TRAE 实战caseanalysis

// example: usingTRAE生成Reactcomponent
import React, { useState } from 'react';

// 当输入"const ProductCard"时, TRAE会智能生成component模板
const ProductCard = ({ product }) => {
    const [isHovered, setIsHovered] = useState(false);
    
    return (
        <div className={`product-card ${isHovered ? 'hovered' : ''}`} 
             onMouseEnter={() => setIsHovered(true)}
             onMouseLeave={() => setIsHovered(false)}>
            <img src={product.image} alt={product.name} className="product-image" />
            <div className="product-info">
                <h3 className="product-name">{product.name}</h3>
                <p className="product-price">¥{product.price}</p>
                <button className="add-to-cart-btn">加入购物车</button>
            </div>
        </div>
    );
};

export default ProductCard;

// example: usingTRAE生成Redux相关code
// Action Types
const ADD_TO_CART = 'ADD_TO_CART';
const REMOVE_FROM_CART = 'REMOVE_FROM_CART';

// Action Creators
const addToCart = (product) => ({
    type: ADD_TO_CART,
    payload: product
});

const removeFromCart = (productId) => ({
    type: REMOVE_FROM_CART,
    payload: productId
});

// Reducer
const initialState = {
    items: [],
    total: 0
};

const cartReducer = (state = initialState, action) => {
    switch (action.type) {
        case ADD_TO_CART:
            // TRAE会智能补全reducer逻辑
            const newItems = [...state.items, action.payload];
            return {
                ...state,
                items: newItems,
                total: newItems.reduce((sum, item) => sum + item.price, 0)
            };
        case REMOVE_FROM_CART:
            // TRAE会智能生成移除商品 逻辑
            const updatedItems = state.items.filter(item => item.id !== action.payload);
            return {
                ...state,
                items: updatedItems,
                total: updatedItems.reduce((sum, item) => sum + item.price, 0)
            };
        default:
            return state;
    }
};

export { addToCart, removeFromCart, cartReducer };

# example: usingTRAEfordataanalysis
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# 读取data
# TRAE会智能补全read_csvfunction and parameter
sales_data = pd.read_csv('sales_data.csv', parse_dates=['order_date'])

# data清洗
# TRAE会检测并修复codeerror
missing_values = sales_data.isnull().sum()
sales_data = sales_data.dropna(subset=['quantity', 'price'])

# data转换
# TRAE会智能生成data转换code
sales_data['total_sales'] = sales_data['quantity'] * sales_data['price']
sales_data['month'] = sales_data['order_date'].dt.month
sales_data['year'] = sales_data['order_date'].dt.year

# dataanalysis
# TRAE会providingdataanalysis建议
daily_sales = sales_data.groupby('order_date')['total_sales'].sum().reset_index()
monthly_sales = sales_data.groupby(['year', 'month'])['total_sales'].sum().reset_index()
product_sales = sales_data.groupby('product_id')['total_sales'].sum().sort_values(ascending=False).reset_index()

# example: usingTRAE生成datavisualizationcode

# 月度销售趋势graph
plt.figure(figsize=(12, 6))
sns.lineplot(data=monthly_sales, x='month', y='total_sales', hue='year')
plt.title('月度销售趋势')
plt.xlabel('月份')
plt.ylabel('销售额')
plt.grid(True)
plt.legend(title='年份')
plt.savefig('monthly_sales_trend.png')
plt.close()

# 产品销售排名
plt.figure(figsize=(10, 8))
sns.barplot(data=product_sales.head(10), x='total_sales', y='product_id', orient='h')
plt.title('产品销售排名 ( before 10名) ')
plt.xlabel('销售额')
plt.ylabel('产品ID')
plt.grid(axis='x')
plt.savefig('product_sales_ranking.png')
plt.close()

# example: usingTRAE构建机器Learningmodel
import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 构建model
# TRAE会智能生成CNNmodelstructure
model = models.Sequential([
    # 卷积层1
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),
    layers.MaxPooling2D((2, 2)),
    
    # 卷积层2
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    
    # 卷积层3
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    
    # 卷积层4
    layers.Conv2D(128, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    
    # 全连接层
    layers.Flatten(),
    layers.Dense(512, activation='relu'),
    layers.Dropout(0.5),  # TRAE建议添加Dropout层防止过拟合
    layers.Dense(5, activation='softmax')  # 5种花卉classification
])

# 编译model
# TRAE会智能推荐optimization器 and 损失function
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# example: usingTRAEoptimizationmodel训练

# data增强
# TRAE建议添加data增强防止过拟合
train_datagen = ImageDataGenerator(
    rescale=1./255,
    rotation_range=40,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    fill_mode='nearest')

# testdata只需要归一化
validation_datagen = ImageDataGenerator(rescale=1./255)

# 加载data
train_generator = train_datagen.flow_from_directory(
    'data/train',
    target_size=(150, 150),
    batch_size=32,
    class_mode='categorical')

validation_generator = validation_datagen.flow_from_directory(
    'data/validation',
    target_size=(150, 150),
    batch_size=32,
    class_mode='categorical')

# model训练
# TRAE建议添加EarlyStoppingcallback
from tensorflow.keras.callbacks import EarlyStopping

early_stopping = EarlyStopping(monitor='val_loss', patience=5)

history = model.fit(
    train_generator,
    steps_per_epoch=100,
    epochs=50,
    validation_data=validation_generator,
    validation_steps=50,
    callbacks=[early_stopping])