mirror of
https://github.com/NanjingForestryUniversity/SCNet.git
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134 lines
3.2 KiB
Plaintext
134 lines
3.2 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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"collapsed": true,
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"pycharm": {
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"name": "#%% md\n"
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}
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},
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"source": [
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"# Model comparison"
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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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"## PLS"
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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": 44,
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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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"shape of data:\n",
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"x_train: (8183, 102), y_train: (8183, 1),\n",
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"x_test: (3508, 102), y_test: (3508, 1)\n"
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]
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}
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],
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"source": [
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"from sklearn.neural_network import MLPRegressor\n",
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"from sklearn.svm import SVR\n",
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"import numpy as np\n",
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"from scipy.io import loadmat\n",
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"from sklearn.cross_decomposition import PLSRegression\n",
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"from sklearn.metrics import mean_squared_error, r2_score\n",
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"\n",
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"data = loadmat('./dataset/mango/mango_dm_split.mat')\n",
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"x_train, y_train, x_test, y_test = data['x_train'], data['y_train'], data['x_test'], data['y_test']\n",
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"print(f\"shape of data:\\n\"\n",
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" f\"x_train: {x_train.shape}, y_train: {y_train.shape},\\n\"\n",
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" f\"x_test: {x_test.shape}, y_test: {y_test.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": "code",
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"execution_count": 45,
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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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"PLS RMSE: 0.7512262994028881 %\n",
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"PLS R^2: 0.8748209692384972\n",
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"SVR RMSE: 2.870635692210643 %\n",
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"SVR R^2: 0.5216575965112935\n",
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"MLP RMSE: 4.919371298214537 %\n",
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"MLP R^2: 0.18027080314424337\n"
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]
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}
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],
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"source": [
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"pls = PLSRegression(n_components=20)\n",
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"svr = SVR(kernel=\"rbf\", degree=30, gamma=\"scale\")\n",
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"mlp = MLPRegressor(hidden_layer_sizes=(60, 50, ))\n",
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"pls = pls.fit(x_train, y_train.ravel())\n",
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"svr = svr.fit(x_train, y_train.ravel())\n",
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"mlp = mlp.fit(x_train, y_train.ravel())\n",
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"\n",
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"models = {'PLS': pls, \"SVR\": svr, \"MLP\": mlp}\n",
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"results = {model_name: model.predict(x_test).reshape((-1, )) for model_name, model in models.items()}\n",
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"for model_name, model_result in results.items():\n",
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" print(model_name, \"RMSE: \", mean_squared_error(y_test, model_result)/np.mean(y_test)*100, \"%\")\n",
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" print(model_name, \"R^2: \", r2_score(y_test, model_result))"
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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": 45,
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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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} |