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stress_at_work_analysis
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Rename methods to make them consistent with regression methods.

master
junos 2023-05-18 18:55:31 +02:00
parent 45441c288d
commit 1318ae3609
1 changed files with 6 additions and 6 deletions
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12
machine_learning/helper.py
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@ -445,7 +445,7 @@ def run_all_classification_models(
scores_df = pd.DataFrame(dummy_score)[test_metrics] scores_df = pd.DataFrame(dummy_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"]) scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df["method"] = "Dummy" scores_df["method"] = "dummy_classifier"
scores = pd.concat([scores, scores_df]) scores = pd.concat([scores, scores_df])
del dummy_class del dummy_class
del dummy_score del dummy_score
@ -465,7 +465,7 @@ def run_all_classification_models(
scores_df = pd.DataFrame(log_reg_scores)[test_metrics] scores_df = pd.DataFrame(log_reg_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"]) scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df["method"] = "logistic_reg" scores_df["method"] = "logistic_regression"
scores = pd.concat([scores, scores_df]) scores = pd.concat([scores, scores_df])
del logistic_regression del logistic_regression
del log_reg_scores del log_reg_scores
@ -485,7 +485,7 @@ def run_all_classification_models(
scores_df = pd.DataFrame(svc_scores)[test_metrics] scores_df = pd.DataFrame(svc_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"]) scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df["method"] = "svc" scores_df["method"] = "SVC"
scores = pd.concat([scores, scores_df]) scores = pd.concat([scores, scores_df])
del svc del svc
del svc_scores del svc_scores
@ -525,7 +525,7 @@ def run_all_classification_models(
scores_df = pd.DataFrame(sgdc_scores)[test_metrics] scores_df = pd.DataFrame(sgdc_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"]) scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df["method"] = "stochastic_gradient_descent" scores_df["method"] = "stochastic_gradient_descent_classifier"
scores = pd.concat([scores, scores_df]) scores = pd.concat([scores, scores_df])
del sgdc del sgdc
del sgdc_scores del sgdc_scores
@ -545,7 +545,7 @@ def run_all_classification_models(
scores_df = pd.DataFrame(rfc_scores)[test_metrics] scores_df = pd.DataFrame(rfc_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"]) scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df["method"] = "random_forest" scores_df["method"] = "random_forest_classifier"
scores = pd.concat([scores, scores_df]) scores = pd.concat([scores, scores_df])
del rfc del rfc
del rfc_scores del rfc_scores
@ -565,7 +565,7 @@ def run_all_classification_models(
scores_df = pd.DataFrame(xgb_scores)[test_metrics] scores_df = pd.DataFrame(xgb_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"]) scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df["method"] = "xgboost" scores_df["method"] = "XGBoost_classifier"
scores = pd.concat([scores, scores_df]) scores = pd.concat([scores, scores_df])
del xgb_classifier del xgb_classifier
del xgb_scores del xgb_scores