sparse matrix length is ambiguous

Question

I'm very new to machine learning so this question might sound stupid. i'm following a tutorial on Text Classification but I'm facing an error that I don't have any idea about how to solve.

This is the code I have (it is basically what it is found in the tutorial)

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)

When I reach the last line, I get an error

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

I guess I'll have to perform some kind of transformation on the data I'm using, or that I should try to load those data in a different way. I tried to search on Stackoverflow already but - being new to all this - I couldn't find anything helpful.

How do I make this work? Ideally I'd like to get not only the solution but also a brief explaination about why the error happened and what the solution does in order to solve it.

thanks!

Can you try casting sparse matrix to dense like `X_train.todense()` and passing the result to `model.fit()`? — Sergey Bushmanov, Mar 11 '19 at 15:08
@FrancoPiccolo The last one history = model.fit(X_train, y_train, nb_epoch=100, verbose=False, validation_data=(X_test, y_test), batch_size=10) — fatmau, Mar 11 '19 at 15:08

score 6 · Accepted Answer · answered Mar 11 '19 at 15:47

The reason you're facing this difficulty is that your X_train and X_test are of type <class scipy.sparse.csr.csr_matrix> whereas your model expects it to be a numpy array.

Try casting them to dense and you're fine to go:

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

Venkatachalam · Answer 2 · 2019-03-12T10:28:38.730

Not sure, why are you getting error for this script.

The following script is working fine; even with sparse matrix. May be give a try in your machine.

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

Exactly, I too think so. Todense is a costly operation. Probably updating the package could be a better solution. — Venkatachalam, Mar 11 '19 at 15:54

sparse matrix length is ambiguous

2 Answers2