104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
import requests
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import json
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import matplotlib.pyplot as plt
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import base64
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from io import BytesIO
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# API基础URL
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API_BASE_URL = 'http://localhost:5000/api'
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# 示例1: 获取产品列表
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def get_products():
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response = requests.get(f'{API_BASE_URL}/products')
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return response.json()
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# 示例2: 训练模型
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def train_model(product_id, model_type='mlstm'):
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data = {
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'product_id': product_id,
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'model_type': model_type,
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'parameters': {
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'look_back': 14,
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'future_days': 7,
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'batch_size': 64,
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'epochs': 100
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}
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}
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response = requests.post(f'{API_BASE_URL}/training', json=data)
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return response.json()
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# 示例3: 查询训练状态
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def check_training_status(task_id):
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response = requests.get(f'{API_BASE_URL}/training/{task_id}')
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return response.json()
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# 示例4: 使用模型预测
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def predict_sales(product_id, model_type='mlstm'):
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data = {
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'product_id': product_id,
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'model_type': model_type,
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'future_days': 7,
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'include_visualization': True
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}
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response = requests.post(f'{API_BASE_URL}/prediction', json=data)
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result = response.json()
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# 如果包含可视化，显示图表
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if 'data' in result and 'visualization' in result['data']:
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img_data = base64.b64decode(result['data']['visualization'])
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img = plt.imread(BytesIO(img_data))
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plt.figure(figsize=(12, 6))
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plt.imshow(img)
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plt.axis('off')
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plt.show()
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return result
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# 示例5: 比较不同模型
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def compare_models(product_id):
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data = {
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'product_id': product_id,
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'model_types': ['mlstm', 'transformer', 'kan'],
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'include_visualization': True
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}
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response = requests.post(f'{API_BASE_URL}/prediction/compare', json=data)
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return response.json()
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# 示例6: 获取模型列表
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def list_models(product_id=None, model_type=None):
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params = {}
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if product_id:
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params['product_id'] = product_id
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if model_type:
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params['model_type'] = model_type
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response = requests.get(f'{API_BASE_URL}/models', params=params)
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return response.json()
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# 示例代码使用
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if __name__ == '__main__':
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# 获取产品列表
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products = get_products()
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print(json.dumps(products, indent=2))
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# 选择第一个产品进行训练
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if products['status'] == 'success' and products['data']:
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product_id = products['data'][0]['product_id']
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# 训练模型
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training_result = train_model(product_id)
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print(json.dumps(training_result, indent=2))
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# 如果训练成功启动，检查状态
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if training_result['status'] == 'success':
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task_id = training_result['data']['task_id']
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status = check_training_status(task_id)
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print(json.dumps(status, indent=2))
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# 使用模型预测
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predictions = predict_sales(product_id)
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print(json.dumps(predictions, indent=2))
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# 比较不同模型
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comparison = compare_models(product_id)
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print(json.dumps(comparison, indent=2)) |