Feature range parameter support - Unit Tests

[CodeBeaverAI]

I started working from Feature range parameter support.

👋 I'm an AI agent who writes, runs, and maintains Unit Tests. I even highlight the bugs I spot! I'm free for open-source repos.
🔄 8 test files added and 12 test files updated to reflect recent changes.
🐛 Found 7 bugs
🛠️ 503/641 tests passed

🔄 Test Updates

I've added or updated 13 tests. They all pass ☑️
Updated Tests:

• mlxtend/frequent_patterns/tests/test_association_rules.py 🩹

  Fixed: mlxtend/frequent_patterns/tests/test_association_rules.py::test_default

• mlxtend/evaluate/tests/test_bias_variance_decomp.py 🩹

  Fixed: mlxtend/evaluate/tests/test_bias_variance_decomp.py::test_01_loss_bagging

• mlxtend/evaluate/tests/test_bias_variance_decomp.py 🩹

  Fixed: mlxtend/evaluate/tests/test_bias_variance_decomp.py::test_mse_bagging

• mlxtend/evaluate/tests/test_bias_variance_decomp.py 🩹

  Fixed: mlxtend/evaluate/tests/test_bias_variance_decomp.py::test_keras

• mlxtend/feature_selection/tests/test_column_selector.py 🩹

  Fixed: mlxtend/feature_selection/tests/test_column_selector.py::test_ColumnSelector_with_dataframe

• mlxtend/feature_selection/tests/test_column_selector.py 🩹

  Fixed: mlxtend/feature_selection/tests/test_column_selector.py::test_ColumnSelector_with_dataframe_and_int_columns

• mlxtend/feature_selection/tests/test_column_selector.py 🩹

  Fixed: mlxtend/feature_selection/tests/test_column_selector.py::test_ColumnSelector_with_dataframe_drop_axis

• mlxtend/feature_selection/tests/test_column_selector.py 🩹

  Fixed: mlxtend/feature_selection/tests/test_column_selector.py::test_ColumnSelector_with_dataframe_in_gridsearch

• mlxtend/preprocessing/tests/test_transactionencoder.py 🩹

  Fixed: mlxtend/preprocessing/tests/test_transactionencoder.py::test_inverse_transform

• mlxtend/classifier/tests/test_logistic_regression.py 🩹

  Fixed: mlxtend/classifier/tests/test_logistic_regression.py::test_invalid_labels_1

• mlxtend/classifier/tests/test_perceptron.py 🩹

  Fixed: mlxtend/classifier/tests/test_perceptron.py::test_invalid_labels_1

• mlxtend/classifier/tests/test_adaline.py 🩹

  Fixed: mlxtend/classifier/tests/test_adaline.py::test_invalid_labels_1

New Tests:

• mlxtend/_base/tests/test_base_model.py

🐛 Bug Detection

Potential issues found in the following files:

• mlxtend/classifier/multilayerperceptron.py

  The error message shows that np.float_ was removed in NumPy 2.0 and should be replaced with np.float64. The MLxtend code uses np.float_ inside the _fit method (when calling _one_hot), which causes the error regardless of the tests. This error is not due to the way the tests are written or the test settings; rather, it is due to the use of a deprecated/removed NumPy alias in the implementation code of MultiLayerPerceptron.

To fix the error, the dtype argument should be updated from np.float_ to np.float64 (or another appropriate float type). This change in the code is the necessary correction.
Thus, the error was caused by a bug in the code being tested.

Test Error Log

``` mlxtend/classifier/tests/test_multilayerperceptron.py::test_multiclass_gd_acc: def test_multiclass_gd_acc(): mlp = MLP(epochs=20, eta=0.05, hidden_layers=[10], minibatches=1, random_seed=1) > mlp.fit(X, y) mlxtend/classifier/tests/test_multilayerperceptron.py:28: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ mlxtend/_base/_classifier.py:81: in fit self._fit(X=X, y=y, init_params=init_params) mlxtend/classifier/multilayerperceptron.py:147: in _fit y_enc = self._one_hot(y=y, n_labels=self.n_classes, dtype=np.float_) _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ attr = 'float_' def __getattr__(attr): # Warn for expired attributes import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_progress_1: def test_progress_1():
mlp = MLP(
epochs=1,
eta=0.05,
hidden_layers=[10],
minibatches=1,
print_progress=1,
random_seed=1,
)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:43:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_progress_2: def test_progress_2():
mlp = MLP(
epochs=1,
eta=0.05,
hidden_layers=[10],
minibatches=1,
print_progress=2,
random_seed=1,
)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:55:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_progress_3: def test_progress_3():
mlp = MLP(
epochs=1,
eta=0.05,
hidden_layers=[10],
minibatches=1,
print_progress=3,
random_seed=1,
)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:67:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_predict_proba: def test_predict_proba():
mlp = MLP(epochs=20, eta=0.05, hidden_layers=[10], minibatches=1, random_seed=1)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:72:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_multiclass_sgd_acc: def test_multiclass_sgd_acc():
mlp = MLP(
epochs=20, eta=0.05, hidden_layers=[25], minibatches=len(y), random_seed=1
)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:83:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_multiclass_minibatch_acc: def test_multiclass_minibatch_acc():
mlp = MLP(epochs=20, eta=0.05, hidden_layers=[25], minibatches=5, random_seed=1)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:90:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_binary_gd: def test_binary_gd():
mlp = MLP(epochs=20, eta=0.05, hidden_layers=[25], minibatches=5, random_seed=1)

  mlp.fit(X_bin, y_bin)

mlxtend/classifier/tests/test_multilayerperceptron.py:109:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_score_function: def test_score_function():
mlp = MLP(epochs=20, eta=0.05, hidden_layers=[25], minibatches=5, random_seed=1)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:115:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_decay_function: def test_decay_function():
mlp = MLP(
epochs=20,
eta=0.05,
decrease_const=0.01,
hidden_layers=[25],
minibatches=5,
random_seed=1,
)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:130:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_momentum_1: def test_momentum_1():
mlp = MLP(
epochs=20,
eta=0.05,
momentum=0.1,
hidden_layers=[25],
minibatches=len(y),
random_seed=1,
)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:146:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_multilayerperceptron.py::test_retrain: def test_retrain():
mlp = MLP(epochs=5, eta=0.05, hidden_layers=[10], minibatches=len(y), random_seed=1)

  mlp.fit(X, y)

mlxtend/classifier/tests/test_multilayerperceptron.py:154:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/multilayerperceptron.py:147: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError

</details>
- `mlxtend/classifier/softmax_regression.py`
  > The error originates from the fact that the source code uses “np.float_” as the dtype in the _one_hot call. In NumPy 2.0, np.float_ was removed (or redefined) and replaced by np.float64. This change in NumPy’s API causes an AttributeError when the code still references np.float_. The tests trigger this error when they call SoftmaxRegression.fit(), but the test itself is not erroneous; instead, it’s the underlying library code (the implementation of SoftmaxRegression) that is using the outdated attribute. Therefore, the error is due to a bug in the code being tested, which needs to be updated to use np.float64 instead of np.float_.
  <details><summary>Test Error Log</summary>

mlxtend/classifier/tests/test_softmax_regression.py::test_binary_logistic_regression_gd: def test_binary_logistic_regression_gd():
t = np.array([[0.13, -0.12], [-3.07, 3.05]])
lr = SoftmaxRegression(epochs=200, eta=0.005, minibatches=1, random_seed=1)

  lr.fit(X_bin, y_bin)

mlxtend/classifier/tests/test_softmax_regression.py:43:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/softmax_regression.py:145: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_softmax_regression.py::test_refit_weights: def test_refit_weights():
t = np.array([[0.13, -0.12], [-3.07, 3.05]])
lr = SoftmaxRegression(epochs=100, eta=0.005, minibatches=1, random_seed=1)

  lr.fit(X_bin, y_bin)

mlxtend/classifier/tests/test_softmax_regression.py:52:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/softmax_regression.py:145: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_softmax_regression.py::test_binary_logistic_regression_sgd: def test_binary_logistic_regression_sgd():
t = np.array([[0.13, -0.12], [-3.06, 3.05]])
lr = SoftmaxRegression(epochs=200, eta=0.005, minibatches=len(y_bin), random_seed=1)

  lr.fit(X_bin, y_bin)  # 0, 1 class

mlxtend/classifier/tests/test_softmax_regression.py:66:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/softmax_regression.py:145: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_softmax_regression.py::test_progress_1: def test_progress_1():
lr = SoftmaxRegression(
epochs=1, eta=0.005, minibatches=1, print_progress=1, random_seed=1
)

  lr.fit(X_bin, y_bin)  # 0, 1 class

mlxtend/classifier/tests/test_softmax_regression.py:76:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/softmax_regression.py:145: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib
        return ctypeslib
    elif attr == "exceptions":
        import numpy.exceptions as exceptions
        return exceptions
    elif attr == "testing":
        import numpy.testing as testing
        return testing
    elif attr == "matlib":
        import numpy.matlib as matlib
        return matlib
    elif attr == "f2py":
        import numpy.f2py as f2py
        return f2py
    elif attr == "typing":
        import numpy.typing as typing
        return typing
    elif attr == "rec":
        import numpy.rec as rec
        return rec
    elif attr == "char":
        import numpy.char as char
        return char
    elif attr == "array_api":
        raise AttributeError("`numpy.array_api` is not available from "
                             "numpy 2.0 onwards", name=None)
    elif attr == "core":
        import numpy.core as core
        return core
    elif attr == "strings":
        import numpy.strings as strings
        return strings
    elif attr == "distutils":
        if 'distutils' in __numpy_submodules__:
            import numpy.distutils as distutils
            return distutils
        else:
            raise AttributeError("`numpy.distutils` is not available from "
                                 "Python 3.12 onwards", name=None)

    if attr in __future_scalars__:
        # And future warnings for those that will change, but also give
        # the AttributeError
        warnings.warn(
            f"In the future `np.{attr}` will be defined as the "
            "corresponding NumPy scalar.", FutureWarning, stacklevel=2)

    if attr in __former_attrs__:
        raise AttributeError(__former_attrs__[attr], name=None)

    if attr in __expired_attributes__:

      raise AttributeError(

            f"`np.{attr}` was removed in the NumPy 2.0 release. "
            f"{__expired_attributes__[attr]}",
            name=None
        )

E AttributeError: np.float_ was removed in the NumPy 2.0 release. Use np.float64 instead.
/usr/local/lib/python3.11/site-packages/numpy/init.py:400: AttributeError
mlxtend/classifier/tests/test_softmax_regression.py::test_progress_2: def test_progress_2():
lr = SoftmaxRegression(
epochs=1, eta=0.005, minibatches=1, print_progress=2, random_seed=1
)

  lr.fit(X_bin, y_bin)  # 0, 1 class

mlxtend/classifier/tests/test_softmax_regression.py:84:

---

mlxtend/_base/_classifier.py:81: in fit
self._fit(X=X, y=y, init_params=init_params)
mlxtend/classifier/softmax_regression.py:145: in _fit
y_enc = self.one_hot(y=y, n_labels=self.n_classes, dtype=np.float)

---

attr = 'float_'
def getattr(attr):
# Warn for expired attributes
import warnings

    if attr == "linalg":
        import numpy.linalg as linalg
        return linalg
    elif attr == "fft":
        import numpy.fft as fft
        return fft
    elif attr == "dtypes":
        import numpy.dtypes as dtypes
        return dtypes
    elif attr == "random":
        import numpy.random as random
        return random
    elif attr == "polynomial":
        import numpy.polynomial as polynomial
        return polynomial
    elif attr == "ma":
        import numpy.ma as ma
        return ma
    elif attr == "ctypeslib":
        import numpy.ctypeslib as ctypeslib