[U] Update

2022-10-01 13:34:56 -04:00
parent 57df75093d
commit 428d5afc58
189 changed files with 569260 additions and 51 deletions
@@ -0,0 +1,81 @@
+import numpy as np
+import pandas as pd
+import joblib
+from sklearn.datasets import make_classification
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import precision_recall_fscore_support
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from imblearn.over_sampling import RandomOverSampler
+def print_prec(y_test, y_pred):
+    print(f'y Test Label: {np.array([int(x) for x in y_test])}')
+    print(f'y Prediction: {np.array([int(x) for x in y_pred])}')
+
+    prec, rec, f1, _ = precision_recall_fscore_support(y_test, y_pred, average='binary')
+    acc = accuracy_score(y_test,y_pred)
+    print(f'Precision: {prec * 100:0.1f}')
+    print(f'Recall:    {rec * 100:0.1f}')
+    print(f'Accuracy:  {acc*100:0.1f}')
+    print(f'F1:        {f1 * 100:0.1f}')
+
+
+if __name__ == '__main__':
+    fm = pd.read_csv('fm-features.csv')
+    d4j = pd.read_csv('d4j-features.csv')
+    bdj = pd.read_csv('bdj-features.csv')
+    all = pd.read_csv('data-all.absolute-features.csv')
+    #X = fm.drop(columns=['LABEL','Time'],axis = 1)  # features
+    #Y = fm['LABEL']  # labels
+    clf = RandomForestClassifier()
+    rs= RandomOverSampler(random_state=0)
+    Y_all_test=all['LABEL']
+    X_all_test=all.drop(columns=['LABEL','Time'],axis = 1)
+    X_train, X_test, y_train, y_test = train_test_split(X_all_test, Y_all_test, test_size=0.2)
+    #print(X_train)
+    X_resampled, y_resampled = rs.fit_resample(X_train, y_train)
+    clf.fit(X_resampled,y_resampled)
+
+    #clf.fit(X, Y)
+    # Y_bdj_test=bdj['LABEL']
+    # X_bdj_test=bdj.drop(columns=['LABEL','Time'],axis = 1)
+    # Y_d4j_test=d4j['LABEL']
+    # X_d4j_test=d4j.drop(columns=['LABEL','Time'],axis = 1)
+    # # bdj_pred=clf.predict(X_bdj_test)
+    # # d4j_pred=clf.predict(X_d4j_test)
+    all_pred=clf.predict(X_test)
+    # # print_prec(Y_bdj_test,bdj_pred)
+    # # print_prec(Y_d4j_test,d4j_pred)
+    print_prec(y_test,all_pred)
+    # 800 667 667 727
+    joblib.dump(clf, "./random_forest.joblib")
+    importance=[0.04336795, 0.11223876, 0.0586915,  0.03840793, 0.07281997, 0.06005508,
+                0.08044522, 0.05864273, 0.07693864, 0.08805545, 0.05911849, 0.09307329,
+                0.0981109,  0.06003408,]
+    features = X_all_test.columns
+    print(features)
+    print(importance)
+    print(clf.feature_importances_)
+    # X_train, X_test, y_train, y_test = train_test_split(X_bdj_test, Y_bdj_test, test_size=0.33, random_state=42)
+    # clf.fit(X_train,y_train)
+    #
+    # bdj_pred=clf.predict(X_test)
+    # d4j_pred=clf.predict(X_d4j_test)
+    # all_pred=clf.predict(X_all_test)
+    # print_prec(y_test,bdj_pred)
+    # print_prec(Y_d4j_test,d4j_pred)
+    # print_prec(Y_all_test,all_pred)
+    #
+    #
+    # X_train, X_test, y_train, y_test = train_test_split(X_d4j_test, Y_d4j_test, test_size=0.33, random_state=42)
+    # clf.fit(X_train,y_train)
+    #
+    # bdj_pred=clf.predict(X_bdj_test)
+    # d4j_pred=clf.predict(X_test)
+    # all_pred=clf.predict(X_all_test)
+    # print_prec(Y_bdj_test,bdj_pred)
+    # print_prec(y_test,d4j_pred)
+    # print_prec(Y_all_test,all_pred)
+
+    # print(bdj.corr(method='spearman').sort_values(by=['LABEL'])['LABEL'])
+    # print(d4j.corr(method='spearman').sort_values(by=['LABEL'])['LABEL'])
+    # print(fm.corr(method='spearman').sort_values(by=['LABEL'])['LABEL'])