Merge pull request #189 from automl/development

Version 1.3.2

Author: sarah-segel

CHANGELOG.md

@@ -1,3 +1,8 @@
+# Version 1.3.2
+
+## Features
+- Allow Importance and ablation path analysis for multi-objective runs.
+
 # Version 1.3.1
 
 ## Quality of Life

Makefile

@@ -2,7 +2,7 @@
 # are usually completed in github actions.
 
 SHELL := /bin/bash
-VERSION := 1.3.1
+VERSION := 1.3.2
 
 NAME := DeepCAVE
 PACKAGE_NAME := deepcave

deepcave/__init__.py

@@ -44,7 +44,7 @@
     "Source Code": "https://github.com/automl/deepcave",
 }
 copyright = f"Copyright {datetime.date.today().strftime('%Y')}, {author}"
-version = "1.3.1"
+version = "1.3.2"
 
 _exec_file = sys.argv[0]
 _exec_files = ["server.py", "worker.py", "sphinx-build"]

deepcave/evaluators/ablation.py

@@ -245,7 +245,7 @@ def _ablation(
         max_hp_difference = -np.inf
 
         for hp in hp_it:
-            if hp in hp in incumbent_config.keys() and hp in self.default_config.keys():
+            if hp in incumbent_config.keys() and hp in self.default_config.keys():
                 config_copy = copy.copy(self.default_config)
                 config_copy[hp] = incumbent_config[hp]
 

deepcave/evaluators/mo_ablation.py

@@ -0,0 +1,350 @@
+# Copyright 2021-2024 The DeepCAVE Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# noqa: D400
+"""
+# Ablation Paths
+
+This module evaluates the ablation paths.
+
+Ablation Paths is a method to analyze the importance of hyperparameters in a configuration space.
+Starting from a default configuration, the default configuration is iteratively changed to the
+incumbent configuration by changing one hyperparameter at a time, choosing the
+hyperparameter that leads to the largest improvement in the objective function at each step.
+
+## Classes:
+    - Ablation: Provide an evaluator of the ablation paths.
+"""
+
+from typing import Any, List, Optional, Tuple, Union
+
+import copy
+
+import numpy as np
+import pandas as pd
+
+from deepcave.evaluators.ablation import Ablation
+from deepcave.evaluators.epm.random_forest_surrogate import RandomForestSurrogate
+from deepcave.runs import AbstractRun
+from deepcave.runs.objective import Objective
+from deepcave.utils.multi_objective_importance import get_weightings
+
+
+class MOAblation(Ablation):
+    """
+    Provide an evaluator of the ablation paths.
+
+    Override: Multi-Objective case
+
+    Properties
+    ----------
+    run : AbstractRun
+        The run to analyze.
+    cs : ConfigurationSpace
+        The configuration space of the run.
+    hp_names : List[str]
+        A list of the hyperparameter names.
+    performances : Optional[Dict[Any, Any]]
+        A dictionary containing the performances for each HP.
+    improvements : Optional[Dict[Any, Any]]
+        A dictionary containing the improvements over the respective previous step for each HP.
+    objectives : Optional[Union[Objective, List[Objective]]]
+        The objective(s) of the run.
+    default_config : Configurations
+        The default configuration of this configuration space.
+        Gets changed step by step towards the incumbent configuration.
+    """
+
+    def __init__(self, run: AbstractRun):
+        super().__init__(run)
+        self.models: List = []
+        self.df_importances = pd.DataFrame([])
+
+    def get_importances(self) -> str:
+        """
+        Return the importance scores.
+
+        Returns
+        -------
+        Dict
+            Dictionary with Hyperparameter names and the corresponding importance scores and
+            variances.
+
+        Raises
+        ------
+        RuntimeError
+            If the important scores are not calculated.
+        """
+        if self.df_importances is None:
+            raise RuntimeError("Importance scores must be calculated first.")
+
+        return self.df_importances.to_json()
+
+    def predict(self, cfg: list[Any], weighting: np.ndarray) -> Tuple[float, float]:
+        """
+        Predict the performance of the input configuration.
+
+        The model results are weighted by the input weightings and summed.
+
+        Parameters
+        ----------
+        cfg : Dict
+            Configuration.
+        weighting : List[float]
+            Weightings.
+
+        Returns
+        -------
+        mean : float
+             The mean of the weighted sum of predictions.
+        var : float
+             The variance of the weighted sum of predictions.
+        """
+        mean, var = 0, 0
+        for model, w in zip(self.models, weighting):
+            pred, var_ = model.predict(np.array([cfg]))
+            mean += w * pred[0]
+            var += w * var_[0]
+        return mean, var
+
+    def calculate(
+        self,
+        objectives: Optional[Union[Objective, List[Objective]]],  # noqa
+        budget: Optional[Union[int, float]] = None,  # noqa
+        n_trees: int = 50,  # noqa
+        seed: int = 0,  # noqa
+    ) -> None:
+        """
+        Calculate the MO ablation path performances and improvements.
+
+        Parameters
+        ----------
+        objectives : Optional[Union[Objective, List[Objective]]]
+            The objective(s) to be considered.
+        budget : Optional[Union[int, float]]
+            The budget to be considered. If None, all budgets of the run are considered.
+            Default is None.
+        n_trees : int
+            The number of trees for the surrogate model.
+            Default is 50.
+        seed : int
+            The seed for the surrogate model.
+            Default is 0.
+        """
+        assert isinstance(objectives, list)
+        for objective in objectives:
+            assert isinstance(objective, Objective)
+
+        df = self.run.get_encoded_data(objectives, budget, specific=True, include_config_ids=True)
+
+        # Obtain all configurations with theirs costs
+        df = df.dropna(subset=[obj.name for obj in objectives])
+        X = df[list(self.run.configspace.keys())].to_numpy()
+
+        # normalize objectives
+        objectives_normed = list()
+        for obj in objectives:
+            normed = obj.name + "_normed"
+            df[normed] = (df[obj.name] - df[obj.name].min()) / (
+                df[obj.name].max() - df[obj.name].min()
+            )
+
+            if obj.optimize == "upper":
+                df[normed] = 1 - df[normed]
+            objectives_normed.append(normed)
+
+            # train one model per objective
+            Y = df[normed].to_numpy()
+            model = RandomForestSurrogate(self.cs, seed=seed, n_trees=n_trees)
+            model._fit(X, Y)
+            self.models.append(model)
+
+        weightings = get_weightings(objectives_normed, df)
+
+        # calculate importance for each weighting generated from the pareto efficient points
+        for w in weightings:
+            df_res = self.calculate_ablation_path(df, objectives_normed, w, budget)
+            if df_res is None:
+                columns = ["hp_name", "importance", "variance", "new_performance", "weight"]
+                self.df_importances = pd.DataFrame(
+                    0, index=np.arange(len(self.hp_names) + 1), columns=columns
+                )
+                self.df_importances["hp_name"] = ["Default"] + self.hp_names
+                return
+            df_res["weight"] = w[0]
+            self.df_importances = pd.concat([self.df_importances, df_res])
+        self.df_importances = self.df_importances.reset_index(drop=True)
+
+    def calculate_ablation_path(
+        self,
+        df: pd.DataFrame,
+        objectives_normed: List[str],
+        weighting: np.ndarray,
+        budget: Optional[Union[int, float]],
+    ) -> pd.DataFrame:
+        """
+        Calculate the ablation path performances.
+
+        Parameters
+        ----------
+        df : pd.DataFrame
+            Dataframe with encoded data.
+        objectives_normed : List[str]
+            The normed objective names to be considered.
+        weighting : np.ndarray
+            The weighting of the objective values.
+        budget : Optional[Union[int, float]]
+            The budget to be considered. If None, all budgets of the run are considered.
+            Default is None.
+
+        Returns
+        -------
+        df : pd.DataFrame
+            Dataframe with results of the ablation calculation.
+        """
+        # Get the incumbent configuration
+        incumbent_cfg_id = np.argmin(
+            sum(df[obj] * w for obj, w in zip(objectives_normed, weighting))
+        )
+        incumbent_config = self.run.get_config(df.iloc[incumbent_cfg_id]["config_id"])
+
+        # Get the default configuration
+        self.default_config = self.cs.get_default_configuration()
+        default_encode = self.run.encode_config(self.default_config, specific=True)
+
+        # Obtain the predicted cost of the default and incumbent configuration
+        def_cost, def_std = self.predict(default_encode, weighting)
+        inc_cost, _ = self.predict(
+            self.run.encode_config(incumbent_config, specific=True), weighting
+        )
+
+        if inc_cost > def_cost:
+            self.logger.warning(
+                "The predicted incumbent objective is worse than the predicted default "
+                f"objective for budget: {budget}. Aborting ablation path calculation."
+            )
+            return None
+        else:
+            # Copy the hps names as to not remove objects from the original list
+            hp_it = self.hp_names.copy()
+            df_abl = pd.DataFrame([])
+            df_abl = pd.concat(
+                [
+                    df_abl,
+                    pd.DataFrame(
+                        {
+                            "hp_name": "Default",
+                            "importance": 0,
+                            "variance": def_std,
+                            "new_performance": def_cost,
+                        },
+                        index=[0],
+                    ),
+                ]
+            )
+
+            for i in range(len(hp_it)):
+                # Get the results of the current ablation iteration
+                continue_ablation, max_hp, max_hp_cost, max_hp_std = self.ablation(
+                    budget, incumbent_config, def_cost, hp_it, weighting
+                )
+
+                if not continue_ablation:
+                    break
+
+                diff = def_cost - max_hp_cost
+                def_cost = max_hp_cost
+
+                df_abl = pd.concat(
+                    [
+                        df_abl,
+                        pd.DataFrame(
+                            {
+                                "hp_name": max_hp,
+                                "importance": diff,
+                                "variance": max_hp_std,
+                                "new_performance": max_hp_cost,
+                            },
+                            index=[i + 1],
+                        ),
+                    ]
+                )
+
+                # Remove the current best hp for keeping the order right
+                hp_it.remove(max_hp)
+            return df_abl.reset_index(drop=True)
+
+    def ablation(
+        self,
+        budget: Optional[Union[int, float]],
+        incumbent_config: Any,
+        def_cost: Any,
+        hp_it: List[str],
+        weighting: np.ndarray[Any, Any],
+    ) -> Tuple[Any, Any, Any, Any]:
+        """
+        Calculate the ablation importance for each hyperparameter.
+
+        Parameters
+        ----------
+        budget: Optional[Union[int, float]]
+            The budget of the run.
+        incumbent_config: Any
+            The incumbent configuration.
+        def_cost: Any
+            The default cost.
+        hp_it: List[str]
+            A list of the HPs that still have to be looked at.
+        weighting : np.ndarray[Any, Any]
+            The weighting of the objective values.
+
+        Returns
+        -------
+        Tuple[Any, Any, Any, Any]
+            continue_ablation, max_hp, max_hp_performance, max_hp_std
+        """
+        max_hp = ""
+        max_hp_difference = 0
+
+        for hp in hp_it:
+            if hp in incumbent_config.keys() and hp in self.default_config.keys():
+                config_copy = copy.copy(self.default_config)
+                config_copy[hp] = incumbent_config[hp]
+
+                new_cost, _ = self.predict(
+                    self.run.encode_config(config_copy, specific=True), weighting
+                )
+                difference = def_cost - new_cost
+
+                # Check for the maximum difference hyperparameter in this round
+                if difference > max_hp_difference:
+                    max_hp = hp
+                    max_hp_difference = difference
+            else:
+                continue
+        hp_count = len(list(self.cs.keys()))
+        if max_hp != "":
+            # For the maximum impact hyperparameter, switch the default with the incumbent value
+            self.default_config[max_hp] = incumbent_config[max_hp]
+            max_hp_cost, max_hp_std = self.predict(
+                self.run.encode_config(self.default_config, specific=True), weighting
+            )
+            return True, max_hp, max_hp_cost, max_hp_std
+        else:
+            self.logger.info(
+                f"End ablation at step {hp_count - len(hp_it) + 1}/{hp_count} "
+                f"for budget {budget} (remaining hyperparameters not activate in incumbent or "
+                "default configuration)."
+            )
+            return False, None, None, None