I am trying to build a object classification model, but when trying to print out the classification report it returned a value error.
ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets
This is my current code:
train_size = int(len(df) * 0.7,) train_text = df['cleansed_text'][:train_size] train_cat = df['category'][:train_size] test_text = df['cleansed_text'][train_size:] test_cat = df['category'][train_size:] max_words = 2500 tokenize = text.Tokenizer(num_words=max_words, char_level=False) tokenize.fit_on_texts(train_text) x_train = tokenize.texts_to_matrix(train_text) x_test = tokenize.texts_to_matrix(test_text) encoder = LabelEncoder() encoder.fit(train_cat) y_train = encoder.transform(train_cat) y_test = encoder.transform(test_cat) num_classes = np.max(y_train) + 1 y_train = utils.to_categorical(y_train, num_classes) y_test = utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Dense(256, input_shape=(max_words,)))
model.add(Dropout(0.5))
model.add(Dense(256,))
model.add(Dropout(0.5))
model.add(Activation('relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
history = model.fit(x_train, y_train,
batch_size=32,
epochs=10,
verbose=1,
validation_split=0.1)
from sklearn.metrics import classification_report
y_test_arg=np.argmax(y_test,axis=1)
Y_pred = np.argmax(model.predict(x_test),axis=1)
print('Confusion Matrix')
print(confusion_matrix(y_test_arg, Y_pred))
print(classification_report(y_test_arg, y_pred, labels=[1,2,3,4,5]))
However, when I attempt to print out the classification report, it ran into this error:
21/21 [==============================] - 0s 2ms/step
Confusion Matrix
[[138 1 6 0 2]
[ 0 102 3 0 2]
[ 3 2 121 1 2]
[ 1 0 1 157 0]
[ 0 3 0 0 123]]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [56], in <cell line: 8>()
5 print('Confusion Matrix')
6 print(confusion_matrix(y_test_arg, Y_pred))
----> 8 print(classification_report(y_test_arg, y_pred, labels=[1,2,3,4,5]))
File ~anaconda3libsite-packagessklearnmetrics_classification.py:2110, in classification_report(y_true, y_pred, labels, target_names, sample_weight, digits, output_dict, zero_division)
1998 def classification_report(
1999 y_true,
2000 y_pred,
(...)
2007 zero_division="warn",
2008 ):
2009 """Build a text report showing the main classification metrics.
2010
2011 Read more in the :ref:`User Guide <classification_report>`.
(...)
2107 <BLANKLINE>
2108 """
-> 2110 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
2112 if labels is None:
2113 labels = unique_labels(y_true, y_pred)
File ~anaconda3libsite-packagessklearnmetrics_classification.py:93, in _check_targets(y_true, y_pred)
90 y_type = {"multiclass"}
92 if len(y_type) > 1:
---> 93 raise ValueError(
94 "Classification metrics can't handle a mix of {0} and {1} targets".format(
95 type_true, type_pred
96 )
97 )
99 # We can't have more than one value on y_type => The set is no more needed
100 y_type = y_type.pop()
ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets
y_test_arg
array([3, 3, 1, 0, 4, 1, 0, 4, 3, 4, 1, 1, 2, 2, 3, 0, 0, 4, 1, 3, 2, 0,
4, 1, 2, 3, 1, 2, 2, 4, 3, 2, 0, 2, 1, 4, 3, 2, 1, 1, 0, 3, 4, 4,
3, 1, 4, 2, 4, 3, 2, 2, 3, 1, 3, 2, 3, 4, 1, 3, 1, 0, 0, 1, 1, 1,
4, 3, 0, 0, 2, 2, 0, 2, 1, 3, 3, 4, 2, 3, 0, 3, 0, 4, 3, 3, 0, 1,
3, 3, 4, 3, 0, 2, 0, 1, 4, 1, 2, 0, 1, 2, 1, 2, 2, 0, 3, 3, 3, 4,
4, 3, 2, 1, 4, 3, 1, 0, 1, 2, 0, 3, 4, 0, 3, 2, 0, 1, 1, 1, 2, 1,
2, 1, 3, 1, 3, 2, 2, 0, 2, 4, 3, 4, 3, 0, 2, 4, 1, 1, 2, 1, 2, 3,
3, 2, 0, 4, 3, 2, 2, 1, 3, 2, 2, 0, 4, 4, 0, 4, 3, 3, 0, 2, 0, 4,
3, 4, 2, 1, 3, 0, 3, 1, 4, 4, 3, 2, 3, 0, 3, 0, 3, 3, 1, 1, 0, 4,
4, 0, 4, 0, 0, 3, 3, 2, 3, 4, 3, 4, 3, 3, 0, 0, 4, 3, 0, 4, 4, 2,
3, 0, 1, 1, 4, 2, 3, 3, 4, 0, 4, 1, 1, 2, 2, 0, 1, 3, 1, 1, 0, 3,
2, 4, 0, 3, 1, 4, 2, 2, 3, 3, 0, 0, 0, 0, 0, 1, 0, 2, 2, 4, 4, 1,
2, 1, 0, 2, 3, 3, 0, 4, 0, 4, 3, 0, 0, 2, 3, 3, 2, 2, 1, 1, 2, 0,
2, 2, 0, 4, 2, 2, 2, 2, 2, 1, 1, 4, 2, 3, 2, 3, 4, 3, 3, 3, 1, 4,
1, 4, 3, 4, 3, 3, 1, 1, 0, 1, 1, 2, 0, 3, 4, 4, 2, 0, 3, 0, 1, 3,
2, 1, 3, 3, 0, 2, 4, 4, 0, 0, 3, 2, 1, 3, 3, 2, 1, 4, 3, 1, 0, 2,
3, 2, 4, 1, 3, 2, 0, 1, 2, 1, 2, 3, 2, 0, 0, 2, 0, 4, 3, 0, 1, 0,
3, 3, 1, 4, 2, 4, 2, 2, 3, 3, 3, 0, 4, 1, 0, 3, 0, 3, 0, 4, 0, 0,
0, 0, 3, 3, 3, 0, 0, 1, 0, 0, 0, 3, 3, 3, 4, 0, 3, 3, 3, 0, 1, 4,
4, 4, 2, 0, 0, 4, 0, 4, 3, 3, 2, 2, 2, 3, 3, 2, 2, 4, 0, 3, 3, 3,
3, 0, 3, 0, 0, 0, 0, 3, 2, 3, 4, 4, 3, 4, 0, 1, 0, 3, 0, 4, 4, 2,
1, 0, 1, 0, 4, 2, 1, 2, 1, 1, 4, 0, 4, 4, 0, 2, 3, 1, 0, 2, 1, 0,
4, 3, 4, 2, 3, 2, 0, 2, 2, 0, 0, 0, 4, 2, 0, 2, 0, 1, 2, 3, 2, 2,
3, 1, 4, 4, 0, 4, 3, 0, 0, 2, 3, 4, 4, 4, 3, 1, 3, 2, 0, 2, 2, 1,
4, 0, 4, 3, 1, 1, 3, 0, 1, 4, 4, 3, 1, 0, 2, 2, 2, 4, 4, 0, 2, 0,
2, 2, 1, 3, 4, 0, 4, 1, 4, 4, 3, 2, 3, 3, 2, 1, 1, 0, 2, 2, 3, 0,
0, 4, 0, 4, 4, 3, 0, 2, 3, 0, 0, 3, 4, 3, 4, 1, 3, 3, 1, 0, 4, 3,
3, 2, 4, 0, 2, 3, 3, 2, 1, 4, 4, 4, 0, 3, 1, 1, 4, 0, 2, 4, 3, 3,
4, 4, 2, 0, 3, 1, 1, 3, 1, 4, 4, 0, 0, 0, 3, 3, 4, 3, 0, 4, 0, 0,
3, 0, 2, 0, 0, 4, 0, 4, 2, 4, 1, 2, 4, 1, 3, 2, 1, 0, 4, 0, 4, 1,
4, 3, 0, 0, 2, 1, 2, 3], dtype=int64)
y_pred
array([[2.6148611e-05, 1.2884392e-06, 8.0136197e-06, 9.9993646e-01,
2.8027451e-05],
[1.1888630e-08, 1.9621881e-07, 6.0117927e-08, 9.9999917e-01,
4.2087538e-07],
[2.4368815e-06, 9.9999702e-01, 2.0465748e-07, 9.2730332e-08,
2.5044619e-07],
...,
[8.7212893e-04, 9.9891293e-01, 7.5106349e-05, 7.0842376e-05,
6.8954141e-05],
[1.2511186e-02, 5.9731454e-05, 9.8512655e-01, 3.0246837e-04,
2.0000227e-03],
[5.9550672e-07, 7.1766672e-06, 2.0012515e-06, 9.9999011e-01,
1.1376539e-07]], dtype=float32)
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Answer
Your problem is caused by the presence of continuous-multioutput target values in y_test_arg or Y_pred. I think this error was generated in the below code:
y_test_arg=np.argmax(y_test,axis=1) Y_pred = np.argmax(model.predict(x_test),axis=1)
It would help if you rounded your predictions in Y_pred before calculating classification_report. You can see this question