def main():
config = get_config()
persister = config['model_persister']
model = persister.read()
dataset = fetch_openml('wine-quality-red')
for x, y in zip(dataset.feature_names, model.feature_importances_):
print(x + ': ' + str(y))
def test_multiple_files(self, get_config, config1_fname, config2_fname,
monkeypatch):
monkeypatch.setitem(os.environ, 'PALLADIUM_CONFIG',
','.join([config1_fname, config2_fname]))
monkeypatch.setitem(os.environ, 'ENV1', 'one')
monkeypatch.setitem(os.environ, 'ENV2', 'two')
config = get_config()
assert config['env'] == 'two'
assert config['here'] == os.path.dirname(config1_fname)
def predict(features):
# Get hold of the Palladium configuration in config.py:
config = get_config()
# Use the model_persister to load the trained model:
model = config['model_persister'].read()
# From here on, it's plain scikit-learn:
result = model.predict_proba([features])[0]
for class_, proba in zip(model.classes_, result):
print("{}: {:.1f}%".format(class_, proba * 100))
def predict(features):
# Get hold of the Palladium configuration in config.py:
config = get_config()
# Use the model_persister to load the trained model:
model = config['model_persister'].read()
# From here on, it's plain scikit-learn:
importances = model.feature_importances_
indices = np.argsort(importances)[::-1]
dataset = fetch_openml("wine-quality-red")
# Print the feature ranking
print("Feature ranking:")
for i in indices:
print(f"{dataset.feature_names[i]} {importances[i]}")
def test_extras(self, get_config):
assert get_config(foo='bar')['foo'] == 'bar'
def __init__(self):
from palladium.config import get_config
self.cfg = get_config().copy()
def test_pld_config_key(self, get_config, config1_fname, monkeypatch):
monkeypatch.setitem(os.environ, 'PALLADIUM_CONFIG', config1_fname)
monkeypatch.setitem(os.environ, 'ENV1', 'one')
config = get_config()
assert config['blocking'].__pld_config_key__ == 'blocking'
def get_me_config():
cfg[threading.get_ident()] = get_config().copy()
def test_variables(self, get_config, config1_fname, monkeypatch):
monkeypatch.setitem(os.environ, 'PALLADIUM_CONFIG', config1_fname)
monkeypatch.setitem(os.environ, 'ENV1', 'one')
config = get_config()
assert config['env'] == 'one'
assert config['here'] == os.path.dirname(config1_fname)
def test_default_config(self, get_config, config1_fname, monkeypatch):
here = os.path.dirname(config1_fname)
monkeypatch.setitem(os.environ, 'ENV1', 'one')
with cwd(here):
config = get_config()
assert config['here'] == here
def predict(features):
model = get_config()['model_persister'].read()
result = model.predict_proba([features])[0]
for class_, proba in zip(model.classes_, result):
print("{}: {:.1f}%".format(class_, proba*100))
