def save(self, model_path):
if not model_path.endswith(fs.sep):
model_path = model_path + fs.sep
if not fs.exists(model_path):
fs.mkdirs(model_path, exist_ok=True)
stub = copy.copy(self)
estimators = self.estimators
if estimators is not None:
stub.estimators = [None for _ in estimators] # keep size
if estimators is not None:
for i, est in enumerate(estimators):
est_pkl = f'{model_path}{i}.pkl'
est_model = f'{model_path}{i}.model'
for t in [est_pkl, est_model]:
if fs.exists(t):
fs.rm(t)
if est is None:
continue
with fs.open(est_pkl, 'wb') as f:
pickle.dump(est, f, protocol=pickle.HIGHEST_PROTOCOL)
if hasattr(est, 'save') and hasattr(est, 'load'):
est.save(est_model)
with fs.open(f'{model_path}ensemble.pkl', 'wb') as f:
pickle.dump(stub, f, protocol=pickle.HIGHEST_PROTOCOL)
def test_save_load(self):
import time
from hypernets.utils import fs
filepath = f'{type(self).__name__}_{time.strftime("%Y%m%d%H%M%S")}'
self.dt.save(filepath)
assert fs.exists(f'{filepath}/dt.pkl')
assert fs.exists(f'{filepath}/dnn_nets.h5')
newdt = deeptable.DeepTable.load(filepath)
print(newdt.config)
preds = newdt.predict(self.X_test)
assert preds.shape, (200, )
def _prepare_cache_dir(self, cache_home, clear_cache=False):
if cache_home is None:
cache_home = 'cache'
if cache_home[-1] == '/':
cache_home = cache_home[:-1]
cache_home = os.path.expanduser(f'{cache_home}')
if not fs.exists(cache_home):
fs.makedirs(cache_home, exist_ok=True)
else:
if clear_cache:
fs.rm(cache_home, recursive=True)
fs.mkdirs(cache_home, exist_ok=True)
cache_dir = f'{cache_home}/{self.signature}'
if not fs.exists(cache_dir):
fs.makedirs(cache_dir, exist_ok=True)
return cache_dir
def test_dataframe_fs(self):
file_path = f'/{type(self).__name__}/test_df_fs.parquet'
df = dsutils.load_bank()
p.store(df, file_path, filesystem=fs)
assert fs.exists(file_path)
# read it
df_read = p.load(file_path, filesystem=fs)
assert self.is_same_df(df, df_read)
def load(model_path):
if not model_path.endswith(fs.sep):
model_path = model_path + fs.sep
with fs.open(f'{model_path}ensemble.pkl', 'rb') as f:
stub = pickle.load(f)
if stub.estimators is not None:
for i in range(len(stub.estimators)):
if fs.exists(f'{model_path}{i}.pkl'):
with fs.open(f'{model_path}{i}.pkl', 'rb') as f:
est = pickle.load(f)
if fs.exists(f'{model_path}{i}.model') and hasattr(
est, 'load'):
est = est.load(f'{model_path}{i}.model')
stub.estimators[i] = est
return stub
def clear(cache_dir=None, fn=None):
assert fn is None or callable(fn)
if cache_dir is None:
cache_dir = cfg.cache_dir
if callable(fn):
cache_dir = f'{cache_dir}{fs.sep}{".".join([fn.__module__, fn.__qualname__])}'
if fs.exists(cache_dir):
fs.rm(cache_dir, recursive=True)
fs.mkdirs(cache_dir, exist_ok=True)
def _prepare_output_dir(self, home_dir, nets):
if home_dir is None:
home_dir = 'dt_output'
if home_dir[-1] == '/':
home_dir = home_dir[:-1]
running_dir = f'dt_{datetime.datetime.now().__format__("%Y%m%d %H%M%S")}_{"_".join(nets)}'
output_path = os.path.expanduser(f'{home_dir}/{running_dir}/')
if not fs.exists(output_path):
fs.makedirs(output_path, exist_ok=True)
return output_path
def load_transformers_from_cache(self):
transformer_path = f'{self.cache_dir}/transformers.pkl'
if fs.exists(transformer_path):
try:
with fs.open(transformer_path, 'rb') as input:
preprocessor = pickle.load(input)
self.__dict__.update(preprocessor.__dict__)
return True
except Exception as e:
logger.error(e)
fs.rm(transformer_path)
return False
def load_deepmodel(self, filepath):
if fs.exists(filepath):
print(f'Load model from: {filepath}.')
dm = DeepModel(self.task,
self.num_classes,
self.config,
self.preprocessor.categorical_columns,
self.preprocessor.continuous_columns,
model_file=filepath)
return dm
else:
raise ValueError(f'Invalid model filename:{filepath}.')
def save_transformed_X_y_to_cache(self, sign, X, y):
filepath = f'{self.cache_dir}/X_y_{sign}.pkl.gz'
try:
# x_t = X.copy(deep=True)
X.insert(0, 'saved__y__', y)
with fs.open(filepath, mode='wb') as f:
X.to_pickle(f, compression='gzip')
return True
except Exception as e:
logger.error(e)
if fs.exists(filepath):
fs.rm(filepath)
return False
def get_transformed_X_y_from_cache(self, sign):
file_x_y = f'{self.cache_dir}/X_y_{sign}.pkl.gz'
X_t, y_t = None, None
if fs.exists(file_x_y):
try:
with fs.open(file_x_y, mode='rb') as f:
df = pd.read_pickle(f, compression='gzip')
y_t = df.pop('saved__y__')
X_t = df
except Exception as e:
logger.error(e)
fs.rm(file_x_y)
return X_t, y_t
def save(self, filepath, deepmodel_basename=None):
if filepath[-1] != '/':
filepath = filepath + '/'
if not fs.exists(filepath):
fs.makedirs(filepath, exist_ok=True)
num_model = len(self.__modelset.get_modelinfos())
for mi in self.__modelset.get_modelinfos():
if isinstance(mi.model, str):
dm = self.load_deepmodel(mi.model)
mi.model = dm
if not isinstance(mi.model, DeepModel):
raise ValueError(
f'Currently does not support saving non-DeepModel models.')
if num_model == 1 and deepmodel_basename is not None:
mi.name = deepmodel_basename
self.__current_model = deepmodel_basename
modelfile = f'{filepath}{mi.name}.h5'
mi.model.save(modelfile)
mi.model = modelfile
with fs.open(f'{filepath}dt.pkl', 'wb') as output:
pickle.dump(self, output, protocol=4)
def _cache_call(*args, **kwargs):
assert len(args) > 0
obj = None
cache_path = None
loaded = False
result = None
tb = _get_tool_box_for_cache(*args, **kwargs)
try:
for c in callbacks:
c.on_enter(fn, *args, **kwargs)
# bind arguments
bind_args = sig.bind(*args, **kwargs)
bind_args.apply_defaults()
obj = bind_args.arguments.get('self', None)
# calc cache_key
key_items = {}
arg_kwargs = bind_args.arguments.get('kwargs', {}).copy()
arg_items = {
k: v
for k, v in bind_args.arguments.items() if k not in [
'self',
]
} # as dict
arg_items.update(arg_kwargs)
if arg_keys is not None and len(arg_keys) > 0:
key_items.update({k: arg_items.get(k) for k in arg_keys})
else:
key_items.update(arg_items)
if attr_keys is not None:
key_items.update({k: getattr(obj, k, None) for k in attr_keys})
elif isinstance(obj, BaseEstimator) and 'params_' not in key_items:
key_items['params_'] = obj.get_params(deep=False)
if attrs_to_restore is not None:
key_items['attrs_to_restore_'] = attrs_to_restore
cache_key = tb.data_hasher()(key_items)
# join cache_path
if not fs.exists(cache_dir):
fs.mkdirs(cache_dir, exist_ok=True)
cache_path = f'{cache_dir}{fs.sep}{cache_key}'
# detect and load cache
if fs.exists(f'{cache_path}.meta'):
# load
cached_data, meta = _load_cache(tb, cache_path)
for c in callbacks:
c.on_apply(fn, cached_data, *args, **kwargs)
# restore attributes
if attrs_to_restore is not None:
cached_attributes = meta.get('attributes', {})
for k in attrs_to_restore:
setattr(obj, k, cached_attributes.get(k))
if meta['strategy'] == _STRATEGY_DATA:
result = cached_data
else: # strategy==transform
if isinstance(transformer, str):
tfn = getattr(obj, transformer)
assert callable(tfn)
result = tfn(*args[1:],
**kwargs) # exclude args[0]==self
elif callable(transformer):
result = transformer(*args, **kwargs)
loaded = True
except SkipCache:
pass
except Exception as e:
logger.warning(e)
if not loaded:
result = fn(*args, **kwargs)
if cache_path is not None and not loaded:
try:
for c in callbacks:
c.on_store(fn, result, *args, **kwargs)
# store cache
cache_strategy = strategy if strategy is not None else cfg.cache_strategy
if cache_strategy == _STRATEGY_TRANSFORM and (
result is None or transformer is not None):
cache_data = None
meta = {'strategy': _STRATEGY_TRANSFORM}
else:
cache_data = result
meta = {'strategy': _STRATEGY_DATA}
if attrs_to_restore is not None:
meta['attributes'] = {
k: getattr(obj, k, None)
for k in attrs_to_restore
}
if isinstance(obj, BaseEstimator):
meta['params_'] = obj.get_params(deep=False) # for info
_store_cache(tb, cache_path, cache_data, meta=meta)
for c in callbacks:
c.on_leave(fn, *args, **kwargs)
except Exception as e:
logger.warning(e)
return result
def fit_cross_validation(
estimator_type,
fit_fn,
X,
y,
X_test=None,
score_fn=roc_auc_score,
estimator_params={},
categorical_feature=None,
task_type=consts.TASK_BINARY,
num_folds=5,
stratified=True,
iterators=None,
batch_size=None,
preds_filepath=None,
):
print("Start cross validation")
print(
f'X.Shape={np.shape(X)}, y.Shape={np.shape(y)}, batch_size={batch_size}'
)
# Cross validation model
if iterators is None:
if stratified:
iterators = StratifiedKFold(n_splits=num_folds,
shuffle=True,
random_state=1001)
else:
iterators = KFold(n_splits=num_folds,
shuffle=True,
random_state=1001)
print(f'Iterators:{iterators}')
if len(y.shape) > 1:
oof_proba = np.zeros(y.shape)
else:
oof_proba = np.zeros((y.shape[0], 1))
y = np.array(y)
if preds_filepath is None and os.environ.get(
consts.ENV_DEEPTABLES_HOME) is not None:
preds_filepath = os.environ.get(consts.ENV_DEEPTABLES_HOME)
if preds_filepath is None:
preds_filepath = f'./preds_{estimator_type}_{datetime.datetime.now().__format__("%Y_%m_%d %H:%M:%S")}/'
if not fs.exists(preds_filepath):
fs.makedirs(preds_filepath, exist_ok=True)
for n_fold, (train_idx, valid_idx) in enumerate(iterators.split(X, y)):
print(f'\nFold:{n_fold + 1}\n')
x_train_fold, y_train_fold = X.iloc[train_idx], y[train_idx]
x_val_fold, y_val_fold = X.iloc[valid_idx], y[valid_idx]
model = fit_fn(
x_train_fold,
y_train_fold,
x_val_fold,
y_val_fold,
cat_vars=categorical_feature,
task=task_type,
estimator_params=estimator_params,
)
print(f'Fold {n_fold + 1} finished.')
proba = model.predict_proba(x_val_fold)[:, 1:2]
oof_proba[valid_idx] = proba
test_fold_proba = model.predict_proba(X_test)
score = round(score_fn(y_val_fold, proba), 5)
file = f'{preds_filepath}{score}_fold{n_fold + 1}.csv'
with fs.open(file, 'w', encoding='utf-8') as f:
pd.DataFrame(test_fold_proba).to_csv(f, index=False)
print(f'Fold {n_fold + 1} Score:{score}')
if oof_proba.shape[-1] == 1:
oof_proba = oof_proba.reshape(-1)
print(f'OOF score:{score_fn(y, oof_proba)}')
return oof_proba
