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ab8be8f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import unittest
import pytest
import sys
from qlib.tests import TestAutoData
from qlib.data.dataset import TSDatasetH, TSDataSampler
import numpy as np
import pandas as pd
import time
from qlib.data.dataset.handler import DataHandlerLP
class TestDataset(TestAutoData):
@pytest.mark.slow
def testTSDataset(self):
tsdh = TSDatasetH(
handler={
"class": "Alpha158",
"module_path": "qlib.contrib.data.handler",
"kwargs": {
"start_time": "2017-01-01",
"end_time": "2020-08-01",
"fit_start_time": "2017-01-01",
"fit_end_time": "2017-12-31",
"instruments": "csi300",
"infer_processors": [
{"class": "FilterCol", "kwargs": {"col_list": ["RESI5", "WVMA5", "RSQR5"]}},
{"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature", "clip_outlier": "true"}},
{"class": "Fillna", "kwargs": {"fields_group": "feature"}},
],
"learn_processors": [
"DropnaLabel",
{"class": "CSRankNorm", "kwargs": {"fields_group": "label"}}, # CSRankNorm
],
},
},
segments={
"train": ("2017-01-01", "2017-12-31"),
"valid": ("2018-01-01", "2018-12-31"),
"test": ("2019-01-01", "2020-08-01"),
},
)
tsds_train = tsdh.prepare("train", data_key=DataHandlerLP.DK_L) # Test the correctness
tsds = tsdh.prepare("valid", data_key=DataHandlerLP.DK_L)
t = time.time()
for idx in np.random.randint(0, len(tsds_train), size=2000):
_ = tsds_train[idx]
print(f"2000 sample takes {time.time() - t}s")
t = time.time()
for _ in range(20):
data = tsds_train[np.random.randint(0, len(tsds_train), size=2000)]
print(data.shape)
print(f"2000 sample(batch index) * 20 times takes {time.time() - t}s")
# The dimension of sample is same as tabular data, but it will return timeseries data of the sample
# We have two method to get the time-series of a sample
# 1) sample by int index directly
tsds[len(tsds) - 1]
# 2) sample by <datetime,instrument> index
data_from_ds = tsds["2017-12-31", "SZ300315"]
# Check the data
# Get data from DataFrame Directly
data_from_df = (
tsdh.handler.fetch(data_key=DataHandlerLP.DK_L)
.loc(axis=0)["2017-01-01":"2017-12-31", "SZ300315"]
.iloc[-30:]
.values
)
equal = np.isclose(data_from_df, data_from_ds)
self.assertTrue(equal[~np.isnan(data_from_df)].all())
if False:
# 3) get both index and data
# NOTE: We don't want to reply on pytorch, so this test can't be included. It is just a example
from torch.utils.data import DataLoader
from qlib.model.utils import IndexSampler
i = len(tsds) - 1
idx = tsds.get_index()
tsds[i]
idx[i]
s_w_i = IndexSampler(tsds)
test_loader = DataLoader(s_w_i)
s_w_i[3]
for data, i in test_loader:
break
print(data.shape)
print(idx[i])
class TestTSDataSampler(unittest.TestCase):
def test_TSDataSampler(self):
"""
Test TSDataSampler for issue #1716
"""
datetime_list = ["2000-01-31", "2000-02-29", "2000-03-31", "2000-04-30", "2000-05-31"]
instruments = ["000001", "000002", "000003", "000004", "000005"]
index = pd.MultiIndex.from_product(
[pd.to_datetime(datetime_list), instruments], names=["datetime", "instrument"]
)
data = np.random.randn(len(datetime_list) * len(instruments))
test_df = pd.DataFrame(data=data, index=index, columns=["factor"])
dataset = TSDataSampler(test_df, datetime_list[0], datetime_list[-1], step_len=2)
print()
print("--------------dataset[0]--------------")
print(dataset[0])
print("--------------dataset[1]--------------")
print(dataset[1])
assert len(dataset[0]) == 2
self.assertTrue(np.isnan(dataset[0][0]))
self.assertEqual(dataset[0][1], dataset[1][0])
self.assertEqual(dataset[1][1], dataset[2][0])
self.assertEqual(dataset[2][1], dataset[3][0])
def test_TSDataSampler2(self):
"""
Extra test TSDataSampler to prevent incorrect filling of nan for the values at the front
"""
datetime_list = ["2000-01-31", "2000-02-29", "2000-03-31", "2000-04-30", "2000-05-31"]
instruments = ["000001", "000002", "000003", "000004", "000005"]
index = pd.MultiIndex.from_product(
[pd.to_datetime(datetime_list), instruments], names=["datetime", "instrument"]
)
data = np.random.randn(len(datetime_list) * len(instruments))
test_df = pd.DataFrame(data=data, index=index, columns=["factor"])
dataset = TSDataSampler(test_df, datetime_list[2], datetime_list[-1], step_len=3)
print()
print("--------------dataset[0]--------------")
print(dataset[0])
print("--------------dataset[1]--------------")
print(dataset[1])
for i in range(3):
self.assertFalse(np.isnan(dataset[0][i]))
self.assertFalse(np.isnan(dataset[1][i]))
self.assertEqual(dataset[0][1], dataset[1][0])
self.assertEqual(dataset[0][2], dataset[1][1])
if __name__ == "__main__":
unittest.main(verbosity=10)
# User could use following code to run test when using line_profiler
# td = TestDataset()
# td.setUpClass()
# td.testTSDataset()
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