def test_group_lasso():
"""Group Lasso test."""
n_samples, n_features = 100, 90
# assign group ids
groups = np.zeros(90)
groups[0:29] = 1
groups[30:59] = 2
groups[60:] = 3
# sample random coefficients
beta0 = np.random.normal(0.0, 1.0, 1)
beta = np.random.normal(0.0, 1.0, n_features)
beta[groups == 2] = 0.
# create an instance of the GLM class
glm_group = GLM(distr='softplus', alpha=1.)
# simulate training data
Xr = np.random.normal(0.0, 1.0, [n_samples, n_features])
yr = simulate_glm(glm_group.distr, beta0, beta, Xr)
# scale and fit
scaler = StandardScaler().fit(Xr)
glm_group.fit(scaler.transform(Xr), yr)
def test_multinomial():
"""Test all multinomial functionality"""
glm_mn = GLM(distr='multinomial', reg_lambda=np.array([0.0, 0.1, 0.2]),
learning_rate = 2e-1, tol=1e-10)
X = np.array([[-1, -2, -3], [4, 5, 6]])
y = np.array([1, 0])
# test gradient
beta = np.zeros([4, 2])
grad_beta0, grad_beta = glm_mn._grad_L2loss(beta[0], beta[1:], 0, X, y)
assert_true(grad_beta0[0] != grad_beta0[1])
glm_mn.fit(X, y)
y_pred_proba = glm_mn.predict_proba(X)
assert_equal(y_pred_proba.shape, (3, X.shape[0], 2)) # n_lambdas x n_samples x n_classes
# pick one as yhat
yhat = y_pred_proba[0]
# uniform prediction
ynull = np.ones(yhat.shape) / yhat.shape[1]
# pseudo_R2 should be greater than 0
assert_true(glm_mn[-1].score(X, y) > 0.)
assert_equal(len(glm_mn.simulate(glm_mn.fit_[0]['beta0'],
glm_mn.fit_[0]['beta'],
X)),
X.shape[0])
# check that score is computed for sliced estimator
scorelist = glm_mn[-1].score(X, y)
assert_equal(scorelist.shape[0], 1)
# check that score is computed for all lambdas
scorelist = glm_mn.score(X, y)
assert_equal(scorelist.shape[0], y_pred_proba.shape[0])
def test_compare_sklearn(solver):
"""Test results against sklearn."""
def rmse(a, b):
return np.sqrt(np.mean((a - b) ** 2))
X, Y, coef_ = make_regression(
n_samples=1000, n_features=500,
noise=0.1, n_informative=10, coef=True,
random_state=42)
alpha = 0.1
l1_ratio = 0.5
clf = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, tol=1e-5)
clf.fit(X, Y)
glm = GLM(distr='gaussian', alpha=l1_ratio, reg_lambda=alpha,
solver=solver, tol=1e-6, max_iter=500)
glm.fit(X, Y)
y_sk = clf.predict(X)
y_pg = glm.predict(X)
assert abs(rmse(Y, y_sk) - rmse(Y, y_pg)) < 0.5
glm = GLM(distr='gaussian', alpha=l1_ratio, reg_lambda=alpha,
solver=solver, tol=1e-6, max_iter=5, fit_intercept=False)
glm.fit(X, Y)
assert glm.beta0_ == 0.
glm.predict(X)
def test_multinomial():
"""Test all multinomial functionality"""
glm_mn = GLM(distr='multinomial', reg_lambda=np.array([0.0, 0.1, 0.2]),
learning_rate = 2e-1, tol=1e-10)
X = np.array([[-1, -2, -3], [4, 5, 6]])
y = np.array([1, 0])
# test gradient
beta = np.zeros([4, 2])
grad_beta0, grad_beta = glm_mn._grad_L2loss(beta[0], beta[1:], 0, X, y)
assert_true(grad_beta0[0] != grad_beta0[1])
glm_mn.fit(X, y)
y_pred = glm_mn.predict(X)
assert_equal(y_pred.shape, (3, X.shape[0], 2)) # n_lambdas x n_samples x n_classes
# pick one as yhat
yhat = y_pred[0]
# uniform prediction
ynull = np.ones(yhat.shape) / yhat.shape[1]
# pseudo_R2 should be greater than 0
assert_true(glm_mn.score(y, yhat, ynull, method='pseudo_R2') > 0.)
glm_mn.score(y, yhat)
assert_equal(len(glm_mn.simulate(glm_mn.fit_[0]['beta0'],
glm_mn.fit_[0]['beta'],
X)),
X.shape[0])
# these should raise an exception
assert_raises(ValueError, glm_mn.score, y, y, y, 'pseudo_R2')
assert_raises(ValueError, glm_mn.score, y, y, None, 'deviance')
def test_multinomial():
"""Test all multinomial functionality"""
glm = GLM(distr='multinomial', reg_lambda=np.array([0.0, 0.1, 0.2]), tol=1e-10)
X = np.array([[-1, -2, -3], [4, 5, 6]])
y = np.array([1, 0])
# test gradient
beta = np.zeros([4, 2])
grad_beta0, grad_beta = glm.grad_L2loss(beta[0], beta[1:], 0, X, y)
assert grad_beta0[0] != grad_beta0[1]
glm.fit(X, y)
y_pred = glm.predict(X)
assert_equal(y_pred.shape, (3, X.shape[0], 2)) # n_lambdas x n_samples x n_classes
# pick one as yhat
yhat = y_pred[0]
# uniform prediction
ynull = np.ones(yhat.shape) / yhat.shape[1]
# pseudo_R2 should be greater than 0
assert_true(glm.pseudo_R2(y, yhat, ynull) > 0.)
glm.deviance(y, yhat)
assert_equal(len(glm.simulate(glm.fit_[0]['beta0'],
glm.fit_[0]['beta'],
X)),
X.shape[0])
# these should raise an exception
try:
glm.pseudo_R2(y, y, y)
assert False
except Exception:
assert True
try:
glm.deviance(y, y)
assert False
except Exception:
assert True
def glm_bernoulli_pyglmnet(Xr, Yr, Xt):
#poissonexp isn't listed as an option for distr?
#glm = GLM(distr='poissonexp', alpha=0., reg_lambda=[0.], tol=1e-6)
glm = GLM(distr='binomial', alpha=0., reg_lambda=[0.], tol=1e-6)
glm.fit(Xr, Yr)
Yt = glm.predict(Xt)[0]
return Yt
def test_multinomial():
"""Test all multinomial functionality"""
glm_mn = GLM(distr='multinomial',
reg_lambda=np.array([0.0, 0.1, 0.2]),
learning_rate=2e-1,
tol=1e-10)
X = np.array([[-1, -2, -3], [4, 5, 6]])
y = np.array([1, 0])
# test gradient
beta = np.zeros([4, 2])
grad_beta0, grad_beta = glm_mn._grad_L2loss(beta[0], beta[1:], 0, X, y)
assert_true(grad_beta0[0] != grad_beta0[1])
glm_mn.fit(X, y)
y_pred = glm_mn.predict(X)
assert_equal(y_pred.shape,
(3, X.shape[0], 2)) # n_lambdas x n_samples x n_classes
# pick one as yhat
yhat = y_pred[0]
# uniform prediction
ynull = np.ones(yhat.shape) / yhat.shape[1]
# pseudo_R2 should be greater than 0
assert_true(glm_mn.score(y, yhat, ynull, method='pseudo_R2') > 0.)
glm_mn.score(y, yhat)
assert_equal(
len(glm_mn.simulate(glm_mn.fit_[0]['beta0'], glm_mn.fit_[0]['beta'],
X)), X.shape[0])
# these should raise an exception
assert_raises(ValueError, glm_mn.score, y, y, y, 'pseudo_R2')
assert_raises(ValueError, glm_mn.score, y, y, None, 'deviance')
def test_group_lasso():
"""Group Lasso test."""
n_samples, n_features = 100, 90
# assign group ids
groups = np.zeros(90)
groups[0:29] = 1
groups[30:59] = 2
groups[60:] = 3
# sample random coefficients
beta0 = np.random.normal(0.0, 1.0, 1)
beta = np.random.normal(0.0, 1.0, n_features)
beta[groups == 2] = 0.
# create an instance of the GLM class
glm_group = GLM(distr='softplus', alpha=1.)
# simulate training data
Xr = np.random.normal(0.0, 1.0, [n_samples, n_features])
yr = simulate_glm(glm_group.distr, beta0, beta, Xr)
# scale and fit
scaler = StandardScaler().fit(Xr)
glm_group.fit(scaler.transform(Xr), yr)
def test_tikhonov():
"""Tikhonov regularization test."""
n_samples, n_features = 100, 10
# design covariance matrix of parameters
Gam = 15.
PriorCov = np.zeros([n_features, n_features])
for i in np.arange(0, n_features):
for j in np.arange(i, n_features):
PriorCov[i, j] = np.exp(-Gam * 1. / (np.float(n_features) ** 2) *
(np.float(i) - np.float(j)) ** 2)
PriorCov[j, i] = PriorCov[i, j]
if i == j:
PriorCov[i, j] += 0.01
PriorCov = 1. / np.max(PriorCov) * PriorCov
# sample parameters as multivariate normal
beta0 = np.random.randn()
beta = np.random.multivariate_normal(np.zeros(n_features), PriorCov)
# sample train and test data
glm_sim = GLM(distr='softplus', score_metric='pseudo_R2')
X = np.random.randn(n_samples, n_features)
y = simulate_glm(glm_sim.distr, beta0, beta, X)
from sklearn.cross_validation import train_test_split
Xtrain, Xtest, ytrain, ytest = \
train_test_split(X, y, test_size=0.5, random_state=42)
# design tikhonov matrix
[U, S, V] = np.linalg.svd(PriorCov, full_matrices=False)
Tau = np.dot(np.diag(1. / np.sqrt(S)), U)
Tau = 1. / np.sqrt(np.float(n_samples)) * Tau / Tau.max()
# fit model with batch gradient
glm_tikhonov = GLM(distr='softplus',
alpha=0.0,
Tau=Tau,
solver='batch-gradient',
tol=1e-5,
score_metric='pseudo_R2')
glm_tikhonov.fit(Xtrain, ytrain)
R2_train, R2_test = dict(), dict()
R2_train['tikhonov'] = glm_tikhonov.score(Xtrain, ytrain)
R2_test['tikhonov'] = glm_tikhonov.score(Xtest, ytest)
# fit model with cdfast
glm_tikhonov = GLM(distr='softplus',
alpha=0.0,
Tau=Tau,
solver='cdfast',
tol=1e-5,
score_metric='pseudo_R2')
glm_tikhonov.fit(Xtrain, ytrain)
R2_train, R2_test = dict(), dict()
R2_train['tikhonov'] = glm_tikhonov.score(Xtrain, ytrain)
R2_test['tikhonov'] = glm_tikhonov.score(Xtest, ytest)
def test_tikhonov():
"""Tikhonov regularization test."""
n_samples, n_features = 100, 10
# design covariance matrix of parameters
Gam = 15.
PriorCov = np.zeros([n_features, n_features])
for i in np.arange(0, n_features):
for j in np.arange(i, n_features):
PriorCov[i, j] = np.exp(-Gam * 1. / (np.float(n_features) ** 2) *
(np.float(i) - np.float(j)) ** 2)
PriorCov[j, i] = PriorCov[i, j]
if i == j:
PriorCov[i, j] += 0.01
PriorCov = 1. / np.max(PriorCov) * PriorCov
# sample parameters as multivariate normal
beta0 = np.random.randn()
beta = np.random.multivariate_normal(np.zeros(n_features), PriorCov)
# sample train and test data
glm_sim = GLM(distr='softplus', score_metric='pseudo_R2')
X = np.random.randn(n_samples, n_features)
y = simulate_glm(glm_sim.distr, beta0, beta, X)
from sklearn.model_selection import train_test_split
Xtrain, Xtest, ytrain, ytest = \
train_test_split(X, y, test_size=0.5, random_state=42)
# design tikhonov matrix
[U, S, V] = np.linalg.svd(PriorCov, full_matrices=False)
Tau = np.dot(np.diag(1. / np.sqrt(S)), U)
Tau = 1. / np.sqrt(np.float(n_samples)) * Tau / Tau.max()
# fit model with batch gradient
glm_tikhonov = GLM(distr='softplus',
alpha=0.0,
Tau=Tau,
solver='batch-gradient',
tol=1e-3,
score_metric='pseudo_R2')
glm_tikhonov.fit(Xtrain, ytrain)
R2_train, R2_test = dict(), dict()
R2_train['tikhonov'] = glm_tikhonov.score(Xtrain, ytrain)
R2_test['tikhonov'] = glm_tikhonov.score(Xtest, ytest)
# fit model with cdfast
glm_tikhonov = GLM(distr='softplus',
alpha=0.0,
Tau=Tau,
solver='cdfast',
tol=1e-3,
score_metric='pseudo_R2')
glm_tikhonov.fit(Xtrain, ytrain)
R2_train, R2_test = dict(), dict()
R2_train['tikhonov'] = glm_tikhonov.score(Xtrain, ytrain)
R2_test['tikhonov'] = glm_tikhonov.score(Xtest, ytest)
def test_glmnet():
"""Test glmnet."""
scaler = StandardScaler()
n_samples, n_features = 1000, 100
density = 0.1
n_lambda = 10
# coefficients
beta0 = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0)
beta = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0, [n_features, 1])
distrs = ['softplus', 'poisson', 'gaussian', 'binomial']
solvers = ['batch-gradient', 'cdfast']
score_metric = 'pseudo_R2'
learning_rate = 2e-1
for solver in solvers:
for distr in distrs:
glm = GLM(distr, learning_rate=learning_rate,
solver=solver, score_metric=score_metric)
assert_true(repr(glm))
np.random.seed(glm.random_state)
X_train = np.random.normal(0.0, 1.0, [n_samples, n_features])
y_train = glm.simulate(beta0, beta, X_train)
X_train = scaler.fit_transform(X_train)
glm.fit(X_train, y_train)
beta_ = glm.fit_[-1]['beta'][:]
assert_allclose(beta[:], beta_, atol=0.5) # check fit
y_pred = glm.predict(scaler.transform(X_train))
assert_equal(y_pred.shape, (n_lambda, X_train.shape[0]))
# checks for slicing.
glm = glm[:3]
glm_copy = glm.copy()
assert_true(glm_copy is not glm)
assert_equal(len(glm.reg_lambda), 3)
y_pred = glm[:2].predict(scaler.transform(X_train))
assert_equal(y_pred.shape, (2, X_train.shape[0]))
y_pred = glm[2].predict(scaler.transform(X_train))
assert_equal(y_pred.shape, (X_train.shape[0], ))
assert_raises(IndexError, glm.__getitem__, [2])
glm.score(X_train, y_train)
# don't allow slicing if model has not been fit yet.
glm_poisson = GLM(distr='softplus')
assert_raises(ValueError, glm_poisson.__getitem__, 2)
# test fit_predict
glm_poisson.fit_predict(X_train, y_train)
assert_raises(ValueError, glm_poisson.fit_predict, X_train[None, ...], y_train)
def test_glmnet():
"""Test glmnet."""
scaler = StandardScaler()
n_samples, n_features = 1000, 100
density = 0.1
n_lambda = 10
# coefficients
beta0 = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0)
beta = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0, [n_features, 1])
distrs = ['poisson', 'poissonexp', 'normal', 'binomial']
solvers = ['batch-gradient', 'cdfast']
learning_rate = 2e-1
for solver in solvers:
for distr in distrs:
glm = GLM(distr, learning_rate=learning_rate, solver=solver)
assert_true(repr(glm))
np.random.seed(glm.random_state)
X_train = np.random.normal(0.0, 1.0, [n_samples, n_features])
y_train = glm.simulate(beta0, beta, X_train)
X_train = scaler.fit_transform(X_train)
glm.fit(X_train, y_train)
beta_ = glm.fit_[-1]['beta'][:]
assert_allclose(beta[:], beta_, atol=0.5) # check fit
y_pred = glm.predict(scaler.transform(X_train))
assert_equal(y_pred.shape, (n_lambda, X_train.shape[0]))
# checks for slicing.
glm = glm[:3]
glm_copy = glm.copy()
assert_true(glm_copy is not glm)
assert_equal(len(glm.reg_lambda), 3)
y_pred = glm[:2].predict(scaler.transform(X_train))
assert_equal(y_pred.shape, (2, X_train.shape[0]))
y_pred = glm[2].predict(scaler.transform(X_train))
assert_equal(y_pred.shape, (X_train.shape[0], ))
assert_raises(IndexError, glm.__getitem__, [2])
glm.score(y_train, y_pred)
# don't allow slicing if model has not been fit yet.
glm_poisson = GLM(distr='poisson')
assert_raises(ValueError, glm_poisson.__getitem__, 2)
# test fit_predict
glm_poisson.fit_predict(X_train, y_train)
assert_raises(ValueError, glm_poisson.fit_predict, X_train[None, ...],
y_train)
def test_cv():
"""Simple CV check"""
X, y = make_regression()
model_mn = GLM(distr='normal', alpha=0.01, reg_lambda=np.array([0.0, 0.1, 0.2]))
model_mn.fit(X, y)
cv = KFold(X.shape[0], 5)
# check that it returns 5 scores
assert_equal(len(cross_val_score(model_mn, X, y, cv=cv, scoring=simple_cv_scorer)), 5)
def test_glmnet():
"""Test glmnet."""
scaler = StandardScaler()
n_samples, n_features = 10000, 100
density = 0.1
n_lambda = 10
# coefficients
beta0 = np.random.rand()
beta = sps.rand(n_features, 1, density=density).toarray()
distrs = ['poisson', 'poissonexp', 'normal', 'binomial']
for distr in distrs:
# FIXME: why do we need such this learning rate for 'poissonexp'?
learning_rate = 1e-5 if distr == 'poissonexp' else 1e-4
glm = GLM(distr, learning_rate=learning_rate)
assert_true(repr(glm))
np.random.seed(glm.random_state)
X_train = np.random.normal(0.0, 1.0, [n_samples, n_features])
y_train = glm.simulate(beta0, beta, X_train)
X_train = scaler.fit_transform(X_train)
glm.fit(X_train, y_train)
beta_ = glm.fit_[-2]['beta'][:]
assert_allclose(beta[:], beta_, atol=0.1) # check fit
density_ = np.sum(beta_ > 0.1) / float(n_features)
assert_allclose(density_, density, atol=0.05) # check density
y_pred = glm.predict(scaler.transform(X_train))
assert_equal(y_pred.shape, (n_lambda, X_train.shape[0]))
# checks for slicing.
glm = glm[:3]
glm_copy = glm.copy()
assert_true(glm_copy is not glm)
assert_equal(len(glm.reg_lambda), 3)
y_pred = glm[:2].predict(scaler.transform(X_train))
assert_equal(y_pred.shape, (2, X_train.shape[0]))
y_pred = glm[2].predict(scaler.transform(X_train))
assert_equal(y_pred.shape, (X_train.shape[0], ))
assert_raises(IndexError, glm.__getitem__, [2])
glm.deviance(y_train, y_pred)
# don't allow slicing if model has not been fit yet.
glm = GLM(distr='poisson')
assert_raises(ValueError, glm.__getitem__, 2)
# test fit_predict
glm.fit_predict(X_train, y_train)
assert_raises(ValueError, glm.fit_predict, X_train[None, ...], y_train)
def test_cv():
"""Simple CV check"""
# XXX: don't use scikit-learn for tests.
X, y = make_regression()
glm_normal = GLM(distr='gaussian', alpha=0.01,
reg_lambda=[0.0, 0.1, 0.2])
glm_normal.fit(X, y)
cv = KFold(X.shape[0], 5)
# check that it returns 5 scores
assert_equal(len(cross_val_score(glm_normal, X, y, cv=cv,
scoring=simple_cv_scorer)), 5)
def test_api_input():
"""Test that the input value of y can be of different types."""
random_state = 1
state = np.random.RandomState(random_state)
n_samples, n_features = 100, 5
X = state.normal(0, 1, (n_samples, n_features))
y = state.normal(0, 1, (n_samples, ))
glm = GLM(distr='gaussian')
# Test that a list will not work - the types have to be ndarray
with pytest.raises(ValueError):
glm.fit(X, list(y))
# Test that ValueError is raised when the shapes mismatch
with pytest.raises(ValueError):
GLM().fit(X, y[3:])
# This would work without errors
glm.fit(X, y)
glm.predict(X)
glm.score(X, y)
glm = GLM(distr='gaussian', solver='test')
with pytest.raises(ValueError, match="solver must be one of"):
glm.fit(X, y)
with pytest.raises(ValueError, match="fit_intercept must be"):
glm = GLM(distr='gaussian', fit_intercept='blah')
glm = GLM(distr='gaussian', max_iter=2)
with pytest.warns(UserWarning, match='Reached max number of iterat'):
glm.fit(X, y)
def test_cv():
"""Simple CV check."""
# XXX: don't use scikit-learn for tests.
X, y = make_regression()
glm_normal = GLM(distr='gaussian', alpha=0.01,
reg_lambda=0.1)
glm_normal.fit(X, y)
cv = KFold(X.shape[0], 5)
# check that it returns 5 scores
assert_equal(len(cross_val_score(glm_normal, X, y, cv=cv,
scoring=simple_cv_scorer)), 5)
def test_pseudoR2():
"""Test pseudo r2."""
n_samples, n_features = 1000, 100
beta0 = np.random.rand()
beta = np.random.normal(0.0, 1.0, n_features)
# sample train and test data
glm_sim = GLM(score_metric='pseudo_R2')
X = np.random.randn(n_samples, n_features)
y = simulate_glm(glm_sim.distr, beta0, beta, X)
glm_sim.fit(X, y)
score = glm_sim.score(X, y)
assert (isinstance(score, float))
def test_deviance():
"""Test deviance."""
n_samples, n_features = 1000, 100
beta0 = np.random.normal(0.0, 1.0, 1)
beta = np.random.normal(0.0, 1.0, n_features)
# sample train and test data
glm_sim = GLM(score_metric='deviance')
X = np.random.randn(n_samples, n_features)
y = simulate_glm(glm_sim.distr, beta0, beta, X)
glm_sim.fit(X, y)
score = glm_sim.score(X, y)
assert_true(isinstance(score, float))
def test_accuracy():
"""Testing accuracy."""
n_samples, n_features, n_classes = 1000, 100, 2
beta0 = np.random.normal(0.0, 1.0, 1)
beta = np.random.normal(0.0, 1.0, (n_features, n_classes))
# sample train and test data
glm_sim = GLM(distr='binomial', score_metric='accuracy')
X = np.random.randn(n_samples, n_features)
y = simulate_glm(glm_sim.distr, beta0, beta, X)
y = np.argmax(y, axis=1)
glm_sim.fit(X, y)
score = glm_sim.score(X, y)
assert_true(isinstance(score, float))
def test_group_lasso():
"""Group Lasso test."""
n_samples, n_features = 100, 90
# assign group ids
groups = np.zeros(90)
groups[0:29] = 1
groups[30:59] = 2
groups[60:] = 3
# sample random coefficients
beta0 = np.random.normal(0.0, 1.0, 1)
beta = np.random.normal(0.0, 1.0, n_features)
beta[groups == 2] = 0.
# create an instance of the GLM class
glm_group = GLM(distr='softplus', alpha=1., reg_lambda=0.2, group=groups)
# simulate training data
np.random.seed(glm_group.random_state)
Xr = np.random.normal(0.0, 1.0, [n_samples, n_features])
yr = simulate_glm(glm_group.distr, beta0, beta, Xr)
# scale and fit
scaler = StandardScaler().fit(Xr)
glm_group.fit(scaler.transform(Xr), yr)
# count number of nonzero coefs for each group.
# in each group, coef must be [all nonzero] or [all zero].
beta = glm_group.beta_
group_ids = np.unique(groups)
for group_id in group_ids:
if group_id == 0:
continue
target_beta = beta[groups == group_id]
n_nonzero = (target_beta != 0.0).sum()
assert n_nonzero in (len(target_beta), 0)
# one of the groups must be [all zero]
assert np.any([
beta[groups == group_id].sum() == 0 for group_id in group_ids
if group_id != 0
])
def test_glmnet():
"""Test glmnet."""
scaler = StandardScaler()
n_samples, n_features = 100, 10
# coefficients
beta0 = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0)
beta = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0, (n_features,))
distrs = ['softplus', 'gaussian', 'poisson', 'binomial', 'probit']
solvers = ['batch-gradient', 'cdfast']
score_metric = 'pseudo_R2'
learning_rate = 2e-1
for solver in solvers:
for distr in distrs:
glm = GLM(distr,
learning_rate=learning_rate,
solver=solver,
score_metric=score_metric)
assert_true(repr(glm))
np.random.seed(glm.random_state)
X_train = np.random.normal(0.0, 1.0, [n_samples, n_features])
y_train = simulate_glm(glm.distr, beta0, beta, X_train)
X_train = scaler.fit_transform(X_train)
glm.fit(X_train, y_train)
beta_ = glm.beta_
assert_allclose(beta, beta_, atol=0.5) # check fit
y_pred = glm.predict(scaler.transform(X_train))
assert_equal(y_pred.shape[0], X_train.shape[0])
# test fit_predict
glm_poisson = GLM(distr='softplus')
glm_poisson.fit_predict(X_train, y_train)
assert_raises(ValueError, glm_poisson.fit_predict, X_train[None, ...],
y_train)
def test_glmnet():
"""Test glmnet."""
scaler = StandardScaler()
n_samples, n_features = 100, 10
# coefficients
beta0 = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0)
beta = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0, (n_features,))
distrs = ['softplus', 'gaussian', 'poisson', 'binomial', 'probit']
solvers = ['batch-gradient', 'cdfast']
score_metric = 'pseudo_R2'
learning_rate = 2e-1
for solver in solvers:
for distr in distrs:
glm = GLM(distr, learning_rate=learning_rate,
solver=solver, score_metric=score_metric)
assert_true(repr(glm))
np.random.seed(glm.random_state)
X_train = np.random.normal(0.0, 1.0, [n_samples, n_features])
y_train = simulate_glm(glm.distr, beta0, beta, X_train)
X_train = scaler.fit_transform(X_train)
glm.fit(X_train, y_train)
beta_ = glm.beta_
assert_allclose(beta, beta_, atol=0.5) # check fit
y_pred = glm.predict(scaler.transform(X_train))
assert_equal(y_pred.shape[0], X_train.shape[0])
# test fit_predict
glm_poisson = GLM(distr='softplus')
glm_poisson.fit_predict(X_train, y_train)
assert_raises(ValueError, glm_poisson.fit_predict,
X_train[None, ...], y_train)
def test_glmnet():
"""Test glmnet."""
glm = GLM(distr='poisson')
scaler = StandardScaler()
n_samples, n_features = 10000, 100
density = 0.1
# coefficients
beta0 = np.random.rand()
beta = sps.rand(n_features, 1, density=density).toarray()
X_train = np.random.normal(0.0, 1.0, [n_samples, n_features])
y_train = glm.simulate(beta0, beta, X_train)
X_train = scaler.fit_transform(X_train)
glm.fit(X_train, y_train)
beta_ = glm.fit_params[-2]['beta'][:]
assert_allclose(beta[:], beta_, atol=0.1) # check fit
density_ = np.sum(beta_ > 0.1) / float(n_features)
assert_allclose(density_, density, atol=0.02) # check density
def test_multinomial():
"""Test all multinomial functionality"""
glm_mn = GLM(distr='multinomial',
reg_lambda=np.array([0.0, 0.1, 0.2]),
learning_rate=2e-1,
tol=1e-10)
X = np.array([[-1, -2, -3], [4, 5, 6]])
y = np.array([1, 0])
# test gradient
beta = np.zeros([4, 2])
grad_beta0, grad_beta = glm_mn._grad_L2loss(beta[0], beta[1:], 0, X, y)
assert_true(grad_beta0[0] != grad_beta0[1])
glm_mn.fit(X, y)
y_pred_proba = glm_mn.predict_proba(X)
assert_equal(y_pred_proba.shape,
(3, X.shape[0], 2)) # n_lambdas x n_samples x n_classes
# pick one as yhat
yhat = y_pred_proba[0]
# uniform prediction
ynull = np.ones(yhat.shape) / yhat.shape[1]
# pseudo_R2 should be greater than 0
assert_true(glm_mn[-1].score(X, y) > 0.)
assert_equal(
len(glm_mn.simulate(glm_mn.fit_[0]['beta0'], glm_mn.fit_[0]['beta'],
X)), X.shape[0])
# check that score is computed for sliced estimator
scorelist = glm_mn[-1].score(X, y)
assert_equal(scorelist.shape[0], 1)
# check that score is computed for all lambdas
scorelist = glm_mn.score(X, y)
assert_equal(scorelist.shape[0], y_pred_proba.shape[0])
def test_api_input_types_y():
"""Test that the input value of y can be of different types."""
random_state = 1
state = np.random.RandomState(random_state)
n_samples, n_features = 100, 5
X = state.normal(0, 1, (n_samples, n_features))
y = state.normal(0, 1, (n_samples, ))
glm = GLM(distr='gaussian')
# Test that a list will not work - the types have to be ndarray
with pytest.raises(ValueError):
glm.fit(X, list(y))
# Test that ValueError is raised when the shapes mismatch
with pytest.raises(ValueError):
GLM().fit(X, y[3:])
# This would work without errors
glm.fit(X, y)
glm.predict(X)
glm.score(X, y)
n_samples, n_features = X.shape
########################################################
# Split the data into training and test sets
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.33, random_state=0)
########################################################
# Fit a gaussian distributed GLM with elastic net regularization
# use the default value for reg_lambda
glm = GLM(distr='gaussian', alpha=0.05, score_metric='pseudo_R2')
# fit model
glm.fit(X_train, y_train)
# score the test set prediction
y_test_hat = glm[-1].predict(X_test)
print("test set pseudo $R^2$ = %f" % glm[-1].score(X_test, y_test))
########################################################
# Plot the true and predicted test set target values
plt.plot(y_test[:50], 'ko-')
plt.plot(y_test_hat[:50], 'ro-')
plt.legend(['true', 'pred'], frameon=False)
plt.xlabel('Counties')
plt.ylabel('Per capita violent crime')
plt.tick_params(axis='y', right='off')
def get_benchmarks(self, X_train, y_train, X_test, y_test):
"""
"""
n_repeats = self.n_repeats
distr = self.distr
res = dict()
for env in self.envs:
res[env] = dict()
if env == 'pyglmnet':
# initialize model
model = GLM(distr=distr,
reg_lambda=[self.reg_lambda],
alpha=self.alpha,
solver='batch-gradient',
score_metric='pseudo_R2')
# fit-predict-score
model.fit(X_train, y_train)
y_test_hat = model[-1].predict(X_test)
y_test_hat = np.squeeze(y_test_hat)
if distr in ['gaussian', 'poisson']:
res[env]['score'] = \
r2_score(y_test, y_test_hat)
elif distr == 'binomial':
res[env]['score'] = \
accuracy_score(y_test,
(y_test_hat > 0.5).astype(int))
# time
tmp = list()
for r in range(n_repeats):
start = time.time()
model.fit(X_train, y_train)
stop = time.time()
tmp.append(stop - start)
res[env]['time'] = np.min(tmp) * 1e3
if env == 'sklearn':
if distr in ['gaussian', 'binomial']:
# initialize model
if distr == 'gaussian':
model = ElasticNet(alpha=self.reg_lambda,
l1_ratio=self.alpha)
elif distr == 'binomial':
model = SGDClassifier(loss='log',
penalty='elasticnet',
alpha=self.reg_lambda,
l1_ratio=self.alpha)
# fit-predict-score
model.fit(X_train, y_train)
y_test_hat = model.predict(X_test)
res[env]['score'] = model.score(X_test, y_test)
# time
tmp = list()
for r in range(n_repeats):
start = time.time()
model.fit(X_train, y_train)
stop = time.time()
tmp.append(stop - start)
res[env]['time'] = np.min(tmp) * 1e3
else:
res[env]['score'] = -999.
res[env]['time'] = -999.
if env == 'statsmodels':
# initialize model
if distr == 'gaussian':
model = sm.GLM(y_train,
sm.add_constant(X_train),
family=sm.families.Gaussian())
elif distr == 'binomial':
model = sm.GLM(y_train,
sm.add_constant(X_train),
family=sm.families.Binomial())
elif distr == 'poisson':
model = sm.GLM(y_train,
sm.add_constant(X_train),
family=sm.families.Poisson())
# fit-predict-score
statsmodels_res = model.fit()
y_test_hat = model.predict(statsmodels_res.params,
exog=sm.add_constant(X_test))
y_test_hat = np.array(y_test_hat)
if distr in ['gaussian', 'poisson']:
res[env]['score'] = \
r2_score(y_test, y_test_hat)
elif distr == 'binomial':
res[env]['score'] = \
accuracy_score(y_test,
(y_test_hat > 0.5).astype(int))
# time
tmp = list()
for r in range(n_repeats):
start = time.time()
statsmodels_res = model.fit()
stop = time.time()
tmp.append(stop - start)
res[env]['time'] = np.min(tmp) * 1e3
if env == 'R':
# initialize model
glmnet = importr('glmnet')
predict = robjects.r('predict')
# fit-predict-score
try:
fit = glmnet.glmnet(X_train,
y_train,
family=distr,
alpha=self.alpha,
nlambda=1)
tmp = predict(fit, newx=X_test, s=0)
y_test_hat = np.zeros(y_test.shape[0])
for i in range(y_test.shape[0]):
y_test_hat[i] = tmp[i]
if distr in ['gaussian', 'poisson']:
res[env]['score'] = \
r2_score(y_test, y_test_hat)
elif distr == 'binomial':
res[env]['score'] = \
accuracy_score(y_test,
(y_test_hat > 0.5).astype(int))
# time
tmp = list()
for r in range(n_repeats):
start = time.time()
fit = glmnet.glmnet(X_train,
y_train,
family=distr,
alpha=self.alpha,
nlambda=1)
stop = time.time()
tmp.append(stop - start)
res[env]['time'] = np.min(tmp) * 1e3
except:
res[env]['score'] = -999.
res[env]['time'] = -999.
return res
def get_benchmarks(self, X_train, y_train, X_test, y_test):
"""
"""
n_repeats = self.n_repeats
distr = self.distr
res = dict()
for env in self.envs:
res[env] = dict()
if env == 'pyglmnet':
# initialize model
model = GLM(distr=distr,
reg_lambda=[self.reg_lambda],
alpha=self.alpha,
solver='batch-gradient',
score_metric='pseudo_R2')
# fit-predict-score
model.fit(X_train, y_train)
y_test_hat = model[-1].predict(X_test)
y_test_hat = np.squeeze(y_test_hat)
if distr in ['gaussian', 'poisson']:
res[env]['score'] = \
r2_score(y_test, y_test_hat)
elif distr == 'binomial':
res[env]['score'] = \
accuracy_score(y_test,
(y_test_hat > 0.5).astype(int))
# time
tmp = list()
for r in range(n_repeats):
start = time.time()
model.fit(X_train, y_train)
stop = time.time()
tmp.append(stop - start)
res[env]['time'] = np.min(tmp) * 1e3
if env == 'sklearn':
if distr in ['gaussian', 'binomial']:
# initialize model
if distr == 'gaussian':
model = ElasticNet(alpha=self.reg_lambda,
l1_ratio=self.alpha)
elif distr == 'binomial':
model = SGDClassifier(loss='log',
penalty='elasticnet',
alpha=self.reg_lambda,
l1_ratio=self.alpha)
# fit-predict-score
model.fit(X_train, y_train)
y_test_hat = model.predict(X_test)
res[env]['score'] = model.score(X_test, y_test)
# time
tmp = list()
for r in range(n_repeats):
start = time.time()
model.fit(X_train, y_train)
stop = time.time()
tmp.append(stop - start)
res[env]['time'] = np.min(tmp) * 1e3
else:
res[env]['score'] = -999.
res[env]['time'] = -999.
if env == 'statsmodels':
# initialize model
if distr == 'gaussian':
model = sm.GLM(y_train,
sm.add_constant(X_train),
family=sm.families.Gaussian())
elif distr == 'binomial':
model = sm.GLM(y_train,
sm.add_constant(X_train),
family=sm.families.Binomial())
elif distr == 'poisson':
model = sm.GLM(y_train,
sm.add_constant(X_train),
family=sm.families.Poisson())
# fit-predict-score
statsmodels_res = model.fit()
y_test_hat = model.predict(statsmodels_res.params,
exog=sm.add_constant(X_test))
y_test_hat = np.array(y_test_hat)
if distr in ['gaussian', 'poisson']:
res[env]['score'] = \
r2_score(y_test, y_test_hat)
elif distr == 'binomial':
res[env]['score'] = \
accuracy_score(y_test,
(y_test_hat > 0.5).astype(int))
# time
tmp = list()
for r in range(n_repeats):
start = time.time()
statsmodels_res = model.fit()
stop = time.time()
tmp.append(stop - start)
res[env]['time'] = np.min(tmp) * 1e3
if env == 'R':
# initialize model
glmnet = importr('glmnet')
predict = robjects.r('predict')
# fit-predict-score
try:
fit = glmnet.glmnet(X_train,
y_train,
family=distr,
alpha=self.alpha,
nlambda=1)
tmp = predict(fit, newx=X_test, s=0)
y_test_hat = np.zeros(y_test.shape[0])
for i in range(y_test.shape[0]):
y_test_hat[i] = tmp[i]
if distr in ['gaussian', 'poisson']:
res[env]['score'] = \
r2_score(y_test, y_test_hat)
elif distr == 'binomial':
res[env]['score'] = \
accuracy_score(y_test,
(y_test_hat > 0.5).astype(int))
# time
tmp = list()
for r in range(n_repeats):
start = time.time()
fit = glmnet.glmnet(X_train,
y_train,
family=distr,
alpha=self.alpha,
nlambda=1)
stop = time.time()
tmp.append(stop - start)
res[env]['time'] = np.min(tmp) * 1e3
except Exception:
res[env]['score'] = -999.
res[env]['time'] = -999.
return res
########################################################
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=10000, n_classes=5,
n_informative=100, n_features=100, n_redundant=0)
########################################################
########################################################
# Fit the model
########################################################
########################################################
from pyglmnet import GLM
glm_mn = GLM(distr='multinomial', alpha=0.01,
reg_lambda=np.array([0.02, 0.01]), verbose=False)
glm_mn.threshold = 1e-5
glm_mn.fit(X, y)
########################################################
########################################################
# Predict and score the output
########################################################
y_pred = glm_mn[-1].predict(X)
print('Percentage correct = %f percent.' % (y_pred == y).mean())
########################################################
n_samples, n_features = X.shape
########################################################
# Split the data into training and test sets
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.33, random_state=0)
########################################################
# Fit a gaussian distributed GLM with elastic net regularization
# use the default value for reg_lambda
glm = GLM(distr='gaussian', alpha=0.05, score_metric='pseudo_R2')
# fit model
glm.fit(X_train, y_train)
# score the test set prediction
y_test_hat = glm[-1].predict(X_test)
print ("test set pseudo $R^2$ = %f" % glm[-1].score(X_test, y_test))
########################################################
# Plot the true and predicted test set target values
plt.plot(y_test[:50], 'ko-')
plt.plot(y_test_hat[:50], 'ro-')
plt.legend(['true', 'pred'], frameon=False)
plt.xlabel('Counties')
plt.ylabel('Per capita violent crime')
plt.tick_params(axis='y', right='off')
Xr = np.random.normal(0.0, 1.0, [n_samples, n_features]) yr = glm_poisson.simulate(beta0, beta, Xr) # testing data Xt = np.random.normal(0.0, 1.0, [n_samples, n_features]) yt = glm_poisson.simulate(beta0, beta, Xt) ########################################################## # Fit the model # ^^^^^^^^^^^^^ # Fitting the model is accomplished by a single GLM method called `fit()`. ########################################################## scaler = StandardScaler().fit(Xr) glm_poisson.fit(scaler.transform(Xr), yr) ########################################################## # Slicing the model object # ^^^^^^^^^^^^^^^^^^^^^^^^ # Although the model is fit to all values of reg_lambda specified by a regularization # path, often we are only interested in further analysis for a particular value of # ``reg_lambda``. We can easily do this by slicing the object. # # For instance ``model[0]`` returns an object identical to model but with ``.fit_`` # as a dictionary corresponding to the estimated coefficients for ``reg_lambda[0]``. ########################################################## # Visualize the fit coefficients # ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ # The estimated coefficients are stored in an instance variable called ``.fit_``
beta0 = np.random.normal(0.0, 1.0, 1).flatten()
beta = sps.rand(10, 1, 1)
beta = np.array(beta.todense()).flatten()
# Generate random training data by using the previous betas
train_x = np.random.normal(0.0, 1.0, [10000, 10])
train_y = simulate_glm("neg-binomial", beta0, beta, train_x)
# plot the data distribution
sns.set(color_codes=True)
sns.distplot(train_y)
plt.show()
# Create the GLM and train it
glm = GLM(distr="neg-binomial", max_iter=10000)
glm.fit(train_x, train_y)
# Print the betas and the beta0 to check for correctness
print("")
print(glm.beta0_)
print(glm.beta_)
print("")
print(beta0)
print(beta)
# Generate test data
# simulate testing data
X_test = np.random.normal(0.0, 1.0, [1000, 10])
y_test = simulate_glm("poisson", beta0, beta, X_test)
# predict using fitted model on the test data
# %% import statsmodels.api as sm mod = sm.GLM(df['cnt'] / df['offset'], df[np.arange(10)], family=sm.families.Poisson()) mod = mod.fit() mod.summary() # %% from pyglmnet import GLM # create an instance of the GLM class glm = GLM(distr='poisson') glm = glm.fit(df[np.arange(10)].values, df['cnt'].values/df['offset'].values) glm # %% glm.get_params() # %% import keras inl = keras.layers.Input((10,)) out = keras.layers.Dense(1, use_bias=False)(inl) out = keras.layers.Lambda(lambda x: keras.backend.exp(x))(out) model = keras.models.Model(inl, out) model.compile(keras.optimizers.Adam(1e-3), 'poisson') model.summary()
# training data Xr = np.random.normal(0.0, 1.0, [n_samples, n_features]) yr = glm_poissonexp.simulate(beta0, beta, Xr) # testing data Xt = np.random.normal(0.0, 1.0, [n_samples, n_features]) yt = glm_poissonexp.simulate(beta0, beta, Xt) ######################################################## # Fit model to training data ######################################################## scaler = StandardScaler().fit(Xr) glm_poissonexp.fit(scaler.transform(Xr),yr); ######################################################## # Use one model to predict ######################################################## m = glm_poissonexp[-1] this_model_param = m.fit_ yrhat = m.predict(scaler.transform(Xr)) ythat = m.predict(scaler.transform(Xt)) ######################################################## # Visualize predicted output ########################################################
n_features=100,
n_redundant=0)
########################################################
########################################################
# Fit the model
########################################################
########################################################
from pyglmnet import GLM
glm_mn = GLM(distr='multinomial',
alpha=0.01,
reg_lambda=np.array([0.02, 0.01]),
verbose=False)
glm_mn.threshold = 1e-5
glm_mn.fit(X, y)
########################################################
########################################################
# Predict and score the output
########################################################
y_pred = glm_mn[-1].predict(X).argmax(axis=1)
print('Percentage correct = %f percent.' % (y_pred == y).mean())
########################################################
def test_glmnet():
"""Test glmnet."""
raises(ValueError, GLM, distr='blah')
raises(ValueError, GLM, distr='gaussian', max_iter=1.8)
n_samples, n_features = 100, 10
# coefficients
beta0 = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0)
beta = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0, (n_features,))
distrs = ['softplus', 'gaussian', 'poisson', 'binomial', 'probit']
solvers = ['batch-gradient', 'cdfast']
score_metric = 'pseudo_R2'
learning_rate = 2e-1
random_state = 0
for distr in distrs:
betas_ = list()
for solver in solvers:
np.random.seed(random_state)
X_train = np.random.normal(0.0, 1.0, [n_samples, n_features])
y_train = simulate_glm(distr, beta0, beta, X_train, sample=False)
alpha = 0.
reg_lambda = 0.
loss_trace = list()
def callback(beta):
Tau = None
eta = 2.0
group = None
loss_trace.append(
_loss(distr, alpha, Tau, reg_lambda, X_train, y_train, eta,
group, beta))
glm = GLM(distr,
learning_rate=learning_rate,
reg_lambda=reg_lambda,
tol=1e-3,
max_iter=5000,
alpha=alpha,
solver=solver,
score_metric=score_metric,
random_state=random_state,
callback=callback)
assert (repr(glm))
glm.fit(X_train, y_train)
# verify loss decreases
assert (np.all(np.diff(loss_trace) <= 1e-7))
# verify loss at convergence = loss when beta=beta_
l_true = _loss(distr, 0., np.eye(beta.shape[0]), 0., X_train,
y_train, 2.0, None, np.concatenate(([beta0], beta)))
assert_allclose(loss_trace[-1], l_true, rtol=1e-4, atol=1e-5)
# beta=beta_ when reg_lambda = 0.
assert_allclose(beta, glm.beta_, rtol=0.05, atol=1e-2)
betas_.append(glm.beta_)
y_pred = glm.predict(X_train)
assert (y_pred.shape[0] == X_train.shape[0])
# compare all solvers pairwise to make sure they're close
for i, first_beta in enumerate(betas_[:-1]):
for second_beta in betas_[i + 1:]:
assert_allclose(first_beta, second_beta, rtol=0.05, atol=1e-2)
# test fit_predict
glm_poisson = GLM(distr='softplus')
glm_poisson.fit_predict(X_train, y_train)
raises(ValueError, glm_poisson.fit_predict, X_train[None, ...], y_train)
print(position_array.shape)
pl.figure()
for n in range(n_frames):
pl.scatter(all_position[n, 0:4], all_position[n, 4:8], s=2, c='k')
pl.show()
# GLM
glm = GLM(distr='gaussian', alpha=0.05)
X = np.delete(all_position, 0, axis=1)
y = all_position[:, 0]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=42)
scaler = StandardScaler().fit(X_train)
glm.fit(scaler.transform(X_train), y_train)
yhat = glm.predict(scaler.transform(X))
# print(glm.score(X_test, Y_test))
#
# plot
pl.figure()
pl.plot(y, marker='x', color='b', label='observed')
pl.plot(yhat[9, :], marker='o', color='r', label='trained')
pl.show()
# training data
Xr = np.random.normal(0.0, 1.0, [n_samples, n_features])
yr = glm_poissonexp.simulate(beta0, beta, Xr)
# testing data
Xt = np.random.normal(0.0, 1.0, [n_samples, n_features])
yt = glm_poissonexp.simulate(beta0, beta, Xt)
########################################################
# Fit model to training data
########################################################
scaler = StandardScaler().fit(Xr)
glm_poissonexp.fit(scaler.transform(Xr), yr)
########################################################
# Gradient of loss function
########################################################
grad_beta0, grad_beta = glm_poissonexp._grad_L2loss(
glm_poissonexp.fit_[-1]['beta0'], glm_poissonexp.fit_[-1]['beta'], 0.01,
Xr, yr)
print(grad_beta[:5])
########################################################
# Use one model to predict
########################################################
plt.ylabel('time bin of response')
plt.title('Sample first 50 rows of design' ' matrix created using Hankel')
plt.show()
########################################################
# **Fitting and predicting with a linear-Gaussian GLM**
#
# For a general linear model, the observed spikes can be
# thought of an underlying parameter
# :math:`\beta_0, \beta` that control the spiking.
#
# You can simply use linear Gaussian GLM with no regularization
# to predict the spike counts.
glm_lg = GLM(distr='gaussian', reg_lambda=0.0, score_metric='pseudo_R2')
glm_lg.fit(Xdsgn, y)
# predict spike counts
ypred_lg = glm_lg.predict(Xdsgn)
########################################################
# **Fitting and predicting with a Poisson GLM**
#
# We can also assume that there is a non-linear function governing
# the underlying the firing patterns.
# In pyglmnet, we use an exponential inverse link function
# for the Poisson distribution.
glm_poisson = GLM(distr='poisson',
alpha=0.05,
learning_rate=1.0,
def test_glmnet(distr, reg_lambda, fit_intercept, solver):
"""Test glmnet."""
raises(ValueError, GLM, distr='blah')
raises(ValueError, GLM, distr='gaussian', max_iter=1.8)
n_samples, n_features = 100, 10
# coefficients
beta0 = 0.
if fit_intercept:
beta0 = 1. / (np.float(n_features) + 1.) * \
np.random.normal(0.0, 1.0)
beta = 1. / (np.float(n_features) + int(fit_intercept)) * \
np.random.normal(0.0, 1.0, (n_features,))
score_metric = 'pseudo_R2'
learning_rate = 2e-1
random_state = 0
betas_ = list()
if not (distr == 'gamma' and solver == 'cdfast'):
np.random.seed(random_state)
theta = 1.0
X_train = np.random.normal(0.0, 1.0, [n_samples, n_features])
y_train = simulate_glm(distr, beta0, beta, X_train, theta=theta,
sample=False)
alpha = 0.
loss_trace = list()
eta = 2.0
group = None
Tau = None
def callback(beta):
Tau = None
loss_trace.append(
_loss(distr, alpha, Tau, reg_lambda,
X_train, y_train, eta, theta, group, beta,
fit_intercept=fit_intercept))
glm = GLM(distr, learning_rate=learning_rate,
reg_lambda=reg_lambda, tol=1e-5, max_iter=5000,
alpha=alpha, solver=solver, score_metric=score_metric,
random_state=random_state, callback=callback,
fit_intercept=fit_intercept, theta=theta)
assert(repr(glm))
glm.fit(X_train, y_train)
# verify loss decreases
assert(np.all(np.diff(loss_trace) <= 1e-7))
# true loss and beta should be recovered when reg_lambda == 0
if reg_lambda == 0.:
# verify loss at convergence = loss when beta=beta_
l_true = _loss(distr, alpha, Tau, reg_lambda,
X_train, y_train, eta, theta, group,
np.concatenate(([beta0], beta)))
assert_allclose(loss_trace[-1], l_true, rtol=1e-4, atol=1e-5)
# beta=beta_ when reg_lambda = 0.
assert_allclose(beta, glm.beta_, rtol=0.05, atol=1e-2)
betas_.append(glm.beta_)
y_pred = glm.predict(X_train)
assert(y_pred.shape[0] == X_train.shape[0])
# compare all solvers pairwise to make sure they're close
for i, first_beta in enumerate(betas_[:-1]):
for second_beta in betas_[i + 1:]:
assert_allclose(first_beta, second_beta, rtol=0.05, atol=1e-2)
# test fit_predict
glm_poisson = GLM(distr='softplus')
glm_poisson.fit_predict(X_train, y_train)
raises(ValueError, glm_poisson.fit_predict,
X_train[None, ...], y_train)
base=np.exp(1)))
#set up the lasso model
glm = GLM(distr="binomial",
tol=1e-2,
score_metric="pseudo_R2",
alpha=1.0,
reg_lambda=np.logspace(np.log(100), np.log(0.01), 5, base=np.exp(1)))
print("gl_glm: ", gl_glm)
print("glm: ", glm)
##########################################################
# Fit models
gl_glm.fit(Xtrain, ytrain)
glm.fit(Xtrain, ytrain)
##########################################################
# Visualize model scores on test set
plt.figure()
plt.semilogx(gl_glm.reg_lambda, gl_glm.score(Xtest, ytest), 'go-')
plt.semilogx(gl_glm.reg_lambda, gl_glm.score(Xtrain, ytrain), 'go--')
plt.semilogx(glm.reg_lambda, glm.score(Xtest, ytest), 'ro-')
plt.semilogx(glm.reg_lambda, glm.score(Xtrain, ytrain), 'ro--')
plt.legend(
['Group Lasso: test', 'Group Lasso: train', 'Lasso: test', 'Lasso: train'],
frameon=False,
loc='best')
plt.xlabel('$\lambda$')
reg_lambda=np.array([0.02, 0.01]), learning_rate=1e-3 ,verbose=False,)
#initial values for the coefficients
beta0 = np.random.normal(0.0, 1.0, 1)
beta = sps.rand(n_features, 1, 0.1)
beta = np.array(beta.todense())
model.threshold = 1e-5
#scaler = StandardScaler().fit(X_train)
#model.fit(scaler.transform(X_train),y_train)
# Fitting the model
model.fit(X_train,y_train)
#ploting the fit coefficients
# TODO: fix this graph
fit_param = model[0].fit_
plt.plot(beta[:], 'bo', label ='bo')
plt.plot(fit_param['beta'][:], 'ro', label='ro')
plt.xlabel('samples')
plt.ylabel('outputs')
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=1,
ncol=2, borderaxespad=0.)
plt.show()
# Makin the predictions base on fit model
yt_predicted = model[-1].predict(X_test)
# Fit models
from sklearn.cross_validation import train_test_split
Xtrain, Xtest, Ytrain, Ytest = train_test_split(features,
spike_counts,
test_size=0.2,
random_state=42)
########################################################
from pyglmnet import utils
n_samples = Xtrain.shape[0]
Tau = utils.tikhonov_from_prior(prior_cov, n_samples)
glm = GLM(distr='poisson', alpha=0., Tau=Tau, score_metric='pseudo_R2')
glm.fit(Xtrain, Ytrain)
cvopt_lambda = glm.score(Xtest, Ytest).argmax()
print("train score: %f" % glm[cvopt_lambda].score(Xtrain, Ytrain))
print("test score: %f" % glm[cvopt_lambda].score(Xtest, Ytest))
weights = glm[cvopt_lambda].fit_['beta']
########################################################
# Visualize
for time_bin_ in range(n_temporal_basis):
RF = strf_model.make_image_from_spatial_basis(
spatial_basis,
weights[range(time_bin_, n_spatial_basis * n_temporal_basis,
n_temporal_basis)])
plt.subplot(1, n_temporal_basis, time_bin_ + 1)
def fit(self, X, Y, get_history_terms=True):
"""
Fits the model to the data in X to predict the response Y.
Imports models and creates model instance as well.
Parameters
----------
X: float, n_samples x n_features, features of interest
Y: float, n_samples x 1, population activity
get_history_terms = Boolean. Whether to compute the temporal features.
Note that if spike_history and cov_history are False,
no history will be computed anyways and the flag does nothing.
"""
if self.default_params:
warnings.warn(
'\n Using default hyperparameters. Consider optimizing on' +
' a held-out dataset using, e.g. hyperopt or random search')
# make the covariate matrix. Include spike or covariate history?
# The different methods here are to satisfy the needs of recurrent keras
# models
if get_history_terms:
if self.tunemodel == 'lstm':
X, Y = self.get_all_with_history_keras(X, Y)
else:
X, Y = self.get_all_with_history(X, Y)
if self.tunemodel == 'glm':
model = GLM(**self.params)
model.fit(X, Y)
# we want the last of the regularization path
self.model = model[-1]
elif self.tunemodel == 'feedforward_nn':
if np.ndim(X) == 1:
X = np.transpose(np.atleast_2d(X))
params = self.params
model = Sequential()
model.add(
Dense(params['n1'],
input_dim=np.shape(X)[1],
kernel_initializer='glorot_normal',
activation='relu',
kernel_regularizer=l2(params['l2'])))
model.add(Dropout(params['dropout']))
model.add(BatchNormalization())
model.add(
Dense(params['n2'],
kernel_initializer='glorot_normal',
activation='relu',
kernel_regularizer=l2(params['l2'])))
model.add(BatchNormalization())
model.add(Dense(1, activation='softplus'))
optim = adam(lr=params['lr'],
clipnorm=params['clipnorm'],
decay=params['decay'],
beta_1=1 - params['b1'],
beta_2=1 - params['b2'])
model.compile(
loss='poisson',
optimizer=optim,
)
hist = model.fit(X,
Y,
batch_size=128,
epochs=30,
verbose=self.verbose)
self.model = model
elif self.tunemodel == 'xgboost':
dtrain = xgb.DMatrix(X, label=Y)
num_round = 200
self.model = xgb.train(self.params, dtrain, num_round)
elif self.tunemodel == 'random_forest':
self.model = RandomForestRegressor(**self.params)
self.model.fit(X, Y)
elif self.tunemodel == 'lstm':
if np.ndim(X) == 1:
X = np.transpose(np.atleast_2d(X))
params = self.params
model = Sequential() #Declare model
#Add recurrent layer
model.add(LSTM(int(params['n_units']),input_shape=(X.shape[1],X.shape[2]),\
dropout_W=params['dropout'],dropout_U=params['dropout']))
#Within recurrent layer, include dropout
model.add(Dropout(params['dropout'])
) #Dropout some units (recurrent layer output units)
#Add dense connections to output layer
model.add(Dense(1, activation='softplus'))
#Fit model (and set fitting parameters)
model.compile(loss='poisson',
optimizer='rmsprop',
metrics=['accuracy'])
model.fit(X,
Y,
epochs=int(params['epochs']),
batch_size=int(params['batch_size']),
verbose=self.verbose) #Fit the model
self.model = model
else: #using predefined model
self.model.fit(X, Y)
np.shape(prior_cov)
########################################################
# Fit models
from sklearn.cross_validation import train_test_split
Xtrain, Xtest, Ytrain, Ytest = train_test_split(features, spike_counts, test_size=0.2, random_state=42)
########################################################
from pyglmnet import utils
n_samples = Xtrain.shape[0]
Tau = utils.tikhonov_from_prior(prior_cov, n_samples)
glm = GLM(distr='poisson', alpha=0., Tau=Tau, score_metric='pseudo_R2')
glm.fit(Xtrain, Ytrain)
cvopt_lambda = glm.score(Xtest, Ytest).argmax()
print("train score: %f" % glm[cvopt_lambda].score(Xtrain, Ytrain))
print("test score: %f" % glm[cvopt_lambda].score(Xtest, Ytest))
weights = glm[cvopt_lambda].fit_['beta']
########################################################
# Visualize
for time_bin_ in range(n_temporal_basis):
RF = strf_model.make_image_from_spatial_basis(spatial_basis,
weights[range(time_bin_,
n_spatial_basis * n_temporal_basis,
n_temporal_basis)])
plt.subplot(1, n_temporal_basis, time_bin_+1)
