def run_ESRNN():
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
path_daily = r'C:\Users\xxxli\Desktop\Daily'
dic_daily = preprocess.read_file(path_daily)
series_list = []
for k, v in dic_daily.items():
ticker_name = k
df, cat = v
df = preprocess.single_price(df, ticker_name) # column = [ticker]
series_list.append(DataSeries(cat, 'daily', df))
collect = DataCollection('universe daily', series_list)
train_dc, test_dc = collect.split(numTest = 24)
m = ModelESRNN( max_epochs = 15,
batch_size = 32, dilations=[[1,3], [7, 14]],
input_size = 12, output_size = 24,
device = device)
m.train(train_dc)
y_test = m.predict(test_dc)
y_test_df = y_test.to_df()
y_test_df.to_csv('hyper_ESRNN_1.csv')
def test_MP_class(self):
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
path_monthly = os.path.join('test','Data','Monthly')
dic_monthly = DP.read_file(path_monthly)
n_assets = 1
time_series_group = []
for i in range(n_assets):
df = dic_monthly[list(dic_monthly.keys())[i]]
ds = DataSeries('ETF', 'monthly', df[0])
time_series_group.append(ds)
input_dc = DataCollection('test1', time_series_group)
m = ModelESRNN(seasonality = [12], input_size = 4, output_size = 12, device=device)
train_dc, test_dc = input_dc.split(numTest = 12)
m.train(train_dc)
forecast_dc = m.predict(test_dc)
# train_dc.to_df().to_csv('insample.csv')
test_dc.to_df().to_csv('test.csv')
# forecast_dc.to_df().to_csv('forecast.csv')
mn = MN.ModelNaive2(2, train_dc)
naive2_dc = mn.fit_and_generate_prediction(12, 'MS')
naive2_dc.to_df().to_csv('naive.csv')
mp = MP.ModelPerformance("test model performance", 2, test_dc, forecast_dc, train_dc, naive2_dc)
mase = MP.MASE(test_dc.to_df(), forecast_dc.to_df(), train_dc.to_df(), 2)
smape = MP.sMAPE(test_dc.to_df(), forecast_dc.to_df())
mape = MP.MAPE(mp.y_df, mp.y_hat_df)
r2 = MP.R2(test_dc.to_df(), forecast_dc.to_df())
rmse = MP.RMSE(test_dc.to_df(), forecast_dc.to_df())
owa = MP.OWA(test_dc.to_df(), forecast_dc.to_df(), train_dc.to_df(), naive2_dc.to_df(), 2)
u1 = MP.Theil_U1(test_dc.to_df(), forecast_dc.to_df())
u2 = MP.Theil_U2(test_dc.to_df(), forecast_dc.to_df())
mp.MASE()
mp.sMAPE()
mp.MAPE()
mp.R2()
mp.RMSE()
mp.OWA()
mp.Theil_U1()
mp.Theil_U2()
self.assertAlmostEqual(mp.metrics['sMAPE'], smape)
self.assertAlmostEqual(mp.metrics['MAPE'], mape)
self.assertAlmostEqual(mp.metrics['R2'], r2)
self.assertAlmostEqual(mp.metrics['RMSE'], rmse)
self.assertAlmostEqual(mp.metrics['MASE'], mase)
self.assertAlmostEqual(mp.metrics['OWA'], owa)
self.assertAlmostEqual(mp.metrics['Theil_U1'], u1)
self.assertAlmostEqual(mp.metrics['Theil_U2'], u2)
def test_Naive2(self):
path_monthly = os.path.join('test', 'Data', 'Monthly')
dic_monthly = preprocess.read_file(path_monthly)
series_list = []
for k, v in dic_monthly.items():
df, cat = v
df = preprocess.single_price(df, k)
series_list.append(DataSeries(cat, 'monthly', df))
collect = DataCollection('test1', series_list)
train_dc, test_dc = collect.split(numTest=12)
m = ModelNaive2(12, train_dc, test_dc)
y_hat_Naive2_dc = m.fit_and_generate_prediction(12, freq='MS')
y_hat_Naive2_dc.to_df().to_csv('test_Naive2_result.csv')
def test_ESRNN(self):
# An example of how to use ESRNN
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
path_daily = os.path.join('test','Data','daily')
dic_daily = preprocess.read_file(path_daily)
series_list = []
for k, v in dic_daily.items():
df, cat = v
df = preprocess.single_price(df, k)
series_list.append(DataSeries(cat, 'daily', df))
collect = DataCollection('test1', series_list)
m = ModelESRNN(max_epochs = 5, seasonality = [], batch_size = 64, input_size = 12, output_size = 12, device = device)
train_dc, test_dc = collect.split(numTest = 12)
m.train(train_dc)
y_test = m.predict(test_dc)
assert(isinstance(y_test, DataCollection))
y_test_df = y_test.to_df()
y_test_df.to_csv('predict_result.csv')
def validation_rolling(input_dc: DataCollection,
num_split: int,
numTest: int,
max_epochs=15,
batch_size=1,
batch_size_test=128,
freq_of_test=-1,
learning_rate=1e-3,
lr_scheduler_step_size=9,
lr_decay=0.9,
per_series_lr_multip=1.0,
gradient_eps=1e-8,
gradient_clipping_threshold=20,
rnn_weight_decay=0,
noise_std=0.001,
level_variability_penalty=80,
testing_percentile=50,
training_percentile=50,
ensemble=False,
cell_type='LSTM',
state_hsize=40,
dilations=[[1, 2], [4, 8]],
add_nl_layer=False,
seasonality=[4],
input_size=4,
output_size=8,
frequency=None,
max_periods=20,
random_seed=1):
import time
scores_list = []
train_val_dic = {}
device = 'cuda' if torch.cuda.is_available() else 'cpu'
for i in range(num_split):
train, validation = input_dc.split(numTest=numTest)
train_val_dic[i] = [train, validation]
input_dc = train
# record score of error
total_score = 0
elapse = 0
for i in range(num_split - 1, -1, -1):
train_dc = train_val_dic[i][0]
validation_dc = train_val_dic[i][1]
validation_df = validation_dc.to_df()
start_time = time.time()
m = ModelESRNN(max_epochs=max_epochs,
batch_size=batch_size,
batch_size_test=batch_size_test,
freq_of_test=freq_of_test,
learning_rate=learning_rate,
lr_scheduler_step_size=lr_scheduler_step_size,
lr_decay=lr_decay,
per_series_lr_multip=per_series_lr_multip,
gradient_eps=gradient_eps,
gradient_clipping_threshold=gradient_clipping_threshold,
rnn_weight_decay=rnn_weight_decay,
noise_std=noise_std,
level_variability_penalty=level_variability_penalty,
testing_percentile=testing_percentile,
training_percentile=training_percentile,
ensemble=ensemble,
cell_type=cell_type,
state_hsize=state_hsize,
dilations=dilations,
add_nl_layer=add_nl_layer,
seasonality=seasonality,
input_size=input_size,
output_size=output_size,
frequency=frequency,
max_periods=max_periods,
random_seed=random_seed,
device=device)
m.train(train_dc)
y_predict = m.predict(validation_dc)
y_predict_df = y_predict.to_df()
score = MP.MAPE(validation_df, y_predict_df)
elapse += time.time() - start_time
scores_list.append(score)
total_score += score
score = total_score / num_split
return score, scores_list, elapse / num_split, (max_epochs, batch_size,
input_size, output_size)
def validation_simple(
input_dc: DataCollection,
numTest: int,
max_epochs=15,
batch_size=1,
batch_size_test=128,
freq_of_test=-1,
learning_rate=1e-3,
lr_scheduler_step_size=9,
lr_decay=0.9,
per_series_lr_multip=1.0,
gradient_eps=1e-8,
gradient_clipping_threshold=20,
rnn_weight_decay=0,
noise_std=0.001,
level_variability_penalty=80,
testing_percentile=50,
training_percentile=50,
ensemble=False,
cell_type='LSTM',
state_hsize=40,
dilations=[[1, 2], [4, 8]],
add_nl_layer=False,
seasonality=[4],
input_size=4,
output_size=8,
frequency=None,
max_periods=20,
random_seed=1,
):
train_dc, validation_dc = input_dc.split(numTest=numTest)
validation_df = validation_dc.to_df()
device = 'cuda' if torch.cuda.is_available() else 'cpu'
m = ModelESRNN(max_epochs=max_epochs,
batch_size=batch_size,
batch_size_test=batch_size_test,
freq_of_test=freq_of_test,
learning_rate=learning_rate,
lr_scheduler_step_size=lr_scheduler_step_size,
lr_decay=lr_decay,
per_series_lr_multip=per_series_lr_multip,
gradient_eps=gradient_eps,
gradient_clipping_threshold=gradient_clipping_threshold,
rnn_weight_decay=rnn_weight_decay,
noise_std=noise_std,
level_variability_penalty=level_variability_penalty,
testing_percentile=testing_percentile,
training_percentile=training_percentile,
ensemble=ensemble,
cell_type=cell_type,
state_hsize=state_hsize,
dilations=dilations,
add_nl_layer=add_nl_layer,
seasonality=seasonality,
input_size=input_size,
output_size=output_size,
frequency=frequency,
max_periods=max_periods,
random_seed=random_seed,
device=device)
m.train(train_dc)
y_predict = m.predict(validation_dc)
y_predict_df = y_predict.to_df()
score = MP.MAPE(validation_df, y_predict_df)
return score, (max_epochs, batch_size, input_size, output_size)
noise_std = 0.001
level_variability_penalty = 80
state_hsize = 40
dilation = [[1]]
add_nl_layer = False
seasonality = [5]
# action
path = os.path.join('test', 'Data', 'Daily')
dic = preprocess.read_file(path)
series_list = []
for k, v in dic.items():
df, cat = v
df = preprocess.single_price(df, k)
series_list.append(DataSeries(cat, 'daily', df))
collect = DataCollection('RollingValidation', series_list)
input_dc, _ = collect.split(numTest=2 * numTest)
score, _ = validation_simple(
input_dc,
numTest=numTest,
max_epochs=max_epochs,
batch_size=batch_size,
learning_rate=learning_rate,
lr_scheduler_step_size=lr_scheduler_step_size,
lr_decay=lr_decay,
noise_std=noise_std,
level_variability_penalty=level_variability_penalty,
state_hsize=state_hsize,
dilations=dilation,
add_nl_layer=add_nl_layer,
seasonality=seasonality,
[[1],[5,10]],
[[1],[5,20]],
[[1],[3,5,10]],
[[1],[3,5,20]],
[[1],[5,10,20]],
[[1,3],[5]],
[[1,5],[10]],
[[1,5],[20]],
[[1,3],[5,10]],[[1,3,5],[10]],
[[1,3],[5,20]],[[1,3,5],[20]],
[[1,5],[10,20]],[[1,5,10],[20]]
]
dil_one = [[[1,5]]]
# train/test split
input_dc, test_dc = collect.split(numTest = 2 * 90)
score_list = []
time_list = []
for dn in dil_one:
import time
start_time = time.time()
score, _ = Vn.validation_simple(input_dc, numTest=90, max_epochs=15, batch_size=64,
learning_rate=1e-3, lr_scheduler_step_size=9, lr_decay=0.9,
noise_std=0.001,level_variability_penalty=80,
state_hsize=40, dilations=dn,
add_nl_layer=False, seasonality=[5],
input_size=5, output_size=90,frequency='D',random_seed=1
)
print("--- dilation :", dn," ---")
print("--- score of this config is %s ---" % score)
class Test_Data(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.a = pd.DataFrame([10.2, 12, 32.1, 9.32],
columns=['fakeSPY'],
index=pd.to_datetime([
'2020-01-01', '2020-02-01', '2020-03-01',
'2020-04-01'
]))
self.a_series = DataSeries('ETF', 'monthly', self.a)
self.b = pd.DataFrame([2.3, 3.6, 4.5],
columns=['fakeTreasury'],
index=pd.to_datetime(
['2019-12-12', '2020-02-05', '2020-09-13']))
self.b_series = DataSeries('Bond', 'monthly', self.b)
self.c_collection = DataCollection('trial',
[self.a_series, self.b_series])
# For test_the_rest_of_entire_dataset():
self.a_entire = pd.DataFrame([10.2, 12, 32.1, 9.32, 11.5, 9.7],
columns=['fakeSPY'],
index=pd.to_datetime([
'2020-01-01', '2020-02-01',
'2020-03-01', '2020-04-01',
'2020-05-01', '2020-06-01'
]))
self.a_series_entire = DataSeries('ETF', 'monthly', self.a_entire)
self.b_entire = pd.DataFrame([2.3, 3.6, 4.5, 5.5],
columns=['fakeTreasury'],
index=pd.to_datetime([
'2019-12-12', '2020-02-05',
'2020-09-13', '2020-10-13'
]))
self.b_series_entire = DataSeries('Bond', 'monthly', self.b_entire)
self.c_collection_entire = DataCollection(
'trial', [self.a_series_entire, self.b_series_entire])
self.a_exp = pd.DataFrame([11.5, 9.7],
columns=['fakeSPY'],
index=pd.to_datetime(
['2020-05-01', '2020-06-01']))
self.a_series_exp = DataSeries('ETF', 'monthly', self.a_exp)
self.b_exp = pd.DataFrame([5.5],
columns=['fakeTreasury'],
index=pd.to_datetime(['2020-10-13']))
self.b_series_exp = DataSeries('Bond', 'monthly', self.b_exp)
self.c_collection_exp = DataCollection(
'trial', [self.a_series_exp, self.b_series_exp])
def test_DataSeries_basic(self):
a = self.a
a_series = self.a_series
assert (len(a_series) == 4)
assert (str(a_series) == 'monthly fakeSPY')
assert (a_series.get_ticker() == 'fakeSPY')
assert (a_series.get_category() == 'ETF')
assert (a_series.get_freq() == 'monthly')
assert (a.equals(a_series.get_ts()))
# test deep copy
a_copy = a_series.copy()
assert (a_copy != a_series
and a_copy.get_ts().equals(a_series.get_ts()))
assert (isinstance(a_series.to_Series(), pd.Series))
def test_DataSeries_add_sub(self):
diff = self.a_series_entire - self.a_series
assert (self.compareSeries(diff, self.a_series_exp))
a_plus = diff + self.a_series
assert (self.compareSeries(a_plus, self.a_series_entire))
def test_DataSeries_to_list(self):
lst = self.a_series.to_list()
assert (lst == [10.2, 12, 32.1, 9.32])
def test_last_index(self):
assert (self.a_series.get_last_date() == pd.to_datetime('2020-04-01'))
def test_DataSeries_split_and_trim(self):
# test split
a_train, a_test = self.a_series.split(pct=0.75)
assert (isinstance(a_train, DataSeries))
assert (isinstance(a_test, DataSeries))
assert (len(a_train) == 3)
assert (len(a_test) == 1)
assert (self.a.iloc[:3].equals(a_train.get_ts()))
assert (self.a.iloc[3:].equals(a_test.get_ts()))
# test trim
trimed = self.a_series.trim('2020-02-01', '2020-03-01')
assert (len(trimed) == 2)
assert (self.a.loc['2020-02-01':'2020-03-01'].equals(trimed.get_ts()))
@staticmethod
def compareSeries(a, b):
flag = True
if not isinstance(a, DataSeries):
print("\n The first item is not a DataSeries object")
return False
if not isinstance(b, DataSeries):
print("\n The Second item is not a DataSeries object")
return False
if a == b:
print("\n The two items are the same object")
flag = False
if len(a) != len(b):
print("\n The two items does not have the same length")
flag = False
if str(a) != str(b):
print("\n The two items does not have the same ticker")
flag = False
if a.get_category() != b.get_category():
print("\n The two items does not have the same category")
flag = False
if not a.get_ts().equals(b.get_ts()):
print("\n The two items does not have the same time series")
flag = False
if not a.get_freq() == b.get_freq():
print("\n The two items does not have the same frequency")
flag = False
return flag
def test_DataCollection_basic(self):
assert (len(self.c_collection) == 2)
assert (self.c_collection.get_freq() == 'monthly')
for item, compare in zip(self.c_collection,
[self.a_series, self.b_series]):
assert (self.compareSeries(item, compare))
def test_DataCollection_add_sub(self):
res = self.c_collection_entire - self.c_collection
expected = self.c_collection_exp
for r, e in zip(res, expected):
assert (self.compareSeries(r, e))
res_plus = res + self.c_collection
for r, e in zip(res_plus, self.c_collection_entire):
assert (self.compareSeries(r, e))
def test_DataCollection_get_series(self):
item1 = self.c_collection[1]
assert (self.compareSeries(item1, self.b_series))
item2 = self.c_collection.get_series('fakeSPY')
assert (self.compareSeries(item2, self.a_series))
def test_DataCollection_copy(self):
c = self.c_collection.copy()
assert (c != self.c_collection)
assert (c.label == self.c_collection.label)
assert (c.get_freq() == self.c_collection.get_freq())
for one, two in zip(c, self.c_collection):
assert (self.compareSeries(one, two))
def test_DataCollection_summary(self):
pass
def test_DataCollection_split(self):
train, test = self.c_collection.split(pct=0.75)
assert (str(train) == 'trial')
assert (train.freq == 'monthly')
assert (str(test) == 'trial')
assert (test.freq == 'monthly')
compare = [self.a_series.split(0.75), self.b_series.split(0.75)]
compare_train, compare_test = zip(*compare)
train_col, test_col = list(compare_train), list(compare_test)
for i, item in enumerate(train):
assert (self.compareSeries(item, train_col[i]))
for i, item in enumerate(test):
assert (self.compareSeries(item, test_col[i]))
def test_DataCollection_list(self):
assert (self.c_collection.ticker_list() == ['fakeSPY', 'fakeTreasury'])
assert (self.c_collection.category_list() == ['ETF', 'Bond'])
assert (self.c_collection.last_date_list() == pd.to_datetime(
['2020-04-01', '2020-09-13']).to_list())
assert (self.c_collection.to_list() == [[10.2, 12, 32.1, 9.32],
[2.3, 3.6, 4.5]])
def test_DataCollection_add(self):
d = pd.DataFrame([11, 22],
columns=['fakeZZZ'],
index=pd.to_datetime(['2019-1-12', '2019-02-05']))
d_series = DataSeries('Bond', 'monthly', d)
c_plus = self.c_collection.copy()
c_plus.add(d_series)
compare = [self.a_series, self.b_series, d_series]
for i, item in enumerate(c_plus):
assert (self.compareSeries(item, compare[i]))
def test_DataCollection_df(self):
df = self.c_collection.to_df()
compare = pd.concat([self.a, self.b], axis=1)
assert (df.equals(compare))
def test_price_to_return(self):
pass