Reap rarely used/unused methods and properties (#3333)

Summary:

There were very few instances of usage of the methods this removes, so this diff changes the callsites and reaps the methods.

Reviewed By: saitcakmak

Differential Revision: D69313269

ax/core/experiment.py

@@ -14,7 +14,7 @@
 from collections import defaultdict, OrderedDict
 from collections.abc import Hashable, Iterable, Mapping
 from datetime import datetime
-from functools import partial, reduce
+from functools import partial
 
 from typing import Any, cast
 
@@ -337,18 +337,17 @@ def arms_by_signature_for_deduplication(self) -> dict[str, Arm]:
         return arms_dict
 
     @property
-    def sum_trial_sizes(self) -> int:
-        """Sum of numbers of arms attached to each trial in this experiment."""
-        return reduce(lambda a, b: a + len(b.arms_by_name), self._trials.values(), 0)
+    def metrics(self) -> dict[str, Metric]:
+        """The metrics attached to the experiment."""
+        optimization_config_metrics: dict[str, Metric] = {}
+        if self.optimization_config is not None:
+            optimization_config_metrics = self.optimization_config.metrics
+        return {**self._tracking_metrics, **optimization_config_metrics}
 
     @property
     def num_abandoned_arms(self) -> int:
         """How many arms attached to this experiment are abandoned."""
-        abandoned = set()
-        for trial in self.trials.values():
-            for x in trial.abandoned_arms:
-                abandoned.add(x)
-        return len(abandoned)
+        return len({aa for t in self.trials.values() for aa in t.abandoned_arms})
 
     @property
     def optimization_config(self) -> OptimizationConfig | None:
@@ -477,14 +476,6 @@ def remove_tracking_metric(self, metric_name: str) -> Experiment:
         del self._tracking_metrics[metric_name]
         return self
 
-    @property
-    def metrics(self) -> dict[str, Metric]:
-        """The metrics attached to the experiment."""
-        optimization_config_metrics: dict[str, Metric] = {}
-        if self.optimization_config is not None:
-            optimization_config_metrics = self.optimization_config.metrics
-        return {**self._tracking_metrics, **optimization_config_metrics}
-
     def _metrics_by_class(
         self, metrics: list[Metric] | None = None
     ) -> dict[type[Metric], list[Metric]]:
@@ -500,6 +491,7 @@ def _metrics_by_class(
 
     def fetch_data_results(
         self,
+        trial_indices: Iterable[int] | None = None,
         metrics: list[Metric] | None = None,
         combine_with_last_data: bool = False,
         overwrite_existing_data: bool = False,
@@ -528,43 +520,9 @@ def fetch_data_results(
         """
 
         return self._lookup_or_fetch_trials_results(
-            trials=list(self.trials.values()),
-            metrics=metrics,
-            combine_with_last_data=combine_with_last_data,
-            overwrite_existing_data=overwrite_existing_data,
-            **kwargs,
-        )
-
-    def fetch_trials_data_results(
-        self,
-        trial_indices: Iterable[int],
-        metrics: list[Metric] | None = None,
-        combine_with_last_data: bool = False,
-        overwrite_existing_data: bool = False,
-        **kwargs: Any,
-    ) -> dict[int, dict[str, MetricFetchResult]]:
-        """Fetches data for specific trials on the experiment.
-
-        If a metric fetch fails, the Exception will be captured in the
-        MetricFetchResult along with a message.
-
-        NOTE: For metrics that are not available while trial is running, the data
-        may be retrieved from cache on the experiment. Data is cached on the experiment
-        via calls to `experiment.attach_data` and whether a given metric class is
-        available while trial is running is determined by the boolean returned from its
-        `is_available_while_running` class method.
-
-        Args:
-            trial_indices: Indices of trials, for which to fetch data.
-            metrics: If provided, fetch data for these metrics instead of the ones
-                defined on the experiment.
-            kwargs: keyword args to pass to underlying metrics' fetch data functions.
-
-        Returns:
-            A nested Dictionary from trial_index => metric_name => result
-        """
-        return self._lookup_or_fetch_trials_results(
-            trials=self.get_trials_by_indices(trial_indices=trial_indices),
+            trials=self.get_trials_by_indices(trial_indices=trial_indices)
+            if trial_indices is not None
+            else list(self.trials.values()),
             metrics=metrics,
             combine_with_last_data=combine_with_last_data,
             overwrite_existing_data=overwrite_existing_data,
@@ -573,6 +531,7 @@ def fetch_trials_data_results(
 
     def fetch_data(
         self,
+        trial_indices: Iterable[int] | None = None,
         metrics: list[Metric] | None = None,
         combine_with_last_data: bool = False,
         overwrite_existing_data: bool = False,
@@ -600,63 +559,15 @@ def fetch_data(
             Data for the experiment.
         """
 
-        results = self._lookup_or_fetch_trials_results(
-            trials=list(self.trials.values()),
+        results = self.fetch_data_results(
+            trial_indices=trial_indices,
             metrics=metrics,
             combine_with_last_data=combine_with_last_data,
             overwrite_existing_data=overwrite_existing_data,
             **kwargs,
         )
-
-        base_metric_cls = (
-            MapMetric if self.default_data_constructor == MapData else Metric
-        )
-
-        return base_metric_cls._unwrap_experiment_data_multi(
-            results=results,
-        )
-
-    def fetch_trials_data(
-        self,
-        trial_indices: Iterable[int],
-        metrics: list[Metric] | None = None,
-        combine_with_last_data: bool = False,
-        overwrite_existing_data: bool = False,
-        **kwargs: Any,
-    ) -> Data:
-        """Fetches data for specific trials on the experiment.
-
-        NOTE: For metrics that are not available while trial is running, the data
-        may be retrieved from cache on the experiment. Data is cached on the experiment
-        via calls to `experiment.attach_data` and whetner a given metric class is
-        available while trial is running is determined by the boolean returned from its
-        `is_available_while_running` class method.
-
-        NOTE: This can be lossy (ex. a MapData could get implicitly cast to a Data and
-        lose rows) if Experiment.default_data_type is misconfigured!
-
-        Args:
-            trial_indices: Indices of trials, for which to fetch data.
-            metrics: If provided, fetch data for these metrics instead of the ones
-                defined on the experiment.
-            kwargs: Keyword args to pass to underlying metrics' fetch data functions.
-
-        Returns:
-            Data for the specific trials on the experiment.
-        """
-
-        results = self._lookup_or_fetch_trials_results(
-            trials=self.get_trials_by_indices(trial_indices=trial_indices),
-            metrics=metrics,
-            combine_with_last_data=combine_with_last_data,
-            overwrite_existing_data=overwrite_existing_data,
-            **kwargs,
-        )
-
-        base_metric_cls = (
-            MapMetric if self.default_data_constructor == MapData else Metric
-        )
-        return base_metric_cls._unwrap_experiment_data_multi(
+        use_map_data = self.default_data_constructor == MapData
+        return (MapMetric if use_map_data else Metric)._unwrap_experiment_data_multi(
             results=results,
         )
 

ax/core/multi_type_experiment.py

@@ -8,7 +8,7 @@
 
 import logging
 from collections.abc import Sequence
-from typing import Any
+from typing import Any, Iterable
 
 from ax.core.arm import Arm
 from ax.core.base_trial import BaseTrial, TrialStatus
@@ -254,6 +254,7 @@ def remove_tracking_metric(self, metric_name: str) -> "MultiTypeExperiment":
     @copy_doc(Experiment.fetch_data)
     def fetch_data(
         self,
+        trial_indices: Iterable[int] | None = None,
         metrics: list[Metric] | None = None,
         combine_with_last_data: bool = False,
         overwrite_existing_data: bool = False,
@@ -265,11 +266,15 @@ def fetch_data(
         return self.default_data_constructor.from_multiple_data(
             [
                 (
-                    trial.fetch_data(**kwargs, metrics=metrics)
+                    trial.fetch_data(metrics=metrics, **kwargs)
                     if trial.status.expecting_data
                     else Data()
                 )
-                for trial in self.trials.values()
+                for trial in (
+                    self.get_trials_by_indices(trial_indices=trial_indices)
+                    if trial_indices is not None
+                    else self.trials.values()
+                )
             ]
         )
 

ax/core/tests/test_experiment.py

@@ -9,6 +9,7 @@
 import logging
 from collections import OrderedDict
 from enum import unique
+from functools import reduce
 from unittest.mock import MagicMock, patch
 
 import pandas as pd
@@ -608,7 +609,9 @@ def test_NumArmsNoDeduplication(self) -> None:
         arm = get_arm()
         exp.new_batch_trial().add_arm(arm)
         trial = exp.new_batch_trial().add_arm(arm)
-        self.assertEqual(exp.sum_trial_sizes, 2)
+        self.assertEqual(
+            reduce(lambda a, b: a + len(b.arms_by_name), exp._trials.values(), 0), 2
+        )
         self.assertEqual(len(exp.arms_by_name), 1)
         trial.mark_arm_abandoned(trial.arms[0].name)
         self.assertEqual(exp.num_abandoned_arms, 1)
@@ -666,34 +669,34 @@ def test_FetchTrialsData(self) -> None:
         batch_1 = exp.trials[1]
         batch_0.mark_completed()
         batch_1.mark_completed()
-        batch_0_data = exp.fetch_trials_data(trial_indices=[0])
+        batch_0_data = exp.fetch_data(trial_indices=[0])
         self.assertEqual(set(batch_0_data.df["trial_index"].values), {0})
         self.assertEqual(
             set(batch_0_data.df["arm_name"].values), {a.name for a in batch_0.arms}
         )
-        batch_1_data = exp.fetch_trials_data(trial_indices=[1])
+        batch_1_data = exp.fetch_data(trial_indices=[1])
         self.assertEqual(set(batch_1_data.df["trial_index"].values), {1})
         self.assertEqual(
             set(batch_1_data.df["arm_name"].values), {a.name for a in batch_1.arms}
         )
         self.assertEqual(
-            exp.fetch_trials_data(trial_indices=[0, 1]),
+            exp.fetch_data(trial_indices=[0, 1]),
             Data.from_multiple_data([batch_0_data, batch_1_data]),
         )
 
         self.assertEqual(len(exp.data_by_trial[0]), 2)
 
         with self.assertRaisesRegex(ValueError, ".* not associated .*"):
-            exp.fetch_trials_data(trial_indices=[2])
+            exp.fetch_data(trial_indices=[2])
         # Try to fetch data when there are only metrics and no attached data.
         exp.remove_tracking_metric(metric_name="b")  # Remove implemented metric.
         exp.add_tracking_metric(Metric(name="b"))  # Add unimplemented metric.
-        self.assertEqual(len(exp.fetch_trials_data(trial_indices=[0]).df), 5)
+        self.assertEqual(len(exp.fetch_data(trial_indices=[0]).df), 5)
         # Try fetching attached data.
         exp.attach_data(batch_0_data)
         exp.attach_data(batch_1_data)
-        self.assertEqual(exp.fetch_trials_data(trial_indices=[0]), batch_0_data)
-        self.assertEqual(exp.fetch_trials_data(trial_indices=[1]), batch_1_data)
+        self.assertEqual(exp.fetch_data(trial_indices=[0]), batch_0_data)
+        self.assertEqual(exp.fetch_data(trial_indices=[1]), batch_1_data)
         self.assertEqual(set(batch_0_data.df["trial_index"].values), {0})
         self.assertEqual(
             set(batch_0_data.df["arm_name"].values), {a.name for a in batch_0.arms}
@@ -1444,38 +1447,40 @@ def test_FetchTrialsData(self) -> None:
         batch_1 = exp.trials[1]
         batch_0.mark_completed()
         batch_1.mark_completed()
-        batch_0_data = exp.fetch_trials_data(trial_indices=[0])
+        batch_0_data = exp.fetch_data(trial_indices=[0])
         self.assertEqual(set(batch_0_data.df["trial_index"].values), {0})
         self.assertEqual(
             set(batch_0_data.df["arm_name"].values), {a.name for a in batch_0.arms}
         )
-        batch_1_data = exp.fetch_trials_data(trial_indices=[1])
+        batch_1_data = exp.fetch_data(trial_indices=[1])
         self.assertEqual(set(batch_1_data.df["trial_index"].values), {1})
         self.assertEqual(
             set(batch_1_data.df["arm_name"].values), {a.name for a in batch_1.arms}
         )
         self.assertEqual(
-            exp.fetch_trials_data(trial_indices=[0, 1]).df.shape[0],
+            exp.fetch_data(trial_indices=[0, 1]).df.shape[0],
             len(exp.arms_by_name) * 2,
         )
 
         with self.assertRaisesRegex(ValueError, ".* not associated .*"):
-            exp.fetch_trials_data(trial_indices=[2])
+            exp.fetch_data(trial_indices=[2])
         # Try to fetch data when there are only metrics and no attached data.
         exp.remove_tracking_metric(metric_name="branin")  # Remove implemented metric.
         exp.add_tracking_metric(
             BraninMetric(name="branin", param_names=["x1", "x2"])
         )  # Add unimplemented metric.
-        # pyre-fixme[16]: `Data` has no attribute `map_df`.
-        self.assertEqual(len(exp.fetch_trials_data(trial_indices=[0]).map_df), 10)
+        self.assertEqual(
+            len(assert_is_instance(exp.fetch_data(trial_indices=[0]), MapData).map_df),
+            10,
+        )
         # Try fetching attached data.
         exp.attach_data(batch_0_data)
         exp.attach_data(batch_1_data)
         pd.testing.assert_frame_equal(
-            exp.fetch_trials_data(trial_indices=[0]).df, batch_0_data.df
+            exp.fetch_data(trial_indices=[0]).df, batch_0_data.df
         )
         pd.testing.assert_frame_equal(
-            exp.fetch_trials_data(trial_indices=[1]).df, batch_1_data.df
+            exp.fetch_data(trial_indices=[1]).df, batch_1_data.df
         )
         self.assertEqual(set(batch_0_data.df["trial_index"].values), {0})
         self.assertEqual(

ax/service/scheduler.py

@@ -1991,7 +1991,7 @@ def _fetch_and_process_trials_data_results(
                     self.experiment, MultiTypeExperiment
                 ).metrics_for_trial_type(trial_type=none_throws(self.trial_type))
                 kwargs["metrics"] = metrics
-            results = self.experiment.fetch_trials_data_results(
+            results = self.experiment.fetch_data_results(
                 trial_indices=trial_indices,
                 **kwargs,
             )

ax/service/tests/test_ax_client.py

@@ -2599,7 +2599,7 @@ def helper_test_get_pareto_optimal_points_from_sobol_step(
         # Check that the data in the frontier matches the observed data
         # (it should be in the original, un-transformed space)
         input_data = (
-            ax_client.experiment.fetch_trials_data([idx_of_frontier_point])
+            ax_client.experiment.fetch_data(trial_indices=[idx_of_frontier_point])
             .df["mean"]
             .values
         )