add fix for ga

autotm/algorithms_for_tuning/genetic_algorithm/ga.py

@@ -701,7 +701,7 @@ def apply_nelder_mead(self, starting_points_set, num_gen, num_iterations=2):
             new_population.append(make_individual(dto=solution_dto))
         return new_population
 
-    def run(self, verbose=False) -> Individual:
+    def run(self, verbose=False, visualize_results=False) -> Individual:
         self.evaluations_counter = 0
         ftime = str(int(time.time()))
 
@@ -1018,7 +1018,10 @@ def run(self, verbose=False) -> Individual:
             best_solution = population[0]
             log_best_solution(best_solution, alg_args=" ".join(sys.argv), is_tmp=True)
 
-        self.metric_collector.save_and_visualise_trace()
+        if visualize_results:
+            self.metric_collector.save_and_visualise_trace()
+        else:
+            self.metric_collector.save_trace()
 
         logger.info(f"Y: {y}")
         best_individual = population[0]

autotm/visualization/dynamic_tracker.py

@@ -1,5 +1,6 @@
 import os
 import time
+import warnings
 
 import numpy as np
 import pandas as pd
@@ -189,7 +190,11 @@ def get_metric_df(self):
                 cur_df = pd.DataFrame(cur_df_dict)
                 cur_df[GENERATION_COL] = gen
                 dfs.append(cur_df)
-            self.mutation_df = pd.concat(dfs)
+            if len(dfs) > 0:
+                self.mutation_df = pd.concat(dfs)
+            else:
+                warnings.warn("No mutations changes have been found to save", RuntimeWarning)
+                self.mutation_df = pd.DataFrame([])
         if self.crossover_df is not None:
             print("Crossover df already exists")
         else:
@@ -227,11 +232,13 @@ def write_metrics_to_file(self):
             )
         )
 
-    def save_and_visualise_trace(self, plot_mutation_effectiveness=False):
+    def save_trace(self):
         self.get_metric_df()
-        # save params
         self.write_metrics_to_file()
 
+    def save_and_visualise_trace(self, plot_mutation_effectiveness=False):
+        self.save_trace()
+
         # traces vis
         graph_template = "plotly_white"
 

tests/integration/test_fit_predict.py

@@ -17,8 +17,8 @@ def check_predictions(autotm: AutoTM, df: pd.DataFrame, mixtures: ArrayLike):
 
     assert n_samples_mixture == n_samples
     assert n_topics_mixture == n_topics
-
-    # TODO: check for nullability of the content in mixtures
+    assert (~mixtures.isna()).all().all()
+    assert (~mixtures.isnull()).all().all()
 
 
 def test_fit_predict():