def test_prev_state_methods(self):
## 1) Build dataset
## ================================================
X, y = datasets.make_classification(n_samples=5,
n_features=20,
n_informative=2)
Xy = {"X": X, "y": y}
methods = Methods(*[TOY_CLF(v_lambda=v_lambda)
for v_lambda in [2, 1]])
methods.run(**Xy)
ps_methods = WarmStartMethods(*[TOY_CLF(v_lambda=v_lambda)
for v_lambda in [2, 1]])
ps_methods.run(**Xy)
self.assertTrue(compare_two_node(methods, ps_methods))
self.assertTrue(comp_2wf_reduce_res(methods, ps_methods))
def test_prev_state_methods(self):
## 1) Build dataset
## ================================================
X, y = datasets.make_classification(n_samples=5,
n_features=20,
n_informative=2)
Xy = {"X": X, "y": y}
methods = Methods(*[TOY_CLF(v_lambda=v_lambda)
for v_lambda in [2, 1]])
methods.run(**Xy)
ps_methods = WarmStartMethods(*[TOY_CLF(v_lambda=v_lambda)
for v_lambda in [2, 1]])
ps_methods.run(**Xy)
self.assertTrue(compare_two_node(methods, ps_methods))
self.assertTrue(comp_2wf_reduce_res(methods, ps_methods))
## 1) Build dataset
## ================================================
X, y = datasets.make_classification(n_samples=10,
n_features=5,
n_informative=2,
random_state=1)
Xy = {"X": X, "y": y}
## 2) Build Methods
## ================================================
print("Methods ===================================")
methods = Methods(*[TOY_CLF(v_lambda=v_lambda) for v_lambda in [2, 1]])
print(methods.run(**Xy))
## 3) Build WarmStartMethods like Methods
## ================================================
## WarmStartMethods
## / \
## TOY_CLF(v_lambda=2) TOY_CLF(v_lambda=1)
##
## 1. WarmStartMethods will look for different argumenets as signature
## For example, here is v_lambda, there are different for each leaf
## 2. And then run TOY_CLF(v_lambda=2).transform
## 3. Except v_lambda, WarmStartMethods copy all the other parameters
## from TOY_CLF(v_lambda=2) to TOY_CLF(v_lambda=1) as initialization
## 4. Finally call TOY_CLF(v_lambda=1).transform
print("WarmStartMethods ==========================")
ps_methods = WarmStartMethods(
*[TOY_CLF(v_lambda=v_lambda) for v_lambda in [2, 1]])
print(ps_methods.run(**Xy))
## ================================================
X, y = datasets.make_classification(n_samples=10,
n_features=5,
n_informative=2,
random_state=1)
Xy = {"X": X, "y": y}
## 2) Build Methods
## ================================================
print "Methods ==================================="
methods = Methods(*[TOY_CLF(v_lambda=v_lambda)
for v_lambda in [2, 1]])
print methods.run(**Xy)
## 3) Build WarmStartMethods like Methods
## ================================================
## WarmStartMethods
## / \
## TOY_CLF(v_lambda=2) TOY_CLF(v_lambda=1)
##
## 1. WarmStartMethods will look for different argumenets as signature
## For example, here is v_lambda, there are different for each leaf
## 2. And then run TOY_CLF(v_lambda=2).transform
## 3. Except v_lambda, WarmStartMethods copy all the other parameters
## from TOY_CLF(v_lambda=2) to TOY_CLF(v_lambda=1) as initialization
## 4. Finally call TOY_CLF(v_lambda=1).transform
print "WarmStartMethods =========================="
ps_methods = WarmStartMethods(*[TOY_CLF(v_lambda=v_lambda)
for v_lambda in [2, 1]])
print ps_methods.run(**Xy)
