“Unknown label type” with numpy and scikit
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X = np.array(...,dtype=np.float64).astype(np.float)
y = np.array(...,dtype=np.float64).astype(np.float)
print(X)
[[-0.5 0. ]
[ 0. 0. ]
[ 0. 0. ]
[ 0. 0. ]
[ 0. -3.5]
[-3.5 -3.5]
[-3.5 -2. ]
[-2. 0. ]
[ 0. 0. ]
[ 0. -3. ]]
print(y)
[-3.5 -3.5 -3.5 -3.5 -3.5 -2. -3. -3. -3. 1. ]
(10, 2)
(10,)
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(X, Y)
No matter what I do I get
Traceback (most recent call last):
File "scripts/wave-pool.py", line 174, in <module>
clf.fit(X, y)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/naive_bayes.py", line 192, in fit
sample_weight=sample_weight)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/naive_bayes.py", line 355, in _partial_fit
if _check_partial_fit_first_call(self, classes):
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/utils/multiclass.py", line 320, in _check_partial_fit_first_call
clf.classes_ = unique_labels(classes)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/utils/multiclass.py", line 96, in unique_labels
raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([-3.5, -3. , -2. , 1. ]),)
numpy scikit-learn
asked yesterday
Ben
7,55721014
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0
down vote
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X = np.array(...,dtype=np.float64).astype(np.float)
y = np.array(...,dtype=np.float64).astype(np.float)
print(X)
[[-0.5 0. ]
[ 0. 0. ]
[ 0. 0. ]
[ 0. 0. ]
[ 0. -3.5]
[-3.5 -3.5]
[-3.5 -2. ]
[-2. 0. ]
[ 0. 0. ]
[ 0. -3. ]]
print(y)
[-3.5 -3.5 -3.5 -3.5 -3.5 -2. -3. -3. -3. 1. ]
(10, 2)
(10,)
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(X, Y)
No matter what I do I get
Traceback (most recent call last):
File "scripts/wave-pool.py", line 174, in <module>
clf.fit(X, y)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/naive_bayes.py", line 192, in fit
sample_weight=sample_weight)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/naive_bayes.py", line 355, in _partial_fit
if _check_partial_fit_first_call(self, classes):
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/utils/multiclass.py", line 320, in _check_partial_fit_first_call
clf.classes_ = unique_labels(classes)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/utils/multiclass.py", line 96, in unique_labels
raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([-3.5, -3. , -2. , 1. ]),)
numpy scikit-learn
asked yesterday
Ben
7,55721014
1
Apparently it doesn't like using floats as 'labels'. Integers work. You may need to dig into thesklearndocs to learn what constitutes valid learning data.
– hpaulj
yesterday
Classifier != regressor.
– Matthieu Brucher
yesterday
@hpaulj Ah that makes sense. Converting to strings for equality would be more appropriate for classes
– Ben
6 hours ago
add a comment |
up vote
0
down vote
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up vote
0
down vote
favorite
X = np.array(...,dtype=np.float64).astype(np.float)
y = np.array(...,dtype=np.float64).astype(np.float)
print(X)
[[-0.5 0. ]
[ 0. 0. ]
[ 0. 0. ]
[ 0. 0. ]
[ 0. -3.5]
[-3.5 -3.5]
[-3.5 -2. ]
[-2. 0. ]
[ 0. 0. ]
[ 0. -3. ]]
print(y)
[-3.5 -3.5 -3.5 -3.5 -3.5 -2. -3. -3. -3. 1. ]
(10, 2)
(10,)
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(X, Y)
No matter what I do I get
Traceback (most recent call last):
File "scripts/wave-pool.py", line 174, in <module>
clf.fit(X, y)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/naive_bayes.py", line 192, in fit
sample_weight=sample_weight)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/naive_bayes.py", line 355, in _partial_fit
if _check_partial_fit_first_call(self, classes):
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/utils/multiclass.py", line 320, in _check_partial_fit_first_call
clf.classes_ = unique_labels(classes)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/utils/multiclass.py", line 96, in unique_labels
raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([-3.5, -3. , -2. , 1. ]),)
numpy scikit-learn
asked yesterday
Ben
7,55721014
X = np.array(...,dtype=np.float64).astype(np.float)
y = np.array(...,dtype=np.float64).astype(np.float)
print(X)
[[-0.5 0. ]
[ 0. 0. ]
[ 0. 0. ]
[ 0. 0. ]
[ 0. -3.5]
[-3.5 -3.5]
[-3.5 -2. ]
[-2. 0. ]
[ 0. 0. ]
[ 0. -3. ]]
print(y)
[-3.5 -3.5 -3.5 -3.5 -3.5 -2. -3. -3. -3. 1. ]
(10, 2)
(10,)
from sklearn.naive_bayes import GaussianNB
clf = GaussianNB()
clf.fit(X, Y)
No matter what I do I get
Traceback (most recent call last):
File "scripts/wave-pool.py", line 174, in <module>
clf.fit(X, y)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/naive_bayes.py", line 192, in fit
sample_weight=sample_weight)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/naive_bayes.py", line 355, in _partial_fit
if _check_partial_fit_first_call(self, classes):
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/utils/multiclass.py", line 320, in _check_partial_fit_first_call
clf.classes_ = unique_labels(classes)
File "/home/ben/.local/lib/python2.7/site-packages/sklearn/utils/multiclass.py", line 96, in unique_labels
raise ValueError("Unknown label type: %s" % repr(ys))
ValueError: Unknown label type: (array([-3.5, -3. , -2. , 1. ]),)
numpy scikit-learn
numpy scikit-learn
asked yesterday
Ben
7,55721014
asked yesterday
Ben
7,55721014
asked yesterday
Ben
7,55721014
asked yesterday
Ben
7,55721014
asked yesterday
Ben
7,55721014
7,55721014
1
Apparently it doesn't like using floats as 'labels'. Integers work. You may need to dig into thesklearndocs to learn what constitutes valid learning data.
– hpaulj
yesterday
Classifier != regressor.
– Matthieu Brucher
yesterday
@hpaulj Ah that makes sense. Converting to strings for equality would be more appropriate for classes
– Ben
6 hours ago
add a comment |
1
Apparently it doesn't like using floats as 'labels'. Integers work. You may need to dig into thesklearndocs to learn what constitutes valid learning data.
– hpaulj
yesterday
Classifier != regressor.
– Matthieu Brucher
yesterday
@hpaulj Ah that makes sense. Converting to strings for equality would be more appropriate for classes
– Ben
6 hours ago
1
1
Apparently it doesn't like using floats as 'labels'. Integers work. You may need to dig into the
sklearn docs to learn what constitutes valid learning data.– hpaulj
yesterday
Apparently it doesn't like using floats as 'labels'. Integers work. You may need to dig into the
sklearn docs to learn what constitutes valid learning data.– hpaulj
yesterday
Classifier != regressor.
– Matthieu Brucher
yesterday
Classifier != regressor.
– Matthieu Brucher
yesterday
@hpaulj Ah that makes sense. Converting to strings for equality would be more appropriate for classes
– Ben
6 hours ago
@hpaulj Ah that makes sense. Converting to strings for equality would be more appropriate for classes
– Ben
6 hours ago
add a comment |
1 Answer
1
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oldest
votes
up vote
0
down vote
"doesn't like using floats as 'labels'. Integers work" hpaulj
answered 3 hours ago
Ben
7,55721014
add a comment |
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
0
down vote
"doesn't like using floats as 'labels'. Integers work" hpaulj
answered 3 hours ago
Ben
7,55721014
add a comment |
up vote
0
down vote
"doesn't like using floats as 'labels'. Integers work" hpaulj
answered 3 hours ago
Ben
7,55721014
add a comment |
up vote
0
down vote
up vote
0
down vote
"doesn't like using floats as 'labels'. Integers work" hpaulj
answered 3 hours ago
Ben
7,55721014
"doesn't like using floats as 'labels'. Integers work" hpaulj
answered 3 hours ago
Ben
7,55721014
answered 3 hours ago
Ben
7,55721014
answered 3 hours ago
Ben
7,55721014
answered 3 hours ago
Ben
7,55721014
7,55721014
add a comment |
add a comment |
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1
Apparently it doesn't like using floats as 'labels'. Integers work. You may need to dig into the
sklearndocs to learn what constitutes valid learning data.– hpaulj
yesterday
Classifier != regressor.
– Matthieu Brucher
yesterday
@hpaulj Ah that makes sense. Converting to strings for equality would be more appropriate for classes
– Ben
6 hours ago