C. Benjamins: Upgrade to ConfigSpace 1.x (#1124)

development/10_experimental.html

@@ -913,6 +913,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/1_installation.html

@@ -915,6 +915,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/2_package_overview.html

@@ -915,6 +915,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/3_getting_started.html

@@ -915,6 +915,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/4_minimal_example.html

@@ -915,6 +915,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/5_api.html

@@ -916,6 +916,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/6_references.html

@@ -915,6 +915,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/7_glossary.html

@@ -915,6 +915,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/8_faq.html

@@ -915,6 +915,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/9_license.html

@@ -914,6 +914,11 @@
        smac.utils.multi_objective
       </a>
      </li>
+     <li class="toctree-l3">
+      <a class="reference internal" href="api/smac.utils.numpyencoder.html">
+       smac.utils.numpyencoder
+      </a>
+     </li>
      <li class="toctree-l3">
       <a class="reference internal" href="api/smac.utils.pareto_front.html">
        smac.utils.pareto_front

development/_downloads/0422a2b52bca2c17c0ab975a6ae7e8db/2_parego.py

@@ -54,14 +54,14 @@ def configspace(self) -> ConfigurationSpace:
         learning_rate = Categorical("learning_rate", ["constant", "invscaling", "adaptive"], default="constant")
         learning_rate_init = Float("learning_rate_init", (0.0001, 1.0), default=0.001, log=True)
 
-        cs.add_hyperparameters([n_layer, n_neurons, activation, solver, batch_size, learning_rate, learning_rate_init])
+        cs.add([n_layer, n_neurons, activation, solver, batch_size, learning_rate, learning_rate_init])
 
         use_lr = EqualsCondition(child=learning_rate, parent=solver, value="sgd")
         use_lr_init = InCondition(child=learning_rate_init, parent=solver, values=["sgd", "adam"])
         use_batch_size = InCondition(child=batch_size, parent=solver, values=["sgd", "adam"])
 
         # We can also add multiple conditions on hyperparameters at once:
-        cs.add_conditions([use_lr, use_batch_size, use_lr_init])
+        cs.add([use_lr, use_batch_size, use_lr_init])
 
         return cs
 

development/_downloads/422686ca3edb22afd49c1684c91a3eb9/5_continue.py

@@ -47,7 +47,7 @@ class QuadraticFunction:
     def configspace(self) -> ConfigurationSpace:
         cs = ConfigurationSpace(seed=0)
         x = Float("x", (-5, 5), default=-5)
-        cs.add_hyperparameters([x])
+        cs.add([x])
 
         return cs
 

development/_downloads/43217042d929e9cbc326a2703a4cb336/4_callback.py

@@ -27,7 +27,7 @@ def configspace(self) -> ConfigurationSpace:
         cs = ConfigurationSpace(seed=0)
         x0 = Float("x0", (-5, 10), default=-3)
         x1 = Float("x1", (-5, 10), default=-4)
-        cs.add_hyperparameters([x0, x1])
+        cs.add([x0, x1])
 
         return cs
 

development/_downloads/4486aac575ef4e866fdafc4318420ee3/5_continue.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "from __future__ import annotations\n\nfrom ConfigSpace import Configuration, ConfigurationSpace, Float\n\nfrom smac import Callback\nfrom smac import HyperparameterOptimizationFacade as HPOFacade\nfrom smac import Scenario\nfrom smac.main.smbo import SMBO\nfrom smac.runhistory import TrialInfo, TrialValue\n\n__copyright__ = \"Copyright 2021, AutoML.org Freiburg-Hannover\"\n__license__ = \"3-clause BSD\"\n\n\nclass StopCallback(Callback):\n    def __init__(self, stop_after: int):\n        self._stop_after = stop_after\n\n    def on_tell_end(self, smbo: SMBO, info: TrialInfo, value: TrialValue) -> bool | None:\n        \"\"\"Called after the stats are updated and the trial is added to the runhistory. Optionally, returns false\n        to gracefully stop the optimization.\n        \"\"\"\n        if smbo.runhistory.finished == self._stop_after:\n            return False\n\n        return None\n\n\nclass QuadraticFunction:\n    @property\n    def configspace(self) -> ConfigurationSpace:\n        cs = ConfigurationSpace(seed=0)\n        x = Float(\"x\", (-5, 5), default=-5)\n        cs.add_hyperparameters([x])\n\n        return cs\n\n    def train(self, config: Configuration, seed: int = 0) -> float:\n        \"\"\"Returns the y value of a quadratic function with a minimum at x=0.\"\"\"\n        x = config[\"x\"]\n        return x * x\n\n\nif __name__ == \"__main__\":\n    model = QuadraticFunction()\n\n    # Scenario object specifying the optimization \"environment\"\n    scenario = Scenario(model.configspace, deterministic=True, n_trials=50)\n    stop_after = 10\n\n    # Now we use SMAC to find the best hyperparameters\n    smac = HPOFacade(\n        scenario,\n        model.train,  # We pass the target function here\n        callbacks=[StopCallback(stop_after=stop_after)],\n        overwrite=True,  # Overrides any previous results that are found that are inconsistent with the meta-data\n    )\n\n    incumbent = smac.optimize()\n    assert smac.runhistory.finished == stop_after\n\n    # Now, we want to continue the optimization\n    # Make sure, we don't overwrite the last run\n    smac2 = HPOFacade(\n        scenario,\n        model.train,\n        overwrite=False,\n    )\n\n    # Check whether we get the same incumbent\n    assert smac.intensifier.get_incumbent() == smac2.intensifier.get_incumbent()\n    assert smac2.runhistory.finished == stop_after\n\n    # And now we finish the optimization\n    incumbent2 = smac2.optimize()\n\n    default_cost = smac.validate(model.configspace.get_default_configuration())\n    print(f\"Default cost: {default_cost}\")\n\n    incumbent_cost = smac.validate(incumbent)\n    print(f\"Incumbent cost of first run: {incumbent_cost}\")\n\n    incumbent_cost = smac2.validate(incumbent2)\n    print(f\"Incumbent cost of continued run: {incumbent_cost}\")"
+        "from __future__ import annotations\n\nfrom ConfigSpace import Configuration, ConfigurationSpace, Float\n\nfrom smac import Callback\nfrom smac import HyperparameterOptimizationFacade as HPOFacade\nfrom smac import Scenario\nfrom smac.main.smbo import SMBO\nfrom smac.runhistory import TrialInfo, TrialValue\n\n__copyright__ = \"Copyright 2021, AutoML.org Freiburg-Hannover\"\n__license__ = \"3-clause BSD\"\n\n\nclass StopCallback(Callback):\n    def __init__(self, stop_after: int):\n        self._stop_after = stop_after\n\n    def on_tell_end(self, smbo: SMBO, info: TrialInfo, value: TrialValue) -> bool | None:\n        \"\"\"Called after the stats are updated and the trial is added to the runhistory. Optionally, returns false\n        to gracefully stop the optimization.\n        \"\"\"\n        if smbo.runhistory.finished == self._stop_after:\n            return False\n\n        return None\n\n\nclass QuadraticFunction:\n    @property\n    def configspace(self) -> ConfigurationSpace:\n        cs = ConfigurationSpace(seed=0)\n        x = Float(\"x\", (-5, 5), default=-5)\n        cs.add([x])\n\n        return cs\n\n    def train(self, config: Configuration, seed: int = 0) -> float:\n        \"\"\"Returns the y value of a quadratic function with a minimum at x=0.\"\"\"\n        x = config[\"x\"]\n        return x * x\n\n\nif __name__ == \"__main__\":\n    model = QuadraticFunction()\n\n    # Scenario object specifying the optimization \"environment\"\n    scenario = Scenario(model.configspace, deterministic=True, n_trials=50)\n    stop_after = 10\n\n    # Now we use SMAC to find the best hyperparameters\n    smac = HPOFacade(\n        scenario,\n        model.train,  # We pass the target function here\n        callbacks=[StopCallback(stop_after=stop_after)],\n        overwrite=True,  # Overrides any previous results that are found that are inconsistent with the meta-data\n    )\n\n    incumbent = smac.optimize()\n    assert smac.runhistory.finished == stop_after\n\n    # Now, we want to continue the optimization\n    # Make sure, we don't overwrite the last run\n    smac2 = HPOFacade(\n        scenario,\n        model.train,\n        overwrite=False,\n    )\n\n    # Check whether we get the same incumbent\n    assert smac.intensifier.get_incumbent() == smac2.intensifier.get_incumbent()\n    assert smac2.runhistory.finished == stop_after\n\n    # And now we finish the optimization\n    incumbent2 = smac2.optimize()\n\n    default_cost = smac.validate(model.configspace.get_default_configuration())\n    print(f\"Default cost: {default_cost}\")\n\n    incumbent_cost = smac.validate(incumbent)\n    print(f\"Incumbent cost of first run: {incumbent_cost}\")\n\n    incumbent_cost = smac2.validate(incumbent2)\n    print(f\"Incumbent cost of continued run: {incumbent_cost}\")"
       ]
     }
   ],