301 lines
8.3 KiB
Plaintext
301 lines
8.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 作物病害分类模型训练示例\n",
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"\n",
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"本 Notebook 演示如何使用作物数据集训练机器学习模型，完成病害严重程度分类任务。\n",
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"\n",
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"**场景：** AI 模型训练（分类任务）\n",
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"\n",
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"**数据集：** CSV 作物病害标注表\n",
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"\n",
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"**模型：** 随机森林 / 决策树分类器"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. 环境准备与数据加载"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import matplotlib\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.preprocessing import LabelEncoder\n",
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"from sklearn.metrics import (\n",
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" classification_report, confusion_matrix, accuracy_score,\n",
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" ConfusionMatrixDisplay\n",
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")\n",
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"import os\n",
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"\n",
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"matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Micro Hei']\n",
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"matplotlib.rcParams['axes.unicode_minus'] = False\n",
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"\n",
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"DATA_DIR = os.path.dirname(os.path.abspath(''))\n",
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"print('环境准备完成')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 加载数据\n",
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"df = pd.read_csv(f'{DATA_DIR}/csv/作物病害标注表.csv')\n",
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"print(f'数据集大小: {df.shape}')\n",
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. 数据预处理"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 特征工程\n",
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"le_crop = LabelEncoder()\n",
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"le_disease = LabelEncoder()\n",
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"le_part = LabelEncoder()\n",
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"le_region = LabelEncoder()\n",
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"le_severity = LabelEncoder()\n",
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"\n",
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"df['作物编码'] = le_crop.fit_transform(df['作物'])\n",
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"df['病害编码'] = le_disease.fit_transform(df['病害名称'])\n",
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"df['部位编码'] = le_part.fit_transform(df['发病部位'])\n",
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"df['地区编码'] = le_region.fit_transform(df['地区'])\n",
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"df['严重程度编码'] = le_severity.fit_transform(df['严重程度'])\n",
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"\n",
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"# 选择特征\n",
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"feature_cols = ['作物编码', '病害编码', '部位编码', '地区编码', '温度_℃', '湿度_%', '经度', '纬度']\n",
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"X = df[feature_cols]\n",
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"y = df['严重程度编码']\n",
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"\n",
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"print(f'特征维度: {X.shape}')\n",
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"print(f'标签分布:\\n{pd.Series(y).value_counts().sort_index()}')\n",
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"print(f'\\n标签映射:')\n",
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"for i, cls in enumerate(le_severity.classes_):\n",
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" print(f' {i}: {cls}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. 划分训练集和测试集"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, X_test, y_train, y_test = train_test_split(\n",
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" X, y, test_size=0.2, random_state=42, stratify=y\n",
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")\n",
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"print(f'训练集大小: {X_train.shape[0]}')\n",
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"print(f'测试集大小: {X_test.shape[0]}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. 训练随机森林模型"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 训练随机森林\n",
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"rf_model = RandomForestClassifier(\n",
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" n_estimators=100,\n",
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" max_depth=10,\n",
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" random_state=42,\n",
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" n_jobs=-1\n",
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")\n",
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"rf_model.fit(X_train, y_train)\n",
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"\n",
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"# 预测\n",
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"y_pred_rf = rf_model.predict(X_test)\n",
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"acc_rf = accuracy_score(y_test, y_pred_rf)\n",
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"print(f'随机森林准确率: {acc_rf:.4f}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. 训练决策树模型（对比）"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 训练决策树\n",
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"dt_model = DecisionTreeClassifier(max_depth=8, random_state=42)\n",
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"dt_model.fit(X_train, y_train)\n",
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"\n",
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"y_pred_dt = dt_model.predict(X_test)\n",
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"acc_dt = accuracy_score(y_test, y_pred_dt)\n",
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"print(f'决策树准确率: {acc_dt:.4f}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 6. 模型评估"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 分类报告\n",
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"print('=' * 50)\n",
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"print('随机森林 - 分类报告')\n",
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"print('=' * 50)\n",
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"print(classification_report(y_test, y_pred_rf, target_names=le_severity.classes_))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 混淆矩阵可视化\n",
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"fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
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"\n",
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"ConfusionMatrixDisplay.from_predictions(\n",
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" y_test, y_pred_rf,\n",
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" display_labels=le_severity.classes_,\n",
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" cmap='Blues', ax=axes[0]\n",
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")\n",
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"axes[0].set_title(f'随机森林混淆矩阵 (准确率={acc_rf:.4f})')\n",
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"\n",
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"ConfusionMatrixDisplay.from_predictions(\n",
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" y_test, y_pred_dt,\n",
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" display_labels=le_severity.classes_,\n",
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" cmap='Oranges', ax=axes[1]\n",
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")\n",
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"axes[1].set_title(f'决策树混淆矩阵 (准确率={acc_dt:.4f})')\n",
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"\n",
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"plt.tight_layout()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 7. 特征重要性分析"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 特征重要性\n",
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"importances = rf_model.feature_importances_\n",
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"indices = np.argsort(importances)[::-1]\n",
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"\n",
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"plt.figure(figsize=(10, 6))\n",
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"plt.bar(range(len(importances)), importances[indices], align='center', color='steelblue')\n",
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"plt.xticks(range(len(importances)), [feature_cols[i] for i in indices], rotation=45)\n",
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"plt.ylabel('重要性分数')\n",
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"plt.title('随机森林 - 特征重要性排序')\n",
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"plt.tight_layout()\n",
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"plt.show()\n",
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"\n",
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"print('特征重要性排名:')\n",
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"for rank, idx in enumerate(indices, 1):\n",
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" print(f' {rank}. {feature_cols[idx]}: {importances[idx]:.4f}')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 8. 模型预测示例"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 模拟新样本预测\n",
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"new_samples = pd.DataFrame({\n",
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" '作物编码': le_crop.transform(['水稻', '小麦', '玉米']),\n",
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" '病害编码': le_disease.transform(['稻瘟病', '锈病', '大斑病']),\n",
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" '部位编码': le_part.transform(['叶片', '叶片', '穗部']),\n",
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" '地区编码': le_region.transform(['长江中下游', '华北平原', '东北平原']),\n",
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" '温度_℃': [28.5, 22.0, 25.3],\n",
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" '湿度_%': [85.0, 65.0, 78.0],\n",
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" '经度': [116.4, 114.5, 123.4],\n",
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" '纬度': [30.5, 38.0, 41.8],\n",
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"})\n",
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"\n",
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"predictions = rf_model.predict(new_samples)\n",
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"pred_labels = le_severity.inverse_transform(predictions)\n",
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"\n",
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"print('预测结果:')\n",
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"print('-' * 50)\n",
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"for i, (_, row) in enumerate(new_samples.iterrows()):\n",
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" crop_name = le_crop.inverse_transform([row['作物编码']])[0]\n",
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" disease = le_disease.inverse_transform([row['病害编码']])[0]\n",
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" print(f'样本{i+1}: {crop_name}/{disease} => 预测严重程度: {pred_labels[i]}')n",
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"\n",
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"print('\\n模型训练和预测流程演示完成！')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.10.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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} |