完成新的dt模型

05_model_training.ipynb

@@ -15,8 +15,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pickle\n",
@@ -45,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "outputs": [],
    "source": [
     "data_path = r'data/envi20220802.txt'\n",
@@ -72,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "outputs": [],
    "source": [
     "data = read_envi_ascii(data_path)"
@@ -86,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "outputs": [
     {
      "name": "stdout",
@@ -114,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "outputs": [],
    "source": [
     "data_x = [d for class_name, d in data.items() if class_name in name_dict.keys()]\n",
@@ -142,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "outputs": [
     {
      "name": "stdout",
@@ -179,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "outputs": [
     {
      "name": "stdout",
@@ -215,6 +224,237 @@
     }
    }
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "outputs": [],
+   "source": [
+    "from models import DecisionTree\n",
+    "from sklearn.model_selection import train_test_split"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "outputs": [],
+   "source": [
+    "train_x, test_x, train_y, test_y = train_test_split(x_resampled, y_resampled, test_size=0.2)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "outputs": [],
+   "source": [
+    "tree = DecisionTree(class_weight={1:20})"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [],
+   "source": [
+    "tree = tree.fit(train_x, train_y)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 模型评估"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 多分类精度"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "outputs": [],
+   "source": [
+    "pred_y = tree.predict(test_x)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       1.00      1.00      1.00       312\n",
+      "         1.0       0.98      0.97      0.98       289\n",
+      "         2.0       0.97      1.00      0.99       312\n",
+      "         3.0       0.95      0.99      0.97       278\n",
+      "         4.0       0.98      0.92      0.95       288\n",
+      "         5.0       0.98      0.98      0.98       270\n",
+      "\n",
+      "    accuracy                           0.98      1749\n",
+      "   macro avg       0.98      0.98      0.98      1749\n",
+      "weighted avg       0.98      0.98      0.98      1749\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "print(classification_report(y_pred=pred_y, y_true=test_y))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 二分类精度"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [],
+   "source": [
+    "test_y[test_y <= 1] = 0\n",
+    "test_y[test_y > 1] = 1\n",
+    "pred_y = tree.predict_bin(test_x)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       1.00      1.00      1.00       598\n",
+      "         1.0       1.00      1.00      1.00      1151\n",
+      "\n",
+      "    accuracy                           1.00      1749\n",
+      "   macro avg       1.00      1.00      1.00      1749\n",
+      "weighted avg       1.00      1.00      1.00      1749\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(y_true=pred_y, y_pred=pred_y))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "# 模型保存"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "path = datetime.datetime.now().strftime(f\"models/pixel_%Y-%m-%d_%H-%M.model\")\n",
+    "with open(path, 'wb') as f:\n",
+    "    pickle.dump(tree, f)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
   {
    "cell_type": "code",
    "execution_count": 8,

config.py

@@ -19,7 +19,7 @@ class Config:
 
     # 光谱模型参数
     blk_size = 4
-    pixel_model_path = r"./models/dt.p"
+    pixel_model_path = r"./models/pixel_2022-08-02_15-22.model"
     blk_model_path = r"./models/rf_4x4_c22_20_sen8_9.model"
     spec_size_threshold = 3