稀疏矩阵长度不明确

Question

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

这是我的代码（基本上是在教程中找到的）

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，我得到一个错误

"TypeError: 稀疏矩阵长度不明确；使用 getnnz() 或 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 Stackoverflow 上进行搜索，但是 - 对这一切都很陌生 - 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，我不仅想获得解决方案，还想简要说明错误发生的原因以及解决方案为了解决它做了什么。

谢谢！

Answer 1

您面临此困难的原因是您的 X_train 和 X_test 是 <class scipy.sparse.csr.csr_matrix> 类型，而您的模型希望它是一个 numpy 数组。

尝试将它们铸造成稠密的，你就可以了：

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

Answer 2

不确定，为什么您收到此脚本的错误。

以下脚本运行良好；即使是稀疏矩阵。可以在你的机器上试一试。

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