MCP在简化数据探索、分析方面的场景化解决方案

import requests
from tencentcloud.common.profile.client_profile import ClientProfile
from tencentcloud.common.profile.http_profile import HttpProfile
from tencentcloud.common.credential import Credential
from tencentcloud.mcp.v20210524 import mcp_client, models

# 设置腾讯云凭证
secret_id = 'your_secret_id'
secret_key = 'your_secret_key'
region = 'ap-guangzhou'

# 创建客户端配置
http_profile = HttpProfile()
http_profile.reqTimeout = 60  # 请求超时时间
http_profile.endpoint = "mcp.tencentcloudapi.com"  # MCP API 端点

client_profile = ClientProfile()
client_profile.httpProfile = http_profile

# 创建客户端
cred = Credential(secret_id, secret_key)
client = mcp_client.McpClient(cred, region, client_profile)

# 配置数据源
def configure_data_source(dataset_id, source_type, source_params):
    req = models.ConfigureDataSourceRequest()
    params = {
        "DatasetId": dataset_id,
        "SourceType": source_type,
        "SourceParams": source_params
    }
    req.from_json_string(json.dumps(params))

    response = client.ConfigureDataSource(req)
    print(response.to_json_string())

# 示例调用
configure_data_source('your_dataset_id', 'mysql', {
    "host": "your_mysql_host",
    "port": 3306,
    "username": "your_username",
    "password": "your_password",
    "database": "your_database",
    "table": "your_table"
})

import pandas as pd
import requests
from tencentcloud.common.profile.client_profile import ClientProfile
from tencentcloud.common.profile.http_profile import HttpProfile
from tencentcloud.common.credential import Credential
from tencentcloud.mcp.v20210524 import mcp_client, models

# 设置腾讯云凭证
secret_id = 'your_secret_id'
secret_key = 'your_secret_key'
region = 'ap-guangzhou'

# 创建客户端配置
http_profile = HttpProfile()
http_profile.reqTimeout = 60  # 请求超时时间
http_profile.endpoint = "mcp.tencentcloudapi.com"  # MCP API 端点

client_profile = ClientProfile()
client_profile.httpProfile = http_profile

# 创建客户端
cred = Credential(secret_id, secret_key)
client = mcp_client.McpClient(cred, region, client_profile)

# 获取数据集并进行探索
def explore_data(dataset_id):
    req = models.DescribeDatasetRequest()
    params = {
        "DatasetId": dataset_id
    }
    req.from_json_string(json.dumps(params))

    resp = client.DescribeDataset(req)
    data = resp.to_json_string()
    df = pd.read_json(data)
    
    # 显示数据集的基本信息
    print(df.head())
    print(df.describe())

# 示例调用
explore_data('your_dataset_id')

from pyflink.dataset import ExecutionEnvironment
from pyflink.table import TableConfig, DataTypes, BatchTableEnvironment, EnvironmentSettings
from pyflink.table.descriptors import Schema, OldCsv, FileSystem
import tensorflow as tf
from tencentcloud.automl.v20181010 import automl_client, models

# 设置Flink环境
env = ExecutionEnvironment.get_execution_environment()
t_config = TableConfig()
t_env = BatchTableEnvironment.create(env, t_config)

# 设置数据流处理的表结构
t_env.connect(FileSystem().path('/path/to/input')) \
    .with_format(OldCsv()
                 .field('timestamp', DataTypes.TIMESTAMP())
                 .field('value', DataTypes.DOUBLE())) \
    .with_schema(Schema()
                 .field('timestamp', DataTypes.TIMESTAMP())
                 .field('value', DataTypes.DOUBLE())) \
    .register_table_source("source")

# 注册结果表
t_env.connect(FileSystem().path('/path/to/output')) \
    .with_format(OldCsv()
                 .field('timestamp', DataTypes.TIMESTAMP())
                 .field('average_value', DataTypes.DOUBLE())) \
    .with_schema(Schema()
                 .field('timestamp', DataTypes.TIMESTAMP())
                 .field('average_value', DataTypes.DOUBLE())) \
    .register_table_sink("sink")

# 执行实时数据分析
t_env.scan("source") \
    .group_by("timestamp") \
    .select("avg(value) as average_value") \
    .insert_into("sink")

t_env.execute("Real-time Data Analysis")

# 使用TensorFlow进行机器学习模型训练
def train_model(dataset_id):
    # 获取数据集
    df = get_data(dataset_id)
    
    # 准备数据
    X = df[['feature1', 'feature2']]
    y = df['target']
    
    # 构建模型
    model = tf.keras.models.Sequential([
        tf.keras.layers.Dense(10, activation='relu', input_shape=(X.shape[1],)),
        tf.keras.layers.Dense(1)
    ])
    
    # 编译模型
    model.compile(optimizer=tf.keras.optimizers.Adam(0.01), loss='mean_squared_error')
    
    # 训练模型
    model.fit(X, y, epochs=10)
    
    # 保存模型
    model.save("/path/to/model.h5")

# 示例调用
train_model('your_dataset_id')

# 使用AutoML进行自动特征工程
def generate_feature_engineering_request(dataset_id):
    request = models.GenerateFeatureEngineeringRequest()
    params = {
        "DatasetId": dataset_id,
        "OutputPath": "/path/to/output"
    }
    request.from_json_string(json.dumps(params))

    response = client.GenerateFeatureEngineering(request)
    print(response.to_json_string())

# 示例调用
generate_feature_engineering_request('your_dataset_id')