repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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spegg | spegg-master/tests/googletest/googlemock/scripts/generator/cpp/gmock_class_test.py | #!/usr/bin/env python
#
# Copyright 2009 Neal Norwitz All Rights Reserved.
# Portions Copyright 2009 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ... | 11,356 | 24.293987 | 78 | py |
spegg | spegg-master/tests/googletest/googlemock/scripts/generator/cpp/utils.py | #!/usr/bin/env python
#
# Copyright 2007 Neal Norwitz
# Portions Copyright 2007 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 1,153 | 26.47619 | 74 | py |
spegg | spegg-master/tests/googletest/googlemock/scripts/generator/cpp/__init__.py | 0 | 0 | 0 | py | |
spegg | spegg-master/tests/googletest/googlemock/scripts/generator/cpp/ast.py | #!/usr/bin/env python
#
# Copyright 2007 Neal Norwitz
# Portions Copyright 2007 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 62,773 | 35.201845 | 82 | py |
spegg | spegg-master/tests/googletest/googlemock/scripts/generator/cpp/tokenize.py | #!/usr/bin/env python
#
# Copyright 2007 Neal Norwitz
# Portions Copyright 2007 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 9,752 | 32.864583 | 79 | py |
deephyper | deephyper-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Note: To use the 'upload' functionality of this file, you must:
# $ pip install twine
import os
import platform
import sys
from shutil import rmtree
from setuptools import Command, setup
# path of the directory where this file is located
here = os.path.abspath(os.pa... | 5,761 | 25.552995 | 93 | py |
deephyper | deephyper-master/examples/plot_from_serial_to_parallel_hyperparameter_search.py | # -*- coding: utf-8 -*-
"""
From Serial to Parallel Evaluations
===================================
**Author(s)**: Romain Egele.
This example demonstrates the advantages of parallel evaluations over serial evaluations. We start by defining an artificial black-box ``run``-function by using the Ackley function:
.. ima... | 3,321 | 39.512195 | 590 | py |
deephyper | deephyper-master/examples/plot_transfer_learning_for_hps.py | # -*- coding: utf-8 -*-
"""
Transfer Learning for Hyperparameter Search
===========================================
**Author(s)**: Romain Egele.
In this example we present how to apply transfer-learning for hyperparameter search. Let's assume you have a bunch of similar tasks for example the search of neural networks... | 3,293 | 36.431818 | 1,014 | py |
deephyper | deephyper-master/examples/black_box_util.py | """Set of Black-Box functions useful to build examples.
"""
import time
import numpy as np
from deephyper.evaluator import profile
def ackley(x, a=20, b=0.2, c=2 * np.pi):
d = len(x)
s1 = np.sum(x**2)
s2 = np.sum(np.cos(c * x))
term1 = -a * np.exp(-b * np.sqrt(s1 / d))
term2 = -np.exp(s2 / d)
... | 820 | 26.366667 | 68 | py |
deephyper | deephyper-master/examples/plot_notify_failures_hyperparameter_search.py | # -*- coding: utf-8 -*-
"""
Notify Failures in Hyperparameter optimization
==============================================
**Author(s)**: Romain Egele.
This example demonstrates how to handle failure of objectives in hyperparameter search. In many cases such as software auto-tuning (where we minimize the run-time of ... | 3,235 | 42.146667 | 760 | py |
deephyper | deephyper-master/examples/plot_profile_worker_utilization.py | # -*- coding: utf-8 -*-
"""
Profile the Worker Utilization
==============================
**Author(s)**: Romain Egele.
This example demonstrates the advantages of parallel evaluations over serial evaluations. We start by defining an artificial black-box ``run``-function by using the Ackley function:
.. image:: https... | 3,542 | 31.805556 | 657 | py |
deephyper | deephyper-master/tests/conftest.py | import pytest
# -- Control skipping of tests according to command line option
def pytest_addoption(parser):
parser.addoption(
"--run",
default="fast,hps",
help="Select tests to run.",
)
def pytest_collection_modifyitems(config, items):
selected_marks = set(config.getoption("--ru... | 1,138 | 28.973684 | 83 | py |
deephyper | deephyper-master/tests/test_quickstart.py | import pytest
def run(job):
# The suggested parameters are accessible in job.parameters (dict)
x = job.parameters["x"]
b = job.parameters["b"]
if job.parameters["function"] == "linear":
y = x + b
elif job.parameters["function"] == "cubic":
y = x**3 + b
# Maximization!
ret... | 1,432 | 25.537037 | 88 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_space.py | import pytest
import numbers
import numpy as np
import os
import yaml
from tempfile import NamedTemporaryFile
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from numpy.testing import assert_raises_regex
from deephyper.skopt impor... | 27,583 | 31.186698 | 88 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_optimizer.py | import numpy as np
import pytest
from sklearn.multioutput import MultiOutputRegressor
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from numpy.testing import assert_raises
from deephyper.skopt import gp_minimize
from deephyper.skopt import forest_minimize
from deephyper.skopt.ben... | 14,307 | 28.501031 | 87 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_gp_opt.py | import numpy as np
from numpy.testing import assert_array_equal
import pytest
from deephyper.skopt import gp_minimize
from deephyper.skopt.benchmarks import bench1
from deephyper.skopt.benchmarks import bench2
from deephyper.skopt.benchmarks import bench3
from deephyper.skopt.benchmarks import bench4
from deephyper.sk... | 5,703 | 26.291866 | 87 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_gpr.py | import pytest
import numpy as np
from deephyper.skopt.learning import GaussianProcessRegressor
@pytest.mark.hps
def test_gpr_uses_noise():
"""Test that gpr is using WhiteKernel"""
X = np.random.normal(size=[100, 2])
Y = np.random.normal(size=[100])
g_gaussian = GaussianProcessRegressor(noise="gauss... | 431 | 23 | 61 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_common.py | from functools import partial
from itertools import product
import numpy as np
from scipy.optimize import OptimizeResult
import pytest
from numpy.testing import assert_almost_equal
from numpy.testing import assert_array_less
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_e... | 15,995 | 30.612648 | 88 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_parallel_cl.py | """This script contains set of functions that test parallel optimization with
skopt, where constant liar parallelization strategy is used.
"""
from numpy.testing import assert_equal
from numpy.testing import assert_raises
from deephyper.skopt.space import Real
from deephyper.skopt import Optimizer
from deephyper.sko... | 5,448 | 31.825301 | 79 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_deprecation.py | from functools import partial
from itertools import product
import pytest
from deephyper.skopt import gp_minimize
from deephyper.skopt import forest_minimize
from deephyper.skopt import gbrt_minimize
from deephyper.skopt import Optimizer
from deephyper.skopt.learning import ExtraTreesRegressor
# dummy_minimize does... | 896 | 28.9 | 81 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_plots.py | """Scikit-optimize plotting tests."""
import numpy as np
import pytest
from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score
from numpy.testing import assert_array_almost_equal
from deephyper.skopt.space import Integer, Catego... | 6,050 | 31.88587 | 88 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_benchmarks.py | import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_almost_equal
from deephyper.skopt.benchmarks import branin
from deephyper.skopt.benchmarks import hart6
@pytest.mark.hps
def test_branin():
xstars = np.asarray([(-np.pi, 12.275), (+np.pi, 2.275)... | 700 | 25.961538 | 78 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_sampler.py | import pytest
import numbers
import numpy as np
import os
import yaml
from tempfile import NamedTemporaryFile
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from numpy.testing import a... | 10,894 | 25.444175 | 83 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_dummy_opt.py | import pytest
from deephyper.skopt import dummy_minimize
from deephyper.skopt.benchmarks import bench1
from deephyper.skopt.benchmarks import bench2
from deephyper.skopt.benchmarks import bench3
def check_minimize(func, y_opt, dimensions, margin, n_calls):
r = dummy_minimize(func, dimensions, n_calls=n_calls, ran... | 812 | 28.035714 | 73 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_forest_opt.py | from functools import partial
from sklearn.tree import DecisionTreeClassifier
import pytest
from deephyper.skopt import gbrt_minimize
from deephyper.skopt import forest_minimize
from deephyper.skopt.benchmarks import bench1
from deephyper.skopt.benchmarks import bench2
from deephyper.skopt.benchmarks import bench3
fro... | 2,602 | 29.267442 | 87 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_transformers.py | import pytest
import numbers
import numpy as np
from numpy.testing import assert_raises
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from numpy.testing import assert_raises_regex
from deephyper.skopt.space import LogN, Normalize
from deephyper.skopt.space.transformers import Strin... | 4,002 | 32.082645 | 84 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_acquisition.py | import numpy as np
import pytest
from scipy import optimize
from sklearn.multioutput import MultiOutputRegressor
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_raises
from deephyper.skopt.acquisition import _gaussian_acquisition
from ... | 5,933 | 32.150838 | 80 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_callbacks.py | import pytest
import numpy as np
import os
from collections import namedtuple
from deephyper.skopt import dummy_minimize
from deephyper.skopt import gp_minimize
from deephyper.skopt.benchmarks import bench1
from deephyper.skopt.benchmarks import bench3
from deephyper.skopt.callbacks import TimerCallback
from deephype... | 3,800 | 29.902439 | 85 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_searchcv.py | """Test scikit-optimize based implementation of hyperparameter
search with interface similar to those of GridSearchCV
"""
import pytest
from sklearn.datasets import load_iris, make_classification
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC, Li... | 13,971 | 28.352941 | 87 | py |
deephyper | deephyper-master/tests/deephyper/skopt/test_utils.py | import pytest
import tempfile
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from numpy.testing import assert_raises
import numpy as np
from deephyper.skopt import gp_minimize, forest_minimize
from deephyper.skopt import load
from deephyper.skopt import dump
from deephyper.skopt i... | 9,985 | 30.304075 | 83 | py |
deephyper | deephyper-master/tests/deephyper/evaluator/test_evaluator.py | import unittest
from collections import Counter
import pandas as pd
import pytest
def run(job, y=0):
return job["x"] + y
def run_many_results(job, y=0):
return {"objective": job["x"], "metadata": {"y": y}}
class TestEvaluator(unittest.TestCase):
@pytest.mark.fast
@pytest.mark.hps
def test_imp... | 9,383 | 28.142857 | 133 | py |
deephyper | deephyper-master/tests/deephyper/evaluator/test_distributed_evaluator.py | """
mpirun -np 2 python test_distributed_evaluator.py
"""
import os
import sys
import time
PYTHON = sys.executable
SCRIPT = os.path.abspath(__file__)
import pytest
import deephyper.test
def run(config):
r = config["r"]
if r == 1:
time.sleep(2)
print(f"r={r}")
return config["r"]
def _test_... | 1,630 | 24.092308 | 83 | py |
deephyper | deephyper-master/tests/deephyper/evaluator/test_mpi_comm_evaluator.py | import os
import sys
import time
import pytest
PYTHON = sys.executable
SCRIPT = os.path.abspath(__file__)
import deephyper.test
from deephyper.evaluator import Evaluator
def run(config):
job_id = config["job_id"]
print(f"job {job_id}...")
if job_id > 3:
time.sleep(2)
print(f"job {job_id} do... | 1,299 | 22.214286 | 71 | py |
deephyper | deephyper-master/tests/deephyper/evaluator/test_decorator.py | import unittest
import pytest
from deephyper.evaluator import profile
@profile
def run_profile(config):
return config["x"]
@pytest.mark.fast
@pytest.mark.hps
class TestDecorator(unittest.TestCase):
def test_profile(self):
output = run_profile({"x": 0})
assert "timestamp_end" in output["me... | 424 | 18.318182 | 54 | py |
deephyper | deephyper-master/tests/deephyper/evaluator/test_queued_evaluator.py | import pytest
import unittest
def run(config, dequed=None):
return config["x"] + dequed[0]
class TestQueuedEvaluator(unittest.TestCase):
@pytest.mark.fast
@pytest.mark.hps
def test_queued_serial_evaluator(self):
from deephyper.evaluator import SerialEvaluator, queued
QueuedSerialEva... | 2,317 | 26.270588 | 66 | py |
deephyper | deephyper-master/tests/deephyper/evaluator/storage/test_memory_storage.py | import unittest
import pytest
from deephyper.evaluator import Evaluator, RunningJob
from deephyper.evaluator.storage import MemoryStorage
def run_0(job: RunningJob) -> dict:
return {
"objective": job.parameters["x"],
"metadata": {"storage_id": id(job.storage)},
}
@pytest.mark.fast
@pytest.m... | 3,427 | 32.281553 | 71 | py |
deephyper | deephyper-master/tests/deephyper/evaluator/storage/test_redis_storage.py | import unittest
import pytest
from deephyper.evaluator import Evaluator, RunningJob
def run_0(job: RunningJob) -> dict:
if not (job.storage.connected):
job.storage.connect()
job.storage.store_job_metadata(job.id, "foo", 0)
return {
"objective": job.parameters["x"],
"metadata": {"s... | 3,926 | 32.853448 | 75 | py |
deephyper | deephyper-master/tests/deephyper/stopper/test__median_stopper.py | import pytest
import numpy as np
from deephyper.evaluator import RunningJob
from deephyper.problem import HpProblem
from deephyper.search.hps import CBO
from deephyper.stopper import MedianStopper
def run(job: RunningJob) -> dict:
assert isinstance(job.stopper, MedianStopper)
max_budget = 50
objective... | 1,491 | 22.3125 | 75 | py |
deephyper | deephyper-master/tests/deephyper/stopper/test__sha_stopper.py | import unittest
import pytest
import numpy as np
from deephyper.evaluator import RunningJob
from deephyper.problem import HpProblem
from deephyper.search.hps import CBO
from deephyper.stopper import SuccessiveHalvingStopper
def run(job: RunningJob) -> dict:
assert isinstance(job.stopper, SuccessiveHalvingStopp... | 1,502 | 22.857143 | 75 | py |
deephyper | deephyper-master/tests/deephyper/stopper/test__idle_stopper.py | import pytest
import numpy as np
from deephyper.evaluator import RunningJob
from deephyper.problem import HpProblem
from deephyper.search.hps import CBO
from deephyper.stopper import IdleStopper
def run(job: RunningJob) -> dict:
assert isinstance(job.stopper, IdleStopper)
max_budget = 50
objective_i =... | 1,390 | 21.435484 | 75 | py |
deephyper | deephyper-master/tests/deephyper/nas/test_node.py | import unittest
import pytest
@pytest.mark.fast
@pytest.mark.nas
class NodeTest(unittest.TestCase):
def test_mirror_node(self):
import tensorflow as tf
from deephyper.nas.node import MirrorNode, VariableNode
from deephyper.nas.operation import operation
Dense = operation(tf.keras... | 1,118 | 21.836735 | 63 | py |
deephyper | deephyper-master/tests/deephyper/nas/test_dense_skipco_factory.py | import pytest
@pytest.mark.fast
@pytest.mark.nas
def test_search_space():
from deephyper.nas.spacelib.tabular import DenseSkipCoSpace
space = DenseSkipCoSpace(input_shape=(10,), output_shape=(1,)).build()
model = space.sample()
| 243 | 21.181818 | 74 | py |
deephyper | deephyper-master/tests/deephyper/nas/test_one_layer_factory.py | import pytest
@pytest.mark.fast
@pytest.mark.nas
def test_search_space():
from deephyper.nas.spacelib.tabular import OneLayerSpace
space = OneLayerSpace(input_shape=(10,), output_shape=(1,)).build()
model = space.sample()
| 237 | 20.636364 | 71 | py |
deephyper | deephyper-master/tests/deephyper/nas/test_new_api.py | import pytest
@pytest.mark.fast
@pytest.mark.nas
def test_basic_space(verbose=0):
import tensorflow as tf
from deephyper.nas import KSearchSpace
from deephyper.nas.node import VariableNode, ConstantNode
from deephyper.nas.operation import operation, Identity
Dense = operation(tf.keras.layers.Den... | 1,177 | 24.608696 | 88 | py |
deephyper | deephyper-master/tests/deephyper/nas/test_trainer_keras_regressor.py | import unittest
import pytest
@pytest.mark.slow
@pytest.mark.nas
class TrainerKerasRegressorTest(unittest.TestCase):
def test_trainer_regressor_train_valid_with_one_input(self):
import sys
from random import random
import deephyper.core.utils
import numpy as np
from deeph... | 4,879 | 33.125874 | 85 | py |
deephyper | deephyper-master/tests/deephyper/nas/test_keras_search_space.py | import unittest
import pytest
@pytest.mark.nas
class TestKSearchSpace(unittest.TestCase):
def test_create(self):
import tensorflow as tf
from deephyper.nas import KSearchSpace
from deephyper.nas.node import VariableNode
from deephyper.nas.operation import operation
Dense ... | 2,481 | 28.2 | 66 | py |
deephyper | deephyper-master/tests/deephyper/nas/run/test_single_loss.py | import pytest
import numpy as np
def load_data(dim=10):
"""
Generate data for linear function -sum(x_i).
Return:
Tuple of Numpy arrays: ``(train_X, train_y), (valid_X, valid_y)``.
"""
rs = np.random.RandomState(42)
size = 100000
prop = 0.80
a, b = 0, 100
d = b - a
x = ... | 1,507 | 23.721311 | 74 | py |
deephyper | deephyper-master/tests/deephyper/nas/run/test_multi_loss.py | import pytest
import numpy as np
def load_data(dim=100):
"""
Generate data for linear function -sum(x_i).
Return:
Tuple of Numpy arrays: ``(train_X, train_y), (valid_X, valid_y)``.
"""
rs = np.random.RandomState(42)
size = 100000
prop = 0.80
a, b = 0, 100
d = b - a
x =... | 1,794 | 27.046875 | 76 | py |
deephyper | deephyper-master/tests/deephyper/nas/run/test_single_loss_multi_var.py | import pytest
import numpy as np
def load_data(dim=10):
"""
Generate data for linear function -sum(x_i).
Return:
Tuple of Numpy arrays: ``(train_X, train_y), (valid_X, valid_y)``.
"""
rs = np.random.RandomState(42)
size = 100000
prop = 0.80
a, b = 0, 100
d = b - a
x =... | 1,540 | 23.854839 | 74 | py |
deephyper | deephyper-master/tests/deephyper/problem/test_problem.py | import unittest
import pytest
@pytest.mark.hps
class HpProblemTest(unittest.TestCase):
def test_add_good_dim(self):
import ConfigSpace as cs
import ConfigSpace.hyperparameters as csh
from deephyper.problem import HpProblem
pb = HpProblem()
p0 = pb.add_hyperparameter((-10... | 4,405 | 30.248227 | 84 | py |
deephyper | deephyper-master/tests/deephyper/keras/layers/padding_test.py | import pytest
@pytest.mark.fast
@pytest.mark.nas
def test_padding_layer():
import tensorflow as tf
import numpy as np
from deephyper.keras.layers import Padding
model = tf.keras.Sequential()
model.add(Padding([[1, 1]]))
data = np.random.random((3, 1))
shape_data = np.shape(data)
asse... | 450 | 20.47619 | 46 | py |
deephyper | deephyper-master/tests/deephyper/search/hps/test_dbo_max_evals.py | import os
import sys
import pytest
PYTHON = sys.executable
SCRIPT = os.path.abspath(__file__)
import deephyper.test
def _test_dbo_max_evals(tmp_path):
import time
import numpy as np
from deephyper.problem import HpProblem
from deephyper.search.hps import MPIDistributedBO
d = 10
domain = (-... | 1,760 | 23.458333 | 78 | py |
deephyper | deephyper-master/tests/deephyper/search/hps/test__cbo_mpi.py | import os
import sys
import pytest
PYTHON = sys.executable
SCRIPT = os.path.abspath(__file__)
import deephyper.test
def _test_mpi_timeout(tmp_path):
"""Test if the timeout condition is working properly when the run-function runs indefinitely."""
import time
from deephyper.problem import HpProblem
fr... | 1,516 | 25.614035 | 100 | py |
deephyper | deephyper-master/tests/deephyper/search/hps/test_parallel_cbo_manual.py | import os
import shutil
import sys
import pytest
PYTHON = sys.executable
SCRIPT = os.path.abspath(__file__)
import deephyper.test
def _test_parallel_cbo_manual():
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
from deephyper.problem import HpProblem
def run(job):
... | 1,971 | 22.2 | 78 | py |
deephyper | deephyper-master/tests/deephyper/search/hps/test_dbo_timeout.py | import os
import shutil
import sys
import pytest
PYTHON = sys.executable
SCRIPT = os.path.abspath(__file__)
import deephyper.test
def _test_dbo_timeout():
import time
import numpy as np
from deephyper.problem import HpProblem
from deephyper.search.hps import MPIDistributedBO
d = 10
domain ... | 1,836 | 22.551282 | 70 | py |
deephyper | deephyper-master/tests/deephyper/search/hps/test__cbo.py | import pytest
@pytest.mark.hps
def test_cbo_random_seed(tmp_path):
import numpy as np
from deephyper.evaluator import Evaluator
from deephyper.problem import HpProblem
from deephyper.search.hps import CBO
problem = HpProblem()
problem.add_hyperparameter((0.0, 10.0), "x")
def run(config):... | 8,589 | 23.403409 | 83 | py |
deephyper | deephyper-master/tests/deephyper/search/nas/test_random_mpicomm.py | import os
import sys
import time
import pytest
PYTHON = sys.executable
SCRIPT = os.path.abspath(__file__)
import deephyper.test
def _test_random_search_mpicomm():
"""Example to execute:
mpirun -np 4 python test_random_mpicomm.py
"""
from deephyper.evaluator import Evaluator
from deephyper.nas... | 1,206 | 21.773585 | 75 | py |
deephyper | deephyper-master/tests/deephyper/search/nas/test_agebo.py | import unittest
import pytest
@pytest.mark.slow
@pytest.mark.nas
class AgEBOTest(unittest.TestCase):
def test_agebo_without_hp(self):
from deephyper.test.nas import linearReg
from deephyper.evaluator import Evaluator
from deephyper.nas.run import run_debug_arch
from deephyper.sear... | 1,697 | 29.321429 | 84 | py |
deephyper | deephyper-master/tests/deephyper/search/nas/test_random.py | import unittest
import pytest
@pytest.mark.slow
@pytest.mark.nas
class RandomTest(unittest.TestCase):
def test_random_search(self):
import numpy as np
from deephyper.evaluator import Evaluator
from deephyper.nas.run import run_debug_arch
from deephyper.search.nas import Random
... | 1,847 | 30.322034 | 85 | py |
deephyper | deephyper-master/tests/deephyper/search/nas/test_regevomixed.py | import unittest
import pytest
@pytest.mark.slow
@pytest.mark.nas
class RegevoMixedTest(unittest.TestCase):
def test_regovomixed_without_hp(self):
import numpy as np
from deephyper.test.nas import linearReg
from deephyper.evaluator import Evaluator
from deephyper.nas.run import run... | 2,048 | 29.132353 | 84 | py |
deephyper | deephyper-master/tests/deephyper/search/nas/test_nas.py | import pytest
import unittest
@pytest.mark.slow
@pytest.mark.nas
class TestNeuralArchitectureSearchAlgorithms(unittest.TestCase):
def evaluate_search(self, search_cls, problem):
from deephyper.evaluator import Evaluator
from deephyper.nas.run import run_debug_arch
# Test "max_evals" stopp... | 2,944 | 36.278481 | 82 | py |
deephyper | deephyper-master/tests/deephyper/search/nas/test_ambsmixed.py | import unittest
import pytest
@pytest.mark.slow
@pytest.mark.nas
class AgEBOTest(unittest.TestCase):
def test_ambsmixed_without_hp(self):
import numpy as np
from deephyper.test.nas import linearReg
from deephyper.evaluator import Evaluator
from deephyper.nas.run import run_debug_a... | 1,803 | 29.066667 | 84 | py |
deephyper | deephyper-master/tests/deephyper/search/nas/test_regevo.py | import unittest
import pytest
@pytest.mark.slow
@pytest.mark.nas
class RegevoTest(unittest.TestCase):
def test_regovo_with_hp(self):
from deephyper.test.nas import linearRegHybrid
from deephyper.evaluator import Evaluator
from deephyper.nas.run import run_debug_arch
from deephyper... | 1,545 | 30.55102 | 84 | py |
deephyper | deephyper-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 8,809 | 27.79085 | 107 | py |
deephyper | deephyper-master/docs/examples/plot_from_serial_to_parallel_hyperparameter_search.py | # -*- coding: utf-8 -*-
"""
From Serial to Parallel Evaluations
===================================
**Author(s)**: Romain Egele.
This example demonstrates the advantages of parallel evaluations over serial evaluations. We start by defining an artificial black-box ``run``-function by using the Ackley function:
.. ima... | 3,321 | 39.512195 | 590 | py |
deephyper | deephyper-master/docs/examples/plot_transfer_learning_for_hps.py | # -*- coding: utf-8 -*-
"""
Transfer Learning for Hyperparameter Search
===========================================
**Author(s)**: Romain Egele.
In this example we present how to apply transfer-learning for hyperparameter search. Let's assume you have a bunch of similar tasks for example the search of neural networks... | 3,293 | 36.431818 | 1,014 | py |
deephyper | deephyper-master/docs/examples/plot_notify_failures_hyperparameter_search.py | # -*- coding: utf-8 -*-
"""
Notify Failures in Hyperparameter optimization
==============================================
**Author(s)**: Romain Egele.
This example demonstrates how to handle failure of objectives in hyperparameter search. In many cases such as software auto-tuning (where we minimize the run-time of ... | 3,235 | 42.146667 | 760 | py |
deephyper | deephyper-master/docs/examples/plot_profile_worker_utilization.py | # -*- coding: utf-8 -*-
"""
Profile the Worker Utilization
==============================
**Author(s)**: Romain Egele.
This example demonstrates the advantages of parallel evaluations over serial evaluations. We start by defining an artificial black-box ``run``-function by using the Ackley function:
.. image:: https... | 3,542 | 31.805556 | 657 | py |
deephyper | deephyper-master/deephyper/__version__.py | VERSION = (0, 5, 0)
__version__ = ".".join(map(str, VERSION))
# alpha/beta/rc tags
__version_suffix__ = ""
| 109 | 14.714286 | 41 | py |
deephyper | deephyper-master/deephyper/__init__.py | """
DeepHyper is a distributed machine learning (`AutoML <https://en.wikipedia.org/wiki/Automated_machine_learning>`_) package for automating the development of deep neural networks for scientific applications. It can run on a single laptop as well as on 1,000 of nodes.
It comprises different tools such as:
* Optimiz... | 1,721 | 49.647059 | 266 | py |
deephyper | deephyper-master/deephyper/core/parser.py | import argparse
import inspect
from inspect import signature
def add_arguments_from_signature(parser, obj, prefix="", exclude=[]):
"""Add arguments to parser base on obj default keyword parameters.
:meta private:
Args:
parser (ArgumentParser)): the argument parser to which we want to add argumen... | 1,822 | 28.885246 | 99 | py |
deephyper | deephyper-master/deephyper/core/__init__.py | 0 | 0 | 0 | py | |
deephyper | deephyper-master/deephyper/core/cli/_new_problem.py | """
Create a DeepHyper Problem
--------------------------
Command line to create a new problem sub-package in a DeepHyper projet package.
It can be used with:
.. code-block:: console
$ deephyper new-problem hps problem_name
"""
import glob
import os
import pathlib
from jinja2 import Template
def add_subparse... | 2,325 | 27.024096 | 89 | py |
deephyper | deephyper-master/deephyper/core/cli/_start_project.py | """
Start a DeepHyper Project
-------------------------
Command line to create a new DeepHyper project package. The package is automatically installed to the current virtual Python environment.
It can be used with:
.. code-block:: console
$ deephyper start-project project_name
"""
import os
import pathlib
impor... | 1,811 | 29.2 | 155 | py |
deephyper | deephyper-master/deephyper/core/cli/_hps.py | """
Hyperparameter Search
---------------------
Command line to execute hyperparameter search.
.. code-block:: bash
$ deephyper hps ambs --help
usage: deephyper hps ambs [-h] --problem PROBLEM --evaluator EVALUATOR [--random-state RANDOM_STATE] [--log-dir LOG_DIR] [--verbose VERBOSE] [--surrogate-model SURR... | 6,917 | 34.659794 | 203 | py |
deephyper | deephyper-master/deephyper/core/cli/_nodelist.py | import sys
import socket
def _theta_nodelist(node_str):
# string like: 1001-1005,1030,1034-1200
node_ids = []
ranges = node_str.split(",")
lo = None
hi = None
for node_range in ranges:
lo, *hi = node_range.split("-")
lo = int(lo)
if hi:
hi = int(hi[0])
... | 938 | 22.475 | 66 | py |
deephyper | deephyper-master/deephyper/core/cli/__init__.py | # for the documentation
from . import _cli, _hps, _nas, _new_problem, _start_project
commands = [_cli, _hps, _nas, _new_problem, _start_project]
__doc__ = ""
for c in commands:
__doc__ += c.__doc__
| 205 | 19.6 | 60 | py |
deephyper | deephyper-master/deephyper/core/cli/_nas.py | """
Neural Architecture Search
--------------------------
Command line to execute neural architecture search or joint hyperparameter and neural architecture search.
.. code-block:: bash
$ deephyper nas regevo --help
usage: deephyper nas regevo [-h] --problem PROBLEM --evaluator EVALUATOR [--random-state RAN... | 6,849 | 34.677083 | 210 | py |
deephyper | deephyper-master/deephyper/core/cli/_cli.py | """DeepHyper command line interface.
It can be used in the shell with:
.. code-block:: console
$ deephyper --help
usage: deephyper [-h] {hps,nas,new-problem,ray-cluster,ray-submit,start-project} ...
DeepHyper command line.
positional arguments:
{hps,nas,new-problem,ray-cluster,ray-submit,start... | 1,600 | 23.257576 | 122 | py |
deephyper | deephyper-master/deephyper/core/cli/_cobalt_nodelist.py | import os
# Adapted from 'get_job_nodelist()' found in the following project:
# https://github.com/argonne-lcf/balsam/blob/main/balsam/platform/compute_node/alcf_thetaknl_node.py
def nodelist():
"""Get all compute nodes allocated in the current job context.
:meta private:
"""
node_str = os.environ["... | 796 | 23.90625 | 100 | py |
deephyper | deephyper-master/deephyper/core/analytics/_topk.py | """
Top-K Configuration
-------------------
A command line to extract the top-k best configuration from a DeepHyper execution.
It can be used with:
.. code-block:: console
$ deephyper-analytics --help
usage: deephyper-analytics topk [-h] [-k K] [-o OUTPUT] path
positional arguments:
path ... | 4,269 | 27.278146 | 104 | py |
deephyper | deephyper-master/deephyper/core/analytics/_dashboard.py | """
Dashboard
---------
A tool to open an interactive dashboard in the browser to help analyse DeepHyper results.
It can be used such as:
.. code-block:: console
$ deephyper-analytics dashboard --database db.json
Then an interactive dashboard will appear in your browser.
"""
import os
import subprocess
HERE = ... | 1,222 | 22.075472 | 89 | py |
deephyper | deephyper-master/deephyper/core/analytics/_analytics.py | """Analytics command line interface for DeepHyper.
It can be used with:
.. code-block:: console
$ deephyper-analytics --help
Command line to analysis the outputs produced by DeepHyper.
positional arguments:
{dashboard,notebook,quickplot,topk}
Kind of analytics.
d... | 1,364 | 22.947368 | 81 | py |
deephyper | deephyper-master/deephyper/core/analytics/__init__.py | # for the documentation
from . import _topk, _quick_plot, _dashboard
from ._db_manager import DBManager, Query
commands = [_topk, _quick_plot, _dashboard]
__all__ = ["DBManager", "Query"]
__doc__ = "Provides command lines tools to visualize results from DeepHyper.\n\n"
for c in commands:
__doc__ += c.__doc__
| 317 | 25.5 | 81 | py |
deephyper | deephyper-master/deephyper/core/analytics/_db_manager.py | """
Database
---------
A tool to interact with a database of Deephyper results.
To view the database run:
.. code-block:: console
$ deephyper-analytics database --view '' --database db.json
To add an entry to the database run:
.. code-block:: console
$ deephyper-analytics database --add $log_dir --databas... | 9,098 | 30.814685 | 306 | py |
deephyper | deephyper-master/deephyper/core/analytics/_quick_plot.py | """
Quick Plot
----------
A tool to have quick and simple visualization from your data.
It can be use such as:
.. code-block:: console
$ deephyper-analytics quickplot nas_big_data/combo/exp_sc21/combo_1gpu_8_age/infos/results.csv
$ deephyper-analytics quickplot save/history/*.json --xy time val_r2
$ dee... | 5,701 | 22.561983 | 118 | py |
deephyper | deephyper-master/deephyper/core/analytics/dashboard/_pyplot.py | import json
from datetime import datetime
import matplotlib
import matplotlib.pyplot as plt
import streamlit as st
from deephyper.core.exceptions import DeephyperRuntimeError
width = 8
height = width / 1.618
fontsize = 18
matplotlib.rcParams.update(
{
"font.size": fontsize,
"figure.figsize": (widt... | 3,507 | 19.045714 | 84 | py |
deephyper | deephyper-master/deephyper/core/analytics/dashboard/_views.py | import abc
import os
import sys
import altair as alt
import pandas as pd
import streamlit as st
from deephyper.core.analytics import DBManager
from st_aggrid import AgGrid, GridOptionsBuilder, ColumnsAutoSizeMode
class View(abc.ABC):
@abc.abstractmethod
def show(self):
...
class Dashboard(View):
... | 20,546 | 31.927885 | 131 | py |
deephyper | deephyper-master/deephyper/core/analytics/dashboard/__init__.py | 0 | 0 | 0 | py | |
deephyper | deephyper-master/deephyper/core/exceptions/loading.py | """Exceptions related with imports of modules/attributes/scripts.
"""
from deephyper.core.exceptions import DeephyperError
class GenericLoaderError(DeephyperError):
"""Raised when the generic_loader function is failing."""
def __init__(self, target, attr, error_source, custom_msg=""):
self.target = t... | 655 | 28.818182 | 82 | py |
deephyper | deephyper-master/deephyper/core/exceptions/problem.py | """Exceptions related with problem definition.
"""
from deephyper.core.exceptions import DeephyperError
class SpaceDimNameOfWrongType(DeephyperError):
"""Raised when a dimension name of the space is not a string."""
def __init__(self, value):
self.value = value
def __str__(self):
return... | 2,811 | 28.291667 | 107 | py |
deephyper | deephyper-master/deephyper/core/exceptions/__init__.py | """Deephyper exceptions
"""
# ! Root exceptions
class DeephyperError(Exception):
"""Root deephyper exception."""
class DeephyperRuntimeError(RuntimeError):
"""Raised when an error is detected in deephyper and that doesn’t fall in any of the other categories. The associated value is a string indicating what... | 770 | 23.870968 | 180 | py |
deephyper | deephyper-master/deephyper/core/exceptions/nas/space.py | from deephyper.core.exceptions.nas import NASError
class WrongSequenceToSetOperations(NASError):
"""Raised when a sequence of actions is not of the same lenght as the number of variable nodes of the search_space."""
def __init__(self, sequence_given, sequence_valid):
self.sequence_given = sequence_gi... | 1,838 | 30.706897 | 169 | py |
deephyper | deephyper-master/deephyper/core/exceptions/nas/__init__.py | """Neural architecture search exceptions.
"""
from deephyper.core.exceptions import DeephyperError
class NASError(DeephyperError):
"""Root neural architecture search exception."""
| 187 | 19.888889 | 52 | py |
deephyper | deephyper-master/deephyper/core/utils/_timeout.py | import multiprocessing
import multiprocessing.pool
from deephyper.core.exceptions import SearchTerminationError
def terminate_on_timeout(timeout, func, *args, **kwargs):
"""High order function to wrap the call of a function in a thread to monitor its execution time."""
pool = multiprocessing.pool.ThreadPoo... | 608 | 29.45 | 103 | py |
deephyper | deephyper-master/deephyper/core/utils/_import.py | import importlib
def load_attr(str_full_module):
"""Loadd attribute from module.
Args:
str_full_module (str): string of the form ``{module_name}.{attr}``.
Returns:
Any: the attribute.
"""
if type(str_full_module) == str:
split_full = str_full_module.split(".")
str... | 525 | 24.047619 | 75 | py |
deephyper | deephyper-master/deephyper/core/utils/_files.py | import pathlib
def ensure_dh_folder_exists():
"""Creates a ``".deephyper"`` directory in the user home directory."""
home = pathlib.Path.home()
deephyper_dir = home.joinpath(".deephyper")
deephyper_dir.mkdir(parents=False, exist_ok=True)
return deephyper_dir.as_posix()
| 292 | 28.3 | 74 | py |
deephyper | deephyper-master/deephyper/core/utils/__init__.py | from ._import import load_attr
__all__ = ["load_attr"]
| 56 | 13.25 | 30 | py |
deephyper | deephyper-master/deephyper/core/utils/_introspection.py | import inspect
import json
def _get_init_param_names(cls):
"""Get parameter names for the estimator"""
# fetch the constructor
init = cls.__init__
if init is object.__init__:
# No explicit constructor to introspect
return []
# introspect the constructor arguments to find the model... | 2,257 | 28.324675 | 82 | py |
deephyper | deephyper-master/deephyper/skopt/callbacks.py | """Monitor and influence the optimization procedure via callbacks.
Callbacks are callables which are invoked after each iteration of the optimizer
and are passed the results "so far". Callbacks can monitor progress, or stop
the optimization early by returning `True`.
"""
try:
from collections.abc import Callable
... | 9,497 | 27.183976 | 87 | py |
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