model_red = catboost.CatBoostClassifier() model_red_GPU = catboost.CatBoostClassifier() model_red.load_model("./catboost modele i wyniki/12 depth/CPU RED basic") model_red_GPU.load_model( "./catboost modele i wyniki/12 depth/GPU RED basic") if model_red.tree_count_ > 10 and model_red_GPU.tree_count_ > 10: os.remove("./catboost modele i wyniki/12 depth/CPU RED basic") os.remove("./catboost modele i wyniki/12 depth/GPU RED basic") model_red.shrink(model_red.tree_count_, model_red.tree_count_ - 2) model_red_GPU.shrink(model_red_GPU.tree_count_, model_red_GPU.tree_count_ - 2) model_red.save_model( "./catboost modele i wyniki/12 depth/CPU RED basic") model_red_GPU.save_model( "./catboost modele i wyniki/12 depth/GPU RED basic") red_data_training, red_data_test, red_quality_training, red_quality_test, white_data_training, white_data_test, white_quality_training, white_quality_test = wines_import.read_data( False) red_quality_predicted_CPU = model_red.predict(red_data_test) red_quality_predicted_GPU = model_red_GPU.predict(red_data_test) print("\nCatboost uczony CPU wyniki\n") print( metrics.classification_report(red_quality_test, red_quality_predicted_CPU, zero_division=0)) print(metrics.confusion_matrix(red_quality_test, red_quality_predicted_CPU)) print("\nCatboost uczony GPU wyniki\n") print( metrics.classification_report(red_quality_test, red_quality_predicted_GPU, zero_division=0))
#import needed library from wines_import import read_data import pandas as pd import time #import linear model and train_test_split from sklearn.linear_model import LogisticRegression from sklearn import metrics #Read the data file data=pd.read_csv('../Data/winequality-white.csv') <<<<<<< HEAD red_data_training, red_data_test, red_quality_training, red_quality_test,white_data_training,white_data_test, white_quality_training, white_quality_test = read_data(True) time_before=time.time() reg = LogisticRegression(solver='saga', random_state=42, max_iter=2000, multi_class='auto') ======= red_data_training, red_data_test, red_quality_training, red_quality_test,white_data_training,white_data_test, white_quality_training, white_quality_test = read_data(False) reg = LogisticRegression() >>>>>>> 103021ef48f3900bc979295db7647819d0bb5109 reg.fit(white_data_training, white_quality_training) y_pred = reg.predict(white_data_test) time_after=time.time() print("Regression coefficient is ", reg.coef_) #classification report print(metrics.classification_report(white_quality_test, y_pred)) Conf_Mat = metrics.confusion_matrix(white_quality_test, y_pred) print("The confusion matrix is\n", Conf_Mat) #print("Accuracy is ",metrics.accuracy_score(white_quality_test, y_pred)) print("Czas wykonania: ",round(time_after-time_before,3)," sekund")