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https://github.com/newnius/YAO-optimizer.git
synced 2025-06-06 06:41:55 +00:00
robust, allow unseen features & labels
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parent
a178107abd
commit
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@ -1,10 +1,7 @@
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@ -438,14 +437,21 @@
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18
serve.py
18
serve.py
@ -41,7 +41,7 @@ def train_models(job):
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feature_data = traindata.iloc[:, 1:-1]
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label_data = traindata.iloc[:, -1]
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X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.01)
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x_train, x_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.01)
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params = {
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'n_estimators': 70,
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'max_depth': 13,
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@ -51,14 +51,14 @@ def train_models(job):
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}
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# print(params)
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model = RandomForestRegressor(**params)
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model.fit(X_train, y_train)
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model.fit(x_train, y_train)
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# save the model to disk
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modelname = './data/' + job + '_' + label + '.sav'
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pickle.dump(model, open(modelname, 'wb'))
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# 对测试集进行预测
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y_pred = model.predict(X_test)
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y_pred = model.predict(x_test)
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# 计算准确率
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MSE = mean_squared_error(y_test, y_pred)
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RMSE = np.sqrt(MSE)
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@ -76,7 +76,10 @@ def predict(job, features):
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values = [job]
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for feature in models[job]['features']:
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if feature in features:
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values.append(features[feature])
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else:
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values.append(0)
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testfile = './data/' + job + '.' + str(random.randint(1000, 9999)) + '.csv'
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t = ['job']
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@ -131,8 +134,6 @@ class MyHandler(BaseHTTPRequestHandler):
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try:
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job = query.get('job')[0]
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features = json.loads(query.get('features')[0])
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msg = {'code': 0, 'error': ""}
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pred, success = predict(job, features)
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if not success:
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@ -170,8 +171,15 @@ class MyHandler(BaseHTTPRequestHandler):
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for label in models[job]['labels']:
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values = [job]
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for feature in models[job]['features']:
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if feature in features:
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values.append(features[feature])
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else:
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values.append(0)
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if label in labels:
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values.append(labels[label])
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else:
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values.append(0)
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if flag:
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t = ['job']
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t.extend(models[job]['features'])
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