stress_at_work_analysis/machine_learning/classification_models.py

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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)