fix imports (#119)

* fix importa * replace deprecated & fix legenda * minor * fix imports * minor * minor
aimclub · Jun 14, 2023 · 1a52230 · 1a52230
1 parent 072f027
commit 1a52230
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Showing 9 changed files with 19 additions and 14 deletions.
diff --git a/.github/workflows/unit-build.yml b/.github/workflows/unit-build.yml
@@ -31,7 +31,6 @@ jobs:
         pip install .[docs]
         pip install .[profilers]
         pip install pytest-cov
-        pip install -r requirements_adaptive.txt
     - name: Test with pytest
       run: |
         pytest --cov=golem test/unit

diff --git a/examples/molecule_search/guacamol_benchmark.py b/examples/molecule_search/guacamol_benchmark.py
@@ -99,7 +99,7 @@ def generate_optimized_molecules(self, scoring_function: ScoringFunction, number
         # Take only the first graph's appearance in history
         individuals \
             = list({hash(self.graph_gen_params.adapter.restore(ind.graph)): ind
-                    for gen in history.individuals
+                    for gen in history.generations
                     for ind in reversed(list(gen))}.values())
 
         top_individuals = sorted(individuals,

diff --git a/experiments/mab/mab_synthetic_experiment_helper.py b/experiments/mab/mab_synthetic_experiment_helper.py
@@ -126,9 +126,9 @@ def show_action_probabilities(self, bandit_type: MutationAgentTypeEnum, stats_ac
         else:
             centers = sorted(self.cluster.cluster_centers_)
             for i in range(self.cluster.n_clusters):
-                titles = [title + f' for cluster with center {int(centers[i])}' for title in titles]
+                titles_centers = [title + f' for cluster with center {int(centers[i])}' for title in titles]
                 plot_action_values(stats=stats_action_value_log[i], action_tags=actions,
-                                   titles=titles)
+                                   titles=titles_centers)
                 plt.show()
 
     def show_average_action_probabilities(self, show_action_probabilities: dict, actions):
@@ -193,7 +193,7 @@ def initial_population_func(graph_size: List[int] = None, pop_size: int = None,
     launch_num = 1
     target_size = 50
 
-    bandits_to_compare = [MutationAgentTypeEnum.contextual_bandit]
+    bandits_to_compare = [MutationAgentTypeEnum.contextual_bandit, MutationAgentTypeEnum.bandit]
     setup_parameters_func = partial(setup_parameters, target_size=target_size, trial_timeout=timeout)
     initial_population_func = partial(initial_population_func,
                                       graph_size=[random.randint(5, 10) for _ in range(10)] +
@@ -204,5 +204,5 @@ def initial_population_func(graph_size: List[int] = None, pop_size: int = None,
                                           n_clusters=2, is_visualize=True)
     helper.compare_bandits(initial_population_func=initial_population_func,
                            setup_parameters=setup_parameters_func)
-    # helper.show_boxplots()
-    # helper.show_fitness_lines()
+    helper.show_boxplots()
+    helper.show_fitness_lines()
diff --git a/golem/core/optimisers/adaptive/neural_mab.py b/golem/core/optimisers/adaptive/neural_mab.py
@@ -2,7 +2,13 @@
 import math
 from typing import List, Any, Union, Dict
 
-import torch
+from golem.utilities.requirements_notificator import warn_requirement
+
+try:
+    import torch
+except ModuleNotFoundError:
+    warn_requirement('torch', 'other_requirements/requirements_adaptive.txt')
+
 import numpy as np
 from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy
 from mabwiser.utils import Arm, Constants, Num

diff --git a/golem/visualisation/opt_history/diversity.py b/golem/visualisation/opt_history/diversity.py
@@ -46,7 +46,7 @@ def plot_diversity_dynamic_gif(history: 'OptHistory',
     metric_names = history.objective.metric_names
     # dtype=float removes None, puts np.nan
     # indexed by [population, metric, individual] after transpose (.T)
-    pops = history.individuals[1:-1]  # ignore initial pop and final choices
+    pops = history.generations[1:-1]  # ignore initial pop and final choices
     fitness_distrib = [np.array([ind.fitness.values for ind in pop], dtype=float).T
                        for pop in pops]
 
@@ -95,7 +95,7 @@ def update_axes(iframe: int):
 def plot_diversity_dynamic(history: 'OptHistory',
                            show: bool = True, save_path: Optional[str] = None, dpi: int = 100):
     labels = history.objective.metric_names
-    h = history.individuals[:-1]  # don't consider final choices
+    h = history.generations[:-1]  # don't consider final choices
     xs = np.arange(len(h))
 
     # Compute diversity by metrics

diff --git a/golem/visualisation/opt_history/fitness_line.py b/golem/visualisation/opt_history/fitness_line.py
@@ -294,7 +294,7 @@ def plot_average_fitness_line_per_generations(axis: plt.Axes, histories, label:
 
     fitness_value_per_generation = []
     for history in histories:
-        generations = history.individuals
+        generations = history.generations
         for gen_num, gen in enumerate(generations):
             for ind in gen:
                 if ind.native_generation != gen_num:

diff --git a/golem/visualisation/opt_viz_extra.py b/golem/visualisation/opt_viz_extra.py
@@ -47,7 +47,7 @@ def pareto_gif_create(self,
                           objectives_names: Tuple[str] = ('ROC-AUC', 'Complexity')):
         files = []
         pareto_fronts = self.history.archive_history
-        individuals = self.history.individuals
+        individuals = self.history.generations
         array_for_analysis = individuals if individuals else pareto_fronts
         all_objectives = extract_objectives(array_for_analysis, objectives_numbers)
         min_x, max_x = min(all_objectives[0]) - 0.01, max(all_objectives[0]) + 0.01
@@ -173,7 +173,7 @@ def _create_boxplot(self, individuals: List[Any], generation_num: int = None,
         plt.savefig(path, bbox_inches='tight')
 
     def boxplots_gif_create(self, objectives_names: Tuple[str] = ('ROC-AUC', 'Complexity')):
-        individuals = self.history.individuals
+        individuals = self.history.generations
         objectives = extract_objectives(individuals)
         objectives = list(itertools.chain(*objectives))
         min_y, max_y = min(objectives), max(objectives)

diff --git a/requirements_adaptive.txt → other_requirements/requirements_adaptive.txt b/requirements_adaptive.txt → other_requirements/requirements_adaptive.txt
diff --git a/test/unit/optimizers/test_composing_history.py b/test/unit/optimizers/test_composing_history.py
@@ -66,7 +66,7 @@ def _test_individuals_in_history(history: OptHistory):
     uids = set()
     ids = set()
     for ind in itertools.chain(*history.generations):
-        # All individuals in `history.individuals` must have a native generation.
+        # All individuals in `history.generations` must have a native generation.
         assert ind.has_native_generation
         assert ind.fitness
         if ind.native_generation == 0: