def test_trial_results():
assert OSPREY_BIN is not None
cwd = os.path.abspath(os.curdir)
dirname = tempfile.mkdtemp()
try:
os.chdir(dirname)
subprocess.check_call([
OSPREY_BIN, 'skeleton', '-t', 'random_example', '-f', 'config.yaml'
])
subprocess.check_call([OSPREY_BIN, 'worker', 'config.yaml', '-n', '5'])
assert os.path.exists('osprey-trials.db')
config = Config('config.yaml')
df = config.trial_results()
assert df.shape[0] == 5
for key in Trial.__table__.columns.keys():
assert key in df.columns
finally:
os.chdir(cwd)
shutil.rmtree(dirname)
def data_from_config(config_file):
"""
Returns features, scores, elapsed time in seconds etc.
and searchspace from a config file
"""
config = Config(config_file)
session = config.trials()
searchspace = config.search_space()
history = [[t.parameters, t.test_scores, t.status, t.elapsed]
for t in session.query(Trial).all()]
return get_data(history, searchspace) + (searchspace, )
def test_search_space():
config = Config.fromdict({
'search_space': {
'intvar': {'type': 'int', 'min': 1, 'max': 2},
'logivar': {'type': 'int', 'min': 1, 'max': 2, 'warp': 'log'},
'fvar': {'type': 'float', 'min': 1, 'max': 3.5},
'logfvar': {'type': 'float', 'min': 1, 'max': 2.5, 'warp': 'log'},
'enumvar': {'type': 'enum', 'choices': [1, False]},
'jumpivar': {'type': 'jump', 'min': 1, 'max': 3, 'num': 3, 'var_type': int},
'jumpfvar': {'type': 'jump', 'min': 1, 'max': 3, 'num': 3, 'var_type': float},
'logjumpivar': {'type': 'jump', 'min': 10, 'max': 1000, 'num': 3, 'warp': 'log', 'var_type': int},
'logjumpfvar': {'type': 'jump', 'min': 10, 'max': 1000, 'num': 3, 'warp': 'log', 'var_type': float}
}}, check_fields=False)
searchspace = config.search_space()
assert searchspace['intvar'] == IntVariable('intvar', 1, 2, warp=None)
assert searchspace['logivar'] == IntVariable('logivar', 1, 2, warp='log')
assert searchspace['fvar'] == FloatVariable('fvar', 1, 3.5, warp=None)
assert searchspace['logfvar'] == FloatVariable('logfvar', 1, 2.5,
warp='log')
assert searchspace['enumvar'] == EnumVariable('enumvar', [1, False])
assert searchspace['jumpivar'] == EnumVariable('jumpivar', [1, 2, 3])
assert searchspace['jumpfvar'] == EnumVariable('jumpfvar', [1.0, 2.0, 3.0])
assert searchspace['logjumpivar'] == EnumVariable('logjumpivar', [10, 100, 1000])
assert searchspace['logjumpfvar'] == EnumVariable('logjumpfvar', [10.0, 100.0, 1000.0])
def test_search_engine_moe_2():
config = Config.fromdict({
'strategy': {'name': 'moe', 'params': {'url': 'abc'}}
}, check_fields=False)
strat = config.strategy()
assert isinstance(strat, MOE)
assert strat.url == 'abc'
def test_estimator_entry_point():
config = Config.fromdict(
{'estimator': {
'entry_point': 'sklearn.cluster.KMeans',
}},
check_fields=False)
assert isinstance(config.estimator(), KMeans)
def test_estimator_entry_point():
config = Config.fromdict({
'estimator': {
'entry_point': 'sklearn.cluster.KMeans',
}
}, check_fields=False)
assert isinstance(config.estimator(), KMeans)
def test_stratified_cv():
from sklearn.cross_validation import StratifiedShuffleSplit
config = Config.fromdict({
'cv': {'name': 'stratifiedshufflesplit', 'params': {'n_iter': 10}}
}, check_fields=False)
cv = config.cv(range(100), np.random.randint(2, size=100))
assert isinstance(cv, StratifiedShuffleSplit)
assert cv.n_iter == 10
def test_estimator_eval_2():
config = Config.fromdict(
{'estimator': {
'eval': 'KMeans()',
'eval_scope': ['sklearn'],
}},
check_fields=False)
assert isinstance(config.estimator(), KMeans)
def test_estimator_eval_2():
config = Config.fromdict({
'estimator': {
'eval': 'KMeans()',
'eval_scope': ['sklearn'],
}
}, check_fields=False)
assert isinstance(config.estimator(), KMeans)
def test_cv_1():
from sklearn.cross_validation import ShuffleSplit
for name in ['shufflesplit', 'ShuffleSplit']:
config = Config.fromdict({
'cv': {'name': name, 'params': {'n_iter': 10}}
}, check_fields=False)
cv = config.cv(range(100))
assert isinstance(cv, ShuffleSplit)
assert cv.n_iter == 10
def test_estimator_pickle():
with tempfile.NamedTemporaryFile('w+b', 0) as f:
cPickle.dump(KMeans(), f)
config = Config.fromdict({
'estimator': {'pickle': f.name}
}, check_fields=False)
assert isinstance(config.estimator(), KMeans)
def test_estimator_pickle():
with tempfile.NamedTemporaryFile('w+b', 0) as f:
cPickle.dump(KMeans(), f)
config = Config.fromdict({'estimator': {
'pickle': f.name
}},
check_fields=False)
assert isinstance(config.estimator(), KMeans)
def test_estimator_entry_point_params():
config = Config.fromdict({
'estimator': {
'entry_point': 'sklearn.cluster.KMeans',
'params': {
'n_clusters': 15
}
}
}, check_fields=False)
assert isinstance(config.estimator(), KMeans)
assert config.estimator().n_clusters == 15
def test_estimator_entry_point_params():
config = Config.fromdict(
{
'estimator': {
'entry_point': 'sklearn.cluster.KMeans',
'params': {
'n_clusters': 15
}
}
},
check_fields=False)
assert isinstance(config.estimator(), KMeans)
assert config.estimator().n_clusters == 15
def test_stratified_cv():
from sklearn.model_selection import StratifiedShuffleSplit
config = Config.fromdict(
{'cv': {
'name': 'stratifiedshufflesplit',
'params': {
'n_splits': 10
}
}},
check_fields=False)
cv = config.cv(range(100), np.random.randint(2, size=100))
assert isinstance(cv, StratifiedShuffleSplit)
assert cv.n_splits == 10
def test_search_space():
config = Config.fromdict({
'search_space': {
'intvar': {'type': 'int', 'min': 1, 'max': 2},
'fvar': {'type': 'float', 'min': 1, 'max': 3.5},
'logvar': {'type': 'float', 'min': 1, 'max': 2.5, 'warp': 'log'},
'enumvar': {'type': 'enum', 'choices': [1, False]},
}}, check_fields=False)
searchspace = config.search_space()
assert searchspace['intvar'] == IntVariable('intvar', 1, 2)
assert searchspace['fvar'] == FloatVariable('fvar', 1, 3.5, warp=None)
assert searchspace['logvar'] == FloatVariable('logvar', 1, 2.5, warp='log')
assert searchspace['enumvar'] == EnumVariable('enumvar', [1, False])
def test_cv_1():
from sklearn.model_selection import ShuffleSplit
for name in ['shufflesplit', 'ShuffleSplit']:
config = Config.fromdict(
{'cv': {
'name': name,
'params': {
'n_splits': 10
}
}},
check_fields=False)
cv = config.cv(range(100))
assert isinstance(cv, ShuffleSplit)
assert cv.n_splits == 10
def test_trial_results():
assert OSPREY_BIN is not None
cwd = os.path.abspath(os.curdir)
dirname = tempfile.mkdtemp()
try:
os.chdir(dirname)
subprocess.check_call([OSPREY_BIN, 'skeleton', '-t', 'random_example',
'-f', 'config.yaml'])
subprocess.check_call([OSPREY_BIN, 'worker', 'config.yaml', '-n', '5'])
assert os.path.exists('osprey-trials.db')
config = Config('config.yaml')
df = config.trial_results()
assert df.shape[0] == 5
for key in Trial.__table__.columns.keys():
assert key in df.columns
finally:
os.chdir(cwd)
shutil.rmtree(dirname)
def test_1():
assert OSPREY_BIN is not None
cwd = os.path.abspath(os.curdir)
dirname = tempfile.mkdtemp()
try:
os.chdir(dirname)
subprocess.check_call([OSPREY_BIN, 'skeleton', '-t', 'msmbuilder',
'-f', 'config.yaml'])
assert os.path.exists('config.yaml')
with open('config.yaml', 'rb') as f:
yaml.load(f)
Config('config.yaml')
finally:
os.chdir(cwd)
shutil.rmtree(dirname)
def test_search_engine_bayes():
config = Config.fromdict({
'strategy': {'name': 'bayes'}
}, check_fields=False)
assert isinstance(config.strategy(), Bayes)
def test_search_engine_hyperopt_tpe():
config = Config.fromdict({
'strategy': {'name': 'hyperopt_tpe'}
}, check_fields=False)
assert isinstance(config.strategy(), HyperoptTPE)
def test_search_engine_moe_1():
config = Config.fromdict({
'strategy': {'name': 'moe', 'params': {'url': 'sdfsdf'}}
}, check_fields=False)
assert isinstance(config.strategy(), MOE)
def test_search_engine_hyperopt_tpe():
config = Config.fromdict({'strategy': {
'name': 'hyperopt_tpe'
}},
check_fields=False)
assert isinstance(config.strategy(), HyperoptTPE)
def test_strategy_random():
config = Config.fromdict({
'strategy': {'name': 'random'}
}, check_fields=False)
assert isinstance(config.strategy(), RandomSearch)
def test_scoring():
config = Config.fromdict({'scoring': 'sdfsfsdf'}, check_fields=False)
assert config.scoring() is 'sdfsfsdf'
def test_search_engine_bayes():
config = Config.fromdict({'strategy': {
'name': 'bayes'
}},
check_fields=False)
assert isinstance(config.strategy(), Bayes)
def test_search_space():
config = Config.fromdict(
{
'search_space': {
'intvar': {
'type': 'int',
'min': 1,
'max': 2
},
'logivar': {
'type': 'int',
'min': 1,
'max': 2,
'warp': 'log'
},
'fvar': {
'type': 'float',
'min': 1,
'max': 3.5
},
'logfvar': {
'type': 'float',
'min': 1,
'max': 2.5,
'warp': 'log'
},
'enumvar': {
'type': 'enum',
'choices': [1, False]
},
'jumpivar': {
'type': 'jump',
'min': 1,
'max': 3,
'num': 3,
'var_type': int
},
'jumpfvar': {
'type': 'jump',
'min': 1,
'max': 3,
'num': 3,
'var_type': float
},
'logjumpivar': {
'type': 'jump',
'min': 10,
'max': 1000,
'num': 3,
'warp': 'log',
'var_type': int
},
'logjumpfvar': {
'type': 'jump',
'min': 10,
'max': 1000,
'num': 3,
'warp': 'log',
'var_type': float
}
}
},
check_fields=False)
searchspace = config.search_space()
assert searchspace['intvar'] == IntVariable('intvar', 1, 2, warp=None)
assert searchspace['logivar'] == IntVariable('logivar', 1, 2, warp='log')
assert searchspace['fvar'] == FloatVariable('fvar', 1, 3.5, warp=None)
assert searchspace['logfvar'] == FloatVariable('logfvar',
1,
2.5,
warp='log')
assert searchspace['enumvar'] == EnumVariable('enumvar', [1, False])
assert searchspace['jumpivar'] == EnumVariable('jumpivar', [1, 2, 3])
assert searchspace['jumpfvar'] == EnumVariable('jumpfvar', [1.0, 2.0, 3.0])
assert searchspace['logjumpivar'] == EnumVariable('logjumpivar',
[10, 100, 1000])
assert searchspace['logjumpfvar'] == EnumVariable('logjumpfvar',
[10.0, 100.0, 1000.0])
def test_random_seed():
config = Config.fromdict({'random_seed': 42}, check_fields=False)
assert config.random_seed() == 42
if len(argv) != 4:
print('usage: sample_db.py config.yaml sample_size n_samples')
exit(1)
inp_file = argv[1]
num = int(argv[2]) # TOTAL size of samples to use e.g. 100
iter = int(argv[3]) # Number of splits e.g. 5
# This will give 5 splits of 20 samples each.100
if num % iter != 0:
print('sample_size not strictly divisible by n_samples')
exit(1)
# Get original database and history
config1 = Config(inp_file)
df1 = config1.trial_results()
hist1 = config1.trials().query(Trial).all()
# Main loop
for name, group in df1.groupby('project_name'):
# Sample the group
sample = group.sample(num, random_state=42)
cv = KFold(n_splits=iter, random_state=42)
all_keep = sample['id'].values
for i, (_, test_idx) in enumerate(cv.split(all_keep)):
keep = all_keep[test_idx]
db2 = make_session('sqlite:///osprey-trials-{0}-{1}.db'.format(int(num/iter), i), project_name=name)
def test_scoring():
config = Config.fromdict({
'scoring': 'sdfsfsdf'
}, check_fields=False)
assert config.scoring() is 'sdfsfsdf'
# Imports
from osprey.config import Config
import sys
if len(sys.argv) != 2:
print('Usage: count_project_trails [config file]')
# Load Configuation File
my_config = sys.argv[1]
config = Config(my_config)
# Retrieve Trial Results
df = config.trial_results()
print(df['project_name'].value_counts())
results = {
'id': id_num,
'cse_train_scores': train_scores,
'cse_train_gaps': train_gaps,
'cse_train_n_timescales': train_n_timescales,
'cse_test_scores': test_scores
}
return results
if __name__ == "__main__":
np.random.seed(42)
config = Config(config_path)
trials = config.trial_results()
trials = trials.sort_values(by='mean_test_score', ascending=False)
# Select the top ten percent
trials = trials.iloc[160:, :]
trial_configs = [get_parameters(irow) for irow in trials.iterrows()]
n_cpu = int(os.environ['SLURM_JOB_CPUS_PER_NODE'])
print('Number of cpus detected {}'.format(n_cpu))
pool = Pool(n_cpu)
results = pool.imap_unordered(run_trial, trial_configs)
results = list(results)
all_ids = [x['id'] for x in results]
def test_strategy_random():
config = Config.fromdict({'strategy': {
'name': 'random'
}},
check_fields=False)
assert isinstance(config.strategy(), RandomSearch)
# # Load the data
# In[4]:
root_dir = 'fs-peptide'
# Load Configuation Files
databases = {
'bayesian': root_dir + '/gp-m52-ei-tica-indv/config-all_tor.yaml',
'random': root_dir + '/rand-tica-indv/config_random-all_tor.yaml',
'sobol': root_dir + '/sobol-tica-indv/config-all_tor.yaml',
'tpe': root_dir + '/tpe-s20-g25-tica-indv/config-all_tor.yaml'
}
all_dfs = []
for k, v in databases.items():
config = Config(v)
df = config.trial_results()
df['method'] = k
all_dfs.append(df)
df_all = pd.concat(all_dfs)
# In[7]:
df_all.head()
# # Drop unnecessary columns and rename
# In[67]:
df = df_all.loc[:, [
def test_random_seed():
config = Config.fromdict({
'random_seed': 42
}, check_fields=False)
assert config.random_seed() == 42
