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allow plot to plot multitargets, add test

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This commit is contained in:
robcaulk 2022-09-17 19:17:44 +02:00
parent 1c92734f39
commit 2c23effbf2
4 changed files with 75 additions and 35 deletions
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2
freqtrade/freqai/freqai_interface.py
View File

@ -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):

68
freqtrade/freqai/utils.py
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@ -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")

5
freqtrade/plot/plotting.py
View File

@ -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}")

35
tests/freqai/test_freqai_interface.py
View File

@ -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))