Add initial version of auto estimator

pyzoo/zoo/automl/model/base_pytorch_model.py

@@ -40,15 +40,28 @@ def __init__(self, model_creator, optimizer_creator, loss_creator,
         self.onnx_model = None
         self.onnx_model_built = False
 
+    def _create_loss(self):
+        if isinstance(self.loss_creator, torch.nn.modules.loss._Loss):
+            self.criterion = self.loss_creator
+        else:
+            self.criterion = self.loss_creator(self.config)
+
+    def _create_optimizer(self):
+        if issubclass(self.optimizer_creator, torch.optim.Optimizer):
+            # use torch default parameter values if user pass optimizer name or optimizer class.
+            self.optimizer = self.optimizer_creator(self.model.parameters())
+        else:
+            self.optimizer = self.optimizer_creator(self.model, self.config)
+
     def build(self, config):
         # check config and update
         self._check_config(**config)
         self.config = config
         # build model
         self.model = self.model_creator(config)
         self.model_built = True
-        self.optimizer = self.optimizer_creator(self.model, self.config)
-        self.criterion = self.loss_creator(self.config)
+        self._create_loss()
+        self._create_optimizer()
 
     def _reshape_input(self, x):
         if x.ndim == 1:

pyzoo/zoo/orca/automl/auto_estimator.py

@@ -0,0 +1,105 @@
+#
+# Copyright 2018 Analytics Zoo Authors.
+#
+# 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
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+class AutoEstimator:
+    def __init__(self, model_builder, searcher):
+        self.model_builder = model_builder
+        self.searcher = searcher
+
+    @staticmethod
+    def from_torch(*,
+                   model_creator,
+                   optimizer,
+                   loss,
+                   logs_dir,
+                   resources_per_trial,
+                   name,
+                   ):
+        """
+        Create an AutoEstimator for torch.
+
+        :param model_creator: PyTorch model creator function.
+        :param optimizer: PyTorch optimizer creator function or pytorch optimizer name (string).
+        :param loss: PyTorch loss instance or PyTorch loss creator function
+            or pytorch loss name (string).
+        :param logs_dir: Local directory to save logs and results.
+        :param resources_per_trial: Dict. resources for each trial. e.g. {"cpu": 2}.
+        :param name: Name of the auto estimator.
+        :return: an AutoEstimator object.
+        """
+        from zoo.orca.automl.pytorch_model_utils import validate_pytorch_loss, \
+            validate_pytorch_optim
+        from zoo.automl.model import ModelBuilder
+        from zoo.automl.search import SearchEngineFactory
+        loss = validate_pytorch_loss(loss)
+        optimizer = validate_pytorch_optim(optimizer)
+        model_builder = ModelBuilder.from_pytorch(model_creator=model_creator,
+                                                  optimizer_creator=optimizer,
+                                                  loss_creator=loss)
+        searcher = SearchEngineFactory.create_engine(backend="ray",
+                                                     logs_dir=logs_dir,
+                                                     resources_per_trial=resources_per_trial,
+                                                     name=name)
+        return AutoEstimator(model_builder=model_builder, searcher=searcher)
+
+    @staticmethod
+    def from_keras(*,
+                   model_creator,
+                   logs_dir,
+                   resources_per_trial,
+                   name,
+                   ):
+        """
+        Create an AutoEstimator for tensorflow keras.
+
+        :param model_creator: Tensorflow keras model creator function.
+        :param logs_dir: Local directory to save logs and results.
+        :param resources_per_trial: Dict. resources for each trial. e.g. {"cpu": 2}.
+        :param name: Name of the auto estimator.
+        :return: an AutoEstimator object.
+        """
+        from zoo.automl.model import ModelBuilder
+        from zoo.automl.search import SearchEngineFactory
+        model_builder = ModelBuilder.from_tfkeras(model_creator=model_creator)
+        searcher = SearchEngineFactory.create_engine(backend="ray",
+                                                     logs_dir=logs_dir,
+                                                     resources_per_trial=resources_per_trial,
+                                                     name=name)
+        return AutoEstimator(model_builder=model_builder, searcher=searcher)
+
+    def fit(self,
+            data,
+            recipe=None,
+            metric=None,
+            search_alg=None,
+            search_alg_params=None,
+            scheduler=None,
+            scheduler_params=None,
+            ):
+
+        self.searcher.compile(data=data,
+                              model_create_func=self.model_builder,
+                              recipe=recipe,
+                              metric=metric,
+                              search_alg=search_alg,
+                              search_alg_params=search_alg_params,
+                              scheduler=scheduler,
+                              scheduler_params=scheduler_params)
+        analysis = self.searcher.run()
+        return analysis
+
+
+

pyzoo/zoo/orca/automl/pytorch_model_utils.py

@@ -0,0 +1,50 @@
+#
+# Copyright 2018 Analytics Zoo Authors.
+#
+# 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
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import torch
+
+PYTORCH_LOSS_NAMES = {s for s in dir(torch.nn.modules) if s.endswith("Loss")}
+PYTORCH_OPTIM_NAMES = {s for s in dir(torch.optim) if any(c.isupper() for c in s)} - {'Optimizer'}
+
+
+def validate_pytorch_loss(loss):
+    import types
+    if isinstance(loss, str):
+        if loss in PYTORCH_LOSS_NAMES:
+            return getattr(torch.nn.modules, loss)()
+        raise ValueError(f'Must provide a valid torch loss name among {PYTORCH_LOSS_NAMES}')
+
+    if isinstance(loss, torch.nn.modules.loss._Loss) or \
+            isinstance(loss, types.FunctionType):
+        return loss
+
+    raise ValueError("Must provide a valid pytorch loss name or a pytorch loss instance"
+                     "or a pytorch loss creator function ")
+
+
+def validate_pytorch_optim(optim):
+    import types
+    if isinstance(optim, str):
+        if optim in PYTORCH_OPTIM_NAMES:
+            return getattr(torch.optim, optim)
+        raise ValueError(f'Must provide a valid torch optimizer name among {PYTORCH_OPTIM_NAMES}')
+
+    if issubclass(optim, torch.optim.Optimizer) or\
+            isinstance(optim, types.FunctionType):
+        return optim
+
+    raise ValueError("Must provide a valid pytorch optimizer name or a pytorch optimizer class" 
+                     "or a pytorch optimizer creator function ")