def test_boston(self):
print("Loading datasets...")
boston_dataset = load_boston()
df_train = pd.DataFrame(boston_dataset.data)
df_train.columns = boston_dataset.feature_names
self.y = pd.Series(boston_dataset.target)
self.X = df_train
self.X_train, \
self.X_test, \
self.y_train, \
self.y_test = train_test_split(self.X, self.y, test_size=0.2, random_state=42)
rs = RandomSearcher(tiny_dt_space, optimize_direction=OptimizeDirection.Maximize, )
hdt = HyperDT(rs,
callbacks=[SummaryCallback(), FileStorageLoggingCallback(rs, output_dir=f'hotexamples_com/hyn_logs')],
reward_metric='RootMeanSquaredError',
dnn_params={
'hidden_units': ((256, 0, False), (256, 0, False)),
'dnn_activation': 'relu',
},
)
hdt.search(self.X_train, self.y_train, self.X_test, self.y_test, max_trials=3)
best_trial = hdt.get_best_trial()
estimator = hdt.final_train(best_trial.space_sample, self.X, self.y)
score = estimator.predict(self.X_test)
result = estimator.evaluate(self.X_test, self.y_test)
assert result
assert isinstance(estimator.model, DeepTable)
def test_bankdata(self):
rs = RandomSearcher(
tiny_dt_space,
optimize_direction=OptimizeDirection.Maximize,
)
hdt = HyperDT(
rs,
callbacks=[
SummaryCallback(),
FileStorageLoggingCallback(rs,
output_dir=f'hotexamples_com/hyn_logs')
],
# reward_metric='accuracy',
reward_metric='AUC',
dnn_params={
'hidden_units': ((256, 0, False), (256, 0, False)),
'dnn_activation': 'relu',
},
)
df = dsutils.load_bank().sample(n=2000, random_state=9527)
df.drop(['id'], axis=1, inplace=True)
df_train, df_test = train_test_split(df,
test_size=0.2,
random_state=42)
y = df_train.pop('y')
y_test = df_test.pop('y')
hdt.search(
df_train,
y,
df_test,
y_test,
max_trials=3,
)
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)
result = estimator.evaluate(df_test, y_test)
assert len(score) == len(y_test)
assert result
assert isinstance(estimator.model, DeepTable)
homedir = f'{consts.PROJECT_NAME}_run_dt_{time.strftime("%Y%m%d%H%M%S")}'
disk_trial_store = DiskTrialStore(f'hotexamples_com/trial_store')
# searcher = MCTSSearcher(mini_dt_space, max_node_space=0,optimize_direction=OptimizeDirection.Maximize)
# searcher = RandomSearcher(mini_dt_space, optimize_direction=OptimizeDirection.Maximize)
searcher = EvolutionSearcher(mini_dt_space,
200,
100,
regularized=True,
candidates_size=30,
optimize_direction=OptimizeDirection.Maximize)
hdt = HyperDT(searcher,
callbacks=[
SummaryCallback(),
FileStorageLoggingCallback(searcher,
output_dir=f'hotexamples_com/hyn_logs')
],
reward_metric='AUC',
earlystopping_patience=1)
space = mini_dt_space()
assert space.combinations == 589824
space2 = default_dt_space()
assert space2.combinations == 3559292928
df = dsutils.load_adult()
# df.drop(['id'], axis=1, inplace=True)
df_train, df_test = train_test_split(df, test_size=0.2, random_state=42)
X = df_train
y = df_train.pop(14)
y_test = df_test.pop(14)