add random_state

osprey/config.py

@@ -13,6 +13,7 @@
                    serialized to a database specified in this section.
  - cv:             specification for cross-validation.
  - scoring:        the score function used in cross-validation. (optional)
+ - random_seed:    random seed to be used. (optional)
 """
 
 import sys
@@ -50,6 +51,7 @@
     'strategy':        ['name', 'params'],
     'cv':              (int, dict),
     'scoring':         (str, type(None)),
+    'random_seed':     (int, type(None)),
 }
 
 
@@ -309,6 +311,11 @@ def scoring(self):
         assert isinstance(scoring, (str, type(None)))
         return scoring
 
+    def random_seed(self):
+        random_seed = self.get_section('random_seed')
+        assert isinstance(random_seed, (int, type(None)))
+        return random_seed
+
     def cv(self, X, y=None):
         cv = self.get_section('cv')
         if isinstance(cv, int):

osprey/data/default_config.yaml

@@ -1,4 +1,6 @@
 trials:
   project_name: default
 
-scoring: !!null
+scoring: !!null
+
+random_seed: !!null

osprey/data/grid_example.yaml

@@ -29,3 +29,5 @@ dataset_loader:
 
 trials:
     uri: sqlite:///osprey-trials.db
+
+random_seed: 42

osprey/data/random_example.yaml

@@ -25,3 +25,5 @@ dataset_loader:
 
 trials:
     uri: sqlite:///osprey-trials.db
+
+random_seed: 42

osprey/data/sklearn_skeleton_config.yaml

@@ -27,3 +27,5 @@ dataset_loader:
 
 trials:
     uri: sqlite:///osprey-trials.db
+
+random_seed: 42

osprey/execute_worker.py

@@ -35,6 +35,7 @@ def execute(args, parser):
     strategy = config.strategy()
     config_sha1 = config.sha1()
     scoring = config.scoring()
+    random_seed = config.random_seed()
     project_name = config.project_name()
 
     if is_msmbuilder_estimator(estimator):
@@ -78,7 +79,7 @@ def signal_hander(signum, frame):
 
         s = run_single_trial(
             estimator=estimator, params=params, trial_id=trial_id,
-            scoring=scoring, X=X, y=y, cv=cv,
+            scoring=scoring, random_seed=random_seed, X=X, y=y, cv=cv,
             sessionbuilder=config.trialscontext)
 
         statuses[i] = s
@@ -122,14 +123,15 @@ def initialize_trial(strategy, searchspace, estimator, config_sha1,
     return trial_id, params
 
 
-def run_single_trial(estimator, params, trial_id, scoring, X, y, cv,
-                     sessionbuilder):
+def run_single_trial(estimator, params, trial_id, scoring, random_seed,
+                     X, y, cv, sessionbuilder):
 
     status = None
 
     try:
         score = fit_and_score_estimator(
-            estimator, params, cv=cv, scoring=scoring, X=X, y=y, verbose=1)
+            estimator, params, cv=cv, scoring=scoring, random_seed=random_seed,
+            X=X, y=y, verbose=1)
         with sessionbuilder() as session:
             trial = session.query(Trial).get(trial_id)
             trial.mean_test_score = score['mean_test_score']

osprey/fit_estimator.py

@@ -19,8 +19,8 @@
 
 
 def fit_and_score_estimator(estimator, parameters, cv, X, y=None, scoring=None,
-                            iid=True, n_jobs=1, verbose=1,
-                            pre_dispatch='2*n_jobs'):
+                            random_seed=None, iid=True, n_jobs=1,
+                            verbose=1, pre_dispatch='2*n_jobs'):
     """Fit and score an estimator with cross-validation
 
     This function is basically a copy of sklearn's
@@ -39,6 +39,9 @@ def fit_and_score_estimator(estimator, parameters, cv, X, y=None, scoring=None,
         The scores on the training and test sets, as well as the mean test set
         score.
     """
+
+    np.random.seed(random_seed)
+
     scorer = check_scoring(estimator, scoring=scoring)
     n_samples = num_samples(X)
     X, y = check_arrays(X, y, allow_lists=True, sparse_format='csr',