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 = GLMCV(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.predict(X_test)
print ("test set pseudo $R^2$ = %f" % glm.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')
plt.tick_params(axis='x', top='off')
ax = plt.gca()
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
from sklearn.model_selection 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 = GLMCV(distr='poisson', alpha=0., Tau=Tau, score_metric='pseudo_R2', cv=3)
glm.fit(Xtrain, Ytrain)
print("train score: %f" % glm.score(Xtrain, Ytrain))
print("test score: %f" % glm.score(Xtest, Ytest))
weights = glm.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)
plt.imshow(RF, cmap='Blues', interpolation='none')
titletext = str(centers[time_bin_])
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 = GLMCV(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.predict(X_test)
print ("test set pseudo $R^2$ = %f" % glm.score(X_test, y_test))
########################################################
# Now use plain grid search cv to compare
import numpy as np # noqa
from sklearn.model_selection import GridSearchCV # noqa
from sklearn.cross_validation import StratifiedKFold # noqa
cv = StratifiedKFold(y_train, 3)
reg_lambda = np.logspace(np.log(0.5), np.log(0.01), 10,
base=np.exp(1))
param_grid = [{'reg_lambda': reg_lambda}]
glm = GLM(distr='gaussian', alpha=0.05, score_metric='pseudo_R2')