Azalea (on HyDEV-Daisy)
82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
import numpy as np
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import pandas as pd
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import joblib
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from sklearn.datasets import make_classification
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.metrics import precision_recall_fscore_support
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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from imblearn.over_sampling import RandomOverSampler
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def print_prec(y_test, y_pred):
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print(f'y Test Label: {np.array([int(x) for x in y_test])}')
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print(f'y Prediction: {np.array([int(x) for x in y_pred])}')
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prec, rec, f1, _ = precision_recall_fscore_support(y_test, y_pred, average='binary')
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acc = accuracy_score(y_test,y_pred)
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print(f'Precision: {prec * 100:0.1f}')
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print(f'Recall: {rec * 100:0.1f}')
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print(f'Accuracy: {acc*100:0.1f}')
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print(f'F1: {f1 * 100:0.1f}')
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if __name__ == '__main__':
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fm = pd.read_csv('fm-features.csv')
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d4j = pd.read_csv('d4j-features.csv')
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bdj = pd.read_csv('bdj-features.csv')
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all = pd.read_csv('data-all.absolute-features.csv')
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#X = fm.drop(columns=['LABEL','Time'],axis = 1) # features
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#Y = fm['LABEL'] # labels
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clf = RandomForestClassifier()
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rs= RandomOverSampler(random_state=0)
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Y_all_test=all['LABEL']
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X_all_test=all.drop(columns=['LABEL','Time'],axis = 1)
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X_train, X_test, y_train, y_test = train_test_split(X_all_test, Y_all_test, test_size=0.2)
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#print(X_train)
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X_resampled, y_resampled = rs.fit_resample(X_train, y_train)
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clf.fit(X_resampled,y_resampled)
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#clf.fit(X, Y)
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# Y_bdj_test=bdj['LABEL']
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# X_bdj_test=bdj.drop(columns=['LABEL','Time'],axis = 1)
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# Y_d4j_test=d4j['LABEL']
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# X_d4j_test=d4j.drop(columns=['LABEL','Time'],axis = 1)
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# # bdj_pred=clf.predict(X_bdj_test)
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# # d4j_pred=clf.predict(X_d4j_test)
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all_pred=clf.predict(X_test)
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# # print_prec(Y_bdj_test,bdj_pred)
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# # print_prec(Y_d4j_test,d4j_pred)
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print_prec(y_test,all_pred)
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# 800 667 667 727
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joblib.dump(clf, "./random_forest.joblib")
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importance=[0.04336795, 0.11223876, 0.0586915, 0.03840793, 0.07281997, 0.06005508,
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0.08044522, 0.05864273, 0.07693864, 0.08805545, 0.05911849, 0.09307329,
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0.0981109, 0.06003408,]
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features = X_all_test.columns
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print(features)
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print(importance)
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print(clf.feature_importances_)
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# X_train, X_test, y_train, y_test = train_test_split(X_bdj_test, Y_bdj_test, test_size=0.33, random_state=42)
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# clf.fit(X_train,y_train)
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#
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# bdj_pred=clf.predict(X_test)
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# d4j_pred=clf.predict(X_d4j_test)
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# all_pred=clf.predict(X_all_test)
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# print_prec(y_test,bdj_pred)
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# print_prec(Y_d4j_test,d4j_pred)
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# print_prec(Y_all_test,all_pred)
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#
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#
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# X_train, X_test, y_train, y_test = train_test_split(X_d4j_test, Y_d4j_test, test_size=0.33, random_state=42)
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# clf.fit(X_train,y_train)
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#
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# bdj_pred=clf.predict(X_bdj_test)
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# d4j_pred=clf.predict(X_test)
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# all_pred=clf.predict(X_all_test)
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# print_prec(Y_bdj_test,bdj_pred)
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# print_prec(y_test,d4j_pred)
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# print_prec(Y_all_test,all_pred)
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# print(bdj.corr(method='spearman').sort_values(by=['LABEL'])['LABEL'])
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# print(d4j.corr(method='spearman').sort_values(by=['LABEL'])['LABEL'])
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# print(fm.corr(method='spearman').sort_values(by=['LABEL'])['LABEL'])
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