def rfecv_quick_method(image="rfecv_quick_method.png"):
X, y = load_credit()
_, ax = plt.subplots()
cv = StratifiedKFold(5)
visualizer = rfecv(RandomForestClassifier(), X=X, y=y, ax=ax, cv=cv, scoring='f1_weighted')
visualizer.show(outpath=os.path.join(IMAGES, image))
def rfecv_credit_example(image="rfecv_credit.png"):
X, y = load_credit()
_, ax = plt.subplots()
cv = StratifiedKFold(5)
oz = RFECV(RandomForestClassifier(), ax=ax, cv=cv, scoring="f1_weighted")
oz.fit(X, y)
oz.poof(outpath=os.path.join(IMAGES, image))
def test_rank1d_quick_method(self):
"""
Test Rank1d quick method
"""
X, y = load_credit()
viz = rank1d(X, y, show=False)
assert isinstance(viz, Rank1D)
self.assert_images_similar(viz, tol=0.1)
def rfecv():
X, y = load_credit()
model = RandomForestClassifier(n_estimators=10)
oz = RFECV(model, cv=3, scoring="f1_weighted", ax=newfig())
oz.fit(X, y)
savefig(oz, "rfecv_sklearn_example")
def pca():
X, y = load_credit()
colors = np.array(["r" if yi else "b" for yi in y])
oz = PCA(scale=True, color=colors, proj_dim=3)
oz.fit_transform(X, y)
savefig(oz, "pca_projection_3d")
def rank2d():
X, y = load_credit()
oz = Rank2D(algorithm="covariance", ax=newfig())
oz.fit_transform(X, y)
savefig(oz, "rank2d_covariance")
def rank1d():
X, y = load_credit()
oz = Rank1D(algorithm="shapiro", ax=newfig())
oz.fit_transform(X, y)
savefig(oz, "rank1d_shapiro")
