feature_columns 的项目必须是 _FeatureColumn
Items of feature_columns must be a _FeatureColumn
我收到这个错误：
ValueError: Items of feature_columns must be a _FeatureColumn. Given
(type ): Index(['CreditScore',
'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard',
'IsActiveMember', 'EstimatedSalary', 'Exited'],
dtype='object').
我正在使用 tensorFlow 库。我想得到预测结果，但我不能 运行 m.train(input_fn=get_input_fn ,steps=5000) 代码。无论我做什么，我总是得到同样的错误。我在下面使用了这些输入函数，但没有任何改变。
def input_fn_train():
x=tf.constant(df_train.astype(np.float64)),
y=tf.constant(df_train[LABEL].astype(np.float64))
return x, y
和
def get_input_fn(data_set, num_epochs=None, shuffle=False):
return tf.estimator.inputs.pandas_input_fn(
x=pd.DataFrame({k: data_set[k].values for k in data_set.columns}),
y=pd.Series(data_set[LABEL].values), num_epochs=num_epochs,
shuffle=shuffle)
我不知道该怎么办。错误是关于什么的？我一直在谷歌搜索但从未找到有用的东西。我该如何处理这个错误。代码如下。谢谢！
import pandas as pd
import tensorflow as tf
import numpy as np
import tempfile
COLS= ["RowNumber","CustomerId","Surname","CreditScore","Geography",
"Gender","Age","Tenure","Balance","NumOfProducts","HasCrCard",
"IsActiveMember","EstimatedSalary","Exited"]
FEATURES = ["CreditScore","Age","Tenure","Balance","NumOfProducts",
"HasCrCard","IsActiveMember", "EstimatedSalary"]
LABEL="Exited"
df_train = pd.read_csv("Churn_Modelling.csv", skipinitialspace=True,
header=0)
df_test = pd.read_csv("Churn_Modelling.csv", skipinitialspace=True,
header=0)
test_label = df_test[LABEL].astype(float)
df_test.drop("Surname", axis = 1, inplace=True)
df_test.drop("RowNumber", axis = 1, inplace=True)
df_test.drop("CustomerId", axis = 1, inplace=True)
df_train.drop("CustomerId", axis = 1, inplace=True)
df_train.drop("Surname", axis = 1, inplace=True)
df_train.drop("RowNumber", axis = 1, inplace=True)
df_train.drop("Geography", axis = 1, inplace=True)
df_train.drop("Gender", axis = 1, inplace=True)
def get_input_fn():
return {'x': tf.constant(df_train[FEATURES].as_matrix(), tf.float32,
df_train.shape),
'y': tf.constant(df_train[LABEL].as_matrix(), tf.float32,
df_train.shape)
}
df=df_train.select_dtypes(exclude=['object'])
numeric_cols=df.columns
m = tf.estimator.LinearClassifier(model_dir=model_dir, feature_columns=
[numeric_cols])
m.train(input_fn=get_input_fn ,steps=5000)
results = m.evaluate(input_fn= get_input_fn(df_test, num_epochs=1,
shuffle=False),steps=None)
y = m.predict(input_fn=get_input_fn(df_test, num_epochs=1, shuffle=False))
pred = list(y)
rowNumber=0
for i in pred:
print(str(rowNumber)+': '+str(pred[i]))
rowNumber=rowNumber+1
您的第一个错误是您创建 tf.estimator.LinearClassifier 的方式。您正在将数据帧索引 df.columns 传递到 feature_columns，但应该传递 tensorflow feature columns 的列表。这些列应该定义它是数字的还是分类的，在后一种情况下是编码类型。
其次，输入函数可以大大简化，因为您正在读取 pandas 数据帧。只需使用 tf.estimator.inputs.pandas_input_fn.
您的 .csv 很可能不同，我制作了一个具有一些值的虚拟值。所以这是一种读取输入并正确拟合模型的方法：
import pandas as pd
import tensorflow as tf
FEATURES = ["CreditScore", "Age", "Tenure", "Balance", "NumOfProducts",
"HasCrCard", "IsActiveMember", "EstimatedSalary", "Exited"]
credit_score = tf.feature_column.numeric_column("CreditScore")
age = tf.feature_column.numeric_column("Age")
tenure = tf.feature_column.numeric_column("Tenure")
balance = tf.feature_column.numeric_column("Balance")
num_of_products = tf.feature_column.numeric_column("NumOfProducts")
has_card = tf.feature_column.categorical_column_with_vocabulary_list("HasCrCard", ["True", "False"])
is_active_member = tf.feature_column.categorical_column_with_vocabulary_list("IsActiveMember", ["True", "False"])
estimated_salary = tf.feature_column.numeric_column("EstimatedSalary")
feature_columns = [credit_score, age, tenure, balance, num_of_products, has_card, is_active_member, estimated_salary]
def input_fn(num_epochs=None, shuffle=True, batch_size=100):
df = pd.read_csv('Churn_Modelling.csv',
names=FEATURES,
dtype={'HasCrCard': str, 'IsActiveMember': str},
skipinitialspace=True,
header=0)
df = df.dropna(how='any', axis=0) # remove NaN elements
labels = df["Exited"]
return tf.estimator.inputs.pandas_input_fn(x=df,
y=labels,
batch_size=batch_size,
num_epochs=num_epochs,
shuffle=shuffle,
num_threads=5)
model = tf.estimator.LinearClassifier(model_dir=None,
feature_columns=feature_columns)
model.train(input_fn=input_fn(), steps=100)
运行正常。
import pandas as pd
import tensorflow as tf
import tempfile
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
def split_data(data, rate, label):
data = data.dropna()
train_data, test_data = train_test_split(data, test_size=rate)
train_label = train_data[label]
train_data = train_data.drop(label, 1)
test_label = test_data[label]
test_data = test_data.drop(label, 1)
return train_data, train_label, test_data, test_label
LABEL = "Exited"
data = pd.read_csv("Churn_Modelling.csv", skipinitialspace=True,
header=0)
data.drop("Surname", axis=1, inplace=True)
data.drop("RowNumber", axis=1, inplace=True)
data.drop("CustomerId", axis=1, inplace=True)
data.drop("Geography", axis=1, inplace=True)
data.drop("Gender", axis=1, inplace=True)
x_train, y_train, x_test, y_test = split_data(data, 0.20, LABEL)
def get_input_fn_train():
input_fn = tf.estimator.inputs.pandas_input_fn(
x=x_train,
y=y_train,
shuffle=False
)
return input_fn
def get_input_fn_test():
input_fn = tf.estimator.inputs.pandas_input_fn(
x=x_test,
y=y_test,
shuffle=False
)
return input_fn
feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input_fn
(get_input_fn_train())
model_dir = tempfile.mkdtemp()
m = tf.estimator.LinearClassifier(model_dir=model_dir,
feature_columns=feature_columns)
# train data
m.train(input_fn=get_input_fn_train(), steps=5000)
# you can get accuracy, accuracy_baseline, auc, auc_precision_recall,
#average_loss, global_step, label/mean, lossprediction/mean
results = m.evaluate(input_fn=get_input_fn_test(), steps=None)
print("model directory = %s" % model_dir)
for key in sorted(results):
print("%s: %s" % (key, results[key]))
# get prediction results
y = m.predict(input_fn=get_input_fn_test())
predictions = list(y)
pred1=pd.DataFrame(data=predictions)
prediction=pd.DataFrame(data=pred1['class_ids'])
pred=[]
for row in prediction["class_ids"]:
pred.append(row[0])
rowNumber = 0
for i in pred:
print(str(rowNumber) + ': ' + str(i))
rowNumber = rowNumber + 1
def calculate(prediction, LABEL):
arr = {"accuracy": accuracy_score(prediction, LABEL),
"report": classification_report(prediction, LABEL),
"Confusion_Matrix": confusion_matrix(prediction, LABEL),
"F1 score": f1_score(prediction, LABEL),
"Recall Score": recall_score(prediction, LABEL),
"cohen_kappa": cohen_kappa_score(prediction, LABEL)
}
return arr
pred2 = pd.DataFrame(data=pred)
print(calculate(pred2.round(), y_test))
我将对@Maxim 的回答（谢谢，顺便说一句）做一些小改动，post 使用随机 numpy 数据的最小工作示例。这似乎 运行 在我的 windows 机器上很好。请注意 由于我的特定硬件。
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import pandas as pd
import numpy as np
import tensorflow as tf
FEATURES = ["CreditScore", "Age", "Tenure", "Balance", "NumOfProducts", "EstimatedSalary", "Exited"]
credit_score = tf.feature_column.numeric_column("CreditScore")
age = tf.feature_column.numeric_column("Age")
tenure = tf.feature_column.numeric_column("Tenure")
balance = tf.feature_column.numeric_column("Balance")
num_of_products = tf.feature_column.numeric_column("NumOfProducts")
estimated_salary = tf.feature_column.numeric_column("EstimatedSalary")
feature_columns = [credit_score, age, tenure, balance, num_of_products, estimated_salary]
def input_fn(num_epochs=None, shuffle=True, batch_size=100):
N_features = len(FEATURES)
print(N_features)
N_examples = 5000
X_train = np.random.rand(N_examples,N_features)
Y_train = np.random.rand(N_examples)
columns = [str(i) for i in range(N_features)]
columns = FEATURES
df = pd.DataFrame(data = X_train, columns = columns)
labels = df["Exited"]
return tf.estimator.inputs.pandas_input_fn(x=df,
y=labels,
batch_size=batch_size,
num_epochs=num_epochs,
shuffle=shuffle,
num_threads=5)
model = tf.estimator.LinearClassifier(model_dir='model_dir',
feature_columns=feature_columns)
model.train(input_fn=input_fn(), steps=100)
我收到这个错误：
ValueError: Items of feature_columns must be a _FeatureColumn. Given (type ): Index(['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited'], dtype='object').
我正在使用 tensorFlow 库。我想得到预测结果，但我不能 运行 m.train(input_fn=get_input_fn ,steps=5000) 代码。无论我做什么，我总是得到同样的错误。我在下面使用了这些输入函数，但没有任何改变。
def input_fn_train():
x=tf.constant(df_train.astype(np.float64)),
y=tf.constant(df_train[LABEL].astype(np.float64))
return x, y
和
def get_input_fn(data_set, num_epochs=None, shuffle=False):
return tf.estimator.inputs.pandas_input_fn(
x=pd.DataFrame({k: data_set[k].values for k in data_set.columns}),
y=pd.Series(data_set[LABEL].values), num_epochs=num_epochs,
shuffle=shuffle)
我不知道该怎么办。错误是关于什么的？我一直在谷歌搜索但从未找到有用的东西。我该如何处理这个错误。代码如下。谢谢！
import pandas as pd
import tensorflow as tf
import numpy as np
import tempfile
COLS= ["RowNumber","CustomerId","Surname","CreditScore","Geography",
"Gender","Age","Tenure","Balance","NumOfProducts","HasCrCard",
"IsActiveMember","EstimatedSalary","Exited"]
FEATURES = ["CreditScore","Age","Tenure","Balance","NumOfProducts",
"HasCrCard","IsActiveMember", "EstimatedSalary"]
LABEL="Exited"
df_train = pd.read_csv("Churn_Modelling.csv", skipinitialspace=True,
header=0)
df_test = pd.read_csv("Churn_Modelling.csv", skipinitialspace=True,
header=0)
test_label = df_test[LABEL].astype(float)
df_test.drop("Surname", axis = 1, inplace=True)
df_test.drop("RowNumber", axis = 1, inplace=True)
df_test.drop("CustomerId", axis = 1, inplace=True)
df_train.drop("CustomerId", axis = 1, inplace=True)
df_train.drop("Surname", axis = 1, inplace=True)
df_train.drop("RowNumber", axis = 1, inplace=True)
df_train.drop("Geography", axis = 1, inplace=True)
df_train.drop("Gender", axis = 1, inplace=True)
def get_input_fn():
return {'x': tf.constant(df_train[FEATURES].as_matrix(), tf.float32,
df_train.shape),
'y': tf.constant(df_train[LABEL].as_matrix(), tf.float32,
df_train.shape)
}
df=df_train.select_dtypes(exclude=['object'])
numeric_cols=df.columns
m = tf.estimator.LinearClassifier(model_dir=model_dir, feature_columns=
[numeric_cols])
m.train(input_fn=get_input_fn ,steps=5000)
results = m.evaluate(input_fn= get_input_fn(df_test, num_epochs=1,
shuffle=False),steps=None)
y = m.predict(input_fn=get_input_fn(df_test, num_epochs=1, shuffle=False))
pred = list(y)
rowNumber=0
for i in pred:
print(str(rowNumber)+': '+str(pred[i]))
rowNumber=rowNumber+1
您的第一个错误是您创建 tf.estimator.LinearClassifier 的方式。您正在将数据帧索引 df.columns 传递到 feature_columns，但应该传递 tensorflow feature columns 的列表。这些列应该定义它是数字的还是分类的，在后一种情况下是编码类型。
其次，输入函数可以大大简化，因为您正在读取 pandas 数据帧。只需使用 tf.estimator.inputs.pandas_input_fn.
您的 .csv 很可能不同，我制作了一个具有一些值的虚拟值。所以这是一种读取输入并正确拟合模型的方法：
import pandas as pd
import tensorflow as tf
FEATURES = ["CreditScore", "Age", "Tenure", "Balance", "NumOfProducts",
"HasCrCard", "IsActiveMember", "EstimatedSalary", "Exited"]
credit_score = tf.feature_column.numeric_column("CreditScore")
age = tf.feature_column.numeric_column("Age")
tenure = tf.feature_column.numeric_column("Tenure")
balance = tf.feature_column.numeric_column("Balance")
num_of_products = tf.feature_column.numeric_column("NumOfProducts")
has_card = tf.feature_column.categorical_column_with_vocabulary_list("HasCrCard", ["True", "False"])
is_active_member = tf.feature_column.categorical_column_with_vocabulary_list("IsActiveMember", ["True", "False"])
estimated_salary = tf.feature_column.numeric_column("EstimatedSalary")
feature_columns = [credit_score, age, tenure, balance, num_of_products, has_card, is_active_member, estimated_salary]
def input_fn(num_epochs=None, shuffle=True, batch_size=100):
df = pd.read_csv('Churn_Modelling.csv',
names=FEATURES,
dtype={'HasCrCard': str, 'IsActiveMember': str},
skipinitialspace=True,
header=0)
df = df.dropna(how='any', axis=0) # remove NaN elements
labels = df["Exited"]
return tf.estimator.inputs.pandas_input_fn(x=df,
y=labels,
batch_size=batch_size,
num_epochs=num_epochs,
shuffle=shuffle,
num_threads=5)
model = tf.estimator.LinearClassifier(model_dir=None,
feature_columns=feature_columns)
model.train(input_fn=input_fn(), steps=100)
运行正常。
import pandas as pd
import tensorflow as tf
import tempfile
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
def split_data(data, rate, label):
data = data.dropna()
train_data, test_data = train_test_split(data, test_size=rate)
train_label = train_data[label]
train_data = train_data.drop(label, 1)
test_label = test_data[label]
test_data = test_data.drop(label, 1)
return train_data, train_label, test_data, test_label
LABEL = "Exited"
data = pd.read_csv("Churn_Modelling.csv", skipinitialspace=True,
header=0)
data.drop("Surname", axis=1, inplace=True)
data.drop("RowNumber", axis=1, inplace=True)
data.drop("CustomerId", axis=1, inplace=True)
data.drop("Geography", axis=1, inplace=True)
data.drop("Gender", axis=1, inplace=True)
x_train, y_train, x_test, y_test = split_data(data, 0.20, LABEL)
def get_input_fn_train():
input_fn = tf.estimator.inputs.pandas_input_fn(
x=x_train,
y=y_train,
shuffle=False
)
return input_fn
def get_input_fn_test():
input_fn = tf.estimator.inputs.pandas_input_fn(
x=x_test,
y=y_test,
shuffle=False
)
return input_fn
feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input_fn
(get_input_fn_train())
model_dir = tempfile.mkdtemp()
m = tf.estimator.LinearClassifier(model_dir=model_dir,
feature_columns=feature_columns)
# train data
m.train(input_fn=get_input_fn_train(), steps=5000)
# you can get accuracy, accuracy_baseline, auc, auc_precision_recall,
#average_loss, global_step, label/mean, lossprediction/mean
results = m.evaluate(input_fn=get_input_fn_test(), steps=None)
print("model directory = %s" % model_dir)
for key in sorted(results):
print("%s: %s" % (key, results[key]))
# get prediction results
y = m.predict(input_fn=get_input_fn_test())
predictions = list(y)
pred1=pd.DataFrame(data=predictions)
prediction=pd.DataFrame(data=pred1['class_ids'])
pred=[]
for row in prediction["class_ids"]:
pred.append(row[0])
rowNumber = 0
for i in pred:
print(str(rowNumber) + ': ' + str(i))
rowNumber = rowNumber + 1
def calculate(prediction, LABEL):
arr = {"accuracy": accuracy_score(prediction, LABEL),
"report": classification_report(prediction, LABEL),
"Confusion_Matrix": confusion_matrix(prediction, LABEL),
"F1 score": f1_score(prediction, LABEL),
"Recall Score": recall_score(prediction, LABEL),
"cohen_kappa": cohen_kappa_score(prediction, LABEL)
}
return arr
pred2 = pd.DataFrame(data=pred)
print(calculate(pred2.round(), y_test))
我将对@Maxim 的回答（谢谢，顺便说一句）做一些小改动，post 使用随机 numpy 数据的最小工作示例。这似乎 运行 在我的 windows 机器上很好。请注意
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import pandas as pd
import numpy as np
import tensorflow as tf
FEATURES = ["CreditScore", "Age", "Tenure", "Balance", "NumOfProducts", "EstimatedSalary", "Exited"]
credit_score = tf.feature_column.numeric_column("CreditScore")
age = tf.feature_column.numeric_column("Age")
tenure = tf.feature_column.numeric_column("Tenure")
balance = tf.feature_column.numeric_column("Balance")
num_of_products = tf.feature_column.numeric_column("NumOfProducts")
estimated_salary = tf.feature_column.numeric_column("EstimatedSalary")
feature_columns = [credit_score, age, tenure, balance, num_of_products, estimated_salary]
def input_fn(num_epochs=None, shuffle=True, batch_size=100):
N_features = len(FEATURES)
print(N_features)
N_examples = 5000
X_train = np.random.rand(N_examples,N_features)
Y_train = np.random.rand(N_examples)
columns = [str(i) for i in range(N_features)]
columns = FEATURES
df = pd.DataFrame(data = X_train, columns = columns)
labels = df["Exited"]
return tf.estimator.inputs.pandas_input_fn(x=df,
y=labels,
batch_size=batch_size,
num_epochs=num_epochs,
shuffle=shuffle,
num_threads=5)
model = tf.estimator.LinearClassifier(model_dir='model_dir',
feature_columns=feature_columns)
model.train(input_fn=input_fn(), steps=100)