plot features as html instead of png

This commit is contained in:
initrv 2022-09-16 21:47:12 +03:00
parent b707a6da35
commit 86aa875bc9
2 changed files with 25 additions and 38 deletions

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@ -1,13 +1,9 @@
import logging import logging
from datetime import datetime, timezone from datetime import datetime, timezone
# for plot_feature_importance
from pathlib import Path from pathlib import Path
import numpy as np import numpy as np
import pandas as pd 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.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider 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. Plot Best and Worst Features by importance for CatBoost model.
Called once per sub-train. Called once per sub-train.
Required: pip install kaleido
Usage: plot_feature_importance( Usage: plot_feature_importance(
model=model, model=model,
feature_names=dk.training_features_list, feature_names=dk.training_features_list,
pair=pair, pair=pair,
train_dir=dk.data_path) 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 # Gather feature importance from model
if "catboost.core" in str(model.__class__): 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__): elif "lightgbm.sklearn" in str(model.__class__):
fi = model.feature_importances_ feature_importance = model.feature_importances_
else: else:
raise NotImplementedError(f"Cannot extract feature importance for {model.__class__}") raise NotImplementedError(f"Cannot extract feature importance for {model.__class__}")
# Data preparation # Data preparation
fi_df = pd.DataFrame({ fi_df = pd.DataFrame({
"feature_names": np.array(feature_names), "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_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1] fi_df_worst = fi_df.nsmallest(count_max, "feature_importance")[::-1]
# Plotting # 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 = make_subplots(rows=1, cols=2, horizontal_spacing=0.5)
fig.add_trace( fig = add_feature_trace(fig, fi_df_top, 1)
go.Bar( fig = add_feature_trace(fig, fi_df_worst, 2)
x=fi_df_top["feature_importance"], fig.update_layout(title_text=f"Best and Worst Features {pair}")
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
)
# Create directory and save image # Store plot file
model_dir, train_name = str(train_dir).rsplit("/", 1) model_dir, train_name = str(train_dir).rsplit("/", 1)
fi_dir = Path(f"{model_dir}/feature_importance/{pair.split('/')[0]}") fi_dir = Path(f"{model_dir}/feature_importance/{pair.split('/')[0]}")
fi_dir.mkdir(parents=True, exist_ok=True) store_plot_file(fig, f"{train_name}.html", fi_dir)
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")

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