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Merge pull request #7322 from freqtrade/add-inlier-metric

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Add inlier metric
This commit is contained in:
Matthias
2022-09-07 07:05:57 +02:00
committed by GitHub
parent 3454a52b95 d7585161b2
commit c08c82bc40
6 changed files with 300 additions and 68 deletions
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36
tests/freqai/conftest.py
View File

@@ -81,6 +81,37 @@ def get_patched_freqaimodel(mocker, freqaiconf):
return freqaimodel
def make_unfiltered_dataframe(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
freqai.dk.pair = "ADA/BTC"
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
new_timerange = TimeRange.parse_timerange("20180120-20180130")
corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(
data_load_timerange, freqai.dk.pair, freqai.dk
)
unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
strategy, corr_dataframes, base_dataframes, freqai.dk.pair
)
unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
return freqai, unfiltered_dataframe
def make_data_dictionary(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
@@ -92,12 +123,11 @@ def make_data_dictionary(mocker, freqai_conf):
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
freqai.dk.pair = "ADA/BTC"
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(data_load_timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(

72
tests/freqai/test_freqai_datakitchen.py
View File

@@ -6,7 +6,8 @@ import pytest
from freqtrade.exceptions import OperationalException
from tests.conftest import log_has_re
from tests.freqai.conftest import get_patched_data_kitchen, make_data_dictionary
from tests.freqai.conftest import (get_patched_data_kitchen, make_data_dictionary,
make_unfiltered_dataframe)
@pytest.mark.parametrize(
@@ -91,3 +92,72 @@ def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf,
"SVM detected 8.09%",
caplog,
)
def test_compute_inlier_metric(mocker, freqai_conf, caplog):
freqai = make_data_dictionary(mocker, freqai_conf)
freqai_conf['freqai']['feature_parameters'].update({"inlier_metric_window": 10})
freqai.dk.compute_inlier_metric(set_='train')
assert log_has_re(
"Inlier metric computed and added to features.",
caplog,
)
def test_add_noise_to_training_features(mocker, freqai_conf):
freqai = make_data_dictionary(mocker, freqai_conf)
freqai_conf['freqai']['feature_parameters'].update({"noise_standard_deviation": 0.1})
freqai.dk.add_noise_to_training_features()
def test_remove_beginning_points_from_data_dict(mocker, freqai_conf):
freqai = make_data_dictionary(mocker, freqai_conf)
freqai.dk.remove_beginning_points_from_data_dict(set_='train')
def test_principal_component_analysis(mocker, freqai_conf, caplog):
freqai = make_data_dictionary(mocker, freqai_conf)
freqai.dk.principal_component_analysis()
assert log_has_re(
"reduced feature dimension by",
caplog,
)
def test_normalize_data(mocker, freqai_conf):
freqai = make_data_dictionary(mocker, freqai_conf)
data_dict = freqai.dk.data_dictionary
freqai.dk.normalize_data(data_dict)
assert len(freqai.dk.data) == 56
def test_filter_features(mocker, freqai_conf):
freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
freqai.dk.find_features(unfiltered_dataframe)
filtered_df, labels = freqai.dk.filter_features(
unfiltered_dataframe,
freqai.dk.training_features_list,
freqai.dk.label_list,
training_filter=True,
)
assert len(filtered_df.columns) == 26
def test_make_train_test_datasets(mocker, freqai_conf):
freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
freqai.dk.find_features(unfiltered_dataframe)
features_filtered, labels_filtered = freqai.dk.filter_features(
unfiltered_dataframe,
freqai.dk.training_features_list,
freqai.dk.label_list,
training_filter=True,
)
data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
assert data_dictionary
assert len(data_dictionary) == 7
assert len(data_dictionary['train_features'].index) == 1916

34
tests/freqai/test_freqai_interface.py
View File

@@ -17,7 +17,7 @@ def is_arm() -> bool:
return "arm" in machine or "aarch64" in machine
def test_train_model_in_series_LightGBM(mocker, freqai_conf):
def test_extract_data_and_train_model_LightGBM(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
@@ -35,7 +35,8 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
@@ -45,7 +46,7 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path))
def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
def test_extract_data_and_train_model_LightGBMMultiModel(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
freqai_conf.update({"freqaimodel": "LightGBMRegressorMultiTarget"})
@@ -64,7 +65,8 @@ def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert len(freqai.dk.label_list) == 2
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
@@ -77,7 +79,7 @@ def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
def test_train_model_in_series_Catboost(mocker, freqai_conf):
def test_extract_data_and_train_model_Catboost(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "CatboostRegressor"})
# freqai_conf.get('freqai', {}).update(
@@ -98,8 +100,8 @@ def test_train_model_in_series_Catboost(mocker, freqai_conf):
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
@@ -110,7 +112,7 @@ def test_train_model_in_series_Catboost(mocker, freqai_conf):
@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
def test_train_model_in_series_CatboostClassifier(mocker, freqai_conf):
def test_extract_data_and_train_model_CatboostClassifier(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "CatboostClassifier"})
freqai_conf.update({"strategy": "freqai_test_classifier"})
@@ -130,8 +132,8 @@ def test_train_model_in_series_CatboostClassifier(mocker, freqai_conf):
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
@@ -141,7 +143,7 @@ def test_train_model_in_series_CatboostClassifier(mocker, freqai_conf):
shutil.rmtree(Path(freqai.dk.full_path))
def test_train_model_in_series_LightGBMClassifier(mocker, freqai_conf):
def test_extract_data_and_train_model_LightGBMClassifier(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"freqaimodel": "LightGBMClassifier"})
freqai_conf.update({"strategy": "freqai_test_classifier"})
@@ -161,8 +163,8 @@ def test_train_model_in_series_LightGBMClassifier(mocker, freqai_conf):
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
@@ -296,7 +298,8 @@ def test_follow_mode(mocker, freqai_conf):
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
@@ -345,7 +348,8 @@ def test_principal_component_analysis(mocker, freqai_conf):
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl")