# Resolver global, by value, by id.
('log', {'key': None}),
('log', {'key_id': None}),
# Resolver conf, by value, by id.
('log2', {'x_id': None}),
('log2', {'x': None}),
],
'priority': 2,
}
},
}
if __name__ == '__main__':
# Execute configuration(s).
objects = pycnfg.run(CNFG, resolve_none=True)
# => 42
# => 42
# => 42
# => 42
# => 42
# => 42
# Storage for produced object(s).
print(objects)
# => {'logger__default': <Logger default (INFO)>,
# 'path__default': 'pycnfg/examples/complex',
# 'x__1': 'a',
# 'x__2': 'c',
# 'y__conf': {'b': 2, 'c': 42, 'print': 252}}
}
},
'key': {
'conf': {
'init': 'b',
},
},
'val': {
'conf': {
'init': 24,
},
'conf2': {
'init': '42',
}
},
}
if __name__ == '__main__':
for cnfg in [CNFG_1, CNFG_2, CNFG_3, CNFG_4]:
# Execute configuration(s).
objects = pycnfg.run(cnfg,
dcnfg={},
update_expl=True,
resolve_none=True)
# => 42
# Storage for produced object(s).
print(objects['section_id__configuration_id'])
# => {'a': 7, 'b': 42}
print('=' * 79)
'return_train_score':
True,
},
}),
# Validate 'sgd' pipeline on 'train' and 'test' subsets of
# 'train' dataset with 'r2' scorer (after optimization).
('validate', {
'pipeline_id': 'pipeline__sgd',
'dataset_id': 'dataset__train',
'subset_id': ['train', 'test'],
'metric_id': ['metric__r2', 'metric__mse'],
}),
# Predict with 'sgd' pipeline on whole 'test' dataset.
('predict', {
'pipeline_id': 'pipeline__sgd',
'dataset_id': 'dataset__test',
'subset_id': '',
}),
# Dump 'sgd' pipeline on disk.
('dump', {
'pipeline_id': 'pipeline__sgd',
'dirpath': None
}),
],
},
},
}
if __name__ == '__main__':
objects = pycnfg.run(CNFG, dcnfg={})
},
},
# Separate section for 'gs_params' kwarg.
'gs_params': {
'conf': {
'priority': 3,
'init': {
'n_iter':
None,
'n_jobs':
1,
'refit':
'metric__r2',
'cv':
sklearn.model_selection.KFold(n_splits=3,
shuffle=True,
random_state=42),
'verbose':
1,
'pre_dispatch':
'n_jobs',
'return_train_score':
True,
},
},
},
}
if __name__ == '__main__':
objects = pycnfg.run(CNFG, dcnfg=mlshell.CNFG)
'y': {
'conf': {
'init': {'b': 2, 'c': 42},
'producer': CustomProducer,
'steps': [
('__init__', {'path_id': 'path__default',
'logger_id': 'logger__default'}),
('set', {'key': 'x__1', 'val': 7}),
('log', {'key': 'x__2'}),
('log', {'key_id': 'x__2'}),
],
'priority': 2,
}
},
}
if __name__ == '__main__':
# Execute configuration(s).
objects = pycnfg.run(CNFG)
# => 42
# => 42
# Storage for produced object(s).
print(objects)
# => {'logger__default': <Logger default (INFO)>,
# 'path__default': 'pycnfg/examples/complex',
# 'x__1': 'a',
# 'x__2': 'c',
# 'y__conf': {'b': 2, 'c': 42, 'a': 7}}
def test_run(id_, args, kwargs, expected):
"""
- Delete previous test output if exist.
- Start mlshell.run.py.
- Check current output with original.
"""
# Remove results for test.
results_path = expected['results_path']
shutil.rmtree(results_path, ignore_errors=True)
# Start code.
# [future] attempts to run classification with n_jobs>1
# global cnfg_default
# sys.modules['cnfg_default'] = get_params(args[0], 'cnfg_default')
# import cnfg_default
# #from cnfg_default import custom_score_metric
objects = pycnfg.run(oid='default', *args, **kwargs)
tmp = {k: type(v).__name__ for k, v in objects.items()}
print('OBJECTS:')
print(tmp)
# Compare results:
# * Compare objects (keys and str of values).
objects_ = expected['objects']
objects = {k: type(v).__name__ for k, v in objects.items()}
if objects != objects_:
print(set(objects.items()) ^ set(objects_.items()))
assert objects == objects_
# for k, v in objects.items():
# assert k in objects_
# assert type(v).__name__ == objects_[k]
# * Compare predictions csv(all available).
pred_path = glob.glob(f"{results_path}/models/*_pred.csv")
pred_path_ = glob.glob(expected['pred_path'])
assert len(pred_path) == len(pred_path_)
for act, exp in zip(sorted(pred_path), sorted(pred_path_)):
file_diff(act, exp)
assert filecmp.cmp(act, exp)
# * Compare test logs.
logs_path = glob.glob(f"{results_path}/logs*/*_test.log")[0]
logs_path_ = expected['logs_path']
file_diff(logs_path, logs_path_)
assert filecmp.cmp(logs_path, logs_path_)
# * Compare runs dataframe, non-universe columns.
runs_path = f"{results_path}/runs"
runs_path_ = expected['runs_path']
df = runs_loader(runs_path)
df_ = runs_loader(runs_path_)
# First False/True for each element, then check all by columns.
# col1 True
# col2 False
# dtype: bool
df_diff = df.eq(df_).all()
# Column names that are not equal.
columns = sorted(list(df_diff[df_diff == False].dropna().index))
# columns_eq = sorted(list(df_diff[df_diff==True].dropna().index))
columns_ = expected['columns_diff']
print('DIFF:\n', columns)
time.sleep(1)
# assert columns == columns_
# * Compare model.
model_path = glob.glob(f"{results_path}/models/*.model")
model_path_ = glob.glob(expected['model_path'])
assert len(model_path) == len(model_path_)
for act, exp in zip(sorted(model_path), sorted(model_path_)):
assert filecmp.cmp(act, exp)
return
# Otherwise can`t pickle when n_jobs>1, need import to address scope.
# custom_score_metric = get_params('classification/conf.py',
# 'custom_score_metric')
# sys.modules[f'custom_score_metric'] = custom_score_metric
def test_run(id_, args, kwargs, expected):
objects = pycnfg.run(*args, **kwargs)
assert objects == expected, f"test={id_}"