From 24f557ce038a1ede082b4953b3e17084c800c2d7 Mon Sep 17 00:00:00 2001 From: Scott-Lindauer_sasinst Date: Tue, 29 Oct 2024 14:24:21 -0400 Subject: [PATCH] fix: remove double named files --- .../DecisionTreeClassifier2.pickle | Bin 9019 -> 0 bytes .../DecisionTreeClassifier2.zip | Bin 462645 -> 0 bytes .../score_DecisionTreeClassifier2.py | 55 - .../GradientBoosting/GradientBoosting2.pickle | Bin 1348098 -> 0 bytes .../GradientBoosting/GradientBoosting2.zip | Bin 3007583 -> 0 bytes .../score_GradientBoosting2.py | 55 - .../RandomForest/RandomForest2.pickle | Bin 1784860 -> 0 bytes .../hmeqModels/RandomForest/RandomForest2.zip | Bin 3837665 -> 0 bytes .../RandomForest/score_RandomForest2.py | 55 - .../pzmm_generate_complete_mode_card.ipynb | 1951 ----------------- 10 files changed, 2116 deletions(-) delete mode 100644 examples/data/hmeqModels/DecisionTreeClassifier/DecisionTreeClassifier2.pickle delete mode 100644 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"DecisionTreeClassifier2.pickle", "rb") as pickle_model: - model = pickle.load(pickle_model) - -def score(LOAN, MORTDUE, VALUE, YOJ, DEROG, DELINQ, CLAGE, NINQ, CLNO, DEBTINC): - "Output: EM_CLASSIFICATION, EM_EVENTPROBABILITY" - - try: - global model - except NameError: - with open(Path(settings.pickle_path) / "DecisionTreeClassifier2.pickle", "rb") as pickle_model: - model = pickle.load(pickle_model) - - - index=None - if not isinstance(LOAN, pd.Series): - index=[0] - input_array = pd.DataFrame( - {"LOAN": LOAN, "MORTDUE": MORTDUE, "VALUE": VALUE, "YOJ": YOJ, "DEROG": DEROG, - "DELINQ": DELINQ, "CLAGE": CLAGE, "NINQ": NINQ, "CLNO": CLNO, "DEBTINC": - DEBTINC}, index=index - ) - input_array = impute_missing_values(input_array) - prediction = model.predict_proba(input_array).tolist() - - # Check for numpy values and convert to a CAS readable representation - if isinstance(prediction, np.ndarray): - prediction = prediction.tolist() - - if input_array.shape[0] == 1: - if prediction[0][1] > 0.5: - EM_CLASSIFICATION = "1" - else: - EM_CLASSIFICATION = "0" - return EM_CLASSIFICATION, prediction[0][1] - else: - df = pd.DataFrame(prediction) - proba = df[1] - classifications = np.where(df[1] > 0.5, '1', '0') - return pd.DataFrame({'EM_CLASSIFICATION': classifications, 'EM_EVENTPROBABILITY': proba}) - -def impute_missing_values(data): - impute_values = \ - {'VALUE': 101776.04874145007, 'DEBTINC': 33.779915349235246, 'NINQ': - 1.1860550458715597, 'DELINQ': 0.4494423791821561, 'CLAGE': 179.7662751900465, - 'MORTDUE': 73760.817199559, 'DEROG': 0.2545696877380046, 'YOJ': - 8.922268135904499, 'CLNO': 21.29609620076682, 'LOAN': 18607.96979865772} - return data.replace(' .', np.nan).fillna(impute_values).apply(pd.to_numeric, errors='ignore') diff --git a/examples/data/hmeqModels/GradientBoosting/GradientBoosting2.pickle b/examples/data/hmeqModels/GradientBoosting/GradientBoosting2.pickle deleted file mode 100644 index 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