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# -*- coding: utf-8 -*-
"""
Unit tests for the GenSVM class
"""
from __future__ import division, print_function
import numpy as np
import unittest
import warnings
from sklearn.datasets import load_digits, load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import maxabs_scale
from gensvm import GenSVM
class GenSVMTestCase(unittest.TestCase):
def test_init_1(self):
""" GENSVM: Sanity check for __init__ """
clf = GenSVM()
self.assertEqual(clf.p, 1.0)
self.assertEqual(clf.lmd, 1e-5)
self.assertEqual(clf.kappa, 0.0)
self.assertEqual(clf.epsilon, 1e-6)
self.assertEqual(clf.weights, "unit")
self.assertEqual(clf.kernel, "linear")
self.assertEqual(clf.gamma, "auto")
self.assertEqual(clf.coef, 1.0)
self.assertEqual(clf.degree, 2.0)
self.assertEqual(clf.kernel_eigen_cutoff, 1e-8)
self.assertEqual(clf.verbose, 0)
self.assertEqual(clf.random_state, None)
self.assertEqual(clf.max_iter, 1e8)
def test_init_invalid_p(self):
""" GENSVM: Check raises for invalid p """
with self.assertRaises(ValueError) as err1:
GenSVM(p=2.6)
exc1 = err1.exception
self.assertEqual(
exc1.args, ("Value for p should be within [1, 2]; got p = 2.6",)
)
with self.assertRaises(ValueError) as err2:
GenSVM(p=0.3)
exc2 = err2.exception
self.assertEqual(
exc2.args, ("Value for p should be within [1, 2]; got p = 0.3",)
)
def test_init_invalid_kappa(self):
""" GENSVM: Check raises for invalid kappa """
with self.assertRaises(ValueError) as err1:
GenSVM(kappa=-1.0)
exc1 = err1.exception
self.assertEqual(
exc1.args,
("Value for kappa should be larger " "than -1; got kappa = -1.0",),
)
def test_init_invalid_lmd(self):
""" GENSVM: Check raises for invalid lmd """
with self.assertRaises(ValueError) as err1:
GenSVM(lmd=-3.0)
exc1 = err1.exception
self.assertEqual(
exc1.args,
("Value for lmd should be larger than 0; got lmd = -3.0",),
)
def test_init_invalid_epsilon(self):
""" GENSVM: Check raises for invalid epsilon """
with self.assertRaises(ValueError) as err1:
GenSVM(epsilon=-1.0)
exc1 = err1.exception
self.assertEqual(
exc1.args,
(
"Value for epsilon should be larger than 0; got "
"epsilon = -1.0",
),
)
def test_init_invalid_gamma(self):
""" GENSVM: Check raises for invalid gamma """
with self.assertRaises(ValueError) as err1:
GenSVM(gamma=0.0)
exc1 = err1.exception
self.assertEqual(exc1.args, ("A gamma value of 0.0 is invalid",))
def test_init_invalid_weights(self):
""" GENSVM: Check raises for invalid weights """
with self.assertRaises(ValueError) as err1:
GenSVM(weights="other")
exc1 = err1.exception
self.assertEqual(
exc1.args,
(
"Unknown weight parameter specified. Should be "
"'unit' or 'group'; got 'other'",
),
)
def test_init_invalid_kernel(self):
""" GENSVM: Check raises for invalid kernel """
with self.assertRaises(ValueError) as err1:
GenSVM(kernel="other")
exc1 = err1.exception
self.assertEqual(
exc1.args,
(
"Unknown kernel specified. Should be "
"'linear', 'rbf', 'poly', or 'sigmoid'; got 'other'",
),
)
def test_invalid_y(self):
""" GENSVM: Check raises for invalid y type """
clf = GenSVM()
X = np.random.random((20, 4))
y = np.random.random((20,))
with self.assertRaises(ValueError) as err:
clf.fit(X, y)
exc = err.exception
self.assertEqual(
exc.args, ("Label type not allowed for GenSVM: 'continuous'",)
)
def test_invalid_seed_V_linear(self):
""" GENSVM: Test for invalid seed_V shape """
clf = GenSVM()
X = np.random.random((20, 4))
y = np.random.randint(1, 4, (20,))
seed_V = np.random.random((10, 2))
with self.assertRaises(ValueError) as err:
clf.fit(X, y, seed_V=seed_V)
exc = err.exception
self.assertEqual(
exc.args, ("Seed V must have shape [5, 2], but has shape [10, 2]",)
)
def test_invalid_seed_V_nonlinear(self):
""" GENSVM: Test for seed_V for nonlinear kernels """
clf = GenSVM(kernel="rbf")
X = np.random.random((20, 4))
y = np.random.randint(1, 4, (20,))
seed_V = np.random.random((5, 2))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
clf.fit(X, y, seed_V=seed_V)
self.assertEqual(len(w), 1)
msg = str(w[0].message)
self.assertEqual(
msg,
(
"Warm starts are only supported for the linear kernel. "
"The seed_V parameter will be ignored."
),
)
def test_fit_predict_strings(self):
""" GENSVM: Test fit and predict with string targets """
digits = load_digits(4)
n_samples = len(digits.images)
X = digits.images.reshape(n_samples, -1)
y = digits.target
labels = np.array(["zero", "one", "two", "three"])
yy = labels[y]
X_train, X_test, y_train, y_test = train_test_split(X, yy)
clf = GenSVM(epsilon=1e-3) # faster testing
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
pred_set = set(y_pred)
label_set = set(labels)
self.assertTrue(pred_set.issubset(label_set))
def test_fit_nonlinear(self):
""" GENSVM: Fit and predict with nonlinear kernel """
data = load_iris()
X = data.data
y = data.target_names[data.target]
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=123
)
clf = GenSVM(kernel="rbf", gamma=10, max_iter=5000, random_state=123)
clf.fit(X_train, y_train)
pred = clf.predict(X_test, trainX=X_train)
self.assertTrue(
set(pred).issubset(set(["versicolor", "virginica", "setosa"]))
)
def test_fit_with_seed(self):
""" GENSVM: Test fit with seeding """
# This is based on the unit test for gensvm_train in the C library
n_obs = 10
n_var = 3
n_class = 4
X = np.zeros((n_obs, n_var + 1))
X[0, 0] = 1.0000000000000000
X[0, 1] = 0.8056271362589000
X[0, 2] = 0.4874175854113872
X[0, 3] = 0.4453015882771756
X[1, 0] = 1.0000000000000000
X[1, 1] = 0.7940590105180981
X[1, 2] = 0.1861049005485224
X[1, 3] = 0.8469394287449229
X[2, 0] = 1.0000000000000000
X[2, 1] = 0.0294257611061681
X[2, 2] = 0.0242717976065267
X[2, 3] = 0.5039128672814752
X[3, 0] = 1.0000000000000000
X[3, 1] = 0.1746563833537603
X[3, 2] = 0.9135736087631979
X[3, 3] = 0.5270258081021366
X[4, 0] = 1.0000000000000000
X[4, 1] = 0.0022298761599785
X[4, 2] = 0.3773482059713607
X[4, 3] = 0.8009654729622842
X[5, 0] = 1.0000000000000000
X[5, 1] = 0.6638830667081945
X[5, 2] = 0.6467607601353914
X[5, 3] = 0.0434948735457108
X[6, 0] = 1.0000000000000000
X[6, 1] = 0.0770493004546461
X[6, 2] = 0.3699566427075194
X[6, 3] = 0.7863539761080217
X[7, 0] = 1.0000000000000000
X[7, 1] = 0.2685233952731509
X[7, 2] = 0.8539966432782011
X[7, 3] = 0.0967159557826836
X[8, 0] = 1.0000000000000000
X[8, 1] = 0.1163951898554611
X[8, 2] = 0.7667861436369238
X[8, 3] = 0.5031912600213351
X[9, 0] = 1.0000000000000000
X[9, 1] = 0.2290251898688216
X[9, 2] = 0.4401981048538806
X[9, 3] = 0.0884616753393881
X = X[:, 1:]
y = np.array([2, 1, 3, 2, 3, 2, 4, 1, 3, 4])
seed_V = np.zeros((n_var + 1, n_class - 1))
seed_V[0, 0] = 0.8233234072519983
seed_V[0, 1] = 0.7701104553132680
seed_V[0, 2] = 0.1102697774064020
seed_V[1, 0] = 0.7956168453294307
seed_V[1, 1] = 0.3267543833513200
seed_V[1, 2] = 0.8659836346403005
seed_V[2, 0] = 0.5777227081256917
seed_V[2, 1] = 0.3693175185473680
seed_V[2, 2] = 0.2728942849022845
seed_V[3, 0] = 0.4426030703804438
seed_V[3, 1] = 0.2456426390463990
seed_V[3, 2] = 0.2665038412777220
clf = GenSVM(
p=1.2143,
kappa=0.90298,
lmd=0.00219038,
epsilon=1e-15,
weights="unit",
kernel="linear",
)
clf.fit(X, y, seed_V=seed_V)
V = clf.combined_coef_
eps = 1e-7
self.assertLess(abs(V[0, 0] - -1.1907736868272805), eps)
self.assertLess(abs(V[0, 1] - 1.8651287814979396), eps)
self.assertLess(abs(V[0, 2] - 1.7250030581662932), eps)
self.assertLess(abs(V[1, 0] - 0.7925100058806183), eps)
self.assertLess(abs(V[1, 1] - -3.6093428916761665), eps)
self.assertLess(abs(V[1, 2] - -1.3394018960329377), eps)
self.assertLess(abs(V[2, 0] - 1.5203132433193016), eps)
self.assertLess(abs(V[2, 1] - -1.9118604362643852), eps)
self.assertLess(abs(V[2, 2] - -1.7939246097629342), eps)
self.assertLess(abs(V[3, 0] - 0.0658817457370326), eps)
self.assertLess(abs(V[3, 1] - 0.6547924025329720), eps)
self.assertLess(abs(V[3, 2] - -0.6773346708737853), eps)
def test_fit_with_weights(self):
""" GENSVM: Test fit with sample weights """
X, y = load_iris(return_X_y=True)
weights = np.random.random((X.shape[0],))
clf = GenSVM()
clf.fit(X, y, sample_weight=weights)
# with seeding
V = clf.combined_coef_
weights = np.random.random((X.shape[0],))
clf.fit(X, y, sample_weight=weights, seed_V=V)
clf = GenSVM(kernel="rbf")
clf.fit(X, y, sample_weight=weights)
if __name__ == "__main__":
unittest.main()
|
