def test_normal_mixture_hard(self):
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel(
np.arange(-3, 4),
np.random.uniform(1, 2, 7).round(2)),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel(np.arange(-3, 4) * 0.1,
np.ones(7),
updates=["mu", "sigma"]),
gradient_descent=GradientDescent(np.array([0.3, 0.1, 0.3]).reshape(
(-1, 1)),
inertia=0.9,
annealing=2000,
last_learning_rate=0.001),
)
for i in range(5000):
competition.iteration()
params = competition.generatives[-1]._params
print params.shape
true_params = competition.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.2)
def test_normal_1000(self):
np.random.seed(0)
size_batch = 1000
adversarials = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalModel(1, 2),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalModel(0, 1, updates=["mu", "sigma"]),
gradient_descent=GradientDescent(0.03, 0.9),
)
for i in range(200):
adversarials.iteration()
params = adversarials.generatives[-1]._params
true_params = adversarials.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.02)
def test_normal_1000(self):
np.random.seed(0)
size_batch = 1000
adversarials = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalModel(1, 2),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalModel(0, 1, updates=["mu", "sigma"]),
gradient_descent=GradientDescent(0.03, 0.9),
)
for i in range(200):
adversarials.iteration()
params = adversarials.generatives[-1]._params
true_params = adversarials.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.02)
def test_normal_mixture(self):
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel([-3, 3], [1, 1]),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel(
[-1, 1], [1, 1], updates=["mu", "sigma"]),
gradient_descent=GradientDescent(
0.1, inertia=0.9, annealing=1000, last_learning_rate=0.01),
)
for i in range(2000):
competition.iteration()
params = competition.generatives[-1]._params
true_params = competition.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.1)
def test_normal_mixture(self):
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel([-3, 3], [1, 1]),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel([-1, 1], [1, 1],
updates=["mu", "sigma"]),
gradient_descent=GradientDescent(0.1,
inertia=0.9,
annealing=1000,
last_learning_rate=0.01),
)
for i in range(2000):
competition.iteration()
params = competition.generatives[-1]._params
true_params = competition.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.1)
def test_normal_mixture_hard(self):
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel(
np.arange(-3, 4), np.random.uniform(1, 2, 7).round(2)),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel(
np.arange(-3, 4) * 0.1, np.ones(7), updates=["mu", "sigma"]),
gradient_descent=GradientDescent(
np.array([0.3, 0.1, 0.3]).reshape((-1, 1)), inertia=0.9,
annealing=2000, last_learning_rate=0.001),
)
for i in range(5000):
competition.iteration()
params = competition.generatives[-1]._params
print params.shape
true_params = competition.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.2)
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel([-3, 3], [1, 1]),
discriminative=pipeline.make_pipeline(preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel([-1, 1], [1, 1],
updates=["mu", "sigma"]),
gradient_descent=GradientDescent(0.01, 0.5),
)
print(competition)
for i in range(1000):
if i % 200 == 0:
competition.plot()
plt.show()
pass
competition.iteration()
print("final model %s" % competition.generatives[-1])
competition.plot_params()
plt.show()
competition.plot_auc()
plt.show()
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalModel(1, 2),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalModel(
0, 1, updates=["mu", "sigma"]),
gradient_descent=GradientDescent(
0.03, inertia=0.0, annealing=100),
)
print(competition)
for i in range(200):
if i % 50 == 0:
competition.plot()
plt.show()
pass
competition.iteration()
print("final model %s" % competition.generatives[-1])
competition.plot_params()
plt.show()
competition.plot_auc()
plt.show()
print(competition)
for i in range(1001):
if i % 50 == 0:
plt.figure()
competition.plot()
pyplot.savefig('Pooling.png')
pyplot.close()
plt.figure()
competition1.plot()
competition2.plot()
pyplot.savefig('Separating.png')
pyplot.close()
pass
competition.iteration()
competition1.iteration()
competition2.iteration()
print("final model pooling %s" % competition.generatives[-1])
print("final model separating %s" % competition1.generatives[-1], competition2.generatives[-1])
#separated = GenerativeNormalMixtureModel([competition1.generatives[-1].params["mu"], competition2.generatives[-1].params["mu"]], [competition1.generatives[-1].params["sigma"], competition2.generatives[-1].params["sigma"]])
separated1 = GenerativeNormalModel(competition1.generatives[-1].params["mu"], competition1.generatives[-1].params["sigma"])
separated2 = GenerativeNormalModel(competition2.generatives[-1].params["mu"], competition2.generatives[-1].params["sigma"])
pooled = GenerativeNormalQuasiMixtureModel(competition.generatives[-1].params["mu"], competition.generatives[-1].params["sigma"])
#true_mixture_model = GenerativeNormalMixtureModel([mu_param, -mu_param], [sigma_param, sigma_param])
true_mixture_model2 = GenerativeNormalQuasiMixtureModel(mu_param,sigma_param)
plt.figure()
xplot = np.arange(-10, 10, 0.1).reshape((-1, 1))
