projektAI/venv/Lib/site-packages/sklearn/linear_model/tests/test_perceptron.py

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2021-06-06 22:13:05 +02:00
import numpy as np
import scipy.sparse as sp
from sklearn.utils._testing import assert_allclose
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_raises
from sklearn.utils import check_random_state
from sklearn.datasets import load_iris
from sklearn.linear_model import Perceptron
iris = load_iris()
random_state = check_random_state(12)
indices = np.arange(iris.data.shape[0])
random_state.shuffle(indices)
X = iris.data[indices]
y = iris.target[indices]
X_csr = sp.csr_matrix(X)
X_csr.sort_indices()
class MyPerceptron:
def __init__(self, n_iter=1):
self.n_iter = n_iter
def fit(self, X, y):
n_samples, n_features = X.shape
self.w = np.zeros(n_features, dtype=np.float64)
self.b = 0.0
for t in range(self.n_iter):
for i in range(n_samples):
if self.predict(X[i])[0] != y[i]:
self.w += y[i] * X[i]
self.b += y[i]
def project(self, X):
return np.dot(X, self.w) + self.b
def predict(self, X):
X = np.atleast_2d(X)
return np.sign(self.project(X))
def test_perceptron_accuracy():
for data in (X, X_csr):
clf = Perceptron(max_iter=100, tol=None, shuffle=False)
clf.fit(data, y)
score = clf.score(data, y)
assert score > 0.7
def test_perceptron_correctness():
y_bin = y.copy()
y_bin[y != 1] = -1
clf1 = MyPerceptron(n_iter=2)
clf1.fit(X, y_bin)
clf2 = Perceptron(max_iter=2, shuffle=False, tol=None)
clf2.fit(X, y_bin)
assert_array_almost_equal(clf1.w, clf2.coef_.ravel())
def test_undefined_methods():
clf = Perceptron(max_iter=100)
for meth in ("predict_proba", "predict_log_proba"):
assert_raises(AttributeError, lambda x: getattr(clf, x), meth)
def test_perceptron_l1_ratio():
"""Check that `l1_ratio` has an impact when `penalty='elasticnet'`"""
clf1 = Perceptron(l1_ratio=0, penalty='elasticnet')
clf1.fit(X, y)
clf2 = Perceptron(l1_ratio=0.15, penalty='elasticnet')
clf2.fit(X, y)
assert clf1.score(X, y) != clf2.score(X, y)
# check that the bounds of elastic net which should correspond to an l1 or
# l2 penalty depending of `l1_ratio` value.
clf_l1 = Perceptron(penalty='l1').fit(X, y)
clf_elasticnet = Perceptron(l1_ratio=1, penalty='elasticnet').fit(X, y)
assert_allclose(clf_l1.coef_, clf_elasticnet.coef_)
clf_l2 = Perceptron(penalty='l2').fit(X, y)
clf_elasticnet = Perceptron(l1_ratio=0, penalty='elasticnet').fit(X, y)
assert_allclose(clf_l2.coef_, clf_elasticnet.coef_)