Merge pull request #52 from lixfz/master

Rename trail to trial

Author: lixfz

deeptables/models/hyper_dt.py

@@ -201,14 +201,14 @@ def _get_estimator(self, space_sample):
         estimator = DTEstimator(space_sample, self.cache_preprocessed_data, self.cache_home, **self.config_kwargs)
         return estimator
 
-    def export_trail_configuration(self, trail):
+    def export_trial_configuration(self, trial):
         default_conf = ModelConfig()
-        new_conf = trail.space_sample.DT_Module.config
+        new_conf = trial.space_sample.DT_Module.config
         conf_set = []
         for f in default_conf._fields:
             if new_conf.__getattribute__(f) != default_conf.__getattribute__(f):
                 conf_set.append(f'\n\t{f}={new_conf.__getattribute__(f)}')
-        str = f'ModelConfig({",".join(conf_set)})\n\nfit params:{trail.space_sample.fit_params.param_values}'
+        str = f'ModelConfig({",".join(conf_set)})\n\nfit params:{trial.space_sample.fit_params.param_values}'
         return str
 
 

tests/models/hyper_dt_regression_test.py

@@ -42,9 +42,9 @@ def test_boston(self):
                           'dnn_activation': 'relu',
                       },
                       )
-        hdt.search(self.X_train, self.y_train, self.X_test, self.y_test, max_trails=3)
+        hdt.search(self.X_train, self.y_train, self.X_test, self.y_test, max_trials=3)
 
-        best_trial = hdt.get_best_trail()
+        best_trial = hdt.get_best_trial()
 
         estimator = hdt.final_train(best_trial.space_sample, self.X, self.y)
         score = estimator.predict(self.X_test)

tests/models/hyper_dt_test.py

@@ -23,7 +23,7 @@ def bankdata(self):
         hdt = HyperDT(rs,
                       callbacks=[SummaryCallback(), FileLoggingCallback(rs, output_dir=f'{homedir}/hyn_logs')],
                       reward_metric='accuracy',
-                      max_trails=3,
+                      max_trials=3,
                       dnn_params={
                           'hidden_units': ((256, 0, False), (256, 0, False)),
                           'dnn_activation': 'relu',
@@ -37,8 +37,8 @@ def bankdata(self):
         y_test = df_test.pop('y')
 
         hdt.search(df_train, y, df_test, y_test)
-        assert hdt.best_model
-        best_trial = hdt.get_best_trail()
+        best_trial = hdt.get_best_trial()
+        assert best_trial
 
         estimator = hdt.final_train(best_trial.space_sample, df_train, y)
         score = estimator.predict(df)
@@ -80,8 +80,8 @@ def test_hyper_dt(self):
 
         y = np.random.randint(0, 2, size=(100), dtype='int')
         df = pd.DataFrame({'x1': x1, 'x2': x2, 'x3': x3, 'x4': x4})
-        hdt.search(df, y, df, y, max_trails=3, epochs=1)
-        best_trial = hdt.get_best_trail()
+        hdt.search(df, y, df, y, max_trials=3, epochs=1)
+        best_trial = hdt.get_best_trial()
         model = hdt.load_estimator(best_trial.model_file)
         assert model
         score = model.predict(df)

tests/models/run_dt.py

@@ -12,12 +12,12 @@
 from hypernets.core.callbacks import SummaryCallback, FileLoggingCallback
 from hypernets.searchers.mcts_searcher import MCTSSearcher
 from hypernets.searchers.evolution_searcher import EvolutionSearcher
-from hypernets.core.trial import DiskTrailStore
+from hypernets.core.trial import DiskTrialStore
 from deeptables.datasets import dsutils
 from sklearn.model_selection import train_test_split
 from .. import homedir
 
-disk_trail_store = DiskTrailStore(f'{homedir}/trail_store')
+disk_trial_store = DiskTrialStore(f'{homedir}/trial_store')
 
 # searcher = MCTSSearcher(mini_dt_space, max_node_space=0,optimize_direction=OptimizeDirection.Maximize)
 # searcher = RandomSearcher(mini_dt_space, optimize_direction=OptimizeDirection.Maximize)
@@ -41,9 +41,9 @@
 y = df_train.pop(14)
 y_test = df_test.pop(14)
 # dataset_id='adult_whole_data',
-hdt.search(df_train, y, df_test, y_test, max_trails=2000, batch_size=256, epochs=10, verbose=1, )
-assert hdt.best_model
-best_trial = hdt.get_best_trail()
+hdt.search(df_train, y, df_test, y_test, max_trials=2000, batch_size=256, epochs=10, verbose=1, )
+best_trial = hdt.get_best_trial()
+assert best_trial
 
 estimator = hdt.final_train(best_trial.space_sample, df_train, y)
 score = estimator.predict(df_test)