diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py index 3fa8a801b..c6b45d61b 100644 --- a/freqtrade/freqai/freqai_interface.py +++ b/freqtrade/freqai/freqai_interface.py @@ -563,7 +563,7 @@ class IFreqaiModel(ABC): self.dd.pair_to_end_of_training_queue(pair) self.dd.save_data(model, pair, dk) - if self.freqai_info["feature_parameters"].get("plot_feature_importance", False): + if self.freqai_info["feature_parameters"].get("plot_feature_importance", True): plot_feature_importance(model, pair, dk) if self.freqai_info.get("purge_old_models", False): diff --git a/freqtrade/freqai/utils.py b/freqtrade/freqai/utils.py index 34528acdd..3f278d436 100644 --- a/freqtrade/freqai/utils.py +++ b/freqtrade/freqai/utils.py @@ -1,6 +1,5 @@ import logging from datetime import datetime, timezone -from pathlib import Path from typing import Any import numpy as np @@ -153,37 +152,42 @@ def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen, from freqtrade.plot.plotting import go, make_subplots, store_plot_file # Extract feature importance from model - if "catboost.core" in str(model.__class__): - feature_importance = model.get_feature_importance() - elif "lightgbm.sklearn" in str(model.__class__): - feature_importance = model.feature_importances_ - else: - # TODO: Add support for more libraries - raise NotImplementedError(f"Cannot extract feature importance from {model.__class__}") + models = {} + if 'FreqaiMultiOutputRegressor' in str(model.__class__): + for estimator, label in zip(model.estimators_, dk.label_list): + models[label] = estimator - # Data preparation - fi_df = pd.DataFrame({ - "feature_names": np.array(dk.training_features_list), - "feature_importance": np.array(feature_importance) - }) - fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1] - fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1] + for label in models: + mdl = models[label] + if "catboost.core" in str(mdl.__class__): + feature_importance = mdl.get_feature_importance() + elif "lightgbm.sklearn" or "xgb" in str(mdl.__class__): + feature_importance = mdl.feature_importances_ + else: + # TODO: Add support for more libraries + raise NotImplementedError(f"Cannot extract feature importance from {mdl.__class__}") - # Plotting - def add_feature_trace(fig, fi_df, col): - return fig.add_trace( - go.Bar( - x=fi_df["feature_importance"], - y=fi_df["feature_names"], - orientation='h', showlegend=False - ), row=1, col=col - ) - fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5) - fig = add_feature_trace(fig, fi_df_top, 1) - fig = add_feature_trace(fig, fi_df_worst, 2) - fig.update_layout(title_text=f"Best and worst features by importance {pair}") + # Data preparation + fi_df = pd.DataFrame({ + "feature_names": np.array(dk.training_features_list), + "feature_importance": np.array(feature_importance) + }) + fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1] + fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1] - # Store plot file - model_dir, train_name = str(dk.data_path).rsplit("/", 1) - fi_dir = Path(f"{model_dir}/feature_importance/{pair.split('/')[0]}") - store_plot_file(fig, f"{train_name}.html", fi_dir) + # Plotting + def add_feature_trace(fig, fi_df, col): + return fig.add_trace( + go.Bar( + x=fi_df["feature_importance"], + y=fi_df["feature_names"], + orientation='h', showlegend=False + ), row=1, col=col + ) + fig = make_subplots(rows=1, cols=2, horizontal_spacing=0.5) + fig = add_feature_trace(fig, fi_df_top, 1) + fig = add_feature_trace(fig, fi_df_worst, 2) + fig.update_layout(title_text=f"Best and worst features by importance {pair}") + + store_plot_file(fig, f"{dk.model_filename}-{label}.html", dk.data_path, + include_plotlyjs="cdn") diff --git a/freqtrade/plot/plotting.py b/freqtrade/plot/plotting.py index f8e95300a..8a00c7899 100644 --- a/freqtrade/plot/plotting.py +++ b/freqtrade/plot/plotting.py @@ -601,7 +601,8 @@ def generate_plot_filename(pair: str, timeframe: str) -> str: return file_name -def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False) -> None: +def store_plot_file(fig, filename: str, directory: Path, + auto_open: bool = False, include_plotlyjs=True) -> None: """ Generate a plot html file from pre populated fig plotly object :param fig: Plotly Figure to plot @@ -614,7 +615,7 @@ def store_plot_file(fig, filename: str, directory: Path, auto_open: bool = False _filename = directory.joinpath(filename) plot(fig, filename=str(_filename), - auto_open=auto_open) + auto_open=auto_open, include_plotlyjs=include_plotlyjs) logger.info(f"Stored plot as {_filename}") diff --git a/tests/freqai/test_freqai_interface.py b/tests/freqai/test_freqai_interface.py index a66594d7f..b600cc535 100644 --- a/tests/freqai/test_freqai_interface.py +++ b/tests/freqai/test_freqai_interface.py @@ -315,3 +315,38 @@ def test_principal_component_analysis(mocker, freqai_conf): assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl") shutil.rmtree(Path(freqai.dk.full_path)) + + +def test_plot_feature_importance(mocker, freqai_conf): + + from freqtrade.freqai.utils import plot_feature_importance + + freqai_conf.update({"timerange": "20180110-20180130"}) + freqai_conf.get("freqai", {}).get("feature_parameters", {}).update( + {"princpial_component_analysis": "true"}) + + 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) + timerange = TimeRange.parse_timerange("20180110-20180130") + freqai.dd.load_all_pair_histories(timerange, freqai.dk) + + freqai.dd.pair_dict = MagicMock() + + data_load_timerange = TimeRange.parse_timerange("20180110-20180130") + new_timerange = TimeRange.parse_timerange("20180120-20180130") + + freqai.extract_data_and_train_model( + new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange) + + model = freqai.dd.load_data("ADA/BTC", freqai.dk) + + plot_feature_importance(model, "ADA/BTC", freqai.dk) + + assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}.html") + + shutil.rmtree(Path(freqai.dk.full_path))