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_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_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_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])
########################################################
# 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
########################################################
m = glm_poissonexp[-1]
this_model_param = m.fit_
yrhat = m.predict(scaler.transform(Xr))
ythat = m.predict(scaler.transform(Xt))
########################################################
# Visualize predicted output
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 ######################################################## m = glm_poissonexp[-1] this_model_param = m.fit_ yrhat = m.predict(scaler.transform(Xr)) ythat = m.predict(scaler.transform(Xt)) ######################################################## # Visualize predicted output
