i.e. remove the features that have the same value in all samples.
Examples
--------
>>> X = [[0, 2, 0, 3],
... [0, 1, 4, 3],
... [0, 1, 1, 3]]
>>> selector = VarianceThreshold(0.0)
>>> selector.fit_transform(X)
array([[2, 0],
[1, 4],
[1, 1]])
"""
def __init__(self, threshold=0.0):
self.threshold = threshold
def _check_X(self, X):
if not isinstance(X, np.ndarray):
X = np.asarray(X)
return X
def fit_transform(self, X):
_X = self._check_X(X)
return _X[:, (np.std(_X, axis=0) > self.threshold + 1e-8)]
if __name__ == '__main__':
# run corresponding tests
from utils.testing import run_tests
run_tests(__file__)
self.best_model_ = self.model
if self.save_models:
self.best_model_.save(filepath=os.path.join(
self.dirpath, self._best_model_name()),
**self.save_params)
# verbosing
if self.verbose:
print_inline(
" - best acc.: {0:.4f} +/- 2 * {1:.3f} at {2}\n"
.format(self.best_score_, self.best_std_,
self.best_params_))
# convert lists to np.ndarray
for key in (['mean_score', 'std_score', 'params'] + [
'split{0}_{1}'.format(k, s) for k in xrange(self.n_splits)
for s in ('score', 'train_time', 'test_time')
]):
self.cv_results_[key] = np.asarray(self.cv_results_[key])
return self
def to_df(self):
import pandas as pd
return pd.DataFrame.from_dict(self.cv_results_).fillna('')
if __name__ == '__main__':
# run corresponding tests
import tests.test_model_selection as t
from utils.testing import run_tests
run_tests(__file__, t)
MULTI_THREADED_SOLUTIONS, 1, args.max_thds + 1,
CombingRunner, build_logger, SOLUTIONS_FOLDER,
CXX_COMPILER_PATH)
test_cases = []
for name in listdir(args.tests):
with open(os.path.join(args.tests, name), 'r') as f:
_, size = f.readline(), int(f.readline())
test_cases.append((size, os.path.join(args.tests, name)))
if args.real_data:
tests_pairs = [(74437, 91721), (124884, 134226)]
else:
tests_pairs = [(100000, 100000), (50000, 50000)]
tests = []
for i in range(len(test_cases)):
for j in range(len(test_cases)):
x, y = test_cases[i], test_cases[j]
if x[1] != y[1] and (x[0], y[0]) in tests_pairs:
tests.append((x[0], CombingTest(x[1], y[1])))
tests.sort(key=lambda x: x[0])
_, tests = zip(*tests)
tests = list(tests)
run_tests(runners, tests, args.repeats, args.resultcsvfile, default_logger,
True)
SINGLE_THREADED_SOLUTIONS = [
'braid_multiplication_sequential_non_optimized',
'braid_multiplication_sequential_memory',
'braid_multiplication_sequential_precompute'
]
MULTI_THREADED_SOLUTIONS = [
'braid_multiplication_parallel',
]
SOLUTIONS_FOLDER = 'braid_mults_solutions' # where we put our ready to run implementations
CSV_FILE = 'braid_mul.csv'
REPEATS = 10
default_logger = Logger('log.txt')
build_logger = Logger('build_log.txt')
if __name__ == '__main__':
arg_parser = ArgumentParser()
arg_parser.add_argument('depth', type=int, help='Depth for level paralellism')
args = arg_parser.parse_args()
runners = compile_programs(SINGLE_THREADED_SOLUTIONS, MULTI_THREADED_SOLUTIONS, 0, args.depth + 1, BraidMulRunner,
build_logger, SOLUTIONS_FOLDER, CXX_COMPILER_PATH)
n = [1000, 5000, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000,150000,200000, 250000,
300000,350000,400000,450000,500000,550000,600000,650000,700000,750000,800000,850000,900000,950000,1000000,
2000000, 5000000, 7500000, 10000000]
tests = [BraidMulTest(str(x), '42') for x in n]
run_tests(runners, tests, REPEATS, CSV_FILE, default_logger, False)
