votes up 1

invalid kernel: '(param0)'

Package:
Exception Class:
ValueError

Raise code

        # we're using clone() in the GenerativeBayes classifier,
        # so we can't do this kind of logic in __init__
        self._choose_algorithm(self.algorithm, self.metric)

        if bandwidth <= 0:
            raise ValueError("bandwidth must be positive")
        if kernel not in VALID_KERNELS:
            raise ValueError("invalid kernel: '{0}'".format(kernel))

    def _choose_algorithm(self, algorithm, metric):
        # given the algorithm string + metric string, choose the optimal
        # algorithm to compute the result.
        if algorithm == 'auto':
            # use KD Tree if possible
            if metric in KDTree.valid_metrics:
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Ways to fix

votes up 3 votes down

Invalid value of kernel was given. This happens if unsupported value of kernel is given or if there is any kind typo in the value.

Code to reproduce the exception:

from sklearn.neighbors import KernelDensity
import numpy as np
rng = np.random.RandomState(42)
X = rng.random_sample((100, 3))
kde = KernelDensity(kernel='gaussin', bandwidth=0.5).fit(X)
log_density = kde.score_samples(X[:3])
log_density

---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-5b276da75b3a> in <module>()  3 rng = np.random.RandomState(42)  4 X = rng.random_sample((100, 3)) ----> 5 kde = KernelDensity(kernel='gaussin', bandwidth=0.5).fit(X)  6 log_density = kde.score_samples(X[:3])  7 log_density 
/usr/local/lib/python3.7/dist-packages/sklearn/neighbors/_kde.py in __init__(self, bandwidth, algorithm, kernel, metric, atol, rtol, breadth_first, leaf_size, metric_params)  111 raise ValueError("bandwidth must be positive")  112 if kernel not in VALID_KERNELS: --> 113 raise ValueError("invalid kernel: '{0}'".format(kernel))  114   115 def _choose_algorithm(self, algorithm, metric): 
ValueError: invalid kernel: 'gaussin'

Fixed:

from sklearn.neighbors import KernelDensity
import numpy as np
rng = np.random.RandomState(42)
X = rng.random_sample((100, 3))
kde = KernelDensity(kernel='gaussian', bandwidth=0.5).fit(X)
log_density = kde.score_samples(X[:3])
log_density

array([-1.52955942, -1.51462041, -1.60244657])

Nov 05, 2021 kellemnegasi answer
kellemnegasi 29.0k

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