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RandomForest_Model.py
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RandomForest_Model.py
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from sklearn.ensemble import RandomForestClassifier
from Utility import getData, printMetrics, getMetrics, logAndSave, logAndSaveV2, getAnnealingData
splitData = False
if splitData:
X_train, X_test, y_train, y_test = getData(useImbalancer=True, useStratify=True)
else:
X_train, y_train = getData(splitData=splitData, useImbalancer=False, useStratify=True)
X_test, y_test = None, None
X_train, X_test, y_train, y_test = getAnnealingData()
def RandomForestModel(splitData, X_train, X_test, y_train, y_test):
clf = RandomForestClassifier(max_depth=14)
clf.fit(X_train, y_train.ravel())
if splitData:
y_preds = clf.predict(X_test)
printMetrics(y_test, y_preds)
val_acc, val_pre, val_recall, val_auc, val_f1 = getMetrics(y_test, y_preds)
else:
val_acc, val_pre, val_recall, val_auc, val_f1 = 0, 0, 0, 0, 0
y_preds = clf.predict(X_train)
acc, pre, recall, auc, f1 = getMetrics(y_train, y_preds)
val_metrics = (val_acc, val_pre, val_recall, val_auc, val_f1)
metrics = (acc, pre, recall, auc, f1)
# print("acc-" + str(acc) + "\tprecision-" + str(pre) + "\trecall-" + str(recall) + "\tauc-" + str(auc) + "\tf1-" + str(f1) + "\tval_accuracy-" + str(val_acc) + "\tval_precision-" + str(val_pre) + "\tval_recall-" + str(val_recall) + "\tval_auc-" + str(val_auc) + "\tval_f1-" + str(val_f1) + "\n")
logAndSave(name_of_model="RandomForestClassifier", clf=clf, metrics=metrics, val_metrics=val_metrics)
def RandomForestModelV2(X_train, X_test, y_train, y_test):
multi_class = True
clf = RandomForestClassifier(max_depth=14)
clf.fit(X_train, y_train)
y_preds = clf.predict(X_test)
# printMetrics(y_test, y_preds, multi_class=multi_class)
val_acc, val_pre, val_recall, val_auc, val_f1 = getMetrics(y_test, y_preds, multi_class=multi_class)
y_preds = clf.predict(X_train)
# printMetrics(y_train, y_preds, multi_class=multi_class)
acc, pre, recall, auc, f1 = getMetrics(y_train, y_preds, multi_class=multi_class)
val_metrics = (val_acc, val_pre, val_recall, val_auc, val_f1)
metrics = (acc, pre, recall, auc, f1)
# print("acc-" + str(acc) + "\tprecision-" + str(pre) + "\trecall-" + str(recall) + "\tauc-" + str(auc) + "\tval_accuracy-" + str(val_acc) + "\tval_precision-" + str(val_pre) + "\tval_recall-" + str(val_recall) + "\tval_auc-" + str(val_auc) + "\n")
logAndSaveV2(name_of_model="RandomForestModelV2", clf=clf, metrics=metrics, val_metrics=val_metrics)
if __name__ == "__main__":
# RandomForestModel(splitData, X_train, X_test, y_train, y_test)
RandomForestModelV2(X_train, X_test, y_train, y_test)