From 2bb95657d8a546c2654e6f1c12ca5f2dacdf5373 Mon Sep 17 00:00:00 2001 From: junos Date: Thu, 8 Dec 2022 10:02:13 +0100 Subject: [PATCH] Finish classification presentation. --- presentation/classification.py | 294 ++------------------------------- 1 file changed, 18 insertions(+), 276 deletions(-) diff --git a/presentation/classification.py b/presentation/classification.py index 3acefcb..0ff2874 100644 --- a/presentation/classification.py +++ b/presentation/classification.py @@ -30,28 +30,34 @@ from sklearn.model_selection import LeaveOneGroupOut, cross_validate from sklearn.dummy import DummyClassifier from sklearn.impute import SimpleImputer -from lightgbm import LGBMClassifier import xgboost as xg from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all" +from pathlib import Path + nb_dir = os.path.split(os.getcwd())[0] if nb_dir not in sys.path: sys.path.append(nb_dir) import machine_learning.labels import machine_learning.model +from machine_learning.helper import run_all_classification_models # %% [markdown] # # RAPIDS models # %% [markdown] # ## Set script's parameters +# + +# %% cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could be regarded as a hyperparameter) n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs # %% jupyter={"source_hidden": true} -model_input = pd.read_csv("../data/stressfulness_event_nonstandardized/input_appraisal_stressfulness_event_mean.csv") +filename = Path("E:/STRAWresults/inputData/stressfulness_event/input_appraisal_stressfulness_event_mean.csv") +model_input = pd.read_csv(filename) # %% jupyter={"source_hidden": true} index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] @@ -59,11 +65,11 @@ model_input.set_index(index_columns, inplace=True) model_input['target'].value_counts() # %% jupyter={"source_hidden": true} -# bins = [-10, -1, 1, 10] # bins for z-scored targets -bins = [0, 1, 4] # bins for stressfulness (1-4) target -model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'high'], retbins=True, right=True) #['low', 'medium', 'high'] +bins = [-10, -1, 1, 10] # bins for z-scored targets +# bins = [0, 1, 4] # bins for stressfulness (1-4) target +model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'medium', 'high'], retbins=True, right=True) #['low', 'medium', 'high'] model_input['target'].value_counts(), edges -# model_input = model_input[model_input['target'] != "medium"] +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'].value_counts() @@ -98,7 +104,6 @@ if not categorical_features.empty: 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": true} cv_method = None # Defaults to 5 k-folds in cross_validate method @@ -113,273 +118,10 @@ if cv_method_str == 'logo' or cv_method_str == 'half_logo': # %% jupyter={"source_hidden": true} imputer = SimpleImputer(missing_values=np.nan, strategy='median') -# %% [markdown] -# ### Baseline: Dummy Classifier (most frequent) -dummy_class = DummyClassifier(strategy="most_frequent") +# %% +final_scores = run_all_classification_models(imputer.fit_transform(train_x), data_y, data_groups, cv_method) -# %% jupyter={"source_hidden": true} -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', 'average_precision', 'recall', 'f1') -) -# %% jupyter={"source_hidden": true} -print("Acc", np.mean(dummy_classifier['test_accuracy'])) -print("Precision", np.mean(dummy_classifier['test_average_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] -# ### Logistic Regression - -# %% jupyter={"source_hidden": true} -logistic_regression = linear_model.LogisticRegression() - -# %% jupyter={"source_hidden": true} -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') -) -# %% jupyter={"source_hidden": true} -print("Acc", 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])) - -# %% [markdown] -# ### Support Vector Machine - -# %% jupyter={"source_hidden": true} -svc = svm.SVC() - -# %% jupyter={"source_hidden": true} -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') -) -# %% jupyter={"source_hidden": true} -print("Acc", 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": true} -gaussian_nb = naive_bayes.GaussianNB() - -# %% jupyter={"source_hidden": true} -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": true} -print("Acc", 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": true} -sgdc = linear_model.SGDClassifier() - -# %% jupyter={"source_hidden": true} -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": true} -print("Acc", 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": true} -knn = neighbors.KNeighborsClassifier() - -# %% jupyter={"source_hidden": true} -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": true} -print("Acc", 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": true} -dtree = tree.DecisionTreeClassifier() - -# %% jupyter={"source_hidden": true} -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": true} -print("Acc", 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": true} -rfc = ensemble.RandomForestClassifier() - -# %% jupyter={"source_hidden": true} -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') -) -# %% jupyter={"source_hidden": true} -print("Acc", 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] -# ### Gradient Boosting Classifier - -# %% jupyter={"source_hidden": true} -gbc = ensemble.GradientBoostingClassifier() - -# %% jupyter={"source_hidden": true} -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": true} -print("Acc", 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": true} -lgbm = LGBMClassifier() - -# %% jupyter={"source_hidden": true} -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": true} -print("Acc", 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": true} -xgb_classifier = xg.sklearn.XGBClassifier() - -# %% jupyter={"source_hidden": true} -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": true} -print("Acc", 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])) +# %% +final_scores.index.name = "metric" +final_scores = final_scores.set_index(["method", final_scores.index]) +final_scores.to_csv("event_stressfulness_lmh_lh_scores.csv")