Unable to convert array of bytes/strings into decimal numbers with dtype='numeric'

Package:

scikit-learn

47032

Exception Class:

ValueError

Raise code

"0.24 and will be removed in 1.1 (renaming of 0.26). Please "
                "convert your data to numeric values explicitly instead.",
                FutureWarning, stacklevel=2
            )
            try:
                array = array.astype(np.float64)
            except ValueError as e:
                raise ValueError(
                    "Unable to convert array of bytes/strings "
                    "into decimal numbers with dtype='numeric'") from e
        if not allow_nd and array.ndim >= 3:
            raise ValueError("Found array with dim %d. %s expected <= 2."
                             % (array.ndim, estimator_name))

        if force_all_finite:
            _ass

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Links to the raise (1)

https://github.com/scikit-learn/scikit-learn/blob/c67518350f91072f9d37ed09c5ef7edf555b6cf6/sklearn/utils/validation.py#L709

Ways to fix

Error code:

from sklearn.utils.validation import check_array
import numpy as np 

X = np.array([('str','res')])
array = check_array(X)
print(array)

check_array - Input validation on an array, list, sparse matrix, or similar.

By default, the input is checked to be a non-empty 2D array containing only finite values.

If the dtype of the array is an object, attempt converting to float, raising on failure.

Fix code:

from sklearn.utils.validation import check_array
import numpy as np 

X = np.array([(3,6)])
array = check_array(X)
print(array)

Note: scikit-learn version is 0.24.2

Sep 18, 2021

anonim 13.0k

sc_X = StandardScaler()

X2_train = sc_X.fit_transform(X_train)

X2_test = sc_X.fit_transform(X_test)

y2_train = y_train

y2_test = y_test

y3 = y

X3 = df.drop(['GDP ($ per capita)','Country','Population', 'Area (sq. mi.)', 'Coastline (coast/area ratio)', 'Arable (%)',

'Crops (%)', 'Other (%)', 'Climate', 'Deathrate', 'Industry'], axis=1)

X3_train, X3_test, y3_train, y3_test = train_test_split(X3, y3, test_size=0.2, random_state=101)

lm1 = LinearRegression()

lm1.fit(X_train,y_train)

lm2 = LinearRegression()

lm2.fit(X2_train,y2_train)

lm3 = LinearRegression()

lm3.fit(X3_train,y3_train)

Apr 19, 2022

hamad264khan 1.5k

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