Merge pull request #572 from autonomio/allow_paramspace_as_params

Scan allows ParamSpace as input

docs/Scan.md

@@ -17,7 +17,7 @@ Argument | Input | Description
 --------- | ------- | -----------
 `x` | array or list of arrays | prediction features
 `y` | array or list of arrays | prediction outcome variable
-`params` | dict | the parameter dictionary
+`params` | dict or ParamSpace object | the parameter dictionary or the ParamSpace object after splitting
 `model` | function | the Keras model as a function
 `experiment_name` | str | Used for creating the experiment logging folder
 `x_val` | array or list of arrays | validation data for x

setup.py

@@ -18,7 +18,7 @@
 URL = 'http://autonom.io'
 LICENSE = 'MIT'
 DOWNLOAD_URL = 'https://github.com/autonomio/talos/'
-VERSION = '1.1.0'
+VERSION = '1.1.1'
 
 
 try:

talos/parameters/DistributeParamSpace.py

@@ -16,6 +16,10 @@ def __init__(self,
         params | object | ParamSpace class object
         machines | int | number of machines to split for
 
+        NOTE: `Scan()` limits will not be applied if ParamSpace object
+        is passed directly into `Scan()` as `params` argument so they
+        should be passed directly into `DistributeParamSpace` instead.
+
         '''
 
         from talos.parameters.ParamSpace import ParamSpace

talos/scan/scan_prepare.py

@@ -16,19 +16,30 @@ def scan_prepare(self):
     elif self.x_val is not None and self.y_val is not None:
         self.custom_val_split = True
 
-    # create reference for parameter keys
-    self._param_dict_keys = sorted(list(self.params.keys()))
-
-    # create the parameter object and move to self
-    from ..parameters.ParamSpace import ParamSpace
-    self.param_object = ParamSpace(params=self.params,
-                                   param_keys=self._param_dict_keys,
-                                   random_method=self.random_method,
-                                   fraction_limit=self.fraction_limit,
-                                   round_limit=self.round_limit,
-                                   time_limit=self.time_limit,
-                                   boolean_limit=self.boolean_limit
-                                   )
+    # handle the case where self.params is dictionary
+    if isinstance(self.params, dict):
+
+        # create reference for parameter keys
+        self._param_dict_keys = sorted(list(self.params.keys()))
+
+        # create the parameter object and move to self
+        from ..parameters.ParamSpace import ParamSpace
+        self.param_object = ParamSpace(params=self.params,
+                                       param_keys=self._param_dict_keys,
+                                       random_method=self.random_method,
+                                       fraction_limit=self.fraction_limit,
+                                       round_limit=self.round_limit,
+                                       time_limit=self.time_limit,
+                                       boolean_limit=self.boolean_limit)
+
+    # handle the case when self.params already is ParamSpace object
+    elif 'talos.parameters.ParamSpace.ParamSpace' in str(type(self.params)):
+
+        self._param_dict_keys = sorted(list(self.params.param_keys))
+        self.param_object = self.params
+
+    else:
+        raise TypeError('params has to be either dict or ParamSpace object.')
 
     # mark that it's a first round
     self.first_round = True

tests/commands/test_latest.py

@@ -24,7 +24,9 @@ def test_latest():
 
     from talos.parameters.DistributeParamSpace import DistributeParamSpace
 
-    _ = DistributeParamSpace(params=p, param_keys=p.keys(), machines=5)
+    param_spaces = DistributeParamSpace(params=p,
+                                        param_keys=p.keys(),
+                                        machines=100)
 
     def iris_model(x_train, y_train, x_val, y_val, params):
 
@@ -49,6 +51,17 @@ def iris_model(x_train, y_train, x_val, y_val, params):
 
         return out, model
 
+    print(type(param_spaces.param_spaces[0]))
+
+    scan_object = talos.Scan(x,
+                             y,
+                             model=iris_model,
+                             params=param_spaces.param_spaces[0],
+                             experiment_name='test_latest',
+                             round_limit=5,
+                             reduction_method='gamify',
+                             save_weights=False)
+
     scan_object = talos.Scan(x, y,
                              model=iris_model,
                              params=p,