def test_repeat_partition():
# tests that if we use identical partitions the average is the same
# as the estimate for the full data
np.random.seed(435265)
N = 200
p = 10
m = 1
beta = np.random.normal(size=p)
beta = beta * np.random.randint(0, 2, p)
X = np.random.normal(size=(N, p))
y = X.dot(beta) + np.random.normal(size=N)
def _rep_data_gen(endog, exog, partitions):
"""partitions data"""
n_exog = exog.shape[0]
n_part = np.ceil(n_exog / partitions)
ii = 0
while ii < n_exog:
yield endog, exog
ii += int(n_part)
nv_mod = DistributedModel(m,
estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitOLSnv = nv_mod.fit(_rep_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
ols_mod = OLS(y, X)
fitOLS = ols_mod.fit_regularized(alpha=0.1)
assert_allclose(fitOLSnv.params, fitOLS.params)
def test_repeat_partition():
# tests that if we use identical partitions the average is the same
# as the estimate for the full data
np.random.seed(435265)
N = 200
p = 10
m = 1
beta = np.random.normal(size=p)
beta = beta * np.random.randint(0, 2, p)
X = np.random.normal(size=(N, p))
y = X.dot(beta) + np.random.normal(size=N)
def _rep_data_gen(endog, exog, partitions):
"""partitions data"""
n_exog = exog.shape[0]
n_part = np.ceil(n_exog / partitions)
ii = 0
while ii < n_exog:
yield endog, exog
ii += int(n_part)
nv_mod = DistributedModel(m, estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitOLSnv = nv_mod.fit(_rep_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
ols_mod = OLS(y, X)
fitOLS = ols_mod.fit_regularized(alpha=0.1)
assert_allclose(fitOLSnv.params, fitOLS.params)
def test_debiased_v_average():
# tests that the debiased method performs better than the standard
# average. Does this for both OLS and GLM.
np.random.seed(435265)
N = 200
p = 10
m = 4
beta = np.random.normal(size=p)
beta = beta * np.random.randint(0, 2, p)
X = np.random.normal(size=(N, p))
y = X.dot(beta) + np.random.normal(size=N)
db_mod = DistributedModel(m)
fitOLSdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.2})
olsdb = np.linalg.norm(fitOLSdb.params - beta)
n_mod = DistributedModel(m,
estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitOLSn = n_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.2})
olsn = np.linalg.norm(fitOLSn.params - beta)
assert_(olsdb < olsn)
prob = 1 / (1 + np.exp(-X.dot(beta) + np.random.normal(size=N)))
y = 1. * (prob > 0.5)
db_mod = DistributedModel(m,
model_class=GLM,
init_kwds={"family": Binomial()})
fitGLMdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.2})
glmdb = np.linalg.norm(fitGLMdb.params - beta)
n_mod = DistributedModel(m,
model_class=GLM,
init_kwds={"family": Binomial()},
estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitGLMn = n_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.2})
glmn = np.linalg.norm(fitGLMn.params - beta)
assert_(glmdb < glmn)
def test_debiased_v_average():
# tests that the debiased method performs better than the standard
# average. Does this for both OLS and GLM.
np.random.seed(435265)
N = 200
p = 10
m = 4
beta = np.random.normal(size=p)
beta = beta * np.random.randint(0, 2, p)
X = np.random.normal(size=(N, p))
y = X.dot(beta) + np.random.normal(size=N)
db_mod = DistributedModel(m)
fitOLSdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.2})
olsdb = np.linalg.norm(fitOLSdb.params - beta)
n_mod = DistributedModel(m, estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitOLSn = n_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.2})
olsn = np.linalg.norm(fitOLSn.params - beta)
assert_(olsdb < olsn)
prob = 1 / (1 + np.exp(-X.dot(beta) + np.random.normal(size=N)))
y = 1. * (prob > 0.5)
db_mod = DistributedModel(m, model_class=GLM,
init_kwds={"family": Binomial()})
fitGLMdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.2})
glmdb = np.linalg.norm(fitGLMdb.params - beta)
n_mod = DistributedModel(m, model_class=GLM,
init_kwds={"family": Binomial()},
estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitGLMn = n_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.2})
glmn = np.linalg.norm(fitGLMn.params - beta)
assert_(glmdb < glmn)
def test_single_partition():
# tests that the results make sense if we have a single partition
np.random.seed(435265)
N = 200
p = 10
m = 1
beta = np.random.normal(size=p)
beta = beta * np.random.randint(0, 2, p)
X = np.random.normal(size=(N, p))
y = X.dot(beta) + np.random.normal(size=N)
# test regularized OLS v. naive
db_mod = DistributedModel(m)
fitOLSdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0})
nv_mod = DistributedModel(m,
estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitOLSnv = nv_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0})
ols_mod = OLS(y, X)
fitOLS = ols_mod.fit(alpha=0)
assert_allclose(fitOLSdb.params, fitOLS.params)
assert_allclose(fitOLSnv.params, fitOLS.params)
# test regularized
nv_mod = DistributedModel(m,
estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitOLSnv = nv_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
ols_mod = OLS(y, X)
fitOLS = ols_mod.fit_regularized(alpha=0.1)
assert_allclose(fitOLSnv.params, fitOLS.params)
def test_larger_p():
# tests when p > N / m for the debiased and naive case
np.random.seed(435265)
N = 40
p = 40
m = 5
beta = np.random.normal(size=p)
beta = beta * np.random.randint(0, 2, p)
X = np.random.normal(size=(N, p))
y = X.dot(beta) + np.random.normal(size=N)
db_mod = DistributedModel(m)
fitOLSdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
assert_equal(np.sum(np.isnan(fitOLSdb.params)), 0)
nv_mod = DistributedModel(m, estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitOLSnv = nv_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
assert_equal(np.sum(np.isnan(fitOLSnv.params)), 0)
def test_single_partition():
# tests that the results make sense if we have a single partition
np.random.seed(435265)
N = 200
p = 10
m = 1
beta = np.random.normal(size=p)
beta = beta * np.random.randint(0, 2, p)
X = np.random.normal(size=(N, p))
y = X.dot(beta) + np.random.normal(size=N)
# test regularized OLS v. naive
db_mod = DistributedModel(m)
fitOLSdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0})
nv_mod = DistributedModel(m, estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitOLSnv = nv_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0})
ols_mod = OLS(y, X)
fitOLS = ols_mod.fit(alpha=0)
assert_allclose(fitOLSdb.params, fitOLS.params)
assert_allclose(fitOLSnv.params, fitOLS.params)
# test regularized
nv_mod = DistributedModel(m, estimation_method=_est_regularized_naive,
join_method=_join_naive)
fitOLSnv = nv_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
ols_mod = OLS(y, X)
fitOLS = ols_mod.fit_regularized(alpha=0.1)
assert_allclose(fitOLSnv.params, fitOLS.params)
def test_fit_joblib():
# tests that the results of all the intermediate steps
# remains correct for joblib fit, does this for OLS and GLM
# and a variety of model sizes
#
# regression test
np.random.seed(435265)
X = np.random.normal(size=(50, 3))
y = np.random.randint(0, 2, size=50)
mod = DistributedModel(1, model_class=OLS)
fit = mod.fit(_data_gen(y, X, 1), parallel_method="joblib",
fit_kwds={"alpha": 0.5})
assert_allclose(fit.params, np.array([-0.191606, -0.012565, -0.351398]),
atol=1e-6, rtol=0)
mod = DistributedModel(2, model_class=OLS)
fit = mod.fit(_data_gen(y, X, 2), parallel_method="joblib",
fit_kwds={"alpha": 0.5})
assert_allclose(fit.params, np.array([-0.157416, -0.029643, -0.471653]),
atol=1e-6, rtol=0)
mod = DistributedModel(3, model_class=OLS)
fit = mod.fit(_data_gen(y, X, 3), parallel_method="joblib",
fit_kwds={"alpha": 0.5})
assert_allclose(fit.params, np.array([-0.124891, -0.050934, -0.403354]),
atol=1e-6, rtol=0)
mod = DistributedModel(1, model_class=GLM,
init_kwds={"family": Binomial()})
fit = mod.fit(_data_gen(y, X, 1), parallel_method="joblib",
fit_kwds={"alpha": 0.5})
assert_allclose(fit.params, np.array([-0.164515, -0.412854, -0.223955]),
atol=1e-6, rtol=0)
mod = DistributedModel(2, model_class=GLM,
init_kwds={"family": Binomial()})
fit = mod.fit(_data_gen(y, X, 2), parallel_method="joblib",
fit_kwds={"alpha": 0.5})
assert_allclose(fit.params, np.array([-0.142513, -0.360324, -0.295485]),
atol=1e-6, rtol=0)
mod = DistributedModel(3, model_class=GLM,
init_kwds={"family": Binomial()})
fit = mod.fit(_data_gen(y, X, 3), parallel_method="joblib",
fit_kwds={"alpha": 0.5})
assert_allclose(fit.params, np.array([-0.110487, -0.306431, -0.243921]),
atol=1e-6, rtol=0)
def test_non_zero_params():
# tests that the thresholding does not cause any issues
np.random.seed(435265)
N = 200
p = 10
m = 5
beta = np.random.normal(size=p)
beta = beta * np.random.randint(0, 2, p)
X = np.random.normal(size=(N, p))
y = X.dot(beta) + np.random.normal(size=N)
db_mod = DistributedModel(m, join_kwds={"threshold": 0.13})
fitOLSdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
ols_mod = OLS(y, X)
fitOLS = ols_mod.fit_regularized(alpha=0.1)
nz_params_db = 1 * (fitOLSdb.params != 0)
nz_params_ols = 1 * (fitOLS.params != 0)
assert_allclose(nz_params_db, nz_params_ols)
def test_non_zero_params():
# tests that the thresholding does not cause any issues
np.random.seed(435265)
N = 200
p = 10
m = 5
beta = np.random.normal(size=p)
beta = beta * np.random.randint(0, 2, p)
X = np.random.normal(size=(N, p))
y = X.dot(beta) + np.random.normal(size=N)
db_mod = DistributedModel(m, join_kwds={"threshold": 0.13})
fitOLSdb = db_mod.fit(_data_gen(y, X, m), fit_kwds={"alpha": 0.1})
ols_mod = OLS(y, X)
fitOLS = ols_mod.fit_regularized(alpha=0.1)
nz_params_db = 1 * (fitOLSdb.params != 0)
nz_params_ols = 1 * (fitOLS.params != 0)
assert_allclose(nz_params_db, nz_params_ols)
ii += int(n_part)
# Next we generate some random data to serve as an example.
X = np.random.normal(size=(1000, 25))
beta = np.random.normal(size=25)
beta *= np.random.randint(0, 2, size=25)
y = norm.rvs(loc=X.dot(beta))
m = 5
# This is the most basic fit, showing all of the defaults, which are to
# use OLS as the model class, and the debiasing procedure.
debiased_OLS_mod = DistributedModel(m)
debiased_OLS_fit = debiased_OLS_mod.fit(zip(_endog_gen(y, m), _exog_gen(X, m)),
fit_kwds={"alpha": 0.2})
# Then we run through a slightly more complicated example which uses the
# GLM model class.
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.genmod.families import Gaussian
debiased_GLM_mod = DistributedModel(m,
model_class=GLM,
init_kwds={"family": Gaussian()})
debiased_GLM_fit = debiased_GLM_mod.fit(zip(_endog_gen(y, m), _exog_gen(X, m)),
fit_kwds={"alpha": 0.2})
# We can also change the `estimation_method` and the `join_method`. The
# below example show how this works for the standard OLS case. Here we
ii += int(n_part)
# Next we generate some random data to serve as an example.
X = np.random.normal(size=(1000, 25))
beta = np.random.normal(size=25)
beta *= np.random.randint(0, 2, size=25)
y = norm.rvs(loc=X.dot(beta))
m = 5
# This is the most basic fit, showing all of the defaults, which are to
# use OLS as the model class, and the debiasing procedure.
debiased_OLS_mod = DistributedModel(m)
debiased_OLS_fit = debiased_OLS_mod.fit(
zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={"alpha": 0.2})
# Then we run through a slightly more complicated example which uses the
# GLM model class.
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.genmod.families import Gaussian
debiased_GLM_mod = DistributedModel(
m, model_class=GLM, init_kwds={"family": Gaussian()})
debiased_GLM_fit = debiased_GLM_mod.fit(
zip(_endog_gen(y, m), _exog_gen(X, m)), fit_kwds={"alpha": 0.2})
# We can also change the `estimation_method` and the `join_method`. The
# below example show how this works for the standard OLS case. Here we
# using a naive averaging approach instead of the debiasing procedure.
