[tune] Hyperopt: Directly accept category variables instead of indices (#12715)

* [tune] Hyperopt: Directly accept category variables instead of indices * Fix interrupt test * Update python/ray/tune/suggest/hyperopt.py Co-authored-by: Richard Liaw <rliaw@berkeley.edu> * Apply suggestions from code review * Update python/ray/tune/suggest/hyperopt.py Co-authored-by: Richard Liaw <rliaw@berkeley.edu> * lint Co-authored-by: Richard Liaw <rliaw@berkeley.edu>
2026-07-13 08:10:21 +08:00 · 2020-12-12 10:40:53 +01:00
parent 0b1fbc5e83
commit 42c70be073
3 changed files with 68 additions and 21 deletions
@@ -40,12 +40,12 @@ if __name__ == "__main__":
        {
            "width": 1,
            "height": 2,
-            "activation": 0  # Activation will be relu
+            "activation": "relu"  # Activation will be relu
        },
        {
            "width": 4,
            "height": 2,
-            "activation": 1  # Activation will be tanh
+            "activation": "tanh"  # Activation will be tanh
        }
    ]

@@ -58,11 +58,9 @@ class HyperOptSearch(Searcher):
            minimizing or maximizing the metric attribute.
        points_to_evaluate (list): Initial parameter suggestions to be run
            first. This is for when you already have some good parameters
-            you want hyperopt to run first to help the TPE algorithm
-            make better suggestions for future parameters. Needs to be
-            a list of dict of hyperopt-named variables.
-            Choice variables should be indicated by their index in the
-            list (see example)
+            you want to run first to help the algorithm make better suggestions
+            for future parameters. Needs to be a list of dict containing the
+            configurations.
        n_initial_points (int): number of random evaluations of the
            objective function before starting to aproximate it with
            tree parzen estimators. Defaults to 20.
@@ -86,7 +84,7 @@ class HyperOptSearch(Searcher):
        current_best_params = [{
            'width': 10,
            'height': 0,
-            'activation': 0, # The index of "relu"
+            'activation': "relu",
        }]

        hyperopt_search = HyperOptSearch(
@@ -109,7 +107,7 @@ class HyperOptSearch(Searcher):
        current_best_params = [{
            'width': 10,
            'height': 0,
-            'activation': 0, # The index of "relu"
+            'activation': "relu",
        }]

        hyperopt_search = HyperOptSearch(
@@ -137,7 +135,6 @@ class HyperOptSearch(Searcher):
            "HyperOpt must be installed! Run `pip install hyperopt`.")
        if mode:
            assert mode in ["min", "max"], "`mode` must be 'min' or 'max'."
-        from hyperopt.fmin import generate_trials_to_calculate
        super(HyperOptSearch, self).__init__(
            metric=metric,
            mode=mode,
@@ -157,15 +154,9 @@ class HyperOptSearch(Searcher):
                hpo.tpe.suggest, n_startup_jobs=n_initial_points)
        if gamma is not None:
            self.algo = partial(self.algo, gamma=gamma)
-        if points_to_evaluate is None:
-            self._hpopt_trials = hpo.Trials()
-            self._points_to_evaluate = 0
-        else:
-            assert isinstance(points_to_evaluate, (list, tuple))
-            self._hpopt_trials = generate_trials_to_calculate(
-                points_to_evaluate)
-            self._hpopt_trials.refresh()
-            self._points_to_evaluate = len(points_to_evaluate)
+
+        self._points_to_evaluate = points_to_evaluate
+
        self._live_trial_mapping = {}
        if random_state_seed is None:
            self.rstate = np.random.RandomState()
@@ -184,12 +175,68 @@ class HyperOptSearch(Searcher):
            self._setup_hyperopt()

    def _setup_hyperopt(self):
+        from hyperopt.fmin import generate_trials_to_calculate
+
        if self._metric is None and self._mode:
            # If only a mode was passed, use anonymous metric
            self._metric = DEFAULT_METRIC

+        if self._points_to_evaluate is None:
+            self._hpopt_trials = hpo.Trials()
+            self._points_to_evaluate = 0
+        else:
+            assert isinstance(self._points_to_evaluate, (list, tuple))
+
+            for i in range(len(self._points_to_evaluate)):
+                config = self._points_to_evaluate[i]
+                self._convert_categories_to_indices(config)
+
+            self._hpopt_trials = generate_trials_to_calculate(
+                self._points_to_evaluate)
+            self._hpopt_trials.refresh()
+            self._points_to_evaluate = len(self._points_to_evaluate)
+
        self.domain = hpo.Domain(lambda spc: spc, self._space)

+    def _convert_categories_to_indices(self, config):
+        """Convert config parameters for categories into hyperopt-compatible
+        representations where instead the index of the category is expected."""
+
+        def _lookup(config_dict, space_dict, key):
+            if isinstance(config_dict[key], dict):
+                for k in config_dict[key]:
+                    _lookup(config_dict[key], space_dict[key], k)
+            else:
+                if isinstance(space_dict[key], hpo.base.pyll.Apply) \
+                   and space_dict[key].name == "switch":
+                    if len(space_dict[key].pos_args) > 0:
+                        categories = [
+                            a.obj for a in space_dict[key].pos_args[1:]
+                            if a.name == "literal"
+                        ]
+                        try:
+                            idx = categories.index(config_dict[key])
+                        except ValueError as exc:
+                            msg = f"Did not find category with value " \
+                                  f"`{config_dict[key]}` in " \
+                                  f"hyperopt parameter `{key}`. "
+
+                            if isinstance(config_dict[key], int):
+                                msg += "In previous versions, a numerical " \
+                                       "index was expected for categorical " \
+                                       "values of `points_to_evaluate`, " \
+                                       "but in ray>=1.2.0, the categorical " \
+                                       "value is expected to be directly " \
+                                       "provided. "
+
+                            msg += "Please make sure the specified category " \
+                                   "is valid."
+                            raise ValueError(msg) from exc
+                        config_dict[key] = idx
+
+        for k in config:
+            _lookup(config, self._space, k)
+
    def set_search_properties(self, metric: Optional[str], mode: Optional[str],
                              config: Dict) -> bool:
        if self.domain:
@@ -28,12 +28,12 @@ if __name__ == "__main__":
        {
            "width": 1,
            "height": 2,
-            "activation": 0  # Activation will be relu
+            "activation": "relu"  # Activation will be relu
        },
        {
            "width": 4,
            "height": 2,
-            "activation": 1  # Activation will be tanh
+            "activation": "tanh"  # Activation will be tanh
        }
    ]
    algo = HyperOptSearch(