TypeError: 无法为列构建 TypeSpec

Question

我试图从这里的数据集中的值“名称”、“平台”、“流派”、“出版商”和“年份”来预测全球销售额：https : //www.kaggle.com/gregorut/videogamesales

这是我训练模型的代码：

from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import clear_output
from six.moves import urllib

import tensorflow as tf

dftrain = pd.read_csv('./vgsales_eval.csv')
dfeval = pd.read_csv('./vgsales_train.csv')

print(dftrain[dftrain.isnull().any(axis=1)])

y_train = dftrain.pop('Global_Sales')
y_eval = dfeval.pop('Global_Sales')

CATEGORICAL_COLUMNS = ['Name', 'Platform', 'Genre', 'Publisher']
NUMERIC_COLUMNS = ['Year']

feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
  vocabulary = dftrain[feature_name].unique()  # gets a list of all unique values from given feature column
  feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))

for feature_name in NUMERIC_COLUMNS:
  feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.int64))

print(feature_columns)

def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):
  def input_function():  
    ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))  
    if shuffle:
      ds = ds.shuffle(1000)  
    ds = ds.batch(batch_size).repeat(num_epochs)  
    return ds
  return input_function  

train_input_fn = make_input_fn(dftrain, y_train)  
eval_input_fn = make_input_fn(dfeval, y_eval, num_epochs=1, shuffle=False)

linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)
linear_est.train(train_input_fn)

我收到以下错误：

Traceback (most recent call last):
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\util\structure.py", line 93, in normalize_element
    spec = type_spec_from_value(t, use_fallback=False)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\util\structure.py", line 466, in type_spec_from_value
    (element, type(element).__name__))
TypeError: Could not build a TypeSpec for 0                 Tecmo Koei
1       Nippon Ichi Software
2                    Ubisoft
3                 Activision
4                      Atari
                ...
6594                   Kemco
6595              Infogrames
6596              Activision
6597                7G//AMES
6598                 Wanadoo
Name: Publisher, Length: 6599, dtype: object with type Series

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "c:\Users\kuhn-\Documents\Github\Tensorflow_Test\VideoGameSales_Test\main.py", line 45, in <module>
    linear_est.train(train_input_fn)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 349, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1175, in _train_model
    return self._train_model_default(input_fn, hooks, saving_listeners)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1201, in _train_model_default
    self._get_features_and_labels_from_input_fn(input_fn, ModeKeys.TRAIN))
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1037, in _get_features_and_labels_from_input_fn
    self._call_input_fn(input_fn, mode))
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow_estimator\python\estimator\estimator.py", line 1130, in _call_input_fn
    return input_fn(**kwargs)
  File "c:\Users\kuhn-\Documents\Github\Tensorflow_Test\VideoGameSales_Test\main.py", line 34, in input_function
    ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 682, in from_tensor_slices
    return TensorSliceDataset(tensors)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\ops\dataset_ops.py", line 3001, in __init__
    element = structure.normalize_element(element)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\data\util\structure.py", line 98, in normalize_element
    ops.convert_to_tensor(t, name="component_%d" % i))
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1499, in convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\constant_op.py", line 338, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\constant_op.py", line 264, in constant
    allow_broadcast=True)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\constant_op.py", line 282, in _constant_impl
    allow_broadcast=allow_broadcast))
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 563, in make_tensor_proto
    append_fn(tensor_proto, proto_values)
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 155, in SlowAppendObjectArrayToTensorProto
    tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\tensor_util.py", line 155, in <listcomp>
    tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
  File "C:\Users\kuhn-\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\util\compat.py", line 87, in as_bytes
    (bytes_or_text,))
TypeError: Expected binary or unicode string, got nan

我在这里做错了什么？是数据集有问题还是我必须以不同的方式读取值？

Answer 1

这基本上与null您获取的数据中存在的值有关，您需要在加载数据时对其进行处理。

我做了一些改变。

1. 要删除空值的记录，您也可以df.fillna根据数据类型，根据需要填写的列和值来执行。
2. 我已将列Year数据类型从更改float为int。因为这会导致另一个问题tensor_slices。

下面是修改后的代码，其数据与您所获取的数据相同。

df = pd.read_csv('/content/vgsales.csv')
# print(df.head())
print(df[df.isnull().any(axis=1)])
# df.fillna('', inplace=True)
df.dropna(how="any",inplace = True)
df.Year = df.Year.astype(int) 

CATEGORICAL_COLUMNS = ['Name', 'Platform', 'Genre', 'Publisher']
NUMERIC_COLUMNS = ['Year'] 

feature_columns = []
for feature_name in CATEGORICAL_COLUMNS:
  vocabulary = df[feature_name].unique()  # gets a list of all unique values from given feature column
  feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(feature_name, vocabulary))

for feature_name in NUMERIC_COLUMNS:
  feature_columns.append(tf.feature_column.numeric_column(feature_name, dtype=tf.int64))

print(feature_columns)

def make_input_fn(data_df, label_df, num_epochs=10, shuffle=True, batch_size=32):
  def input_function():  
    ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))  
    if shuffle:
      ds = ds.shuffle(1000)  
    ds = ds.batch(batch_size).repeat(num_epochs)  
    return ds
  return input_function  

train_input_fn = make_input_fn(df, y_train)  
linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)