mirror of
https://github.com/NanjingForestryUniversity/supermachine-tobacco.git
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Merge remote-tracking branch 'origin/master'
This commit is contained in:
commit
660711f6e6
@ -15,8 +15,17 @@
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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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"execution_count": 3,
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\FEIJINTI\\miniconda3\\envs\\deepo\\lib\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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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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@ -45,7 +54,7 @@
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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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"execution_count": 4,
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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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@ -72,7 +81,7 @@
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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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"execution_count": 5,
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"outputs": [],
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"source": [
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"data = read_envi_ascii(data_path)"
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@ -86,7 +95,7 @@
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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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"execution_count": 6,
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"outputs": [
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{
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"name": "stdout",
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@ -114,7 +123,7 @@
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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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"execution_count": 7,
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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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@ -142,7 +151,7 @@
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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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"execution_count": 8,
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"outputs": [
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{
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"name": "stdout",
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@ -179,7 +188,7 @@
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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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"execution_count": 9,
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"outputs": [
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{
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"name": "stdout",
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@ -215,6 +224,237 @@
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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": 10,
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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": 11,
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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": 12,
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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": 13,
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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": 14,
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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": 15,
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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 1.00 312\n",
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" 1.0 0.98 0.97 0.98 289\n",
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" 2.0 0.97 1.00 0.99 312\n",
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" 3.0 0.95 0.99 0.97 278\n",
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" 4.0 0.98 0.92 0.95 288\n",
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" 5.0 0.98 0.98 0.98 270\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": 16,
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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": 17,
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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 1.00 598\n",
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" 1.0 1.00 1.00 1.00 1151\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": 18,
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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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"cell_type": "code",
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"execution_count": 8,
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@ -19,7 +19,7 @@ class Config:
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# 光谱模型参数
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blk_size = 4
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pixel_model_path = r"./models/dt.p"
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pixel_model_path = r"./models/pixel_2022-08-02_15-22.model"
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blk_model_path = r"./models/rf_4x4_c22_20_sen8_9.model"
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spec_size_threshold = 3
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@ -168,5 +168,5 @@ if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Run image test or ')
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tester = TestMain()
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tester.pony_run(test_path=r'/home/lzy/2022.7.30/tobacco_v1_0/saved_img/',
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test_rgb=False, test_spectra=False, get_delta=False)
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test_rgb=True, test_spectra=True, get_delta=False)
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@ -260,7 +260,7 @@ class PixelModelML:
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self.dt = pickle.load(f)
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def predict(self, feature):
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pixel_result_array = self.dt.predict(feature)
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pixel_result_array = self.dt.predict_bin(feature)
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return pixel_result_array
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@ -409,7 +409,7 @@ class SpecDetector(Detector):
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if x_yellow.shape[0] == 0:
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return non_yellow_things
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else:
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tobacco = self.pixel_model_ml.predict_bin(x_yellow) < 0.5
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tobacco = self.pixel_model_ml.predict(x_yellow) < 0.5
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non_yellow_things[yellow_things] = ~tobacco
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# 杂质mask中将背景赋值为0,将杂质赋值为1
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