allow plot to plot multitargets, add test

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):

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__):
+    models = {}
-        feature_importance = model.get_feature_importance()
+    if 'FreqaiMultiOutputRegressor' in str(model.__class__):
-    elif "lightgbm.sklearn" in str(model.__class__):
+        for estimator, label in zip(model.estimators_, dk.label_list):
-        feature_importance = model.feature_importances_
+            models[label] = estimator
-    else:
-        # TODO: Add support for more libraries
-        raise NotImplementedError(f"Cannot extract feature importance from {model.__class__}")
 
-    # Data preparation
+    for label in models:
-    fi_df = pd.DataFrame({
+        mdl = models[label]
-        "feature_names": np.array(dk.training_features_list),
+        if "catboost.core" in str(mdl.__class__):
-        "feature_importance": np.array(feature_importance)
+            feature_importance = mdl.get_feature_importance()
-    })
+        elif "lightgbm.sklearn" or "xgb" in str(mdl.__class__):
-    fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
+            feature_importance = mdl.feature_importances_
-    fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
+        else:
+            # TODO: Add support for more libraries
+            raise NotImplementedError(f"Cannot extract feature importance from {mdl.__class__}")
 
-    # Plotting
+        # Data preparation
-    def add_feature_trace(fig, fi_df, col):
+        fi_df = pd.DataFrame({
-        return fig.add_trace(
+            "feature_names": np.array(dk.training_features_list),
-            go.Bar(
+            "feature_importance": np.array(feature_importance)
-                x=fi_df["feature_importance"],
+        })
-                y=fi_df["feature_names"],
+        fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
-                orientation='h', showlegend=False
+        fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
-            ), 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
+        # Plotting
-    model_dir, train_name = str(dk.data_path).rsplit("/", 1)
+        def add_feature_trace(fig, fi_df, col):
-    fi_dir = Path(f"{model_dir}/feature_importance/{pair.split('/')[0]}")
+            return fig.add_trace(
-    store_plot_file(fig, f"{train_name}.html", fi_dir)
+                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")

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}")
 
 

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))