def test_forecast_tcmf(self):
model = TCMFForecaster(y_iters=1,
init_FX_epoch=1,
max_FX_epoch=1,
max_TCN_epoch=1,
alt_iters=2)
horizon = np.random.randint(1, 50)
# construct data
id = np.arange(300)
data = np.random.rand(300, 480)
input = dict({'data': data})
with self.assertRaises(Exception) as context:
model.fit(input)
self.assertTrue("key `y` doesn't exist in x" in str(context.exception))
input = dict({'id': id, 'y': data})
with self.assertRaises(Exception) as context:
model.is_distributed()
self.assertTrue('You should run fit before calling is_distributed()'
in str(context.exception))
model.fit(input)
assert not model.is_distributed()
with self.assertRaises(Exception) as context:
model.fit(input)
self.assertTrue('This model has already been fully trained' in str(context.exception))
with self.assertRaises(Exception) as context:
model.fit(input, incremental=True)
self.assertTrue('NotImplementedError' in context.exception.__class__.__name__)
with tempfile.TemporaryDirectory() as tempdirname:
model.save(tempdirname)
loaded_model = TCMFForecaster.load(tempdirname, distributed=False)
yhat = model.predict(x=None, horizon=horizon)
yhat_loaded = loaded_model.predict(x=None, horizon=horizon)
yhat_id = yhat_loaded["id"]
assert (yhat_id == id).all()
yhat = yhat["prediction"]
yhat_loaded = yhat_loaded["prediction"]
assert yhat.shape == (300, horizon)
assert (yhat == yhat_loaded).all()
target_value = np.random.rand(300, horizon)
target_value = dict({"y": target_value})
assert model.evaluate(x=None, target_value=target_value, metric=['mse'])
def test_forecast_tcmf_without_id(self):
model = TCMFForecaster(y_iters=1,
init_FX_epoch=1,
max_FX_epoch=1,
max_TCN_epoch=1,
alt_iters=2)
horizon = np.random.randint(1, 50)
# construct data
id = np.arange(200)
data = np.random.rand(300, 480)
input = dict({'y': "abc"})
with self.assertRaises(Exception) as context:
model.fit(input)
self.assertTrue("the value of y should be an ndarray" in str(context.exception))
input = dict({'id': id, 'y': data})
with self.assertRaises(Exception) as context:
model.fit(input)
self.assertTrue("the length of the id array should be equal to the number of"
in str(context.exception))
input = dict({'y': data})
model.fit(input)
assert not model.is_distributed()
with self.assertRaises(Exception) as context:
model.fit(input)
self.assertTrue('This model has already been fully trained' in str(context.exception))
with tempfile.TemporaryDirectory() as tempdirname:
model.save(tempdirname)
loaded_model = TCMFForecaster.load(tempdirname, distributed=False)
yhat = model.predict(x=None, horizon=horizon)
yhat_loaded = loaded_model.predict(x=None, horizon=horizon)
assert "id" not in yhat_loaded
yhat = yhat["prediction"]
yhat_loaded = yhat_loaded["prediction"]
assert yhat.shape == (300, horizon)
assert (yhat == yhat_loaded).all()
target_value = np.random.rand(300, horizon)
target_value_fake = dict({"data": target_value})
with self.assertRaises(Exception) as context:
model.evaluate(x=None, target_value=target_value_fake, metric=['mse'])
self.assertTrue("key y doesn't exist in y" in str(context.exception))
target_value = dict({"y": target_value})
model.evaluate(x=None, target_value=target_value, metric=['mse'])
def test_forecast_tcmf_xshards(self):
from zoo.orca import OrcaContext
import zoo.orca.data.pandas
import pandas as pd
OrcaContext.pandas_read_backend = "pandas"
def preprocessing(df, id_name, y_name):
id = df.index
data = df.to_numpy()
result = dict({id_name: id, y_name: data})
return result
def postprocessing(pred_results, output_dt_col_name):
id_arr = pred_results["id"]
pred_results = pred_results["prediction"]
pred_results = np.concatenate((np.expand_dims(id_arr, axis=1), pred_results), axis=1)
final_df = pd.DataFrame(pred_results, columns=["id"] + output_dt_col_name)
final_df.id = final_df.id.astype("int")
final_df = final_df.set_index("id")
final_df.columns.name = "datetime"
final_df = final_df.unstack().reset_index().rename({0: "prediction"}, axis=1)
return final_df
def get_pred(d):
return d["prediction"]
model = TCMFForecaster(y_iters=1,
init_FX_epoch=1,
max_FX_epoch=1,
max_TCN_epoch=1,
alt_iters=2)
with tempfile.NamedTemporaryFile() as temp:
data = np.random.rand(300, 480)
df = pd.DataFrame(data)
df.to_csv(temp.name)
shard = zoo.orca.data.pandas.read_csv(temp.name)
shard.cache()
shard_train = shard.transform_shard(preprocessing, 'id', 'data')
with self.assertRaises(Exception) as context:
model.fit(shard_train)
self.assertTrue("key `y` doesn't exist in x" in str(context.exception))
shard_train = shard.transform_shard(preprocessing, 'cid', 'y')
with self.assertRaises(Exception) as context:
model.fit(shard_train)
self.assertTrue("key `id` doesn't exist in x" in str(context.exception))
with self.assertRaises(Exception) as context:
model.is_distributed()
self.assertTrue('You should run fit before calling is_distributed()'
in str(context.exception))
shard_train = shard.transform_shard(preprocessing, 'id', 'y')
model.fit(shard_train)
assert model.is_distributed()
with self.assertRaises(Exception) as context:
model.fit(shard_train)
self.assertTrue('This model has already been fully trained' in str(context.exception))
with self.assertRaises(Exception) as context:
model.fit(shard_train, incremental=True)
self.assertTrue('NotImplementedError' in context.exception.__class__.__name__)
with tempfile.TemporaryDirectory() as tempdirname:
model.save(tempdirname + "/model")
loaded_model = TCMFForecaster.load(tempdirname + "/model", distributed=True)
horizon = np.random.randint(1, 50)
yhat_shard_origin = model.predict(x=None, horizon=horizon)
yhat_list_origin = yhat_shard_origin.collect()
yhat_list_origin = list(map(get_pred, yhat_list_origin))
yhat_shard = loaded_model.predict(x=None, horizon=horizon)
yhat_list = yhat_shard.collect()
yhat_list = list(map(get_pred, yhat_list))
yhat_origin = np.concatenate(yhat_list_origin)
yhat = np.concatenate(yhat_list)
assert yhat.shape == (300, horizon)
assert (yhat == yhat_origin).all()
output_dt_col_name = pd.date_range(start='2020-05-01', periods=horizon, freq='H').to_list()
yhat_df_shards = yhat_shard.transform_shard(postprocessing, output_dt_col_name)
final_df_list = yhat_df_shards.collect()
final_df = pd.concat(final_df_list)
final_df.sort_values("datetime", inplace=True)
assert final_df.shape == (300 * horizon, 3)
OrcaContext.pandas_read_backend = "spark"