bring classifier/rl up to new paradigm. ensure tests pass. remove old code. add documentation, add new example transform
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robcaulk
@@ -10,8 +10,8 @@ from freqtrade.data.dataprovider import DataProvider
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from freqtrade.exceptions import OperationalException
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from tests.conftest import get_patched_exchange # , log_has_re
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from tests.freqai.conftest import (get_patched_data_kitchen, get_patched_freqai_strategy,
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make_unfiltered_dataframe) # make_data_dictionary,
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from tests.freqai.conftest import make_unfiltered_dataframe # make_data_dictionary,
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from tests.freqai.conftest import get_patched_data_kitchen, get_patched_freqai_strategy
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from tests.freqai.test_freqai_interface import is_mac
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@@ -72,68 +72,6 @@ def test_check_if_model_expired(mocker, freqai_conf):
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shutil.rmtree(Path(dk.full_path))
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# def test_use_DBSCAN_to_remove_outliers(mocker, freqai_conf, caplog):
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# freqai = make_data_dictionary(mocker, freqai_conf)
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# # freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 1})
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# freqai.dk.use_DBSCAN_to_remove_outliers(predict=False)
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# assert log_has_re(r"DBSCAN found eps of 1\.7\d\.", caplog)
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# def test_compute_distances(mocker, freqai_conf):
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# freqai = make_data_dictionary(mocker, freqai_conf)
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# freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 1})
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# avg_mean_dist = freqai.dk.compute_distances()
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# assert round(avg_mean_dist, 2) == 1.98
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# def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf, caplog):
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# freqai = make_data_dictionary(mocker, freqai_conf)
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# freqai_conf['freqai']['feature_parameters'].update({"outlier_protection_percentage": 0.1})
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# freqai.dk.use_SVM_to_remove_outliers(predict=False)
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# assert log_has_re(
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# "SVM detected 7.83%",
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# caplog,
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# )
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# def test_compute_inlier_metric(mocker, freqai_conf, caplog):
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# freqai = make_data_dictionary(mocker, freqai_conf)
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# freqai_conf['freqai']['feature_parameters'].update({"inlier_metric_window": 10})
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# freqai.dk.compute_inlier_metric(set_='train')
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# assert log_has_re(
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# "Inlier metric computed and added to features.",
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# caplog,
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# )
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# def test_add_noise_to_training_features(mocker, freqai_conf):
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# freqai = make_data_dictionary(mocker, freqai_conf)
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# freqai_conf['freqai']['feature_parameters'].update({"noise_standard_deviation": 0.1})
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# freqai.dk.add_noise_to_training_features()
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# def test_remove_beginning_points_from_data_dict(mocker, freqai_conf):
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# freqai = make_data_dictionary(mocker, freqai_conf)
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# freqai.dk.remove_beginning_points_from_data_dict(set_='train')
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# def test_principal_component_analysis(mocker, freqai_conf, caplog):
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# freqai = make_data_dictionary(mocker, freqai_conf)
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# freqai.dk.principal_component_analysis()
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# assert log_has_re(
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# "reduced feature dimension by",
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# caplog,
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# )
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# def test_normalize_data(mocker, freqai_conf):
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# freqai = make_data_dictionary(mocker, freqai_conf)
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# data_dict = freqai.dk.data_dictionary
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# freqai.dk.normalize_data(data_dict)
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# assert any('_max' in entry for entry in freqai.dk.data.keys())
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# assert any('_min' in entry for entry in freqai.dk.data.keys())
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def test_filter_features(mocker, freqai_conf):
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freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
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freqai.dk.find_features(unfiltered_dataframe)
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@@ -74,6 +74,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
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freqai_conf = make_rl_config(freqai_conf)
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# test the RL guardrails
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freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
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freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 2})
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freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
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if 'test_3ac' in model or 'test_4ac' in model:
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@@ -162,7 +163,6 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, s
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
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assert len(freqai.dk.data['training_features_list']) == 14
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shutil.rmtree(Path(freqai.dk.full_path))
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@@ -218,7 +218,6 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
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f"{freqai.dk.model_filename}_model{model_file_extension}").exists()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
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shutil.rmtree(Path(freqai.dk.full_path))
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@@ -283,9 +282,6 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
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_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = base_df[freqai_conf["timeframe"]]
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for i in range(5):
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df[f'%-constant_{i}'] = i
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metadata = {"pair": "LTC/BTC"}
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freqai.dk.set_paths('LTC/BTC', None)
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freqai.start_backtesting(df, metadata, freqai.dk, strategy)
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@@ -293,14 +289,6 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
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assert len(model_folders) == num_files
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Trade.use_db = True
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assert log_has_re(
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"Removed features ",
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caplog,
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)
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assert log_has_re(
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"Removed 5 features from prediction features, ",
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caplog,
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)
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Backtesting.cleanup()
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shutil.rmtree(Path(freqai.dk.full_path))
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@@ -425,36 +413,6 @@ def test_backtesting_fit_live_predictions(mocker, freqai_conf, caplog):
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_principal_component_analysis(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
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{"princpial_component_analysis": "true"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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freqai.dk.live = True
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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freqai.dd.pair_dict = MagicMock()
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.dk.set_paths('ADA/BTC', None)
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freqai.extract_data_and_train_model(
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new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl")
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_plot_feature_importance(mocker, freqai_conf):
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from freqtrade.freqai.utils import plot_feature_importance
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