def test_covtype_memorize():
with timer("Cuda treelearn"):
forest = RandomForestClassifier(bootstrap = False)
forest.fit(x, y, bfs_threshold = 500000)
with timer("Predict"):
diff, total = util.test_diff(forest.predict(x), y)
print "%s(Wrong)/%s(Total). The error rate is %f." % (diff, total, diff/float(total))
assert diff == 0, "Didn't perfectly memorize, got %d wrong" % diff
def test_digits_memorize():
with timer("Cuda treelearn"):
forest = RandomForestClassifier(n_estimators = n_estimators/2, bootstrap = False)
forest.fit(x, y)
with timer("Predict"):
diff, total = util.test_diff(forest.predict(x), y)
print "%s (Wrong) / %s (Total). The error rate is %f." % (diff, total, diff/float(total))
assert diff == 0, "Didn't memorize, got %d wrong" % diff
def test_covtype_memorize():
with timer("Cuda treelearn"):
forest = RandomForestClassifier(bootstrap = False)
forest.fit(x, y, bfs_threshold = 500000)
with timer("Predict"):
diff, total = util.test_diff(forest.predict(x), y)
print("%s(Wrong)/%s(Total). The error rate is %f." % (diff, total, diff/float(total)))
assert diff == 0, "Didn't perfectly memorize, got %d wrong" % diff
def benchmark_hybrid(dataset, bfs_threshold=None):
x_train, y_train = load_data(dataset)
# Just use this forest to compile the code.
throw_away = hybridForest(n_estimators=1, bootstrap=bootstrap, max_features=None, cpu_classifier=WiseRF)
throw_away.fit(x_train, y_train, bfs_threshold=bfs_threshold)
with timer("%s benchmark hybrid (bfs_threshold = %s)" % (dataset, bfs_threshold)):
forest = hybridForest(n_estimators=n_estimators, bootstrap=bootstrap, max_features=None, cpu_classifier=WiseRF)
forest.fit(x_train, y_train, bfs_threshold=bfs_threshold)
forest = None
def benchmark_cuda(dataset, bfs_threshold = None):
x_train, y_train = load_data(dataset)
#Just use this forest to compile the code.
throw_away = RandomForestClassifier(n_estimators = 1, bootstrap = bootstrap, verbose = False,
max_features = None, debug = debug)
throw_away.fit(x_train, y_train, bfs_threshold = bfs_threshold)
with timer("%s benchmark cuda (bfs_threshold = %s)" % (dataset, bfs_threshold)):
forest = RandomForestClassifier(n_estimators = n_estimators, bootstrap = bootstrap, verbose = verbose,
max_features = None, debug = debug)
forest.fit(x_train, y_train, bfs_threshold = bfs_threshold)
forest = None
def benchmark_hybrid(dataset, bfs_threshold=None):
x_train, y_train = load_data(dataset)
#Just use this forest to compile the code.
throw_away = hybridForest(n_estimators=2,
bootstrap=bootstrap,
max_features=None)
throw_away.fit(x_train, y_train, bfs_threshold=bfs_threshold)
with timer("%s benchmark hybrid (bfs_threshold = %s)" %
(dataset, bfs_threshold)):
forest = hybridForest(n_estimators=n_estimators,
bootstrap=bootstrap,
n_jobs=2,
max_features=None)
forest.fit(x_train, y_train, bfs_threshold=bfs_threshold)
forest = None
def benchmark_cuda(dataset, bfs_threshold=None):
x_train, y_train = load_data(dataset)
#Just use this forest to compile the code.
throw_away = RandomForestClassifier(n_estimators=1,
bootstrap=bootstrap,
verbose=False,
max_features=None,
debug=debug)
throw_away.fit(x_train, y_train, bfs_threshold=bfs_threshold)
with timer("%s benchmark cuda (bfs_threshold = %s)" %
(dataset, bfs_threshold)):
forest = RandomForestClassifier(n_estimators=n_estimators,
bootstrap=bootstrap,
verbose=verbose,
max_features=None,
debug=debug)
forest.fit(x_train, y_train, bfs_threshold=bfs_threshold)
forest = None