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