{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Machine learning classification\n", "\n", "Below is an example for using the `ml.classificator` class to classify spectral data.\n", "It wraps scikit-learn algorithms in a similar way compared to the `rp.mlregression` class.\n", " \n", "## Fake data generation\n", "\n", "First we generate 5 fake signals that can be measured modulo some noise. More complex examples can be generated if wanted." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of samples = 100\n", "Number of labels = 100\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import numpy as np\n", "np.random.seed(42) # fixing the seed\n", "import matplotlib.pyplot as plt\n", "import rampy as rp\n", "from sklearn.model_selection import train_test_split\n", "\n", "# The X axis\n", "x = np.arange(0, 1000, 1.0)\n", "\n", "# The perfect 5 signals\n", "spectra_1 = rp.gaussian(x, 10.0, 300., 25.) + rp.lorentzian(x, 15., 650., 50.)\n", "spectra_2 = rp.gaussian(x, 20.0, 350., 25.) + rp.gaussian(x, 25.0, 380., 20.) + rp.lorentzian(x, 15., 630., 50.)\n", "spectra_3 = rp.gaussian(x, 10.0, 500., 50.) + rp.lorentzian(x, 15.0, 520., 10.) + rp.gaussian(x, 25., 530., 3.)\n", "spectra_4 = rp.gaussian(x, 10.0, 100., 5.) + rp.lorentzian(x, 30.0, 110., 3.) + rp.gaussian(x, 5., 900., 10.)\n", "spectra_5 = rp.gaussian(x, 10.0, 600., 200.)\n", "\n", "# the number of observations of each signal\n", "number_of_spectra = 20\n", "\n", "# generating a dataset (will be shuffled later during the train-test split)\n", "dataset = np.hstack((np.ones((len(x),number_of_spectra))*spectra_1.reshape(-1,1),\n", " np.ones((len(x),number_of_spectra))*spectra_2.reshape(-1,1),\n", " np.ones((len(x),number_of_spectra))*spectra_3.reshape(-1,1),\n", " np.ones((len(x),number_of_spectra))*spectra_4.reshape(-1,1),\n", " np.ones((len(x),number_of_spectra))*spectra_5.reshape(-1,1)\n", " )).T\n", "\n", "# add noise\n", "noise_scale = 2.0\n", "dataset = dataset + np.random.normal(scale=noise_scale,size=(len(dataset),len(x)))\n", "\n", "# create numeric labels\n", "labels = np.vstack((np.tile(np.array([1]).reshape(-1,1),number_of_spectra),\n", " np.tile(np.array([2]).reshape(-1,1),number_of_spectra),\n", " np.tile(np.array([3]).reshape(-1,1),number_of_spectra),\n", " np.tile(np.array([4]).reshape(-1,1),number_of_spectra),\n", " np.tile(np.array([5]).reshape(-1,1),number_of_spectra),\n", " )).reshape(-1,1)\n", "\n", "print('Number of samples = {}'.format(dataset.shape[0]))\n", "print('Number of labels = {}'.format(labels.shape[0]))\n", "\n", "# Do figure\n", "plt.figure(figsize=(8,4))\n", "\n", "plt.subplot(1,2,1)\n", "plt.title('(a) The 5 \"perfect\" signals')\n", "plt.plot(x, spectra_1, label='signal 1')\n", "plt.plot(x, spectra_2, label='signal 2')\n", "plt.plot(x, spectra_3, label='signal 3')\n", "plt.plot(x, spectra_4, label='signal 4')\n", "plt.plot(x, spectra_5, label='signal 5')\n", "plt.xlabel('X axis')\n", "plt.ylabel('Ideal signals')\n", "plt.legend()\n", "\n", "plt.subplot(1,2,2)\n", "plt.title('(b) Noisy observations')\n", "plt.plot(x, dataset[0*number_of_spectra:1*number_of_spectra,:].T, color=\"C0\",alpha=0.1)\n", "plt.plot(x, dataset[1*number_of_spectra:2*number_of_spectra,:].T, color=\"C1\",alpha=0.1)\n", "plt.plot(x, dataset[2*number_of_spectra:3*number_of_spectra,:].T, color=\"C2\",alpha=0.1)\n", "plt.plot(x, dataset[3*number_of_spectra:4*number_of_spectra,:].T, color=\"C3\",alpha=0.1)\n", "plt.plot(x, dataset[4*number_of_spectra:5*number_of_spectra,:].T, color=\"C4\",alpha=0.1)\n", "\n", "plt.xlabel('X axis')\n", "plt.ylabel('Noisy observed signals (1000 times each)')\n", "\n", "plt.tight_layout()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Machine Learning example of treatment\n", "\n", "Below a quick example of how you will do a classification of the signals using scikit-learn functions." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "- Classifier:Nearest Neighbors is scoring 1.0.\n", "\n", "- Classifier:SVC is scoring 1.0.\n", "\n", "- Classifier:Gaussian Process is scoring 0.09090909090909091.\n", "\n", "- Classifier:Decision Tree is scoring 0.8787878787878788.\n", "\n", "- Classifier:Random Forest is scoring 1.0.\n", "\n", "- Classifier:Neural Net is scoring 1.0.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/charles/miniconda3/envs/gpvisc/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "- Classifier:AdaBoost is scoring 1.0.\n", "\n", "- Classifier:Naive Bayes is scoring 1.0.\n", "\n", "- Classifier:QDA is scoring 0.5757575757575758.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/charles/miniconda3/envs/gpvisc/lib/python3.11/site-packages/sklearn/discriminant_analysis.py:947: UserWarning: Variables are collinear\n", " warnings.warn(\"Variables are collinear\")\n" ] } ], "source": [ "from sklearn.neural_network import MLPClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.gaussian_process import GaussianProcessClassifier\n", "from sklearn.gaussian_process.kernels import RBF\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n", "\n", "X = dataset\n", "y = labels\n", "\n", "# shufling\n", "from sklearn.utils import shuffle\n", "X, y = shuffle(X, y, random_state=42)\n", "\n", "#\n", "# TRain/Test split\n", "#\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n", "\n", "#\n", "# Initiate classifiers\n", "#\n", "\n", "# The classifiers with default parameters\n", "classifiers = [\n", " KNeighborsClassifier(),\n", " SVC(),\n", " GaussianProcessClassifier(),\n", " DecisionTreeClassifier(),\n", " RandomForestClassifier(),\n", " MLPClassifier(),\n", " AdaBoostClassifier(),\n", " GaussianNB(), QuadraticDiscriminantAnalysis()]\n", "\n", "# Their names\n", "names = [\"Nearest Neighbors\", \"SVC\", \"Gaussian Process\",\n", " \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n", " \"Naive Bayes\", \"QDA\"]\n", "\n", "# iterate over classifiers\n", "for name, clf in zip(names, classifiers):\n", " clf.fit(X_train, y_train.ravel())\n", " score = clf.score(X_test, y_test.ravel())\n", " print('\\n- Classifier:'+name+' is scoring '+str(score)+'.')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## ML classification with rampy\n", "\n", "Now we use the rampy.mlclassificator to do the exact same thing as above, but in a more concise manner!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "- Classifier:Nearest Neighbors is scoring (test:) 1.0.\n", "\n", "- Classifier:SVC is scoring (test:) 1.0.\n", "\n", "- Classifier:Gaussian Process is scoring (test:) 0.09090909090909091.\n", "\n", "- Classifier:Decision Tree is scoring (test:) 0.8787878787878788.\n", "\n", "- Classifier:Random Forest is scoring (test:) 1.0.\n", "\n", "- Classifier:Neural Net is scoring (test:) 1.0.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/charles/miniconda3/envs/gpvisc/lib/python3.11/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "- Classifier:AdaBoost is scoring (test:) 0.9696969696969697.\n", "\n", "- Classifier:Naive Bayes is scoring (test:) 1.0.\n", "\n", "- Classifier:QDA is scoring (test:) 0.5757575757575758.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/charles/miniconda3/envs/gpvisc/lib/python3.11/site-packages/sklearn/discriminant_analysis.py:947: UserWarning: Variables are collinear\n", " warnings.warn(\"Variables are collinear\")\n" ] } ], "source": [ "# initiate model\n", "MLC = rp.mlclassificator(X_train, y_train, X_test = X_test, y_test=y_test, scaling=False)\n", "\n", "# iterate over classifiers\n", "for name in names:\n", " MLC.algorithm = name\n", " MLC.fit()\n", " score = MLC.model.score(MLC.X_test, MLC.y_test)\n", " print('\\n- Classifier:'+name+' is scoring (test:) '+str(score)+'.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We obtain the same results, great! Now using mlclassificator, you can do automatic train/test split and scaling. Let's see if this improves the results from the Decision Tree classifier:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "- Classifier SVC is scoring (test:) 1.0.\n" ] } ], "source": [ "# initiate model\n", "MLC = rp.mlclassificator(X, y, \n", " scaling=True, \n", " test_size=0.33, \n", " random_state=42, \n", " algorithm = 'SVC')\n", "\n", "MLC.fit()\n", "\n", "# if you ask for scaling, do not forget to pass the scaled data to the model when scoring!!\n", "score = MLC.model.score(MLC.X_test_sc, MLC.y_test)\n", "print('\\n- Classifier SVC is scoring (test:) '+str(score)+'.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also try to tune the hyperparameters of other algorithms, such as Decision Tree:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "- Classifier Decision Tree is scoring (test:) 0.8787878787878788.\n" ] } ], "source": [ "# we define the _params as we would do with the scikit-learn class:\n", "params = {'max_depth': 10}\n", "\n", "# initiate model\n", "MLC = rp.mlclassificator(X, y, \n", " scaling=True, \n", " test_size=0.33, \n", " random_state=42, \n", " algorithm = 'Decision Tree',\n", " params_ = params)\n", "MLC.fit()\n", "score = MLC.model.score(MLC.X_test_sc, MLC.y_test)\n", "print('\\n- Classifier Decision Tree is scoring (test:) '+str(score)+'.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also try to improve the Gaussian Process classifier using for instance the [Matern kernel](https://scikit-learn.org/stable/modules/gaussian_process.html#gp-kernels):" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "- Classifier GP is scoring (test:) 1.0.\n" ] } ], "source": [ "# we define the _params as we would do with the scikit-learn GP class:\n", "# we actually will try a linear kernel\n", "from sklearn.gaussian_process.kernels import Matern\n", "params = {'kernel': 1.0*Matern()}\n", "\n", "# initiate model\n", "MLC = rp.mlclassificator(X, y, \n", " scaling=True, \n", " test_size=0.33, \n", " random_state=42, \n", " algorithm = 'Gaussian Process',\n", " params_ = params)\n", "MLC.fit()\n", "score = MLC.model.score(MLC.X_test_sc, MLC.y_test)\n", "print('\\n- Classifier GP is scoring (test:) '+str(score)+'.')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "gpvisc", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 4 }