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stress_at_work_analysis
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Free up memory during model building.

master
junos 2023-05-10 21:44:40 +02:00
parent b505fb2b6a
commit 35c09374dd
1 changed files with 26 additions and 2 deletions
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28
machine_learning/helper.py
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@ -191,10 +191,12 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "dummy"
scores = pd.concat([scores, scores_df])
del dummy_regr
del dummy_regr_scores
lin_reg_rapids = linear_model.LinearRegression()
lin_reg = linear_model.LinearRegression()
lin_reg_scores = cross_validate(
lin_reg_rapids,
lin_reg,
X=data_x,
y=data_y,
groups=data_groups,
@ -209,6 +211,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "linear_reg"
scores = pd.concat([scores, scores_df])
del lin_reg
del lin_reg_scores
ridge_reg = linear_model.Ridge(alpha=0.5)
ridge_reg_scores = cross_validate(
@ -226,6 +230,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "ridge_reg"
scores = pd.concat([scores, scores_df])
del ridge_reg
del ridge_reg_scores
lasso_reg = linear_model.Lasso(alpha=0.1)
lasso_reg_score = cross_validate(
@ -243,6 +249,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "lasso_reg"
scores = pd.concat([scores, scores_df])
del lasso_reg
del lasso_reg_score
bayesian_ridge_reg = linear_model.BayesianRidge()
bayesian_ridge_reg_score = cross_validate(
@ -260,6 +268,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "bayesian_ridge"
scores = pd.concat([scores, scores_df])
del bayesian_ridge_reg
del bayesian_ridge_reg_score
ransac_reg = linear_model.RANSACRegressor()
ransac_reg_score = cross_validate(
@ -277,6 +287,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "RANSAC"
scores = pd.concat([scores, scores_df])
del ransac_reg
del ransac_reg_score
svr = svm.SVR()
svr_score = cross_validate(
@ -294,6 +306,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "SVR"
scores = pd.concat([scores, scores_df])
del svr
del svr_score
kridge = kernel_ridge.KernelRidge()
kridge_score = cross_validate(
@ -311,6 +325,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "kernel_ridge"
scores = pd.concat([scores, scores_df])
del kridge
del kridge_score
gpr = gaussian_process.GaussianProcessRegressor()
gpr_score = cross_validate(
@ -328,6 +344,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "gaussian_proc"
scores = pd.concat([scores, scores_df])
del gpr
del gpr_score
rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
rfr_score = cross_validate(
@ -345,6 +363,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "random_forest"
scores = pd.concat([scores, scores_df])
del rfr
del rfr_score
xgb = XGBRegressor()
xgb_score = cross_validate(
@ -362,6 +382,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "XGBoost"
scores = pd.concat([scores, scores_df])
del xgb
del xgb_score
ada = ensemble.AdaBoostRegressor()
ada_score = cross_validate(
@ -379,6 +401,8 @@ def run_all_regression_models(
scores_df = scores_df.agg(["max", np.nanmedian]).transpose()
scores_df["method"] = "ADA_boost"
scores = pd.concat([scores, scores_df])
del ada
del ada_score
return scores