From f58d20ffc227d9277b65ad9d6834787645de2aaa Mon Sep 17 00:00:00 2001
From: junos <junos.lukan@ijs.si>
Date: Wed, 10 May 2023 23:17:44 +0200
Subject: [PATCH] Update classification runner.

---
 exploration/ml_pipeline_classification.py | 467 +++-------------------
 1 file changed, 54 insertions(+), 413 deletions(-)

diff --git a/exploration/ml_pipeline_classification.py b/exploration/ml_pipeline_classification.py
index 04087f0..709cdc2 100644
--- a/exploration/ml_pipeline_classification.py
+++ b/exploration/ml_pipeline_classification.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.13.0
+#       jupytext_version: 1.14.5
 #   kernelspec:
 #     display_name: straw2analysis
 #     language: python
@@ -18,445 +18,86 @@
 import os
 import sys
 
-import numpy as np
-import matplotlib.pyplot as plt
 import pandas as pd
-
-from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble 
-from sklearn.model_selection import LeaveOneGroupOut, cross_validate, StratifiedKFold
-from sklearn.dummy import DummyClassifier
-from sklearn.impute import SimpleImputer
-
-from lightgbm import LGBMClassifier
-import xgboost as xg
 from IPython.core.interactiveshell import InteractiveShell
+
+from machine_learning.helper import (
+    impute_encode_categorical_features,
+    prepare_cross_validator,
+    prepare_sklearn_data_format,
+    run_all_classification_models,
+)
+
 InteractiveShell.ast_node_interactivity = "all"
 
 nb_dir = os.path.split(os.getcwd())[0]
 if nb_dir not in sys.path:
     sys.path.append(nb_dir)
-    
-import machine_learning.helper
 
-# %% [markdown]
-# # RAPIDS models
 
-# %% [markdown]
-# ## Set script's parameters
-#
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
-cv_method_str = 'logo' # logo, half_logo, 5kfold # Cross-validation method (could be regarded as a hyperparameter)
-n_sl = 3 # Number of largest/smallest accuracies (of particular CV) outputs
-undersampling = False # (bool) If True this will train and test data on balanced dataset (using undersampling method)
+# %%
+CV_METHOD = "logo"  # logo, half_logo, 5kfold
+# Cross-validation method (could be regarded as a hyperparameter)
+N_SL = 3  # Number of largest/smallest accuracies (of particular CV) outputs
+UNDERSAMPLING = False
+# (bool) If True this will train and test data on balanced dataset
+# (using undersampling method)
 
 # %% jupyter={"source_hidden": false, "outputs_hidden": false}
-model_input = pd.read_csv("../data/stressfulness_event_with_target_0_ver2/input_appraisal_stressfulness_event_mean.csv")
-# model_input = model_input[model_input.columns.drop(list(model_input.filter(regex='empatica_temperature')))]
+model_input = pd.read_csv(
+    "E:/STRAWresults/20230415/daily/input_PANAS_negative_affect_mean.csv"
+)
+# model_input =
+# model_input[model_input.columns.drop(
+# list(model_input.filter(regex='empatica_temperature'))
+# )]
 
 # %% jupyter={"source_hidden": false, "outputs_hidden": false}
-index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
-model_input.set_index(index_columns, inplace=True)
-model_input['target'].value_counts()
+model_input["target"].value_counts()
 
 # %% jupyter={"source_hidden": false, "outputs_hidden": false}
 # bins = [-10, 0, 10] # bins for z-scored targets
-bins = [-1, 0, 4] # bins for stressfulness (0-4) target
-model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'high'], retbins=True, right=True) #['low', 'medium', 'high']
-model_input['target'].value_counts(), edges
+bins = [-1, 0, 4]  # bins for stressfulness (0-4) target
+model_input["target"], edges = pd.cut(
+    model_input.target, bins=bins, labels=["low", "high"], retbins=True, right=True
+)  # ['low', 'medium', 'high']
+model_input["target"].value_counts(), edges
 # model_input = model_input[model_input['target'] != "medium"]
-model_input['target'] = model_input['target'].astype(str).apply(lambda x: 0 if x == "low" else 1)
+model_input["target"] = (
+    model_input["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
+)
 
-model_input['target'].value_counts()
+model_input["target"].value_counts()
 
 # %% jupyter={"source_hidden": false, "outputs_hidden": false}
 # UnderSampling
-if undersampling:
-    no_stress = model_input[model_input['target'] == 0]
-    stress = model_input[model_input['target'] == 1]
-    
+if UNDERSAMPLING:
+    no_stress = model_input[model_input["target"] == 0]
+    stress = model_input[model_input["target"] == 1]
+
     no_stress = no_stress.sample(n=len(stress))
-    model_input = pd.concat([stress,no_stress], axis=0)
-
-#   model_input_new = pd.DataFrame(columns=model_input.columns)
-#   for pid in model_input["pid"].unique():
-#     stress = model_input[(model_input["pid"] == pid) & (model_input['target'] == 1)]
-#     no_stress = model_input[(model_input["pid"] == pid) & (model_input['target'] == 0)]
-#     if (len(stress) == 0):
-#       continue
-#     if (len(no_stress) == 0):
-#       continue
-#     model_input_new = pd.concat([model_input_new, stress], axis=0)
-    
-#     no_stress = no_stress.sample(n=min(len(stress), len(no_stress)))
-#     # In case there are more stress samples than no_stress, take all instances of no_stress.
-#     model_input_new = pd.concat([model_input_new, no_stress], axis=0)
-#     model_input = model_input_new   
-#     model_input_new = pd.concat([model_input_new, no_stress], axis=0)
+    model_input = pd.concat([stress, no_stress], axis=0)
 
 
 # %% jupyter={"source_hidden": false, "outputs_hidden": false}
-if cv_method_str == 'half_logo':
-    model_input['pid_index'] = model_input.groupby('pid').cumcount()
-    model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
-
-    model_input["pid_index"] = (model_input['pid_index'] / model_input['pid_count'] + 1).round()
-    model_input["pid_half"] = model_input["pid"] + "_" +  model_input["pid_index"].astype(int).astype(str)
-
-    data_x, data_y, data_groups = model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input["target"], model_input["pid_half"]
-else:
-    data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
-
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-categorical_feature_colnames = ["gender", "startlanguage"]
-additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
-categorical_feature_colnames += additional_categorical_features
-
-categorical_features = data_x[categorical_feature_colnames].copy()
-mode_categorical_features = categorical_features.mode().iloc[0]
-
-# fillna with mode
-categorical_features = categorical_features.fillna(mode_categorical_features)
-
-# one-hot encoding
-categorical_features = categorical_features.apply(lambda col: col.astype("category"))
-if not categorical_features.empty:
-    categorical_features = pd.get_dummies(categorical_features)
-
-numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
-train_x = pd.concat([numerical_features, categorical_features], axis=1)
-train_x.dtypes
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-cv_method = StratifiedKFold(n_splits=5, shuffle=True) # Defaults to 5 k-folds in cross_validate method
-if cv_method_str == 'logo' or cv_method_str == 'half_logo':
-    cv_method = LeaveOneGroupOut()
-    cv_method.get_n_splits(
-        train_x,
-        data_y,
-        groups=data_groups,
-    )
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-imputer = SimpleImputer(missing_values=np.nan, strategy='median')
-
-# %% [markdown]
-# ### Baseline: Dummy Classifier (most frequent)
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
-dummy_class = DummyClassifier(strategy="most_frequent")
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-dummy_classifier = cross_validate(
-    dummy_class,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    error_score='raise',
-    scoring=('accuracy', 'precision', 'recall', 'f1')
-)
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(dummy_classifier['test_accuracy']))
-print("Acc (mean)", np.mean(dummy_classifier['test_accuracy']))
-print("Precision", np.mean(dummy_classifier['test_precision']))
-print("Recall", np.mean(dummy_classifier['test_recall']))
-print("F1", np.mean(dummy_classifier['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dummy_classifier['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dummy_classifier['test_accuracy'], n_sl)[:n_sl]))
-
-# %% [markdown] nteract={"transient": {"deleting": false}}
-# ### All models
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
-final_scores = machine_learning.helper.run_all_classification_models(imputer.fit_transform(train_x), data_y, data_groups, cv_method)
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
+model_input_encoded = impute_encode_categorical_features(model_input)
 # %%
-final_scores.index.name = "metric"
-final_scores = final_scores.set_index(["method", final_scores.index])
-final_scores.to_csv(f"../presentation/event_stressful_detection_{cv_method_str}.csv")
-
-# %% [markdown]
-# ### Logistic Regression
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-logistic_regression = linear_model.LogisticRegression()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-log_reg_scores = cross_validate(
-    logistic_regression,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    scoring=('accuracy', 'precision', 'recall', 'f1')
+data_x, data_y, data_groups = prepare_sklearn_data_format(
+    model_input_encoded, CV_METHOD
 )
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(log_reg_scores['test_accuracy']))
-print("Acc (mean)", np.mean(log_reg_scores['test_accuracy']))
-print("Precision", np.mean(log_reg_scores['test_precision']))
-print("Recall", np.mean(log_reg_scores['test_recall']))
-print("F1", np.mean(log_reg_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-log_reg_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(log_reg_scores['test_accuracy'], n_sl)[:n_sl]))
+cross_validator = prepare_cross_validator(data_x, data_y, data_groups, CV_METHOD)
 
-# %% [markdown]
-# ### Support Vector Machine
+# %%
+data_y.head()
 
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-svc = svm.SVC()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-svc_scores = cross_validate(
-    svc,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    scoring=('accuracy', 'precision', 'recall', 'f1')
+# %%
+data_y.tail()
+# %%
+data_y.shape
+# %%
+scores = run_all_classification_models(data_x, data_y, data_groups, cross_validator)
+# %%
+scores.to_csv(
+    "../presentation/JCQ_supervisor_support_regression_" + CV_METHOD + ".csv",
+    index=False,
 )
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(svc_scores['test_accuracy']))
-print("Acc (mean)", np.mean(svc_scores['test_accuracy']))
-print("Precision", np.mean(svc_scores['test_precision']))
-print("Recall", np.mean(svc_scores['test_recall']))
-print("F1", np.mean(svc_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-svc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(svc_scores['test_accuracy'], n_sl)[:n_sl]))
-
-# %% [markdown]
-# ### Gaussian Naive Bayes
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-gaussian_nb = naive_bayes.GaussianNB()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-gaussian_nb_scores = cross_validate(
-    gaussian_nb,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    error_score='raise',
-    scoring=('accuracy', 'precision', 'recall', 'f1')
-)
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(gaussian_nb_scores['test_accuracy']))
-print("Acc (mean)", np.mean(gaussian_nb_scores['test_accuracy']))
-print("Precision", np.mean(gaussian_nb_scores['test_precision']))
-print("Recall", np.mean(gaussian_nb_scores['test_recall']))
-print("F1", np.mean(gaussian_nb_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-gaussian_nb_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gaussian_nb_scores['test_accuracy'], n_sl)[:n_sl]))
-
-# %% [markdown]
-# ### Stochastic Gradient Descent Classifier
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-sgdc = linear_model.SGDClassifier()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-sgdc_scores = cross_validate(
-    sgdc,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    error_score='raise',
-    scoring=('accuracy', 'precision', 'recall', 'f1')
-)
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(sgdc_scores['test_accuracy']))
-print("Acc (mean)", np.mean(sgdc_scores['test_accuracy']))
-print("Precision", np.mean(sgdc_scores['test_precision']))
-print("Recall", np.mean(sgdc_scores['test_recall']))
-print("F1", np.mean(sgdc_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-sgdc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(sgdc_scores['test_accuracy'], n_sl)[:n_sl]))
-
-# %% [markdown]
-# ### K-nearest neighbors
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-knn = neighbors.KNeighborsClassifier()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-knn_scores = cross_validate(
-    knn,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    error_score='raise',
-    scoring=('accuracy', 'precision', 'recall', 'f1')
-)
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(knn_scores['test_accuracy']))
-print("Acc (mean)", np.mean(knn_scores['test_accuracy']))
-print("Precision", np.mean(knn_scores['test_precision']))
-print("Recall", np.mean(knn_scores['test_recall']))
-print("F1", np.mean(knn_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-knn_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(knn_scores['test_accuracy'], n_sl)[:n_sl]))
-
-# %% [markdown]
-# ### Decision Tree
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-dtree = tree.DecisionTreeClassifier()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-dtree_scores = cross_validate(
-    dtree,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    error_score='raise',
-    scoring=('accuracy', 'precision', 'recall', 'f1')
-)
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(dtree_scores['test_accuracy']))
-print("Acc (mean)", np.mean(dtree_scores['test_accuracy']))
-print("Precision", np.mean(dtree_scores['test_precision']))
-print("Recall", np.mean(dtree_scores['test_recall']))
-print("F1", np.mean(dtree_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-dtree_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(dtree_scores['test_accuracy'], n_sl)[:n_sl]))
-
-# %% [markdown]
-# ### Random Forest Classifier
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-rfc = ensemble.RandomForestClassifier()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-rfc_scores = cross_validate(
-    rfc,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    error_score='raise',
-    scoring=('accuracy', 'precision', 'recall', 'f1'), 
-    return_estimator=True
-)
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(rfc_scores['test_accuracy']))
-print("Acc (mean)", np.mean(rfc_scores['test_accuracy']))
-print("Precision", np.mean(rfc_scores['test_precision']))
-print("Recall", np.mean(rfc_scores['test_recall']))
-print("F1", np.mean(rfc_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-rfc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(rfc_scores['test_accuracy'], n_sl)[:n_sl]))
-
-# %% [markdown]
-# ### Feature importance (RFC)
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-rfc_es_fimp = pd.DataFrame(columns=list(train_x.columns))
-for idx, estimator in enumerate(rfc_scores['estimator']):
-    feature_importances = pd.DataFrame(estimator.feature_importances_,
-                                       index = list(train_x.columns),
-                                        columns=['importance'])
-    # print("\nFeatures sorted by their score for estimator {}:".format(idx))
-    # print(feature_importances.sort_values('importance', ascending=False).head(10))                                    
-    rfc_es_fimp = pd.concat([rfc_es_fimp, feature_importances]).groupby(level=0).mean()
-
-pd.set_option('display.max_rows', 100)
-print(rfc_es_fimp.sort_values('importance', ascending=False).head(30))
-
-rfc_es_fimp.sort_values('importance', ascending=False).head(30).plot.bar()
-
-rfc_es_fimp.sort_values('importance', ascending=False).tail(30).plot.bar()
-
-train_x['empatica_temperature_cr_stdDev_X_SO_mean'].value_counts()
-
-# %% [markdown]
-# ### Gradient Boosting Classifier
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-gbc = ensemble.GradientBoostingClassifier()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-gbc_scores = cross_validate(
-    gbc,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    error_score='raise',
-    scoring=('accuracy', 'precision', 'recall', 'f1')
-)
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(gbc_scores['test_accuracy']))
-print("Acc (mean)", np.mean(gbc_scores['test_accuracy']))
-print("Precision", np.mean(gbc_scores['test_precision']))
-print("Recall", np.mean(gbc_scores['test_recall']))
-print("F1", np.mean(gbc_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-gbc_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(gbc_scores['test_accuracy'], n_sl)[:n_sl]))
-
-# %% [markdown]
-# ### LGBM Classifier
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-lgbm = LGBMClassifier()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-lgbm_scores = cross_validate(
-    lgbm,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    error_score='raise',
-    scoring=('accuracy', 'precision', 'recall', 'f1')
-)
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(lgbm_scores['test_accuracy']))
-print("Acc (mean)", np.mean(lgbm_scores['test_accuracy']))
-print("Precision", np.mean(lgbm_scores['test_precision']))
-print("Recall", np.mean(lgbm_scores['test_recall']))
-print("F1", np.mean(lgbm_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-lgbm_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(lgbm_scores['test_accuracy'], n_sl)[:n_sl]))
-
-# %% [markdown]
-# ### XGBoost Classifier
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-xgb_classifier = xg.sklearn.XGBClassifier()
-
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-xgb_classifier_scores = cross_validate(
-    xgb_classifier,
-    X=imputer.fit_transform(train_x),
-    y=data_y,
-    groups=data_groups,
-    cv=cv_method,
-    n_jobs=-1,
-    error_score='raise',
-    scoring=('accuracy', 'precision', 'recall', 'f1')
-)
-# %% jupyter={"source_hidden": false, "outputs_hidden": false}
-print("Acc (median)", np.nanmedian(xgb_classifier_scores['test_accuracy']))
-print("Acc (mean)", np.mean(xgb_classifier_scores['test_accuracy']))
-print("Precision", np.mean(xgb_classifier_scores['test_precision']))
-print("Recall", np.mean(xgb_classifier_scores['test_recall']))
-print("F1", np.mean(xgb_classifier_scores['test_f1']))
-print(f"Largest {n_sl} ACC:", np.sort(-np.partition(-xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl])[::-1])
-print(f"Smallest {n_sl} ACC:", np.sort(np.partition(xgb_classifier_scores['test_accuracy'], n_sl)[:n_sl]))
-