def test_target_explained_variance(self):
np.random.seed(0)
clf = PCA()
clf.fit(self.X)
ax = skplt.plot_pca_component_variance(clf, target_explained_variance=0)
ax = skplt.plot_pca_component_variance(clf, target_explained_variance=0.5)
ax = skplt.plot_pca_component_variance(clf, target_explained_variance=1)
ax = skplt.plot_pca_component_variance(clf, target_explained_variance=1.5)
def test_target_explained_variance(self):
np.random.seed(0)
clf = PCA()
clf.fit(self.X)
ax = skplt.plot_pca_component_variance(clf, target_explained_variance=0)
ax = skplt.plot_pca_component_variance(clf, target_explained_variance=0.5)
ax = skplt.plot_pca_component_variance(clf, target_explained_variance=1)
ax = skplt.plot_pca_component_variance(clf, target_explained_variance=1.5)
def test_ax(self):
np.random.seed(0)
clf = PCA()
clf.fit(self.X)
fig, ax = plt.subplots(1, 1)
out_ax = skplt.plot_pca_component_variance(clf)
assert ax is not out_ax
out_ax =skplt.plot_pca_component_variance(clf, ax=ax)
assert ax is out_ax
def test_ax(self):
np.random.seed(0)
clf = PCA()
clf.fit(self.X)
fig, ax = plt.subplots(1, 1)
out_ax = skplt.plot_pca_component_variance(clf)
assert ax is not out_ax
out_ax = skplt.plot_pca_component_variance(clf, ax=ax)
assert ax is out_ax
rf = rf.fit(X, y)
skplt.plot_feature_importances(rf, feature_names=['petal length', 'petal width',
'sepal length', 'sepal width'])
plt.show()
#kjøre PCA med 50 variable og se hvor mye dimensjonene forklarer varians
from sklearn.decomposition import PCA
from sklearn.datasets import load_digits as load_data
import scikitplot.plotters as skplt
import matplotlib.pyplot as plt
pca = decomposition.PCA()
pca.fit(X)
skplt.plot_pca_component_variance(pca)
plt.show()
# Plot the PCA spectrum
pca.fit(X)
plt.figure(1, figsize=(4, 3))
plt.clf()
plt.axes([.2, .2, .7, .7])
plt.plot(pca.explained_variance_, linewidth=2)
plt.axis('tight')
plt.xlabel('n_components')
plt.ylabel('explained_variance_')
###############################################################################
# Prediction
"""An example showing the plot_pca_component_variance method used by a scikit-learn PCA object""" from sklearn.decomposition import PCA from sklearn.datasets import load_digits as load_data import scikitplot.plotters as skplt import matplotlib.pyplot as plt X, y = load_data(return_X_y=True) pca = PCA(random_state=1) pca.fit(X) skplt.plot_pca_component_variance(pca) plt.show()