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Я запустил этот код

 import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.covariance import OAS


n_train = 20  # samples for training
n_test = 200  # samples for testing
n_averages = 50  # how often to repeat classification
n_features_max = 75  # maximum number of features
step = 4  # step size for the calculation


def generate_data(n_samples, n_features):
    """Generate random blob-ish data with noisy features.

    This returns an array of input data with shape `(n_samples, n_features)`
    and an array of `n_samples` target labels.

    Only one feature contains discriminative information, the other features
    contain only noise.
    """
    X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])

    # add non-discriminative features
    if n_features > 1:
        X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])
    return X, y


acc_clf1, acc_clf2, acc_clf3 = [], [], []
n_features_range = range(1, n_features_max   1, step)
for n_features in n_features_range:
    score_clf1, score_clf2, score_clf3 = 0, 0, 0
    for _ in range(n_averages):
        X, y = generate_data(n_train, n_features)

        clf1 = LinearDiscriminantAnalysis(solver='lsqr',
                                          shrinkage='auto').fit(X, y)
        clf2 = LinearDiscriminantAnalysis(solver='lsqr',
                                          shrinkage=None).fit(X, y)
        oa = OAS(store_precision=False, assume_centered=False)
        clf3 = LinearDiscriminantAnalysis(solver='lsqr',
                                          covariance_estimator=oa).fit(X, y)

        X, y = generate_data(n_test, n_features)
        score_clf1  = clf1.score(X, y)
        score_clf2  = clf2.score(X, y)
        score_clf3  = clf3.score(X, y)

    acc_clf1.append(score_clf1 / n_averages)
    acc_clf2.append(score_clf2 / n_averages)
    acc_clf3.append(score_clf3 / n_averages)

features_samples_ratio = np.array(n_features_range) / n_train

plt.plot(features_samples_ratio, acc_clf1, linewidth=2,
         label="Linear Discriminant Analysis with Ledoit Wolf", color='navy')
plt.plot(features_samples_ratio, acc_clf2, linewidth=2,
         label="Linear Discriminant Analysis", color='gold')
plt.plot(features_samples_ratio, acc_clf3, linewidth=2,
         label="Linear Discriminant Analysis with OAS", color='red')

plt.xlabel('n_features / n_samples')
plt.ylabel('Classification accuracy')

plt.legend(loc=3, prop={'size': 12})
plt.suptitle('Linear Discriminant Analysis vs. '   'n'
               'Shrinkage Linear Discriminant Analysis vs. '   'n'
               'OAS Linear Discriminant Analysis (1 discriminative feature)')
plt.show()
 

вот пример из sklearn

и у меня есть эта ошибка:

 TypeError: __init__() got an unexpected keyword argument 'covariance_estimator'
 

из этой строки

 clf3 = LinearDiscriminantAnalysis(solver='lsqr',
                                  covariance_estimator=oa).fit(X, y)
 

Мне это кажется синтаксически правильным. Итак, я не уверен, что пошло не так, или для этого аргумента могут быть зависимости от версий(?).

Любые идеи будут оценены по достоинству.

Скорее всего, проблема связана с версией python или версией sklearn. Этот пример отлично подходит для меня.

 /usr/bin/python3.9
Python 3.9.5 (default, May 19 2021, 11:32:47) 
[GCC 9.3.0] on linux
 

 ls -d /usr/local/lib/python3.9/dist-packages/{scikit,sklearn}*dist-info
/usr/local/lib/python3.9/dist-packages/scikit_learn-1.0.dist-info
/usr/local/lib/python3.9/dist-packages/sklearn-0.0.dist-info
 

 #!/usr/bin/python3.9
import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import make_blobs
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.covariance import OAS


n_train = 20  # samples for training
n_test = 200  # samples for testing
n_averages = 50  # how often to repeat classification
n_features_max = 75  # maximum number of features
step = 4  # step size for the calculation


def generate_data(n_samples, n_features):
    """Generate random blob-ish data with noisy features.

    This returns an array of input data with shape `(n_samples, n_features)`
    and an array of `n_samples` target labels.

    Only one feature contains discriminative information, the other features
    contain only noise.
    """
    X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])

    # add non-discriminative features
    if n_features > 1:
        X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])
    return X, y


acc_clf1, acc_clf2, acc_clf3 = [], [], []
n_features_range = range(1, n_features_max   1, step)
for n_features in n_features_range:
    score_clf1, score_clf2, score_clf3 = 0, 0, 0
    for _ in range(n_averages):
        X, y = generate_data(n_train, n_features)

        clf1 = LinearDiscriminantAnalysis(solver='lsqr',
                                          shrinkage='auto').fit(X, y)
        clf2 = LinearDiscriminantAnalysis(solver='lsqr',
                                          shrinkage=None).fit(X, y)
        oa = OAS(store_precision=False, assume_centered=False)
        clf3 = LinearDiscriminantAnalysis(solver='lsqr',
                                          covariance_estimator=oa).fit(X, y)

        X, y = generate_data(n_test, n_features)
        score_clf1  = clf1.score(X, y)
        score_clf2  = clf2.score(X, y)
        score_clf3  = clf3.score(X, y)

    acc_clf1.append(score_clf1 / n_averages)
    acc_clf2.append(score_clf2 / n_averages)
    acc_clf3.append(score_clf3 / n_averages)

features_samples_ratio = np.array(n_features_range) / n_train

plt.plot(features_samples_ratio, acc_clf1, linewidth=2,
         label="Linear Discriminant Analysis with Ledoit Wolf", color='navy')
plt.plot(features_samples_ratio, acc_clf2, linewidth=2,
         label="Linear Discriminant Analysis", color='gold')
plt.plot(features_samples_ratio, acc_clf3, linewidth=2,
         label="Linear Discriminant Analysis with OAS", color='red')

plt.xlabel('n_features / n_samples')
plt.ylabel('Classification accuracy')

plt.legend(loc=3, prop={'size': 12})
plt.suptitle('Linear Discriminant Analysis vs. '   'n'
               'Shrinkage Linear Discriminant Analysis vs. '   'n'
               'OAS Linear Discriminant Analysis (1 discriminative feature)')
plt.show()
 

Результат сценария:

https://i.stack.imgur.com/L6WeG.png