import streamlit as st
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import plotly.express as px
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import plotly.graph_objects as go
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from app.services.extruder_service import ExtruderService
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from app.services.main_process_service import MainProcessService
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from sklearn.preprocessing import StandardScaler, MinMaxScaler
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error
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from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
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from sklearn.svm import SVR
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from sklearn.neural_network import MLPRegressor
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# 尝试导入深度学习库
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use_deep_learning = False
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try:
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import torch
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import torch.nn as nn
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import torch.optim as optim
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use_deep_learning = True
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# 检测GPU是否可用
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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st.success(f"使用设备: {device}")
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except ImportError:
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st.warning("未检测到PyTorch，深度学习模型将不可用。请安装pytorch以使用LSTM/GRU模型。")
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# PyTorch深度学习模型定义
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class LSTMModel(nn.Module):
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def __init__(self, input_dim, hidden_dim=64, num_layers=2):
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super(LSTMModel, self).__init__()
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self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
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self.fc1 = nn.Linear(hidden_dim, 32)
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self.dropout = nn.Dropout(0.2)
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self.fc2 = nn.Linear(32, 1)
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def forward(self, x):
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out, _ = self.lstm(x)
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out = out[:, -1, :]
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out = torch.relu(self.fc1(out))
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out = self.dropout(out)
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out = self.fc2(out)
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return out
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class GRUModel(nn.Module):
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def __init__(self, input_dim, hidden_dim=64, num_layers=2):
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super(GRUModel, self).__init__()
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self.gru = nn.GRU(input_dim, hidden_dim, num_layers, batch_first=True)
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self.fc1 = nn.Linear(hidden_dim, 32)
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self.dropout = nn.Dropout(0.2)
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self.fc2 = nn.Linear(32, 1)
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def forward(self, x):
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out, _ = self.gru(x)
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out = out[:, -1, :]
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out = torch.relu(self.fc1(out))
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out = self.dropout(out)
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out = self.fc2(out)
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return out
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class BiLSTMModel(nn.Module):
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def __init__(self, input_dim, hidden_dim=64, num_layers=2):
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super(BiLSTMModel, self).__init__()
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self.bilstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True, bidirectional=True)
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self.fc1 = nn.Linear(hidden_dim * 2, 32)
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self.dropout = nn.Dropout(0.2)
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self.fc2 = nn.Linear(32, 1)
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def forward(self, x):
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out, _ = self.bilstm(x)
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out = out[:, -1, :]
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out = torch.relu(self.fc1(out))
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out = self.dropout(out)
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out = self.fc2(out)
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return out
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def show_metered_weight_advanced():
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# 初始化服务
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extruder_service = ExtruderService()
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main_process_service = MainProcessService()
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# 页面标题
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st.title("米重高级预测分析")
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# 初始化会话状态
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if 'ma_start_date' not in st.session_state:
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st.session_state['ma_start_date'] = datetime.now().date() - timedelta(days=7)
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if 'ma_end_date' not in st.session_state:
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st.session_state['ma_end_date'] = datetime.now().date()
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if 'ma_quick_select' not in st.session_state:
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st.session_state['ma_quick_select'] = "最近7天"
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if 'ma_model_type' not in st.session_state:
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st.session_state['ma_model_type'] = 'RandomForest'
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if 'ma_sequence_length' not in st.session_state:
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st.session_state['ma_sequence_length'] = 10
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# 默认特征列表（不再允许用户选择）
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default_features = ['螺杆转速', '机头压力', '流程主速', '螺杆温度',
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'后机筒温度', '前机筒温度', '机头温度']
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# 定义回调函数
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def update_dates(qs):
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st.session_state['ma_quick_select'] = qs
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today = datetime.now().date()
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if qs == "今天":
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st.session_state['ma_start_date'] = today
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st.session_state['ma_end_date'] = today
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elif qs == "最近3天":
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st.session_state['ma_start_date'] = today - timedelta(days=3)
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st.session_state['ma_end_date'] = today
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elif qs == "最近7天":
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st.session_state['ma_start_date'] = today - timedelta(days=7)
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st.session_state['ma_end_date'] = today
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elif qs == "最近30天":
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st.session_state['ma_start_date'] = today - timedelta(days=30)
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st.session_state['ma_end_date'] = today
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def on_date_change():
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st.session_state['ma_quick_select'] = "自定义"
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# 查询条件区域
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with st.expander("🔍 查询配置", expanded=True):
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# 添加自定义 CSS 实现响应式换行
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st.markdown("""
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<style>
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/* 强制列容器换行 */
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[data-testid="stExpander"] [data-testid="column"] {
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flex: 1 1 120px !important;
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min-width: 120px !important;
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}
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/* 针对日期输入框列稍微加宽一点 */
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@media (min-width: 768px) {
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[data-testid="stExpander"] [data-testid="column"]:nth-child(6),
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[data-testid="stExpander"] [data-testid="column"]:nth-child(7) {
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flex: 2 1 180px !important;
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min-width: 180px !important;
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}
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}
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</style>
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""", unsafe_allow_html=True)
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# 创建布局
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cols = st.columns([1, 1, 1, 1, 1, 1.5, 1.5, 1])
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options = ["今天", "最近3天", "最近7天", "最近30天", "自定义"]
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for i, option in enumerate(options):
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with cols[i]:
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# 根据当前选择状态决定按钮类型
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button_type = "primary" if st.session_state['ma_quick_select'] == option else "secondary"
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if st.button(option, key=f"btn_ma_{option}", width='stretch', type=button_type):
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update_dates(option)
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st.rerun()
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with cols[5]:
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start_date = st.date_input(
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"开始日期",
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label_visibility="collapsed",
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key="ma_start_date",
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on_change=on_date_change
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)
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with cols[6]:
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end_date = st.date_input(
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"结束日期",
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label_visibility="collapsed",
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key="ma_end_date",
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on_change=on_date_change
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)
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with cols[7]:
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query_button = st.button("🚀 开始分析", key="ma_query", width='stretch')
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# 模型配置
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st.markdown("---")
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st.write("🤖 **模型配置**")
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model_cols = st.columns(2)
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with model_cols[0]:
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# 模型类型选择
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model_options = ['RandomForest', 'GradientBoosting', 'SVR', 'MLP']
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if use_deep_learning:
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model_options.extend(['LSTM', 'GRU', 'BiLSTM'])
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model_type = st.selectbox(
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"模型类型",
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options=model_options,
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key="ma_model_type",
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help="选择用于预测的模型类型"
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)
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with model_cols[1]:
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# 序列长度（仅适用于深度学习模型）
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if model_type in ['LSTM', 'GRU', 'BiLSTM']:
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sequence_length = st.slider(
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"序列长度",
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min_value=5,
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max_value=30,
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value=st.session_state['ma_sequence_length'],
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step=1,
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help="用于深度学习模型的时间序列长度",
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key="ma_sequence_length"
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)
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else:
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st.session_state['ma_sequence_length'] = 10
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st.write("序列长度: 10 (默认，仅适用于深度学习模型)")
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# 转换为datetime对象
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start_dt = datetime.combine(start_date, datetime.min.time())
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end_dt = datetime.combine(end_date, datetime.max.time())
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# 查询处理
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if query_button:
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with st.spinner("正在获取数据..."):
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# 1. 获取完整的挤出机数据
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df_extruder_full = extruder_service.get_extruder_data(start_dt, end_dt)
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# 2. 获取主流程控制数据
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df_main_speed = main_process_service.get_cutting_setting_data(start_dt, end_dt)
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df_temp = main_process_service.get_temperature_control_data(start_dt, end_dt)
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# 检查是否有数据
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has_data = any([
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df_extruder_full is not None and not df_extruder_full.empty,
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df_main_speed is not None and not df_main_speed.empty,
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df_temp is not None and not df_temp.empty
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])
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if not has_data:
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st.warning("所选时间段内未找到任何数据，请尝试调整查询条件。")
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# 清除缓存数据
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for key in ['cached_extruder_full', 'cached_main_speed', 'cached_temp', 'last_query_start', 'last_query_end']:
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if key in st.session_state:
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del st.session_state[key]
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return
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# 缓存数据到会话状态
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st.session_state['cached_extruder_full'] = df_extruder_full
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st.session_state['cached_main_speed'] = df_main_speed
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st.session_state['cached_temp'] = df_temp
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st.session_state['last_query_start'] = start_dt
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st.session_state['last_query_end'] = end_dt
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# 数据处理和分析
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if all(key in st.session_state for key in ['cached_extruder_full', 'cached_main_speed', 'cached_temp']):
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with st.spinner("正在分析数据..."):
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# 获取缓存数据
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df_extruder_full = st.session_state['cached_extruder_full']
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df_main_speed = st.session_state['cached_main_speed']
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df_temp = st.session_state['cached_temp']
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# 检查是否有数据
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has_data = any([
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df_extruder_full is not None and not df_extruder_full.empty,
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df_main_speed is not None and not df_main_speed.empty,
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df_temp is not None and not df_temp.empty
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])
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if not has_data:
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st.warning("所选时间段内未找到任何数据，请尝试调整查询条件。")
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return
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# 数据整合与预处理
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def integrate_data(df_extruder_full, df_main_speed, df_temp):
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# 确保挤出机数据存在
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if df_extruder_full is None or df_extruder_full.empty:
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return None
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# 创建只包含米重和时间的主数据集
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df_merged = df_extruder_full[['time', 'metered_weight', 'screw_speed_actual', 'head_pressure']].copy()
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# 整合主流程数据
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if df_main_speed is not None and not df_main_speed.empty:
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df_main_speed = df_main_speed[['time', 'process_main_speed']]
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df_merged = pd.merge_asof(
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df_merged.sort_values('time'),
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df_main_speed.sort_values('time'),
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on='time',
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direction='nearest',
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tolerance=pd.Timedelta('1min')
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)
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# 整合温度数据
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if df_temp is not None and not df_temp.empty:
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temp_cols = ['time', 'nakata_extruder_screw_display_temp',
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'nakata_extruder_rear_barrel_display_temp',
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'nakata_extruder_front_barrel_display_temp',
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'nakata_extruder_head_display_temp']
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df_temp_subset = df_temp[temp_cols].copy()
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df_merged = pd.merge_asof(
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df_merged.sort_values('time'),
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df_temp_subset.sort_values('time'),
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on='time',
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direction='nearest',
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tolerance=pd.Timedelta('1min')
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)
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# 重命名列以提高可读性
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df_merged.rename(columns={
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'screw_speed_actual': '螺杆转速',
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'head_pressure': '机头压力',
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'process_main_speed': '流程主速',
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'nakata_extruder_screw_display_temp': '螺杆温度',
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'nakata_extruder_rear_barrel_display_temp': '后机筒温度',
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'nakata_extruder_front_barrel_display_temp': '前机筒温度',
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'nakata_extruder_head_display_temp': '机头温度'
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}, inplace=True)
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# 清理数据
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df_merged.dropna(subset=['metered_weight'], inplace=True)
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return df_merged
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# 执行数据整合
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df_analysis = integrate_data(df_extruder_full, df_main_speed, df_temp)
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if df_analysis is None or df_analysis.empty:
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st.warning("数据整合失败，请检查数据质量或调整时间范围。")
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return
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# 重命名米重列
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df_analysis.rename(columns={'metered_weight': '米重'}, inplace=True)
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# --- 原始数据趋势图 ---
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st.subheader("📈 原始数据趋势图")
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# 创建趋势图
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fig_trend = go.Figure()
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# 添加米重数据
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if df_extruder_full is not None and not df_extruder_full.empty:
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fig_trend.add_trace(go.Scatter(
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x=df_extruder_full['time'],
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y=df_extruder_full['metered_weight'],
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name='米重 (Kg/m)',
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mode='lines',
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line=dict(color='blue', width=2)
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))
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# 添加螺杆转速
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fig_trend.add_trace(go.Scatter(
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x=df_extruder_full['time'],
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y=df_extruder_full['screw_speed_actual'],
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name='螺杆转速 (RPM)',
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mode='lines',
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line=dict(color='green', width=1.5),
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yaxis='y2'
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))
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# 添加机头压力
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fig_trend.add_trace(go.Scatter(
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x=df_extruder_full['time'],
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y=df_extruder_full['head_pressure'],
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name='机头压力',
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mode='lines',
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line=dict(color='orange', width=1.5),
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yaxis='y3'
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))
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# 添加流程主速
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if df_main_speed is not None and not df_main_speed.empty:
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fig_trend.add_trace(go.Scatter(
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x=df_main_speed['time'],
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y=df_main_speed['process_main_speed'],
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name='流程主速 (M/Min)',
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mode='lines',
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line=dict(color='red', width=1.5),
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yaxis='y4'
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))
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# 添加温度数据
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if df_temp is not None and not df_temp.empty:
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# 螺杆温度
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fig_trend.add_trace(go.Scatter(
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x=df_temp['time'],
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y=df_temp['nakata_extruder_screw_display_temp'],
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name='螺杆温度 (°C)',
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mode='lines',
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line=dict(color='purple', width=1),
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yaxis='y5'
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))
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# 配置趋势图布局
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fig_trend.update_layout(
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title='原始数据趋势',
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xaxis=dict(
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title='时间',
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rangeslider=dict(visible=True),
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type='date'
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),
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yaxis=dict(
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title='米重 (Kg/m)',
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title_font=dict(color='blue'),
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tickfont=dict(color='blue')
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),
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yaxis2=dict(
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title='螺杆转速 (RPM)',
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title_font=dict(color='green'),
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tickfont=dict(color='green'),
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overlaying='y',
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side='right'
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),
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yaxis3=dict(
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title='机头压力',
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title_font=dict(color='orange'),
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tickfont=dict(color='orange'),
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overlaying='y',
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side='right',
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anchor='free',
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position=0.85
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),
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yaxis4=dict(
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title='流程主速 (M/Min)',
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title_font=dict(color='red'),
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tickfont=dict(color='red'),
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overlaying='y',
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side='right',
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anchor='free',
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position=0.75
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),
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yaxis5=dict(
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title='温度 (°C)',
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title_font=dict(color='purple'),
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tickfont=dict(color='purple'),
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overlaying='y',
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side='left',
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anchor='free',
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position=0.15
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),
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legend=dict(
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orientation="h",
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yanchor="bottom",
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y=1.02,
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xanchor="right",
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x=1
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),
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height=600,
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margin=dict(l=100, r=200, t=100, b=100),
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hovermode='x unified'
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)
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# 显示趋势图
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st.plotly_chart(fig_trend, width='stretch', config={'scrollZoom': True})
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# --- 高级预测分析 ---
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st.subheader("📊 高级预测分析")
|
|
# 检查所有默认特征是否在数据中
|
missing_features = [f for f in default_features if f not in df_analysis.columns]
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if missing_features:
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st.warning(f"数据中缺少以下特征: {', '.join(missing_features)}")
|
else:
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# 准备数据
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X = df_analysis[default_features]
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y = df_analysis['米重']
|
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# 清理数据中的NaN值
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combined = pd.concat([X, y], axis=1)
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combined_clean = combined.dropna()
|
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# 检查清理后的数据量
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if len(combined_clean) < 30:
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st.warning("数据量不足，无法进行有效的预测分析")
|
else:
|
# 重新分离X和y
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X_clean = combined_clean[default_features]
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y_clean = combined_clean['米重']
|
|
# 特征工程：添加时间相关特征
|
# 确保使用时间列作为索引
|
if 'time' in combined_clean.columns:
|
# 将time列设置为索引
|
combined_clean = combined_clean.set_index('time')
|
|
# 创建时间特征
|
time_features = pd.DataFrame(index=combined_clean.index)
|
time_features['hour'] = combined_clean.index.hour
|
time_features['minute'] = combined_clean.index.minute
|
time_features['second'] = combined_clean.index.second
|
time_features['time_of_day'] = time_features['hour'] * 3600 + time_features['minute'] * 60 + time_features['second']
|
else:
|
# 如果没有time列，创建空的时间特征
|
time_features = pd.DataFrame(index=combined_clean.index)
|
time_features['hour'] = 0
|
time_features['minute'] = 0
|
time_features['second'] = 0
|
time_features['time_of_day'] = 0
|
|
# 添加滞后特征
|
for feature in default_features:
|
for lag in [1, 2, 3]:
|
time_features[f'{feature}_lag{lag}'] = X_clean[feature].shift(lag)
|
time_features[f'{feature}_diff{lag}'] = X_clean[feature].diff(lag)
|
|
# 添加滚动统计特征
|
for feature in default_features:
|
time_features[f'{feature}_rolling_mean'] = X_clean[feature].rolling(window=5).mean()
|
time_features[f'{feature}_rolling_std'] = X_clean[feature].rolling(window=5).std()
|
time_features[f'{feature}_rolling_min'] = X_clean[feature].rolling(window=5).min()
|
time_features[f'{feature}_rolling_max'] = X_clean[feature].rolling(window=5).max()
|
|
# 清理滞后特征和滚动统计特征产生的NaN值
|
time_features.dropna(inplace=True)
|
|
# 对齐X和y
|
common_index = time_features.index.intersection(y_clean.index)
|
X_final = pd.concat([X_clean.loc[common_index], time_features.loc[common_index]], axis=1)
|
y_final = y_clean.loc[common_index]
|
|
# 检查最终数据量
|
if len(X_final) < 20:
|
st.warning("特征工程后数据量不足，无法进行有效的预测分析")
|
else:
|
# 分割训练集和测试集
|
X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size=0.2, random_state=42)
|
|
# 数据标准化
|
scaler_X = StandardScaler()
|
scaler_y = MinMaxScaler()
|
|
X_train_scaled = scaler_X.fit_transform(X_train)
|
X_test_scaled = scaler_X.transform(X_test)
|
y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1)).ravel()
|
y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1)).ravel()
|
|
# 模型训练
|
model = None
|
y_pred = None
|
|
try:
|
if model_type == 'RandomForest':
|
# 随机森林回归
|
model = RandomForestRegressor(n_estimators=100, random_state=42)
|
model.fit(X_train, y_train)
|
y_pred = model.predict(X_test)
|
|
elif model_type == 'GradientBoosting':
|
# 梯度提升回归
|
model = GradientBoostingRegressor(n_estimators=100, random_state=42)
|
model.fit(X_train, y_train)
|
y_pred = model.predict(X_test)
|
|
elif model_type == 'SVR':
|
# 支持向量回归
|
model = SVR(kernel='rbf', C=1.0, gamma='scale')
|
model.fit(X_train_scaled, y_train_scaled)
|
y_pred_scaled = model.predict(X_test_scaled)
|
y_pred = scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).ravel()
|
|
elif model_type == 'MLP':
|
# 多层感知器回归
|
model = MLPRegressor(hidden_layer_sizes=(100, 50), max_iter=500, random_state=42)
|
model.fit(X_train_scaled, y_train_scaled)
|
y_pred_scaled = model.predict(X_test_scaled)
|
y_pred = scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).ravel()
|
|
elif use_deep_learning and model_type in ['LSTM', 'GRU', 'BiLSTM']:
|
# 准备时间序列数据
|
sequence_length = st.session_state['ma_sequence_length']
|
|
def create_sequences(X, y, seq_length):
|
X_seq = []
|
y_seq = []
|
# 确保X和y的长度一致
|
min_len = min(len(X), len(y))
|
# 确保X和y的长度至少为seq_length + 1
|
if min_len <= seq_length:
|
return np.array([]), np.array([])
|
# 截断X和y到相同长度
|
X_trimmed = X[:min_len]
|
y_trimmed = y[:min_len]
|
# 创建序列
|
for i in range(len(X_trimmed) - seq_length):
|
X_seq.append(X_trimmed[i:i+seq_length])
|
y_seq.append(y_trimmed[i+seq_length])
|
return np.array(X_seq), np.array(y_seq)
|
|
# 为深度学习模型创建序列
|
X_train_seq, y_train_seq = create_sequences(X_train_scaled, y_train_scaled, sequence_length)
|
X_test_seq, y_test_seq = create_sequences(X_test_scaled, y_test_scaled, sequence_length)
|
|
# 检查创建的序列是否为空
|
if len(X_train_seq) == 0 or len(y_train_seq) == 0:
|
st.warning(f"数据量不足，无法创建有效的LSTM序列。需要至少 {sequence_length + 1} 个样本，当前只有 {min(len(X_train_scaled), len(y_train_scaled))} 个样本。")
|
# 使用随机森林作为备选模型
|
model = RandomForestRegressor(n_estimators=100, random_state=42)
|
model.fit(X_train, y_train)
|
y_pred = model.predict(X_test)
|
else:
|
# 转换为PyTorch张量并移动到设备
|
X_train_tensor = torch.tensor(X_train_seq, dtype=torch.float32).to(device)
|
y_train_tensor = torch.tensor(y_train_seq, dtype=torch.float32).unsqueeze(1).to(device)
|
X_test_tensor = torch.tensor(X_test_seq, dtype=torch.float32).to(device)
|
y_test_tensor = torch.tensor(y_test_seq, dtype=torch.float32).unsqueeze(1).to(device)
|
|
# 构建PyTorch模型并移动到设备
|
input_dim = X_train_scaled.shape[1]
|
|
if model_type == 'LSTM':
|
deep_model = LSTMModel(input_dim).to(device)
|
elif model_type == 'GRU':
|
deep_model = GRUModel(input_dim).to(device)
|
elif model_type == 'BiLSTM':
|
deep_model = BiLSTMModel(input_dim).to(device)
|
|
# 定义损失函数和优化器
|
criterion = nn.MSELoss()
|
optimizer = optim.Adam(deep_model.parameters(), lr=0.001)
|
|
# 显示使用的设备
|
st.info(f"使用设备: {device}")
|
|
# 训练模型
|
num_epochs = 50
|
batch_size = 32
|
|
for epoch in range(num_epochs):
|
deep_model.train()
|
optimizer.zero_grad()
|
|
# 前向传播
|
outputs = deep_model(X_train_tensor)
|
loss = criterion(outputs, y_train_tensor)
|
|
# 反向传播和优化
|
loss.backward()
|
optimizer.step()
|
|
# 预测
|
deep_model.eval()
|
with torch.no_grad():
|
y_pred_scaled_tensor = deep_model(X_test_tensor)
|
y_pred_scaled = y_pred_scaled_tensor.numpy().ravel()
|
y_pred = scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).ravel()
|
|
# 将y_test_seq转换回原始尺度
|
y_test_actual = scaler_y.inverse_transform(y_test_seq.reshape(-1, 1)).ravel()
|
|
# 保存模型
|
model = deep_model
|
|
# 计算评估指标
|
if model_type in ['LSTM', 'GRU', 'BiLSTM']:
|
# 使用转换后的y_test_seq作为真实值
|
r2 = r2_score(y_test_actual, y_pred)
|
mse = mean_squared_error(y_test_actual, y_pred)
|
mae = mean_absolute_error(y_test_actual, y_pred)
|
rmse = np.sqrt(mse)
|
else:
|
# 使用原始的y_test作为真实值
|
r2 = r2_score(y_test, y_pred)
|
mse = mean_squared_error(y_test, y_pred)
|
mae = mean_absolute_error(y_test, y_pred)
|
rmse = np.sqrt(mse)
|
|
# 显示模型性能
|
metrics_cols = st.columns(2)
|
with metrics_cols[0]:
|
st.metric("R² 得分", f"{r2:.4f}")
|
st.metric("均方误差 (MSE)", f"{mse:.6f}")
|
with metrics_cols[1]:
|
st.metric("平均绝对误差 (MAE)", f"{mae:.6f}")
|
st.metric("均方根误差 (RMSE)", f"{rmse:.6f}")
|
|
# --- 实际值与预测值对比 ---
|
st.subheader("🔄 实际值与预测值对比")
|
|
# 创建对比数据
|
if model_type in ['LSTM', 'GRU', 'BiLSTM']:
|
# 使用转换后的y_test_actual
|
compare_df = pd.DataFrame({
|
'实际值': y_test_actual,
|
'预测值': y_pred
|
})
|
else:
|
# 使用原始的y_test
|
compare_df = pd.DataFrame({
|
'实际值': y_test,
|
'预测值': y_pred
|
})
|
compare_df = compare_df.sort_index()
|
|
# 创建对比图
|
fig_compare = go.Figure()
|
fig_compare.add_trace(go.Scatter(
|
x=compare_df.index,
|
y=compare_df['实际值'],
|
name='实际值',
|
mode='lines+markers',
|
line=dict(color='blue', width=2)
|
))
|
fig_compare.add_trace(go.Scatter(
|
x=compare_df.index,
|
y=compare_df['预测值'],
|
name='预测值',
|
mode='lines+markers',
|
line=dict(color='red', width=2, dash='dash')
|
))
|
fig_compare.update_layout(
|
title=f'测试集: 实际米重 vs 预测米重 ({model_type})',
|
xaxis=dict(title='时间'),
|
yaxis=dict(title='米重 (Kg/m)'),
|
legend=dict(orientation='h', yanchor='bottom', y=1.02, xanchor='right', x=1),
|
height=400
|
)
|
st.plotly_chart(fig_compare, width='stretch')
|
|
# --- 残差分析 ---
|
st.subheader("📉 残差分析")
|
|
# 计算残差
|
if model_type in ['LSTM', 'GRU', 'BiLSTM']:
|
# 使用转换后的y_test_actual
|
residuals = y_test_actual - y_pred
|
else:
|
# 使用原始的y_test
|
residuals = y_test - y_pred
|
|
# 创建残差图
|
fig_residual = go.Figure()
|
fig_residual.add_trace(go.Scatter(
|
x=y_pred,
|
y=residuals,
|
mode='markers',
|
marker=dict(color='green', size=8, opacity=0.6)
|
))
|
fig_residual.add_shape(
|
type="line",
|
x0=y_pred.min(),
|
y0=0,
|
x1=y_pred.max(),
|
y1=0,
|
line=dict(color="red", width=2, dash="dash")
|
)
|
fig_residual.update_layout(
|
title='残差图',
|
xaxis=dict(title='预测值'),
|
yaxis=dict(title='残差'),
|
height=400
|
)
|
st.plotly_chart(fig_residual, width='stretch')
|
|
# --- 特征重要性（如果模型支持） ---
|
if model_type in ['RandomForest', 'GradientBoosting']:
|
st.subheader("⚖️ 特征重要性分析")
|
|
# 计算特征重要性
|
feature_importance = pd.DataFrame({
|
'特征': X_train.columns,
|
'重要性': model.feature_importances_
|
})
|
feature_importance = feature_importance.sort_values('重要性', ascending=False)
|
|
# 创建特征重要性图
|
fig_importance = px.bar(
|
feature_importance,
|
x='特征',
|
y='重要性',
|
title='特征重要性',
|
color='重要性',
|
color_continuous_scale='viridis'
|
)
|
fig_importance.update_layout(
|
xaxis=dict(tickangle=-45),
|
height=400
|
)
|
st.plotly_chart(fig_importance, width='stretch')
|
|
# --- 预测功能 ---
|
st.subheader("🔮 米重预测")
|
|
# 创建预测表单
|
st.write("输入特征值进行米重预测:")
|
predict_cols = st.columns(2)
|
input_features = {}
|
|
for i, feature in enumerate(default_features):
|
with predict_cols[i % 2]:
|
# 获取特征的统计信息
|
min_val = X_clean[feature].min()
|
max_val = X_clean[feature].max()
|
mean_val = X_clean[feature].mean()
|
|
input_features[feature] = st.number_input(
|
f"{feature}",
|
key=f"ma_pred_{feature}",
|
value=float(mean_val),
|
min_value=float(min_val),
|
max_value=float(max_val),
|
step=0.1
|
)
|
|
if st.button("预测米重"):
|
# 准备预测数据
|
input_df = pd.DataFrame([input_features])
|
|
# 添加时间特征（使用当前时间）
|
current_time = datetime.now()
|
time_features_input = pd.DataFrame({
|
'hour': [current_time.hour],
|
'minute': [current_time.minute],
|
'second': [current_time.second],
|
'time_of_day': [current_time.hour * 3600 + current_time.minute * 60 + current_time.second]
|
})
|
|
# 添加滞后特征（使用输入值作为替代）
|
for feature in default_features:
|
for lag in [1, 2, 3]:
|
time_features_input[f'{feature}_lag{lag}'] = input_features[feature]
|
time_features_input[f'{feature}_diff{lag}'] = 0.0
|
|
# 添加滚动统计特征（使用输入值作为替代）
|
for feature in default_features:
|
time_features_input[f'{feature}_rolling_mean'] = input_features[feature]
|
time_features_input[f'{feature}_rolling_std'] = 0.0
|
time_features_input[f'{feature}_rolling_min'] = input_features[feature]
|
time_features_input[f'{feature}_rolling_max'] = input_features[feature]
|
|
# 合并特征
|
input_combined = pd.concat([input_df, time_features_input], axis=1)
|
|
# 预测
|
if model_type in ['SVR', 'MLP']:
|
input_scaled = scaler_X.transform(input_combined)
|
prediction_scaled = model.predict(input_scaled)
|
predicted_weight = scaler_y.inverse_transform(prediction_scaled.reshape(-1, 1)).ravel()[0]
|
elif use_deep_learning and model_type in ['LSTM', 'GRU', 'BiLSTM']:
|
# 为深度学习模型创建序列
|
input_scaled = scaler_X.transform(input_combined)
|
# 重复输入以创建序列
|
input_seq = np.tile(input_scaled, (sequence_length, 1)).reshape(1, sequence_length, -1)
|
# 转换为PyTorch张量并移动到设备
|
input_tensor = torch.tensor(input_seq, dtype=torch.float32).to(device)
|
# 预测
|
model.eval()
|
with torch.no_grad():
|
prediction_scaled_tensor = model(input_tensor)
|
prediction_scaled = prediction_scaled_tensor.cpu().numpy().ravel()[0]
|
predicted_weight = scaler_y.inverse_transform(prediction_scaled.reshape(-1, 1)).ravel()[0]
|
else:
|
predicted_weight = model.predict(input_combined)[0]
|
|
# 显示预测结果
|
st.success(f"预测米重: {predicted_weight:.4f} Kg/m")
|
|
# --- 数据预览 ---
|
st.subheader("🔍 数据预览")
|
st.dataframe(df_analysis.head(20), width='stretch')
|
|
# --- 导出数据 ---
|
st.subheader("💾 导出数据")
|
# 将数据转换为CSV格式
|
csv = df_analysis.to_csv(index=False)
|
# 创建下载按钮
|
st.download_button(
|
label="导出整合后的数据 (CSV)",
|
data=csv,
|
file_name=f"metered_weight_advanced_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
|
mime="text/csv",
|
help="点击按钮导出整合后的米重分析数据"
|
)
|
except Exception as e:
|
st.error(f"模型训练或预测失败: {str(e)}")
|
|
else:
|
# 提示用户点击开始分析按钮
|
st.info("请选择时间范围并点击'开始分析'按钮获取数据。")
|
