[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

hyperopt/algobase.py

@@ -1,5 +1,6 @@
 """ Support code for new-style search algorithms.
 """
+
 import copy
 from collections import deque
 

hyperopt/atpe.py

@@ -380,10 +380,10 @@ def createHyperparameterConfigForHyperoptDomain(domain):
             data = {"type": "object", "properties": {}}
 
             for key in domain.params:
-                data["properties"][
-                    key
-                ] = Hyperparameter.createHyperparameterConfigForHyperoptDomain(
-                    domain.params[key]
+                data["properties"][key] = (
+                    Hyperparameter.createHyperparameterConfigForHyperoptDomain(
+                        domain.params[key]
+                    )
                 )
 
                 if "name" not in data["properties"][key]:
@@ -394,9 +394,9 @@ def createHyperparameterConfigForHyperoptDomain(domain):
             data = {"type": "object", "properties": {}}
 
             for item in domain.named_args:
-                data["properties"][
-                    item[0]
-                ] = Hyperparameter.createHyperparameterConfigForHyperoptDomain(item[1])
+                data["properties"][item[0]] = (
+                    Hyperparameter.createHyperparameterConfigForHyperoptDomain(item[1])
+                )
 
             return data
         elif domain.name == "switch":
@@ -1133,9 +1133,9 @@ def computePrimarySecondary():
                                     secondary.name
                                 ]
                         elif atpeParams["secondaryLockingMode"] == "random":
-                            lockedValues[
-                                secondary.name
-                            ] = self.chooseRandomValueForParameter(secondary)
+                            lockedValues[secondary.name] = (
+                                self.chooseRandomValueForParameter(secondary)
+                            )
 
                 elif atpeParams["secondaryProbabilityMode"] == "correlation":
                     probability = max(
@@ -1158,9 +1158,9 @@ def computePrimarySecondary():
                                     secondary.name
                                 ]
                         elif atpeParams["secondaryLockingMode"] == "random":
-                            lockedValues[
-                                secondary.name
-                            ] = self.chooseRandomValueForParameter(secondary)
+                            lockedValues[secondary.name] = (
+                                self.chooseRandomValueForParameter(secondary)
+                            )
 
             # Now last step, we filter results prior to sending them into ATPE
             for resultIndex, result in enumerate(results):

hyperopt/criteria.py

@@ -1,5 +1,6 @@
 """Criteria for Bayesian optimization
 """
+
 import numpy as np
 import scipy.stats
 

hyperopt/graph_viz.py

@@ -3,6 +3,7 @@
 in a search space.
 
 """
+
 import io
 from .pyll_utils import expr_to_config
 

hyperopt/hp.py

@@ -3,6 +3,7 @@
     from hyperopt import hp
     hp.uniform('x', 0, 1)
 """
+
 from .pyll_utils import hp_choice as choice
 from .pyll_utils import hp_randint as randint
 from .pyll_utils import hp_pchoice as pchoice

hyperopt/ipy.py

@@ -4,6 +4,7 @@
 Author: James Bergstra <james.bergstra@gmail.com>
 Licensed: MIT
 """
+
 from time import sleep, time
 
 import numpy as np

hyperopt/mongoexp.py

@@ -89,6 +89,7 @@
 finalize the job in the database.
 
 """
+
 import copy
 
 # import hashlib
@@ -497,9 +498,9 @@ def reserve(self, host_id, cond=None, exp_key=None):
             raise ValueError("refusing to reserve owned job")
         else:
             cond["owner"] = None
-            cond[
-                "state"
-            ] = JOB_STATE_NEW  # theoretically this is redundant, theoretically
+            cond["state"] = (
+                JOB_STATE_NEW  # theoretically this is redundant, theoretically
+            )
 
         try:
             rval = self.jobs.find_and_modify(

hyperopt/pyll/stochastic.py

@@ -1,6 +1,7 @@
 """
 Constructs for annotating base graphs.
 """
+
 import sys
 import numpy as np
 from .base import scope, as_apply, dfs, rec_eval, clone

hyperopt/rand.py

@@ -1,6 +1,7 @@
 """
 Random search - presented as hyperopt.fmin_random
 """
+
 import logging
 import numpy as np
 

hyperopt/rdists.py

@@ -2,6 +2,7 @@
 Extra distributions to complement scipy.stats
 
 """
+
 import numpy as np
 import numpy.random as mtrand
 import scipy.stats

hyperopt/tests/test_base.py

@@ -46,9 +46,9 @@ def ok_trial(tid, *args, **kwargs):
 def create_fake_trial(tid, loss=None, status=STATUS_OK, state=JOB_STATE_DONE):
     return dict(
         tid=tid,
-        result={"status": status, "loss": loss}
-        if loss is not None
-        else {"status": status},
+        result=(
+            {"status": status, "loss": loss} if loss is not None else {"status": status}
+        ),
         spec={"a": 1},
         misc={
             "tid": tid,

hyperopt/tests/unit/test_ipy.py

@@ -8,6 +8,7 @@
 
 
 """
+
 import sys
 from nose import SkipTest
 

hyperopt/tests/unit/test_plotting.py

@@ -5,6 +5,7 @@
 then the plots actually appear.
 
 """
+
 import unittest
 import os
 

hyperopt/tests/unit/test_rdists.py

@@ -89,7 +89,7 @@ def test_distribution_rvs(self):
 
 
 def check_d_samples(dfn, n, rtol=1e-2, atol=1e-2):
-    counts = defaultdict(lambda: 0)
+    counts = defaultdict(int)
     # print 'sample', dfn.rvs(size=n)
     inc = 1 / n
     for s in dfn.rvs(size=n):

hyperopt/tpe.py

@@ -1,6 +1,7 @@
 """
 Graphical model (GM)-based optimization algorithm using Theano
 """
+
 import logging
 import time