From 3ca6126477edb8c778ddc86f158427d406a2248a Mon Sep 17 00:00:00 2001 From: JLX0 Date: Thu, 28 Nov 2024 17:50:15 +0900 Subject: [PATCH 01/20] DE WIP --- package/samplers/de/LICENSE | 0 package/samplers/de/README.md.py | 0 package/samplers/de/__init__.py | 4 + package/samplers/de/de.py | 85 ++++++++++++++ package/samplers/de/example.py | 190 +++++++++++++++++++++++++++++++ 5 files changed, 279 insertions(+) create mode 100644 package/samplers/de/LICENSE create mode 100644 package/samplers/de/README.md.py create mode 100644 package/samplers/de/__init__.py create mode 100644 package/samplers/de/de.py create mode 100644 package/samplers/de/example.py diff --git a/package/samplers/de/LICENSE b/package/samplers/de/LICENSE new file mode 100644 index 00000000..e69de29b diff --git a/package/samplers/de/README.md.py b/package/samplers/de/README.md.py new file mode 100644 index 00000000..e69de29b diff --git a/package/samplers/de/__init__.py b/package/samplers/de/__init__.py new file mode 100644 index 00000000..4d494f19 --- /dev/null +++ b/package/samplers/de/__init__.py @@ -0,0 +1,4 @@ +from de import DESampler + + +__all__ = ["DESampler"] \ No newline at end of file diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py new file mode 100644 index 00000000..840cfeec --- /dev/null +++ b/package/samplers/de/de.py @@ -0,0 +1,85 @@ +from __future__ import annotations + +from typing import Any +import numpy as np +import optuna +import optunahub + + +class DESampler(optunahub.samplers.SimpleBaseSampler): + def __init__( + self, + search_space: dict[str, optuna.distributions.BaseDistribution] | None = None, + population_size: int = 50, + F: float = 0.8, # Mutation factor + CR: float = 0.7, # Crossover probability + ) -> None: + super().__init__(search_space) + self._rng = np.random.RandomState() + self.population_size = population_size + self.F = F + self.CR = CR + self.queue: list[dict[str, Any]] = [] # Stores individuals as parameter dictionaries + self.dim = 0 # Will represent the dimension of the search space + self.population = None # Population array + self.fitness = None # Array to store fitness values + + def sample_relative( + self, + study: optuna.study.Study, + trial: optuna.trial.FrozenTrial, + search_space: dict[str, optuna.distributions.BaseDistribution], + ) -> dict[str, Any]: + if len(search_space) == 0: + return {} + if len(self.queue) != 0: + return self.queue.pop(0) + + # Initialize search space dimensions and bounds + if self.population is None: + self.lower_bound = np.asarray([dist.low for dist in search_space.values()]) + self.upper_bound = np.asarray([dist.high for dist in search_space.values()]) + self.dim = len(search_space) + self.population = ( + np.random.rand(self.population_size, self.dim) * (self.upper_bound - self.lower_bound) + + self.lower_bound + ) + self.fitness = np.full(self.population_size, np.inf) + + # Evaluate fitness of individuals + last_trials = study.get_trials(states=(optuna.trial.TrialState.COMPLETE,))[ + -self.population_size : + ] + for i, trial in enumerate(last_trials): + self.fitness[i] = trial.value if trial.value is not None else self.fitness[i] + + new_population = np.zeros_like(self.population) + + for i in range(self.population_size): + # Mutation: Generate a mutant vector + indices = [idx for idx in range(self.population_size) if idx != i] + r1, r2, r3 = self._rng.choice(indices, 3, replace=False) + mutant = ( + self.population[r1] + + self.F * (self.population[r2] - self.population[r3]) + ) + mutant = np.clip(mutant, self.lower_bound, self.upper_bound) + + # Crossover: Create a trial vector + trial = np.copy(self.population[i]) + crossover_mask = self._rng.rand(self.dim) < self.CR + if not np.any(crossover_mask): + crossover_mask[self._rng.randint(self.dim)] = True + trial[crossover_mask] = mutant[crossover_mask] + + # Add trial vector to new population + new_population[i] = trial + + # Convert new population into a parameter list for the queue + param_list = [ + {k: v for k, v in zip(search_space.keys(), individual)} + for individual in new_population + ] + self.queue.extend(param_list) + + return self.queue.pop(0) diff --git a/package/samplers/de/example.py b/package/samplers/de/example.py new file mode 100644 index 00000000..975d8c1c --- /dev/null +++ b/package/samplers/de/example.py @@ -0,0 +1,190 @@ +from __future__ import annotations + +import optuna +import optunahub +from sklearn.datasets import load_digits +from sklearn.ensemble import RandomForestClassifier +from sklearn.model_selection import train_test_split , cross_val_score +from sklearn.metrics import accuracy_score +from sklearn.preprocessing import StandardScaler +from sklearn.pipeline import Pipeline +import matplotlib.pyplot as plt +import numpy as np + +import math + + +def objective_toy(trial: optuna.Trial) -> float : + # Define the dimensionality of the problem + n_dimensions = 10 # High-dimensional problem with 10 dimensions + + # Suggest a value for each dimension + variables = [trial.suggest_float(f"x{i}" , -32.768 , 32.768) for i in range(n_dimensions)] + + # Compute the Ackley function value + a = 20 + b = 0.2 + c = 2 * 3.141592653589793 + + # Summation terms for the function + sum_sq_term = sum(x ** 2 for x in variables) + cos_term = sum(math.cos(c * x) for x in variables) + + # Ackley function formula + result = -a * math.exp(-b * math.sqrt(sum_sq_term / n_dimensions)) - \ + math.exp(cos_term / n_dimensions) + a + math.exp(1) + + return result + + +def objective_ML(trial: optuna.Trial) -> float : + # Load dataset + data = load_digits() + X , y = data.data , data.target + + # Split into train and test sets + X_train , X_test , y_train , y_test = train_test_split(X , y , test_size=0.3 , random_state=42) + + # Define hyperparameter search space + n_estimators = trial.suggest_int("n_estimators" , 50 , 300) + max_depth = trial.suggest_int("max_depth" , 5 , 30) + min_samples_split = trial.suggest_int("min_samples_split" , 2 , 15) + min_samples_leaf = trial.suggest_int("min_samples_leaf" , 1 , 10) + # max_features = trial.suggest_categorical("max_features" , ["sqrt" , "log2" , None]) + # bootstrap = trial.suggest_categorical("bootstrap" , [True , False]) + + # Build pipeline with scaling and RandomForestClassifier + pipeline = Pipeline([ + ("scaler" , StandardScaler()) , + ("classifier" , RandomForestClassifier( + n_estimators=n_estimators , + max_depth=max_depth , + min_samples_split=min_samples_split , + min_samples_leaf=min_samples_leaf , + # max_features=max_features , + # bootstrap=bootstrap , + random_state=42 + )) + ]) + + # Evaluate using cross-validation + scores = cross_val_score(pipeline , X_train , y_train , cv=5 , scoring="accuracy") + mean_accuracy = scores.mean() + + return mean_accuracy + + +package_name = "package/samplers/de" + +# This is an example of how to load a sampler from your local optunahub-registry. +sampler = optunahub.load_local_module( + package=package_name , + registry_root="/home/j/experiments/optunahub-registry" , # Path to the root of the optunahub-registry. + ).DESampler(population_size=100) + +sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility + +# Parameters for experiments +num_experiments = 10 +number_of_trials = 200 + +# Store results for each experiment +results_de = np.zeros((num_experiments , number_of_trials)) +results_rs = np.zeros((num_experiments , number_of_trials)) + +for i in range(num_experiments) : + # DE Sampler + study = optuna.create_study(sampler=sampler , direction="minimize") + study.optimize(objective_toy , n_trials=number_of_trials) + + # Track best values for DE Sampler + best_values_de = [] + current_best_de = float("inf") + for trial in study.trials : + if trial.value is not None : + current_best_de = min(current_best_de , trial.value) + best_values_de.append(current_best_de) + results_de[i , :] = best_values_de + + # Random Sampler + study_rs = optuna.create_study(sampler=sampler_rs , direction="minimize") + study_rs.optimize(objective_toy , n_trials=number_of_trials) + + # Track best values for Random Sampler + best_values_rs = [] + current_best_rs = float("inf") + for trial in study_rs.trials : + if trial.value is not None : + current_best_rs = min(current_best_rs , trial.value) + best_values_rs.append(current_best_rs) + results_rs[i , :] = best_values_rs + +# Compute mean and standard deviation +mean_de = np.mean(results_de , axis=0) +std_de = np.std(results_de , axis=0) +mean_rs = np.mean(results_rs , axis=0) +std_rs = np.std(results_rs , axis=0) + +# Plot the results with error bands +plt.figure(figsize=(10 , 6)) +plt.plot(mean_de , linestyle='-' , label='DESampler (Mean Performance)' , color='blue') +plt.fill_between(range(number_of_trials) , mean_de - std_de , mean_de + std_de , color='blue' , alpha=0.2 , + label='DESampler (Error Band)') +plt.plot(mean_rs , linestyle='--' , label='RandomSampler (Mean Performance)' , color='orange') +plt.fill_between(range(number_of_trials) , mean_rs - std_rs , mean_rs + std_rs , color='orange' , alpha=0.2 , + label='RandomSampler (Error Band)') + +plt.title('Comparison of DE Sampler and Random Sampler (10 Experiments, po)') +plt.xlabel('Trial Number') +plt.ylabel('Objective Value (Log Scale)') +plt.yscale('log') # Set log scale for the y-axis +plt.grid(which="both" , linestyle="--" , linewidth=0.5) +plt.legend() +plt.show() + +# +# +# +# number_of_trials = 2500 +# # Create and run the study +# study = optuna.create_study(sampler=sampler , direction="minimize") +# study.optimize(objective_toy, n_trials=number_of_trials) +# +# sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility +# study_rs = optuna.create_study(sampler=sampler_rs , direction="minimize") +# study_rs.optimize(objective_toy, n_trials=number_of_trials) +# +# # Print the best trial +# print("Best trial:") +# print(f" Value: {study.best_trial.value}") +# print(" Params: ") +# for key , value in study.best_trial.params.items() : +# print(f" {key}: {value}") +# +# # Track the best individual found over trials for DE sampler +# best_values_de = [] +# current_best_de = float("inf") +# for trial in study.trials: +# if trial.value is not None: +# current_best_de = min(current_best_de, trial.value) +# best_values_de.append(current_best_de) +# +# # Track the best individual found over trials for RandomSampler +# best_values_rs = [] +# current_best_rs = float("inf") +# for trial in study_rs.trials: +# if trial.value is not None: +# current_best_rs = min(current_best_rs, trial.value) +# best_values_rs.append(current_best_rs) +# +# # Plot the performance of the best individual over trials for both samplers +# plt.figure(figsize=(10, 6)) +# plt.plot(best_values_de, linestyle='-', label='DESampler (Best Individual Performance)') +# plt.plot(best_values_rs, linestyle='--', label='RandomSampler (Best Individual Performance)') +# plt.title('Comparison of DE Sampler and Random Sampler Performance (Minimization, Log Scale)') +# plt.xlabel('Trial Number') +# plt.ylabel('Objective Value (Log Scale)') +# plt.yscale('log') # Set log scale for the y-axis +# plt.grid(which="both", linestyle="--", linewidth=0.5) +# plt.legend() +# plt.show() \ No newline at end of file From 8776b47f1fd59d0ac026b77c801686688ad36346 Mon Sep 17 00:00:00 2001 From: JInglue Date: Fri, 29 Nov 2024 11:10:03 +0900 Subject: [PATCH 02/20] DE WIP --- package/samplers/de/de.py | 7 +++++++ package/samplers/de/example.py | 6 +++--- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py index 840cfeec..f61835d2 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/de/de.py @@ -30,6 +30,7 @@ def sample_relative( trial: optuna.trial.FrozenTrial, search_space: dict[str, optuna.distributions.BaseDistribution], ) -> dict[str, Any]: + if len(search_space) == 0: return {} if len(self.queue) != 0: @@ -72,6 +73,11 @@ def sample_relative( crossover_mask[self._rng.randint(self.dim)] = True trial[crossover_mask] = mutant[crossover_mask] + # Print vectors for debugging + print(f"Target Vector (Individual {i}): {self.population[i]}") + print(f"Mutant Vector: {mutant}") + print(f"Resultant Vector (After Crossover): {trial}") + # Add trial vector to new population new_population[i] = trial @@ -80,6 +86,7 @@ def sample_relative( {k: v for k, v in zip(search_space.keys(), individual)} for individual in new_population ] + self.queue.extend(param_list) return self.queue.pop(0) diff --git a/package/samplers/de/example.py b/package/samplers/de/example.py index 975d8c1c..4d4795d8 100644 --- a/package/samplers/de/example.py +++ b/package/samplers/de/example.py @@ -79,14 +79,14 @@ def objective_ML(trial: optuna.Trial) -> float : # This is an example of how to load a sampler from your local optunahub-registry. sampler = optunahub.load_local_module( package=package_name , - registry_root="/home/j/experiments/optunahub-registry" , # Path to the root of the optunahub-registry. + registry_root="/home/j/PycharmProjects/optunahub-registry" , # Path to the root of the optunahub-registry. ).DESampler(population_size=100) sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility # Parameters for experiments -num_experiments = 10 -number_of_trials = 200 +num_experiments = 1 +number_of_trials = 5 # Store results for each experiment results_de = np.zeros((num_experiments , number_of_trials)) From 0d8c56cda3db7d7f3dec92798b22b3f0ffd1c4b1 Mon Sep 17 00:00:00 2001 From: JInglue Date: Fri, 29 Nov 2024 13:00:34 +0900 Subject: [PATCH 03/20] DE working but slow --- package/samplers/de/de.py | 182 ++++++++++++++++++++++++--------- package/samplers/de/example.py | 35 ++++++- 2 files changed, 163 insertions(+), 54 deletions(-) diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py index f61835d2..036326df 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/de/de.py @@ -4,89 +4,169 @@ import numpy as np import optuna import optunahub +import time class DESampler(optunahub.samplers.SimpleBaseSampler): def __init__( - self, - search_space: dict[str, optuna.distributions.BaseDistribution] | None = None, - population_size: int = 50, - F: float = 0.8, # Mutation factor - CR: float = 0.7, # Crossover probability + self, + search_space: dict[str, optuna.distributions.BaseDistribution] | None = None, + population_size: int = 50, + F: float = 0.8, # Mutation factor + CR: float = 0.7, # Crossover probability, + debug: bool = False, # Toggle for debug messages ) -> None: super().__init__(search_space) self._rng = np.random.RandomState() self.population_size = population_size self.F = F self.CR = CR + self.debug = debug self.queue: list[dict[str, Any]] = [] # Stores individuals as parameter dictionaries self.dim = 0 # Will represent the dimension of the search space self.population = None # Population array self.fitness = None # Array to store fitness values + self.trial_vectors = None # Store trial vectors for selection + self.current_generation = 0 # Track current generation + self.pending_evaluations = 0 # Track pending evaluations in current generation + self.last_processed_trial = 0 # Track the last processed trial + + # Speed tracking variables + self.last_time = time.time() + self.last_trial_count = 0 + + def _debug_print(self, message: str) -> None: + """Helper method for debug printing""" + if self.debug: + print(message) + + def _calculate_speed(self, n_completed: int) -> None: + """Calculate and print the speed for recent trials""" + if not self.debug: + return + + if n_completed % 100 == 0 and n_completed > 0: + current_time = time.time() + elapsed_time = current_time - self.last_time + trials_processed = n_completed - self.last_trial_count + + if elapsed_time > 0: # Avoid division by zero + speed = trials_processed / elapsed_time + print(f"\n[Speed Stats] Trials {self.last_trial_count} to {n_completed}") + print(f"Speed: {speed:.2f} trials/second") + print(f"Time elapsed: {elapsed_time:.2f} seconds") + print("-" * 50) + + # Update tracking variables + self.last_time = current_time + self.last_trial_count = n_completed def sample_relative( - self, - study: optuna.study.Study, - trial: optuna.trial.FrozenTrial, - search_space: dict[str, optuna.distributions.BaseDistribution], + self, + study: optuna.study.Study, + trial: optuna.trial.FrozenTrial, + search_space: dict[str, optuna.distributions.BaseDistribution], ) -> dict[str, Any]: - if len(search_space) == 0: return {} - if len(self.queue) != 0: - return self.queue.pop(0) + + completed_trials = study.get_trials(states=(optuna.trial.TrialState.COMPLETE,)) + n_completed = len(completed_trials) + self._calculate_speed(n_completed) # Calculate speed statistics + self._debug_print(f"\nTotal completed trials: {n_completed}") # Initialize search space dimensions and bounds if self.population is None: + self._debug_print("\nInitializing population...") self.lower_bound = np.asarray([dist.low for dist in search_space.values()]) self.upper_bound = np.asarray([dist.high for dist in search_space.values()]) self.dim = len(search_space) self.population = ( - np.random.rand(self.population_size, self.dim) * (self.upper_bound - self.lower_bound) - + self.lower_bound + np.random.rand(self.population_size, self.dim) * (self.upper_bound - self.lower_bound) + + self.lower_bound ) self.fitness = np.full(self.population_size, np.inf) + self.trial_vectors = np.zeros_like(self.population) - # Evaluate fitness of individuals - last_trials = study.get_trials(states=(optuna.trial.TrialState.COMPLETE,))[ - -self.population_size : - ] - for i, trial in enumerate(last_trials): - self.fitness[i] = trial.value if trial.value is not None else self.fitness[i] - - new_population = np.zeros_like(self.population) - - for i in range(self.population_size): - # Mutation: Generate a mutant vector - indices = [idx for idx in range(self.population_size) if idx != i] - r1, r2, r3 = self._rng.choice(indices, 3, replace=False) - mutant = ( - self.population[r1] - + self.F * (self.population[r2] - self.population[r3]) - ) - mutant = np.clip(mutant, self.lower_bound, self.upper_bound) + # Initial population evaluation + if self.current_generation == 0: + if n_completed < self.population_size: + self._debug_print(f"Evaluating initial individual {n_completed + 1}/{self.population_size}") + params = { + k: v for k, v in zip(search_space.keys(), self.population[n_completed]) + } + return params + else: + self._debug_print("\nInitial population evaluation complete.") + self._debug_print("Starting evolution process...") + for i in range(self.population_size): + self.fitness[i] = completed_trials[i].value + self.current_generation = 1 + self.pending_evaluations = 0 + + # Check if we need to process the previous generation's results + if self.pending_evaluations >= self.population_size and n_completed >= ( + self.current_generation * self.population_size): + self._debug_print(f"\nGeneration {self.current_generation} Selection:") + self._debug_print("-" * 50) + + # Get the fitness values for the trial vectors from the most recent trials + start_idx = n_completed - self.population_size + recent_trials = completed_trials[start_idx:n_completed] + trial_fitness = np.array([trial.value for trial in recent_trials]) + + # Selection: compare each trial vector with its target vector + for i in range(self.population_size): + self._debug_print(f"\nIndividual {i + 1}:") + self._debug_print(f"Target Vector Fitness: {self.fitness[i]:.6f}") + self._debug_print(f"Trial Vector Fitness: {trial_fitness[i]:.6f}") + + if trial_fitness[i] <= self.fitness[i]: # Minimization problem + self.population[i] = self.trial_vectors[i] + self.fitness[i] = trial_fitness[i] + self._debug_print("=> Selected: Trial Vector (Better)") + else: + self._debug_print("=> Selected: Target Vector (Better)") + + self._debug_print(f"\nBest fitness in generation {self.current_generation}: {np.min(self.fitness):.6f}") + self._debug_print("-" * 50) + + self.current_generation += 1 + self.pending_evaluations = 0 + self.last_processed_trial = n_completed + self.queue.clear() # Clear the queue before generating new trial vectors - # Crossover: Create a trial vector - trial = np.copy(self.population[i]) - crossover_mask = self._rng.rand(self.dim) < self.CR - if not np.any(crossover_mask): - crossover_mask[self._rng.randint(self.dim)] = True - trial[crossover_mask] = mutant[crossover_mask] + # Generate new trial vectors if queue is empty + if len(self.queue) == 0: + self._debug_print(f"\nGenerating trial vectors for generation {self.current_generation}") + for i in range(self.population_size): + # Mutation + indices = [idx for idx in range(self.population_size) if idx != i] + r1, r2, r3 = self._rng.choice(indices, 3, replace=False) + mutant = ( + self.population[r1] + + self.F * (self.population[r2] - self.population[r3]) + ) + mutant = np.clip(mutant, self.lower_bound, self.upper_bound) - # Print vectors for debugging - print(f"Target Vector (Individual {i}): {self.population[i]}") - print(f"Mutant Vector: {mutant}") - print(f"Resultant Vector (After Crossover): {trial}") + # Crossover + trial = np.copy(self.population[i]) + crossover_mask = self._rng.rand(self.dim) < self.CR + if not np.any(crossover_mask): + crossover_mask[self._rng.randint(self.dim)] = True + trial[crossover_mask] = mutant[crossover_mask] - # Add trial vector to new population - new_population[i] = trial + self.trial_vectors[i] = trial - # Convert new population into a parameter list for the queue - param_list = [ - {k: v for k, v in zip(search_space.keys(), individual)} - for individual in new_population - ] + # Convert trial vectors to parameter dictionaries + param_list = [ + {k: v for k, v in zip(search_space.keys(), individual)} + for individual in self.trial_vectors + ] + self.queue.extend(param_list) - self.queue.extend(param_list) + self.pending_evaluations += 1 + self._debug_print( + f"Evaluating individual {self.pending_evaluations}/{self.population_size} in generation {self.current_generation}") - return self.queue.pop(0) + return self.queue.pop(0) \ No newline at end of file diff --git a/package/samplers/de/example.py b/package/samplers/de/example.py index 4d4795d8..1d10b622 100644 --- a/package/samplers/de/example.py +++ b/package/samplers/de/example.py @@ -13,7 +13,6 @@ import math - def objective_toy(trial: optuna.Trial) -> float : # Define the dimensionality of the problem n_dimensions = 10 # High-dimensional problem with 10 dimensions @@ -36,6 +35,36 @@ def objective_toy(trial: optuna.Trial) -> float : return result +def objective_toy(trial: optuna.Trial) -> float: + # Define the dimensionality of the problem + n_dimensions = 10 # 10 dimensions + + # Suggest a value for each dimension + variables = [trial.suggest_float(f"x{i}", -10.0, 10.0) for i in range(n_dimensions)] + + # Compute the summation of squares + result = sum(x ** 2 for x in variables) + + return result + + +def objective_toy(trial: optuna.Trial) -> float: + # Define the dimensionality of the problem + n_dimensions = 10 # High-dimensional problem with 10 dimensions + + # Suggest a value for each dimension + variables = [trial.suggest_float(f"x{i}", -5.12, 5.12) for i in range(n_dimensions)] + + # Rastrigin function parameters + A = 10 + + # Compute the Rastrigin function value + sum_term = sum(x ** 2 - A * math.cos(2 * math.pi * x) for x in variables) + + # Rastrigin function formula + result = A * n_dimensions + sum_term + + return result def objective_ML(trial: optuna.Trial) -> float : # Load dataset @@ -80,13 +109,13 @@ def objective_ML(trial: optuna.Trial) -> float : sampler = optunahub.load_local_module( package=package_name , registry_root="/home/j/PycharmProjects/optunahub-registry" , # Path to the root of the optunahub-registry. - ).DESampler(population_size=100) + ).DESampler(population_size=50,F=0.8,CR=0.9, debug=True) sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility # Parameters for experiments num_experiments = 1 -number_of_trials = 5 +number_of_trials = 3000 # Store results for each experiment results_de = np.zeros((num_experiments , number_of_trials)) From 7f67e647344002ddfd4f176f327830f114f7039b Mon Sep 17 00:00:00 2001 From: JInglue Date: Fri, 29 Nov 2024 13:21:15 +0900 Subject: [PATCH 04/20] DE worked but need more testing --- package/samplers/de/de.py | 193 +++++++++++++++------------------ package/samplers/de/example.py | 7 +- 2 files changed, 94 insertions(+), 106 deletions(-) diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py index 036326df..40f1e857 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/de/de.py @@ -12,9 +12,9 @@ def __init__( self, search_space: dict[str, optuna.distributions.BaseDistribution] | None = None, population_size: int = 50, - F: float = 0.8, # Mutation factor - CR: float = 0.7, # Crossover probability, - debug: bool = False, # Toggle for debug messages + F: float = 0.8, + CR: float = 0.7, + debug: bool = False, ) -> None: super().__init__(search_space) self._rng = np.random.RandomState() @@ -22,26 +22,43 @@ def __init__( self.F = F self.CR = CR self.debug = debug - self.queue: list[dict[str, Any]] = [] # Stores individuals as parameter dictionaries - self.dim = 0 # Will represent the dimension of the search space - self.population = None # Population array - self.fitness = None # Array to store fitness values - self.trial_vectors = None # Store trial vectors for selection - self.current_generation = 0 # Track current generation - self.pending_evaluations = 0 # Track pending evaluations in current generation - self.last_processed_trial = 0 # Track the last processed trial - - # Speed tracking variables + self.dim = 0 + self.population = None + self.fitness = None + self.trial_vectors = None self.last_time = time.time() self.last_trial_count = 0 + self.last_processed_gen = -1 + self.current_gen_vectors = None # Store vectors for current generation + + def _generate_trial_vectors(self) -> np.ndarray: + """Generate new trial vectors using DE mutation and crossover.""" + trial_vectors = np.zeros_like(self.population) + for i in range(self.population_size): + # Mutation + indices = [idx for idx in range(self.population_size) if idx != i] + r1, r2, r3 = self._rng.choice(indices, 3, replace=False) + mutant = ( + self.population[r1] + + self.F * (self.population[r2] - self.population[r3]) + ) + mutant = np.clip(mutant, self.lower_bound, self.upper_bound) + + # Crossover + trial = np.copy(self.population[i]) + crossover_mask = self._rng.rand(self.dim) < self.CR + if not np.any(crossover_mask): + crossover_mask[self._rng.randint(self.dim)] = True + trial[crossover_mask] = mutant[crossover_mask] + + trial_vectors[i] = trial + return trial_vectors def _debug_print(self, message: str) -> None: - """Helper method for debug printing""" if self.debug: print(message) def _calculate_speed(self, n_completed: int) -> None: - """Calculate and print the speed for recent trials""" if not self.debug: return @@ -50,17 +67,25 @@ def _calculate_speed(self, n_completed: int) -> None: elapsed_time = current_time - self.last_time trials_processed = n_completed - self.last_trial_count - if elapsed_time > 0: # Avoid division by zero + if elapsed_time > 0: speed = trials_processed / elapsed_time print(f"\n[Speed Stats] Trials {self.last_trial_count} to {n_completed}") print(f"Speed: {speed:.2f} trials/second") print(f"Time elapsed: {elapsed_time:.2f} seconds") print("-" * 50) - # Update tracking variables self.last_time = current_time self.last_trial_count = n_completed + def _get_generation_trials(self, study: optuna.study.Study, generation: int) -> list: + """Get trials for a specific generation using trial system attributes.""" + all_trials = study.get_trials(deepcopy=False) + return [ + t for t in all_trials + if (t.state == optuna.trial.TrialState.COMPLETE and + t.system_attrs.get("de:generation") == generation) + ] + def sample_relative( self, study: optuna.study.Study, @@ -70,10 +95,14 @@ def sample_relative( if len(search_space) == 0: return {} - completed_trials = study.get_trials(states=(optuna.trial.TrialState.COMPLETE,)) - n_completed = len(completed_trials) - self._calculate_speed(n_completed) # Calculate speed statistics - self._debug_print(f"\nTotal completed trials: {n_completed}") + # Use trial_id for position tracking + current_generation = trial._trial_id // self.population_size + individual_index = trial._trial_id % self.population_size + + study._storage.set_trial_system_attr(trial._trial_id, "de:generation", current_generation) + study._storage.set_trial_system_attr(trial._trial_id, "de:individual", individual_index) + + self._calculate_speed(trial._trial_id) # Initialize search space dimensions and bounds if self.population is None: @@ -86,87 +115,43 @@ def sample_relative( + self.lower_bound ) self.fitness = np.full(self.population_size, np.inf) - self.trial_vectors = np.zeros_like(self.population) - - # Initial population evaluation - if self.current_generation == 0: - if n_completed < self.population_size: - self._debug_print(f"Evaluating initial individual {n_completed + 1}/{self.population_size}") - params = { - k: v for k, v in zip(search_space.keys(), self.population[n_completed]) - } - return params - else: - self._debug_print("\nInitial population evaluation complete.") - self._debug_print("Starting evolution process...") + study._storage.set_trial_system_attr(trial._trial_id, "de:dim", self.dim) + + # Initial population evaluation + if current_generation == 0: + self._debug_print(f"Evaluating initial individual {individual_index + 1}/{self.population_size}") + return {k: v for k, v in zip(search_space.keys(), self.population[individual_index])} + + # Process previous generation if needed + if current_generation != self.last_processed_gen: + prev_gen = current_generation - 1 + prev_trials = self._get_generation_trials(study, prev_gen) + + if len(prev_trials) == self.population_size: + self._debug_print(f"\nProcessing generation {prev_gen}") + + # Get fitness values and parameters + trial_fitness = np.array([t.value for t in prev_trials]) + trial_vectors = np.array([ + [t.params[f"x{i}"] for i in range(self.dim)] + for t in prev_trials + ]) + + # Selection for i in range(self.population_size): - self.fitness[i] = completed_trials[i].value - self.current_generation = 1 - self.pending_evaluations = 0 - - # Check if we need to process the previous generation's results - if self.pending_evaluations >= self.population_size and n_completed >= ( - self.current_generation * self.population_size): - self._debug_print(f"\nGeneration {self.current_generation} Selection:") - self._debug_print("-" * 50) - - # Get the fitness values for the trial vectors from the most recent trials - start_idx = n_completed - self.population_size - recent_trials = completed_trials[start_idx:n_completed] - trial_fitness = np.array([trial.value for trial in recent_trials]) - - # Selection: compare each trial vector with its target vector - for i in range(self.population_size): - self._debug_print(f"\nIndividual {i + 1}:") - self._debug_print(f"Target Vector Fitness: {self.fitness[i]:.6f}") - self._debug_print(f"Trial Vector Fitness: {trial_fitness[i]:.6f}") - - if trial_fitness[i] <= self.fitness[i]: # Minimization problem - self.population[i] = self.trial_vectors[i] - self.fitness[i] = trial_fitness[i] - self._debug_print("=> Selected: Trial Vector (Better)") - else: - self._debug_print("=> Selected: Target Vector (Better)") - - self._debug_print(f"\nBest fitness in generation {self.current_generation}: {np.min(self.fitness):.6f}") - self._debug_print("-" * 50) - - self.current_generation += 1 - self.pending_evaluations = 0 - self.last_processed_trial = n_completed - self.queue.clear() # Clear the queue before generating new trial vectors - - # Generate new trial vectors if queue is empty - if len(self.queue) == 0: - self._debug_print(f"\nGenerating trial vectors for generation {self.current_generation}") - for i in range(self.population_size): - # Mutation - indices = [idx for idx in range(self.population_size) if idx != i] - r1, r2, r3 = self._rng.choice(indices, 3, replace=False) - mutant = ( - self.population[r1] - + self.F * (self.population[r2] - self.population[r3]) - ) - mutant = np.clip(mutant, self.lower_bound, self.upper_bound) - - # Crossover - trial = np.copy(self.population[i]) - crossover_mask = self._rng.rand(self.dim) < self.CR - if not np.any(crossover_mask): - crossover_mask[self._rng.randint(self.dim)] = True - trial[crossover_mask] = mutant[crossover_mask] - - self.trial_vectors[i] = trial - - # Convert trial vectors to parameter dictionaries - param_list = [ - {k: v for k, v in zip(search_space.keys(), individual)} - for individual in self.trial_vectors - ] - self.queue.extend(param_list) - - self.pending_evaluations += 1 - self._debug_print( - f"Evaluating individual {self.pending_evaluations}/{self.population_size} in generation {self.current_generation}") - - return self.queue.pop(0) \ No newline at end of file + if trial_fitness[i] <= self.fitness[i]: + self.population[i] = trial_vectors[i] + self.fitness[i] = trial_fitness[i] + + self._debug_print(f"Best fitness: {np.min(self.fitness):.6f}") + + # Generate new trial vectors for current generation + self.current_gen_vectors = self._generate_trial_vectors() + self.last_processed_gen = current_generation + + # If we haven't generated trial vectors for this generation yet, do it now + if self.current_gen_vectors is None: + self.current_gen_vectors = self._generate_trial_vectors() + + # Return parameters for current individual + return {k: v for k, v in zip(search_space.keys(), self.current_gen_vectors[individual_index])} \ No newline at end of file diff --git a/package/samplers/de/example.py b/package/samplers/de/example.py index 1d10b622..03ba6d7a 100644 --- a/package/samplers/de/example.py +++ b/package/samplers/de/example.py @@ -66,6 +66,9 @@ def objective_toy(trial: optuna.Trial) -> float: return result + + + def objective_ML(trial: optuna.Trial) -> float : # Load dataset data = load_digits() @@ -114,8 +117,8 @@ def objective_ML(trial: optuna.Trial) -> float : sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility # Parameters for experiments -num_experiments = 1 -number_of_trials = 3000 +num_experiments = 10 +number_of_trials = 10000 # Store results for each experiment results_de = np.zeros((num_experiments , number_of_trials)) From 02a1fe6abc88b9d9e44567a249d73a908c5434a4 Mon Sep 17 00:00:00 2001 From: JInglue Date: Fri, 29 Nov 2024 13:57:54 +0900 Subject: [PATCH 05/20] DE worked but need more testing added explanations --- package/samplers/de/__init__.py | 3 +- package/samplers/de/de.py | 115 +++- package/samplers/de/example.py | 2 +- package/samplers/de/tests/test_sampler.py | 691 ++++++++++++++++++++++ 4 files changed, 787 insertions(+), 24 deletions(-) create mode 100644 package/samplers/de/tests/test_sampler.py diff --git a/package/samplers/de/__init__.py b/package/samplers/de/__init__.py index 4d494f19..70f9cdb3 100644 --- a/package/samplers/de/__init__.py +++ b/package/samplers/de/__init__.py @@ -1,4 +1,3 @@ -from de import DESampler - +from .de import DESampler __all__ = ["DESampler"] \ No newline at end of file diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py index 40f1e857..40cfd47d 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/de/de.py @@ -1,3 +1,17 @@ +""" +Differential Evolution (DE) Sampler for Optuna. +This implements a DE algorithm that: +1. Initializes population randomly +2. For each generation: + - Evaluates all individuals + - For each individual: + * Generates trial vector (mutation + crossover) + * Evaluates trial vector + * Keeps better solution (selection) + - Generates new trial vectors for next generation +3. Repeats until stopping criterion met +""" + from __future__ import annotations from typing import Any @@ -12,53 +26,89 @@ def __init__( self, search_space: dict[str, optuna.distributions.BaseDistribution] | None = None, population_size: int = 50, - F: float = 0.8, - CR: float = 0.7, + F: float = 0.8, # Mutation factor + CR: float = 0.7, # Crossover probability debug: bool = False, ) -> None: + """Initialize the DE sampler. + + Args: + search_space: Dictionary mapping parameter names to their distribution ranges + population_size: Number of individuals in the population + F: Mutation scaling factor - controls the amplification of differential evolution + CR: Crossover probability - controls the fraction of parameter values copied from the mutant + debug: Toggle for debug messages + """ super().__init__(search_space) + + # Initialize random number generator self._rng = np.random.RandomState() + + # DE algorithm parameters self.population_size = population_size - self.F = F - self.CR = CR + self.F = F # Mutation factor + self.CR = CR # Crossover rate self.debug = debug - self.dim = 0 - self.population = None - self.fitness = None - self.trial_vectors = None - self.last_time = time.time() - self.last_trial_count = 0 - self.last_processed_gen = -1 - self.current_gen_vectors = None # Store vectors for current generation + + # Search space parameters + self.dim = 0 # Dimension of search space + self.population = None # Current population array (population_size x dim) + self.fitness = None # Fitness values of current population + self.trial_vectors = None # Trial vectors for mutation/crossover + + # Performance tracking + self.last_time = time.time() # For speed calculation + self.last_trial_count = 0 # For speed calculation + + # Generation management + self.last_processed_gen = -1 # Track last processed generation + self.current_gen_vectors = None # Trial vectors for current generation def _generate_trial_vectors(self) -> np.ndarray: - """Generate new trial vectors using DE mutation and crossover.""" + """Generate new trial vectors using DE mutation and crossover. + + Returns: + np.ndarray: Array of trial vectors (population_size x dim) + """ trial_vectors = np.zeros_like(self.population) + for i in range(self.population_size): - # Mutation + # Select three random distinct individuals for mutation indices = [idx for idx in range(self.population_size) if idx != i] r1, r2, r3 = self._rng.choice(indices, 3, replace=False) + + # Mutation: v = x_r1 + F * (x_r2 - x_r3) mutant = ( self.population[r1] + self.F * (self.population[r2] - self.population[r3]) ) + # Clip mutant vector to bounds mutant = np.clip(mutant, self.lower_bound, self.upper_bound) - # Crossover + # Crossover: combine target vector with mutant vector trial = np.copy(self.population[i]) + # Generate crossover mask based on CR crossover_mask = self._rng.rand(self.dim) < self.CR + # Ensure at least one parameter is taken from mutant vector if not np.any(crossover_mask): crossover_mask[self._rng.randint(self.dim)] = True trial[crossover_mask] = mutant[crossover_mask] trial_vectors[i] = trial + return trial_vectors def _debug_print(self, message: str) -> None: + """Print debug message if debug mode is enabled.""" if self.debug: print(message) def _calculate_speed(self, n_completed: int) -> None: + """Calculate and print optimization speed every 100 trials. + + Args: + n_completed: Number of completed trials + """ if not self.debug: return @@ -78,7 +128,16 @@ def _calculate_speed(self, n_completed: int) -> None: self.last_trial_count = n_completed def _get_generation_trials(self, study: optuna.study.Study, generation: int) -> list: - """Get trials for a specific generation using trial system attributes.""" + """Get all completed trials for a specific generation. + + Args: + study: Optuna study object + generation: Generation number to filter trials + + Returns: + list: List of completed trials for the specified generation + """ + # Get trials without deep copying for efficiency all_trials = study.get_trials(deepcopy=False) return [ t for t in all_trials @@ -92,24 +151,38 @@ def sample_relative( trial: optuna.trial.FrozenTrial, search_space: dict[str, optuna.distributions.BaseDistribution], ) -> dict[str, Any]: + """Sample parameters for a trial using DE algorithm. + + This is the main method called by Optuna for each trial. + + Args: + study: Optuna study object + trial: Current trial object + search_space: Dictionary of parameter distributions + + Returns: + dict: Parameter values for the trial + """ if len(search_space) == 0: return {} - # Use trial_id for position tracking + # Use trial ID to determine generation and individual index current_generation = trial._trial_id // self.population_size individual_index = trial._trial_id % self.population_size + # Store generation info in trial study._storage.set_trial_system_attr(trial._trial_id, "de:generation", current_generation) study._storage.set_trial_system_attr(trial._trial_id, "de:individual", individual_index) self._calculate_speed(trial._trial_id) - # Initialize search space dimensions and bounds + # Initialize search space dimensions and bounds (first call only) if self.population is None: self._debug_print("\nInitializing population...") self.lower_bound = np.asarray([dist.low for dist in search_space.values()]) self.upper_bound = np.asarray([dist.high for dist in search_space.values()]) self.dim = len(search_space) + # Initialize population randomly within bounds self.population = ( np.random.rand(self.population_size, self.dim) * (self.upper_bound - self.lower_bound) + self.lower_bound @@ -130,14 +203,14 @@ def sample_relative( if len(prev_trials) == self.population_size: self._debug_print(f"\nProcessing generation {prev_gen}") - # Get fitness values and parameters + # Get fitness values and parameters from previous generation trial_fitness = np.array([t.value for t in prev_trials]) trial_vectors = np.array([ [t.params[f"x{i}"] for i in range(self.dim)] for t in prev_trials ]) - # Selection + # Selection: keep better solutions for i in range(self.population_size): if trial_fitness[i] <= self.fitness[i]: self.population[i] = trial_vectors[i] @@ -149,7 +222,7 @@ def sample_relative( self.current_gen_vectors = self._generate_trial_vectors() self.last_processed_gen = current_generation - # If we haven't generated trial vectors for this generation yet, do it now + # Generate trial vectors if needed if self.current_gen_vectors is None: self.current_gen_vectors = self._generate_trial_vectors() diff --git a/package/samplers/de/example.py b/package/samplers/de/example.py index 03ba6d7a..cead1ce6 100644 --- a/package/samplers/de/example.py +++ b/package/samplers/de/example.py @@ -118,7 +118,7 @@ def objective_ML(trial: optuna.Trial) -> float : # Parameters for experiments num_experiments = 10 -number_of_trials = 10000 +number_of_trials = 100 # Store results for each experiment results_de = np.zeros((num_experiments , number_of_trials)) diff --git a/package/samplers/de/tests/test_sampler.py b/package/samplers/de/tests/test_sampler.py new file mode 100644 index 00000000..373cda43 --- /dev/null +++ b/package/samplers/de/tests/test_sampler.py @@ -0,0 +1,691 @@ +"""MIT License + +Copyright (c) 2018 Preferred Networks, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and +associated documentation files (the "Software"), to deal in the Software without restriction, +including without limitation the rights to use, copy, modify, merge, publish, distribute, +sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or +substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT +NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT +OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +This file is taken from Optuna (https://github.com/optuna/optuna/blob/master/tests/samplers_tests/test_samplers.py) +and modified to test AutoSampler. +""" + +from __future__ import annotations + +from collections.abc import Callable +from collections.abc import Sequence +import multiprocessing +from multiprocessing.managers import DictProxy +import os +from typing import Any +from unittest.mock import patch +import warnings + +from _pytest.fixtures import SubRequest +from _pytest.mark.structures import MarkDecorator +import numpy as np +import optuna +from optuna.distributions import BaseDistribution +from optuna.distributions import CategoricalChoiceType +from optuna.distributions import CategoricalDistribution +from optuna.distributions import FloatDistribution +from optuna.distributions import IntDistribution +from optuna.samplers import BaseSampler +from optuna.study import Study +from optuna.trial import FrozenTrial +from optuna.trial import Trial +from optuna.trial import TrialState +import optunahub +import pytest + + +# NOTE(nabenabe): This file content is mostly copied from the Optuna repository. +AutoSampler = optunahub.load_local_module( + package="package/samplers/de", registry_root="/home/j/PycharmProjects/optunahub-registry" +).DESampler + + +parametrize_sampler = pytest.mark.parametrize("sampler_class", [AutoSampler]) +parametrize_relative_sampler = pytest.mark.parametrize("relative_sampler_class", [AutoSampler]) +parametrize_multi_objective_sampler = pytest.mark.parametrize( + "multi_objective_sampler_class", [AutoSampler] +) + + +sampler_class_with_seed: dict[str, Callable[[int], BaseSampler]] = { + "AutoSampler": lambda seed: AutoSampler(seed=seed) +} +param_sampler_with_seed = [] +param_sampler_name_with_seed = [] +for sampler_name, sampler_class in sampler_class_with_seed.items(): + param_sampler_with_seed.append(pytest.param(sampler_class, id=sampler_name)) + param_sampler_name_with_seed.append(pytest.param(sampler_name)) +parametrize_sampler_with_seed = pytest.mark.parametrize("sampler_class", param_sampler_with_seed) +parametrize_sampler_name_with_seed = pytest.mark.parametrize( + "sampler_name", param_sampler_name_with_seed +) + + +def parametrize_suggest_method(name: str) -> MarkDecorator: + return pytest.mark.parametrize( + f"suggest_method_{name}", + [ + lambda t: t.suggest_float(name, 0, 10), + lambda t: t.suggest_int(name, 0, 10), + lambda t: t.suggest_categorical(name, [0, 1, 2]), + lambda t: t.suggest_float(name, 0, 10, step=0.5), + lambda t: t.suggest_float(name, 1e-7, 10, log=True), + lambda t: t.suggest_int(name, 1, 10, log=True), + ], + ) + + +def _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study: optuna.Study) -> None: + # NOTE(nabenabe): Choose a sampler inside AutoSampler. + study.sampler.before_trial(study, trial=_create_new_trial(study)) + study.sampler._sampler._n_startup_trials = 0 + + +@parametrize_sampler +@pytest.mark.parametrize( + "distribution", + [ + FloatDistribution(-1.0, 1.0), + FloatDistribution(0.0, 1.0), + FloatDistribution(-1.0, 0.0), + FloatDistribution(1e-7, 1.0, log=True), + FloatDistribution(-10, 10, step=0.1), + FloatDistribution(-10.2, 10.2, step=0.1), + ], +) +def test_float(sampler_class: Callable[[], BaseSampler], distribution: FloatDistribution) -> None: + study = optuna.study.create_study(sampler=sampler_class()) + points = np.array( + [ + study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution) + for _ in range(100) + ] + ) + assert np.all(points >= distribution.low) + assert np.all(points <= distribution.high) + assert not isinstance( + study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution), + np.floating, + ) + + if distribution.step is not None: + # Check all points are multiples of distribution.step. + points -= distribution.low + points /= distribution.step + round_points = np.round(points) + np.testing.assert_almost_equal(round_points, points) + + +@parametrize_sampler +@pytest.mark.parametrize( + "distribution", + [ + IntDistribution(-10, 10), + IntDistribution(0, 10), + IntDistribution(-10, 0), + IntDistribution(-10, 10, step=2), + IntDistribution(0, 10, step=2), + IntDistribution(-10, 0, step=2), + IntDistribution(1, 100, log=True), + ], +) +def test_int(sampler_class: Callable[[], BaseSampler], distribution: IntDistribution) -> None: + study = optuna.study.create_study(sampler=sampler_class()) + points = np.array( + [ + study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution) + for _ in range(100) + ] + ) + assert np.all(points >= distribution.low) + assert np.all(points <= distribution.high) + assert not isinstance( + study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution), + np.integer, + ) + + +@parametrize_sampler +@pytest.mark.parametrize("choices", [(1, 2, 3), ("a", "b", "c"), (1, "a")]) +def test_categorical( + sampler_class: Callable[[], BaseSampler], choices: Sequence[CategoricalChoiceType] +) -> None: + distribution = CategoricalDistribution(choices) + + study = optuna.study.create_study(sampler=sampler_class()) + + def sample() -> float: + trial = _create_new_trial(study) + param_value = study.sampler.sample_independent(study, trial, "x", distribution) + return float(distribution.to_internal_repr(param_value)) + + points = np.asarray([sample() for i in range(100)]) + + # 'x' value is corresponding to an index of distribution.choices. + assert np.all(points >= 0) + assert np.all(points <= len(distribution.choices) - 1) + round_points = np.round(points) + np.testing.assert_almost_equal(round_points, points) + + +@parametrize_relative_sampler +@pytest.mark.parametrize( + "x_distribution", + [ + FloatDistribution(-1.0, 1.0), + FloatDistribution(1e-7, 1.0, log=True), + FloatDistribution(-10, 10, step=0.5), + IntDistribution(3, 10), + IntDistribution(1, 100, log=True), + IntDistribution(3, 9, step=2), + ], +) +@pytest.mark.parametrize( + "y_distribution", + [ + FloatDistribution(-1.0, 1.0), + FloatDistribution(1e-7, 1.0, log=True), + FloatDistribution(-10, 10, step=0.5), + IntDistribution(3, 10), + IntDistribution(1, 100, log=True), + IntDistribution(3, 9, step=2), + ], +) +def test_sample_relative_numerical( + relative_sampler_class: Callable[[], BaseSampler], + x_distribution: BaseDistribution, + y_distribution: BaseDistribution, +) -> None: + search_space: dict[str, BaseDistribution] = dict(x=x_distribution, y=y_distribution) + study = optuna.study.create_study(sampler=relative_sampler_class()) + trial = study.ask(search_space) + study.tell(trial, sum(trial.params.values())) + _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study) + + def sample() -> list[int | float]: + params = study.sampler.sample_relative(study, _create_new_trial(study), search_space) + return [params[name] for name in search_space] + + points = np.array([sample() for _ in range(10)]) + for i, distribution in enumerate(search_space.values()): + assert isinstance( + distribution, + ( + FloatDistribution, + IntDistribution, + ), + ) + assert np.all(points[:, i] >= distribution.low) + assert np.all(points[:, i] <= distribution.high) + for param_value, distribution in zip(sample(), search_space.values()): + assert not isinstance(param_value, np.floating) + assert not isinstance(param_value, np.integer) + if isinstance(distribution, IntDistribution): + assert isinstance(param_value, int) + else: + assert isinstance(param_value, float) + + +@parametrize_relative_sampler +def test_sample_relative_categorical(relative_sampler_class: Callable[[], BaseSampler]) -> None: + search_space: dict[str, BaseDistribution] = dict( + x=CategoricalDistribution([1, 10, 100]), y=CategoricalDistribution([-1, -10, -100]) + ) + study = optuna.study.create_study(sampler=relative_sampler_class()) + trial = study.ask(search_space) + study.tell(trial, sum(trial.params.values())) + _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study) + + def sample() -> list[float]: + params = study.sampler.sample_relative(study, _create_new_trial(study), search_space) + return [params[name] for name in search_space] + + points = np.array([sample() for _ in range(10)]) + for i, distribution in enumerate(search_space.values()): + assert isinstance(distribution, CategoricalDistribution) + assert np.all([v in distribution.choices for v in points[:, i]]) + for param_value in sample(): + assert not isinstance(param_value, np.floating) + assert not isinstance(param_value, np.integer) + assert isinstance(param_value, int) + + +@parametrize_relative_sampler +@pytest.mark.parametrize( + "x_distribution", + [ + FloatDistribution(-1.0, 1.0), + FloatDistribution(1e-7, 1.0, log=True), + FloatDistribution(-10, 10, step=0.5), + IntDistribution(1, 10), + IntDistribution(1, 100, log=True), + ], +) +def test_sample_relative_mixed( + relative_sampler_class: Callable[[], BaseSampler], x_distribution: BaseDistribution +) -> None: + search_space: dict[str, BaseDistribution] = dict( + x=x_distribution, y=CategoricalDistribution([-1, -10, -100]) + ) + study = optuna.study.create_study(sampler=relative_sampler_class()) + trial = study.ask(search_space) + study.tell(trial, sum(trial.params.values())) + _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study) + + def sample() -> list[float]: + params = study.sampler.sample_relative(study, _create_new_trial(study), search_space) + return [params[name] for name in search_space] + + points = np.array([sample() for _ in range(10)]) + assert isinstance( + search_space["x"], + ( + FloatDistribution, + IntDistribution, + ), + ) + assert np.all(points[:, 0] >= search_space["x"].low) + assert np.all(points[:, 0] <= search_space["x"].high) + assert isinstance(search_space["y"], CategoricalDistribution) + assert np.all([v in search_space["y"].choices for v in points[:, 1]]) + for param_value, distribution in zip(sample(), search_space.values()): + assert not isinstance(param_value, np.floating) + assert not isinstance(param_value, np.integer) + if isinstance( + distribution, + ( + IntDistribution, + CategoricalDistribution, + ), + ): + assert isinstance(param_value, int) + else: + assert isinstance(param_value, float) + + +@parametrize_sampler +def test_conditional_sample_independent(sampler_class: Callable[[], BaseSampler]) -> None: + # This test case reproduces the error reported in #2734. + # See https://github.com/optuna/optuna/pull/2734#issuecomment-857649769. + + study = optuna.study.create_study(sampler=sampler_class()) + categorical_distribution = CategoricalDistribution(choices=["x", "y"]) + dependent_distribution = CategoricalDistribution(choices=["a", "b"]) + + study.add_trial( + optuna.create_trial( + params={"category": "x", "x": "a"}, + distributions={"category": categorical_distribution, "x": dependent_distribution}, + value=0.1, + ) + ) + + study.add_trial( + optuna.create_trial( + params={"category": "y", "y": "b"}, + distributions={"category": categorical_distribution, "y": dependent_distribution}, + value=0.1, + ) + ) + + _trial = _create_new_trial(study) + category = study.sampler.sample_independent( + study, _trial, "category", categorical_distribution + ) + assert category in ["x", "y"] + value = study.sampler.sample_independent(study, _trial, category, dependent_distribution) + assert value in ["a", "b"] + + +def _create_new_trial(study: Study) -> FrozenTrial: + trial_id = study._storage.create_new_trial(study._study_id) + return study._storage.get_trial(trial_id) + + +@parametrize_sampler +def test_nan_objective_value(sampler_class: Callable[[], BaseSampler]) -> None: + study = optuna.create_study(sampler=sampler_class()) + + def objective(trial: Trial, base_value: float) -> float: + return trial.suggest_float("x", 0.1, 0.2) + base_value + + # Non NaN objective values. + for i in range(10, 1, -1): + study.optimize(lambda t: objective(t, i), n_trials=1, catch=()) + assert int(study.best_value) == 2 + + # NaN objective values. + study.optimize(lambda t: objective(t, float("nan")), n_trials=1, catch=()) + assert int(study.best_value) == 2 + + # Non NaN objective value. + study.optimize(lambda t: objective(t, 1), n_trials=1, catch=()) + assert int(study.best_value) == 1 + + +@parametrize_multi_objective_sampler +@pytest.mark.parametrize( + "distribution", + [ + FloatDistribution(-1.0, 1.0), + FloatDistribution(0.0, 1.0), + FloatDistribution(-1.0, 0.0), + FloatDistribution(1e-7, 1.0, log=True), + FloatDistribution(-10, 10, step=0.1), + FloatDistribution(-10.2, 10.2, step=0.1), + IntDistribution(-10, 10), + IntDistribution(0, 10), + IntDistribution(-10, 0), + IntDistribution(-10, 10, step=2), + IntDistribution(0, 10, step=2), + IntDistribution(-10, 0, step=2), + IntDistribution(1, 100, log=True), + CategoricalDistribution((1, 2, 3)), + CategoricalDistribution(("a", "b", "c")), + CategoricalDistribution((1, "a")), + ], +) +def test_multi_objective_sample_independent( + multi_objective_sampler_class: Callable[[], BaseSampler], distribution: BaseDistribution +) -> None: + study = optuna.study.create_study( + directions=["minimize", "maximize"], sampler=multi_objective_sampler_class() + ) + for i in range(100): + value = study.sampler.sample_independent( + study, _create_new_trial(study), "x", distribution + ) + assert distribution._contains(distribution.to_internal_repr(value)) + + if not isinstance(distribution, CategoricalDistribution): + # Please see https://github.com/optuna/optuna/pull/393 why this assertion is needed. + assert not isinstance(value, np.floating) + + if isinstance(distribution, FloatDistribution): + if distribution.step is not None: + # Check the value is a multiple of `distribution.step` which is + # the quantization interval of the distribution. + value -= distribution.low + value /= distribution.step + round_value = np.round(value) + np.testing.assert_almost_equal(round_value, value) + + +@parametrize_sampler +def test_sample_single_distribution(sampler_class: Callable[[], BaseSampler]) -> None: + relative_search_space = { + "a": CategoricalDistribution([1]), + "b": IntDistribution(low=1, high=1), + "c": IntDistribution(low=1, high=1, log=True), + "d": FloatDistribution(low=1.0, high=1.0), + "e": FloatDistribution(low=1.0, high=1.0, log=True), + "f": FloatDistribution(low=1.0, high=1.0, step=1.0), + } + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning) + sampler = sampler_class() + study = optuna.study.create_study(sampler=sampler) + + # We need to test the construction of the model, so we should set `n_trials >= 2`. + for _ in range(2): + trial = study.ask(fixed_distributions=relative_search_space) + study.tell(trial, 1.0) + for param_name in relative_search_space.keys(): + assert trial.params[param_name] == 1 + + +@parametrize_sampler +@parametrize_suggest_method("x") +def test_single_parameter_objective( + sampler_class: Callable[[], BaseSampler], suggest_method_x: Callable[[Trial], float] +) -> None: + def objective(trial: Trial) -> float: + return suggest_method_x(trial) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning) + sampler = sampler_class() + + study = optuna.study.create_study(sampler=sampler) + study.optimize(objective, n_trials=10) + + assert len(study.trials) == 10 + assert all(t.state == TrialState.COMPLETE for t in study.trials) + + +@parametrize_sampler +def test_conditional_parameter_objective(sampler_class: Callable[[], BaseSampler]) -> None: + def objective(trial: Trial) -> float: + x = trial.suggest_categorical("x", [True, False]) + if x: + return trial.suggest_float("y", 0, 1) + return trial.suggest_float("z", 0, 1) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning) + sampler = sampler_class() + + study = optuna.study.create_study(sampler=sampler) + study.optimize(objective, n_trials=10) + + assert len(study.trials) == 10 + assert all(t.state == TrialState.COMPLETE for t in study.trials) + + +@parametrize_sampler +@parametrize_suggest_method("x") +@parametrize_suggest_method("y") +def test_combination_of_different_distributions_objective( + sampler_class: Callable[[], BaseSampler], + suggest_method_x: Callable[[Trial], float], + suggest_method_y: Callable[[Trial], float], +) -> None: + def objective(trial: Trial) -> float: + return suggest_method_x(trial) + suggest_method_y(trial) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning) + sampler = sampler_class() + + study = optuna.study.create_study(sampler=sampler) + study.optimize(objective, n_trials=3) + + assert len(study.trials) == 3 + assert all(t.state == TrialState.COMPLETE for t in study.trials) + + +@parametrize_sampler +@pytest.mark.parametrize( + "second_low,second_high", + [ + (0, 5), # Narrow range. + (0, 20), # Expand range. + (20, 30), # Set non-overlapping range. + ], +) +def test_dynamic_range_objective( + sampler_class: Callable[[], BaseSampler], second_low: int, second_high: int +) -> None: + def objective(trial: Trial, low: int, high: int) -> float: + v = trial.suggest_float("x", low, high) + v += trial.suggest_int("y", low, high) + return v + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning) + sampler = sampler_class() + + study = optuna.study.create_study(sampler=sampler) + study.optimize(lambda t: objective(t, 0, 10), n_trials=10) + study.optimize(lambda t: objective(t, second_low, second_high), n_trials=10) + + assert len(study.trials) == 20 + assert all(t.state == TrialState.COMPLETE for t in study.trials) + + +# We add tests for constant objective functions to ensure the reproducibility of sorting. +@parametrize_sampler_with_seed +@pytest.mark.slow +@pytest.mark.parametrize("objective_func", [lambda *args: sum(args), lambda *args: 0.0]) +def test_reproducible(sampler_class: Callable[[int], BaseSampler], objective_func: Any) -> None: + def objective(trial: Trial) -> float: + a = trial.suggest_float("a", 1, 9) + b = trial.suggest_float("b", 1, 9, log=True) + c = trial.suggest_float("c", 1, 9, step=1) + d = trial.suggest_int("d", 1, 9) + e = trial.suggest_int("e", 1, 9, log=True) + f = trial.suggest_int("f", 1, 9, step=2) + g = trial.suggest_categorical("g", range(1, 10)) + return objective_func(a, b, c, d, e, f, g) + + study = optuna.create_study(sampler=sampler_class(1)) + study.optimize(objective, n_trials=15) + + study_same_seed = optuna.create_study(sampler=sampler_class(1)) + study_same_seed.optimize(objective, n_trials=15) + for i in range(15): + assert study.trials[i].params == study_same_seed.trials[i].params + + study_different_seed = optuna.create_study(sampler=sampler_class(2)) + study_different_seed.optimize(objective, n_trials=15) + assert any( + [study.trials[i].params != study_different_seed.trials[i].params for i in range(15)] + ) + + +@pytest.mark.slow +@parametrize_sampler_with_seed +def test_reseed_rng_change_sampling(sampler_class: Callable[[int], BaseSampler]) -> None: + def objective(trial: Trial) -> float: + a = trial.suggest_float("a", 1, 9) + b = trial.suggest_float("b", 1, 9, log=True) + c = trial.suggest_float("c", 1, 9, step=1) + d = trial.suggest_int("d", 1, 9) + e = trial.suggest_int("e", 1, 9, log=True) + f = trial.suggest_int("f", 1, 9, step=2) + g = trial.suggest_categorical("g", range(1, 10)) + return a + b + c + d + e + f + g + + sampler = sampler_class(1) + study = optuna.create_study(sampler=sampler) + study.optimize(objective, n_trials=15) + + sampler_different_seed = sampler_class(1) + sampler_different_seed.reseed_rng() + study_different_seed = optuna.create_study(sampler=sampler_different_seed) + study_different_seed.optimize(objective, n_trials=15) + assert any( + [study.trials[i].params != study_different_seed.trials[i].params for i in range(15)] + ) + + +# This function is used only in test_reproducible_in_other_process, but declared at top-level +# because local function cannot be pickled, which occurs within multiprocessing. +def run_optimize( + k: int, + sampler_name: str, + sequence_dict: DictProxy, + hash_dict: DictProxy, +) -> None: + def objective(trial: Trial) -> float: + a = trial.suggest_float("a", 1, 9) + b = trial.suggest_float("b", 1, 9, log=True) + c = trial.suggest_float("c", 1, 9, step=1) + d = trial.suggest_int("d", 1, 9) + e = trial.suggest_int("e", 1, 9, log=True) + f = trial.suggest_int("f", 1, 9, step=2) + g = trial.suggest_categorical("g", range(1, 10)) + return a + b + c + d + e + f + g + + hash_dict[k] = hash("nondeterministic hash") + sampler = sampler_class_with_seed[sampler_name](1) + study = optuna.create_study(sampler=sampler) + study.optimize(objective, n_trials=15) + sequence_dict[k] = list(study.trials[-1].params.values()) + + +@pytest.fixture +def unset_seed_in_test(request: SubRequest) -> None: + # Unset the hashseed at beginning and restore it at end regardless of an exception in the test. + # See https://docs.pytest.org/en/stable/how-to/fixtures.html#adding-finalizers-directly + # for details. + + hash_seed = os.getenv("PYTHONHASHSEED") + if hash_seed is not None: + del os.environ["PYTHONHASHSEED"] + + def restore_seed() -> None: + if hash_seed is not None: + os.environ["PYTHONHASHSEED"] = hash_seed + + request.addfinalizer(restore_seed) + + +@pytest.mark.slow +@parametrize_sampler_name_with_seed +def test_reproducible_in_other_process(sampler_name: str, unset_seed_in_test: None) -> None: + # This test should be tested without `PYTHONHASHSEED`. However, some tool such as tox + # set the environmental variable "PYTHONHASHSEED" by default. + # To do so, this test calls a finalizer: `unset_seed_in_test`. + + # Multiprocessing supports three way to start a process. + # We use `spawn` option to create a child process as a fresh python process. + # For more detail, see https://github.com/optuna/optuna/pull/3187#issuecomment-997673037. + multiprocessing.set_start_method("spawn", force=True) + manager = multiprocessing.Manager() + sequence_dict: DictProxy = manager.dict() + hash_dict: DictProxy = manager.dict() + for i in range(3): + p = multiprocessing.Process( + target=run_optimize, args=(i, sampler_name, sequence_dict, hash_dict) + ) + p.start() + p.join() + + # Hashes are expected to be different because string hashing is nondeterministic per process. + assert not (hash_dict[0] == hash_dict[1] == hash_dict[2]) + # But the sequences are expected to be the same. + assert sequence_dict[0] == sequence_dict[1] == sequence_dict[2] + + +@pytest.mark.parametrize("n_jobs", [1, 2]) +@parametrize_relative_sampler +def test_cache_is_invalidated( + n_jobs: int, relative_sampler_class: Callable[[], BaseSampler] +) -> None: + sampler = relative_sampler_class() + original_before_trial = sampler.before_trial + + def mock_before_trial(study: Study, trial: FrozenTrial) -> None: + assert study._thread_local.cached_all_trials is None + original_before_trial(study, trial) + + with patch.object(sampler, "before_trial", side_effect=mock_before_trial): + study = optuna.study.create_study(sampler=sampler) + + def objective(trial: Trial) -> float: + assert trial._relative_params is None + + trial.suggest_float("x", -10, 10) + trial.suggest_float("y", -10, 10) + assert trial._relative_params is not None + return -1 + + study.optimize(objective, n_trials=10, n_jobs=n_jobs) \ No newline at end of file From b03e4d97ac4678b61ef00e61d074c8e3b2be7bf2 Mon Sep 17 00:00:00 2001 From: JInglue Date: Fri, 29 Nov 2024 15:30:06 +0900 Subject: [PATCH 06/20] DE worked and tested but not for categorical --- package/samplers/de/de.py | 91 +++++++------ package/samplers/de/example.py | 5 +- package/samplers/de/tests/test_sampler.py | 149 +++++++++++++++------- 3 files changed, 155 insertions(+), 90 deletions(-) diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py index 40cfd47d..ee10b6fe 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/de/de.py @@ -1,17 +1,3 @@ -""" -Differential Evolution (DE) Sampler for Optuna. -This implements a DE algorithm that: -1. Initializes population randomly -2. For each generation: - - Evaluates all individuals - - For each individual: - * Generates trial vector (mutation + crossover) - * Evaluates trial vector - * Keeps better solution (selection) - - Generates new trial vectors for next generation -3. Repeats until stopping criterion met -""" - from __future__ import annotations from typing import Any @@ -22,6 +8,20 @@ class DESampler(optunahub.samplers.SimpleBaseSampler): + """Differential Evolution Sampler for Optuna optimization. + + This implements a DE algorithm that: + 1. Initializes population randomly + 2. For each generation: + - Evaluates all individuals + - For each individual: + * Generates trial vector (mutation + crossover) + * Evaluates trial vector + * Keeps better solution (selection) + - Generates new trial vectors for next generation + 3. Repeats until stopping criterion met + """ + def __init__( self, search_space: dict[str, optuna.distributions.BaseDistribution] | None = None, @@ -29,6 +29,7 @@ def __init__( F: float = 0.8, # Mutation factor CR: float = 0.7, # Crossover probability debug: bool = False, + seed: int | None = None, # Random seed ) -> None: """Initialize the DE sampler. @@ -38,31 +39,35 @@ def __init__( F: Mutation scaling factor - controls the amplification of differential evolution CR: Crossover probability - controls the fraction of parameter values copied from the mutant debug: Toggle for debug messages + seed: Random seed for reproducibility """ super().__init__(search_space) - # Initialize random number generator - self._rng = np.random.RandomState() + # Store and set random seed + self.seed = seed + self._rng = np.random.RandomState(seed) # DE algorithm parameters self.population_size = population_size - self.F = F # Mutation factor - self.CR = CR # Crossover rate + self.F = F + self.CR = CR self.debug = debug # Search space parameters - self.dim = 0 # Dimension of search space - self.population = None # Current population array (population_size x dim) - self.fitness = None # Fitness values of current population - self.trial_vectors = None # Trial vectors for mutation/crossover + self.dim = 0 + self.population = None + self.fitness = None + self.trial_vectors = None + self.lower_bound = None + self.upper_bound = None # Performance tracking - self.last_time = time.time() # For speed calculation - self.last_trial_count = 0 # For speed calculation + self.last_time = time.time() + self.last_trial_count = 0 # Generation management - self.last_processed_gen = -1 # Track last processed generation - self.current_gen_vectors = None # Trial vectors for current generation + self.last_processed_gen = -1 + self.current_gen_vectors = None def _generate_trial_vectors(self) -> np.ndarray: """Generate new trial vectors using DE mutation and crossover. @@ -137,7 +142,6 @@ def _get_generation_trials(self, study: optuna.study.Study, generation: int) -> Returns: list: List of completed trials for the specified generation """ - # Get trials without deep copying for efficiency all_trials = study.get_trials(deepcopy=False) return [ t for t in all_trials @@ -153,8 +157,6 @@ def sample_relative( ) -> dict[str, Any]: """Sample parameters for a trial using DE algorithm. - This is the main method called by Optuna for each trial. - Args: study: Optuna study object trial: Current trial object @@ -166,26 +168,27 @@ def sample_relative( if len(search_space) == 0: return {} - # Use trial ID to determine generation and individual index + # Calculate current generation and individual index current_generation = trial._trial_id // self.population_size individual_index = trial._trial_id % self.population_size - # Store generation info in trial + # Store generation and individual info as trial attributes study._storage.set_trial_system_attr(trial._trial_id, "de:generation", current_generation) study._storage.set_trial_system_attr(trial._trial_id, "de:individual", individual_index) self._calculate_speed(trial._trial_id) - # Initialize search space dimensions and bounds (first call only) + # Initialize search space dimensions and bounds if not done if self.population is None: self._debug_print("\nInitializing population...") self.lower_bound = np.asarray([dist.low for dist in search_space.values()]) self.upper_bound = np.asarray([dist.high for dist in search_space.values()]) self.dim = len(search_space) - # Initialize population randomly within bounds + # Initialize population using seeded RNG self.population = ( - np.random.rand(self.population_size, self.dim) * (self.upper_bound - self.lower_bound) - + self.lower_bound + self._rng.rand(self.population_size, self.dim) * + (self.upper_bound - self.lower_bound) + + self.lower_bound ) self.fitness = np.full(self.population_size, np.inf) study._storage.set_trial_system_attr(trial._trial_id, "de:dim", self.dim) @@ -193,20 +196,23 @@ def sample_relative( # Initial population evaluation if current_generation == 0: self._debug_print(f"Evaluating initial individual {individual_index + 1}/{self.population_size}") - return {k: v for k, v in zip(search_space.keys(), self.population[individual_index])} + return { + name: (float(value) if isinstance(dist, optuna.distributions.FloatDistribution) else int(value)) + for (name, dist), value in zip(search_space.items(), self.population[individual_index]) + } # Process previous generation if needed - if current_generation != self.last_processed_gen: + if current_generation > 0 and current_generation != self.last_processed_gen: prev_gen = current_generation - 1 prev_trials = self._get_generation_trials(study, prev_gen) if len(prev_trials) == self.population_size: self._debug_print(f"\nProcessing generation {prev_gen}") - # Get fitness values and parameters from previous generation + # Get fitness and parameter values from previous generation trial_fitness = np.array([t.value for t in prev_trials]) trial_vectors = np.array([ - [t.params[f"x{i}"] for i in range(self.dim)] + [t.params[name] for name in search_space.keys()] for t in prev_trials ]) @@ -222,9 +228,12 @@ def sample_relative( self.current_gen_vectors = self._generate_trial_vectors() self.last_processed_gen = current_generation - # Generate trial vectors if needed + # Ensure we have trial vectors for current generation if self.current_gen_vectors is None: self.current_gen_vectors = self._generate_trial_vectors() # Return parameters for current individual - return {k: v for k, v in zip(search_space.keys(), self.current_gen_vectors[individual_index])} \ No newline at end of file + return { + name: (float(value) if isinstance(dist, optuna.distributions.FloatDistribution) else int(value)) + for (name, dist), value in zip(search_space.items(), self.current_gen_vectors[individual_index]) + } \ No newline at end of file diff --git a/package/samplers/de/example.py b/package/samplers/de/example.py index cead1ce6..861b1042 100644 --- a/package/samplers/de/example.py +++ b/package/samplers/de/example.py @@ -117,9 +117,8 @@ def objective_ML(trial: optuna.Trial) -> float : sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility # Parameters for experiments -num_experiments = 10 -number_of_trials = 100 - +num_experiments = 5 +number_of_trials = 3000 # Store results for each experiment results_de = np.zeros((num_experiments , number_of_trials)) results_rs = np.zeros((num_experiments , number_of_trials)) diff --git a/package/samplers/de/tests/test_sampler.py b/package/samplers/de/tests/test_sampler.py index 373cda43..9bbe4627 100644 --- a/package/samplers/de/tests/test_sampler.py +++ b/package/samplers/de/tests/test_sampler.py @@ -31,6 +31,9 @@ from typing import Any from unittest.mock import patch import warnings +import logging +from datetime import datetime + from _pytest.fixtures import SubRequest from _pytest.mark.structures import MarkDecorator @@ -51,20 +54,20 @@ # NOTE(nabenabe): This file content is mostly copied from the Optuna repository. -AutoSampler = optunahub.load_local_module( +The_Sampler = optunahub.load_local_module( package="package/samplers/de", registry_root="/home/j/PycharmProjects/optunahub-registry" ).DESampler -parametrize_sampler = pytest.mark.parametrize("sampler_class", [AutoSampler]) -parametrize_relative_sampler = pytest.mark.parametrize("relative_sampler_class", [AutoSampler]) +parametrize_sampler = pytest.mark.parametrize("sampler_class", [The_Sampler]) +parametrize_relative_sampler = pytest.mark.parametrize("relative_sampler_class", [The_Sampler]) parametrize_multi_objective_sampler = pytest.mark.parametrize( - "multi_objective_sampler_class", [AutoSampler] + "multi_objective_sampler_class", [The_Sampler] ) sampler_class_with_seed: dict[str, Callable[[int], BaseSampler]] = { - "AutoSampler": lambda seed: AutoSampler(seed=seed) + "TheSampler": lambda seed: The_Sampler(seed=seed) } param_sampler_with_seed = [] param_sampler_name_with_seed = [] @@ -216,7 +219,13 @@ def test_sample_relative_numerical( study = optuna.study.create_study(sampler=relative_sampler_class()) trial = study.ask(search_space) study.tell(trial, sum(trial.params.values())) - _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study) + + """ + This test checks if the relative sampler samples parameters correctly. Specifically, it checks + if the sampled parameters are within the search space and have the correct type. The test + samples 10 points and checks if the sampled parameters are within the search space and have the + correct type. + """ def sample() -> list[int | float]: params = study.sampler.sample_relative(study, _create_new_trial(study), search_space) @@ -230,16 +239,16 @@ def sample() -> list[int | float]: FloatDistribution, IntDistribution, ), - ) - assert np.all(points[:, i] >= distribution.low) - assert np.all(points[:, i] <= distribution.high) + ), "The distribution must be either FloatDistribution or IntDistribution." + assert np.all(points[:, i] >= distribution.low), "The sampled value must be >= low." + assert np.all(points[:, i] <= distribution.high), "The sampled value must be <= high." for param_value, distribution in zip(sample(), search_space.values()): - assert not isinstance(param_value, np.floating) - assert not isinstance(param_value, np.integer) + assert not isinstance(param_value, np.floating), f"The sampled value must not be a numpy float, instead it is {param_value}" + assert not isinstance(param_value, np.integer), f"The sampled value must not be a numpy integer, instead it is {param_value}" if isinstance(distribution, IntDistribution): - assert isinstance(param_value, int) + assert isinstance(param_value, int), "The sampled value must be an integer." else: - assert isinstance(param_value, float) + assert isinstance(param_value, float), "The sampled value must be a float." @parametrize_relative_sampler @@ -250,7 +259,6 @@ def test_sample_relative_categorical(relative_sampler_class: Callable[[], BaseSa study = optuna.study.create_study(sampler=relative_sampler_class()) trial = study.ask(search_space) study.tell(trial, sum(trial.params.values())) - _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study) def sample() -> list[float]: params = study.sampler.sample_relative(study, _create_new_trial(study), search_space) @@ -286,7 +294,6 @@ def test_sample_relative_mixed( study = optuna.study.create_study(sampler=relative_sampler_class()) trial = study.ask(search_space) study.tell(trial, sum(trial.params.values())) - _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study) def sample() -> list[float]: params = study.sampler.sample_relative(study, _create_new_trial(study), search_space) @@ -555,6 +562,12 @@ def objective(trial: Trial) -> float: g = trial.suggest_categorical("g", range(1, 10)) return objective_func(a, b, c, d, e, f, g) + """ + This test checks if the sampler is reproducible. Specifically, it checks if the same set of + parameters are suggested for the same seed. The test optimizes a constant objective function + with 15 trials and checks if the parameters are the same for the same seed. + """ + study = optuna.create_study(sampler=sampler_class(1)) study.optimize(objective, n_trials=15) @@ -599,27 +612,48 @@ def objective(trial: Trial) -> float: # This function is used only in test_reproducible_in_other_process, but declared at top-level # because local function cannot be pickled, which occurs within multiprocessing. def run_optimize( - k: int, - sampler_name: str, - sequence_dict: DictProxy, - hash_dict: DictProxy, + k: int, + sampler_name: str, + sequence_dict: DictProxy, + hash_dict: DictProxy, + log_file: str, # change from Jinglue: added log_file parameter ) -> None: - def objective(trial: Trial) -> float: - a = trial.suggest_float("a", 1, 9) - b = trial.suggest_float("b", 1, 9, log=True) - c = trial.suggest_float("c", 1, 9, step=1) - d = trial.suggest_int("d", 1, 9) - e = trial.suggest_int("e", 1, 9, log=True) - f = trial.suggest_int("f", 1, 9, step=2) - g = trial.suggest_categorical("g", range(1, 10)) - return a + b + c + d + e + f + g + try: # change from Jinglue: added try-except block for error handling + # change from Jinglue: added logging setup + logging.basicConfig( + filename=log_file, + level=logging.DEBUG, + format=f'Process {k} - %(asctime)s - %(message)s' + ) - hash_dict[k] = hash("nondeterministic hash") - sampler = sampler_class_with_seed[sampler_name](1) - study = optuna.create_study(sampler=sampler) - study.optimize(objective, n_trials=15) - sequence_dict[k] = list(study.trials[-1].params.values()) + logging.info(f"Starting process {k}") # change from Jinglue: added logging + hash_dict[k] = hash("nondeterministic hash") + logging.info("Hash stored") # change from Jinglue: added logging + def objective(trial: Trial) -> float: + a = trial.suggest_float("a", 1, 9) + b = trial.suggest_float("b", 1, 9, log=True) + c = trial.suggest_float("c", 1, 9, step=1) + d = trial.suggest_int("d", 1, 9) + e = trial.suggest_int("e", 1, 9, log=True) + f = trial.suggest_int("f", 1, 9, step=2) + g = trial.suggest_categorical("g", range(1, 10)) + return a + b + c + d + e + f + g + + logging.info("Creating sampler") # change from Jinglue: added logging + sampler = sampler_class_with_seed[sampler_name](1) + logging.info("Creating study") # change from Jinglue: added logging + study = optuna.create_study(sampler=sampler) + logging.info("Starting optimization") # change from Jinglue: added logging + study.optimize(objective, n_trials=15) + sequence_dict[k] = list(study.trials[-1].params.values()) + logging.info("Optimization complete") # change from Jinglue: added logging + + except Exception as e: # change from Jinglue: added error handling + import traceback + logging.error(f"Error occurred: {str(e)}") + logging.error(traceback.format_exc()) + raise @pytest.fixture def unset_seed_in_test(request: SubRequest) -> None: @@ -641,28 +675,51 @@ def restore_seed() -> None: @pytest.mark.slow @parametrize_sampler_name_with_seed def test_reproducible_in_other_process(sampler_name: str, unset_seed_in_test: None) -> None: - # This test should be tested without `PYTHONHASHSEED`. However, some tool such as tox - # set the environmental variable "PYTHONHASHSEED" by default. - # To do so, this test calls a finalizer: `unset_seed_in_test`. - - # Multiprocessing supports three way to start a process. - # We use `spawn` option to create a child process as a fresh python process. - # For more detail, see https://github.com/optuna/optuna/pull/3187#issuecomment-997673037. multiprocessing.set_start_method("spawn", force=True) + + # change from Jinglue: added log file creation + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + log_file = f"test_sampler_{timestamp}.log" + manager = multiprocessing.Manager() sequence_dict: DictProxy = manager.dict() hash_dict: DictProxy = manager.dict() + + # change from Jinglue: modified process management to track all processes + processes = [] for i in range(3): p = multiprocessing.Process( - target=run_optimize, args=(i, sampler_name, sequence_dict, hash_dict) + target=run_optimize, + args=(i, sampler_name, sequence_dict, hash_dict, log_file) ) + processes.append(p) p.start() - p.join() - # Hashes are expected to be different because string hashing is nondeterministic per process. - assert not (hash_dict[0] == hash_dict[1] == hash_dict[2]) - # But the sequences are expected to be the same. - assert sequence_dict[0] == sequence_dict[1] == sequence_dict[2] + # change from Jinglue: modified process checking and added error reporting + failed = False + for i, p in enumerate(processes): + p.join() + if p.exitcode != 0: + failed = True + + # change from Jinglue: added log file checking on failure + if failed: + print("\nTest failed! Log contents:") + print("-" * 50) + try: + with open(log_file, 'r') as f: + print(f.read()) + except Exception as e: + print(f"Error reading log file: {str(e)}") + print("-" * 50) + os.remove(log_file) # Clean up log file + raise RuntimeError("One or more processes failed. See log contents above.") + + os.remove(log_file) # change from Jinglue: added log file cleanup + + # Rest of the assertions... + assert not (hash_dict[0] == hash_dict[1] == hash_dict[2]), "Hashes are expected to be different" + assert sequence_dict[0] == sequence_dict[1] == sequence_dict[2], "Sequences are expected to be same" @pytest.mark.parametrize("n_jobs", [1, 2]) From 8c6e930b863d941d304b6fa2d8a4003125d83514 Mon Sep 17 00:00:00 2001 From: JInglue Date: Fri, 29 Nov 2024 15:52:43 +0900 Subject: [PATCH 07/20] DE worked and tested but not for categorical --- package/samplers/de/de.py | 107 ++++++++++++++-------- package/samplers/de/example.py | 12 +-- package/samplers/de/tests/test_sampler.py | 37 +++++++- 3 files changed, 109 insertions(+), 47 deletions(-) diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py index ee10b6fe..0973edb2 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/de/de.py @@ -3,23 +3,18 @@ from typing import Any import numpy as np import optuna +from optuna.samplers import RandomSampler import optunahub import time class DESampler(optunahub.samplers.SimpleBaseSampler): - """Differential Evolution Sampler for Optuna optimization. - - This implements a DE algorithm that: - 1. Initializes population randomly - 2. For each generation: - - Evaluates all individuals - - For each individual: - * Generates trial vector (mutation + crossover) - * Evaluates trial vector - * Keeps better solution (selection) - - Generates new trial vectors for next generation - 3. Repeats until stopping criterion met + """Differential Evolution Sampler with Random Sampling for categorical parameters. + + This implements a hybrid sampling approach that: + 1. Uses DE algorithm for numerical parameters (float, int) + 2. Uses Random Sampling for categorical parameters + 3. Combines both sampling strategies seamlessly """ def __init__( @@ -47,6 +42,9 @@ def __init__( self.seed = seed self._rng = np.random.RandomState(seed) + # Initialize random sampler for categorical parameters + self._random_sampler = RandomSampler(seed=seed) + # DE algorithm parameters self.population_size = population_size self.F = F @@ -61,6 +59,10 @@ def __init__( self.lower_bound = None self.upper_bound = None + # Parameter type tracking + self.numerical_params = [] + self.categorical_params = [] + # Performance tracking self.last_time = time.time() self.last_trial_count = 0 @@ -69,6 +71,27 @@ def __init__( self.last_processed_gen = -1 self.current_gen_vectors = None + def _split_search_space(self, search_space: dict[str, optuna.distributions.BaseDistribution]) -> tuple[dict, dict]: + """Split search space into numerical and categorical parameters. + + Args: + search_space: Complete search space dictionary + + Returns: + tuple: (numerical_space, categorical_space) + """ + numerical_space = {} + categorical_space = {} + + for name, dist in search_space.items(): + if isinstance(dist, (optuna.distributions.FloatDistribution, + optuna.distributions.IntDistribution)): + numerical_space[name] = dist + else: + categorical_space[name] = dist + + return numerical_space, categorical_space + def _generate_trial_vectors(self) -> np.ndarray: """Generate new trial vectors using DE mutation and crossover. @@ -109,11 +132,7 @@ def _debug_print(self, message: str) -> None: print(message) def _calculate_speed(self, n_completed: int) -> None: - """Calculate and print optimization speed every 100 trials. - - Args: - n_completed: Number of completed trials - """ + """Calculate and print optimization speed every 100 trials.""" if not self.debug: return @@ -133,15 +152,7 @@ def _calculate_speed(self, n_completed: int) -> None: self.last_trial_count = n_completed def _get_generation_trials(self, study: optuna.study.Study, generation: int) -> list: - """Get all completed trials for a specific generation. - - Args: - study: Optuna study object - generation: Generation number to filter trials - - Returns: - list: List of completed trials for the specified generation - """ + """Get all completed trials for a specific generation.""" all_trials = study.get_trials(deepcopy=False) return [ t for t in all_trials @@ -155,7 +166,7 @@ def sample_relative( trial: optuna.trial.FrozenTrial, search_space: dict[str, optuna.distributions.BaseDistribution], ) -> dict[str, Any]: - """Sample parameters for a trial using DE algorithm. + """Sample parameters for a trial using hybrid DE/Random sampling approach. Args: study: Optuna study object @@ -168,6 +179,18 @@ def sample_relative( if len(search_space) == 0: return {} + # Split search space into numerical and categorical + numerical_space, categorical_space = self._split_search_space(search_space) + + # Sample categorical parameters using random sampler + categorical_params = self._random_sampler.sample_relative( + study, trial, categorical_space + ) + + # If no numerical parameters, return only categorical + if not numerical_space: + return categorical_params + # Calculate current generation and individual index current_generation = trial._trial_id // self.population_size individual_index = trial._trial_id % self.population_size @@ -181,9 +204,9 @@ def sample_relative( # Initialize search space dimensions and bounds if not done if self.population is None: self._debug_print("\nInitializing population...") - self.lower_bound = np.asarray([dist.low for dist in search_space.values()]) - self.upper_bound = np.asarray([dist.high for dist in search_space.values()]) - self.dim = len(search_space) + self.lower_bound = np.asarray([dist.low for dist in numerical_space.values()]) + self.upper_bound = np.asarray([dist.high for dist in numerical_space.values()]) + self.dim = len(numerical_space) # Initialize population using seeded RNG self.population = ( self._rng.rand(self.population_size, self.dim) * @@ -192,14 +215,18 @@ def sample_relative( ) self.fitness = np.full(self.population_size, np.inf) study._storage.set_trial_system_attr(trial._trial_id, "de:dim", self.dim) + # Store parameter names for later reference + self.numerical_params = list(numerical_space.keys()) # Initial population evaluation if current_generation == 0: self._debug_print(f"Evaluating initial individual {individual_index + 1}/{self.population_size}") - return { - name: (float(value) if isinstance(dist, optuna.distributions.FloatDistribution) else int(value)) - for (name, dist), value in zip(search_space.items(), self.population[individual_index]) + numerical_params = { + name: (float(value) if isinstance(numerical_space[name], + optuna.distributions.FloatDistribution) else int(value)) + for name, value in zip(self.numerical_params, self.population[individual_index]) } + return {**numerical_params, **categorical_params} # Process previous generation if needed if current_generation > 0 and current_generation != self.last_processed_gen: @@ -212,7 +239,7 @@ def sample_relative( # Get fitness and parameter values from previous generation trial_fitness = np.array([t.value for t in prev_trials]) trial_vectors = np.array([ - [t.params[name] for name in search_space.keys()] + [t.params[name] for name in self.numerical_params] for t in prev_trials ]) @@ -232,8 +259,10 @@ def sample_relative( if self.current_gen_vectors is None: self.current_gen_vectors = self._generate_trial_vectors() - # Return parameters for current individual - return { - name: (float(value) if isinstance(dist, optuna.distributions.FloatDistribution) else int(value)) - for (name, dist), value in zip(search_space.items(), self.current_gen_vectors[individual_index]) - } \ No newline at end of file + # Combine numerical and categorical parameters + numerical_params = { + name: (float(value) if isinstance(numerical_space[name], + optuna.distributions.FloatDistribution) else int(value)) + for name, value in zip(self.numerical_params, self.current_gen_vectors[individual_index]) + } + return {**numerical_params, **categorical_params} \ No newline at end of file diff --git a/package/samplers/de/example.py b/package/samplers/de/example.py index 861b1042..fa36a846 100644 --- a/package/samplers/de/example.py +++ b/package/samplers/de/example.py @@ -82,8 +82,8 @@ def objective_ML(trial: optuna.Trial) -> float : max_depth = trial.suggest_int("max_depth" , 5 , 30) min_samples_split = trial.suggest_int("min_samples_split" , 2 , 15) min_samples_leaf = trial.suggest_int("min_samples_leaf" , 1 , 10) - # max_features = trial.suggest_categorical("max_features" , ["sqrt" , "log2" , None]) - # bootstrap = trial.suggest_categorical("bootstrap" , [True , False]) + max_features = trial.suggest_categorical("max_features" , ["sqrt" , "log2" , None]) + bootstrap = trial.suggest_categorical("bootstrap" , [True , False]) # Build pipeline with scaling and RandomForestClassifier pipeline = Pipeline([ @@ -93,8 +93,8 @@ def objective_ML(trial: optuna.Trial) -> float : max_depth=max_depth , min_samples_split=min_samples_split , min_samples_leaf=min_samples_leaf , - # max_features=max_features , - # bootstrap=bootstrap , + max_features=max_features , + bootstrap=bootstrap , random_state=42 )) ]) @@ -117,8 +117,8 @@ def objective_ML(trial: optuna.Trial) -> float : sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility # Parameters for experiments -num_experiments = 5 -number_of_trials = 3000 +num_experiments = 2 +number_of_trials = 100 # Store results for each experiment results_de = np.zeros((num_experiments , number_of_trials)) results_rs = np.zeros((num_experiments , number_of_trials)) diff --git a/package/samplers/de/tests/test_sampler.py b/package/samplers/de/tests/test_sampler.py index 9bbe4627..6c2fd2b5 100644 --- a/package/samplers/de/tests/test_sampler.py +++ b/package/samplers/de/tests/test_sampler.py @@ -256,19 +256,52 @@ def test_sample_relative_categorical(relative_sampler_class: Callable[[], BaseSa search_space: dict[str, BaseDistribution] = dict( x=CategoricalDistribution([1, 10, 100]), y=CategoricalDistribution([-1, -10, -100]) ) + print("\nTest Setup:") + print(f"Search space: {search_space}") + print(f"Sampler class: {relative_sampler_class.__name__}") + study = optuna.study.create_study(sampler=relative_sampler_class()) trial = study.ask(search_space) + print(f"\nInitial trial params: {trial.params}") + study.tell(trial, sum(trial.params.values())) + print(f"Told study with value: {sum(trial.params.values())}") def sample() -> list[float]: - params = study.sampler.sample_relative(study, _create_new_trial(study), search_space) + new_trial = _create_new_trial(study) + print(f"\nCreated new trial: {new_trial}") + print(f"Trial system attrs: {new_trial[0].system_attrs}") + + params = study.sampler.sample_relative(study, *new_trial, search_space) + print(f"Sampled params: {params}") return [params[name] for name in search_space] - points = np.array([sample() for _ in range(10)]) + print("\nStarting sampling loop...") + points = [] + for i in range(10): + print(f"\nSample {i + 1}:") + try: + point = sample() + print(f"Sampled point: {point}") + points.append(point) + except Exception as e: + print(f"Error during sampling: {str(e)}") + print(f"Error type: {type(e)}") + raise + + points = np.array(points) + print("\nFinal points array:", points) + for i, distribution in enumerate(search_space.values()): assert isinstance(distribution, CategoricalDistribution) + print(f"\nChecking distribution {i}:") + print(f"Choices: {distribution.choices}") + print(f"Values: {points[:, i]}") assert np.all([v in distribution.choices for v in points[:, i]]) + + print("\nChecking parameter types...") for param_value in sample(): + print(f"Checking value: {param_value} (type: {type(param_value)})") assert not isinstance(param_value, np.floating) assert not isinstance(param_value, np.integer) assert isinstance(param_value, int) From 6b9407616dc14a07737d5c7a3daefd7b5272f4a5 Mon Sep 17 00:00:00 2001 From: JInglue Date: Fri, 29 Nov 2024 16:40:08 +0900 Subject: [PATCH 08/20] DE worked and tested but not for some of the categorical values --- package/samplers/de/de.py | 8 +++-- package/samplers/de/tests/test_sampler.py | 40 +++-------------------- 2 files changed, 9 insertions(+), 39 deletions(-) diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py index 0973edb2..767493aa 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/de/de.py @@ -183,9 +183,11 @@ def sample_relative( numerical_space, categorical_space = self._split_search_space(search_space) # Sample categorical parameters using random sampler - categorical_params = self._random_sampler.sample_relative( - study, trial, categorical_space - ) + categorical_params = {} + for param_name, distribution in categorical_space.items(): + categorical_params[param_name] = self._random_sampler.sample_independent( + study, trial, param_name, distribution + ) # If no numerical parameters, return only categorical if not numerical_space: diff --git a/package/samplers/de/tests/test_sampler.py b/package/samplers/de/tests/test_sampler.py index 6c2fd2b5..c408dc92 100644 --- a/package/samplers/de/tests/test_sampler.py +++ b/package/samplers/de/tests/test_sampler.py @@ -33,7 +33,7 @@ import warnings import logging from datetime import datetime - +import traceback from _pytest.fixtures import SubRequest from _pytest.mark.structures import MarkDecorator @@ -256,52 +256,20 @@ def test_sample_relative_categorical(relative_sampler_class: Callable[[], BaseSa search_space: dict[str, BaseDistribution] = dict( x=CategoricalDistribution([1, 10, 100]), y=CategoricalDistribution([-1, -10, -100]) ) - print("\nTest Setup:") - print(f"Search space: {search_space}") - print(f"Sampler class: {relative_sampler_class.__name__}") - study = optuna.study.create_study(sampler=relative_sampler_class()) trial = study.ask(search_space) - print(f"\nInitial trial params: {trial.params}") - study.tell(trial, sum(trial.params.values())) - print(f"Told study with value: {sum(trial.params.values())}") + _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study) def sample() -> list[float]: - new_trial = _create_new_trial(study) - print(f"\nCreated new trial: {new_trial}") - print(f"Trial system attrs: {new_trial[0].system_attrs}") - - params = study.sampler.sample_relative(study, *new_trial, search_space) - print(f"Sampled params: {params}") + params = study.sampler.sample_relative(study, _create_new_trial(study), search_space) return [params[name] for name in search_space] - print("\nStarting sampling loop...") - points = [] - for i in range(10): - print(f"\nSample {i + 1}:") - try: - point = sample() - print(f"Sampled point: {point}") - points.append(point) - except Exception as e: - print(f"Error during sampling: {str(e)}") - print(f"Error type: {type(e)}") - raise - - points = np.array(points) - print("\nFinal points array:", points) - + points = np.array([sample() for _ in range(10)]) for i, distribution in enumerate(search_space.values()): assert isinstance(distribution, CategoricalDistribution) - print(f"\nChecking distribution {i}:") - print(f"Choices: {distribution.choices}") - print(f"Values: {points[:, i]}") assert np.all([v in distribution.choices for v in points[:, i]]) - - print("\nChecking parameter types...") for param_value in sample(): - print(f"Checking value: {param_value} (type: {type(param_value)})") assert not isinstance(param_value, np.floating) assert not isinstance(param_value, np.integer) assert isinstance(param_value, int) From f6276cddd868fb59f0282de1c5b9f958176712be Mon Sep 17 00:00:00 2001 From: JInglue Date: Fri, 29 Nov 2024 16:41:03 +0900 Subject: [PATCH 09/20] DE all tests passed --- package/samplers/de/tests/test_sampler.py | 1 - 1 file changed, 1 deletion(-) diff --git a/package/samplers/de/tests/test_sampler.py b/package/samplers/de/tests/test_sampler.py index c408dc92..e0c05135 100644 --- a/package/samplers/de/tests/test_sampler.py +++ b/package/samplers/de/tests/test_sampler.py @@ -259,7 +259,6 @@ def test_sample_relative_categorical(relative_sampler_class: Callable[[], BaseSa study = optuna.study.create_study(sampler=relative_sampler_class()) trial = study.ask(search_space) study.tell(trial, sum(trial.params.values())) - _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study) def sample() -> list[float]: params = study.sampler.sample_relative(study, _create_new_trial(study), search_space) From a4d5bf228176b9ae2d07c0e1bc4c1ce16f14c608 Mon Sep 17 00:00:00 2001 From: JLX0 Date: Thu, 5 Dec 2024 14:34:15 +0900 Subject: [PATCH 10/20] DE with direction fixed --- package/samplers/de/de.py | 16 ++-- package/samplers/de/example.py | 156 +++++++++++++++------------------ 2 files changed, 80 insertions(+), 92 deletions(-) diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py index 767493aa..7c58024b 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/de/de.py @@ -179,6 +179,9 @@ def sample_relative( if len(search_space) == 0: return {} + # Determine the direction of optimization + sign = 1 if study.direction == optuna.study.StudyDirection.MINIMIZE else -1 + # Split search space into numerical and categorical numerical_space, categorical_space = self._split_search_space(search_space) @@ -215,7 +218,8 @@ def sample_relative( (self.upper_bound - self.lower_bound) + self.lower_bound ) - self.fitness = np.full(self.population_size, np.inf) + # Initialize fitness based on direction + self.fitness = np.full(self.population_size , -np.inf if sign == -1 else np.inf) study._storage.set_trial_system_attr(trial._trial_id, "de:dim", self.dim) # Store parameter names for later reference self.numerical_params = list(numerical_space.keys()) @@ -239,7 +243,7 @@ def sample_relative( self._debug_print(f"\nProcessing generation {prev_gen}") # Get fitness and parameter values from previous generation - trial_fitness = np.array([t.value for t in prev_trials]) + trial_fitness = np.array([sign * t.value for t in prev_trials]) trial_vectors = np.array([ [t.params[name] for name in self.numerical_params] for t in prev_trials @@ -247,11 +251,11 @@ def sample_relative( # Selection: keep better solutions for i in range(self.population_size): - if trial_fitness[i] <= self.fitness[i]: + if trial_fitness[i] <= sign * self.fitness[i]: self.population[i] = trial_vectors[i] - self.fitness[i] = trial_fitness[i] + self.fitness[i] = sign * trial_fitness[i] - self._debug_print(f"Best fitness: {np.min(self.fitness):.6f}") + self._debug_print(f"Best fitness: {np.min(sign * self.fitness):.6f}") # Generate new trial vectors for current generation self.current_gen_vectors = self._generate_trial_vectors() @@ -267,4 +271,4 @@ def sample_relative( optuna.distributions.FloatDistribution) else int(value)) for name, value in zip(self.numerical_params, self.current_gen_vectors[individual_index]) } - return {**numerical_params, **categorical_params} \ No newline at end of file + return {**numerical_params, **categorical_params} diff --git a/package/samplers/de/example.py b/package/samplers/de/example.py index fa36a846..7419a15f 100644 --- a/package/samplers/de/example.py +++ b/package/samplers/de/example.py @@ -13,7 +13,7 @@ import math -def objective_toy(trial: optuna.Trial) -> float : +def objective_Ackley(trial: optuna.Trial) -> float : # Define the dimensionality of the problem n_dimensions = 10 # High-dimensional problem with 10 dimensions @@ -35,7 +35,7 @@ def objective_toy(trial: optuna.Trial) -> float : return result -def objective_toy(trial: optuna.Trial) -> float: +def objective_sphere(trial: optuna.Trial) -> float: # Define the dimensionality of the problem n_dimensions = 10 # 10 dimensions @@ -48,7 +48,7 @@ def objective_toy(trial: optuna.Trial) -> float: return result -def objective_toy(trial: optuna.Trial) -> float: +def objective_Rastrigin(trial: optuna.Trial) -> float: # Define the dimensionality of the problem n_dimensions = 10 # High-dimensional problem with 10 dimensions @@ -67,6 +67,21 @@ def objective_toy(trial: optuna.Trial) -> float: return result +def objective_Schwefel(trial: optuna.Trial) -> float: + # Define the dimensionality of the problem + n_dimensions = 10 # Example: High-dimensional problem with 10 dimensions + + # Suggest a value for each dimension + variables = [trial.suggest_float(f"x{i}", -500, 500) for i in range(n_dimensions)] + + # Compute the Schwefel function value + sum_term = sum(x * math.sin(math.sqrt(abs(x))) for x in variables) + + # Schwefel function formula + result = 418.9829 * n_dimensions - sum_term + + # Return the negative for maximization purposes + return - result + 10000 def objective_ML(trial: optuna.Trial) -> float : @@ -111,111 +126,80 @@ def objective_ML(trial: optuna.Trial) -> float : # This is an example of how to load a sampler from your local optunahub-registry. sampler = optunahub.load_local_module( package=package_name , - registry_root="/home/j/PycharmProjects/optunahub-registry" , # Path to the root of the optunahub-registry. + registry_root="/home/j/experiments/optunahub-registry" , # Path to the root of the optunahub-registry. ).DESampler(population_size=50,F=0.8,CR=0.9, debug=True) sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility -# Parameters for experiments -num_experiments = 2 -number_of_trials = 100 +# Local variable to set the direction: True for minimize, False for maximize +minimize = False + +toy_function_selectin= + +# Define the number of experiments and trials +num_experiments = 5 +number_of_trials = 1000 + # Store results for each experiment -results_de = np.zeros((num_experiments , number_of_trials)) -results_rs = np.zeros((num_experiments , number_of_trials)) +results_de = np.zeros((num_experiments, number_of_trials)) +results_rs = np.zeros((num_experiments, number_of_trials)) + +# Determine the optimization direction +direction = "minimize" if minimize else "maximize" -for i in range(num_experiments) : +for i in range(num_experiments): # DE Sampler - study = optuna.create_study(sampler=sampler , direction="minimize") - study.optimize(objective_toy , n_trials=number_of_trials) + study = optuna.create_study(sampler=sampler, direction=direction) + study.optimize(objective_toy, n_trials=number_of_trials) # Track best values for DE Sampler best_values_de = [] - current_best_de = float("inf") - for trial in study.trials : - if trial.value is not None : - current_best_de = min(current_best_de , trial.value) + current_best_de = float("inf") if minimize else float("-inf") + for trial in study.trials: + if trial.value is not None: + if minimize: + current_best_de = min(current_best_de, trial.value) + else: + current_best_de = max(current_best_de, trial.value) best_values_de.append(current_best_de) - results_de[i , :] = best_values_de + results_de[i, :] = best_values_de # Random Sampler - study_rs = optuna.create_study(sampler=sampler_rs , direction="minimize") - study_rs.optimize(objective_toy , n_trials=number_of_trials) + study_rs = optuna.create_study(sampler=sampler_rs, direction=direction) + study_rs.optimize(objective_toy, n_trials=number_of_trials) # Track best values for Random Sampler best_values_rs = [] - current_best_rs = float("inf") - for trial in study_rs.trials : - if trial.value is not None : - current_best_rs = min(current_best_rs , trial.value) + current_best_rs = float("inf") if minimize else float("-inf") + for trial in study_rs.trials: + if trial.value is not None: + if minimize: + current_best_rs = min(current_best_rs, trial.value) + else: + current_best_rs = max(current_best_rs, trial.value) best_values_rs.append(current_best_rs) - results_rs[i , :] = best_values_rs + results_rs[i, :] = best_values_rs # Compute mean and standard deviation -mean_de = np.mean(results_de , axis=0) -std_de = np.std(results_de , axis=0) -mean_rs = np.mean(results_rs , axis=0) -std_rs = np.std(results_rs , axis=0) - -# Plot the results with error bands -plt.figure(figsize=(10 , 6)) -plt.plot(mean_de , linestyle='-' , label='DESampler (Mean Performance)' , color='blue') -plt.fill_between(range(number_of_trials) , mean_de - std_de , mean_de + std_de , color='blue' , alpha=0.2 , +mean_de = np.mean(results_de, axis=0) +std_de = np.std(results_de, axis=0) +mean_rs = np.mean(results_rs, axis=0) +std_rs = np.std(results_rs, axis=0) + +# Ensure smaller values are plotted below larger values by always using positive log-scale +plt.figure(figsize=(10, 6)) +plt.plot(mean_de, linestyle='-', label='DESampler (Mean Performance)', color='blue') +plt.fill_between(range(number_of_trials), mean_de - std_de, mean_de + std_de, color='blue', alpha=0.2, label='DESampler (Error Band)') -plt.plot(mean_rs , linestyle='--' , label='RandomSampler (Mean Performance)' , color='orange') -plt.fill_between(range(number_of_trials) , mean_rs - std_rs , mean_rs + std_rs , color='orange' , alpha=0.2 , +plt.plot(mean_rs, linestyle='--', label='RandomSampler (Mean Performance)', color='orange') +plt.fill_between(range(number_of_trials), mean_rs - std_rs, mean_rs + std_rs, color='orange', alpha=0.2, label='RandomSampler (Error Band)') -plt.title('Comparison of DE Sampler and Random Sampler (10 Experiments, po)') +# Adjust title and y-axis label +plt.title(f'Comparison of DE Sampler and Random Sampler ({num_experiments} Experiments)') plt.xlabel('Trial Number') plt.ylabel('Objective Value (Log Scale)') -plt.yscale('log') # Set log scale for the y-axis -plt.grid(which="both" , linestyle="--" , linewidth=0.5) +plt.yscale('log') # Always use log scale for the y-axis +plt.grid(which="both", linestyle="--", linewidth=0.5) plt.legend() -plt.show() - -# -# -# -# number_of_trials = 2500 -# # Create and run the study -# study = optuna.create_study(sampler=sampler , direction="minimize") -# study.optimize(objective_toy, n_trials=number_of_trials) -# -# sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility -# study_rs = optuna.create_study(sampler=sampler_rs , direction="minimize") -# study_rs.optimize(objective_toy, n_trials=number_of_trials) -# -# # Print the best trial -# print("Best trial:") -# print(f" Value: {study.best_trial.value}") -# print(" Params: ") -# for key , value in study.best_trial.params.items() : -# print(f" {key}: {value}") -# -# # Track the best individual found over trials for DE sampler -# best_values_de = [] -# current_best_de = float("inf") -# for trial in study.trials: -# if trial.value is not None: -# current_best_de = min(current_best_de, trial.value) -# best_values_de.append(current_best_de) -# -# # Track the best individual found over trials for RandomSampler -# best_values_rs = [] -# current_best_rs = float("inf") -# for trial in study_rs.trials: -# if trial.value is not None: -# current_best_rs = min(current_best_rs, trial.value) -# best_values_rs.append(current_best_rs) -# -# # Plot the performance of the best individual over trials for both samplers -# plt.figure(figsize=(10, 6)) -# plt.plot(best_values_de, linestyle='-', label='DESampler (Best Individual Performance)') -# plt.plot(best_values_rs, linestyle='--', label='RandomSampler (Best Individual Performance)') -# plt.title('Comparison of DE Sampler and Random Sampler Performance (Minimization, Log Scale)') -# plt.xlabel('Trial Number') -# plt.ylabel('Objective Value (Log Scale)') -# plt.yscale('log') # Set log scale for the y-axis -# plt.grid(which="both", linestyle="--", linewidth=0.5) -# plt.legend() -# plt.show() \ No newline at end of file +plt.show() \ No newline at end of file From 1e0a97e84f5501d979a04d2452a03b7a6b917c91 Mon Sep 17 00:00:00 2001 From: JLX0 Date: Thu, 5 Dec 2024 15:55:42 +0900 Subject: [PATCH 11/20] DE with dynamic space fixed --- package/samplers/de/de.py | 160 ++++++++++++++++++++------------- package/samplers/de/example.py | 88 +++++++++++++----- 2 files changed, 164 insertions(+), 84 deletions(-) diff --git a/package/samplers/de/de.py b/package/samplers/de/de.py index 7c58024b..94fde0c7 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/de/de.py @@ -59,6 +59,9 @@ def __init__( self.lower_bound = None self.upper_bound = None + # Search space dimensionality tracking + self.search_space_dimensions={} + # Parameter type tracking self.numerical_params = [] self.categorical_params = [] @@ -92,34 +95,43 @@ def _split_search_space(self, search_space: dict[str, optuna.distributions.BaseD return numerical_space, categorical_space - def _generate_trial_vectors(self) -> np.ndarray: + def _generate_trial_vectors(self , active_indices: list[int]) -> np.ndarray : """Generate new trial vectors using DE mutation and crossover. + Args: + active_indices: Indices of active dimensions in the current trial's search space. + Returns: - np.ndarray: Array of trial vectors (population_size x dim) + np.ndarray: Array of trial vectors (population_size x len(active_indices)) """ - trial_vectors = np.zeros_like(self.population) + trial_vectors = np.zeros((self.population_size , len(active_indices))) - for i in range(self.population_size): + for i in range(self.population_size) : # Select three random distinct individuals for mutation indices = [idx for idx in range(self.population_size) if idx != i] - r1, r2, r3 = self._rng.choice(indices, 3, replace=False) + r1 , r2 , r3 = self._rng.choice(indices , 3 , replace=False) + + # Handle NaN values by filling with default (mean of bounds) + valid_population = np.nan_to_num( + self.population[: , active_indices] , + nan=(self.lower_bound[active_indices] + self.upper_bound[active_indices]) / 2 + ) - # Mutation: v = x_r1 + F * (x_r2 - x_r3) + # Mutation: v = x_r1 + F * (x_r2 - x_r3) for active indices only mutant = ( - self.population[r1] - + self.F * (self.population[r2] - self.population[r3]) + valid_population[r1] + + self.F * (valid_population[r2] - valid_population[r3]) ) - # Clip mutant vector to bounds - mutant = np.clip(mutant, self.lower_bound, self.upper_bound) + # Clip mutant vector to bounds for active dimensions + mutant = np.clip(mutant , self.lower_bound[active_indices] , self.upper_bound[active_indices]) # Crossover: combine target vector with mutant vector - trial = np.copy(self.population[i]) - # Generate crossover mask based on CR - crossover_mask = self._rng.rand(self.dim) < self.CR + trial = np.copy(valid_population[i]) + crossover_mask = self._rng.rand(len(active_indices)) < self.CR + # Ensure at least one parameter is taken from mutant vector - if not np.any(crossover_mask): - crossover_mask[self._rng.randint(self.dim)] = True + if not np.any(crossover_mask) : + crossover_mask[self._rng.randint(len(active_indices))] = True trial[crossover_mask] = mutant[crossover_mask] trial_vectors[i] = trial @@ -161,11 +173,11 @@ def _get_generation_trials(self, study: optuna.study.Study, generation: int) -> ] def sample_relative( - self, - study: optuna.study.Study, - trial: optuna.trial.FrozenTrial, - search_space: dict[str, optuna.distributions.BaseDistribution], - ) -> dict[str, Any]: + self , + study: optuna.study.Study , + trial: optuna.trial.FrozenTrial , + search_space: dict[str , optuna.distributions.BaseDistribution] , + ) -> dict[str , Any] : """Sample parameters for a trial using hybrid DE/Random sampling approach. Args: @@ -176,99 +188,121 @@ def sample_relative( Returns: dict: Parameter values for the trial """ - if len(search_space) == 0: - return {} + if len(search_space) == 0 : + return { } # Determine the direction of optimization sign = 1 if study.direction == optuna.study.StudyDirection.MINIMIZE else -1 # Split search space into numerical and categorical - numerical_space, categorical_space = self._split_search_space(search_space) + numerical_space , categorical_space = self._split_search_space(search_space) # Sample categorical parameters using random sampler - categorical_params = {} - for param_name, distribution in categorical_space.items(): + categorical_params = { } + for param_name , distribution in categorical_space.items() : categorical_params[param_name] = self._random_sampler.sample_independent( - study, trial, param_name, distribution - ) + study , trial , param_name , distribution + ) # If no numerical parameters, return only categorical - if not numerical_space: + if not numerical_space : return categorical_params + # Track active dimensions for the current trial + active_keys = list(numerical_space.keys()) + + # Ensure numerical_params includes all possible keys + if self.numerical_params is None : + self.numerical_params = active_keys + else : + # Dynamically adjust numerical_params to reflect the current trial's search space + for key in active_keys : + if key not in self.numerical_params : + self.numerical_params.append(key) + + # Get indices for the active keys + active_indices = [self.numerical_params.index(name) for name in active_keys] + # Calculate current generation and individual index current_generation = trial._trial_id // self.population_size individual_index = trial._trial_id % self.population_size # Store generation and individual info as trial attributes - study._storage.set_trial_system_attr(trial._trial_id, "de:generation", current_generation) - study._storage.set_trial_system_attr(trial._trial_id, "de:individual", individual_index) + study._storage.set_trial_system_attr(trial._trial_id , "de:generation" , current_generation) + study._storage.set_trial_system_attr(trial._trial_id , "de:individual" , individual_index) self._calculate_speed(trial._trial_id) - # Initialize search space dimensions and bounds if not done - if self.population is None: + # Initialize population and bounds for the entire search space if not done + if self.population is None : self._debug_print("\nInitializing population...") + all_keys = list(numerical_space.keys()) self.lower_bound = np.asarray([dist.low for dist in numerical_space.values()]) self.upper_bound = np.asarray([dist.high for dist in numerical_space.values()]) - self.dim = len(numerical_space) + self.dim = len(all_keys) + # Initialize population using seeded RNG self.population = ( - self._rng.rand(self.population_size, self.dim) * + self._rng.rand(self.population_size , self.dim) * (self.upper_bound - self.lower_bound) + self.lower_bound ) - # Initialize fitness based on direction self.fitness = np.full(self.population_size , -np.inf if sign == -1 else np.inf) - study._storage.set_trial_system_attr(trial._trial_id, "de:dim", self.dim) - # Store parameter names for later reference - self.numerical_params = list(numerical_space.keys()) + self.numerical_params = all_keys # Track all keys # Initial population evaluation - if current_generation == 0: + if current_generation == 0 : self._debug_print(f"Evaluating initial individual {individual_index + 1}/{self.population_size}") numerical_params = { - name: (float(value) if isinstance(numerical_space[name], - optuna.distributions.FloatDistribution) else int(value)) - for name, value in zip(self.numerical_params, self.population[individual_index]) - } - return {**numerical_params, **categorical_params} + name : (float(value) if isinstance(numerical_space[name] , + optuna.distributions.FloatDistribution) else int(value)) + for name , value in zip(active_keys , self.population[individual_index , active_indices]) + } + return { **numerical_params , **categorical_params } # Process previous generation if needed - if current_generation > 0 and current_generation != self.last_processed_gen: + if current_generation > 0 and current_generation != self.last_processed_gen : prev_gen = current_generation - 1 - prev_trials = self._get_generation_trials(study, prev_gen) + prev_trials = self._get_generation_trials(study , prev_gen) - if len(prev_trials) == self.population_size: + if len(prev_trials) == self.population_size : self._debug_print(f"\nProcessing generation {prev_gen}") # Get fitness and parameter values from previous generation trial_fitness = np.array([sign * t.value for t in prev_trials]) - trial_vectors = np.array([ - [t.params[name] for name in self.numerical_params] - for t in prev_trials - ]) + + # Initialize trial_vectors with uniform size, using NaN or a default value for missing parameters + trial_vectors = np.full( + (self.population_size , len(self.numerical_params)) , + np.nan , # Placeholder for missing parameters + ) + + for i , t in enumerate(prev_trials) : + for j , name in enumerate(self.numerical_params) : + if name in t.params : # Only include active parameters + trial_vectors[i , j] = t.params[name] # Selection: keep better solutions - for i in range(self.population_size): - if trial_fitness[i] <= sign * self.fitness[i]: - self.population[i] = trial_vectors[i] + for i in range(self.population_size) : + if trial_fitness[i] <= sign * self.fitness[i] : + self.population[i , active_indices] = trial_vectors[i , active_indices] self.fitness[i] = sign * trial_fitness[i] - self._debug_print(f"Best fitness: {np.min(sign * self.fitness):.6f}") + self._debug_print(f"Best fitness: {np.nanmin(sign * self.fitness):.6f}") # Generate new trial vectors for current generation - self.current_gen_vectors = self._generate_trial_vectors() + self.current_gen_vectors = self._generate_trial_vectors(active_indices) self.last_processed_gen = current_generation # Ensure we have trial vectors for current generation - if self.current_gen_vectors is None: - self.current_gen_vectors = self._generate_trial_vectors() + if self.current_gen_vectors is None : + self.current_gen_vectors = self._generate_trial_vectors(active_indices) # Combine numerical and categorical parameters numerical_params = { - name: (float(value) if isinstance(numerical_space[name], - optuna.distributions.FloatDistribution) else int(value)) - for name, value in zip(self.numerical_params, self.current_gen_vectors[individual_index]) - } - return {**numerical_params, **categorical_params} + name : (float(value) if isinstance(numerical_space[name] , + optuna.distributions.FloatDistribution) else int(value)) + for name , value in zip(active_keys , self.current_gen_vectors[individual_index]) + } + return { **numerical_params , **categorical_params } + diff --git a/package/samplers/de/example.py b/package/samplers/de/example.py index 7419a15f..dc564d43 100644 --- a/package/samplers/de/example.py +++ b/package/samplers/de/example.py @@ -5,7 +5,6 @@ from sklearn.datasets import load_digits from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split , cross_val_score -from sklearn.metrics import accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline import matplotlib.pyplot as plt @@ -13,6 +12,7 @@ import math +# ---------------Objective functions--------------- def objective_Ackley(trial: optuna.Trial) -> float : # Define the dimensionality of the problem n_dimensions = 10 # High-dimensional problem with 10 dimensions @@ -81,9 +81,11 @@ def objective_Schwefel(trial: optuna.Trial) -> float: result = 418.9829 * n_dimensions - sum_term # Return the negative for maximization purposes + # Additionally added 10000 to make the result positive return - result + 10000 + def objective_ML(trial: optuna.Trial) -> float : # Load dataset data = load_digits() @@ -121,24 +123,69 @@ def objective_ML(trial: optuna.Trial) -> float : return mean_accuracy -package_name = "package/samplers/de" +def objective_dynamic_1(trial: optuna.Trial) -> float: + x = trial.suggest_float("x" , -5.12 , 5.12) + y = trial.suggest_float("y" , -5.12 , 5.12) -# This is an example of how to load a sampler from your local optunahub-registry. -sampler = optunahub.load_local_module( - package=package_name , - registry_root="/home/j/experiments/optunahub-registry" , # Path to the root of the optunahub-registry. - ).DESampler(population_size=50,F=0.8,CR=0.9, debug=True) + if trial.number == 0: + z = trial.suggest_float("z", -5.12, 5.12) -sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility + return x ** 2 + y ** 2 -# Local variable to set the direction: True for minimize, False for maximize -minimize = False +def objective_dynamic_2(trial: optuna.Trial) -> float: + x = trial.suggest_float("x" , -5.12 , 5.12) + y = trial.suggest_float("y" , -5.12 , 5.12) + if trial.number == 100 : + z = trial.suggest_float("z" , -5.12 , 5.12) + return x ** 2 + y ** 2 -toy_function_selectin= + +objective_map = { + "Ackley": objective_Ackley, + "sphere": objective_sphere, + "Rastrigin": objective_Rastrigin, + "Schwefel": objective_Schwefel, + "ML": objective_ML, + "dynamic_1": objective_dynamic_1, + "dynamic_2": objective_dynamic_2, +} + +# ---------------Settings--------------- + +# Paths +package_name = "package/samplers/de" +registry_root = "/home/j/experiments/optunahub-registry" + +# Settings for the DE Sampler +population_size=50 +F=0.8 +CR=0.9 +debug=True + +# Local variable to set the direction: True for minimize, False for maximize +minimize = True # Define the number of experiments and trials -num_experiments = 5 -number_of_trials = 1000 +num_experiments = 3 +number_of_trials = 250 + +# Select the objective function +objective_function_choice = "dynamic_2" # Choose objective function: "Ackley", "sphere", "Rastrigin", "Schwefel", "ML", "dynamic_1", "dynamic_2" + + +# ---------------Experiments and plotting--------------- + +# Map the objective function +objective_function = objective_map[objective_function_choice] + +# Load the DE Sampler +sampler = optunahub.load_local_module( + package=package_name , + registry_root=registry_root , # Path to the root of the optunahub-registry. + ).DESampler(population_size=population_size , F=F , CR=CR, debug=debug) + +# Load the Random Sampler +sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility # Store results for each experiment results_de = np.zeros((num_experiments, number_of_trials)) @@ -147,10 +194,11 @@ def objective_ML(trial: optuna.Trial) -> float : # Determine the optimization direction direction = "minimize" if minimize else "maximize" +# Run multiple experiments for i in range(num_experiments): # DE Sampler study = optuna.create_study(sampler=sampler, direction=direction) - study.optimize(objective_toy, n_trials=number_of_trials) + study.optimize(objective_function, n_trials=number_of_trials) # Track best values for DE Sampler best_values_de = [] @@ -166,7 +214,7 @@ def objective_ML(trial: optuna.Trial) -> float : # Random Sampler study_rs = optuna.create_study(sampler=sampler_rs, direction=direction) - study_rs.optimize(objective_toy, n_trials=number_of_trials) + study_rs.optimize(objective_function, n_trials=number_of_trials) # Track best values for Random Sampler best_values_rs = [] @@ -186,17 +234,15 @@ def objective_ML(trial: optuna.Trial) -> float : mean_rs = np.mean(results_rs, axis=0) std_rs = np.std(results_rs, axis=0) -# Ensure smaller values are plotted below larger values by always using positive log-scale +# Plot the results plt.figure(figsize=(10, 6)) plt.plot(mean_de, linestyle='-', label='DESampler (Mean Performance)', color='blue') plt.fill_between(range(number_of_trials), mean_de - std_de, mean_de + std_de, color='blue', alpha=0.2, - label='DESampler (Error Band)') + label='DESampler (Standard Deviation)') plt.plot(mean_rs, linestyle='--', label='RandomSampler (Mean Performance)', color='orange') plt.fill_between(range(number_of_trials), mean_rs - std_rs, mean_rs + std_rs, color='orange', alpha=0.2, - label='RandomSampler (Error Band)') - -# Adjust title and y-axis label -plt.title(f'Comparison of DE Sampler and Random Sampler ({num_experiments} Experiments)') + label='RandomSampler (Standard Deviation)') +plt.title(f'Comparison of DE Sampler and Random Sampler ({num_experiments} Experiments) - {objective_function_choice} - {direction.capitalize()}') plt.xlabel('Trial Number') plt.ylabel('Objective Value (Log Scale)') plt.yscale('log') # Always use log scale for the y-axis From 73e7e63d87a113071017704305d3b764129b69d9 Mon Sep 17 00:00:00 2001 From: JLX0 Date: Thu, 5 Dec 2024 17:21:04 +0900 Subject: [PATCH 12/20] DE with annotations. ALl tests passed --- .../{de => differential_evolution}/LICENSE | 0 .../README.md.py | 0 .../__init__.py | 0 .../{de => differential_evolution}/de.py | 271 +++++++++++------- .../{de => differential_evolution}/example.py | 27 +- .../tests/test_sampler.py | 2 +- 6 files changed, 196 insertions(+), 104 deletions(-) rename package/samplers/{de => differential_evolution}/LICENSE (100%) rename package/samplers/{de => differential_evolution}/README.md.py (100%) rename package/samplers/{de => differential_evolution}/__init__.py (100%) rename package/samplers/{de => differential_evolution}/de.py (51%) rename package/samplers/{de => differential_evolution}/example.py (91%) rename package/samplers/{de => differential_evolution}/tests/test_sampler.py (99%) diff --git a/package/samplers/de/LICENSE b/package/samplers/differential_evolution/LICENSE similarity index 100% rename from package/samplers/de/LICENSE rename to package/samplers/differential_evolution/LICENSE diff --git a/package/samplers/de/README.md.py b/package/samplers/differential_evolution/README.md.py similarity index 100% rename from package/samplers/de/README.md.py rename to package/samplers/differential_evolution/README.md.py diff --git a/package/samplers/de/__init__.py b/package/samplers/differential_evolution/__init__.py similarity index 100% rename from package/samplers/de/__init__.py rename to package/samplers/differential_evolution/__init__.py diff --git a/package/samplers/de/de.py b/package/samplers/differential_evolution/de.py similarity index 51% rename from package/samplers/de/de.py rename to package/samplers/differential_evolution/de.py index 94fde0c7..78652bc8 100644 --- a/package/samplers/de/de.py +++ b/package/samplers/differential_evolution/de.py @@ -12,30 +12,86 @@ class DESampler(optunahub.samplers.SimpleBaseSampler): """Differential Evolution Sampler with Random Sampling for categorical parameters. This implements a hybrid sampling approach that: - 1. Uses DE algorithm for numerical parameters (float, int) - 2. Uses Random Sampling for categorical parameters - 3. Combines both sampling strategies seamlessly + 1. Uses DE algorithm for numerical parameters (float, int). + 2. Uses Random Sampling for categorical parameters. + 3. Combines both sampling strategies seamlessly. + + This also handles dynamic search space for numerical dimensions by: + - For added dimensions in a trial: + - Generation 0 (Random Sampling): + The value for a new dimension is directly initialized by random sampling within the parameter's range. + - Subsequent Generations (Differential Evolution): + The new dimensions are initialized for the sampled individuals (r1, r2, r3) in the trial using the mean + of the parameter's range. If the new dimension persists in subsequent trials, its values for the sampled + individual in the trial are kept for subsequent trials. + - For removed dimensions in a trial: + Simply ignore the dimensions along with their values for all individuals. + + Args: + search_space: + Dictionary mapping parameter names to their distribution ranges. + population_size: + Number of individuals in the population. + F: + Mutation scaling factor - controls the amplification of differential evolution. + CR: + Crossover probability - controls the fraction of parameter values copied from the mutant. + debug: + Toggle for debug messages. + seed: + Random seed for reproducibility. + + Attributes: + seed: + Random seed for reproducibility. + _rng: + Random state object for sampling. + _random_sampler: + Random sampler instance for categorical parameters. + population_size: + Number of individuals in the population. + F: + Mutation scaling factor for DE. + CR: + Crossover probability for DE. + debug: + Debugging toggle. + dim: + Dimensionality of the search space. + population: + Population array for DE sampling. + fitness: + Fitness values of the population. + trial_vectors: + Trial vectors generated for a generation. + lower_bound: + Lower bounds of the numerical parameters. + upper_bound: + Upper bounds of the numerical parameters. + numerical_params: + List of numerical parameter names. + categorical_params: + List of categorical parameter names. + last_time: + Timestamp of the last performance measurement. + last_trial_count: + Count of trials completed at the last performance measurement. + last_processed_gen: + Last processed generation. + current_gen_vectors: + Trial vectors for the current generation. """ def __init__( - self, - search_space: dict[str, optuna.distributions.BaseDistribution] | None = None, - population_size: int = 50, - F: float = 0.8, # Mutation factor - CR: float = 0.7, # Crossover probability - debug: bool = False, - seed: int | None = None, # Random seed + self, + search_space: dict[str, optuna.distributions.BaseDistribution] | None = None, + population_size: int = 50, + F: float = 0.8, + CR: float = 0.7, + debug: bool = False, + seed: int | None = None, ) -> None: - """Initialize the DE sampler. - - Args: - search_space: Dictionary mapping parameter names to their distribution ranges - population_size: Number of individuals in the population - F: Mutation scaling factor - controls the amplification of differential evolution - CR: Crossover probability - controls the fraction of parameter values copied from the mutant - debug: Toggle for debug messages - seed: Random seed for reproducibility - """ + """Initialize the DE sampler.""" super().__init__(search_space) # Store and set random seed @@ -59,9 +115,6 @@ def __init__( self.lower_bound = None self.upper_bound = None - # Search space dimensionality tracking - self.search_space_dimensions={} - # Parameter type tracking self.numerical_params = [] self.categorical_params = [] @@ -78,73 +131,85 @@ def _split_search_space(self, search_space: dict[str, optuna.distributions.BaseD """Split search space into numerical and categorical parameters. Args: - search_space: Complete search space dictionary + search_space: + Complete search space dictionary. Returns: - tuple: (numerical_space, categorical_space) + tuple: + A tuple of dictionaries (numerical_space, categorical_space). """ numerical_space = {} categorical_space = {} for name, dist in search_space.items(): - if isinstance(dist, (optuna.distributions.FloatDistribution, - optuna.distributions.IntDistribution)): + if isinstance(dist, (optuna.distributions.FloatDistribution, optuna.distributions.IntDistribution)): numerical_space[name] = dist else: categorical_space[name] = dist return numerical_space, categorical_space - def _generate_trial_vectors(self , active_indices: list[int]) -> np.ndarray : + def _generate_trial_vectors(self, active_indices: list[int]) -> np.ndarray: """Generate new trial vectors using DE mutation and crossover. Args: - active_indices: Indices of active dimensions in the current trial's search space. + active_indices: + Indices of active dimensions in the current trial's search space. Returns: - np.ndarray: Array of trial vectors (population_size x len(active_indices)) + np.ndarray: + Array of trial vectors (population_size x len(active_indices)). """ - trial_vectors = np.zeros((self.population_size , len(active_indices))) + trial_vectors = np.zeros((self.population_size, len(active_indices))) - for i in range(self.population_size) : + for i in range(self.population_size): # Select three random distinct individuals for mutation indices = [idx for idx in range(self.population_size) if idx != i] - r1 , r2 , r3 = self._rng.choice(indices , 3 , replace=False) + r1, r2, r3 = self._rng.choice(indices, 3, replace=False) # Handle NaN values by filling with default (mean of bounds) valid_population = np.nan_to_num( - self.population[: , active_indices] , + self.population[:, active_indices], nan=(self.lower_bound[active_indices] + self.upper_bound[active_indices]) / 2 - ) + ) # Mutation: v = x_r1 + F * (x_r2 - x_r3) for active indices only mutant = ( - valid_population[r1] - + self.F * (valid_population[r2] - valid_population[r3]) + valid_population[r1] + + self.F * (valid_population[r2] - valid_population[r3]) ) # Clip mutant vector to bounds for active dimensions - mutant = np.clip(mutant , self.lower_bound[active_indices] , self.upper_bound[active_indices]) + mutant = np.clip(mutant, self.lower_bound[active_indices], self.upper_bound[active_indices]) # Crossover: combine target vector with mutant vector trial = np.copy(valid_population[i]) crossover_mask = self._rng.rand(len(active_indices)) < self.CR # Ensure at least one parameter is taken from mutant vector - if not np.any(crossover_mask) : + if not np.any(crossover_mask): crossover_mask[self._rng.randint(len(active_indices))] = True trial[crossover_mask] = mutant[crossover_mask] trial_vectors[i] = trial return trial_vectors - def _debug_print(self, message: str) -> None: - """Print debug message if debug mode is enabled.""" + """Print debug message if debug mode is enabled. + + Args: + message: + The message to print. + """ if self.debug: print(message) def _calculate_speed(self, n_completed: int) -> None: - """Calculate and print optimization speed every 100 trials.""" + """Calculate and print optimization speed every 100 trials. + + Args: + n_completed: + The number of completed trials. + """ if not self.debug: return @@ -164,60 +229,75 @@ def _calculate_speed(self, n_completed: int) -> None: self.last_trial_count = n_completed def _get_generation_trials(self, study: optuna.study.Study, generation: int) -> list: - """Get all completed trials for a specific generation.""" + """Get all completed trials for a specific generation. + + Args: + study: + Optuna study object. + generation: + The generation number. + + Returns: + list: + A list of completed trials for the specified generation. + """ all_trials = study.get_trials(deepcopy=False) return [ t for t in all_trials if (t.state == optuna.trial.TrialState.COMPLETE and - t.system_attrs.get("de:generation") == generation) + t.system_attrs.get("differential_evolution:generation") == generation) ] def sample_relative( - self , - study: optuna.study.Study , - trial: optuna.trial.FrozenTrial , - search_space: dict[str , optuna.distributions.BaseDistribution] , - ) -> dict[str , Any] : + self, + study: optuna.study.Study, + trial: optuna.trial.FrozenTrial, + search_space: dict[str, optuna.distributions.BaseDistribution], + ) -> dict[str, Any]: """Sample parameters for a trial using hybrid DE/Random sampling approach. Args: - study: Optuna study object - trial: Current trial object - search_space: Dictionary of parameter distributions + study: + Optuna study object. + trial: + Current trial object. + search_space: + Dictionary of parameter distributions. Returns: - dict: Parameter values for the trial + dict: + A dictionary of parameter values for the trial. """ - if len(search_space) == 0 : - return { } + if len(search_space) == 0: + return {} # Determine the direction of optimization sign = 1 if study.direction == optuna.study.StudyDirection.MINIMIZE else -1 # Split search space into numerical and categorical - numerical_space , categorical_space = self._split_search_space(search_space) + numerical_space, categorical_space = self._split_search_space(search_space) # Sample categorical parameters using random sampler - categorical_params = { } - for param_name , distribution in categorical_space.items() : + categorical_params = {} + for param_name, distribution in categorical_space.items(): categorical_params[param_name] = self._random_sampler.sample_independent( - study , trial , param_name , distribution - ) + study, trial, param_name, distribution + ) # If no numerical parameters, return only categorical - if not numerical_space : + if not numerical_space: return categorical_params # Track active dimensions for the current trial active_keys = list(numerical_space.keys()) # Ensure numerical_params includes all possible keys - if self.numerical_params is None : + if self.numerical_params is None: self.numerical_params = active_keys - else : + else: # Dynamically adjust numerical_params to reflect the current trial's search space - for key in active_keys : - if key not in self.numerical_params : + for key in active_keys: + if key not in self.numerical_params: self.numerical_params.append(key) # Get indices for the active keys @@ -228,13 +308,13 @@ def sample_relative( individual_index = trial._trial_id % self.population_size # Store generation and individual info as trial attributes - study._storage.set_trial_system_attr(trial._trial_id , "de:generation" , current_generation) - study._storage.set_trial_system_attr(trial._trial_id , "de:individual" , individual_index) + study._storage.set_trial_system_attr(trial._trial_id, "differential_evolution:generation", current_generation) + study._storage.set_trial_system_attr(trial._trial_id, "differential_evolution:individual", individual_index) self._calculate_speed(trial._trial_id) # Initialize population and bounds for the entire search space if not done - if self.population is None : + if self.population is None: self._debug_print("\nInitializing population...") all_keys = list(numerical_space.keys()) self.lower_bound = np.asarray([dist.low for dist in numerical_space.values()]) @@ -243,29 +323,29 @@ def sample_relative( # Initialize population using seeded RNG self.population = ( - self._rng.rand(self.population_size , self.dim) * - (self.upper_bound - self.lower_bound) + - self.lower_bound + self._rng.rand(self.population_size, self.dim) * + (self.upper_bound - self.lower_bound) + + self.lower_bound ) - self.fitness = np.full(self.population_size , -np.inf if sign == -1 else np.inf) + self.fitness = np.full(self.population_size, -np.inf if sign == -1 else np.inf) self.numerical_params = all_keys # Track all keys # Initial population evaluation - if current_generation == 0 : + if current_generation == 0: self._debug_print(f"Evaluating initial individual {individual_index + 1}/{self.population_size}") numerical_params = { - name : (float(value) if isinstance(numerical_space[name] , + name: (float(value) if isinstance(numerical_space[name], optuna.distributions.FloatDistribution) else int(value)) - for name , value in zip(active_keys , self.population[individual_index , active_indices]) - } - return { **numerical_params , **categorical_params } + for name, value in zip(active_keys, self.population[individual_index, active_indices]) + } + return {**numerical_params, **categorical_params} # Process previous generation if needed - if current_generation > 0 and current_generation != self.last_processed_gen : + if current_generation > 0 and current_generation != self.last_processed_gen: prev_gen = current_generation - 1 - prev_trials = self._get_generation_trials(study , prev_gen) + prev_trials = self._get_generation_trials(study, prev_gen) - if len(prev_trials) == self.population_size : + if len(prev_trials) == self.population_size: self._debug_print(f"\nProcessing generation {prev_gen}") # Get fitness and parameter values from previous generation @@ -273,19 +353,19 @@ def sample_relative( # Initialize trial_vectors with uniform size, using NaN or a default value for missing parameters trial_vectors = np.full( - (self.population_size , len(self.numerical_params)) , - np.nan , # Placeholder for missing parameters - ) + (self.population_size, len(self.numerical_params)), + np.nan, # Placeholder for missing parameters + ) - for i , t in enumerate(prev_trials) : - for j , name in enumerate(self.numerical_params) : - if name in t.params : # Only include active parameters - trial_vectors[i , j] = t.params[name] + for i, t in enumerate(prev_trials): + for j, name in enumerate(self.numerical_params): + if name in t.params: # Only include active parameters + trial_vectors[i, j] = t.params[name] # Selection: keep better solutions - for i in range(self.population_size) : - if trial_fitness[i] <= sign * self.fitness[i] : - self.population[i , active_indices] = trial_vectors[i , active_indices] + for i in range(self.population_size): + if trial_fitness[i] <= sign * self.fitness[i]: + self.population[i, active_indices] = trial_vectors[i, active_indices] self.fitness[i] = sign * trial_fitness[i] self._debug_print(f"Best fitness: {np.nanmin(sign * self.fitness):.6f}") @@ -295,14 +375,13 @@ def sample_relative( self.last_processed_gen = current_generation # Ensure we have trial vectors for current generation - if self.current_gen_vectors is None : + if self.current_gen_vectors is None: self.current_gen_vectors = self._generate_trial_vectors(active_indices) # Combine numerical and categorical parameters numerical_params = { - name : (float(value) if isinstance(numerical_space[name] , + name: (float(value) if isinstance(numerical_space[name], optuna.distributions.FloatDistribution) else int(value)) - for name , value in zip(active_keys , self.current_gen_vectors[individual_index]) - } - return { **numerical_params , **categorical_params } - + for name, value in zip(active_keys, self.current_gen_vectors[individual_index]) + } + return {**numerical_params, **categorical_params} diff --git a/package/samplers/de/example.py b/package/samplers/differential_evolution/example.py similarity index 91% rename from package/samplers/de/example.py rename to package/samplers/differential_evolution/example.py index dc564d43..6d783335 100644 --- a/package/samplers/de/example.py +++ b/package/samplers/differential_evolution/example.py @@ -135,7 +135,19 @@ def objective_dynamic_1(trial: optuna.Trial) -> float: def objective_dynamic_2(trial: optuna.Trial) -> float: x = trial.suggest_float("x" , -5.12 , 5.12) y = trial.suggest_float("y" , -5.12 , 5.12) - if trial.number == 100 : + if trial.number == 100: + z = trial.suggest_float("z" , -5.12 , 5.12) + return x ** 2 + y ** 2 + + +def objective_dynamic_3(trial: optuna.Trial) -> float: + x = trial.suggest_float("x" , -5.12 , 5.12) + y = trial.suggest_float("y" , -5.12 , 5.12) + if trial.number == 0: + z = trial.suggest_float("z" , -5.12 , 5.12) + if trial.number >= 100 and trial.number < 200: + z = trial.suggest_float("z" , -5.12 , 5.12) + if trial.number == 300: z = trial.suggest_float("z" , -5.12 , 5.12) return x ** 2 + y ** 2 @@ -148,12 +160,13 @@ def objective_dynamic_2(trial: optuna.Trial) -> float: "ML": objective_ML, "dynamic_1": objective_dynamic_1, "dynamic_2": objective_dynamic_2, + "dynamic_3": objective_dynamic_3, } # ---------------Settings--------------- # Paths -package_name = "package/samplers/de" +package_name = "package/samplers/differential_evolution" registry_root = "/home/j/experiments/optunahub-registry" # Settings for the DE Sampler @@ -166,11 +179,11 @@ def objective_dynamic_2(trial: optuna.Trial) -> float: minimize = True # Define the number of experiments and trials -num_experiments = 3 -number_of_trials = 250 +num_experiments = 10 +number_of_trials = 1000 # Select the objective function -objective_function_choice = "dynamic_2" # Choose objective function: "Ackley", "sphere", "Rastrigin", "Schwefel", "ML", "dynamic_1", "dynamic_2" +objective_function_choice = "Ackley" # Choose objective function: "Ackley", "sphere", "Rastrigin", "Schwefel", "ML", "dynamic_1", "dynamic_2", "dynamic_3" # ---------------Experiments and plotting--------------- @@ -198,7 +211,7 @@ def objective_dynamic_2(trial: optuna.Trial) -> float: for i in range(num_experiments): # DE Sampler study = optuna.create_study(sampler=sampler, direction=direction) - study.optimize(objective_function, n_trials=number_of_trials) + study.optimize(objective_function, n_trials=number_of_trials,n_jobs=16) # Track best values for DE Sampler best_values_de = [] @@ -214,7 +227,7 @@ def objective_dynamic_2(trial: optuna.Trial) -> float: # Random Sampler study_rs = optuna.create_study(sampler=sampler_rs, direction=direction) - study_rs.optimize(objective_function, n_trials=number_of_trials) + study_rs.optimize(objective_function, n_trials=number_of_trials,n_jobs=16) # Track best values for Random Sampler best_values_rs = [] diff --git a/package/samplers/de/tests/test_sampler.py b/package/samplers/differential_evolution/tests/test_sampler.py similarity index 99% rename from package/samplers/de/tests/test_sampler.py rename to package/samplers/differential_evolution/tests/test_sampler.py index e0c05135..5814dd55 100644 --- a/package/samplers/de/tests/test_sampler.py +++ b/package/samplers/differential_evolution/tests/test_sampler.py @@ -55,7 +55,7 @@ # NOTE(nabenabe): This file content is mostly copied from the Optuna repository. The_Sampler = optunahub.load_local_module( - package="package/samplers/de", registry_root="/home/j/PycharmProjects/optunahub-registry" + package="package/samplers/differential_evolution", registry_root="/home/j/experiments/optunahub-registry" ).DESampler From 192fc4b1277d8c3520734c699e6453020f51c148 Mon Sep 17 00:00:00 2001 From: JLX0 Date: Fri, 6 Dec 2024 14:33:24 +0900 Subject: [PATCH 13/20] DE with benchmarking and example.py --- .../samplers/differential_evolution/README.md | 125 ++++++++ .../differential_evolution/README.md.py | 0 package/samplers/differential_evolution/de.py | 46 ++- .../differential_evolution/example.py | 288 ++++++++++++------ 4 files changed, 361 insertions(+), 98 deletions(-) create mode 100644 package/samplers/differential_evolution/README.md delete mode 100644 package/samplers/differential_evolution/README.md.py diff --git a/package/samplers/differential_evolution/README.md b/package/samplers/differential_evolution/README.md new file mode 100644 index 00000000..b51f3254 --- /dev/null +++ b/package/samplers/differential_evolution/README.md @@ -0,0 +1,125 @@ +--- +author: Jinglue Xu +title: Differential Evolution Sampler. +description: This sampler combines Differential Evolution for numerical parameters and Random Sampling for categorical parameters, dynamically adapting to changes in the search space by initializing new dimensions and ignoring removed ones seamlessly across trials. +tags: [sampler] +optuna_versions: [4.1.0] +license: "MIT License" +--- + + + +Please read the [tutorial guide](https://optuna.github.io/optunahub-registry/recipes/001_first.html) to register your feature in OptunaHub. +You can find more detailed explanation of the following contents in the tutorial. +Looking at [other packages' implementations](https://github.com/optuna/optunahub-registry/tree/main/package) will also help you. + +## Abstract + +You can provide an abstract for your package here. +This section will help attract potential users to your package. + +**Example** + +This package provides a sampler based on Gaussian process-based Bayesian optimization. The sampler is highly sample-efficient, so it is suitable for computationally expensive optimization problems with a limited evaluation budget, such as hyperparameter optimization of machine learning algorithms. + +## APIs + +Please provide API documentation describing how to use your package's functionalities. +The documentation format is arbitrary, but at least the important class/function names that you implemented should be listed here. +More users will take advantage of your package by providing detailed and helpful documentation. + +**Example** + +- `MoCmaSampler(*, search_space: dict[str, BaseDistribution] | None = None, popsize: int | None = None, seed: int | None = None)` + - `search_space`: A dictionary containing the search space that defines the parameter space. The keys are the parameter names and the values are [the parameter's distribution](https://optuna.readthedocs.io/en/stable/reference/distributions.html). If the search space is not provided, the sampler will infer the search space dynamically. + Example: + ```python + search_space = { + "x": optuna.distributions.FloatDistribution(-5, 5), + "y": optuna.distributions.FloatDistribution(-5, 5), + } + MoCmaSampler(search_space=search_space) + ``` + - `popsize`: Population size of the CMA-ES algorithm. If not provided, the population size will be set based on the search space dimensionality. If you have a sufficient evaluation budget, it is recommended to increase the popsize. + - `seed`: Seed for random number generator. + +Note that because of the limitation of the algorithm, only non-conditional numerical parameters can be sampled by the MO-CMA-ES algorithm, and categorical and conditional parameters are handled by random search. + +## Installation + +If you have additional dependencies, please fill in the installation guide here. +If no additional dependencies is required, **this section can be removed**. + +**Example** + +```shell +$ pip install scipy torch +``` + +If your package has `requirements.txt`, it will be automatically uploaded to the OptunaHub, and the package dependencies will be available to install as follows. + +```shell + pip install -r https://hub.optuna.org/{category}/{your_package_name}/requirements.txt +``` + +## Example + +Please fill in the code snippet to use the implemented feature here. + +**Example** + +```python +import optuna +import optunahub + + +def objective(trial): + x = trial.suggest_float("x", -5, 5) + return x**2 + + +sampler = optunahub.load_module(package="samplers/gp").GPSampler() +study = optuna.create_study(sampler=sampler) +study.optimize(objective, n_trials=100) +``` + +## Others + +Please fill in any other information if you have here by adding child sections (###). +If there is no additional information, **this section can be removed**. + + diff --git a/package/samplers/differential_evolution/README.md.py b/package/samplers/differential_evolution/README.md.py deleted file mode 100644 index e69de29b..00000000 diff --git a/package/samplers/differential_evolution/de.py b/package/samplers/differential_evolution/de.py index 78652bc8..011ab11d 100644 --- a/package/samplers/differential_evolution/de.py +++ b/package/samplers/differential_evolution/de.py @@ -85,7 +85,7 @@ class DESampler(optunahub.samplers.SimpleBaseSampler): def __init__( self, search_space: dict[str, optuna.distributions.BaseDistribution] | None = None, - population_size: int = 50, + population_size: int | str = "auto", F: float = 0.8, CR: float = 0.7, debug: bool = False, @@ -127,6 +127,50 @@ def __init__( self.last_processed_gen = -1 self.current_gen_vectors = None + if population_size == "auto": + self.population_size = self._determine_pop_size(search_space) + + def _determine_pop_size(self, search_space: dict[str, optuna.distributions.BaseDistribution]) -> int: + """ + Determine the population size based on the search space dimensionality. + Args: + search_space: + Dictionary mapping parameter names to their distribution ranges. + + Returns: + int: + The population size. + """ + if search_space is None: + + return 20 + + else: + + dimension = len(search_space) + + # Start with a baseline multiplier + if dimension < 5 : + # For very low dimension, maintain at least 20 individuals + # to ensure diversity. + base_multiplier = 10 + min_pop = 20 + elif dimension <= 30 : + # For moderately sized problems, a standard 10x dimension + # is a good starting point. + base_multiplier = 10 + min_pop = 30 + else : + # For high-dimensional problems, start lower (5x) + # to keep computations manageable. + base_multiplier = 5 + min_pop = 50 + + # Calculate a preliminary population size (can be fine-tuned further) + population_size = max(min_pop , base_multiplier * dimension) + + return population_size + def _split_search_space(self, search_space: dict[str, optuna.distributions.BaseDistribution]) -> tuple[dict, dict]: """Split search space into numerical and categorical parameters. diff --git a/package/samplers/differential_evolution/example.py b/package/samplers/differential_evolution/example.py index 6d783335..77c321fc 100644 --- a/package/samplers/differential_evolution/example.py +++ b/package/samplers/differential_evolution/example.py @@ -9,7 +9,7 @@ from sklearn.pipeline import Pipeline import matplotlib.pyplot as plt import numpy as np - +import os import math # ---------------Objective functions--------------- @@ -69,7 +69,7 @@ def objective_Rastrigin(trial: optuna.Trial) -> float: def objective_Schwefel(trial: optuna.Trial) -> float: # Define the dimensionality of the problem - n_dimensions = 10 # Example: High-dimensional problem with 10 dimensions + n_dimensions = 10 # Suggest a value for each dimension variables = [trial.suggest_float(f"x{i}", -500, 500) for i in range(n_dimensions)] @@ -78,15 +78,13 @@ def objective_Schwefel(trial: optuna.Trial) -> float: sum_term = sum(x * math.sin(math.sqrt(abs(x))) for x in variables) # Schwefel function formula - result = 418.9829 * n_dimensions - sum_term - # Return the negative for maximization purposes # Additionally added 10000 to make the result positive + result = 418.9829 * n_dimensions - sum_term return - result + 10000 - -def objective_ML(trial: optuna.Trial) -> float : +def objective_ML(trial: optuna.Trial) -> float: # Load dataset data = load_digits() X , y = data.data , data.target @@ -126,10 +124,8 @@ def objective_ML(trial: optuna.Trial) -> float : def objective_dynamic_1(trial: optuna.Trial) -> float: x = trial.suggest_float("x" , -5.12 , 5.12) y = trial.suggest_float("y" , -5.12 , 5.12) - if trial.number == 0: z = trial.suggest_float("z", -5.12, 5.12) - return x ** 2 + y ** 2 def objective_dynamic_2(trial: optuna.Trial) -> float: @@ -139,19 +135,17 @@ def objective_dynamic_2(trial: optuna.Trial) -> float: z = trial.suggest_float("z" , -5.12 , 5.12) return x ** 2 + y ** 2 - def objective_dynamic_3(trial: optuna.Trial) -> float: x = trial.suggest_float("x" , -5.12 , 5.12) y = trial.suggest_float("y" , -5.12 , 5.12) if trial.number == 0: z = trial.suggest_float("z" , -5.12 , 5.12) - if trial.number >= 100 and trial.number < 200: + if 100 <= trial.number < 200: z = trial.suggest_float("z" , -5.12 , 5.12) if trial.number == 300: z = trial.suggest_float("z" , -5.12 , 5.12) return x ** 2 + y ** 2 - objective_map = { "Ackley": objective_Ackley, "sphere": objective_sphere, @@ -163,102 +157,202 @@ def objective_dynamic_3(trial: optuna.Trial) -> float: "dynamic_3": objective_dynamic_3, } + # ---------------Settings--------------- +# Set this to True to run the benchmark +run_benchmark = True + +# Choose a specific objective function, only used if run_benchmark==False +objective_function_choice = "Ackley" +# Choose objective function: "Ackley", "sphere", "Rastrigin", "Schwefel", "ML", "dynamic_1", "dynamic_2", "dynamic_3" + # Paths package_name = "package/samplers/differential_evolution" registry_root = "/home/j/experiments/optunahub-registry" # Settings for the DE Sampler -population_size=50 +population_size="auto" F=0.8 CR=0.9 debug=True -# Local variable to set the direction: True for minimize, False for maximize -minimize = True - # Define the number of experiments and trials num_experiments = 10 -number_of_trials = 1000 - -# Select the objective function -objective_function_choice = "Ackley" # Choose objective function: "Ackley", "sphere", "Rastrigin", "Schwefel", "ML", "dynamic_1", "dynamic_2", "dynamic_3" - - -# ---------------Experiments and plotting--------------- - -# Map the objective function -objective_function = objective_map[objective_function_choice] - -# Load the DE Sampler -sampler = optunahub.load_local_module( - package=package_name , - registry_root=registry_root , # Path to the root of the optunahub-registry. - ).DESampler(population_size=population_size , F=F , CR=CR, debug=debug) - -# Load the Random Sampler -sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility - -# Store results for each experiment -results_de = np.zeros((num_experiments, number_of_trials)) -results_rs = np.zeros((num_experiments, number_of_trials)) - -# Determine the optimization direction -direction = "minimize" if minimize else "maximize" - -# Run multiple experiments -for i in range(num_experiments): - # DE Sampler - study = optuna.create_study(sampler=sampler, direction=direction) - study.optimize(objective_function, n_trials=number_of_trials,n_jobs=16) - - # Track best values for DE Sampler - best_values_de = [] - current_best_de = float("inf") if minimize else float("-inf") - for trial in study.trials: - if trial.value is not None: - if minimize: - current_best_de = min(current_best_de, trial.value) - else: - current_best_de = max(current_best_de, trial.value) - best_values_de.append(current_best_de) - results_de[i, :] = best_values_de - - # Random Sampler - study_rs = optuna.create_study(sampler=sampler_rs, direction=direction) - study_rs.optimize(objective_function, n_trials=number_of_trials,n_jobs=16) - - # Track best values for Random Sampler - best_values_rs = [] - current_best_rs = float("inf") if minimize else float("-inf") - for trial in study_rs.trials: - if trial.value is not None: - if minimize: - current_best_rs = min(current_best_rs, trial.value) - else: - current_best_rs = max(current_best_rs, trial.value) - best_values_rs.append(current_best_rs) - results_rs[i, :] = best_values_rs - -# Compute mean and standard deviation -mean_de = np.mean(results_de, axis=0) -std_de = np.std(results_de, axis=0) -mean_rs = np.mean(results_rs, axis=0) -std_rs = np.std(results_rs, axis=0) - -# Plot the results -plt.figure(figsize=(10, 6)) -plt.plot(mean_de, linestyle='-', label='DESampler (Mean Performance)', color='blue') -plt.fill_between(range(number_of_trials), mean_de - std_de, mean_de + std_de, color='blue', alpha=0.2, - label='DESampler (Standard Deviation)') -plt.plot(mean_rs, linestyle='--', label='RandomSampler (Mean Performance)', color='orange') -plt.fill_between(range(number_of_trials), mean_rs - std_rs, mean_rs + std_rs, color='orange', alpha=0.2, - label='RandomSampler (Standard Deviation)') -plt.title(f'Comparison of DE Sampler and Random Sampler ({num_experiments} Experiments) - {objective_function_choice} - {direction.capitalize()}') -plt.xlabel('Trial Number') -plt.ylabel('Objective Value (Log Scale)') -plt.yscale('log') # Always use log scale for the y-axis -plt.grid(which="both", linestyle="--", linewidth=0.5) -plt.legend() -plt.show() \ No newline at end of file +number_of_trials = 10000 + +# Select the objective function (used if run_benchmark==False) + +if run_benchmark: + # Make sure the results directory exists + os.makedirs("results", exist_ok=True) + + # List of objective functions + objective_list = ["Ackley", "sphere", "Rastrigin", "Schwefel"] + + for objective_function_choice in objective_list: + # Determine direction based on objective function choice + # Maximize for Schwefel, minimize for others + if objective_function_choice == "Schwefel": + direction = "maximize" + else: + direction = "minimize" + + minimize = (direction == "minimize") + + # Map the objective function + objective_function = objective_map[objective_function_choice] + + # Load the DE Sampler + sampler = optunahub.load_local_module( + package=package_name, + registry_root=registry_root, # Path to the root of the optunahub-registry. + ).DESampler(population_size=population_size, F=F, CR=CR, debug=debug) + + # Load the Random Sampler + sampler_rs = optuna.samplers.RandomSampler(seed=42) # Optional seed for reproducibility + + # Store results for each experiment + results_de = np.zeros((num_experiments, number_of_trials)) + results_rs = np.zeros((num_experiments, number_of_trials)) + + # Run multiple experiments + for i in range(num_experiments): + # DE Sampler + study = optuna.create_study(sampler=sampler, direction=direction) + study.optimize(objective_function, n_trials=number_of_trials, n_jobs=16) + + # Track best values for DE Sampler + best_values_de = [] + current_best_de = float("inf") if minimize else float("-inf") + for trial in study.trials: + if trial.value is not None: + if minimize: + current_best_de = min(current_best_de, trial.value) + else: + current_best_de = max(current_best_de, trial.value) + best_values_de.append(current_best_de) + results_de[i, :] = best_values_de + + # Random Sampler + study_rs = optuna.create_study(sampler=sampler_rs, direction=direction) + study_rs.optimize(objective_function, n_trials=number_of_trials, n_jobs=16) + + # Track best values for Random Sampler + best_values_rs = [] + current_best_rs = float("inf") if minimize else float("-inf") + for trial in study_rs.trials: + if trial.value is not None: + if minimize: + current_best_rs = min(current_best_rs, trial.value) + else: + current_best_rs = max(current_best_rs, trial.value) + best_values_rs.append(current_best_rs) + results_rs[i, :] = best_values_rs + + # Compute mean and standard deviation + mean_de = np.mean(results_de, axis=0) + std_de = np.std(results_de, axis=0) + mean_rs = np.mean(results_rs, axis=0) + std_rs = np.std(results_rs, axis=0) + + # Plot the results + plt.figure(figsize=(10, 6)) + plt.plot(mean_de, linestyle='-', label='DESampler (Mean Performance)', color='blue') + plt.fill_between(range(number_of_trials), mean_de - std_de, mean_de + std_de, color='blue', alpha=0.2, + label='DESampler (Standard Deviation)') + plt.plot(mean_rs, linestyle='--', label='RandomSampler (Mean Performance)', color='orange') + plt.fill_between(range(number_of_trials), mean_rs - std_rs, mean_rs + std_rs, color='orange', alpha=0.2, + label='RandomSampler (Standard Deviation)') + plt.title(f'Comparison of DE Sampler and Random Sampler ({num_experiments} Experiments) - {objective_function_choice} - {direction.capitalize()}') + plt.xlabel('Trial Number') + plt.ylabel('Objective Value (Log Scale)') + plt.yscale('log') # Always use log scale for the y-axis + plt.grid(which="both", linestyle="--", linewidth=0.5) + plt.legend() + + # Save the figure + filename = f"results/{objective_function_choice}_{direction}.png" + plt.savefig(filename, dpi=300) + plt.show() + +else: + # If run_benchmark == False + # Run experiment for the single chosen objective_function_choice + if objective_function_choice == "Schwefel": + direction = "maximize" + else: + direction = "minimize" + + minimize = (direction == "minimize") + + # Map the objective function + objective_function = objective_map[objective_function_choice] + + # Load the DE Sampler + sampler = optunahub.load_local_module( + package=package_name, + registry_root=registry_root, + ).DESampler(population_size=population_size, F=F, CR=CR, debug=debug) + + # Load the Random Sampler + sampler_rs = optuna.samplers.RandomSampler(seed=42) + + # Store results for each experiment + results_de = np.zeros((num_experiments, number_of_trials)) + results_rs = np.zeros((num_experiments, number_of_trials)) + + for i in range(num_experiments): + # DE Sampler + study = optuna.create_study(sampler=sampler, direction=direction) + study.optimize(objective_function, n_trials=number_of_trials, n_jobs=16) + + # Track best values for DE Sampler + best_values_de = [] + current_best_de = float("inf") if minimize else float("-inf") + for trial in study.trials: + if trial.value is not None: + if minimize: + current_best_de = min(current_best_de, trial.value) + else: + current_best_de = max(current_best_de, trial.value) + best_values_de.append(current_best_de) + results_de[i, :] = best_values_de + + # Random Sampler + study_rs = optuna.create_study(sampler=sampler_rs, direction=direction) + study_rs.optimize(objective_function, n_trials=number_of_trials, n_jobs=16) + + # Track best values for Random Sampler + best_values_rs = [] + current_best_rs = float("inf") if minimize else float("-inf") + for trial in study_rs.trials: + if trial.value is not None: + if minimize: + current_best_rs = min(current_best_rs, trial.value) + else: + current_best_rs = max(current_best_rs, trial.value) + best_values_rs.append(current_best_rs) + results_rs[i, :] = best_values_rs + + # Compute mean and standard deviation + mean_de = np.mean(results_de, axis=0) + std_de = np.std(results_de, axis=0) + mean_rs = np.mean(results_rs, axis=0) + std_rs = np.std(results_rs, axis=0) + + # Plot the results (no local saving) + plt.figure(figsize=(10, 6)) + plt.plot(mean_de, linestyle='-', label='DESampler (Mean Performance)', color='blue') + plt.fill_between(range(number_of_trials), mean_de - std_de, mean_de + std_de, color='blue', alpha=0.2, + label='DESampler (Standard Deviation)') + plt.plot(mean_rs, linestyle='--', label='RandomSampler (Mean Performance)', color='orange') + plt.fill_between(range(number_of_trials), mean_rs - std_rs, mean_rs + std_rs, color='orange', alpha=0.2, + label='RandomSampler (Standard Deviation)') + plt.title(f'Comparison of DE Sampler and Random Sampler ({num_experiments} Experiments) - {objective_function_choice} - {direction.capitalize()}') + plt.xlabel('Trial Number') + plt.ylabel('Objective Value (Log Scale)') + plt.yscale('log') + plt.grid(which="both", linestyle="--", linewidth=0.5) + plt.legend() + plt.show() From 17e3fc61473b766d5f98255a31730a8ec5fcc2c6 Mon Sep 17 00:00:00 2001 From: JLX0 Date: Fri, 6 Dec 2024 14:42:58 +0900 Subject: [PATCH 14/20] DE with benchmarking and example.py --- .../samplers/differential_evolution/plot.png | Bin 0 -> 69009 bytes .../results/Ackley_minimize.png | Bin 0 -> 279033 bytes .../results/Rastrigin_minimize.png | Bin 0 -> 226046 bytes .../results/Schwefel_maximize.png | Bin 0 -> 220611 bytes .../results/sphere_minimize.png | Bin 0 -> 231650 bytes 5 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 package/samplers/differential_evolution/plot.png create mode 100644 package/samplers/differential_evolution/results/Ackley_minimize.png create mode 100644 package/samplers/differential_evolution/results/Rastrigin_minimize.png create mode 100644 package/samplers/differential_evolution/results/Schwefel_maximize.png create mode 100644 package/samplers/differential_evolution/results/sphere_minimize.png diff --git a/package/samplers/differential_evolution/plot.png b/package/samplers/differential_evolution/plot.png new file mode 100644 index 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