124 lines
4.4 KiB
Python
124 lines
4.4 KiB
Python
import pandas as pd
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import xgboost as xg
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from lightgbm import LGBMClassifier
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from sklearn import ensemble, linear_model, naive_bayes, neighbors, svm, tree
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from sklearn.dummy import DummyClassifier
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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": {"model": svm.SVC(), "metrics": [0, 0, 0, 0]},
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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": {"model": neighbors.KNeighborsClassifier(), "metrics": [0, 0, 0, 0]},
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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": {"model": LGBMClassifier(), "metrics": [0, 0, 0, 0]},
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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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scores = pd.DataFrame(columns=["method", "metric", "mean"])
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for model_title, model in self.cmodels.items():
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scores_df = pd.DataFrame(columns=["method", "metric", "mean"])
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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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scores_df = pd.concat(
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[
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scores_df,
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pd.DataFrame(
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{
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"method": model_title,
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"metric": "test_accuracy",
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"mean": model["metrics"][0] / n_clusters,
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},
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index=[0],
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),
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],
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ignore_index=True,
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)
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print("Precision:", model["metrics"][1] / n_clusters)
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scores_df = pd.concat(
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[
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scores_df,
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pd.DataFrame(
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{
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"method": model_title,
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"metric": "test_precision",
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"mean": model["metrics"][1] / n_clusters,
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},
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index=[0],
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),
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],
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ignore_index=True,
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)
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print("Recall:", model["metrics"][2] / n_clusters)
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scores_df = pd.concat(
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[
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scores_df,
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pd.DataFrame(
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{
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"method": model_title,
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"metric": "test_recall",
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"mean": model["metrics"][2] / n_clusters,
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},
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index=[0],
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),
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],
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ignore_index=True,
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)
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print("F1:", model["metrics"][3] / n_clusters)
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scores_df = pd.concat(
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[
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scores_df,
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pd.DataFrame(
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{
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"method": model_title,
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"metric": "test_f1",
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"mean": model["metrics"][3] / n_clusters,
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},
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index=[0],
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),
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],
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ignore_index=True,
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)
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scores = pd.concat([scores, scores_df])
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return scores
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