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coco
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train_features = train_features.drop("left_block_R",axis=1)
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train_features = train_features.drop("left_block_G",axis=1)
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train_features = train_features.drop("left_block_B",axis=1)
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#train_features = train_features.drop("left_block_H",axis=1)
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#train_features = train_features.drop("left_block_S",axis=1)
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#train_features = train_features.drop("left_block_V",axis=1)
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train_features = train_features.drop("left_block_l",axis=1)
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train_features = train_features.drop("left_block_a",axis=1)
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train_features = train_features.drop("left_block_b",axis=1)
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train_features = train_features.drop("left_block_R_stddev",axis=1)
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train_features = train_features.drop("left_block_G_stddev",axis=1)
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train_features = train_features.drop("left_block_B_stddev",axis=1)
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#train_features = train_features.drop("left_block_H_stddev",axis=1)
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#train_features = train_features.drop("left_block_S_stddev",axis=1)
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#train_features = train_features.drop("left_block_V_stddev",axis=1)
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train_features = train_features.drop("left_block_l_stddev",axis=1)
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train_features = train_features.drop("left_block_a_stddev",axis=1)
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train_features = train_features.drop("left_block_b_stddev",axis=1)
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train_features = train_features.drop("left_block_R_hist",axis=1)
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train_features = train_features.drop("left_block_G_hist",axis=1)
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train_features = train_features.drop("left_block_B_hist",axis=1)
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#train_features = train_features.drop("left_block_H_hist",axis=1)
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#train_features = train_features.drop("left_block_S_hist",axis=1)
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#train_features = train_features.drop("left_block_V_hist",axis=1)
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train_features = train_features.drop("left_block_l_hist",axis=1)
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train_features = train_features.drop("left_block_a_hist",axis=1)
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train_features = train_features.drop("left_block_b_hist",axis=1)
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train_features = train_features.drop("left_block_R_max",axis=1)
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train_features = train_features.drop("left_block_G_max",axis=1)
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train_features = train_features.drop("left_block_B_max",axis=1)
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#train_features = train_features.drop("left_block_H_max",axis=1)
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#train_features = train_features.drop("left_block_S_max",axis=1)
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#train_features = train_features.drop("left_block_V_max",axis=1)
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train_features = train_features.drop("left_block_l_max",axis=1)
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train_features = train_features.drop("left_block_a_max",axis=1)
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train_features = train_features.drop("left_block_b_max",axis=1)
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train_features = train_features.drop("left_block_R_min",axis=1)
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train_features = train_features.drop("left_block_G_min",axis=1)
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train_features = train_features.drop("left_block_B_min",axis=1)
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#train_features = train_features.drop("left_block_H_min",axis=1)
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#train_features = train_features.drop("left_block_S_min",axis=1)
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#train_features = train_features.drop("left_block_V_min",axis=1)
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train_features = train_features.drop("left_block_l_min",axis=1)
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train_features = train_features.drop("left_block_a_min",axis=1)
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train_features = train_features.drop("left_block_b_min",axis=1)
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##################################################################
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train_features = train_features.drop("right_block_R",axis=1)
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train_features = train_features.drop("right_block_G",axis=1)
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train_features = train_features.drop("right_block_B",axis=1)
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#train_features = train_features.drop("right_block_H",axis=1)
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#train_features = train_features.drop("right_block_S",axis=1)
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#train_features = train_features.drop("right_block_V",axis=1)
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train_features = train_features.drop("right_block_l",axis=1)
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train_features = train_features.drop("right_block_a",axis=1)
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train_features = train_features.drop("right_block_b",axis=1)
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train_features = train_features.drop("right_block_R_stddev",axis=1)
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train_features = train_features.drop("right_block_G_stddev",axis=1)
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train_features = train_features.drop("right_block_B_stddev",axis=1)
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#train_features = train_features.drop("right_block_H_stddev",axis=1)
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#train_features = train_features.drop("right_block_S_stddev",axis=1)
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#train_features = train_features.drop("right_block_V_stddev",axis=1)
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train_features = train_features.drop("right_block_l_stddev",axis=1)
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train_features = train_features.drop("right_block_a_stddev",axis=1)
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train_features = train_features.drop("right_block_b_stddev",axis=1)
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train_features = train_features.drop("right_block_R_hist",axis=1)
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train_features = train_features.drop("right_block_G_hist",axis=1)
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train_features = train_features.drop("right_block_B_hist",axis=1)
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#train_features = train_features.drop("right_block_H_hist",axis=1)
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#train_features = train_features.drop("right_block_S_hist",axis=1)
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#train_features = train_features.drop("right_block_V_hist",axis=1)
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train_features = train_features.drop("right_block_l_hist",axis=1)
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train_features = train_features.drop("right_block_a_hist",axis=1)
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train_features = train_features.drop("right_block_b_hist",axis=1)
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train_features = train_features.drop("right_block_R_max",axis=1)
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train_features = train_features.drop("right_block_G_max",axis=1)
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train_features = train_features.drop("right_block_B_max",axis=1)
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#train_features = train_features.drop("right_block_H_max",axis=1)
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#train_features = train_features.drop("right_block_S_max",axis=1)
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#train_features = train_features.drop("right_block_V_max",axis=1)
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train_features = train_features.drop("right_block_l_max",axis=1)
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train_features = train_features.drop("right_block_a_max",axis=1)
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train_features = train_features.drop("right_block_b_max",axis=1)
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train_features = train_features.drop("right_block_R_min",axis=1)
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train_features = train_features.drop("right_block_G_min",axis=1)
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train_features = train_features.drop("right_block_B_min",axis=1)
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#train_features = train_features.drop("right_block_H_min",axis=1)
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#train_features = train_features.drop("right_block_S_min",axis=1)
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#train_features = train_features.drop("right_block_V_min",axis=1)
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train_features = train_features.drop("right_block_l_min",axis=1)
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train_features = train_features.drop("right_block_a_min",axis=1)
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train_features = train_features.drop("right_block_b_min",axis=1)
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####################################################################
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train_features = train_features.drop("whiteBlock_R",axis=1)
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train_features = train_features.drop("whiteBlock_G",axis=1)
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train_features = train_features.drop("whiteBlock_B",axis=1)
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#train_features = train_features.drop("whiteBlock_H",axis=1)
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#train_features = train_features.drop("whiteBlock_S",axis=1)
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#train_features = train_features.drop("whiteBlock_V",axis=1)
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train_features = train_features.drop("whiteBlock_l",axis=1)
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train_features = train_features.drop("whiteBlock_a",axis=1)
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train_features = train_features.drop("whiteBlock_b",axis=1)
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train_features = train_features.drop("whiteBlock_R_stddev",axis=1)
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train_features = train_features.drop("whiteBlock_G_stddev",axis=1)
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train_features = train_features.drop("whiteBlock_B_stddev",axis=1)
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#train_features = train_features.drop("whiteBlock_H_stddev",axis=1)
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#train_features = train_features.drop("whiteBlock_S_stddev",axis=1)
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#train_features = train_features.drop("whiteBlock_V_stddev",axis=1)
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train_features = train_features.drop("whiteBlock_l_stddev",axis=1)
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train_features = train_features.drop("whiteBlock_a_stddev",axis=1)
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train_features = train_features.drop("whiteBlock_b_stddev",axis=1)
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train_features = train_features.drop("whiteBlock_R_hist",axis=1)
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train_features = train_features.drop("whiteBlock_G_hist",axis=1)
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train_features = train_features.drop("whiteBlock_B_hist",axis=1)
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#train_features = train_features.drop("whiteBlock_H_hist",axis=1)
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#train_features = train_features.drop("whiteBlock_S_hist",axis=1)
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#train_features = train_features.drop("whiteBlock_V_hist",axis=1)
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train_features = train_features.drop("whiteBlock_l_hist",axis=1)
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train_features = train_features.drop("whiteBlock_a_hist",axis=1)
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train_features = train_features.drop("whiteBlock_b_hist",axis=1)
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train_features = train_features.drop("whiteBlock_R_max",axis=1)
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train_features = train_features.drop("whiteBlock_G_max",axis=1)
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train_features = train_features.drop("whiteBlock_B_max",axis=1)
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#train_features = train_features.drop("whiteBlock_H_max",axis=1)
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#train_features = train_features.drop("whiteBlock_S_max",axis=1)
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#train_features = train_features.drop("whiteBlock_V_max",axis=1)
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train_features = train_features.drop("whiteBlock_l_max",axis=1)
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train_features = train_features.drop("whiteBlock_a_max",axis=1)
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train_features = train_features.drop("whiteBlock_b_max",axis=1)
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train_features = train_features.drop("whiteBlock_R_min",axis=1)
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train_features = train_features.drop("whiteBlock_G_min",axis=1)
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train_features = train_features.drop("whiteBlock_B_min",axis=1)
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#train_features = train_features.drop("whiteBlock_H_min",axis=1)
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#train_features = train_features.drop("whiteBlock_S_min",axis=1)
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#train_features = train_features.drop("whiteBlock_V_min",axis=1)
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train_features = train_features.drop("whiteBlock_l_min",axis=1)
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train_features = train_features.drop("whiteBlock_a_min",axis=1)
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train_features = train_features.drop("whiteBlock_b_min",axis=1)
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##################################################################
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#from sklearn.model_selection import KFold
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import accuracy_score
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from sklearn.svm import SVC
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from sklearn.metrics import f1_score
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from sklearn.metrics import precision_score
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from sklearn.metrics import recall_score
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from sklearn.ensemble import ExtraTreesClassifier
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from sklearn.ensemble import AdaBoostClassifier
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# 使用train_features
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from sklearn.model_selection import train_test_split
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X_train ,X_test,y_train,y_test = train_test_split(train_features,train_labels,test_size = 0.3, random_state = 20)
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#X_train ,X_test,y_train,y_test = train_test_split(train_features_9,train_labels,test_size = 0.2, random_state = 20)
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import classification_report
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from sklearn.metrics import classification_report,confusion_matrix
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from sklearn.metrics import f1_score
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from sklearn.metrics import precision_score
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from sklearn.metrics import recall_score
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#rfc = RandomForestClassifier(n_estimators=600)
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#rfc = RandomForestClassifier(n_estimators=50)
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#RandomForest accuracy score: 0.9955591300090916
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rfc = RandomForestClassifier(n_estimators=50,min_samples_leaf=20)
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#RandomForest accuracy score: 0.9772012028813204/0.9803133086229806/0.9811874956290649/0.9852786908175397
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#rfc = RandomForestClassifier(n_estimators=50)
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#RandomForest accuracy score: 0.9955940974893349
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#rfc = RandomForestClassifier(n_estimators=100,min_samples_leaf=50)
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#RandomForest accuracy score: 0.97688649555913
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#rfc = RandomForestClassifier(n_estimators=50,min_samples_leaf=100)
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#RandomForest accuracy score: 0.9669906986502552
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rfc.fit(X_train, y_train)
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rfc_pred = rfc.predict(X_test)
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cr = classification_report(y_test,rfc_pred)
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print(cr)
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cm = confusion_matrix(y_test,rfc_pred)
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print(cm)
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print("---------------------------------\n")
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print ("Accuracy of prediction:",round((cm[0,0]+cm[1,1])/cm.sum(),3))
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print ("RandomForest accuracy score:" , accuracy_score(y_test,rfc_pred))
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print("---------------------------------\n")
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print ("f1 score:" , f1_score(y_test,rfc_pred,average='micro'))
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print ("precision_score:" , precision_score(y_test,rfc_pred,average='micro'))
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print ("recall_score:" , recall_score(y_test,rfc_pred,average='micro'))
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import m2cgen as m2c
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# 导出纯C代码
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c_code = m2c.export_to_c(rfc)
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# 保存文件，用文本模式w，不要wb
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with open("clf/ov_rtree50_f20_20260708.cpp", "w", encoding="utf-8") as f:
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f.write(c_code)
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print("模型C代码导出完成")
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java_code = m2c.export_to_java(rfc)
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with open("clf/ov_rtree50_f20_20260708.java", "w", encoding="utf-8") as f:
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f.write(java_code)
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print("模型java代码导出完成")
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