plot features as html instead of png

freqtrade/freqai/utils.py

@@ -1,13 +1,9 @@
 import logging
 from datetime import datetime, timezone
-# for plot_feature_importance
 from pathlib import Path
 
 import numpy as np
 import pandas as pd
-import plotly.graph_objects as go
-import plotly.io as pio
-from plotly.subplots import make_subplots
 
 from freqtrade.configuration import TimeRange
 from freqtrade.data.dataprovider import DataProvider
@@ -142,64 +138,56 @@ def get_required_data_timerange(
 #         )
 
 
-def plot_feature_importance(model, feature_names, pair, train_dir, count_max=25) -> None:
+def plot_feature_importance(model, feature_names, pair, train_dir, count_max=50) -> None:
     """
         Plot Best and Worst Features by importance for CatBoost model.
         Called once per sub-train.
-
-        Required: pip install kaleido
-
         Usage: plot_feature_importance(
             model=model,
             feature_names=dk.training_features_list,
             pair=pair,
             train_dir=dk.data_path)
     """
+    try:
+        import plotly.graph_objects as go
+        from plotly.subplots import make_subplots
+    except ImportError:
+        logger.exception("Module plotly not found \n Please install using `pip3 install plotly`")
+        exit(1)
+
+    from freqtrade.plot.plotting import store_plot_file
 
     # Gather feature importance from model
     if "catboost.core" in str(model.__class__):
-        fi = model.get_feature_importance()
-
+        feature_importance = model.get_feature_importance()
     elif "lightgbm.sklearn" in str(model.__class__):
-        fi = model.feature_importances_
-
+        feature_importance = model.feature_importances_
     else:
         raise NotImplementedError(f"Cannot extract feature importance for {model.__class__}")
 
     # Data preparation
     fi_df = pd.DataFrame({
         "feature_names": np.array(feature_names),
-        "feature_importance": np.array(fi)
+        "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]
 
     # 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_trace(
-        go.Bar(
-            x=fi_df_top["feature_importance"],
-            y=fi_df_top["feature_names"],
-            orientation='h', showlegend=False
-        ), row=1, col=1
-    )
-    fig.add_trace(
-        go.Bar(
-            x=fi_df_worst["feature_importance"],
-            y=fi_df_worst["feature_names"],
-            orientation='h', showlegend=False
-        ), row=1, col=2
-    )
-    fig.update_layout(
-        title_text=f"Best and Worst Features {pair}",
-        width=1000, height=600
-    )
+    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 {pair}")
 
-    # Create directory and save image
+    # Store plot file
     model_dir, train_name = str(train_dir).rsplit("/", 1)
     fi_dir = Path(f"{model_dir}/feature_importance/{pair.split('/')[0]}")
-    fi_dir.mkdir(parents=True, exist_ok=True)
-
-    pio.write_image(fig, f"{fi_dir}/{train_name}.png", format="png")
-
-    logger.info(f"Freqai saving feature importance plot {fi_dir}/{train_name}.png")
+    store_plot_file(fig, f"{train_name}.html", fi_dir)

requirements-plot.txt

@@ -2,4 +2,3 @@
 -r requirements.txt
 
 plotly==5.10.0
-kaleido==0.2.1