应为二进制或unicode字符串，得到的是nan tensorflow/pandas

Question

我对TensorFlow/机器学习比较陌生，因此有一些困难。我有一个csv格式的数据集here，我想用here这样的熊猫来读取它。它在一个不同的数据集上工作，但我修改和扩展了，但我认为我在这里遗漏了一些重要的东西。基本上，我所要做的就是预测给定数据集的“总体”评级。这是我的代码和回溯：

import pandas as pd
import tensorflow as tf
import tempfile


COLUMNS = ["reviewerID", "asin", "reviewerName", "helpful_0", "helpful_1", "reviewText",
           "overall", "summary", "unixReviewTime"]

CATEGORICAL_COLUMNS = ["reviewerID", "reviewerName", "reviewText", "summary"]
CONTINUOUS_COLUMNS = ["helpful_0", "helpful_1", "unixReviewTime"]

df_train = pd.read_csv('Digital_Music_5.csv', names=COLUMNS, skipinitialspace=True,
                       low_memory=False, skiprows=1)
df_test = pd.read_csv('Digital_Music_5_test.csv', names=COLUMNS,
                      skipinitialspace=True, skiprows=1)

LABEL_COLUMN = "label"


df_train[LABEL_COLUMN] = df_train["overall"]
df_test[LABEL_COLUMN] = df_train["overall"]

print(df_train)


def input_fn(df):
    # Creates a dictionary mapping from each continuous feature column name (k)
    # to the values of that column stored in a constant Tensor.
    continuous_cols = {k: tf.constant(df[k].values)
                       for k in CONTINUOUS_COLUMNS}
    # Creates a dictionary mapping from each categorical feature column name
    # (k) to the values of that column stored in a tf.SparseTensor.
    categorical_cols = {k: tf.SparseTensor(
        indices=[[i, 0] for i in range(df[k].size)],
        values=df[k].values,
        dense_shape=[df[k].size, 1],) for k in CATEGORICAL_COLUMNS}
    # Merges the two dictionaries into one.
    feature_cols = dict(continuous_cols)
    feature_cols.update(categorical_cols)
    # Converts the label column into a constant Tensor.
    label = tf.constant(df[LABEL_COLUMN].values)
    # Returns the feature columns and the label.
    return feature_cols, label


def train_input_fn():
    return input_fn(df_train)


def eval_input_fn():
    return input_fn(df_test)


reviewText = tf.contrib.layers.sparse_column_with_hash_bucket("reviewText", hash_bucket_size=100000)
reviewerID = tf.contrib.layers.sparse_column_with_hash_bucket("reviewerID", hash_bucket_size=100000)
reviewerName = tf.contrib.layers.sparse_column_with_hash_bucket("reviewerName", hash_bucket_size=100000)
summary = tf.contrib.layers.sparse_column_with_hash_bucket("summary", hash_bucket_size=100000)


asin = tf.contrib.layers.real_valued_column("asin")
helpful_0 = tf.contrib.layers.real_valued_column("helpful_0")
helpful_1 = tf.contrib.layers.real_valued_column("helpful_1")
unixReviewTime = tf.contrib.layers.real_valued_column("unixReviewTime")

# reviewText_x_summary = tf.contrib.layers.crossed_column([reviewText, summary], hash_bucket_size=100000)
# reviewerID_x_reviewerName = tf.contrib.layers.crossed_column([reviewerID, reviewerName], hash_bucket_size=100000)
# reviewText_x_reviewerID_x_reviewerName = tf.contrib.layers.crossed_column([reviewText, reviewerID, reviewerName], hash_bucket_size=100000)


model_dir = tempfile.mkdtemp()
m = tf.contrib.learn.LinearClassifier(feature_columns=[reviewText, reviewerName, summary,
                                                       asin, helpful_0, helpful_1, unixReviewTime], optimizer=tf.train.FtrlOptimizer(
                                                                     learning_rate=0.1,
                                                                     l1_regularization_strength=1.0,
                                                                     l2_regularization_strength=1.0),
                                                       model_dir=model_dir)

m.fit(input_fn=train_input_fn, steps=200)
# results = m.evaluate(input_fn=eval_input_fn, steps=1)
# for key in sorted(results):
#     print("{}: {}".format(key, results[key]))

回溯：

^{pr2}$

Answer 1

您的输入数据帧包含空的审阅者姓名和审阅文本，它们由pd.read_csv()映射到NaN，但是TensorFlow需要一个字符串而不是NaN。在

使用以下命令检查空单元格：

df_train[df_train.isnull().any(axis=1)]

您可以使用以下命令将这些nan转换为空字符串

^{pr2}$

或者让pd.read_csv()直接使用na_values=[]创建空字符串而不是nan：

df_train = pd.read_csv('Digital_Music_5.csv', names=COLUMNS, 
                        skipinitialspace=True, low_memory=False, 
                        skiprows=1, na_values=[])