def test_plot_roc(self):
M, labels = uft.generate_correlated_test_matrix(1000)
M_train, M_test, labels_train, labels_test = train_test_split(
M, labels)
clf = RandomForestClassifier(random_state=0)
clf.fit(M_train, labels_train)
score = clf.predict_proba(M_test)[:, -1]
fig = comm.plot_roc(labels_test, score, verbose=False)
self.add_fig_to_report(fig, 'plot_roc')
def test_plot_roc(self):
M, labels = uft.generate_correlated_test_matrix(1000)
M_train, M_test, labels_train, labels_test = train_test_split(
M,
labels)
clf = RandomForestClassifier(random_state=0)
clf.fit(M_train, labels_train)
score = clf.predict_proba(M_test)[:,-1]
fig = dsp.plot_roc(labels_test, score, verbose=False)
self.add_fig_to_report(fig, 'plot_roc')
def test_get_roc_auc(self):
M, labels = uft.generate_correlated_test_matrix(1000)
M_train, M_test, labels_train, labels_test = train_test_split(
M, labels)
clf = RandomForestClassifier(random_state=0)
clf.fit(M_train, labels_train)
score = clf.predict_proba(M_test)[:, -1]
self.assertTrue(
np.allclose(comm.get_roc_auc(labels_test, score, verbose=False),
roc_auc_score(labels_test, score)))
def test_get_roc_auc(self):
M, labels = uft.generate_correlated_test_matrix(1000)
M_train, M_test, labels_train, labels_test = train_test_split(
M,
labels)
clf = RandomForestClassifier(random_state=0)
clf.fit(M_train, labels_train)
score = clf.predict_proba(M_test)[:,-1]
self.assertTrue(np.allclose(
dsp.get_roc_auc(labels_test, score, verbose=False),
roc_auc_score(labels_test, score)))
