From 387ce288cc61b0a2143f9814b57f4e705239ba0c Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 07:47:57 +0200 Subject: [PATCH 01/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/evaluators/ablation.py | 4 +- deepcave/evaluators/mo_ablation.py | 325 ++++++++++++++++++ deepcave/evaluators/mo_fanova.py | 171 +++++++++ deepcave/evaluators/mo_lpi.py | 321 +++++++++++++++++ .../plugins/hyperparameter/ablation_paths.py | 167 ++++++++- .../plugins/hyperparameter/importances.py | 187 +++++++++- 6 files changed, 1139 insertions(+), 36 deletions(-) create mode 100644 deepcave/evaluators/mo_ablation.py create mode 100644 deepcave/evaluators/mo_fanova.py create mode 100644 deepcave/evaluators/mo_lpi.py diff --git a/deepcave/evaluators/ablation.py b/deepcave/evaluators/ablation.py index da14b7ba..ec27cf8f 100644 --- a/deepcave/evaluators/ablation.py +++ b/deepcave/evaluators/ablation.py @@ -242,10 +242,10 @@ def _ablation( continue_ablation, max_hp, max_hp_performance, max_hp_std """ max_hp = "" - max_hp_difference = -np.inf + max_hp_difference = 0 # so that no hp beloy the default cost is chosen for hp in hp_it: - if hp in hp in incumbent_config.keys() and hp in self.default_config.keys(): + if hp in incumbent_config.keys() and hp in self.default_config.keys(): config_copy = copy.copy(self.default_config) config_copy[hp] = incumbent_config[hp] diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py new file mode 100644 index 00000000..52f5070a --- /dev/null +++ b/deepcave/evaluators/mo_ablation.py @@ -0,0 +1,325 @@ +# noqa: D400 +""" +# Ablation Paths + +This module evaluates the ablation paths. + +Ablation Paths is a method to analyze the importance of hyperparameters in a configuration space. +Starting from a default configuration, the default configuration is iteratively changed to the +incumbent configuration by changing one hyperparameter at a time, choosing the +hyperparameter that leads to the largest improvement in the objective function at each step. + +## Classes: + - Ablation: Provide an evaluator of the ablation paths. +""" + +from typing import Any, Dict, List, Optional, Tuple, Union + +import copy + +import numpy as np + +from deepcave.evaluators.epm.random_forest_surrogate import RandomForestSurrogate +from deepcave.evaluators.ablation import Ablation +from deepcave.runs import AbstractRun +from deepcave.runs.objective import Objective +import pandas as pd + +class MOAblation(Ablation): + """ + Provide an evaluator of the ablation paths. + Override: Multi-Objective case + + Properties + ---------- + run : AbstractRun + The run to analyze. + cs : ConfigurationSpace + The configuration space of the run. + hp_names : List[str] + A list of the hyperparameter names. + performances : Optional[Dict[Any, Any]] + A dictionary containing the performances for each HP. + improvements : Optional[Dict[Any, Any]] + A dictionary containing the improvements over the respective previous step for each HP. + objectives : Optional[Union[Objective, List[Objective]]] + The objective(s) of the run. + default_config : Configurations + The default configuration of this configuration space. + Gets changed step by step towards the incumbent configuration. + """ + + def __init__(self, run: AbstractRun): + super().__init__(run) + self.models = [] + self.df_importances = pd.DataFrame([]) + + def get_importances(self): + """ + Return the importance scores. + + Returns + ------- + Dict + Dictionary with Hyperparameter names and the corresponding importance scores and variances. + + Raises + ------ + RuntimeError + If the important scores are not calculated. + """ + if self.df_importances is None: + raise RuntimeError("Importance scores must be calculated first.") + + return self.df_importances.to_json() + + + def get_weightings(self, objectives_normed, df): + """ + Calculates the weighting used for the weighted importance. It uses the points on the pareto-front as weightings + + Parameters + ---------- + objectives_normed : List[str] + the normalized objective names as a list of strings + df : pandas.dataframe + the dataframe containing the encoded data + + Returns + ---------- + weightings : numpy.ndarray[numpy.ndarray] + the weightings as a list of lists + """ + optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) + return df[optimized][objectives_normed].T.apply(lambda values: values / values.sum()).T.to_numpy() + + def is_pareto_efficient(self, costs): + """ + Find the pareto-efficient points. + + Parameters + ---------- + costs : numpy.ndarray + An (n_points, n_costs) array + + Returns + ---------- + is_efficient : numpy.ndarray + A (n_points, ) boolean array, indicating whether each point is Pareto efficient + """ + is_efficient = np.ones(costs.shape[0], dtype=bool) + for i, c in enumerate(costs): + is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all(np.any(costs[i + 1:] > c, axis=1)) + return is_efficient + + + def predict(self, cfg, weighting): + """ + Predicts the performance of the input configuration. The model results are weighted by the input weightings + and summed. + + Parameters + ---------- + cfg : Dict + configuration + weighting : List[float] + weightings + + Returns + ---------- + mean : float + the mean of the weighted sum of predictions + var : float + the variance of the weighted sum of predictions + """ + mean, var = 0, 0 + for model, w in zip(self.models, weighting): + pred, var_ = model.predict(np.array([cfg])) + mean += w * pred[0] + var += w * var_[0] + return mean, var + + def calculate( + self, + objectives: Optional[Union[Objective, List[Objective]]], # noqa + budget: Optional[Union[int, float]] = None, # noqa + n_trees: int = 50, # noqa + seed: int = 0, # noqa + ) -> None: + """ + Calculate the MO ablation path performances and improvements. + + Parameters + ---------- + objectives : Optional[Union[Objective, List[Objective]]] + The objective(s) to be considered. + budget : Optional[Union[int, float]] + The budget to be considered. If None, all budgets of the run are considered. + Default is None. + n_trees : int + The number of trees for the surrogate model. + Default is 50. + seed : int + The seed for the surrogate model. + Default is 0. + """ + for objective in objectives: + assert isinstance(objective, Objective) + + df = self.run.get_encoded_data(objectives, budget, specific=True, include_config_ids=True) + + # Obtain all configurations with theirs costs + df = df.dropna(subset=[obj.name for obj in objectives]) + X = df[list(self.run.configspace.keys())].to_numpy() + + # normalize objectives + objectives_normed = list() + for obj in objectives: + normed = obj.name + '_normed' + df[normed] = (df[obj.name] - df[obj.name].min()) / (df[obj.name].max() - df[obj.name].min()) + objectives_normed.append(normed) + + # train one model per objective + Y = df[normed].to_numpy() + model = RandomForestSurrogate(self.cs, seed=seed, n_trees=n_trees) + model._fit(X, Y) + self.models.append(model) + + weightings = self.get_weightings(objectives_normed, df) + + # calculate importance for each weighting generated from the pareto efficient points + for w in weightings: + df_res = self.calculate_ablation_path(df, objectives_normed, w, budget) + df_res['weight'] = w[0] + self.df_importances = pd.concat([self.df_importances, df_res]) + self.df_importances = self.df_importances.reset_index(drop=True) + + + def calculate_ablation_path(self, df, objectives_normed, weighting, budget): + """ + Calculate the ablation path performances. + + Parameters + ---------- + df : pd.DataFrame + Dataframe with encoded data + objectives_normed : List[str] + The normed objective names to be considered. + weighting : List[float] + The weighting of the objective values. + budget : Optional[Union[int, float]] + The budget to be considered. If None, all budgets of the run are considered. + Default is None. + + Returns + ---------- + df : pd.DataFrame + Dataframe with results of the ablation calculation + """ + # Get the incumbent configuration + incumbent_cfg_id = np.argmin(sum(df[obj] * w for obj, w in zip(objectives_normed, weighting))) + incumbent_config = self.run.get_config(df.iloc[incumbent_cfg_id]['config_id']) + + # Get the default configuration + self.default_config = self.cs.get_default_configuration() + default_encode = self.run.encode_config(self.default_config, specific=True) + + # Obtain the predicted cost of the default and incumbent configuration + def_cost, def_std = self.predict(default_encode, weighting) + inc_cost, _ = self.predict(self.run.encode_config(incumbent_config, specific=True), weighting) + + # TODO make sure objectives are minimized + + if inc_cost > def_cost: + self.logger.warning( + "The predicted incumbent objective is worse than the predicted default " + f"objective for budget: {budget}. Aborting ablation path calculation." + ) + return None + else: + # Copy the hps names as to not remove objects from the original list + hp_it = self.hp_names.copy() + df_abl = pd.DataFrame([]) + df_abl = pd.concat( + [df_abl, pd.DataFrame( + {'hp_name': 'Default', 'importance': 0, 'variance': def_std, 'new_performance': def_cost}, index=[0])]) + + for i in range(len(hp_it)): + # Get the results of the current ablation iteration + continue_ablation, max_hp, max_hp_cost, max_hp_std = self._ablation( + budget, incumbent_config, def_cost, hp_it, weighting + ) + + if not continue_ablation: + break + + diff = def_cost - max_hp_cost + def_cost = max_hp_cost + + df_abl = pd.concat( + [df_abl, pd.DataFrame( + {'hp_name': max_hp, 'importance': diff, 'variance': max_hp_std, 'new_performance': max_hp_cost}, index=[i+1])]) + + # Remove the current best hp for keeping the order right + hp_it.remove(max_hp) + return df_abl.reset_index(drop=True) + + def _ablation( + self, + budget: Optional[Union[int, float]], + incumbent_config: Any, + def_cost: Any, + hp_it: List[str], + weighting: Tuple[float, float] + ) -> Tuple[Any, Any, Any, Any]: + """ + Calculate the ablation importance for each hyperparameter. + + Parameters + ---------- + budget: Optional[Union[int, float]] + The budget of the run. + incumbent_config: Any + The incumbent configuration. + def_cost: Any + The default cost. + hp_it: List[str] + A list of the HPs that still have to be looked at. + weighting : List[float] + The weighting of the objective values. + + Returns + ------- + Tuple[Any, Any, Any, Any] + continue_ablation, max_hp, max_hp_performance, max_hp_std + """ + max_hp = "" + max_hp_difference = 0 + + for hp in hp_it: + if hp in incumbent_config.keys() and hp in self.default_config.keys(): + config_copy = copy.copy(self.default_config) + config_copy[hp] = incumbent_config[hp] + + new_cost, _ = self.predict(self.run.encode_config(config_copy, specific=True), weighting) + difference = def_cost - new_cost + + # Check for the maximum difference hyperparameter in this round + if difference > max_hp_difference: + max_hp = hp + max_hp_difference = difference + else: + continue + hp_count = len(list(self.cs.keys())) + if max_hp != "": + # For the maximum impact hyperparameter, switch the default with the incumbent value + self.default_config[max_hp] = incumbent_config[max_hp] + max_hp_cost, max_hp_std = self.predict(self.run.encode_config(self.default_config, specific=True), weighting) + return True, max_hp, max_hp_cost, max_hp_std + else: + self.logger.info( + f"End ablation at step {hp_count - len(hp_it) + 1}/{hp_count} " + f"for budget {budget} (remaining hyperparameters not activate in incumbent or " + "default configuration)." + ) + return False, None, None, None diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py new file mode 100644 index 00000000..8be85e15 --- /dev/null +++ b/deepcave/evaluators/mo_fanova.py @@ -0,0 +1,171 @@ +# TODO +# noqa: D400 +""" +# fANOVA + +This module provides a tool for assessing the importance of an algorithms Hyperparameters. + +Utilities provide calculation of the data wrt the budget and train the forest on the encoded data. + +## Classes + - fANOVA: Calculate and provide midpoints and sizes. +""" + +from typing import Dict, List, Optional, Tuple, Union + + +import numpy as np +from deepcave.evaluators.epm.fanova_forest import FanovaForest +from deepcave.evaluators.fanova import fANOVA +from deepcave.runs import AbstractRun +from deepcave.runs.objective import Objective +import pandas as pd + +class MOfANOVA(fANOVA): + """ + Multi-Objective fANOVA. + Calculate and provide midpoints and sizes from the forest's split values in order to get the marginals. + Override: to train the random forest with an arbitrary weighting of the objectives (multi-objective case). + """ + + def __init__(self, run: AbstractRun): + if run.configspace is None: + raise RuntimeError("The run needs to be initialized.") + + super().__init__(run) + self.importances_ = None + + def get_weightings(self, objectives_normed, df): + """ + Calculates the weighting used for the weighted importance. It uses the points on the pareto-front as weightings + + Parameters + ---------- + objectives_normed : List[str] + the normalized objective names as a list of strings + df : pandas.dataframe + the dataframe containing the encoded data + + Returns + ---------- + weightings : numpy.ndarray[numpy.ndarray] + the weightings as a list of lists + """ + optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) + return df[optimized][objectives_normed].T.apply(lambda values: values / values.sum()).T.to_numpy() + + def is_pareto_efficient(self, costs): + """ + Find the pareto-efficient points. + + Parameters + ---------- + costs : numpy.ndarray + An (n_points, n_costs) array + + Returns + ---------- + is_efficient : numpy.ndarray + A (n_points, ) boolean array, indicating whether each point is Pareto efficient + """ + is_efficient = np.ones(costs.shape[0], dtype=bool) + for i, c in enumerate(costs): + is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all(np.any(costs[i + 1:] > c, axis=1)) + return is_efficient + + + def calculate( + self, + objectives: Optional[Union[Objective, List[Objective]]] = None, + budget: Optional[Union[int, float]] = None, + n_trees: int = 100, + seed: int = 0, + ) -> None: + """ + Get the data with respect to budget and train the forest on the encoded data. + Calculates weighted fAnova for multiple objectives. + + Note + ---- + Right now, only `n_trees` is used. It can be further specified if needed. + + Parameters + ---------- + objectives : Optional[Union[Objective, List[Objective]]], optional + Considered objectives. By default None. If None, all objectives are considered. + budget : Optional[Union[int, float]], optional + Considered budget. By default None. If None, the highest budget is chosen. + n_trees : int, optional + How many trees should be used. By default 100. + seed : int + Random seed. By default 0. + """ + if objectives is None: + objectives = self.run.get_objectives() + + if budget is None: + budget = self.run.get_highest_budget() + + self.n_trees = n_trees + + # Get data + df = self.run.get_encoded_data( + objectives, budget, specific=True, include_combined_cost=True + ) + X = df[self.hp_names].to_numpy() + + # normalize objectives + objectives_normed = list() + for obj in objectives: + normed = obj.name + '_normed' + df[normed] = (df[obj.name] - df[obj.name].min()) / (df[obj.name].max() - df[obj.name].min()) + objectives_normed.append(normed) + + df_all = pd.DataFrame([]) + weightings = self.get_weightings(objectives_normed, df) + + # calculate importance for each weighting generated from the pareto efficient points + for w in weightings: + Y = sum(df[obj] * weighting for obj, weighting in zip(objectives_normed, w)).to_numpy() + + self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) + self._model.train(X, Y) + df_res = pd.DataFrame(super(MOfANOVA, self).get_importances(hp_names=None)).loc[0:1].T.reset_index() + df_res['weight'] = w[0] + df_all = pd.concat([df_all, df_res]) + self.importances_ = df_all.rename(columns={0: 'importance', 1: 'variance', 'index': 'hp_name'}).reset_index(drop=True) + + + + def get_importances( + self, hp_names: Optional[List[str]] = None, sort: bool = True + ) -> Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]]: + """ + Return the importance scores from the passed Hyperparameter names. + + Parameters + ---------- + hp_names : Optional[List[str]] + Selected Hyperparameter names to get the importance scores from. If None, all + Hyperparameters of the configuration space are used. + sort : bool, optional + Whether the Hyperparameters should be sorted by importance. By default True. + + Returns + ------- + Dict + Dictionary with Hyperparameter names and the corresponding importance scores and variances. + + Raises + ------ + RuntimeError + If the important scores are not calculated. + """ + if self.importances_ is None: + raise RuntimeError("Importance scores must be calculated first.") + + res = self.importances_.sort_values(by='importance', ascending=False) if sort else self.importances_ + if hp_names: + res = res[self.importances_['hp_name'].isin(hp_names)] + return res.to_json() + diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py new file mode 100644 index 00000000..ab5d495b --- /dev/null +++ b/deepcave/evaluators/mo_lpi.py @@ -0,0 +1,321 @@ +# TODO +# noqa: D400 +""" +# LPI + +This module provides utilities to calculate the local parameter importance (LPI). + +## Classes + - LPI: This class calculates the local parameter importance (LPI). +""" + +from typing import Any, Dict, List, Optional, Tuple, Union + +import numpy as np +from ConfigSpace import Configuration +from ConfigSpace.c_util import change_hp_value + +from ConfigSpace.util import impute_inactive_values + +from deepcave.evaluators.epm.fanova_forest import FanovaForest +from deepcave.runs import AbstractRun +from deepcave.runs.objective import Objective +from deepcave.evaluators.lpi import LPI +import pandas as pd + + +# https://github.com/automl/ParameterImportance/blob/f4950593ee627093fc30c0847acc5d8bf63ef84b/pimp/evaluator/local_parameter_importance.py#L27 +class MOLPI(LPI): + """ + Calculate the multi-objective local parameter importance (LPI). + Override: to train the random forest with an arbitrary weighting of the objectives (multi-objective case). + + Properties + ---------- + run : AbstractRun + The AbstractRun to get the importance from. + cs : ConfigurationSpace + The configuration space of the run. + hp_names : List[str] + The names of the Hyperparameters. + variances : Dict[Any, list] + The overall variances per tree. + importances : dict + The importances of the Hyperparameters. + continuous_neighbors : int + The number of neighbors chosen for continuous Hyperparameters. + incumbent : Configuration + The incumbent of the run. + default : Configuration + A configuration containing Hyperparameters with default values. + incumbent_array : numpy.ndarray + The internal vector representation of the incumbent. + seed : int + The seed. If not provided it will be random. + rs : RandomState + A random state with a given seed value. + """ + + def __init__(self, run: AbstractRun): + super().__init__(run) + + def get_weightings(self, objectives_normed, df): + """ + Calculates the weighting used for the weighted importance. It uses the points on the pareto-front as weightings + + Parameters + ---------- + objectives_normed : List[str] + the normalized objective names as a list of strings + df : pandas.dataframe + the dataframe containing the encoded data + + Returns + ---------- + weightings : numpy.ndarray[numpy.ndarray] + the weightings as a list of lists + """ + optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) + return df[optimized][objectives_normed].T.apply(lambda values: values / values.sum()).T.to_numpy() + + def is_pareto_efficient(self, costs): + """ + Find the pareto-efficient points. + + Parameters + ---------- + costs : numpy.ndarray + An (n_points, n_costs) array + + Returns + ---------- + is_efficient : numpy.ndarray + A (n_points, ) boolean array, indicating whether each point is Pareto efficient + """ + is_efficient = np.ones(costs.shape[0], dtype=bool) + for i, c in enumerate(costs): + is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all(np.any(costs[i + 1:] > c, axis=1)) + return is_efficient + + def calculate( + self, + objectives: Optional[Union[Objective, List[Objective]]] = None, + budget: Optional[Union[int, float]] = None, + continous_neighbors: int = 500, + n_trees: int = 10, + seed: int = 0, + ) -> None: + """ + Prepare the data and train a RandomForest model. + + Parameters + ---------- + objectives : Optional[Union[Objective, List[Objective]]], optional + Considered objectives. By default, None. If None, all objectives are considered. + budget : Optional[Union[int, float]], optional + Considered budget. By default, None. If None, the highest budget is chosen. + continuous_neighbors : int, optional + How many neighbors should be chosen for continuous hyperparameters (HPs). + By default, 500. + n_trees : int, optional + The number of trees for the fanova forest. + Default is 10. + seed : Optional[int], optional + The seed. By default None. If None, a random seed is chosen. + """ + if objectives is None: + objectives = self.run.get_objectives() + + if budget is None: + budget = self.run.get_highest_budget() + + # Set variables + self.continous_neighbors = continous_neighbors + self.incumbent, _ = self.run.get_incumbent(budget=budget, objectives=objectives) + self.default = self.cs.get_default_configuration() + self.incumbent_array = self.incumbent.get_array() + + self.seed = seed + self.rs = np.random.RandomState(seed) + + # Get data + df = self.run.get_encoded_data( + objectives=objectives, budget=budget, specific=True, include_combined_cost=True + ) + X = df[self.hp_names].to_numpy() + + # normalize objectives + objectives_normed = list() + for obj in objectives: + normed = obj.name + '_normed' + df[normed] = (df[obj.name] - df[obj.name].min()) / (df[obj.name].max() - df[obj.name].min()) + objectives_normed.append(normed) + + df_all = pd.DataFrame([]) + weightings = self.get_weightings(objectives_normed, df) + + # calculate importance for each weighting generated from the pareto efficient points + for w in weightings: + Y = sum(df[obj] * weighting for obj, weighting in zip(objectives_normed, w)).to_numpy() + # Use same forest as for fanova + self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) + self._model.train(X, Y) + importances = self.calc_one_weighting() + df_res = pd.DataFrame(importances).loc[0:1].T.reset_index() + df_res['weight'] = w[0] + df_all = pd.concat([df_all, df_res]) + self.importances = df_all.rename(columns={0: 'importance', 1: 'variance', 'index':'hp_name'}).reset_index(drop=True) + self.importances = self.importances.applymap(lambda x: max(x, 0) if not isinstance(x, str) else x) # no negative values + + def calc_one_weighting(self): + """ + Prepare the data after a model has be trained for one weighting. + + Returns + ------- + imp_var_dict: dict + Dictionary of importances and variances + """ + # Get neighborhood sampled on an unit-hypercube. + neighborhood = self._get_neighborhood() + + # The delta performance is needed from the default configuration and the incumbent + def_perf, def_var = self._predict_mean_var(self.default) + inc_perf, inc_var = self._predict_mean_var(self.incumbent) + delta = def_perf - inc_perf + + # These are used for plotting and hold the predictions for each neighbor of each parameter. + # That means performances holds the mean, variances the variance of the forest. + performances: Dict[str, List[np.ndarray]] = {} + variances: Dict[str, List[np.ndarray]] = {} + # These are used for importance and hold the corresponding importance/variance over + # neighbors. Only import if NOT quantifying importance via performance-variance across + # neighbors. + + # Nested list of values per tree in random forest. + predictions: Dict[str, List[List[np.ndarray]]] = {} + + # Iterate over parameters + for hp_idx, hp_name in enumerate(self.incumbent.keys()): + if hp_name not in neighborhood: + continue + + performances[hp_name] = [] + variances[hp_name] = [] + predictions[hp_name] = [] + incumbent_added = False + incumbent_idx = 0 + + # Iterate over neighbors + for unit_neighbor, neighbor in zip(neighborhood[hp_name][0], neighborhood[hp_name][1]): + if not incumbent_added: + # Detect incumbent + if unit_neighbor > self.incumbent_array[hp_idx]: + performances[hp_name].append(inc_perf) + variances[hp_name].append(inc_var) + incumbent_added = True + else: + incumbent_idx += 1 + + # Create the neighbor-Configuration object + new_array = self.incumbent_array.copy() + new_array = change_hp_value( + self.cs, new_array, hp_name, unit_neighbor, self.cs.index_of[hp_name] + ) + new_config = impute_inactive_values(Configuration(self.cs, vector=new_array)) + + # Get the leaf values + x = np.array(new_config.get_array()) + leaf_values = self._model.get_leaf_values(x) + + # And the prediction/performance/variance + predictions[hp_name].append([np.mean(tree_pred) for tree_pred in leaf_values]) + performances[hp_name].append(np.mean(predictions[hp_name][-1])) + variances[hp_name].append(np.var(predictions[hp_name][-1])) + + if len(neighborhood[hp_name][0]) > 0: + neighborhood[hp_name][0] = np.insert( + neighborhood[hp_name][0], incumbent_idx, self.incumbent_array[hp_idx] + ) + neighborhood[hp_name][1] = np.insert( + neighborhood[hp_name][1], incumbent_idx, self.incumbent[hp_name] + ) + else: + neighborhood[hp_name][0] = np.array(self.incumbent_array[hp_idx]) + neighborhood[hp_name][1] = [self.incumbent[hp_name]] + + if not incumbent_added: + performances[hp_name].append(inc_perf) + variances[hp_name].append(inc_var) + + # After all neighbors are estimated, look at all performances except the incumbent + perf_before = performances[hp_name][:incumbent_idx] + perf_after = performances[hp_name][incumbent_idx + 1 :] + tmp_perf = perf_before + perf_after + + # Avoid division by zero + if delta == 0: + delta = 1 + + # Creating actual importance value (by normalizing over sum of vars) + num_trees = len(list(predictions.values())[0][0]) + hp_names = list(performances.keys()) + + overall_var_per_tree = {} + for hp_name in hp_names: + hp_variances = [] + for tree_idx in range(num_trees): + variance = np.var([neighbor[tree_idx] for neighbor in predictions[hp_name]]) + hp_variances += [variance] + + overall_var_per_tree[hp_name] = hp_variances + + # Sum up variances per tree across parameters + sum_var_per_tree = [ + sum([overall_var_per_tree[hp_name][tree_idx] for hp_name in hp_names]) + for tree_idx in range(num_trees) + ] + + # Normalize + overall_var_per_tree = { + p: [ + t / sum_var_per_tree[idx] if sum_var_per_tree[idx] != 0.0 else np.nan + for idx, t in enumerate(trees) + ] + for p, trees in overall_var_per_tree.items() + } + imp_var_dict = { k:(np.mean(overall_var_per_tree[k]), np.var(overall_var_per_tree[k])) for k in overall_var_per_tree} + return imp_var_dict + + def get_importances( + self, hp_names: Optional[List[str]] = None, sort: bool = True + ) -> Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]]: + """ + Return the importance scores from the passed Hyperparameter names. + + Parameters + ---------- + hp_names : Optional[List[str]] + Selected Hyperparameter names to get the importance scores from. If None, all + Hyperparameters of the configuration space are used. + sort : bool, optional + Whether the Hyperparameters should be sorted by importance. By default True. + + Returns + ------- + Dict + Dictionary with Hyperparameter names and the corresponding importance scores and variances. + + Raises + ------ + RuntimeError + If the important scores are not calculated. + """ + if self.importances is None: + raise RuntimeError("Importance scores must be calculated first.") + + res = self.importances.sort_values(by='importance', ascending=False) if sort else self.importances + + if hp_names: + res = res[self.importances['hp_name'].isin(hp_names)] + return res.to_json() diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index eb68f60e..bc0bb925 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -25,6 +25,7 @@ - Ablation_Paths: This class provides a plugin for the visualization of the ablation paths. """ +import math from typing import Any, Callable, Dict, List import dash_bootstrap_components as dbc @@ -35,12 +36,13 @@ from deepcave import config from deepcave.evaluators.ablation import Ablation +from deepcave.evaluators.mo_ablation import MOAblation from deepcave.plugins.static import StaticPlugin from deepcave.runs import AbstractRun from deepcave.utils.cast import optional_int from deepcave.utils.layout import get_checklist_options, get_select_options, help_button from deepcave.utils.styled_plotty import get_color, save_image - +import pandas as pd class AblationPaths(StaticPlugin): """ @@ -77,9 +79,19 @@ def get_input_layout(register: Callable) -> List[Any]: [ dbc.Col( [ - dbc.Label("Objective"), + dbc.Label("Objective 1"), + dbc.Select( + id=register("objective_id1", ["value", "options"], type=int), + placeholder="Select objective ...", + ), + ], + md=6, + ), + dbc.Col( + [ + dbc.Label("Objective 2"), dbc.Select( - id=register("objective_id", ["value", "options"], type=int), + id=register("objective_id2", ["value", "options"], type=int), placeholder="Select objective ...", ), ], @@ -220,8 +232,12 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st # Prepare objectives objective_names = run.get_objective_names() objective_ids = run.get_objective_ids() + objective_value1 = inputs["objective_id1"]["value"] + objective_value2 = inputs["objective_id2"]["value"] # in the multi-objective case + objective_options = get_select_options(objective_names, objective_ids) - objective_value = inputs["objective_id"]["value"] + objective_options2 = [dic for dic in objective_options if dic['value'] != objective_value1] # make sure the same objective cannot be chosen twice + objective_options2 += [{'label': 'Select objective ...', 'value': -1}] # add the option to deselect the second objective # Prepare budgets budgets = run.get_budgets(human=True) @@ -229,12 +245,12 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st budget_options = get_checklist_options(budgets, budget_ids) budget_value = inputs["budget_id"]["value"] - hp_names = run.configspace.get_hyperparameter_names() + hp_names = list(run.configspace.keys()) n_hps = inputs["n_hps"]["value"] # Pre-set values - if objective_value is None: - objective_value = objective_ids[0] + if objective_value1 is None: + objective_value1 = objective_ids[0] if n_hps == 0: n_hps = len(hp_names) @@ -245,9 +261,13 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st budget_value = budget_ids[-1] return { - "objective_id": { + "objective_id1": { "options": objective_options, - "value": objective_value, + "value": objective_value1, + }, + "objective_id2": { + "options": objective_options2, + "value": objective_value2, }, "budget_id": { "options": budget_options, @@ -289,7 +309,10 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: RuntimeError If the number of trees is not specified. """ - objective = run.get_objective(inputs["objective_id"]) + objective = run.get_objective(inputs["objective_id1"]) + if inputs["objective_id2"]: + if inputs["objective_id2"] != -1: + objective = [objective, run.get_objective(inputs["objective_id2"])] n_trees = inputs["n_trees"] if n_trees is None: @@ -297,18 +320,23 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: budgets = run.get_budgets(include_combined=True) - evaluator = Ablation(run) + + if isinstance(objective,list): + evaluator = MOAblation(run) + else: + evaluator = Ablation(run) # Collect data data = {} for budget_id, budget in enumerate(budgets): - assert isinstance(budget, (int, float)) assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) - - performances = evaluator.get_ablation_performances() - improvements = evaluator.get_ablation_improvements() - data[budget_id] = [performances, improvements] + if isinstance(objective, list): + data[budget_id] = evaluator.get_importances() + else: + performances = evaluator.get_ablation_performances() + improvements = evaluator.get_ablation_improvements() + data[budget_id] = [performances, improvements] return data # type: ignore @staticmethod @@ -327,6 +355,7 @@ def get_output_layout(register: Callable) -> List[dcc.Graph]: List[dcc.Graph] Layout for the output block. """ + # TODO remove second Figure if MO-Ablation return [ dcc.Graph( register("perf_graph", "figure"), @@ -365,6 +394,11 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]: # type: ignore return [figure1, figure2] The figures of the ablation paths. """ + if inputs["objective_id2"] and inputs["objective_id2"]!=-1: + # MO case: other plot + return AblationPaths.load_outputs_mo(run, inputs, outputs) + + # First selected, should always be shown first selected_budget_id = inputs["budget_id"] objective = run.get_objective(inputs["objective_id"]) @@ -462,3 +496,104 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]: # type: ignore save_image(figure2, "ablation_path_improvement.pdf") return [figure1, figure2] + + @staticmethod + def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore + """ + Multi-objective case for read in raw data and prepare for layout. + + Note + ---- + The passed inputs are cleaned and therefore differ + compared to 'load_inputs' or 'load_dependency_inputs'. + Please see '_clean_inputs' for more information. + + Parameters + ---------- + run + The selected run. + inputs + Input and filter values from the user. + outputs + Raw output from the run. + + Returns + ------- + return [figure1, None] + The figure of the ablation paths. + """ + # First selected, should always be shown first + objective1 = run.get_objective(inputs["objective_id1"]).name + selected_budget_id = inputs["budget_id"] + n_hps = inputs["n_hps"] + + if n_hps == "" or n_hps is None: + raise PreventUpdate + else: + n_hps = int(n_hps) + + # Collect data + data = {} + for budget_id, importances_json in outputs.items(): + # Important to cast budget_id here because of json serialization + budget_id = int(budget_id) + if budget_id != selected_budget_id: + continue + df_importances = pd.read_json(importances_json) + data[budget_id] = df_importances + + # Sort by last fidelity now + idx = data[selected_budget_id].groupby("hp_name")['importance'].max().sort_values(ascending=False).index + idx = list(idx[:n_hps]) + ['Default'] + + df = data[selected_budget_id][data[selected_budget_id]['hp_name'].isin(idx)] # only keep selected hps + + # TODO: necessary? + # convert back to float after json serialization + for col in ['weight', 'importance', 'variance', 'new_performance']: + df[col] = df[col].astype(float) + + df['accuracy'] = np.where(df['hp_name'] == 'Default', 1 - df['new_performance'], + df['importance']) + + grouped_df = df.groupby(['weight', 'hp_name'])['accuracy'].sum().unstack(fill_value=0) + + # Create traces for each hp_name + traces = [] + for column in grouped_df.columns: + traces.append(go.Scatter( + x=grouped_df.index, + y=grouped_df[column], + mode='lines', + stackgroup='one', # This makes the traces stacked + name=column, + hoverinfo='skip', + showlegend=True, + opacity=0.2 + )) + + fig = go.Figure(data=traces) + + # Update the layout + fig.update_layout( + xaxis_title="Weight for " + objective1, + yaxis_title="Importance", + title={ + "text": "Multi-Objective Ablation Path", + "font": {"size": config.FIGURE_FONT_SIZE + 2},}, + xaxis=dict(range=[0, 1], tickangle=-45), + yaxis=dict( + range=[ + math.floor(10 * (1 - ( + df[df['hp_name'] == 'Default']['new_performance'].max() + 0.01))) / 10, + 1 + ] + ), + margin=config.FIGURE_MARGIN, + font=dict(size=config.FIGURE_FONT_SIZE), + ) + + save_image(fig, "ablation_path_performance.pdf") + + + return [fig, None] diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 188bbc21..98630f04 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -36,6 +36,8 @@ from deepcave import config from deepcave.evaluators.fanova import fANOVA as GlobalEvaluator +from deepcave.evaluators.mo_fanova import MOfANOVA +from deepcave.evaluators.mo_lpi import MOLPI from deepcave.evaluators.lpi import LPI as LocalEvaluator from deepcave.plugins.static import StaticPlugin from deepcave.runs import AbstractRun @@ -43,6 +45,7 @@ from deepcave.utils.layout import get_checklist_options, get_select_options, help_button from deepcave.utils.logs import get_logger from deepcave.utils.styled_plotty import get_color, save_image +import pandas as pd logger = get_logger(__name__) @@ -80,15 +83,29 @@ def get_input_layout(register: Callable) -> List[Any]: Layout for the input block. """ return [ - html.Div( + dbc.Row( [ - dbc.Label("Objective"), - dbc.Select( - id=register("objective_id", ["value", "options"], type=int), - placeholder="Select objective ...", + dbc.Col( + [ + dbc.Label("Objective 1"), + dbc.Select( + id=register("objective_id1", ["value", "options"], type=int), + placeholder="Select objective ...", + ), + ], + md=6, + ), + dbc.Col( + [ + dbc.Label("Objective 2"), + dbc.Select( + id=register("objective_id2", ["value", "options"], type=int), + placeholder="Select objective ...", + ), + ], + md=6, ), ], - className="mb-3", ), dbc.Row( [ @@ -206,7 +223,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[str, Any]: # type: ignore # noqa: E501 """ - Work like 'load_inputs' but called after inputs have changed. + Works like 'load_inputs' but called after inputs have changed. Note ---- @@ -228,8 +245,12 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st # Prepare objectives objective_names = run.get_objective_names() objective_ids = run.get_objective_ids() + objective_value1 = inputs["objective_id1"]["value"] + objective_value2 = inputs["objective_id2"]["value"] # in the multi-objective case + objective_options = get_select_options(objective_names, objective_ids) - objective_value = inputs["objective_id"]["value"] + objective_options2 = [dic for dic in objective_options if dic['value'] != objective_value1] # make sure the same objective cannot be chosen twice + objective_options2 += [{'label': 'Select objective ...', 'value': -1}] # add the option to deselect the second objective # Prepare budgets budgets = run.get_budgets(human=True) @@ -243,8 +264,8 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st n_hps = inputs["n_hps"]["value"] # Pre-set values - if objective_value is None: - objective_value = objective_ids[0] + if objective_value1 is None: + objective_value1 = objective_ids[0] if n_hps == 0: n_hps = len(hp_names) @@ -257,9 +278,13 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st budget_value = [budget_ids[-1]] return { - "objective_id": { + "objective_id1": { "options": objective_options, - "value": objective_value, + "value": objective_value1, + }, + "objective_id2": { + "options": objective_options2, + "value": objective_value2, }, "method": { "value": inputs["method"]["value"], @@ -301,7 +326,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: Returns ------- Dict[str, Any] - A serialzied dictionary. + A serialized dictionary. Raises ------ @@ -309,7 +334,10 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. If the method is not found. """ - objective = run.get_objective(inputs["objective_id"]) + objective = run.get_objective(inputs["objective_id1"]) + if inputs["objective_id2"]: + if inputs["objective_id2"] !=-1: + objective = [objective, run.get_objective(inputs["objective_id2"])] method = inputs["method"] n_trees = inputs["n_trees"] @@ -342,10 +370,14 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: hp_names = list(run.configspace.keys()) budgets = run.get_budgets(include_combined=True) + # Initialize the evaluator evaluator: Optional[Union[LocalEvaluator, GlobalEvaluator]] = None - if method == "local": - # Initialize the evaluator + if method == "local" and isinstance(objective,list): + evaluator = MOLPI(run) + elif method == "local": evaluator = LocalEvaluator(run) + elif method == "global" and isinstance(objective,list): + evaluator = MOfANOVA(run) elif method == "global": evaluator = GlobalEvaluator(run) else: @@ -358,9 +390,14 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) importances = evaluator.get_importances(hp_names) - if any(np.isnan(val) for value in importances.values() for val in value): - logger.warning(f"Nan encountered in importance values for budget {budget}.") + if isinstance(objective, list): + if any(pd.read_json(importances)['importance'].isna()): + logger.warning(f"Nan encountered in importance values for budget {budget}.") + else: + if any(np.isnan(val) for value in importances.values() for val in value): + logger.warning(f"Nan encountered in importance values for budget {budget}.") data[budget_id] = importances + return data # type: ignore @staticmethod @@ -410,6 +447,11 @@ def load_outputs(run, inputs, outputs) -> go.Figure: # type: ignore go.figure The figure of the importances. """ + + if inputs["objective_id2"] and inputs["objective_id2"]!=-1: + # MO case: other plot + return Importances.load_ouputs_mo_fanova(run, inputs, outputs) + # First selected, should always be shown first selected_hp_names = inputs["hyperparameter_names"] selected_budget_ids = inputs["budget_ids"] @@ -476,3 +518,112 @@ def load_outputs(run, inputs, outputs) -> go.Figure: # type: ignore save_image(figure, "importances.pdf") return figure + + @staticmethod + def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore + """ + Multi-objective case for read in raw data and prepare for layout. + + Note + ---- + The passed inputs are cleaned and therefore differ + compared to 'load_inputs' or 'load_dependency_inputs'. + Please see '_clean_inputs' for more information. + + Parameters + ---------- + run + The selected run. + inputs + Input and filter values from the user. + outputs + Raw output from the run. + + Returns + ------- + go.figure + The figure of the importances. + """ + + # First selected, should always be shown first + objective1 = run.get_objective(inputs["objective_id1"]).name + selected_hp_names = inputs["hyperparameter_names"] + selected_budget_ids = inputs["budget_ids"] + n_hps = inputs["n_hps"] + + if n_hps == "" or n_hps is None: + raise PreventUpdate + else: + n_hps = int(n_hps) + + if len(selected_hp_names) == 0 or len(selected_budget_ids) == 0: + raise PreventUpdate() + + # Collect data + data = {} + for budget_id, importances_json in outputs.items(): + df_importances = pd.read_json(importances_json) + # Important to cast budget_id here because of json serialization + budget_id = int(budget_id) + if budget_id not in selected_budget_ids: + continue + + df_importances = df_importances[df_importances['hp_name'].isin(selected_hp_names)] # only keep selected hps + data[budget_id] = df_importances + + # Sort by last fidelity now + selected_budget_id = max(selected_budget_ids) + idx = data[selected_budget_id].groupby("hp_name")['importance'].max().sort_values(ascending=False).index + idx = idx[:n_hps] + + # Create the figure + figure = go.Figure() + df = data[selected_budget_id][data[selected_budget_id]['hp_name'].isin(idx)] # only keep top hps + + # TODO: necessary? + # convert back to float after json serialization + for col in ['weight', 'importance', 'variance']: + df[col] = df[col].astype(float) + + # Group by 'hp_name' and plot each group + for group_id, group_data in df.groupby('hp_name'): + # Sort data by the weight column + group_data = group_data.sort_values(by='weight') + + figure.add_trace(go.Scatter( + x=group_data['weight'], + y=group_data['importance'], + mode='lines', + name=group_id, + )) + + # Add the shaded area representing the variance + x = group_data['weight'].to_list() + y1 = group_data['importance'].to_list() - group_data['variance'].to_list() + y2 = group_data['importance'].to_list() + group_data['variance'].to_list() + figure.add_trace(go.Scatter( + x=x+x[::-1], + y=y1+y2[::-1], + fill='toself', + hoverinfo='skip', + showlegend=False, + opacity=0.2, + )) + + # Update the layout for labels, title, and axis limits + figure.update_layout( + xaxis_title='Weight for ' + objective1, + yaxis_title='Importance', + xaxis=dict(range=[0, 1], tickangle=-45), + yaxis=dict(range=[0, df['importance'].max()]), + title={ + "text": "Multi-Objective " + inputs["method"], + "font": {"size": config.FIGURE_FONT_SIZE + 2},}, + margin=config.FIGURE_MARGIN, + font=dict(size=config.FIGURE_FONT_SIZE), + ) + + save_image(figure, "importances.pdf") + + return figure + From 5831ac0637476871e50b892bbc3e6bc51671933b Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 07:56:34 +0200 Subject: [PATCH 02/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 98630f04..f1689d0d 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -598,12 +598,13 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore )) # Add the shaded area representing the variance - x = group_data['weight'].to_list() - y1 = group_data['importance'].to_list() - group_data['variance'].to_list() - y2 = group_data['importance'].to_list() + group_data['variance'].to_list() + x = group_data['weight'] + y1 = (group_data['importance'] - group_data['variance']).to_list() + y2 = (group_data['importance'] + group_data['variance']) + figure.add_trace(go.Scatter( - x=x+x[::-1], - y=y1+y2[::-1], + x=x.tolist()+x[::-1].tolist(), + y=y1+y2[::-1].tolist(), fill='toself', hoverinfo='skip', showlegend=False, From d1297acc7b68880594bf9b3596034913c01ccb6d Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:06:09 +0200 Subject: [PATCH 03/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index f1689d0d..f28151a3 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -580,10 +580,10 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore figure = go.Figure() df = data[selected_budget_id][data[selected_budget_id]['hp_name'].isin(idx)] # only keep top hps - # TODO: necessary? - # convert back to float after json serialization - for col in ['weight', 'importance', 'variance']: - df[col] = df[col].astype(float) + # # TODO: necessary? + # # convert back to float after json serialization + # for col in ['weight', 'importance', 'variance']: + # df[col] = df[col].astype(float) # Group by 'hp_name' and plot each group for group_id, group_data in df.groupby('hp_name'): @@ -595,6 +595,7 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore y=group_data['importance'], mode='lines', name=group_id, + fillcolor=group_id )) # Add the shaded area representing the variance @@ -609,6 +610,7 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore hoverinfo='skip', showlegend=False, opacity=0.2, + fillcolor=group_id )) # Update the layout for labels, title, and axis limits @@ -617,9 +619,6 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore yaxis_title='Importance', xaxis=dict(range=[0, 1], tickangle=-45), yaxis=dict(range=[0, df['importance'].max()]), - title={ - "text": "Multi-Objective " + inputs["method"], - "font": {"size": config.FIGURE_FONT_SIZE + 2},}, margin=config.FIGURE_MARGIN, font=dict(size=config.FIGURE_FONT_SIZE), ) From a4d97638d102b509894845cce16fa29dab934ed3 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:13:23 +0200 Subject: [PATCH 04/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index f28151a3..4dde338e 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -590,12 +590,14 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore # Sort data by the weight column group_data = group_data.sort_values(by='weight') + colors = {label: color for label, color in zip(hps, sns.color_palette('colorblind', n_colors=len(hps)))} + figure.add_trace(go.Scatter( x=group_data['weight'], y=group_data['importance'], mode='lines', name=group_id, - fillcolor=group_id + fillcolor=colors[group_id] )) # Add the shaded area representing the variance @@ -610,7 +612,7 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore hoverinfo='skip', showlegend=False, opacity=0.2, - fillcolor=group_id + fillcolor=colors[group_id] )) # Update the layout for labels, title, and axis limits From ffd98dec369c0bda2ad9721adbfc8ed2073f4d23 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:14:56 +0200 Subject: [PATCH 05/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 4dde338e..0d2dcddb 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -46,6 +46,7 @@ from deepcave.utils.logs import get_logger from deepcave.utils.styled_plotty import get_color, save_image import pandas as pd +import seaborn as sns logger = get_logger(__name__) @@ -590,7 +591,7 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore # Sort data by the weight column group_data = group_data.sort_values(by='weight') - colors = {label: color for label, color in zip(hps, sns.color_palette('colorblind', n_colors=len(hps)))} + colors = {label: color for label, color in zip(idx, sns.color_palette('colorblind', n_colors=len(idx)))} figure.add_trace(go.Scatter( x=group_data['weight'], From 10aace077e60d319c85d59b9741f0ed663b0f0c2 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:19:38 +0200 Subject: [PATCH 06/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 0d2dcddb..e72bf15a 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -46,7 +46,7 @@ from deepcave.utils.logs import get_logger from deepcave.utils.styled_plotty import get_color, save_image import pandas as pd -import seaborn as sns +import plotly.express as px logger = get_logger(__name__) @@ -591,14 +591,15 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore # Sort data by the weight column group_data = group_data.sort_values(by='weight') - colors = {label: color for label, color in zip(idx, sns.color_palette('colorblind', n_colors=len(idx)))} + color_palette = px.colors.qualitative.Plotly # Choose a color palette + colors = {hp: color_palette[i % len(color_palette)] for i, hp in enumerate(idx.unique())} figure.add_trace(go.Scatter( x=group_data['weight'], y=group_data['importance'], mode='lines', name=group_id, - fillcolor=colors[group_id] + line=dict(color=colors[group_id]) )) # Add the shaded area representing the variance From e73f79864562945bd9993a919242a5ca46e77d23 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:21:41 +0200 Subject: [PATCH 07/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index e72bf15a..98d2dda0 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -614,7 +614,8 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore hoverinfo='skip', showlegend=False, opacity=0.2, - fillcolor=colors[group_id] + fillcolor=colors[group_id], + line=colors[group_id], )) # Update the layout for labels, title, and axis limits From 1193e46cea3c3b09e561e35cc09f185fd5525669 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:22:33 +0200 Subject: [PATCH 08/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 98d2dda0..58487bb0 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -615,7 +615,7 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore showlegend=False, opacity=0.2, fillcolor=colors[group_id], - line=colors[group_id], + color=colors[group_id], )) # Update the layout for labels, title, and axis limits From 2b9c8bf868905d87ec76c9b9f3c926c6eee59b54 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:24:26 +0200 Subject: [PATCH 09/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 58487bb0..d92b31d6 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -615,7 +615,7 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore showlegend=False, opacity=0.2, fillcolor=colors[group_id], - color=colors[group_id], + line=dict(color=colors[group_id]) )) # Update the layout for labels, title, and axis limits From 91f4a5c2967ed5e00d6ab29f299842cc8f6c8208 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:26:46 +0200 Subject: [PATCH 10/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index d92b31d6..18033e91 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -615,7 +615,8 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore showlegend=False, opacity=0.2, fillcolor=colors[group_id], - line=dict(color=colors[group_id]) + line=dict(color=colors[group_id]), + mode='lines', )) # Update the layout for labels, title, and axis limits From c11dff16c7b6cc64d2b393f8b79b6ce73981a026 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:32:45 +0200 Subject: [PATCH 11/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 18033e91..2c8ce787 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -577,6 +577,9 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore idx = data[selected_budget_id].groupby("hp_name")['importance'].max().sort_values(ascending=False).index idx = idx[:n_hps] + color_palette = px.colors.qualitative.Plotly # Choose a color palette + colors = {hp: color_palette[i % len(color_palette)] for i, hp in enumerate(list(run.configspace.keys()))} + # Create the figure figure = go.Figure() df = data[selected_budget_id][data[selected_budget_id]['hp_name'].isin(idx)] # only keep top hps @@ -591,8 +594,7 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore # Sort data by the weight column group_data = group_data.sort_values(by='weight') - color_palette = px.colors.qualitative.Plotly # Choose a color palette - colors = {hp: color_palette[i % len(color_palette)] for i, hp in enumerate(idx.unique())} + figure.add_trace(go.Scatter( x=group_data['weight'], From 51c5bb9e644370d9c2b3a0b1fc02ffdd315f7fa5 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:41:58 +0200 Subject: [PATCH 12/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/ablation_paths.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index bc0bb925..58492168 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -557,6 +557,8 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore df['importance']) grouped_df = df.groupby(['weight', 'hp_name'])['accuracy'].sum().unstack(fill_value=0) + color_palette = px.colors.qualitative.Plotly # Choose a color palette + colors = {hp: color_palette[i % len(color_palette)] for i, hp in enumerate(list(run.configspace.keys()))} # Create traces for each hp_name traces = [] @@ -569,7 +571,9 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore name=column, hoverinfo='skip', showlegend=True, - opacity=0.2 + opacity=0.2, + fillcolor=colors[column], + line=dict(color=colors[column]), )) fig = go.Figure(data=traces) @@ -578,9 +582,6 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore fig.update_layout( xaxis_title="Weight for " + objective1, yaxis_title="Importance", - title={ - "text": "Multi-Objective Ablation Path", - "font": {"size": config.FIGURE_FONT_SIZE + 2},}, xaxis=dict(range=[0, 1], tickangle=-45), yaxis=dict( range=[ From ee2ee9e6e4b6f9b0d00bad8b019d0547b40b898b Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:44:01 +0200 Subject: [PATCH 13/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/ablation_paths.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 58492168..434fa2a1 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -43,6 +43,7 @@ from deepcave.utils.layout import get_checklist_options, get_select_options, help_button from deepcave.utils.styled_plotty import get_color, save_image import pandas as pd +import plotly.express as px class AblationPaths(StaticPlugin): """ From c5a2e54a5a39211797349081b47b1dc6e38881f4 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:45:03 +0200 Subject: [PATCH 14/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 434fa2a1..fbac1163 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -559,7 +559,7 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore grouped_df = df.groupby(['weight', 'hp_name'])['accuracy'].sum().unstack(fill_value=0) color_palette = px.colors.qualitative.Plotly # Choose a color palette - colors = {hp: color_palette[i % len(color_palette)] for i, hp in enumerate(list(run.configspace.keys()))} + colors = {hp: color_palette[i % len(color_palette)] for i, hp in enumerate(list(run.configspace.keys())+['Default'])} # Create traces for each hp_name traces = [] From 9454731be6313cdd6b2bdc602fe6092525c2825b Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:55:35 +0200 Subject: [PATCH 15/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/ablation_paths.py | 6 +----- deepcave/plugins/hyperparameter/importances.py | 5 ----- 2 files changed, 1 insertion(+), 10 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index fbac1163..c5e3b571 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -397,6 +397,7 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]: # type: ignore """ if inputs["objective_id2"] and inputs["objective_id2"]!=-1: # MO case: other plot + print(outputs["objective_id2"]) return AblationPaths.load_outputs_mo(run, inputs, outputs) @@ -549,11 +550,6 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore df = data[selected_budget_id][data[selected_budget_id]['hp_name'].isin(idx)] # only keep selected hps - # TODO: necessary? - # convert back to float after json serialization - for col in ['weight', 'importance', 'variance', 'new_performance']: - df[col] = df[col].astype(float) - df['accuracy'] = np.where(df['hp_name'] == 'Default', 1 - df['new_performance'], df['importance']) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 2c8ce787..68fa4d89 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -584,11 +584,6 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore figure = go.Figure() df = data[selected_budget_id][data[selected_budget_id]['hp_name'].isin(idx)] # only keep top hps - # # TODO: necessary? - # # convert back to float after json serialization - # for col in ['weight', 'importance', 'variance']: - # df[col] = df[col].astype(float) - # Group by 'hp_name' and plot each group for group_id, group_data in df.groupby('hp_name'): # Sort data by the weight column From 5688b5f8a6d46e3dca9ad45fb018d3b3bffcdc09 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:56:50 +0200 Subject: [PATCH 16/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index c5e3b571..dc770c96 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -403,7 +403,7 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]: # type: ignore # First selected, should always be shown first selected_budget_id = inputs["budget_id"] - objective = run.get_objective(inputs["objective_id"]) + objective = run.get_objective(inputs["objective_id1"]) n_hps = inputs["n_hps"] show_confidence = inputs["show_confidence"] From adec3b2939d98cb355ac9f91fe166929175141f8 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:56:59 +0200 Subject: [PATCH 17/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/ablation_paths.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index dc770c96..ea626d72 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -397,7 +397,6 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]: # type: ignore """ if inputs["objective_id2"] and inputs["objective_id2"]!=-1: # MO case: other plot - print(outputs["objective_id2"]) return AblationPaths.load_outputs_mo(run, inputs, outputs) From d279a5add34a48ca7eacf44e3b50043ba77d6784 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 08:58:44 +0200 Subject: [PATCH 18/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index ea626d72..c7d8387d 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -593,4 +593,4 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore save_image(fig, "ablation_path_performance.pdf") - return [fig, None] + return [fig, go.Figure()] From 0472a504a1603902f202b59c1870902c35a25ccf Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 09:01:35 +0200 Subject: [PATCH 19/85] Add LPI, fANOVA and ablation paths for two objectives --- .../plugins/hyperparameter/ablation_paths.py | 22 ++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index c7d8387d..82a258cf 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -592,5 +592,25 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore save_image(fig, "ablation_path_performance.pdf") + # create fake figure to hide the second figure which does not exists for MO + white_fig = go.Figure() + white_fig.update_layout( + paper_bgcolor="white", + plot_bgcolor="white", + xaxis=dict( + showgrid=False, + zeroline=False, + visible=False + ), + yaxis=dict( + showgrid=False, + zeroline=False, + visible=False + ), + font=dict( + color="white" + ), + showlegend=False + ) - return [fig, go.Figure()] + return [fig, white_fig] From 392f25200dbca3f11039a285643f9d9efefdb7ab Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 09:05:10 +0200 Subject: [PATCH 20/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 68fa4d89..f25a2451 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -336,6 +336,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the method is not found. """ objective = run.get_objective(inputs["objective_id1"]) + print(inputs["objective_id2"]) if inputs["objective_id2"]: if inputs["objective_id2"] !=-1: objective = [objective, run.get_objective(inputs["objective_id2"])] @@ -451,6 +452,7 @@ def load_outputs(run, inputs, outputs) -> go.Figure: # type: ignore if inputs["objective_id2"] and inputs["objective_id2"]!=-1: # MO case: other plot + print('test') return Importances.load_ouputs_mo_fanova(run, inputs, outputs) # First selected, should always be shown first From c107781ab0ae7e4d0da6533434271743fcd503f6 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 09:07:05 +0200 Subject: [PATCH 21/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/importances.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index f25a2451..80ae99bd 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -336,9 +336,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the method is not found. """ objective = run.get_objective(inputs["objective_id1"]) - print(inputs["objective_id2"]) - if inputs["objective_id2"]: - if inputs["objective_id2"] !=-1: + if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] method = inputs["method"] n_trees = inputs["n_trees"] @@ -450,7 +448,7 @@ def load_outputs(run, inputs, outputs) -> go.Figure: # type: ignore The figure of the importances. """ - if inputs["objective_id2"] and inputs["objective_id2"]!=-1: + if inputs["objective_id2"] not in (None, -1): # MO case: other plot print('test') return Importances.load_ouputs_mo_fanova(run, inputs, outputs) From cdb448064442f395bf663aee528c705faf331854 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 09:09:18 +0200 Subject: [PATCH 22/85] Add LPI, fANOVA and ablation paths for two objectives --- deepcave/plugins/hyperparameter/ablation_paths.py | 7 +++---- deepcave/plugins/hyperparameter/importances.py | 3 +-- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 82a258cf..07300dec 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -311,9 +311,8 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. """ objective = run.get_objective(inputs["objective_id1"]) - if inputs["objective_id2"]: - if inputs["objective_id2"] != -1: - objective = [objective, run.get_objective(inputs["objective_id2"])] + if inputs["objective_id2"] not in (None, -1): + objective = [objective, run.get_objective(inputs["objective_id2"])] n_trees = inputs["n_trees"] if n_trees is None: @@ -395,7 +394,7 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]: # type: ignore return [figure1, figure2] The figures of the ablation paths. """ - if inputs["objective_id2"] and inputs["objective_id2"]!=-1: + if inputs["objective_id2"] not in (None, -1): # MO case: other plot return AblationPaths.load_outputs_mo(run, inputs, outputs) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 80ae99bd..865c7f73 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -337,7 +337,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: """ objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): - objective = [objective, run.get_objective(inputs["objective_id2"])] + objective = [objective, run.get_objective(inputs["objective_id2"])] method = inputs["method"] n_trees = inputs["n_trees"] @@ -450,7 +450,6 @@ def load_outputs(run, inputs, outputs) -> go.Figure: # type: ignore if inputs["objective_id2"] not in (None, -1): # MO case: other plot - print('test') return Importances.load_ouputs_mo_fanova(run, inputs, outputs) # First selected, should always be shown first From e0b7344b83ccd98dc897d801e9c1a4908af78372 Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 11:41:24 +0200 Subject: [PATCH 23/85] run formatter, fix pydocstyle, and add license --- deepcave/evaluators/ablation.py | 2 +- deepcave/evaluators/mo_ablation.py | 137 ++++++++++++------ deepcave/evaluators/mo_fanova.py | 93 ++++++++---- deepcave/evaluators/mo_lpi.py | 104 ++++++++----- .../plugins/hyperparameter/ablation_paths.py | 100 +++++++------ .../plugins/hyperparameter/importances.py | 112 ++++++++------ 6 files changed, 346 insertions(+), 202 deletions(-) diff --git a/deepcave/evaluators/ablation.py b/deepcave/evaluators/ablation.py index ec27cf8f..151f4110 100644 --- a/deepcave/evaluators/ablation.py +++ b/deepcave/evaluators/ablation.py @@ -242,7 +242,7 @@ def _ablation( continue_ablation, max_hp, max_hp_performance, max_hp_std """ max_hp = "" - max_hp_difference = 0 # so that no hp beloy the default cost is chosen + max_hp_difference = 0 # so that no hp beloy the default cost is chosen for hp in hp_it: if hp in incumbent_config.keys() and hp in self.default_config.keys(): diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index 52f5070a..c98588f6 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -1,3 +1,17 @@ +# Copyright 2021-2024 The DeepCAVE Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # noqa: D400 """ # Ablation Paths @@ -13,21 +27,23 @@ - Ablation: Provide an evaluator of the ablation paths. """ -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, List, Optional, Tuple, Union import copy import numpy as np +import pandas as pd -from deepcave.evaluators.epm.random_forest_surrogate import RandomForestSurrogate from deepcave.evaluators.ablation import Ablation +from deepcave.evaluators.epm.random_forest_surrogate import RandomForestSurrogate from deepcave.runs import AbstractRun from deepcave.runs.objective import Objective -import pandas as pd + class MOAblation(Ablation): """ Provide an evaluator of the ablation paths. + Override: Multi-Objective case Properties @@ -51,7 +67,7 @@ class MOAblation(Ablation): def __init__(self, run: AbstractRun): super().__init__(run) - self.models = [] + self.models: List = [] self.df_importances = pd.DataFrame([]) def get_importances(self): @@ -61,7 +77,8 @@ def get_importances(self): Returns ------- Dict - Dictionary with Hyperparameter names and the corresponding importance scores and variances. + Dictionary with Hyperparameter names and the corresponding importance scores and + variances. Raises ------ @@ -73,25 +90,28 @@ def get_importances(self): return self.df_importances.to_json() - - def get_weightings(self, objectives_normed, df): + def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray: """ - Calculates the weighting used for the weighted importance. It uses the points on the pareto-front as weightings + Calculate the weighting for the weighted importance using the points on the pareto-front. Parameters ---------- objectives_normed : List[str] - the normalized objective names as a list of strings + The normalized objective names as a list of strings. df : pandas.dataframe - the dataframe containing the encoded data + The dataframe containing the encoded data. Returns - ---------- + ------- weightings : numpy.ndarray[numpy.ndarray] - the weightings as a list of lists + The weightings as a list of lists. """ optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) - return df[optimized][objectives_normed].T.apply(lambda values: values / values.sum()).T.to_numpy() + return ( + df[optimized][objectives_normed] + .T.apply(lambda values: values / values.sum()) + .T.to_numpy() + ) def is_pareto_efficient(self, costs): """ @@ -100,37 +120,39 @@ def is_pareto_efficient(self, costs): Parameters ---------- costs : numpy.ndarray - An (n_points, n_costs) array + An (n_points, n_costs) array. Returns - ---------- + ------- is_efficient : numpy.ndarray - A (n_points, ) boolean array, indicating whether each point is Pareto efficient + A (n_points, ) boolean array, indicating whether each point is Pareto efficient. """ is_efficient = np.ones(costs.shape[0], dtype=bool) for i, c in enumerate(costs): - is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all(np.any(costs[i + 1:] > c, axis=1)) + is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all( + np.any(costs[i + 1 :] > c, axis=1) + ) return is_efficient - def predict(self, cfg, weighting): """ - Predicts the performance of the input configuration. The model results are weighted by the input weightings - and summed. + Predict the performance of the input configuration. + + The model results are weighted by the input weightings and summed. Parameters ---------- cfg : Dict - configuration + Configuration. weighting : List[float] - weightings + Weightings. Returns - ---------- + ------- mean : float - the mean of the weighted sum of predictions + The mean of the weighted sum of predictions. var : float - the variance of the weighted sum of predictions + The variance of the weighted sum of predictions. """ mean, var = 0, 0 for model, w in zip(self.models, weighting): @@ -175,8 +197,10 @@ def calculate( # normalize objectives objectives_normed = list() for obj in objectives: - normed = obj.name + '_normed' - df[normed] = (df[obj.name] - df[obj.name].min()) / (df[obj.name].max() - df[obj.name].min()) + normed = obj.name + "_normed" + df[normed] = (df[obj.name] - df[obj.name].min()) / ( + df[obj.name].max() - df[obj.name].min() + ) objectives_normed.append(normed) # train one model per objective @@ -190,11 +214,10 @@ def calculate( # calculate importance for each weighting generated from the pareto efficient points for w in weightings: df_res = self.calculate_ablation_path(df, objectives_normed, w, budget) - df_res['weight'] = w[0] + df_res["weight"] = w[0] self.df_importances = pd.concat([self.df_importances, df_res]) self.df_importances = self.df_importances.reset_index(drop=True) - def calculate_ablation_path(self, df, objectives_normed, weighting, budget): """ Calculate the ablation path performances. @@ -202,7 +225,7 @@ def calculate_ablation_path(self, df, objectives_normed, weighting, budget): Parameters ---------- df : pd.DataFrame - Dataframe with encoded data + Dataframe with encoded data. objectives_normed : List[str] The normed objective names to be considered. weighting : List[float] @@ -212,13 +235,15 @@ def calculate_ablation_path(self, df, objectives_normed, weighting, budget): Default is None. Returns - ---------- + ------- df : pd.DataFrame - Dataframe with results of the ablation calculation + Dataframe with results of the ablation calculation. """ # Get the incumbent configuration - incumbent_cfg_id = np.argmin(sum(df[obj] * w for obj, w in zip(objectives_normed, weighting))) - incumbent_config = self.run.get_config(df.iloc[incumbent_cfg_id]['config_id']) + incumbent_cfg_id = np.argmin( + sum(df[obj] * w for obj, w in zip(objectives_normed, weighting)) + ) + incumbent_config = self.run.get_config(df.iloc[incumbent_cfg_id]["config_id"]) # Get the default configuration self.default_config = self.cs.get_default_configuration() @@ -226,7 +251,9 @@ def calculate_ablation_path(self, df, objectives_normed, weighting, budget): # Obtain the predicted cost of the default and incumbent configuration def_cost, def_std = self.predict(default_encode, weighting) - inc_cost, _ = self.predict(self.run.encode_config(incumbent_config, specific=True), weighting) + inc_cost, _ = self.predict( + self.run.encode_config(incumbent_config, specific=True), weighting + ) # TODO make sure objectives are minimized @@ -241,8 +268,19 @@ def calculate_ablation_path(self, df, objectives_normed, weighting, budget): hp_it = self.hp_names.copy() df_abl = pd.DataFrame([]) df_abl = pd.concat( - [df_abl, pd.DataFrame( - {'hp_name': 'Default', 'importance': 0, 'variance': def_std, 'new_performance': def_cost}, index=[0])]) + [ + df_abl, + pd.DataFrame( + { + "hp_name": "Default", + "importance": 0, + "variance": def_std, + "new_performance": def_cost, + }, + index=[0], + ), + ] + ) for i in range(len(hp_it)): # Get the results of the current ablation iteration @@ -257,8 +295,19 @@ def calculate_ablation_path(self, df, objectives_normed, weighting, budget): def_cost = max_hp_cost df_abl = pd.concat( - [df_abl, pd.DataFrame( - {'hp_name': max_hp, 'importance': diff, 'variance': max_hp_std, 'new_performance': max_hp_cost}, index=[i+1])]) + [ + df_abl, + pd.DataFrame( + { + "hp_name": max_hp, + "importance": diff, + "variance": max_hp_std, + "new_performance": max_hp_cost, + }, + index=[i + 1], + ), + ] + ) # Remove the current best hp for keeping the order right hp_it.remove(max_hp) @@ -270,7 +319,7 @@ def _ablation( incumbent_config: Any, def_cost: Any, hp_it: List[str], - weighting: Tuple[float, float] + weighting: Tuple[float, float], ) -> Tuple[Any, Any, Any, Any]: """ Calculate the ablation importance for each hyperparameter. @@ -301,7 +350,9 @@ def _ablation( config_copy = copy.copy(self.default_config) config_copy[hp] = incumbent_config[hp] - new_cost, _ = self.predict(self.run.encode_config(config_copy, specific=True), weighting) + new_cost, _ = self.predict( + self.run.encode_config(config_copy, specific=True), weighting + ) difference = def_cost - new_cost # Check for the maximum difference hyperparameter in this round @@ -314,7 +365,9 @@ def _ablation( if max_hp != "": # For the maximum impact hyperparameter, switch the default with the incumbent value self.default_config[max_hp] = incumbent_config[max_hp] - max_hp_cost, max_hp_std = self.predict(self.run.encode_config(self.default_config, specific=True), weighting) + max_hp_cost, max_hp_std = self.predict( + self.run.encode_config(self.default_config, specific=True), weighting + ) return True, max_hp, max_hp_cost, max_hp_std else: self.logger.info( diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 8be85e15..44d09958 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -1,4 +1,17 @@ -# TODO +# Copyright 2021-2024 The DeepCAVE Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # noqa: D400 """ # fANOVA @@ -13,19 +26,23 @@ from typing import Dict, List, Optional, Tuple, Union - import numpy as np +import pandas as pd + from deepcave.evaluators.epm.fanova_forest import FanovaForest from deepcave.evaluators.fanova import fANOVA from deepcave.runs import AbstractRun from deepcave.runs.objective import Objective -import pandas as pd + class MOfANOVA(fANOVA): """ Multi-Objective fANOVA. - Calculate and provide midpoints and sizes from the forest's split values in order to get the marginals. - Override: to train the random forest with an arbitrary weighting of the objectives (multi-objective case). + + Calculate and provide midpoints and sizes from the forest's split values in order to get + the marginals. + Override: to train the random forest with an arbitrary weighting of the objectives + (multi-objective case). """ def __init__(self, run: AbstractRun): @@ -35,24 +52,28 @@ def __init__(self, run: AbstractRun): super().__init__(run) self.importances_ = None - def get_weightings(self, objectives_normed, df): + def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray: """ - Calculates the weighting used for the weighted importance. It uses the points on the pareto-front as weightings + Calculate the weighting for the weighted importance using the points on the pareto-front. Parameters ---------- objectives_normed : List[str] - the normalized objective names as a list of strings + The normalized objective names as a list of strings. df : pandas.dataframe - the dataframe containing the encoded data + The dataframe containing the encoded data. Returns - ---------- - weightings : numpy.ndarray[numpy.ndarray] - the weightings as a list of lists + ------- + weightings : numpy.ndarray + The weightings. """ optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) - return df[optimized][objectives_normed].T.apply(lambda values: values / values.sum()).T.to_numpy() + return ( + df[optimized][objectives_normed] + .T.apply(lambda values: values / values.sum()) + .T.to_numpy() + ) def is_pareto_efficient(self, costs): """ @@ -61,19 +82,20 @@ def is_pareto_efficient(self, costs): Parameters ---------- costs : numpy.ndarray - An (n_points, n_costs) array + An (n_points, n_costs) array. Returns - ---------- + ------- is_efficient : numpy.ndarray - A (n_points, ) boolean array, indicating whether each point is Pareto efficient + A (n_points, ) boolean array, indicating whether each point is Pareto efficient. """ is_efficient = np.ones(costs.shape[0], dtype=bool) for i, c in enumerate(costs): - is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all(np.any(costs[i + 1:] > c, axis=1)) + is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all( + np.any(costs[i + 1 :] > c, axis=1) + ) return is_efficient - def calculate( self, objectives: Optional[Union[Objective, List[Objective]]] = None, @@ -83,7 +105,8 @@ def calculate( ) -> None: """ Get the data with respect to budget and train the forest on the encoded data. - Calculates weighted fAnova for multiple objectives. + + Calculates weighted fanova for multiple objectives. Note ---- @@ -117,8 +140,10 @@ def calculate( # normalize objectives objectives_normed = list() for obj in objectives: - normed = obj.name + '_normed' - df[normed] = (df[obj.name] - df[obj.name].min()) / (df[obj.name].max() - df[obj.name].min()) + normed = obj.name + "_normed" + df[normed] = (df[obj.name] - df[obj.name].min()) / ( + df[obj.name].max() - df[obj.name].min() + ) objectives_normed.append(normed) df_all = pd.DataFrame([]) @@ -130,12 +155,16 @@ def calculate( self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) self._model.train(X, Y) - df_res = pd.DataFrame(super(MOfANOVA, self).get_importances(hp_names=None)).loc[0:1].T.reset_index() - df_res['weight'] = w[0] + df_res = ( + pd.DataFrame(super(MOfANOVA, self).get_importances(hp_names=None)) + .loc[0:1] + .T.reset_index() + ) + df_res["weight"] = w[0] df_all = pd.concat([df_all, df_res]) - self.importances_ = df_all.rename(columns={0: 'importance', 1: 'variance', 'index': 'hp_name'}).reset_index(drop=True) - - + self.importances_ = df_all.rename( + columns={0: "importance", 1: "variance", "index": "hp_name"} + ).reset_index(drop=True) def get_importances( self, hp_names: Optional[List[str]] = None, sort: bool = True @@ -154,7 +183,8 @@ def get_importances( Returns ------- Dict - Dictionary with Hyperparameter names and the corresponding importance scores and variances. + Dictionary with Hyperparameter names and the corresponding importance scores and + variances. Raises ------ @@ -164,8 +194,11 @@ def get_importances( if self.importances_ is None: raise RuntimeError("Importance scores must be calculated first.") - res = self.importances_.sort_values(by='importance', ascending=False) if sort else self.importances_ + res = ( + self.importances_.sort_values(by="importance", ascending=False) + if sort + else self.importances_ + ) if hp_names: - res = res[self.importances_['hp_name'].isin(hp_names)] + res = res.loc[self.importances_["hp_name"].isin(hp_names)] return res.to_json() - diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index ab5d495b..4cdeeed0 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -1,4 +1,17 @@ -# TODO +# Copyright 2021-2024 The DeepCAVE Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # noqa: D400 """ # LPI @@ -9,26 +22,27 @@ - LPI: This class calculates the local parameter importance (LPI). """ -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Dict, List, Optional, Tuple, Union import numpy as np +import pandas as pd from ConfigSpace import Configuration from ConfigSpace.c_util import change_hp_value - from ConfigSpace.util import impute_inactive_values from deepcave.evaluators.epm.fanova_forest import FanovaForest +from deepcave.evaluators.lpi import LPI from deepcave.runs import AbstractRun from deepcave.runs.objective import Objective -from deepcave.evaluators.lpi import LPI -import pandas as pd # https://github.com/automl/ParameterImportance/blob/f4950593ee627093fc30c0847acc5d8bf63ef84b/pimp/evaluator/local_parameter_importance.py#L27 class MOLPI(LPI): """ Calculate the multi-objective local parameter importance (LPI). - Override: to train the random forest with an arbitrary weighting of the objectives (multi-objective case). + + Override: to train the random forest with an arbitrary weighting of the objectives + (multi-objective case). Properties ---------- @@ -59,42 +73,48 @@ class MOLPI(LPI): def __init__(self, run: AbstractRun): super().__init__(run) - def get_weightings(self, objectives_normed, df): + def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray: """ - Calculates the weighting used for the weighted importance. It uses the points on the pareto-front as weightings + Calculate the weighting for the weighted importance using the points on the pareto-front. Parameters ---------- objectives_normed : List[str] - the normalized objective names as a list of strings + The normalized objective names as a list of strings. df : pandas.dataframe - the dataframe containing the encoded data + The dataframe containing the encoded data. Returns - ---------- + ------- weightings : numpy.ndarray[numpy.ndarray] - the weightings as a list of lists + The weightings as a list of lists. """ optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) - return df[optimized][objectives_normed].T.apply(lambda values: values / values.sum()).T.to_numpy() + return ( + df[optimized][objectives_normed] + .T.apply(lambda values: values / values.sum()) + .T.to_numpy() + ) - def is_pareto_efficient(self, costs): + def is_pareto_efficient(self, costs: np.ndarray) -> np.ndarray: """ Find the pareto-efficient points. Parameters ---------- costs : numpy.ndarray - An (n_points, n_costs) array + An (n_points, n_costs) array. Returns - ---------- + ------- is_efficient : numpy.ndarray - A (n_points, ) boolean array, indicating whether each point is Pareto efficient + A (n_points, ) boolean array, indicating whether each point is Pareto efficient. """ is_efficient = np.ones(costs.shape[0], dtype=bool) for i, c in enumerate(costs): - is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all(np.any(costs[i + 1:] > c, axis=1)) + is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all( + np.any(costs[i + 1 :] > c, axis=1) + ) return is_efficient def calculate( @@ -145,10 +165,13 @@ def calculate( X = df[self.hp_names].to_numpy() # normalize objectives + assert isinstance(objectives, list) objectives_normed = list() for obj in objectives: - normed = obj.name + '_normed' - df[normed] = (df[obj.name] - df[obj.name].min()) / (df[obj.name].max() - df[obj.name].min()) + normed = obj.name + "_normed" + df[normed] = (df[obj.name] - df[obj.name].min()) / ( + df[obj.name].max() - df[obj.name].min() + ) objectives_normed.append(normed) df_all = pd.DataFrame([]) @@ -162,19 +185,23 @@ def calculate( self._model.train(X, Y) importances = self.calc_one_weighting() df_res = pd.DataFrame(importances).loc[0:1].T.reset_index() - df_res['weight'] = w[0] + df_res["weight"] = w[0] df_all = pd.concat([df_all, df_res]) - self.importances = df_all.rename(columns={0: 'importance', 1: 'variance', 'index':'hp_name'}).reset_index(drop=True) - self.importances = self.importances.applymap(lambda x: max(x, 0) if not isinstance(x, str) else x) # no negative values - - def calc_one_weighting(self): + self.importances = df_all.rename( + columns={0: "importance", 1: "variance", "index": "hp_name"} + ).reset_index(drop=True) + self.importances = self.importances.map( + lambda x: max(x, 0) if not isinstance(x, str) else x + ) # no negative values + + def calc_one_weighting(self) -> Dict[str, Tuple[float, float]]: """ Prepare the data after a model has be trained for one weighting. Returns ------- - imp_var_dict: dict - Dictionary of importances and variances + imp_var_dict: Dict[str, Tuple[float, float]] + Dictionary of importances and variances. """ # Get neighborhood sampled on an unit-hypercube. neighborhood = self._get_neighborhood() @@ -248,11 +275,6 @@ def calc_one_weighting(self): performances[hp_name].append(inc_perf) variances[hp_name].append(inc_var) - # After all neighbors are estimated, look at all performances except the incumbent - perf_before = performances[hp_name][:incumbent_idx] - perf_after = performances[hp_name][incumbent_idx + 1 :] - tmp_perf = perf_before + perf_after - # Avoid division by zero if delta == 0: delta = 1 @@ -284,8 +306,11 @@ def calc_one_weighting(self): ] for p, trees in overall_var_per_tree.items() } - imp_var_dict = { k:(np.mean(overall_var_per_tree[k]), np.var(overall_var_per_tree[k])) for k in overall_var_per_tree} - return imp_var_dict + imp_var_dict = { + k: (np.mean(overall_var_per_tree[k]), np.var(overall_var_per_tree[k])) + for k in overall_var_per_tree + } + return imp_var_dict def get_importances( self, hp_names: Optional[List[str]] = None, sort: bool = True @@ -304,7 +329,8 @@ def get_importances( Returns ------- Dict - Dictionary with Hyperparameter names and the corresponding importance scores and variances. + Dictionary with Hyperparameter names and the corresponding importance scores and + variances. Raises ------ @@ -314,8 +340,12 @@ def get_importances( if self.importances is None: raise RuntimeError("Importance scores must be calculated first.") - res = self.importances.sort_values(by='importance', ascending=False) if sort else self.importances + res = ( + self.importances.sort_values(by="importance", ascending=False) + if sort + else self.importances + ) if hp_names: - res = res[self.importances['hp_name'].isin(hp_names)] + res = res.loc[self.importances["hp_name"].isin(hp_names)] return res.to_json() diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 07300dec..3cf142ad 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -25,11 +25,14 @@ - Ablation_Paths: This class provides a plugin for the visualization of the ablation paths. """ -import math from typing import Any, Callable, Dict, List +import math + import dash_bootstrap_components as dbc import numpy as np +import pandas as pd +import plotly.express as px import plotly.graph_objs as go from dash import dcc, html from dash.exceptions import PreventUpdate @@ -42,8 +45,7 @@ from deepcave.utils.cast import optional_int from deepcave.utils.layout import get_checklist_options, get_select_options, help_button from deepcave.utils.styled_plotty import get_color, save_image -import pandas as pd -import plotly.express as px + class AblationPaths(StaticPlugin): """ @@ -234,11 +236,15 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st objective_names = run.get_objective_names() objective_ids = run.get_objective_ids() objective_value1 = inputs["objective_id1"]["value"] - objective_value2 = inputs["objective_id2"]["value"] # in the multi-objective case + objective_value2 = inputs["objective_id2"]["value"] # in the multi-objective case objective_options = get_select_options(objective_names, objective_ids) - objective_options2 = [dic for dic in objective_options if dic['value'] != objective_value1] # make sure the same objective cannot be chosen twice - objective_options2 += [{'label': 'Select objective ...', 'value': -1}] # add the option to deselect the second objective + objective_options2 = [ + dict for dict in objective_options if dict["value"] != objective_value1 + ] # make sure the same objective cannot be chosen twice + objective_options2 += [ + {"label": "Select objective ...", "value": -1} + ] # add the option to deselect the second objective # Prepare budgets budgets = run.get_budgets(human=True) @@ -320,8 +326,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: budgets = run.get_budgets(include_combined=True) - - if isinstance(objective,list): + if isinstance(objective, list): evaluator = MOAblation(run) else: evaluator = Ablation(run) @@ -398,7 +403,6 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]: # type: ignore # MO case: other plot return AblationPaths.load_outputs_mo(run, inputs, outputs) - # First selected, should always be shown first selected_budget_id = inputs["budget_id"] objective = run.get_objective(inputs["objective_id1"]) @@ -543,33 +547,47 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore data[budget_id] = df_importances # Sort by last fidelity now - idx = data[selected_budget_id].groupby("hp_name")['importance'].max().sort_values(ascending=False).index - idx = list(idx[:n_hps]) + ['Default'] + idx = ( + data[selected_budget_id] + .groupby("hp_name")["importance"] + .max() + .sort_values(ascending=False) + .index + ) + idx = list(idx[:n_hps]) + ["Default"] - df = data[selected_budget_id][data[selected_budget_id]['hp_name'].isin(idx)] # only keep selected hps + df = data[selected_budget_id][ + data[selected_budget_id]["hp_name"].isin(idx) + ] # only keep selected hps - df['accuracy'] = np.where(df['hp_name'] == 'Default', 1 - df['new_performance'], - df['importance']) + df["accuracy"] = np.where( + df["hp_name"] == "Default", 1 - df["new_performance"], df["importance"] + ) - grouped_df = df.groupby(['weight', 'hp_name'])['accuracy'].sum().unstack(fill_value=0) + grouped_df = df.groupby(["weight", "hp_name"])["accuracy"].sum().unstack(fill_value=0) color_palette = px.colors.qualitative.Plotly # Choose a color palette - colors = {hp: color_palette[i % len(color_palette)] for i, hp in enumerate(list(run.configspace.keys())+['Default'])} + colors = { + hp: color_palette[i % len(color_palette)] + for i, hp in enumerate(list(run.configspace.keys()) + ["Default"]) + } # Create traces for each hp_name traces = [] for column in grouped_df.columns: - traces.append(go.Scatter( - x=grouped_df.index, - y=grouped_df[column], - mode='lines', - stackgroup='one', # This makes the traces stacked - name=column, - hoverinfo='skip', - showlegend=True, - opacity=0.2, - fillcolor=colors[column], - line=dict(color=colors[column]), - )) + traces.append( + go.Scatter( + x=grouped_df.index, + y=grouped_df[column], + mode="lines", + stackgroup="one", # This makes the traces stacked + name=column, + hoverinfo="skip", + showlegend=True, + opacity=0.2, + fillcolor=colors[column], + line=dict(color=colors[column]), + ) + ) fig = go.Figure(data=traces) @@ -580,9 +598,11 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore xaxis=dict(range=[0, 1], tickangle=-45), yaxis=dict( range=[ - math.floor(10 * (1 - ( - df[df['hp_name'] == 'Default']['new_performance'].max() + 0.01))) / 10, - 1 + math.floor( + 10 * (1 - (df[df["hp_name"] == "Default"]["new_performance"].max() + 0.01)) + ) + / 10, + 1, ] ), margin=config.FIGURE_MARGIN, @@ -596,20 +616,10 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore white_fig.update_layout( paper_bgcolor="white", plot_bgcolor="white", - xaxis=dict( - showgrid=False, - zeroline=False, - visible=False - ), - yaxis=dict( - showgrid=False, - zeroline=False, - visible=False - ), - font=dict( - color="white" - ), - showlegend=False + xaxis=dict(showgrid=False, zeroline=False, visible=False), + yaxis=dict(showgrid=False, zeroline=False, visible=False), + font=dict(color="white"), + showlegend=False, ) return [fig, white_fig] diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 865c7f73..587017fd 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -27,8 +27,12 @@ from typing import Any, Callable, Dict, List, Optional, Union +from io import StringIO + import dash_bootstrap_components as dbc import numpy as np +import pandas as pd +import plotly.express as px import plotly.graph_objs as go from ConfigSpace import ConfigurationSpace, Constant from dash import dcc, html @@ -36,17 +40,15 @@ from deepcave import config from deepcave.evaluators.fanova import fANOVA as GlobalEvaluator +from deepcave.evaluators.lpi import LPI as LocalEvaluator from deepcave.evaluators.mo_fanova import MOfANOVA from deepcave.evaluators.mo_lpi import MOLPI -from deepcave.evaluators.lpi import LPI as LocalEvaluator from deepcave.plugins.static import StaticPlugin from deepcave.runs import AbstractRun from deepcave.utils.cast import optional_int from deepcave.utils.layout import get_checklist_options, get_select_options, help_button from deepcave.utils.logs import get_logger from deepcave.utils.styled_plotty import get_color, save_image -import pandas as pd -import plotly.express as px logger = get_logger(__name__) @@ -247,11 +249,15 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st objective_names = run.get_objective_names() objective_ids = run.get_objective_ids() objective_value1 = inputs["objective_id1"]["value"] - objective_value2 = inputs["objective_id2"]["value"] # in the multi-objective case + objective_value2 = inputs["objective_id2"]["value"] # in the multi-objective case objective_options = get_select_options(objective_names, objective_ids) - objective_options2 = [dic for dic in objective_options if dic['value'] != objective_value1] # make sure the same objective cannot be chosen twice - objective_options2 += [{'label': 'Select objective ...', 'value': -1}] # add the option to deselect the second objective + objective_options2 = [ + dict for dict in objective_options if dict["value"] != objective_value1 + ] # make sure the same objective cannot be chosen twice + objective_options2 += [ + {"label": "Select objective ...", "value": -1} + ] # add the option to deselect the second objective # Prepare budgets budgets = run.get_budgets(human=True) @@ -372,11 +378,11 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: # Initialize the evaluator evaluator: Optional[Union[LocalEvaluator, GlobalEvaluator]] = None - if method == "local" and isinstance(objective,list): + if method == "local" and isinstance(objective, list): evaluator = MOLPI(run) elif method == "local": evaluator = LocalEvaluator(run) - elif method == "global" and isinstance(objective,list): + elif method == "global" and isinstance(objective, list): evaluator = MOfANOVA(run) elif method == "global": evaluator = GlobalEvaluator(run) @@ -391,7 +397,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: importances = evaluator.get_importances(hp_names) if isinstance(objective, list): - if any(pd.read_json(importances)['importance'].isna()): + if any(pd.read_json(StringIO(importances))["importance"].isna()): logger.warning(f"Nan encountered in importance values for budget {budget}.") else: if any(np.isnan(val) for value in importances.values() for val in value): @@ -447,7 +453,6 @@ def load_outputs(run, inputs, outputs) -> go.Figure: # type: ignore go.figure The figure of the importances. """ - if inputs["objective_id2"] not in (None, -1): # MO case: other plot return Importances.load_ouputs_mo_fanova(run, inputs, outputs) @@ -544,7 +549,6 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore go.figure The figure of the importances. """ - # First selected, should always be shown first objective1 = run.get_objective(inputs["objective_id1"]).name selected_hp_names = inputs["hyperparameter_names"] @@ -562,65 +566,80 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore # Collect data data = {} for budget_id, importances_json in outputs.items(): - df_importances = pd.read_json(importances_json) + df_importances = pd.read_json(StringIO(importances_json)) # Important to cast budget_id here because of json serialization budget_id = int(budget_id) if budget_id not in selected_budget_ids: continue - df_importances = df_importances[df_importances['hp_name'].isin(selected_hp_names)] # only keep selected hps + df_importances = df_importances[ + df_importances["hp_name"].isin(selected_hp_names) + ] # only keep selected hps data[budget_id] = df_importances # Sort by last fidelity now selected_budget_id = max(selected_budget_ids) - idx = data[selected_budget_id].groupby("hp_name")['importance'].max().sort_values(ascending=False).index + idx = ( + data[selected_budget_id] + .groupby("hp_name")["importance"] + .max() + .sort_values(ascending=False) + .index + ) idx = idx[:n_hps] color_palette = px.colors.qualitative.Plotly # Choose a color palette - colors = {hp: color_palette[i % len(color_palette)] for i, hp in enumerate(list(run.configspace.keys()))} + colors = { + hp: color_palette[i % len(color_palette)] + for i, hp in enumerate(list(run.configspace.keys())) + } # Create the figure figure = go.Figure() - df = data[selected_budget_id][data[selected_budget_id]['hp_name'].isin(idx)] # only keep top hps + df = data[selected_budget_id][ + data[selected_budget_id]["hp_name"].isin(idx) + ] # only keep top hps # Group by 'hp_name' and plot each group - for group_id, group_data in df.groupby('hp_name'): + for group_id, group_data in df.groupby("hp_name"): # Sort data by the weight column - group_data = group_data.sort_values(by='weight') - - - - figure.add_trace(go.Scatter( - x=group_data['weight'], - y=group_data['importance'], - mode='lines', - name=group_id, - line=dict(color=colors[group_id]) - )) + group_data = group_data.sort_values(by="weight") + + figure.add_trace( + go.Scatter( + x=group_data["weight"], + y=group_data["importance"], + mode="lines", + name=group_id, + line=dict(color=colors[group_id]), + ) + ) # Add the shaded area representing the variance - x = group_data['weight'] - y1 = (group_data['importance'] - group_data['variance']).to_list() - y2 = (group_data['importance'] + group_data['variance']) - - figure.add_trace(go.Scatter( - x=x.tolist()+x[::-1].tolist(), - y=y1+y2[::-1].tolist(), - fill='toself', - hoverinfo='skip', - showlegend=False, - opacity=0.2, - fillcolor=colors[group_id], - line=dict(color=colors[group_id]), - mode='lines', - )) + x = group_data["weight"] + y1 = (group_data["importance"] - group_data["variance"]).to_list() + y2 = group_data["importance"] + group_data["variance"] + + figure.add_trace( + go.Scatter( + x=x.tolist() + x[::-1].tolist(), + y=y1 + y2[::-1].tolist(), + fill="toself", + hoverinfo="skip", + showlegend=False, + opacity=0.2, + fillcolor=colors[group_id], + line=dict(color=colors[group_id]), + mode="lines", + ) + ) # Update the layout for labels, title, and axis limits figure.update_layout( - xaxis_title='Weight for ' + objective1, - yaxis_title='Importance', + xaxis_title="Weight for " + objective1, + yaxis_title="Importance", xaxis=dict(range=[0, 1], tickangle=-45), - yaxis=dict(range=[0, df['importance'].max()]), + yaxis=dict(range=[0, df["importance"].max()]), margin=config.FIGURE_MARGIN, font=dict(size=config.FIGURE_FONT_SIZE), ) @@ -628,4 +647,3 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore save_image(figure, "importances.pdf") return figure - From bf9d2a8a131017f2ffd2ea843e337232ed430e56 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 13:13:13 +0200 Subject: [PATCH 24/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 44d09958..9a2f1011 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -136,10 +136,12 @@ def calculate( objectives, budget, specific=True, include_combined_cost=True ) X = df[self.hp_names].to_numpy() + print(X) # normalize objectives objectives_normed = list() for obj in objectives: + print(obj.name) normed = obj.name + "_normed" df[normed] = (df[obj.name] - df[obj.name].min()) / ( df[obj.name].max() - df[obj.name].min() @@ -165,6 +167,7 @@ def calculate( self.importances_ = df_all.rename( columns={0: "importance", 1: "variance", "index": "hp_name"} ).reset_index(drop=True) + print(self.importances_) def get_importances( self, hp_names: Optional[List[str]] = None, sort: bool = True From 2c14d95841f2d8a91fb5830f4e26236f21a41184 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 13:15:16 +0200 Subject: [PATCH 25/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 9a2f1011..9054d005 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -136,12 +136,10 @@ def calculate( objectives, budget, specific=True, include_combined_cost=True ) X = df[self.hp_names].to_numpy() - print(X) # normalize objectives objectives_normed = list() for obj in objectives: - print(obj.name) normed = obj.name + "_normed" df[normed] = (df[obj.name] - df[obj.name].min()) / ( df[obj.name].max() - df[obj.name].min() @@ -150,10 +148,12 @@ def calculate( df_all = pd.DataFrame([]) weightings = self.get_weightings(objectives_normed, df) + print(weightings) # calculate importance for each weighting generated from the pareto efficient points for w in weightings: Y = sum(df[obj] * weighting for obj, weighting in zip(objectives_normed, w)).to_numpy() + print(Y) self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) self._model.train(X, Y) @@ -162,6 +162,7 @@ def calculate( .loc[0:1] .T.reset_index() ) + print(df_res) df_res["weight"] = w[0] df_all = pd.concat([df_all, df_res]) self.importances_ = df_all.rename( From e1118a16197d7a47219cd362cb17bf3b9dd14752 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 13:16:52 +0200 Subject: [PATCH 26/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 9054d005..bc30b2b5 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -68,6 +68,7 @@ def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.n weightings : numpy.ndarray The weightings. """ + print(df[objectives_normed]) optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) return ( df[optimized][objectives_normed] @@ -89,6 +90,7 @@ def is_pareto_efficient(self, costs): is_efficient : numpy.ndarray A (n_points, ) boolean array, indicating whether each point is Pareto efficient. """ + print('costs', costs) is_efficient = np.ones(costs.shape[0], dtype=bool) for i, c in enumerate(costs): is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all( From 3212f2f34d56b3582bb9888bf6f83d180fc62abd Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 13:19:35 +0200 Subject: [PATCH 27/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index bc30b2b5..c5fcb2c4 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -68,7 +68,6 @@ def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.n weightings : numpy.ndarray The weightings. """ - print(df[objectives_normed]) optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) return ( df[optimized][objectives_normed] @@ -137,7 +136,7 @@ def calculate( df = self.run.get_encoded_data( objectives, budget, specific=True, include_combined_cost=True ) - X = df[self.hp_names].to_numpy() + # normalize objectives objectives_normed = list() @@ -147,10 +146,11 @@ def calculate( df[obj.name].max() - df[obj.name].min() ) objectives_normed.append(normed) - - df_all = pd.DataFrame([]) + df = df.dropna(subset=objectives_normed) + X = df[self.hp_names].to_numpy() weightings = self.get_weightings(objectives_normed, df) print(weightings) + df_all = pd.DataFrame([]) # calculate importance for each weighting generated from the pareto efficient points for w in weightings: From 6fbdffcc43d03555537c0fc6878f5b078bf82ed8 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 13:21:35 +0200 Subject: [PATCH 28/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 4 ---- deepcave/evaluators/mo_lpi.py | 5 +++-- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index c5fcb2c4..01c1e405 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -149,13 +149,11 @@ def calculate( df = df.dropna(subset=objectives_normed) X = df[self.hp_names].to_numpy() weightings = self.get_weightings(objectives_normed, df) - print(weightings) df_all = pd.DataFrame([]) # calculate importance for each weighting generated from the pareto efficient points for w in weightings: Y = sum(df[obj] * weighting for obj, weighting in zip(objectives_normed, w)).to_numpy() - print(Y) self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) self._model.train(X, Y) @@ -164,13 +162,11 @@ def calculate( .loc[0:1] .T.reset_index() ) - print(df_res) df_res["weight"] = w[0] df_all = pd.concat([df_all, df_res]) self.importances_ = df_all.rename( columns={0: "importance", 1: "variance", "index": "hp_name"} ).reset_index(drop=True) - print(self.importances_) def get_importances( self, hp_names: Optional[List[str]] = None, sort: bool = True diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index 4cdeeed0..df4e74f5 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -162,7 +162,7 @@ def calculate( df = self.run.get_encoded_data( objectives=objectives, budget=budget, specific=True, include_combined_cost=True ) - X = df[self.hp_names].to_numpy() + # normalize objectives assert isinstance(objectives, list) @@ -173,7 +173,8 @@ def calculate( df[obj.name].max() - df[obj.name].min() ) objectives_normed.append(normed) - + df = df.dropna(subset=objectives_normed) + X = df[self.hp_names].to_numpy() df_all = pd.DataFrame([]) weightings = self.get_weightings(objectives_normed, df) From d8be54e41bdad61d3f5c86f6e46a7fb2d22ca419 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 13:56:35 +0200 Subject: [PATCH 29/85] Bug fixing --- deepcave/evaluators/mo_lpi.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index df4e74f5..744ce8eb 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -72,6 +72,7 @@ class MOLPI(LPI): def __init__(self, run: AbstractRun): super().__init__(run) + self.importances: Optional[pd.DataFrame] = None def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray: """ From 8a205dab29cd6d0ddda9847e5c4bd5da104cee99 Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 13:58:59 +0200 Subject: [PATCH 30/85] revert to starting max_hp_difference at neg inf, fix warning, immediately update second objective selection --- deepcave/evaluators/ablation.py | 2 +- deepcave/evaluators/mo_ablation.py | 2 +- deepcave/plugins/hyperparameter/ablation_paths.py | 3 ++- deepcave/plugins/hyperparameter/importances.py | 8 ++++---- 4 files changed, 8 insertions(+), 7 deletions(-) diff --git a/deepcave/evaluators/ablation.py b/deepcave/evaluators/ablation.py index 151f4110..251cc830 100644 --- a/deepcave/evaluators/ablation.py +++ b/deepcave/evaluators/ablation.py @@ -242,7 +242,7 @@ def _ablation( continue_ablation, max_hp, max_hp_performance, max_hp_std """ max_hp = "" - max_hp_difference = 0 # so that no hp beloy the default cost is chosen + max_hp_difference = -np.inf for hp in hp_it: if hp in incumbent_config.keys() and hp in self.default_config.keys(): diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index c98588f6..cdc09067 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -343,7 +343,7 @@ def _ablation( continue_ablation, max_hp, max_hp_performance, max_hp_std """ max_hp = "" - max_hp_difference = 0 + max_hp_difference = -np.inf for hp in hp_it: if hp in incumbent_config.keys() and hp in self.default_config.keys(): diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 3cf142ad..8b84799f 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -28,6 +28,7 @@ from typing import Any, Callable, Dict, List import math +from io import StringIO import dash_bootstrap_components as dbc import numpy as np @@ -543,7 +544,7 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore budget_id = int(budget_id) if budget_id != selected_budget_id: continue - df_importances = pd.read_json(importances_json) + df_importances = pd.read_json(StringIO(importances_json)) data[budget_id] = df_importances # Sort by last fidelity now diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 587017fd..22638686 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -251,6 +251,10 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st objective_value1 = inputs["objective_id1"]["value"] objective_value2 = inputs["objective_id2"]["value"] # in the multi-objective case + # Pre-set values + if objective_value1 is None: + objective_value1 = objective_ids[0] + objective_options = get_select_options(objective_names, objective_ids) objective_options2 = [ dict for dict in objective_options if dict["value"] != objective_value1 @@ -270,10 +274,6 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st hp_value = inputs["hyperparameter_names"]["value"] n_hps = inputs["n_hps"]["value"] - # Pre-set values - if objective_value1 is None: - objective_value1 = objective_ids[0] - if n_hps == 0: n_hps = len(hp_names) From 3f37b6b4e1cbc7293a1ec4eb962a3640b4e14f9f Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 14:02:27 +0200 Subject: [PATCH 31/85] Bug fixing --- deepcave/evaluators/mo_lpi.py | 17 ++++------------- 1 file changed, 4 insertions(+), 13 deletions(-) diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index 744ce8eb..866f9961 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -314,9 +314,7 @@ def calc_one_weighting(self) -> Dict[str, Tuple[float, float]]: } return imp_var_dict - def get_importances( - self, hp_names: Optional[List[str]] = None, sort: bool = True - ) -> Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]]: + def get_importances(self, hp_names: List[str]) -> str: """ Return the importance scores from the passed Hyperparameter names. @@ -325,8 +323,6 @@ def get_importances( hp_names : Optional[List[str]] Selected Hyperparameter names to get the importance scores from. If None, all Hyperparameters of the configuration space are used. - sort : bool, optional - Whether the Hyperparameters should be sorted by importance. By default True. Returns ------- @@ -342,12 +338,7 @@ def get_importances( if self.importances is None: raise RuntimeError("Importance scores must be calculated first.") - res = ( - self.importances.sort_values(by="importance", ascending=False) - if sort - else self.importances - ) - if hp_names: - res = res.loc[self.importances["hp_name"].isin(hp_names)] - return res.to_json() + return self.importances.loc[self.importances["hp_name"].isin(hp_names)].to_json() + else: + return self.importances.to_json() From ce5102a3efa1207328b2b8cd8f4e632b8b6c27a0 Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 14:16:09 +0200 Subject: [PATCH 32/85] adapt importance and ablation api example to multi-objective --- examples/api/ablation_paths.py | 6 ++++-- examples/api/importances.py | 6 ++++-- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/examples/api/ablation_paths.py b/examples/api/ablation_paths.py index 83ffad96..a9210bfa 100644 --- a/examples/api/ablation_paths.py +++ b/examples/api/ablation_paths.py @@ -15,13 +15,15 @@ # Instantiate the run run = DeepCAVERun.from_path(Path("logs/DeepCAVE/minimal/run_2")) - objective_id = run.get_objective_ids()[0] + objective_id1 = run.get_objective_ids()[0] + objective_id2 = run.get_objective_ids()[1] budget_id = run.get_budget_ids()[0] # Instantiate the plugin plugin = AblationPaths() inputs = plugin.generate_inputs( - objective_id=objective_id, + objective_id1=objective_id1, + objective_id2=None, # replace with objective_id2 for multi-objective importance budget_id=budget_id, n_hps=100, n_trees=100, diff --git a/examples/api/importances.py b/examples/api/importances.py index de4dad38..eee98617 100644 --- a/examples/api/importances.py +++ b/examples/api/importances.py @@ -15,14 +15,16 @@ # Instantiate the run run = DeepCAVERun.from_path(Path("logs/DeepCAVE/minimal/run_2")) - objective_id = run.get_objective_ids()[0] + objective_id1 = run.get_objective_ids()[0] + objective_id2 = run.get_objective_ids()[1] budget_ids = run.get_budget_ids() # Instantiate the plugin plugin = Importances() inputs = plugin.generate_inputs( hyperparameter_names=list(run.configspace.keys()), - objective_id=objective_id, + objective_id1=objective_id1, + objective_id2=None, # replace with objective_id2 for multi-objective importance budget_ids=budget_ids, method="global", n_hps=3, From 35076583fc6b52a767fc2868561e418a47c5c40c Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 14:20:13 +0200 Subject: [PATCH 33/85] update changelog and version --- CHANGELOG.md | 5 +++++ Makefile | 2 +- deepcave/__init__.py | 2 +- 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6bfe58ff..2a8cfbdd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,3 +1,8 @@ +# Version 1.3.2 + +## Features +- Allow Importance and ablation path analysis for multi-objective runs. + # Version 1.3.1 ## Quality of Life diff --git a/Makefile b/Makefile index 3afee1db..c150b3c0 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,7 @@ # are usually completed in github actions. SHELL := /bin/bash -VERSION := 1.3.1 +VERSION := 1.3.2 NAME := DeepCAVE PACKAGE_NAME := deepcave diff --git a/deepcave/__init__.py b/deepcave/__init__.py index 2e7d2bfe..57a89b70 100644 --- a/deepcave/__init__.py +++ b/deepcave/__init__.py @@ -44,7 +44,7 @@ "Source Code": "https://github.com/automl/deepcave", } copyright = f"Copyright {datetime.date.today().strftime('%Y')}, {author}" -version = "1.3.1" +version = "1.3.2" _exec_file = sys.argv[0] _exec_files = ["server.py", "worker.py", "sphinx-build"] From 0af965cab06b77f25894926b86b4d47377f19028 Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 15:00:21 +0200 Subject: [PATCH 34/85] edit some comments, immediately update second objective selection for ablations --- deepcave/plugins/hyperparameter/ablation_paths.py | 9 ++++----- deepcave/plugins/hyperparameter/importances.py | 2 +- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 8b84799f..9071951b 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -239,6 +239,10 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st objective_value1 = inputs["objective_id1"]["value"] objective_value2 = inputs["objective_id2"]["value"] # in the multi-objective case + # Pre-set values + if objective_value1 is None: + objective_value1 = objective_ids[0] + objective_options = get_select_options(objective_names, objective_ids) objective_options2 = [ dict for dict in objective_options if dict["value"] != objective_value1 @@ -256,10 +260,6 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[st hp_names = list(run.configspace.keys()) n_hps = inputs["n_hps"]["value"] - # Pre-set values - if objective_value1 is None: - objective_value1 = objective_ids[0] - if n_hps == 0: n_hps = len(hp_names) @@ -361,7 +361,6 @@ def get_output_layout(register: Callable) -> List[dcc.Graph]: List[dcc.Graph] Layout for the output block. """ - # TODO remove second Figure if MO-Ablation return [ dcc.Graph( register("perf_graph", "figure"), diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 22638686..b23ba331 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -577,7 +577,7 @@ def load_ouputs_mo_fanova(run, inputs, outputs) -> go.Figure: # type: ignore ] # only keep selected hps data[budget_id] = df_importances - # Sort by last fidelity now + # Keep only n_hps most important hyperparameters according to max importance selected_budget_id = max(selected_budget_ids) idx = ( data[selected_budget_id] From cddf7486053909c8c0ab5d9211d2527f27dc3355 Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 15:01:48 +0200 Subject: [PATCH 35/85] Return zero importance if incumbent is predicted worse than default in ablation paths --- deepcave/evaluators/mo_ablation.py | 6 ++++++ examples/api/ablation_paths.py | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index cdc09067..5d0cd763 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -214,6 +214,12 @@ def calculate( # calculate importance for each weighting generated from the pareto efficient points for w in weightings: df_res = self.calculate_ablation_path(df, objectives_normed, w, budget) + if df_res is None: + columns = ["hp_name", "importance", "variance", "new_performance", "weight"] + self.df_importances = pd.DataFrame(0, index=np.arange(len(self.hp_names) + 1), + columns=columns) + self.df_importances["hp_name"] = ["Default"] + self.hp_names + return df_res["weight"] = w[0] self.df_importances = pd.concat([self.df_importances, df_res]) self.df_importances = self.df_importances.reset_index(drop=True) diff --git a/examples/api/ablation_paths.py b/examples/api/ablation_paths.py index a9210bfa..174a829f 100644 --- a/examples/api/ablation_paths.py +++ b/examples/api/ablation_paths.py @@ -17,7 +17,7 @@ objective_id1 = run.get_objective_ids()[0] objective_id2 = run.get_objective_ids()[1] - budget_id = run.get_budget_ids()[0] + budget_id = run.get_budget_ids()[1] # Instantiate the plugin plugin = AblationPaths() From de06b47f7640318da461e6544876dbb0e2deb55f Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 15:02:34 +0200 Subject: [PATCH 36/85] Formatting --- deepcave/evaluators/mo_ablation.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index 5d0cd763..f070c798 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -216,8 +216,9 @@ def calculate( df_res = self.calculate_ablation_path(df, objectives_normed, w, budget) if df_res is None: columns = ["hp_name", "importance", "variance", "new_performance", "weight"] - self.df_importances = pd.DataFrame(0, index=np.arange(len(self.hp_names) + 1), - columns=columns) + self.df_importances = pd.DataFrame( + 0, index=np.arange(len(self.hp_names) + 1), columns=columns + ) self.df_importances["hp_name"] = ["Default"] + self.hp_names return df_res["weight"] = w[0] From 741b3974468c1103f308abcc5848cd3903e7687b Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 15:27:08 +0200 Subject: [PATCH 37/85] Fix copy of a slice warning --- deepcave/plugins/hyperparameter/ablation_paths.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 9071951b..4c83c43e 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -558,11 +558,10 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore df = data[selected_budget_id][ data[selected_budget_id]["hp_name"].isin(idx) - ] # only keep selected hps + ].copy() # only keep selected hps - df["accuracy"] = np.where( - df["hp_name"] == "Default", 1 - df["new_performance"], df["importance"] - ) + df.loc[df["hp_name"] == "Default", "accuracy"] = 1 - df["new_performance"] + df.loc[df["hp_name"] != "Default", "accuracy"] = df["importance"] grouped_df = df.groupby(["weight", "hp_name"])["accuracy"].sum().unstack(fill_value=0) color_palette = px.colors.qualitative.Plotly # Choose a color palette From 16cbc206a2bcf22d7a2ff578ffa30a56a1a06f9c Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 15:29:07 +0200 Subject: [PATCH 38/85] Move redundant mo-importance methods to utils --- deepcave/evaluators/mo_ablation.py | 47 +------------ deepcave/evaluators/mo_fanova.py | 49 +------------- deepcave/evaluators/mo_lpi.py | 47 +------------ deepcave/utils/multi_objective_importance.py | 71 ++++++++++++++++++++ 4 files changed, 77 insertions(+), 137 deletions(-) create mode 100644 deepcave/utils/multi_objective_importance.py diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index f070c798..4ea531dd 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -38,6 +38,7 @@ from deepcave.evaluators.epm.random_forest_surrogate import RandomForestSurrogate from deepcave.runs import AbstractRun from deepcave.runs.objective import Objective +from deepcave.utils.multi_objective_importance import get_weightings class MOAblation(Ablation): @@ -90,50 +91,6 @@ def get_importances(self): return self.df_importances.to_json() - def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray: - """ - Calculate the weighting for the weighted importance using the points on the pareto-front. - - Parameters - ---------- - objectives_normed : List[str] - The normalized objective names as a list of strings. - df : pandas.dataframe - The dataframe containing the encoded data. - - Returns - ------- - weightings : numpy.ndarray[numpy.ndarray] - The weightings as a list of lists. - """ - optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) - return ( - df[optimized][objectives_normed] - .T.apply(lambda values: values / values.sum()) - .T.to_numpy() - ) - - def is_pareto_efficient(self, costs): - """ - Find the pareto-efficient points. - - Parameters - ---------- - costs : numpy.ndarray - An (n_points, n_costs) array. - - Returns - ------- - is_efficient : numpy.ndarray - A (n_points, ) boolean array, indicating whether each point is Pareto efficient. - """ - is_efficient = np.ones(costs.shape[0], dtype=bool) - for i, c in enumerate(costs): - is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all( - np.any(costs[i + 1 :] > c, axis=1) - ) - return is_efficient - def predict(self, cfg, weighting): """ Predict the performance of the input configuration. @@ -209,7 +166,7 @@ def calculate( model._fit(X, Y) self.models.append(model) - weightings = self.get_weightings(objectives_normed, df) + weightings = get_weightings(objectives_normed, df) # calculate importance for each weighting generated from the pareto efficient points for w in weightings: diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 01c1e405..aea7e421 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -26,13 +26,13 @@ from typing import Dict, List, Optional, Tuple, Union -import numpy as np import pandas as pd from deepcave.evaluators.epm.fanova_forest import FanovaForest from deepcave.evaluators.fanova import fANOVA from deepcave.runs import AbstractRun from deepcave.runs.objective import Objective +from deepcave.utils.multi_objective_importance import get_weightings class MOfANOVA(fANOVA): @@ -52,51 +52,6 @@ def __init__(self, run: AbstractRun): super().__init__(run) self.importances_ = None - def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray: - """ - Calculate the weighting for the weighted importance using the points on the pareto-front. - - Parameters - ---------- - objectives_normed : List[str] - The normalized objective names as a list of strings. - df : pandas.dataframe - The dataframe containing the encoded data. - - Returns - ------- - weightings : numpy.ndarray - The weightings. - """ - optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) - return ( - df[optimized][objectives_normed] - .T.apply(lambda values: values / values.sum()) - .T.to_numpy() - ) - - def is_pareto_efficient(self, costs): - """ - Find the pareto-efficient points. - - Parameters - ---------- - costs : numpy.ndarray - An (n_points, n_costs) array. - - Returns - ------- - is_efficient : numpy.ndarray - A (n_points, ) boolean array, indicating whether each point is Pareto efficient. - """ - print('costs', costs) - is_efficient = np.ones(costs.shape[0], dtype=bool) - for i, c in enumerate(costs): - is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all( - np.any(costs[i + 1 :] > c, axis=1) - ) - return is_efficient - def calculate( self, objectives: Optional[Union[Objective, List[Objective]]] = None, @@ -148,7 +103,7 @@ def calculate( objectives_normed.append(normed) df = df.dropna(subset=objectives_normed) X = df[self.hp_names].to_numpy() - weightings = self.get_weightings(objectives_normed, df) + weightings = get_weightings(objectives_normed, df) df_all = pd.DataFrame([]) # calculate importance for each weighting generated from the pareto efficient points diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index 744ce8eb..1decbcec 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -34,6 +34,7 @@ from deepcave.evaluators.lpi import LPI from deepcave.runs import AbstractRun from deepcave.runs.objective import Objective +from deepcave.utils.multi_objective_importance import get_weightings # https://github.com/automl/ParameterImportance/blob/f4950593ee627093fc30c0847acc5d8bf63ef84b/pimp/evaluator/local_parameter_importance.py#L27 @@ -74,50 +75,6 @@ def __init__(self, run: AbstractRun): super().__init__(run) self.importances: Optional[pd.DataFrame] = None - def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray: - """ - Calculate the weighting for the weighted importance using the points on the pareto-front. - - Parameters - ---------- - objectives_normed : List[str] - The normalized objective names as a list of strings. - df : pandas.dataframe - The dataframe containing the encoded data. - - Returns - ------- - weightings : numpy.ndarray[numpy.ndarray] - The weightings as a list of lists. - """ - optimized = self.is_pareto_efficient(df[objectives_normed].to_numpy()) - return ( - df[optimized][objectives_normed] - .T.apply(lambda values: values / values.sum()) - .T.to_numpy() - ) - - def is_pareto_efficient(self, costs: np.ndarray) -> np.ndarray: - """ - Find the pareto-efficient points. - - Parameters - ---------- - costs : numpy.ndarray - An (n_points, n_costs) array. - - Returns - ------- - is_efficient : numpy.ndarray - A (n_points, ) boolean array, indicating whether each point is Pareto efficient. - """ - is_efficient = np.ones(costs.shape[0], dtype=bool) - for i, c in enumerate(costs): - is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all( - np.any(costs[i + 1 :] > c, axis=1) - ) - return is_efficient - def calculate( self, objectives: Optional[Union[Objective, List[Objective]]] = None, @@ -177,7 +134,7 @@ def calculate( df = df.dropna(subset=objectives_normed) X = df[self.hp_names].to_numpy() df_all = pd.DataFrame([]) - weightings = self.get_weightings(objectives_normed, df) + weightings = get_weightings(objectives_normed, df) # calculate importance for each weighting generated from the pareto efficient points for w in weightings: diff --git a/deepcave/utils/multi_objective_importance.py b/deepcave/utils/multi_objective_importance.py new file mode 100644 index 00000000..e557f82f --- /dev/null +++ b/deepcave/utils/multi_objective_importance.py @@ -0,0 +1,71 @@ +# Copyright 2021-2024 The DeepCAVE Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# noqa: D400 +""" +# Multi-Objective importances + +This module provides utilities for calculating multi-objective importances. +""" + +from typing import List + +import pandas as pd +import numpy as np + + +def is_pareto_efficient(costs): + """ + Find the pareto-efficient points. + + Parameters + ---------- + costs : numpy.ndarray + An (n_points, n_costs) array. + + Returns + ------- + is_efficient : numpy.ndarray + A (n_points, ) boolean array, indicating whether each point is pareto-efficient. + """ + is_efficient = np.ones(costs.shape[0], dtype=bool) + for i, c in enumerate(costs): + is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all( + np.any(costs[i + 1:] > c, axis=1) + ) + return is_efficient + + +def get_weightings(objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray: + """ + Calculate the weighting for the weighted importance using the points on the pareto-front. + + Parameters + ---------- + objectives_normed : List[str] + The normalized objective names as a list of strings. + df : pandas.dataframe + The dataframe containing the encoded data. + + Returns + ------- + weightings : numpy.ndarray + The weightings. + """ + optimized = is_pareto_efficient(df[objectives_normed].to_numpy()) + return ( + df[optimized][objectives_normed] + .T.apply(lambda values: values / values.sum()) + .T.to_numpy() + ) \ No newline at end of file From ce04b844c47ecdaf1d4377b71ed90cb554d65166 Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 15:29:35 +0200 Subject: [PATCH 39/85] Format --- deepcave/evaluators/mo_fanova.py | 1 - deepcave/evaluators/mo_lpi.py | 1 - deepcave/utils/multi_objective_importance.py | 10 ++++------ 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index aea7e421..fb344fa0 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -92,7 +92,6 @@ def calculate( objectives, budget, specific=True, include_combined_cost=True ) - # normalize objectives objectives_normed = list() for obj in objectives: diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index 1decbcec..8166f70b 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -121,7 +121,6 @@ def calculate( objectives=objectives, budget=budget, specific=True, include_combined_cost=True ) - # normalize objectives assert isinstance(objectives, list) objectives_normed = list() diff --git a/deepcave/utils/multi_objective_importance.py b/deepcave/utils/multi_objective_importance.py index e557f82f..eb658392 100644 --- a/deepcave/utils/multi_objective_importance.py +++ b/deepcave/utils/multi_objective_importance.py @@ -21,8 +21,8 @@ from typing import List -import pandas as pd import numpy as np +import pandas as pd def is_pareto_efficient(costs): @@ -42,7 +42,7 @@ def is_pareto_efficient(costs): is_efficient = np.ones(costs.shape[0], dtype=bool) for i, c in enumerate(costs): is_efficient[i] = np.all(np.any(costs[:i] > c, axis=1)) and np.all( - np.any(costs[i + 1:] > c, axis=1) + np.any(costs[i + 1 :] > c, axis=1) ) return is_efficient @@ -65,7 +65,5 @@ def get_weightings(objectives_normed: List[str], df: pd.DataFrame) -> np.ndarray """ optimized = is_pareto_efficient(df[objectives_normed].to_numpy()) return ( - df[optimized][objectives_normed] - .T.apply(lambda values: values / values.sum()) - .T.to_numpy() - ) \ No newline at end of file + df[optimized][objectives_normed].T.apply(lambda values: values / values.sum()).T.to_numpy() + ) From 3d3407f7581a1d33730a9b0d36ff633a354fb31d Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 15:41:30 +0200 Subject: [PATCH 40/85] Bug fixing --- deepcave/evaluators/mo_ablation.py | 12 ++++++------ deepcave/evaluators/mo_fanova.py | 2 +- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index c98588f6..04f38c0e 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -70,7 +70,7 @@ def __init__(self, run: AbstractRun): self.models: List = [] self.df_importances = pd.DataFrame([]) - def get_importances(self): + def get_importances(self) -> str: """ Return the importance scores. @@ -113,7 +113,7 @@ def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.n .T.to_numpy() ) - def is_pareto_efficient(self, costs): + def is_pareto_efficient(self, costs: np.ndarray) -> np.ndarray: """ Find the pareto-efficient points. @@ -218,7 +218,7 @@ def calculate( self.df_importances = pd.concat([self.df_importances, df_res]) self.df_importances = self.df_importances.reset_index(drop=True) - def calculate_ablation_path(self, df, objectives_normed, weighting, budget): + def calculate_ablation_path(self, df: pd.DataFrame, objectives_normed: List[str], weighting: np.ndarray, budget: Optional[Union[int, float]]) -> pd.DataFrame: """ Calculate the ablation path performances. @@ -228,7 +228,7 @@ def calculate_ablation_path(self, df, objectives_normed, weighting, budget): Dataframe with encoded data. objectives_normed : List[str] The normed objective names to be considered. - weighting : List[float] + weighting : np.ndarray The weighting of the objective values. budget : Optional[Union[int, float]] The budget to be considered. If None, all budgets of the run are considered. @@ -284,7 +284,7 @@ def calculate_ablation_path(self, df, objectives_normed, weighting, budget): for i in range(len(hp_it)): # Get the results of the current ablation iteration - continue_ablation, max_hp, max_hp_cost, max_hp_std = self._ablation( + continue_ablation, max_hp, max_hp_cost, max_hp_std = self.ablation( budget, incumbent_config, def_cost, hp_it, weighting ) @@ -313,7 +313,7 @@ def calculate_ablation_path(self, df, objectives_normed, weighting, budget): hp_it.remove(max_hp) return df_abl.reset_index(drop=True) - def _ablation( + def ablation( self, budget: Optional[Union[int, float]], incumbent_config: Any, diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 01c1e405..5bb240f5 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -75,7 +75,7 @@ def get_weightings(self, objectives_normed: List[str], df: pd.DataFrame) -> np.n .T.to_numpy() ) - def is_pareto_efficient(self, costs): + def is_pareto_efficient(self, costs: np.ndarray) -> np.ndarray: """ Find the pareto-efficient points. From 1044d34c1aa2fe56d4b4a14fa6e00f4cb70fe0ad Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 15:55:16 +0200 Subject: [PATCH 41/85] Bug fixing --- deepcave/evaluators/mo_lpi.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index f78aebdd..3d6ee351 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -109,9 +109,9 @@ def calculate( # Set variables self.continous_neighbors = continous_neighbors - self.incumbent, _ = self.run.get_incumbent(budget=budget, objectives=objectives) + # self.incumbent, _ = self.run.get_incumbent(budget=budget, objectives=objectives) self.default = self.cs.get_default_configuration() - self.incumbent_array = self.incumbent.get_array() + # self.incumbent_array = self.incumbent.get_array() self.seed = seed self.rs = np.random.RandomState(seed) @@ -141,6 +141,10 @@ def calculate( # Use same forest as for fanova self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) self._model.train(X, Y) + + incumbent_cfg_id = np.argmin(sum(df[obj] * w for obj, w in zip(objectives_normed, w))) + self.incumbent = self.run.get_config(df.iloc[incumbent_cfg_id]["config_id"]) + self.incumbent_array = self.incumbent.get_array() importances = self.calc_one_weighting() df_res = pd.DataFrame(importances).loc[0:1].T.reset_index() df_res["weight"] = w[0] From f7bd2a3dff4936786c9248e5303f8f04e418fe3d Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 15:57:16 +0200 Subject: [PATCH 42/85] Bug fixing --- deepcave/evaluators/mo_lpi.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index 3d6ee351..031e7ba0 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -144,6 +144,7 @@ def calculate( incumbent_cfg_id = np.argmin(sum(df[obj] * w for obj, w in zip(objectives_normed, w))) self.incumbent = self.run.get_config(df.iloc[incumbent_cfg_id]["config_id"]) + print(df.columns) self.incumbent_array = self.incumbent.get_array() importances = self.calc_one_weighting() df_res = pd.DataFrame(importances).loc[0:1].T.reset_index() From 1aff7d2129dbbd9b1e16199746726b29f38ebe06 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 15:58:19 +0200 Subject: [PATCH 43/85] Bug fixing --- deepcave/evaluators/mo_lpi.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index 031e7ba0..8ce83e44 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -142,9 +142,9 @@ def calculate( self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) self._model.train(X, Y) + print(df.columns) incumbent_cfg_id = np.argmin(sum(df[obj] * w for obj, w in zip(objectives_normed, w))) self.incumbent = self.run.get_config(df.iloc[incumbent_cfg_id]["config_id"]) - print(df.columns) self.incumbent_array = self.incumbent.get_array() importances = self.calc_one_weighting() df_res = pd.DataFrame(importances).loc[0:1].T.reset_index() From a563e1f8e84bf86a348e701dc87cbe38b9b2f1f0 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 16:00:40 +0200 Subject: [PATCH 44/85] Bug fixing --- deepcave/evaluators/mo_lpi.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index 8ce83e44..883c247a 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -109,16 +109,14 @@ def calculate( # Set variables self.continous_neighbors = continous_neighbors - # self.incumbent, _ = self.run.get_incumbent(budget=budget, objectives=objectives) self.default = self.cs.get_default_configuration() - # self.incumbent_array = self.incumbent.get_array() self.seed = seed self.rs = np.random.RandomState(seed) # Get data df = self.run.get_encoded_data( - objectives=objectives, budget=budget, specific=True, include_combined_cost=True + objectives=objectives, budget=budget, specific=True, include_combined_cost=True, include_config_ids=True ) # normalize objectives @@ -142,7 +140,6 @@ def calculate( self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) self._model.train(X, Y) - print(df.columns) incumbent_cfg_id = np.argmin(sum(df[obj] * w for obj, w in zip(objectives_normed, w))) self.incumbent = self.run.get_config(df.iloc[incumbent_cfg_id]["config_id"]) self.incumbent_array = self.incumbent.get_array() From a59181d9cb4ca2d2193f9e6c528cc6f462a24e12 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 16:19:06 +0200 Subject: [PATCH 45/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 4c83c43e..a86279fe 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -593,7 +593,7 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore # Update the layout fig.update_layout( xaxis_title="Weight for " + objective1, - yaxis_title="Importance", + yaxis_title="Sum of the weighted normalized performance", xaxis=dict(range=[0, 1], tickangle=-45), yaxis=dict( range=[ From df65818f93e43012324fa7bdcd7d2c2cf3000dae Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 16:22:47 +0200 Subject: [PATCH 46/85] Bug fixing --- deepcave/runs/converters/dataframe.py | 2 +- deepcave/utils/multi_objective_importance.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/deepcave/runs/converters/dataframe.py b/deepcave/runs/converters/dataframe.py index 7cbc0bce..11e34048 100644 --- a/deepcave/runs/converters/dataframe.py +++ b/deepcave/runs/converters/dataframe.py @@ -122,7 +122,7 @@ def from_path(cls, path: Union[Path, str]) -> "DataFrameRun": return run @staticmethod - def load_objectives(path: Path) -> list[Objective]: + def load_objectives(path: Path) -> List[Objective]: """ Load the objectives of the run from the trials.csv file. diff --git a/deepcave/utils/multi_objective_importance.py b/deepcave/utils/multi_objective_importance.py index eb658392..36275da1 100644 --- a/deepcave/utils/multi_objective_importance.py +++ b/deepcave/utils/multi_objective_importance.py @@ -25,7 +25,7 @@ import pandas as pd -def is_pareto_efficient(costs): +def is_pareto_efficient(costs: np.ndarray) -> np.ndarray: """ Find the pareto-efficient points. 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z){76NVjOAuzE})yce?N3TkWv~Q^CKkN{D8o6TuY(%?Uefa8r{5A5SEc5^U7f)acEd zxb`@ObYH*b&9*r4U&YQkoS@h(e67dhQne+v5dz_p=Vg;Ws-ee3?T#M%J~htCGF zbG{2vLjF)7boz9h@T(=n7dEwrap{Ya6{Kd(Aa6_Ue3}wQ^yTS)->_@NHw5g=&kuTG zt;BGp__}Ytw}Gu%z}V{8dO{rt#YfID|9lTbqz$ZA1&5Y!6)L zT^M)1wkVcnED|=s&b@qtS}v&1g^7R&OgA534=NI?eBO zk`q~!?c1{ft@SPkO|OESjnQ#OuMB==U$&~;_3^n`ViD#*pKdXRbb36xl}mNStTWvO zAwIvhJe)5M2ZCJ;`CCk*((h7b$cvWnE~LN#1*koQh^Y}R^UwX_rE zC8T47&LS1R7X%e?H`&y#M#^_RTMsK0G<^uWf8*?K17P>K1!H{qf%P zZ-0%Md1uW-+cblv)63FNpL<*v6E=C;`C7yBPUu3jdrq)U;{nO=$~tf06_rnSzP$N- z)6bc}0XFmBmm{lm_qtCCnX-J{Iz4Xv9G}mr)6>pA<((NE_NA+F`+A$w=hv2&eai+W zaILIYpB{G4-t_DIy7RM+UUk)n_Df@5uDR!In=iH(xJ=q_|KH!=mp^nC7unTje!r&p z(DjRp-HX4xxOir+_4W&;&`uWEbMN)q`D9P6iQH_IeQnL0(rb~z`?|gWTM%(sMHwU010SvIw1(+rghf!l7VKEvuurx86ZeSt7Fq)@e zK`~lTz(QiQpn!$MXh8uBiP3@r9ulJk1uQ5AwxIZD&$ph*$kXVK3CKO3u6{1-oD!M< D>+{9< From afbc3509e8f4a2e7635fc38fb17bb3f00cd173b7 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:48:28 +0200 Subject: [PATCH 63/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 5 +++-- deepcave/plugins/hyperparameter/importances.py | 1 + 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 7b202859..b2029a2b 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -318,7 +318,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: RuntimeError If the number of trees is not specified. """ - objective: Union[Objective, list[Objective], list[Optional[Objective]], None] = None + objective: Any = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] @@ -329,13 +329,14 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: budgets = run.get_budgets(include_combined=True) + evaluator: Union[Ablation, MOAblation, None] = None if isinstance(objective, list): evaluator = MOAblation(run) else: evaluator = Ablation(run) # Collect data - data = {} + data: Dict[Any,Any] = {} for budget_id, budget in enumerate(budgets): assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index e28f2a5d..9a7471db 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -405,6 +405,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: else: assert not isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances(hp_names) + assert isinstance(importances, Dict) if any(np.isnan(val) for value in importances.values() for val in value): logger.warning(f"Nan encountered in importance values for budget {budget}.") data[budget_id] = importances From 343b6e25f55c738683478effb64a2e9586959f30 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:52:25 +0200 Subject: [PATCH 64/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 4 ++-- deepcave/plugins/hyperparameter/importances.py | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index b2029a2b..cea1b843 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -25,7 +25,7 @@ - Ablation_Paths: This class provides a plugin for the visualization of the ablation paths. """ -from typing import Any, Callable, Dict, List, Union, Optional +from typing import Any, Callable, Dict, List, Union import math from io import StringIO @@ -46,7 +46,6 @@ from deepcave.utils.cast import optional_int from deepcave.utils.layout import get_checklist_options, get_select_options, help_button from deepcave.utils.styled_plotty import get_color, save_image -from deepcave.runs.objective import Objective class AblationPaths(StaticPlugin): @@ -341,6 +340,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) if isinstance(objective, list): + assert isinstance(evaluator, MOAblation) data[budget_id] = evaluator.get_importances() else: performances = evaluator.get_ablation_performances() diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 9a7471db..8086ee58 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -49,7 +49,6 @@ from deepcave.utils.layout import get_checklist_options, get_select_options, help_button from deepcave.utils.logs import get_logger from deepcave.utils.styled_plotty import get_color, save_image -from deepcave.runs.objective import Objective logger = get_logger(__name__) From 721a7e973b2238cc2d4edaca30799ea935d5f986 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:57:18 +0200 Subject: [PATCH 65/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 5 ++--- deepcave/plugins/hyperparameter/importances.py | 1 - 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index bdbbf66a..5326b330 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -115,7 +115,7 @@ def calculate( self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) self._model.train(X, Y) df_res = ( - pd.DataFrame(super(MOfANOVA, self).get_importances(hp_names=None)) + pd.DataFrame(super(MOfANOVA, self).get_importances_(hp_names=None)) .loc[0:1] .T.reset_index() ) @@ -126,8 +126,7 @@ def calculate( ).reset_index(drop=True) def get_importances_( - self, hp_names: Optional[List[str]] = None, depth: int = 1, sort: bool = True - ) -> str: + self, hp_names: Optional[List[str]] = None) -> str: """ Return the importance scores from the passed Hyperparameter names. diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 8086ee58..54699b90 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -402,7 +402,6 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: if any(pd.read_json(StringIO(importances))["importance"].isna()): logger.warning(f"Nan encountered in importance values for budget {budget}.") else: - assert not isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances(hp_names) assert isinstance(importances, Dict) if any(np.isnan(val) for value in importances.values() for val in value): From 38588b1680862254778688bf8d7c3e4988822d8e Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:58:12 +0200 Subject: [PATCH 66/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 5326b330..ee198e2a 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -115,7 +115,7 @@ def calculate( self._model = FanovaForest(self.cs, n_trees=n_trees, seed=seed) self._model.train(X, Y) df_res = ( - pd.DataFrame(super(MOfANOVA, self).get_importances_(hp_names=None)) + pd.DataFrame(super(MOfANOVA, self).get_importances(hp_names=None)) .loc[0:1] .T.reset_index() ) From e14d90c98be524dda11d897a85a741bc767b6b66 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:02:27 +0200 Subject: [PATCH 67/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index ee198e2a..434ac98e 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -153,6 +153,6 @@ def get_importances_( raise RuntimeError("Importance scores must be calculated first.") if hp_names: - return self.importances_.loc[self.importances_["hp_name"].isin(hp_names)] + return self.importances_.loc[self.importances_["hp_name"].isin(hp_names)].to_json() else: return self.importances_.to_json() From ba064271c5ceb5e0a82bbef976094e7069bdff33 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:05:16 +0200 Subject: [PATCH 68/85] Bug fixing --- deepcave/plugins/hyperparameter/importances.py | 1 + 1 file changed, 1 insertion(+) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 54699b90..a6c472bf 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -399,6 +399,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: if isinstance(objective, list): assert isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances_(hp_names) + print(importances) if any(pd.read_json(StringIO(importances))["importance"].isna()): logger.warning(f"Nan encountered in importance values for budget {budget}.") else: From 8bf28031ead42bca4f1c2f7a790c9b23f17646cb Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:06:18 +0200 Subject: [PATCH 69/85] Bug fixing --- deepcave/plugins/hyperparameter/importances.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index a6c472bf..54699b90 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -399,7 +399,6 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: if isinstance(objective, list): assert isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances_(hp_names) - print(importances) if any(pd.read_json(StringIO(importances))["importance"].isna()): logger.warning(f"Nan encountered in importance values for budget {budget}.") else: From 0dc1f64b09f202c0f3ee0a79be8c5b0436bc5060 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:09:12 +0200 Subject: [PATCH 70/85] Bug fixing --- deepcave/evaluators/mo_ablation.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index a3070cfa..69234a68 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -162,6 +162,7 @@ def calculate( if obj.optimize == "upper": df[normed] = 1-df[normed] + print('upper') objectives_normed.append(normed) # train one model per objective @@ -223,8 +224,6 @@ def calculate_ablation_path(self, df: pd.DataFrame, objectives_normed: List[str] self.run.encode_config(incumbent_config, specific=True), weighting ) - # TODO make sure objectives are minimized - if inc_cost > def_cost: self.logger.warning( "The predicted incumbent objective is worse than the predicted default " From 7a6b89fb7882d41cdee300f5862fa9b747ff253e Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:12:27 +0200 Subject: [PATCH 71/85] Bug fixing --- deepcave/evaluators/mo_ablation.py | 1 - 1 file changed, 1 deletion(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index 69234a68..8336da26 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -162,7 +162,6 @@ def calculate( if obj.optimize == "upper": df[normed] = 1-df[normed] - print('upper') objectives_normed.append(normed) # train one model per objective From 26f38ca47ce95dadaab611c3e487d8e326b30ca4 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:14:47 +0200 Subject: [PATCH 72/85] Bug fixing --- deepcave/plugins/hyperparameter/importances.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 54699b90..930ce6c7 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -395,7 +395,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: for budget_id, budget in enumerate(budgets): assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) - + importances: Union[dict[str, tuple[float, float]], dict[Union[str, tuple[str, ...]], tuple[float, float, float, float]],str, None] = None if isinstance(objective, list): assert isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances_(hp_names) From 528fac5ebe9a8d0ec08a348faddb794018163a61 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:15:36 +0200 Subject: [PATCH 73/85] Bug fixing --- deepcave/plugins/hyperparameter/importances.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 930ce6c7..2cce8df5 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -224,7 +224,8 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "budget_ids": {"options": get_checklist_options(), "value": []}, } - def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[str, Any]: # type: ignore # noqa: E501 + def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ + str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. @@ -395,7 +396,8 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: for budget_id, budget in enumerate(budgets): assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) - importances: Union[dict[str, tuple[float, float]], dict[Union[str, tuple[str, ...]], tuple[float, float, float, float]],str, None] = None + importances: Union[dict[str, tuple[float, float]], dict[ + Union[str, tuple[str, ...]], tuple[float, float, float, float]], str, None] = None if isinstance(objective, list): assert isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances_(hp_names) From 9b508e3e9ee7845b57712071421edd784f5846f2 Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 18:17:03 +0200 Subject: [PATCH 74/85] Fix pre-commit and format --- deepcave/evaluators/mo_ablation.py | 10 ++++++++-- deepcave/evaluators/mo_fanova.py | 5 ++--- deepcave/evaluators/mo_lpi.py | 8 ++++++-- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- deepcave/plugins/hyperparameter/importances.py | 5 +++++ 5 files changed, 22 insertions(+), 8 deletions(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index 8336da26..c7c24a89 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -161,7 +161,7 @@ def calculate( ) if obj.optimize == "upper": - df[normed] = 1-df[normed] + df[normed] = 1 - df[normed] objectives_normed.append(normed) # train one model per objective @@ -186,7 +186,13 @@ def calculate( self.df_importances = pd.concat([self.df_importances, df_res]) self.df_importances = self.df_importances.reset_index(drop=True) - def calculate_ablation_path(self, df: pd.DataFrame, objectives_normed: List[str], weighting: np.ndarray, budget: Optional[Union[int, float]]) -> pd.DataFrame: + def calculate_ablation_path( + self, + df: pd.DataFrame, + objectives_normed: List[str], + weighting: np.ndarray, + budget: Optional[Union[int, float]], + ) -> pd.DataFrame: """ Calculate the ablation path performances. diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 434ac98e..fe0bbb0f 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -101,7 +101,7 @@ def calculate( df[obj.name].max() - df[obj.name].min() ) if obj.optimize == "upper": - df[normed] = 1-df[normed] + df[normed] = 1 - df[normed] objectives_normed.append(normed) df = df.dropna(subset=objectives_normed) X = df[self.hp_names].to_numpy() @@ -125,8 +125,7 @@ def calculate( columns={0: "importance", 1: "variance", "index": "hp_name"} ).reset_index(drop=True) - def get_importances_( - self, hp_names: Optional[List[str]] = None) -> str: + def get_importances_(self, hp_names: Optional[List[str]] = None) -> str: """ Return the importance scores from the passed Hyperparameter names. diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index d8a66770..06c72afa 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -116,7 +116,11 @@ def calculate( # Get data df = self.run.get_encoded_data( - objectives=objectives, budget=budget, specific=True, include_combined_cost=True, include_config_ids=True + objectives=objectives, + budget=budget, + specific=True, + include_combined_cost=True, + include_config_ids=True, ) # normalize objectives @@ -128,7 +132,7 @@ def calculate( df[obj.name].max() - df[obj.name].min() ) if obj.optimize == "upper": - df[normed] = 1-df[normed] + df[normed] = 1 - df[normed] objectives_normed.append(normed) df = df.dropna(subset=objectives_normed) X = df[self.hp_names].to_numpy() diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index cea1b843..f5c3d6f3 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -335,7 +335,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: evaluator = Ablation(run) # Collect data - data: Dict[Any,Any] = {} + data: Dict[Any, Any] = {} for budget_id, budget in enumerate(budgets): assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 54699b90..c28baab6 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -392,6 +392,11 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: # Collect data data = {} + importances: Union[ + str, + dict[str, tuple[float, float]], + dict[Union[str, tuple[str, ...]], tuple[float, float, float, float]], + ] for budget_id, budget in enumerate(budgets): assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) From 6216adc2c917dcb16003005033b64c19a355d898 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:19:37 +0200 Subject: [PATCH 75/85] Bug fixing --- deepcave/evaluators/mo_ablation.py | 29 ++++++++++--------- deepcave/evaluators/mo_lpi.py | 16 +++++----- .../plugins/hyperparameter/ablation_paths.py | 7 +++-- .../plugins/hyperparameter/importances.py | 2 +- 4 files changed, 28 insertions(+), 26 deletions(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index 8336da26..e2bab4dd 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -119,11 +119,11 @@ def predict(self, cfg: list[Any], weighting: np.ndarray) -> Tuple[float, float]: return mean, var def calculate( - self, - objectives: Optional[Union[Objective, List[Objective]]], # noqa - budget: Optional[Union[int, float]] = None, # noqa - n_trees: int = 50, # noqa - seed: int = 0, # noqa + self, + objectives: Optional[Union[Objective, List[Objective]]], # noqa + budget: Optional[Union[int, float]] = None, # noqa + n_trees: int = 50, # noqa + seed: int = 0, # noqa ) -> None: """ Calculate the MO ablation path performances and improvements. @@ -157,11 +157,11 @@ def calculate( for obj in objectives: normed = obj.name + "_normed" df[normed] = (df[obj.name] - df[obj.name].min()) / ( - df[obj.name].max() - df[obj.name].min() + df[obj.name].max() - df[obj.name].min() ) if obj.optimize == "upper": - df[normed] = 1-df[normed] + df[normed] = 1 - df[normed] objectives_normed.append(normed) # train one model per objective @@ -186,7 +186,8 @@ def calculate( self.df_importances = pd.concat([self.df_importances, df_res]) self.df_importances = self.df_importances.reset_index(drop=True) - def calculate_ablation_path(self, df: pd.DataFrame, objectives_normed: List[str], weighting: np.ndarray, budget: Optional[Union[int, float]]) -> pd.DataFrame: + def calculate_ablation_path(self, df: pd.DataFrame, objectives_normed: List[str], weighting: np.ndarray, + budget: Optional[Union[int, float]]) -> pd.DataFrame: """ Calculate the ablation path performances. @@ -280,12 +281,12 @@ def calculate_ablation_path(self, df: pd.DataFrame, objectives_normed: List[str] return df_abl.reset_index(drop=True) def ablation( - self, - budget: Optional[Union[int, float]], - incumbent_config: Any, - def_cost: Any, - hp_it: List[str], - weighting: np.ndarray[Any, Any], + self, + budget: Optional[Union[int, float]], + incumbent_config: Any, + def_cost: Any, + hp_it: List[str], + weighting: np.ndarray[Any, Any], ) -> Tuple[Any, Any, Any, Any]: """ Calculate the ablation importance for each hyperparameter. diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index d8a66770..ba009ccd 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -76,12 +76,12 @@ def __init__(self, run: AbstractRun): self.importances: Optional[pd.DataFrame] = None def calculate( - self, - objectives: Optional[Union[Objective, List[Objective]]] = None, - budget: Optional[Union[int, float]] = None, - continous_neighbors: int = 500, - n_trees: int = 10, - seed: int = 0, + self, + objectives: Optional[Union[Objective, List[Objective]]] = None, + budget: Optional[Union[int, float]] = None, + continous_neighbors: int = 500, + n_trees: int = 10, + seed: int = 0, ) -> None: """ Prepare the data and train a RandomForest model. @@ -125,10 +125,10 @@ def calculate( for obj in objectives: normed = obj.name + "_normed" df[normed] = (df[obj.name] - df[obj.name].min()) / ( - df[obj.name].max() - df[obj.name].min() + df[obj.name].max() - df[obj.name].min() ) if obj.optimize == "upper": - df[normed] = 1-df[normed] + df[normed] = 1 - df[normed] objectives_normed.append(normed) df = df.dropna(subset=objectives_normed) X = df[self.hp_names].to_numpy() diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index cea1b843..167aa0b0 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -212,7 +212,8 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "show_confidence": {"options": get_select_options(binary=True), "value": "false"}, } - def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[str, Any]: # type: ignore # noqa: E501 + def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ + str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. @@ -335,7 +336,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: evaluator = Ablation(run) # Collect data - data: Dict[Any,Any] = {} + data: Dict[Any, Any] = {} for budget_id, budget in enumerate(budgets): assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) @@ -476,7 +477,7 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]: # type: ignore barmode="group", title={ "text": "Ablation Path when Iteratively Setting the Hyperparameters to Their " - "Incumbent Value", + "Incumbent Value", "font": {"size": config.FIGURE_FONT_SIZE + 2}, }, yaxis_title=objective.name, diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 2cce8df5..9c2221bc 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -236,7 +236,7 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ Parameters ---------- - run + run : AbstractRun The selected run. inputs : Dict[str, Any] Current content of the inputs. From b15c7e8473d90ca3564986813faf24a21c19a3b3 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:22:23 +0200 Subject: [PATCH 76/85] Bug fixing --- deepcave/plugins/hyperparameter/importances.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 8b154167..9d3ac18b 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -224,7 +224,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "budget_ids": {"options": get_checklist_options(), "value": []}, } - def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ + def load_dependency_inputs(self, run: AbstractRun, _: Any, inputs: Dict[str, Any]) -> Dict[ str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. @@ -401,8 +401,6 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: for budget_id, budget in enumerate(budgets): assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) - importances: Union[dict[str, tuple[float, float]], dict[ - Union[str, tuple[str, ...]], tuple[float, float, float, float]], str, None] = None if isinstance(objective, list): assert isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances_(hp_names) From aa2fc515e226bed0db1e32a02c285bb40d1ceedb Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:24:04 +0200 Subject: [PATCH 77/85] Bug fixing --- deepcave/evaluators/mo_ablation.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index ee61655d..a5c87ec9 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -186,15 +186,13 @@ def calculate( self.df_importances = pd.concat([self.df_importances, df_res]) self.df_importances = self.df_importances.reset_index(drop=True) - def calculate_ablation_path(self, df: pd.DataFrame, objectives_normed: List[str], weighting: np.ndarray, - budget: Optional[Union[int, float]]) -> pd.DataFrame: def calculate_ablation_path( - self, - df: pd.DataFrame, - objectives_normed: List[str], - weighting: np.ndarray, - budget: Optional[Union[int, float]], - ) -> pd.DataFrame: + self, + df: pd.DataFrame, + objectives_normed: List[str], + weighting: np.ndarray, + budget: Optional[Union[int, float]], + ) -> pd.DataFrame: """ Calculate the ablation path performances. From d6ad37e63793b01228e3d72005dc8d574633defc Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:25:05 +0200 Subject: [PATCH 78/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 167aa0b0..d5be095a 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -212,7 +212,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "show_confidence": {"options": get_select_options(binary=True), "value": "false"}, } - def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ + def load_dependency_inputs(self, run: AbstractRun, _: Any, inputs: Dict[str, Any]) -> Dict[ str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. @@ -224,7 +224,7 @@ def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ Parameters ---------- - run + run: AbstractRun The selected run. inputs : Dict[str, Any] Current content of the inputs. From 893b42a1c318e2f888306ebbe2a533a3bd07fd60 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:25:56 +0200 Subject: [PATCH 79/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 4 ++-- deepcave/plugins/hyperparameter/importances.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index d5be095a..5590e49b 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -212,7 +212,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "show_confidence": {"options": get_select_options(binary=True), "value": "false"}, } - def load_dependency_inputs(self, run: AbstractRun, _: Any, inputs: Dict[str, Any]) -> Dict[ + def load_dependency_inputs(self, run: Union[AbstractRun, list[AbstractRun], None], _: Any, inputs: Dict[str, Any]) -> Dict[ str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. @@ -224,7 +224,7 @@ def load_dependency_inputs(self, run: AbstractRun, _: Any, inputs: Dict[str, Any Parameters ---------- - run: AbstractRun + run: Union[AbstractRun, list[AbstractRun], None] The selected run. inputs : Dict[str, Any] Current content of the inputs. diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 9d3ac18b..827e2e86 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -224,7 +224,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "budget_ids": {"options": get_checklist_options(), "value": []}, } - def load_dependency_inputs(self, run: AbstractRun, _: Any, inputs: Dict[str, Any]) -> Dict[ + def load_dependency_inputs(self, run: Union[AbstractRun, list[AbstractRun], None], _: Any, inputs: Dict[str, Any]) -> Dict[ str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. @@ -236,7 +236,7 @@ def load_dependency_inputs(self, run: AbstractRun, _: Any, inputs: Dict[str, Any Parameters ---------- - run : AbstractRun + run : Union[AbstractRun, list[AbstractRun], None] The selected run. inputs : Dict[str, Any] Current content of the inputs. From 1688de9a8c296faaea2fe4d9fb0ad8a0da387ea1 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 18:27:14 +0200 Subject: [PATCH 80/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 4 ++-- deepcave/plugins/hyperparameter/importances.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 5590e49b..0c833d8a 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -212,7 +212,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "show_confidence": {"options": get_select_options(binary=True), "value": "false"}, } - def load_dependency_inputs(self, run: Union[AbstractRun, list[AbstractRun], None], _: Any, inputs: Dict[str, Any]) -> Dict[ + def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. @@ -224,7 +224,7 @@ def load_dependency_inputs(self, run: Union[AbstractRun, list[AbstractRun], None Parameters ---------- - run: Union[AbstractRun, list[AbstractRun], None] + run: The selected run. inputs : Dict[str, Any] Current content of the inputs. diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 827e2e86..0c2cf42d 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -224,7 +224,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "budget_ids": {"options": get_checklist_options(), "value": []}, } - def load_dependency_inputs(self, run: Union[AbstractRun, list[AbstractRun], None], _: Any, inputs: Dict[str, Any]) -> Dict[ + def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. @@ -236,7 +236,7 @@ def load_dependency_inputs(self, run: Union[AbstractRun, list[AbstractRun], None Parameters ---------- - run : Union[AbstractRun, list[AbstractRun], None] + run The selected run. inputs : Dict[str, Any] Current content of the inputs. From 6d785c132c60b1f01ed37c723d1f0faded7a829e Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Thu, 29 Aug 2024 18:32:32 +0200 Subject: [PATCH 81/85] Format --- deepcave/evaluators/mo_ablation.py | 36 +++++++++---------- deepcave/evaluators/mo_lpi.py | 14 ++++---- .../plugins/hyperparameter/ablation_paths.py | 5 ++- .../plugins/hyperparameter/importances.py | 3 +- 4 files changed, 28 insertions(+), 30 deletions(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index a5c87ec9..c7c24a89 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -119,11 +119,11 @@ def predict(self, cfg: list[Any], weighting: np.ndarray) -> Tuple[float, float]: return mean, var def calculate( - self, - objectives: Optional[Union[Objective, List[Objective]]], # noqa - budget: Optional[Union[int, float]] = None, # noqa - n_trees: int = 50, # noqa - seed: int = 0, # noqa + self, + objectives: Optional[Union[Objective, List[Objective]]], # noqa + budget: Optional[Union[int, float]] = None, # noqa + n_trees: int = 50, # noqa + seed: int = 0, # noqa ) -> None: """ Calculate the MO ablation path performances and improvements. @@ -157,7 +157,7 @@ def calculate( for obj in objectives: normed = obj.name + "_normed" df[normed] = (df[obj.name] - df[obj.name].min()) / ( - df[obj.name].max() - df[obj.name].min() + df[obj.name].max() - df[obj.name].min() ) if obj.optimize == "upper": @@ -187,12 +187,12 @@ def calculate( self.df_importances = self.df_importances.reset_index(drop=True) def calculate_ablation_path( - self, - df: pd.DataFrame, - objectives_normed: List[str], - weighting: np.ndarray, - budget: Optional[Union[int, float]], - ) -> pd.DataFrame: + self, + df: pd.DataFrame, + objectives_normed: List[str], + weighting: np.ndarray, + budget: Optional[Union[int, float]], + ) -> pd.DataFrame: """ Calculate the ablation path performances. @@ -286,12 +286,12 @@ def calculate_ablation_path( return df_abl.reset_index(drop=True) def ablation( - self, - budget: Optional[Union[int, float]], - incumbent_config: Any, - def_cost: Any, - hp_it: List[str], - weighting: np.ndarray[Any, Any], + self, + budget: Optional[Union[int, float]], + incumbent_config: Any, + def_cost: Any, + hp_it: List[str], + weighting: np.ndarray[Any, Any], ) -> Tuple[Any, Any, Any, Any]: """ Calculate the ablation importance for each hyperparameter. diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index 1d646d8d..06c72afa 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -76,12 +76,12 @@ def __init__(self, run: AbstractRun): self.importances: Optional[pd.DataFrame] = None def calculate( - self, - objectives: Optional[Union[Objective, List[Objective]]] = None, - budget: Optional[Union[int, float]] = None, - continous_neighbors: int = 500, - n_trees: int = 10, - seed: int = 0, + self, + objectives: Optional[Union[Objective, List[Objective]]] = None, + budget: Optional[Union[int, float]] = None, + continous_neighbors: int = 500, + n_trees: int = 10, + seed: int = 0, ) -> None: """ Prepare the data and train a RandomForest model. @@ -129,7 +129,7 @@ def calculate( for obj in objectives: normed = obj.name + "_normed" df[normed] = (df[obj.name] - df[obj.name].min()) / ( - df[obj.name].max() - df[obj.name].min() + df[obj.name].max() - df[obj.name].min() ) if obj.optimize == "upper": df[normed] = 1 - df[normed] diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 0c833d8a..152d2609 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -212,8 +212,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "show_confidence": {"options": get_select_options(binary=True), "value": "false"}, } - def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ - str, Any]: # type: ignore # noqa: E501 + def load_dependency_inputs(self, run, _, inputs) -> Dict[str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. @@ -477,7 +476,7 @@ def load_outputs(run, inputs, outputs) -> List[go.Figure]: # type: ignore barmode="group", title={ "text": "Ablation Path when Iteratively Setting the Hyperparameters to Their " - "Incumbent Value", + "Incumbent Value", "font": {"size": config.FIGURE_FONT_SIZE + 2}, }, yaxis_title=objective.name, diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 0c2cf42d..fe432bb7 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -224,8 +224,7 @@ def load_inputs(self) -> Dict[str, Dict[str, Any]]: "budget_ids": {"options": get_checklist_options(), "value": []}, } - def load_dependency_inputs(self, run, _: Any, inputs: Dict[str, Any]) -> Dict[ - str, Any]: # type: ignore # noqa: E501 + def load_dependency_inputs(self, run, _, inputs) -> Dict[str, Any]: # type: ignore # noqa: E501 """ Works like 'load_inputs' but called after inputs have changed. 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zipuywQ11>kwdajRB9BCtG*l{{xFZGs>xw}ssIR?db#IS1$oc~8&E1#MF?YtEW z^1}bI60lmO@bN#7ebwL;f=ieO%WJ zYBxryb!@g!?P3==Lmrb+ml8_U1lK#cbHqq zwOVSAF*t-gowu!DyGONNUebsOhN%l4P@LJJ-){rKQgMTGDEFl-mpc}NCn1rj*uy9P zY5Ef-Gm>%Kk#1DL#(S5eryb@V+-(>Hpp9bL05ReW+Ug(0htV5|yoTY1}h&=gl zKfaaH%ghQjsBDA46%woa~kC9w+IZ;d){ZSY;!(eJYGX%_LhIqYO-k^uGaZbqfkJ| zr@`#kmz+9~&+V7~VPt78y!=mr_m+}X?pzTy68W3`hWbZneAuJ u`F~XX{kHq`|1|kOXN9`{{_ivP{ODqU$0O49sh1PSE!CBE9+fItzxZE8xm@u8 literal 0 HcmV?d00001 From 54a70e415018888426e81d7e144be588532aba90 Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Fri, 30 Aug 2024 08:21:01 +0200 Subject: [PATCH 83/85] Adapt ablation and importance api examples --- examples/api/ablation_paths.py | 11 ++++++----- examples/api/importances.py | 4 ++-- 2 files changed, 8 insertions(+), 7 deletions(-) diff --git a/examples/api/ablation_paths.py b/examples/api/ablation_paths.py index 174a829f..85b994a8 100644 --- a/examples/api/ablation_paths.py +++ b/examples/api/ablation_paths.py @@ -13,17 +13,17 @@ if __name__ == "__main__": # Instantiate the run - run = DeepCAVERun.from_path(Path("logs/DeepCAVE/minimal/run_2")) + run = DeepCAVERun.from_path(Path("logs/DeepCAVE/minimal/run_1")) objective_id1 = run.get_objective_ids()[0] - objective_id2 = run.get_objective_ids()[1] - budget_id = run.get_budget_ids()[1] + objective_id2 = None # replace with run.get_objective_ids()[1] for multi-objective importance + budget_id = run.get_budget_ids()[0] # Instantiate the plugin plugin = AblationPaths() inputs = plugin.generate_inputs( objective_id1=objective_id1, - objective_id2=None, # replace with objective_id2 for multi-objective importance + objective_id2=objective_id2, budget_id=budget_id, n_hps=100, n_trees=100, @@ -35,4 +35,5 @@ # Finally, you can load the figure. Here, the filter variables play a role. figure1, figure2 = plugin.load_outputs(run, inputs, outputs) figure1.write_image("examples/api/ablation_paths_performance.png", scale=2.0) - figure2.write_image("examples/api/ablation_paths_improvement.png", scale=2.0) + if not objective_id2: + figure2.write_image("examples/api/ablation_paths_improvement.png", scale=2.0) diff --git a/examples/api/importances.py b/examples/api/importances.py index eee98617..78bc36ac 100644 --- a/examples/api/importances.py +++ b/examples/api/importances.py @@ -16,7 +16,7 @@ run = DeepCAVERun.from_path(Path("logs/DeepCAVE/minimal/run_2")) objective_id1 = run.get_objective_ids()[0] - objective_id2 = run.get_objective_ids()[1] + objective_id2 = None # replace with run.get_objective_ids()[1] for multi-objective importance budget_ids = run.get_budget_ids() # Instantiate the plugin @@ -24,7 +24,7 @@ inputs = plugin.generate_inputs( hyperparameter_names=list(run.configspace.keys()), objective_id1=objective_id1, - objective_id2=None, # replace with objective_id2 for multi-objective importance + objective_id2=objective_id2, budget_ids=budget_ids, method="global", n_hps=3, From 5923991145a41ed508a4b15c43e59303add8b476 Mon Sep 17 00:00:00 2001 From: Sarah Krebs Date: Fri, 30 Aug 2024 08:33:30 +0200 Subject: [PATCH 84/85] Add second objective to importance docs and update images --- deepcave/plugins/objective/pareto_front.py | 4 ++-- docs/images/plugins/ablation_paths.png | Bin 324838 -> 321067 bytes docs/images/plugins/importances.png | Bin 296123 -> 279664 bytes docs/plugins/ablation_paths.rst | 4 ++-- docs/plugins/importances.rst | 2 +- docs/plugins/pareto_front.rst | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/deepcave/plugins/objective/pareto_front.py b/deepcave/plugins/objective/pareto_front.py index f8793afc..25637f01 100644 --- a/deepcave/plugins/objective/pareto_front.py +++ b/deepcave/plugins/objective/pareto_front.py @@ -137,7 +137,7 @@ def get_input_layout(register: Callable) -> List[Any]: [ dbc.Col( [ - dbc.Label("Objective #1"), + dbc.Label("Objective 1"), dbc.Select( id=register("objective_id_1", ["value", "options"], type=int), placeholder="Select objective ...", @@ -147,7 +147,7 @@ def get_input_layout(register: Callable) -> List[Any]: ), dbc.Col( [ - dbc.Label("Objective #2"), + dbc.Label("Objective 2"), dbc.Select( id=register("objective_id_2", ["value", "options"], type=int), placeholder="Select objective ...", diff --git a/docs/images/plugins/ablation_paths.png b/docs/images/plugins/ablation_paths.png index e5f0620fa4810a0d60812a382200652f07be6331..4b7573fe75dc6882a302b8694d7a9b71026c4a33 100644 GIT binary patch literal 321067 zcmdqJXIN8f*Di{>)MWu)3m_n1K|!i?=_(>10!nWIL3)ey5+Eu9Yk`0U=_MeY5IO`B zNR}eKgdSRmN(m51=pocSdH31hD}F!Dx%PE_oPFkXMZ;uf&hdQ0cU>vWZwh+`@{R8mf@ek5&Wm!U%=0tKI*1E24Dvt ze;Y4*CPxpjyS=FQOD}tS4{s;15A6g_1-MA)@FF!YdmA5Tu*Wq+XLoz1$6nW@?p}M~ z>v&D_uB7y}yZ00%K0TNc2xmm`PDxrloLRxnh35 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b/deepcave/evaluators/mo_ablation.py @@ -315,7 +315,7 @@ def ablation( continue_ablation, max_hp, max_hp_performance, max_hp_std """ max_hp = "" - max_hp_difference = -np.inf + max_hp_difference = 0 for hp in hp_it: if hp in incumbent_config.keys() and hp in self.default_config.keys():

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Based on a weighting scheme, the two objectives are scalarized to +form a single objective for which the ablation path is calculated. The resulting plot shows the +contribution of each hyperparameter to the performance for different weightings, including the +default performance. Note that the y-axis displays the sum of the scalarized normalized performance. +The weightings are calculated based on the points on the pareto-front, which is the set of +non-dominated solutions. For more detail on this, please see the paper +`Hyperparameter Importance Analysis for Multi-Objective AutoML +`_. + +.. image:: ../images/plugins/ablation_paths_mo.png + Options ------- * **Objective**: Select the objective function you wish to analyze. diff --git a/docs/plugins/importances.rst b/docs/plugins/importances.rst index 83386433..5ce4a63b 100644 --- a/docs/plugins/importances.rst +++ b/docs/plugins/importances.rst @@ -35,6 +35,19 @@ global impact of hyperparameters. For more details on fANOVA, please refer to th `An Efficient Approach for Assessing Hyperparameter Importance `_. +Multi-Objective Importance +-------------------------- + +Both LPI and fANOVA can be applied to two objectives displaying how the importance changes from one +objective to the other. Based on a weighting scheme, the two objectives are scalarized to form a +single objective for which the importance is calculated. The resulting plot then shows the +scalarized importance for different weightings. The weightings are calculated based on the points +on the pareto front which is the set of non-dominated solutions. For more detail on this, please +see the paper `Hyperparameter Importance Analysis for Multi-Objective AutoML +`_. + +.. image:: ../images/plugins/importances_mo.png + Options ------- From 77c173ab7298dc7317a4214d394696f1a87111e9 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 16:37:21 +0200 Subject: [PATCH 48/85] Bug fixing --- deepcave/evaluators/mo_ablation.py | 7 ++++--- deepcave/evaluators/mo_fanova.py | 1 + deepcave/plugins/hyperparameter/ablation_paths.py | 4 +++- deepcave/plugins/hyperparameter/importances.py | 2 ++ 4 files changed, 10 insertions(+), 4 deletions(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index b1356531..ba08bf28 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -91,7 +91,7 @@ def get_importances(self) -> str: return self.df_importances.to_json() - def predict(self, cfg, weighting): + def predict(self, cfg: dict, weighting: np.ndarray) -> Tuple[float, float]: """ Predict the performance of the input configuration. @@ -142,6 +142,7 @@ def calculate( The seed for the surrogate model. Default is 0. """ + assert isinstance(objectives, list) for objective in objectives: assert isinstance(objective, Objective) @@ -283,7 +284,7 @@ def ablation( incumbent_config: Any, def_cost: Any, hp_it: List[str], - weighting: Tuple[float, float], + weighting: np.ndarray[Any, Any], ) -> Tuple[Any, Any, Any, Any]: """ Calculate the ablation importance for each hyperparameter. @@ -298,7 +299,7 @@ def ablation( The default cost. hp_it: List[str] A list of the HPs that still have to be looked at. - weighting : List[float] + weighting : np.ndarray[Any, Any] The weighting of the objective values. Returns diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index fb344fa0..e89f7a15 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -93,6 +93,7 @@ def calculate( ) # normalize objectives + assert isinstance(objectives, list) objectives_normed = list() for obj in objectives: normed = obj.name + "_normed" diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index a86279fe..77d1c285 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -25,7 +25,7 @@ - Ablation_Paths: This class provides a plugin for the visualization of the ablation paths. """ -from typing import Any, Callable, Dict, List +from typing import Any, Callable, Dict, List, Union import math from io import StringIO @@ -46,6 +46,7 @@ from deepcave.utils.cast import optional_int from deepcave.utils.layout import get_checklist_options, get_select_options, help_button from deepcave.utils.styled_plotty import get_color, save_image +from deepcave.runs.objective import Objective class AblationPaths(StaticPlugin): @@ -317,6 +318,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: RuntimeError If the number of trees is not specified. """ + objective: Union[Objective, list[Objective]] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index b23ba331..a0c5a67e 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -49,6 +49,7 @@ from deepcave.utils.layout import get_checklist_options, get_select_options, help_button from deepcave.utils.logs import get_logger from deepcave.utils.styled_plotty import get_color, save_image +from deepcave.runs.objective import Objective logger = get_logger(__name__) @@ -341,6 +342,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. If the method is not found. """ + objective: Union[Objective, list[Objective]] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] From e556fd4a29dffde6f5fa0c57ac3fb557365ff4d3 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 16:41:24 +0200 Subject: [PATCH 49/85] Bug fixing --- deepcave/evaluators/mo_ablation.py | 2 +- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- deepcave/plugins/hyperparameter/importances.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index ba08bf28..fafd0814 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -91,7 +91,7 @@ def get_importances(self) -> str: return self.df_importances.to_json() - def predict(self, cfg: dict, weighting: np.ndarray) -> Tuple[float, float]: + def predict(self, cfg: list[Any], weighting: np.ndarray) -> Tuple[float, float]: """ Predict the performance of the input configuration. diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 77d1c285..6441f71a 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -318,7 +318,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: RuntimeError If the number of trees is not specified. """ - objective: Union[Objective, list[Objective]] = None + objective: Union[Objective, list[Objective], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index a0c5a67e..ab6d18d7 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -342,7 +342,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. If the method is not found. """ - objective: Union[Objective, list[Objective]] = None + objective: Union[Objective, list[Objective], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] From 344bc121ce1407ac2473b1075b99233e1af7ec42 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 16:43:02 +0200 Subject: [PATCH 50/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 4 ++-- deepcave/plugins/hyperparameter/importances.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 6441f71a..9151bdb9 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -25,7 +25,7 @@ - Ablation_Paths: This class provides a plugin for the visualization of the ablation paths. """ -from typing import Any, Callable, Dict, List, Union +from typing import Any, Callable, Dict, List, Union, Optional import math from io import StringIO @@ -318,7 +318,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: RuntimeError If the number of trees is not specified. """ - objective: Union[Objective, list[Objective], None] = None + objective: Union[Optional[Objective], list[Optional[Objective]], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index ab6d18d7..ac810c1b 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -342,7 +342,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. If the method is not found. """ - objective: Union[Objective, list[Objective], None] = None + objective: Union[Optional[Objective], list[Optional[Objective]], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] From cf2bfaacb8eb3860c2558c63176306ad5d71dd7d Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 16:44:31 +0200 Subject: [PATCH 51/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- deepcave/plugins/hyperparameter/importances.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 9151bdb9..bda0858b 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -318,7 +318,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: RuntimeError If the number of trees is not specified. """ - objective: Union[Optional[Objective], list[Optional[Objective]], None] = None + objective: Union[Objective, list[Optional[Objective]], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index ac810c1b..40cab047 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -342,7 +342,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. If the method is not found. """ - objective: Union[Optional[Objective], list[Optional[Objective]], None] = None + objective: Union[Objective, list[Optional[Objective]], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] From 282bb95f72174512c2c228b6be87b8cadeeaf955 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 16:50:53 +0200 Subject: [PATCH 52/85] Bug fixing --- deepcave/evaluators/lpi.py | 2 +- deepcave/evaluators/mo_ablation.py | 2 +- deepcave/evaluators/mo_fanova.py | 2 +- deepcave/evaluators/mo_lpi.py | 2 +- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- deepcave/plugins/hyperparameter/importances.py | 3 ++- 6 files changed, 7 insertions(+), 6 deletions(-) diff --git a/deepcave/evaluators/lpi.py b/deepcave/evaluators/lpi.py index 8565ea28..76d232af 100644 --- a/deepcave/evaluators/lpi.py +++ b/deepcave/evaluators/lpi.py @@ -249,7 +249,7 @@ def calculate( self.variances = overall_var_per_tree self.importances = importances - def get_importances(self, hp_names: List[str]) -> Dict[str, Tuple[float, float]]: + def get_importances(self, hp_names: List[str]) -> Union[Dict[str, Tuple[float, float]], str]: """ Return the importances. diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index fafd0814..d61268f7 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -71,7 +71,7 @@ def __init__(self, run: AbstractRun): self.models: List = [] self.df_importances = pd.DataFrame([]) - def get_importances(self) -> str: + def get_importances(self, hp_names: Optional[list[str]], depth: int = 0, sort: bool = True) -> dict[Union[str, tuple[str, ...]], tuple[float, float, float, float]]: """ Return the importance scores. diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index e89f7a15..8ab99cbd 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -125,7 +125,7 @@ def calculate( def get_importances( self, hp_names: Optional[List[str]] = None, sort: bool = True - ) -> Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]]: + ) -> Union[Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]], str]: """ Return the importance scores from the passed Hyperparameter names. diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index 883c247a..a181d5a7 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -272,7 +272,7 @@ def calc_one_weighting(self) -> Dict[str, Tuple[float, float]]: } return imp_var_dict - def get_importances(self, hp_names: List[str]) -> str: + def get_importances(self, hp_names: List[str]) -> Union[Dict[str, Tuple[float, float]], str]: """ Return the importance scores from the passed Hyperparameter names. diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index bda0858b..cf61f5ef 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -318,7 +318,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: RuntimeError If the number of trees is not specified. """ - objective: Union[Objective, list[Optional[Objective]], None] = None + objective: Union[Objective, list[Objective], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 40cab047..374beb61 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -342,7 +342,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. If the method is not found. """ - objective: Union[Objective, list[Optional[Objective]], None] = None + objective: Union[Objective, list[Objective], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] @@ -399,6 +399,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: importances = evaluator.get_importances(hp_names) if isinstance(objective, list): + assert isinstance(importances, str) if any(pd.read_json(StringIO(importances))["importance"].isna()): logger.warning(f"Nan encountered in importance values for budget {budget}.") else: From 515f2cabc19b1fd63ab37a0265d2a9471c9ec47c Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 16:57:05 +0200 Subject: [PATCH 53/85] Bug fixing --- deepcave/evaluators/fanova.py | 2 +- deepcave/evaluators/mo_ablation.py | 2 +- deepcave/plugins/hyperparameter/ablation_paths.py | 4 ++-- deepcave/plugins/hyperparameter/importances.py | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/deepcave/evaluators/fanova.py b/deepcave/evaluators/fanova.py index 5876d9c7..dc9d8c3f 100644 --- a/deepcave/evaluators/fanova.py +++ b/deepcave/evaluators/fanova.py @@ -114,7 +114,7 @@ def calculate( def get_importances( self, hp_names: Optional[List[str]] = None, depth: int = 1, sort: bool = True - ) -> Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]]: + ) -> Union[Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]], str]: """ Return the importance scores from the passed Hyperparameter names. diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index d61268f7..fafd0814 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -71,7 +71,7 @@ def __init__(self, run: AbstractRun): self.models: List = [] self.df_importances = pd.DataFrame([]) - def get_importances(self, hp_names: Optional[list[str]], depth: int = 0, sort: bool = True) -> dict[Union[str, tuple[str, ...]], tuple[float, float, float, float]]: + def get_importances(self) -> str: """ Return the importance scores. diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index cf61f5ef..777789cd 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -318,7 +318,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: RuntimeError If the number of trees is not specified. """ - objective: Union[Objective, list[Objective], None] = None + objective: Union[Objective, list[Objective], list[Optional[Objective]], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] @@ -340,7 +340,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) if isinstance(objective, list): - data[budget_id] = evaluator.get_importances() + data[budget_id] = evaluator.get_importances(hp_names=None) else: performances = evaluator.get_ablation_performances() improvements = evaluator.get_ablation_improvements() diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 374beb61..93a5fccb 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -342,7 +342,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. If the method is not found. """ - objective: Union[Objective, list[Objective], None] = None + objective: Union[Objective, list[Objective], list[Optional[Objective]], None] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] From 2ceab26a4d5ba1fbe3a4a72d580ae3f7e01b0b0b Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:03:22 +0200 Subject: [PATCH 54/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 2 +- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 8ab99cbd..f19831bc 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -124,7 +124,7 @@ def calculate( ).reset_index(drop=True) def get_importances( - self, hp_names: Optional[List[str]] = None, sort: bool = True + self, hp_names: Optional[List[str]] = None, depth: int = 1, sort: bool = True ) -> Union[Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]], str]: """ Return the importance scores from the passed Hyperparameter names. diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 777789cd..35fe9997 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -340,7 +340,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) if isinstance(objective, list): - data[budget_id] = evaluator.get_importances(hp_names=None) + data[budget_id] = evaluator.get_importances() else: performances = evaluator.get_ablation_performances() improvements = evaluator.get_ablation_improvements() From 46cc7c4c406e6488bc0f902c5a3d74a43c09356b Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:11:40 +0200 Subject: [PATCH 55/85] Bug fixing --- deepcave/evaluators/mo_ablation.py | 3 +++ deepcave/evaluators/mo_fanova.py | 2 ++ deepcave/evaluators/mo_lpi.py | 2 ++ 3 files changed, 7 insertions(+) diff --git a/deepcave/evaluators/mo_ablation.py b/deepcave/evaluators/mo_ablation.py index fafd0814..a3070cfa 100644 --- a/deepcave/evaluators/mo_ablation.py +++ b/deepcave/evaluators/mo_ablation.py @@ -159,6 +159,9 @@ def calculate( df[normed] = (df[obj.name] - df[obj.name].min()) / ( df[obj.name].max() - df[obj.name].min() ) + + if obj.optimize == "upper": + df[normed] = 1-df[normed] objectives_normed.append(normed) # train one model per objective diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index f19831bc..41cd16a7 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -100,6 +100,8 @@ def calculate( df[normed] = (df[obj.name] - df[obj.name].min()) / ( df[obj.name].max() - df[obj.name].min() ) + if obj.optimize == "upper": + df[normed] = 1-df[normed] objectives_normed.append(normed) df = df.dropna(subset=objectives_normed) X = df[self.hp_names].to_numpy() diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index a181d5a7..fa21494e 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -127,6 +127,8 @@ def calculate( df[normed] = (df[obj.name] - df[obj.name].min()) / ( df[obj.name].max() - df[obj.name].min() ) + if obj.optimize == "upper": + df[normed] = 1-df[normed] objectives_normed.append(normed) df = df.dropna(subset=objectives_normed) X = df[self.hp_names].to_numpy() From 4d357d9d77ee7f3ba38286b8ec7276b7c250cb83 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:24:30 +0200 Subject: [PATCH 56/85] Bug fixing --- deepcave/evaluators/fanova.py | 2 +- deepcave/evaluators/lpi.py | 2 +- deepcave/evaluators/mo_fanova.py | 14 +++++--------- deepcave/evaluators/mo_lpi.py | 2 +- deepcave/plugins/hyperparameter/importances.py | 5 +++-- 5 files changed, 11 insertions(+), 14 deletions(-) diff --git a/deepcave/evaluators/fanova.py b/deepcave/evaluators/fanova.py index dc9d8c3f..5876d9c7 100644 --- a/deepcave/evaluators/fanova.py +++ b/deepcave/evaluators/fanova.py @@ -114,7 +114,7 @@ def calculate( def get_importances( self, hp_names: Optional[List[str]] = None, depth: int = 1, sort: bool = True - ) -> Union[Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]], str]: + ) -> Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]]: """ Return the importance scores from the passed Hyperparameter names. diff --git a/deepcave/evaluators/lpi.py b/deepcave/evaluators/lpi.py index 76d232af..8565ea28 100644 --- a/deepcave/evaluators/lpi.py +++ b/deepcave/evaluators/lpi.py @@ -249,7 +249,7 @@ def calculate( self.variances = overall_var_per_tree self.importances = importances - def get_importances(self, hp_names: List[str]) -> Union[Dict[str, Tuple[float, float]], str]: + def get_importances(self, hp_names: List[str]) -> Dict[str, Tuple[float, float]]: """ Return the importances. diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index 41cd16a7..f26c5e3f 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -125,9 +125,9 @@ def calculate( columns={0: "importance", 1: "variance", "index": "hp_name"} ).reset_index(drop=True) - def get_importances( + def get_importances_( self, hp_names: Optional[List[str]] = None, depth: int = 1, sort: bool = True - ) -> Union[Dict[Union[str, Tuple[str, ...]], Tuple[float, float, float, float]], str]: + ) -> str: """ Return the importance scores from the passed Hyperparameter names. @@ -153,11 +153,7 @@ def get_importances( if self.importances_ is None: raise RuntimeError("Importance scores must be calculated first.") - res = ( - self.importances_.sort_values(by="importance", ascending=False) - if sort - else self.importances_ - ) if hp_names: - res = res.loc[self.importances_["hp_name"].isin(hp_names)] - return res.to_json() + return self.importances_.loc[self.importances_["hp_name"].isin(hp_names)] + else: + return self.importances_.to_json() diff --git a/deepcave/evaluators/mo_lpi.py b/deepcave/evaluators/mo_lpi.py index fa21494e..d8a66770 100644 --- a/deepcave/evaluators/mo_lpi.py +++ b/deepcave/evaluators/mo_lpi.py @@ -274,7 +274,7 @@ def calc_one_weighting(self) -> Dict[str, Tuple[float, float]]: } return imp_var_dict - def get_importances(self, hp_names: List[str]) -> Union[Dict[str, Tuple[float, float]], str]: + def get_importances_(self, hp_names: List[str]) -> str: """ Return the importance scores from the passed Hyperparameter names. diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 93a5fccb..3fb4d13c 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -397,12 +397,13 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) - importances = evaluator.get_importances(hp_names) + if isinstance(objective, list): - assert isinstance(importances, str) + importances = evaluator.get_importances_(hp_names) if any(pd.read_json(StringIO(importances))["importance"].isna()): logger.warning(f"Nan encountered in importance values for budget {budget}.") else: + importances = evaluator.get_importances(hp_names) if any(np.isnan(val) for value in importances.values() for val in value): logger.warning(f"Nan encountered in importance values for budget {budget}.") data[budget_id] = importances From 2cf321334d8c4d2e344ce1ab3d8e26916fdbc255 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:27:56 +0200 Subject: [PATCH 57/85] Bug fixing --- deepcave/plugins/hyperparameter/importances.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 3fb4d13c..f0a153aa 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -342,7 +342,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. If the method is not found. """ - objective: Union[Objective, list[Objective], list[Optional[Objective]], None] = None + objective: Optional[Union[Objective, List[Objective]]] = None objective = run.get_objective(inputs["objective_id1"]) if inputs["objective_id2"] not in (None, -1): objective = [objective, run.get_objective(inputs["objective_id2"])] @@ -399,10 +399,12 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: if isinstance(objective, list): + assert isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances_(hp_names) if any(pd.read_json(StringIO(importances))["importance"].isna()): logger.warning(f"Nan encountered in importance values for budget {budget}.") else: + assert isinstance(evaluator, (LocalEvaluator, GlobalEvaluator)) importances = evaluator.get_importances(hp_names) if any(np.isnan(val) for value in importances.values() for val in value): logger.warning(f"Nan encountered in importance values for budget {budget}.") From 6e0f69a8729b4c872b340477ef6da71e8a13dbb0 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:32:03 +0200 Subject: [PATCH 58/85] Bug fixing --- deepcave/evaluators/mo_fanova.py | 2 +- deepcave/plugins/hyperparameter/importances.py | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/deepcave/evaluators/mo_fanova.py b/deepcave/evaluators/mo_fanova.py index f26c5e3f..bdbbf66a 100644 --- a/deepcave/evaluators/mo_fanova.py +++ b/deepcave/evaluators/mo_fanova.py @@ -24,7 +24,7 @@ - fANOVA: Calculate and provide midpoints and sizes. """ -from typing import Dict, List, Optional, Tuple, Union +from typing import List, Optional, Union import pandas as pd diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index f0a153aa..6c3fdae5 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -397,14 +397,13 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: assert isinstance(budget, (int, float)) evaluator.calculate(objective, budget, n_trees=n_trees, seed=0) - if isinstance(objective, list): assert isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances_(hp_names) if any(pd.read_json(StringIO(importances))["importance"].isna()): logger.warning(f"Nan encountered in importance values for budget {budget}.") else: - assert isinstance(evaluator, (LocalEvaluator, GlobalEvaluator)) + assert not isinstance(evaluator, (MOLPI, MOfANOVA)) importances = evaluator.get_importances(hp_names) if any(np.isnan(val) for value in importances.values() for val in value): logger.warning(f"Nan encountered in importance values for budget {budget}.") From 557182f9d1cef7fed3d93301caf8b69f69596b6e Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:38:13 +0200 Subject: [PATCH 59/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index 35fe9997..e8a33c40 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -595,7 +595,7 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore # Update the layout fig.update_layout( xaxis_title="Weight for " + objective1, - yaxis_title="Sum of the weighted normalized performance", + yaxis_title="Sum of the weighted\n normalized performance", xaxis=dict(range=[0, 1], tickangle=-45), yaxis=dict( range=[ From f1124959cc58a8863fdecc2335fe19c2a345804f Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:39:58 +0200 Subject: [PATCH 60/85] Bug fixing --- deepcave/plugins/hyperparameter/ablation_paths.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/ablation_paths.py b/deepcave/plugins/hyperparameter/ablation_paths.py index e8a33c40..7b202859 100644 --- a/deepcave/plugins/hyperparameter/ablation_paths.py +++ b/deepcave/plugins/hyperparameter/ablation_paths.py @@ -595,7 +595,7 @@ def load_outputs_mo(run, inputs, outputs) -> List[go.Figure]: # type: ignore # Update the layout fig.update_layout( xaxis_title="Weight for " + objective1, - yaxis_title="Sum of the weighted\n normalized performance", + yaxis_title="Sum of the weighted
normalized performance", xaxis=dict(range=[0, 1], tickangle=-45), yaxis=dict( range=[ From bcb87e75c7f0461df1ce5b7e5708499fd0a0d930 Mon Sep 17 00:00:00 2001 From: daphne12345 Date: Thu, 29 Aug 2024 17:43:13 +0200 Subject: [PATCH 61/85] Bug fixing --- deepcave/plugins/hyperparameter/importances.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/deepcave/plugins/hyperparameter/importances.py b/deepcave/plugins/hyperparameter/importances.py index 6c3fdae5..e28f2a5d 100644 --- a/deepcave/plugins/hyperparameter/importances.py +++ b/deepcave/plugins/hyperparameter/importances.py @@ -342,7 +342,7 @@ def process(run: AbstractRun, inputs: Dict[str, Any]) -> Dict[str, Any]: If the number of trees is not specified. If the method is not found. 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