稀疏矩阵长度不明确

Question

我对机器学习还很陌生，所以这个问题听起来可能很愚蠢。我正在关注 tutorial on Text Classification，但我遇到了一个错误，我不知道如何解决。

这是我的代码（基本上是在教程中找到的代码）

import pandas as pd

filepath_dict = {'yelp':   'data/yelp_labelled.txt',
              'amazon': 'data/amazon_cells_labelled.txt',
              'imdb':   'data/imdb_labelled.txt'}

df_list = []
for source, filepath in filepath_dict.items():
df = pd.read_csv(filepath, names=['sentence', 'label'], sep='\t')
df['source'] = source  
df_list.append(df)

df = pd.concat(df_list)
print(df.iloc[0:4])


from sklearn.feature_extraction.text import CountVectorizer

df_yelp = df[df['source'] == 'yelp']

sentences = df_yelp['sentence'].values
y = df_yelp['label'].values

from sklearn.model_selection import train_test_split
sentences_train, sentences_test, y_train, y_test = train_test_split(sentences, y, test_size=0.25, random_state=1000)


from sklearn.feature_extraction.text import CountVectorizer


vectorizer = CountVectorizer()
vectorizer.fit(sentences_train)

X_train = vectorizer.transform(sentences_train)
X_test  = vectorizer.transform(sentences_test)

from keras.models import Sequential
from keras import layers

input_dim = X_train.shape[1] 

model = Sequential()
model.add(layers.Dense(10, input_dim=input_dim, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', 
            optimizer='adam', 
            metrics=['accuracy'])
model.summary()

history = model.fit(X_train, y_train,
nb_epoch=100,
verbose=False,
validation_data=(X_test, y_test),
batch_size=10)

当我到达最后一行时，出现错误

"TypeError: sparse matrix length is ambiguous; use getnnz() or shape[0]"

我想我必须对我正在使用的数据执行某种转换，或者我应该尝试以不同的方式加载这些数据。我已经尝试在 Whosebug 上进行搜索，但是 - 对这一切都是陌生的 - 我找不到任何有用的东西。

我该如何进行这项工作？理想情况下，我不仅希望获得解决方案，还希望获得有关错误发生原因以及解决方案如何解决该错误的简要说明。

谢谢！

Answer 1

您遇到这种困难的原因是您的 X_train 和 X_test 属于 <class scipy.sparse.csr.csr_matrix> 类型，而您的模型期望它是一个 numpy 数组。

尝试将它们投射到密集，你就可以了：

X_train = X_train.todense()
X_test = X_test.todense()

Answer 2

不确定，为什么这个脚本会出错。

以下脚本运行良好；即使是稀疏矩阵。可以在您的机器上试一试。

sentences = ['i want to test this','let us try this',
             'would this work','how about this',
             'even this','this should not work']
y= [0,0,0,0,0,1]
from sklearn.model_selection import train_test_split
sentences_train, sentences_test, y_train, y_test = train_test_split(sentences, y, test_size=0.25, random_state=1000)


from sklearn.feature_extraction.text import CountVectorizer


vectorizer = CountVectorizer()
vectorizer.fit(sentences_train)

X_train = vectorizer.transform(sentences_train)
X_test  = vectorizer.transform(sentences_test)

from keras.models import Sequential
from keras import layers

input_dim = X_train.shape[1] 

model = Sequential()
model.add(layers.Dense(10, input_dim=input_dim, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', 
            optimizer='adam', 
            metrics=['accuracy'])
model.summary()

model.fit(X_train, y_train,
                        epochs=2,
                        verbose=True,
                        validation_data=(X_test, y_test),
                        batch_size=2)

#
Layer (type)                 Output Shape              Param #   
=================================================================
dense_5 (Dense)              (None, 10)                110       
_________________________________________________________________
dense_6 (Dense)              (None, 1)                 11        
=================================================================
Total params: 121
Trainable params: 121
Non-trainable params: 0
_________________________________________________________________
Train on 4 samples, validate on 2 samples
Epoch 1/2
4/4 [==============================] - 1s 169ms/step - loss: 0.7570 - acc: 0.2500 - val_loss: 0.6358 - val_acc: 1.0000
Epoch 2/2
4/4 [==============================] - 0s 3ms/step - loss: 0.7509 - acc: 0.2500 - val_loss: 0.6328 - val_acc: 1.0000

稀疏矩阵长度不明确

sparse matrix length is ambiguous

python

scikit-learn

keras

sklearn-pandas