input_fn=lambda: input_fn(df_test), steps=1)

    for key in sorted(results):
        print("%s: %s"%(key, results[key]))

def main(_):
  train_and_eval()


if __name__ == "__main__":
  tf.app.run()

运行结果：

• accuracy: 0.825686
• accuracy/baseline_label_mean: 0.236226
• accuracy/threshold_0.500000_mean: 0.825686
• auc: 0.820967
• global_step: 202
• labels/actual_label_mean: 0.236226
• labels/prediction_mean: 0.199659
• loss: 0.443123
• precision/positive_threshold_0.500000_mean: 0.766385
• recall/positive_threshold_0.500000_mean: 0.377015

可以将model_type切换为deep,wide,deep_n_wide，查看不同的输出结果！

另外，先将model_type换成wide, 为了防止线性模型的过拟合，可以在LinearClassifier中加上一个optimizer的参数，如下：

m = tf.contrib.learn.LinearClassifier(feature_columns=[
  gender, native_country, education, occupation, workclass, marital_status, race,
  age_buckets, education_x_occupation, age_buckets_x_education_x_occupation],
  optimizer=tf.train.FtrlOptimizer(
    learning_rate=0.1,
    l1_regularization_strength=1.0,
    l2_regularization_strength=1.0),
  model_dir=model_dir)

reference

1. https://archive.ics.uci.edu/ml/datasets/Census+Income
2. https://www.tensorflow.org/tutorials/wide/