def test_LogisticRegression_fit(self):
"""...Test LogisticRegression fit with different solvers and penalties
"""
sto_seed = 179312
raw_features, y = Test.get_train_data()
for fit_intercept in [True, False]:
for penalty in penalties:
if penalty == 'binarsity':
# binarize features
n_cuts = 3
binarizer = FeaturesBinarizer(n_cuts=n_cuts)
features = binarizer.fit_transform(raw_features)
else:
features = raw_features
for solver in solvers:
solver_kwargs = {
'penalty': penalty,
'tol': 1e-5,
'solver': solver,
'verbose': False,
'max_iter': 10,
'fit_intercept': fit_intercept
}
if penalty != 'none':
solver_kwargs['C'] = 100
if penalty == 'binarsity':
solver_kwargs[
'blocks_start'] = binarizer.feature_indices[:-1, ]
solver_kwargs['blocks_length'] = binarizer.n_values
if solver == 'sdca':
solver_kwargs['sdca_ridge_strength'] = 2e-2
if solver in ['sgd', 'svrg', 'sdca']:
solver_kwargs['random_state'] = sto_seed
if solver == 'sgd':
solver_kwargs['step'] = 1.
if solver == 'bfgs':
# BFGS only accepts ProxZero and ProxL2sq for now
if penalty not in ['none', 'l2']:
continue
learner = LogisticRegression(**solver_kwargs)
learner.fit(features, y)
probas = learner.predict_proba(features)[:, 1]
auc = roc_auc_score(y, probas)
self.assertGreater(
auc, 0.7, "solver %s with penalty %s and "
"intercept %s reached too low AUC" %
(solver, penalty, fit_intercept))
def test_predict_proba(self):
"""...Test LogReg predict_proba
"""
X, y = Test.get_train_data(n_features=12, n_samples=300, nnz=0)
learner = LogisticRegression(random_state=32289, tol=1e-13)
learner.fit(X, y)
X_test, y_test = Test.get_train_data(n_features=12, n_samples=5, nnz=0)
predicted_probas = np.array([[0.35851418, 0.64148582],
[0.42549328, 0.57450672],
[0.6749705, 0.3250295],
[0.39684181, 0.60315819],
[0.42732443, 0.57267557]])
np.testing.assert_array_almost_equal(learner.predict_proba(X_test),
predicted_probas,
decimal=3)
def test_LogisticRegression_fit(self):
"""...Test LogisticRegression fit with different solvers and penalties
"""
sto_seed = 179312
X, y = Test.get_train_data()
for fit_intercept in [True, False]:
for penalty in penalties:
for solver in solvers:
solver_kwargs = {
'penalty': penalty,
'tol': 1e-5,
'solver': solver,
'verbose': False,
'max_iter': 10,
'fit_intercept': fit_intercept
}
if penalty != 'none':
solver_kwargs['C'] = 50
if solver == 'sdca':
solver_kwargs['sdca_ridge_strength'] = 2e-2
if solver in ['sgd', 'svrg', 'sdca']:
solver_kwargs['random_state'] = sto_seed
if solver == 'sgd':
solver_kwargs['step'] = 1.
if solver == 'bfgs':
# BFGS only accepts ProxZero and ProxL2sq for now
if penalty not in ['none', 'l2']:
continue
learner = LogisticRegression(**solver_kwargs)
learner.fit(X, y)
probas = learner.predict_proba(X)[:, 1]
auc = roc_auc_score(y, probas)
self.assertGreater(
auc, 0.7, "solver %s with penalty %s and "
"intercept %s reached too low AUC" %
(solver, penalty, fit_intercept))
train_set = fetch_tick_dataset('binary/adult/adult.trn.bz2')
test_set = fetch_tick_dataset('binary/adult/adult.tst.bz2')
clf_tick = LogRegTick(C=1e5, penalty='l1', tol=1e-8)
clf_scikit = LogRegScikit(penalty='l1', tol=1e-8)
t1 = time()
clf_tick.fit(train_set[0], train_set[1])
t_tick = time() - t1
t1 = time()
clf_scikit.fit(train_set[0], train_set[1])
t_scikit = time() - t1
pred_tick = clf_tick.predict_proba(test_set[0])
pred_scikit = clf_scikit.predict_proba(test_set[0])
fpr_tick, tpr_tick, _ = roc_curve(test_set[1], pred_tick[:, 1])
fpr_scikit, tpr_scikit, _ = roc_curve(test_set[1], pred_scikit[:, 1])
plt.figure(figsize=(10, 8))
ax1 = plt.subplot2grid((2, 2), (0, 0))
plt.stem(clf_tick.weights)
plt.title(r'Model-weights in $\mathtt{tick}$', fontsize=16)
plt.ylim((-2, 2.5))
ax2 = plt.subplot2grid((2, 2), (0, 1))
plt.stem(np.ravel(clf_scikit.coef_))
# plt.legend()
==============================================
Binary classification with logistic regression
==============================================
This code perform binary classification on adult dataset with logistic
regression learner (`tick.inference.LogisticRegression`).
"""
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
from tick.inference import LogisticRegression
from tick.dataset import fetch_tick_dataset
train_set = fetch_tick_dataset('binary/adult/adult.trn.bz2')
test_set = fetch_tick_dataset('binary/adult/adult.tst.bz2')
learner = LogisticRegression()
learner.fit(train_set[0], train_set[1])
predictions = learner.predict_proba(test_set[0])
fpr, tpr, _ = roc_curve(test_set[1], predictions[:, 1])
plt.figure(figsize=(6, 5))
plt.plot(fpr, tpr, lw=2)
plt.title("ROC curve on adult dataset (area = {:.2f})".format(auc(fpr, tpr)))
plt.ylabel("True Positive Rate")
plt.xlabel("False Positive Rate")
plt.show()