stress_at_work_analysis/machine_learning/classification_models.py

from sklearn.dummy import DummyClassifier
from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
from lightgbm import LGBMClassifier
import xgboost as xg 

class ClassificationModels():
    
    def __init__(self):
        self.cmodels = self.init_classification_models()
        
    def get_cmodels(self):
        return self.cmodels

    def init_classification_models(self):
        cmodels = {
            'dummy_classifier': {
                'model': DummyClassifier(strategy="most_frequent"),
                'metrics': [0, 0, 0, 0]
            },
            'logistic_regression': {
                'model': linear_model.LogisticRegression(max_iter=1000),
                'metrics': [0, 0, 0, 0]
            },
            'support_vector_machine': {
                'model': svm.SVC(),
                'metrics': [0, 0, 0, 0]
            },
            'gaussian_naive_bayes': {
                'model': naive_bayes.GaussianNB(),
                'metrics': [0, 0, 0, 0]
            },
            'stochastic_gradient_descent_classifier': {
                'model': linear_model.SGDClassifier(),
                'metrics': [0, 0, 0, 0]
            },
            'knn': {
                'model': neighbors.KNeighborsClassifier(),
                'metrics': [0, 0, 0, 0]
            },
            'decision_tree': {
                'model': tree.DecisionTreeClassifier(),
                'metrics': [0, 0, 0, 0]
            },
            'random_forest_classifier': {
                'model': ensemble.RandomForestClassifier(),
                'metrics': [0, 0, 0, 0]
            },
            'gradient_boosting_classifier': {
                'model': ensemble.GradientBoostingClassifier(),
                'metrics': [0, 0, 0, 0]
            },
            'lgbm_classifier': {
                'model': LGBMClassifier(),
                'metrics': [0, 0, 0, 0]
            },
            'XGBoost_classifier': {
                'model': xg.sklearn.XGBClassifier(),
                'metrics': [0, 0, 0, 0]
            }
        }
        
        return cmodels
    
    def get_total_models_scores(self, n_clusters=1):
        for model_title, model in self.cmodels.items():
            print("\n************************************\n")
            print("Current model:", model_title, end="\n")
            print("Acc:", model['metrics'][0]/n_clusters)
            print("Precision:", model['metrics'][1]/n_clusters)
            print("Recall:", model['metrics'][2]/n_clusters)
            print("F1:", model['metrics'][3]/n_clusters)