def test_predict_proba():
from EvoDAG.model import EvoDAGE
m = EvoDAGE(n_estimators=3, n_jobs=2, time_limit=4).fit(X, cl)
pr = m.predict_proba(X)
default_nargs()
print(pr.min(), pr.max())
assert pr.min() >= 0 and pr.max() <= 1
def test_init_e():
from EvoDAG.model import EvoDAGE
m = EvoDAGE(n_estimators=4, n_jobs=4, seed=10,
early_stopping_rounds=100).fit(X, cl)
hy = m.predict(X)
assert (cl == hy).mean() > 0.9
default_nargs()
def test_raw_decision_function():
from EvoDAG.model import EvoDAGE
m = EvoDAGE(n_estimators=3, n_jobs=2, time_limit=4)
m.fit(X, cl)
pr = m.raw_decision_function(X)
default_nargs()
print(pr.shape)
assert pr.shape[1] == np.unique(cl).shape[0] * len(m._m.models)
def test_init_time_limit():
from EvoDAG.model import EvoDAGE
import time
local = time.time()
EvoDAGE(n_estimators=30, n_jobs=2, time_limit=4).fit(X, cl)
default_nargs()
t = time.time() - local
print(t)
assert t <= 6
def test_regression_multiple_outputs():
from EvoDAG.model import EvoDAGE
from SparseArray import SparseArray
y = [
SparseArray.fromlist(cl),
SparseArray.fromlist(cl * -1),
SparseArray.fromlist(cl * -1 + 0.5)
]
m = EvoDAGE(time_limit=4, multiple_outputs=True,
classifier=False).fit(X, y)
assert m
def test_max_training_size():
from EvoDAG.model import EvoDAGE
import time
local = time.time()
try:
EvoDAGE(n_estimators=30, n_jobs=2, time_limit=4,
max_training_size="hola").fit(X, cl)
except TypeError:
default_nargs()
t = time.time() - local
print(t)
assert t <= 6
return
assert False
time_budget = D.info['time_budget'] # <== HERE IS THE TIME BUDGET!
else:
time_budget = max_time
time_budget = float(time_budget)
overall_time_budget = overall_time_budget + time_budget
vprint(verbose, "[+] Cumulated time budget (all tasks so far) %5.2f sec" % (overall_time_budget))
# We do not add the time left over form previous dataset: time_budget += time_left_over
vprint(verbose, "[+] Time budget for this task %5.2f sec" % time_budget)
time_spent = time.time() - start
vprint(verbose, "[+] Remaining time after reading data %5.2f sec" % (time_budget-time_spent))
# ========= Creating a model, knowing its assigned task from D.info['task'].
# The model can also select its hyper-parameters based on other elements of info.
vprint(verbose, "======== Creating model ==========")
time_budget = 3600
M = EvoDAGE(n_jobs=16, classifier=True, time_limit=time_budget, fitness_function='macro-F1', Tanh=False)
print(M)
# ========= Iterating over learning cycles and keeping track of time
time_spent = time.time() - start
vprint(verbose, "[+] Remaining time after building model %5.2f sec" % (time_budget-time_spent))
if time_spent >= time_budget:
vprint(verbose, "[-] Sorry, time budget exceeded, skipping this task")
execution_success = False
continue
time_predict_value = time_to_predict(D)
time_budget = time_budget - time_spent # Remove time spent so far
start = time.time() # Reset the counter
time_spent = 0 # Initialize time spent learning
M.time_limit = time_budget * time_predict_value * 0.9
vprint(verbose, "[+] Time budget to train the model %5.2f sec" % M._time_limit)