mirror of
https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
synced 2025-11-08 14:23:55 +00:00
471 lines
9.2 KiB
Plaintext
471 lines
9.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# 训练像素模型\n",
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"用这个文件可以训练出需要使用的光谱像素点模型"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 1,
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pickle\n",
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"from utils import read_envi_ascii\n",
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"from config import Config\n",
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"from models import ManualTree"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 一些变量"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 2,
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"outputs": [],
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"source": [
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"data_path = r'data/envi20220802.txt'\n",
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"name_dict = {'tobacco': 1, 'yantou':2, 'kazhi':3, 'bomo':4, 'jiaodai':5, 'background':0}"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 构建数据集"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 3,
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"outputs": [],
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"source": [
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"data = read_envi_ascii(data_path)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 4,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"zibian (569, 448)\n",
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"tobacco (1457, 448)\n",
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"yantou (354, 448)\n",
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"kazhi (449, 448)\n",
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"bomo (1154, 448)\n",
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"jiaodai (566, 448)\n",
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"background (1235, 448)\n"
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]
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}
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],
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"source": [
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"_ = [print(class_name, d.shape) for class_name, d in data.items()]"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 5,
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"outputs": [],
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"source": [
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"data_x = [d for class_name, d in data.items() if class_name in name_dict.keys()]\n",
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"data_y = [np.ones((d.shape[0], ))*name_dict[class_name] for class_name, d in data.items() if class_name in name_dict.keys()]\n",
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"data_x, data_y = np.concatenate(data_x), np.concatenate(data_y)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## 取出需要的22个特征谱段"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 6,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"这些是现在的数据: (5215, 448) (5215,)\n",
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"截取其中需要的部分后: (5215, 22) (5215,)\n"
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]
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}
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],
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"source": [
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"print(\"这些是现在的数据: \", data_x.shape, data_y.shape)\n",
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"data_x_cut = data_x[..., Config.bands]\n",
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"print(\"截取其中需要的部分后: \", data_x_cut.shape, data_y.shape)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## 进行样本平衡"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 7,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"这是重采样后的数据: (8742, 22) (8742,)\n"
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]
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}
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],
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"source": [
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"from imblearn.over_sampling import RandomOverSampler\n",
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"ros = RandomOverSampler(random_state=0)\n",
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"x_resampled, y_resampled = ros.fit_resample(data_x_cut, data_y)\n",
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"print('这是重采样后的数据: ', x_resampled.shape, y_resampled.shape)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 进行模型训练\n",
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"分出一部分数据进行训练"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 8,
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"outputs": [],
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"source": [
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"from models import DecisionTree\n",
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"from sklearn.model_selection import train_test_split"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 9,
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"outputs": [],
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"source": [
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"train_x, test_x, train_y, test_y = train_test_split(x_resampled, y_resampled, test_size=0.2)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 11,
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"outputs": [],
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"source": [
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"tree = DecisionTree(class_weight={1:20})"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 12,
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"outputs": [],
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"source": [
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"tree = tree.fit(train_x, train_y)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 模型评估"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## 多分类精度"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 13,
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"outputs": [],
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"source": [
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"pred_y = tree.predict(test_x)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 14,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0.0 1.00 1.00 1.00 304\n",
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" 1.0 0.97 0.98 0.97 275\n",
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" 2.0 0.98 1.00 0.99 297\n",
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" 3.0 0.95 0.99 0.97 293\n",
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" 4.0 0.98 0.91 0.95 316\n",
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" 5.0 0.98 0.98 0.98 264\n",
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"\n",
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" accuracy 0.98 1749\n",
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" macro avg 0.98 0.98 0.98 1749\n",
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"weighted avg 0.98 0.98 0.98 1749\n",
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"\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import classification_report\n",
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"\n",
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"print(classification_report(y_pred=pred_y, y_true=test_y))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## 二分类精度"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 19,
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"outputs": [],
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"source": [
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"test_y[test_y <= 1] = 0\n",
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"test_y[test_y > 1] = 1\n",
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"pred_y = tree.predict_bin(test_x)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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": 20,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0.0 1.00 1.00 1.00 582\n",
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" 1.0 1.00 1.00 1.00 1167\n",
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"\n",
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" accuracy 1.00 1749\n",
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" macro avg 1.00 1.00 1.00 1749\n",
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"weighted avg 1.00 1.00 1.00 1749\n",
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"\n"
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]
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}
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],
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"source": [
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"print(classification_report(y_true=pred_y, y_pred=pred_y))"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"# 模型保存"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%% md\n"
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}
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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": 22,
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"outputs": [],
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"source": [
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"import datetime\n",
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"\n",
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"path = datetime.datetime.now().strftime(f\"models/pixel_%Y-%m-%d_%H-%M.model\")\n",
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"with open(path, 'wb') as f:\n",
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" pickle.dump(tree, f)"
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],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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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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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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} |