From 66b25d54d1a9e266217abf91c5e6cad436e0a4c7 Mon Sep 17 00:00:00 2001 From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= Date: Tue, 11 Mar 2025 19:37:20 +0100 Subject: [PATCH] refactor(qav3): import cleanups MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit Signed-off-by: Jérôme Benoit --- .../freqaimodels/LightGBMRegressorQuickAdapterV35.py | 8 ++++---- .../freqaimodels/XGBoostRegressorQuickAdapterV35.py | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py index a10e1b0..b433644 100644 --- a/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/LightGBMRegressorQuickAdapterV35.py @@ -9,7 +9,7 @@ from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel from freqtrade.freqai.data_kitchen import FreqaiDataKitchen import pandas as pd import numpy as np -import scipy as spy +import scipy as sp import optuna import sklearn import warnings @@ -206,7 +206,7 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): if not warmed_up: f = [0, 0] else: - f = spy.stats.norm.fit(pred_df_full[label]) + f = sp.stats.norm.fit(pred_df_full[label]) dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1] # fit the DI_threshold @@ -216,8 +216,8 @@ class LightGBMRegressorQuickAdapterV35(BaseRegressionModel): else: di_values = pd.to_numeric(pred_df_full["DI_values"], errors="coerce") di_values = di_values.dropna() - f = spy.stats.weibull_min.fit(di_values) - cutoff = spy.stats.weibull_min.ppf( + f = sp.stats.weibull_min.fit(di_values) + cutoff = sp.stats.weibull_min.ppf( self.freqai_info.get("outlier_threshold", 0.999), *f ) diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py index 4a274c6..49608d1 100644 --- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py +++ b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV35.py @@ -9,7 +9,7 @@ from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel from freqtrade.freqai.data_kitchen import FreqaiDataKitchen import pandas as pd import numpy as np -import scipy as spy +import scipy as sp import optuna import sklearn import warnings @@ -209,7 +209,7 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): if not warmed_up: f = [0, 0] else: - f = spy.stats.norm.fit(pred_df_full[label]) + f = sp.stats.norm.fit(pred_df_full[label]) dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1] # fit the DI_threshold @@ -219,8 +219,8 @@ class XGBoostRegressorQuickAdapterV35(BaseRegressionModel): else: di_values = pd.to_numeric(pred_df_full["DI_values"], errors="coerce") di_values = di_values.dropna() - f = spy.stats.weibull_min.fit(di_values) - cutoff = spy.stats.weibull_min.ppf( + f = sp.stats.weibull_min.fit(di_values) + cutoff = sp.stats.weibull_min.ppf( self.freqai_info.get("outlier_threshold", 0.999), *f ) -- 2.43.0