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(mini_dt_space, optimize_direction=OptimizeDirection.Maximize, )
hdt = HyperDT(rs,
callbacks=[SummaryCallback(), FileLoggingCallback(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(frac=0.1, 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)
def test_hyper_dt(self):
rs = RandomSearcher(mini_dt_space, optimize_direction=OptimizeDirection.Maximize, )
hdt = HyperDT(rs,
callbacks=[SummaryCallback()],
reward_metric='accuracy',
dnn_params={
'hidden_units': ((256, 0, False), (256, 0, False)),
'dnn_activation': 'relu',
},
cache_preprocessed_data=True,
cache_home=homedir + '/cache'
)
x1 = np.random.randint(0, 10, size=(100), dtype='int')
x2 = np.random.randint(0, 2, size=(100)).astype('str')
x3 = np.random.randint(0, 2, size=(100)).astype('str')
x4 = np.random.normal(0.0, 1.0, size=(100))
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_trials=3, epochs=1)
best_trial = hdt.get_best_trial()
model = hdt.load_estimator(best_trial.model_file)
assert model
score = model.predict(df)
result = model.evaluate(df, y)
assert len(score) == 100
assert result
assert isinstance(model, DeepTable)
estimator = hdt.final_train(best_trial.space_sample, df, y, epochs=1)
score = estimator.predict(df)
result = estimator.evaluate(df, y)
assert len(score) == 100
assert result
assert isinstance(estimator.model, DeepTable)
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)
# dataset_id='adult_whole_data',
hdt.search(
df_train,
y,
df_test,
y_test,
max_trials=3,
batch_size=256,
epochs=1,
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)
result = estimator.evaluate(df_test, y_test)
print(result)