CODEWITHSUNDEEP
pythonmachine-learningscikit-learnrandom-forest
Marco Giuseppe de Pinto
Marco Giuseppe de Pinto

Reputation: 605

ValueError while fitting Decision Tree Classifier on a dataset

I have created features X and labels y for the dataset I am working on.

At this point, I want to train a random forest classifier on it but I am facing a ValueError while fitting the classifier on the training data: setting an array element with a sequence.

Below the X and y features and the error details:

X:

(array([-8.1530527e-10,  8.9952795e-10, -9.1185753e-10, ...,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([-0.00050612, -0.00057967, -0.00035985, ...,  0.        ,
         0.        ,  0.        ], dtype=float32),
 array([ 6.8139506e-08, -2.3837963e-05, -2.4622474e-05, ...,
         3.1678758e-06, -2.4535689e-06,  0.0000000e+00], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
         6.9306935e-07, -6.6020442e-07,  0.0000000e+00], dtype=float32),
 array([-7.30260945e-05, -1.18022966e-04, -1.08280736e-04, ...,
         8.83421380e-05,  4.97258679e-06,  0.00000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([ 2.3406714e-05,  3.1186773e-05,  4.9467826e-06, ...,
         1.2180173e-07, -9.2944845e-08,  0.0000000e+00], dtype=float32),
 array([ 1.1845550e-06, -1.6399191e-06,  2.5565218e-06, ...,
        -8.7445065e-09,  5.9859917e-09,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([-1.3284328e-05, -7.4090644e-07,  7.2679302e-07, ...,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
         5.0694009e-08, -3.4546797e-08,  0.0000000e+00], dtype=float32),
 array([ 1.5591205e-07, -1.5845627e-07,  1.5362870e-07, ...,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.1608539e-05,
        8.2463991e-09, 0.0000000e+00], dtype=float32),
 array([-3.6192148e-07, -1.4590451e-05, -5.3999561e-06, ...,
        -1.9935460e-05, -3.4417746e-05,  0.0000000e+00], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
        -2.5319534e-07,  2.6521766e-07,  0.0000000e+00], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
        -2.5055220e-08,  1.2936166e-08,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([ 1.3387315e-05,  6.0913658e-07, -5.6471418e-07, ...,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
 array([ 1.7200684e-02,  3.2272514e-02,  3.2961801e-02, ...,
        -1.6286784e-06, -8.5592075e-07,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
        -3.3923173e-11,  2.8026699e-11,  0.0000000e+00], dtype=float32),
 array([-0.00103188, -0.00075814, -0.00051426, ...,  0.        ,
         0.        ,  0.        ], dtype=float32),
 array([ 7.6278877e-07,  2.1624428e-05,  1.1150542e-05, ...,
         1.8263392e-09, -1.5558380e-09,  0.0000000e+00], dtype=float32),
 array([-1.2111740e-07,  6.3130176e-07, -1.8378003e-06, ...,
         1.1309878e-05,  5.4562256e-06,  0.0000000e+00], dtype=float32),
 array([0.00026949, 0.00028119, 0.00020081, ..., 0.00032586, 0.00046612,
        0.        ], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
        -7.8796054e-09,  1.7431153e-08,  0.0000000e+00], dtype=float32),
 array([1.42000988e-06, 1.30781755e-05, 2.77493709e-05, ...,
        0.00000000e+00, 0.00000000e+00, 0.00000000e+00], dtype=float32),
 array([ 2.9161662e-10, -6.3629275e-11, -3.0565092e-10, ...,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
 array([ 2.2051008e-05,  1.6838792e-05,  3.5639907e-05, ...,
         4.5767497e-06, -1.2002213e-05,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
        -2.0104826e-10,  1.6824393e-10,  0.0000000e+00], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
        -4.8303300e-06, -1.2008861e-05,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
        -2.7673337e-07,  2.8604177e-07,  0.0000000e+00], dtype=float32),
 array([-0.00066044, -0.0009837 , -0.00090796, ..., -0.00171516,
        -0.0017666 ,  0.        ], dtype=float32),
 array([ 3.2218946e-11, -5.5296181e-11,  8.9530647e-11, ...,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([-1.3284328e-05, -7.4090644e-07,  7.2679302e-07, ...,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
 array([ 4.9886359e-05,  1.4642075e-04,  4.4365996e-04, ...,
         6.3584002e-07, -6.2395281e-07,  0.0000000e+00], dtype=float32),
 array([-3.2826196e-04,  4.5522624e-03, -8.2306744e-04, ...,
        -2.2519816e-07, -6.2417300e-08,  0.0000000e+00], dtype=float32),
 array([ 3.1686827e-04,  4.6282235e-04,  1.0160641e-04, ...,
        -1.4605960e-05,  6.6572487e-05,  0.0000000e+00], dtype=float32),
 array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,
        -7.1763244e-09, -2.8297892e-08,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([-2.5870585e-07,  4.6514080e-07, -9.5607948e-07, ...,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
 array([ 5.788035e-07, -6.493598e-07,  7.111379e-07, ...,  0.000000e+00,
         0.000000e+00,  0.000000e+00], dtype=float32),
 array([ 2.5118000e-04,  1.4220485e-03,  3.9536849e-04, ...,
         4.5242754e-04, -3.1405249e-05,  0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([ 1.1985266e-07,  2.1360799e-07, -1.1951373e-06, ...,
        -1.3043609e-04,  1.2107374e-06,  0.0000000e+00], dtype=float32),
 array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 2.5944988e-08,
        1.2123945e-07, 0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),
 array([-2.4280996e-06, -1.2362683e-05, -8.5034850e-07, ...,
        -1.0113516e-11,  5.1403621e-12,  0.0000000e+00], dtype=float32),
 array([9.6098862e-05, 1.6449913e-04, 1.1942573e-04, ..., 0.0000000e+00,
        0.0000000e+00, 0.0000000e+00], dtype=float32),
 array([ 1.3284328e-05,  7.4090644e-07, -7.2679302e-07, ...,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype=float32),
 array([ 2.4700081e-05,  2.9454704e-05,  8.0751715e-06, ...,
         1.2746801e-07, -1.6574201e-06,  0.0000000e+00], dtype=float32),
 array([8.4619669e-06, 9.7476968e-06, 2.0182479e-05, ..., 2.1081217e-11,
        4.0220186e-10, 0.0000000e+00], dtype=float32),
 array([0., 0., 0., ..., 0., 0., 0.], dtype=float32))

y below

('08',
 '08',
 '06',
 '05',
 '05',
 '04',
 '06',
 '07',
 '01',
 '04',
 '03',
 '07',
 '03',
 '01',
 '03',
 '03',
 '02',
 '02',
 '02',
 '02',
 '05',
 '06',
 '04',
 '08',
 '07',
 '06',
 '04',
 '05',
 '07',
 '02',
 '08',
 '01',
 '08',
 '03',
 '08',
 '02',
 '03',
 '06',
 '04',
 '07',
 '04',
 '07',
 '05',
 '06',
 '08',
 '08',
 '04',
 '05',
 '05',
 '04',
 '06',
 '07',
 '05',
 '07',
 '01',
 '06',
 '02',
 '02',
 '03',
 '03')

Code for the classifier plus the train/test split:

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

from sklearn.tree import DecisionTreeClassifier
dtree = DecisionTreeClassifier()
dtree.fit(X_train, y_train)

Error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-70-b6417fbfb8de> in <module>()
      1 from sklearn.tree import DecisionTreeClassifier
      2 dtree = DecisionTreeClassifier()
----> 3 dtree.fit(X_train, y_train)

/usr/local/lib/python3.6/dist-packages/sklearn/tree/tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    788             sample_weight=sample_weight,
    789             check_input=check_input,
--> 790             X_idx_sorted=X_idx_sorted)
    791         return self
    792 

/usr/local/lib/python3.6/dist-packages/sklearn/tree/tree.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
    114         random_state = check_random_state(self.random_state)
    115         if check_input:
--> 116             X = check_array(X, dtype=DTYPE, accept_sparse="csc")
    117             y = check_array(y, ensure_2d=False, dtype=None)
    118             if issparse(X):

/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    431                                       force_all_finite)
    432     else:
--> 433         array = np.array(array, dtype=dtype, order=order, copy=copy)
    434 
    435         if ensure_2d:

ValueError: setting an array element with a sequence.

EDIT1: I converted both X and y into numpy arrays but the error I am receiving is the same, details below

import numpy as np
X = np.asarray(X)
y = np.asarray(y)


X.shape, y.shape

Output:

((60,), (60,))

Upvotes: 2

Views: 2540

Answers (1)

Luca Massaron
Luca Massaron

Reputation: 1809

It appears that the problem is your X. Probably one of the arrays constituting it has a different length, that causes the tuple that you have build, and that is transformed into a Numpy array by Scikit-learn when processed by the DecisionTreeClassifier, to transform into a vector of strings, which are not what the decision tree function expects to process.

Just check this code snippet:

X1 = (array([-8.1530527e-10,  8.9952795e-10, -9.1185753e-10,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype='float32'),
 array([0., 0., 0., 0., 0., 0.], dtype='float32'),
 array([0., 0., 0., 0., 0., 0.], dtype='float32'))

X2 = (array([-8.1530527e-10,  8.9952795e-10, -9.1185753e-10,
         0.0000000e+00,  0.0000000e+00,  0.0000000e+00], dtype='float32'),
 array([0., 0., 0., 0., 0., 0., 1], dtype='float32'),
 array([0., 0., 0., 0., 0., 0.], dtype='float32'))

print("X1:", np.array(X1).dtype, "\nX2:", np.array(X2).dtype)

By just changing the second element of X2 with the addition of a further number causes the X2 array to turn into a string array (object type).

Upvotes: 1

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