728 lines
250 KiB
Plaintext
728 lines
250 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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"source": [
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"<!-- dom:TITLE: Data Analysis and Machine Learning: Preprocessing and Dimensionality Reduction -->\n",
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"# Data Analysis and Machine Learning: Preprocessing and Dimensionality Reduction\n",
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"<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
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"<!-- Author: --> \n",
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"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
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"\n",
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"Date: **Oct 17, 2019**\n",
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"\n",
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"Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"## Reducing the number of degrees of freedom, overarching view\n",
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"\n",
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"Many Machine Learning problems involve thousands or even millions of features for each training\n",
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"instance. Not only does this make training extremely slow, it can also make it much harder to find a good\n",
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"solution, as we will see. This problem is often referred to as the curse of dimensionality.\n",
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"Fortunately, in real-world problems, it is often possible to reduce the number of features considerably,\n",
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"turning an intractable problem into a tractable one.\n",
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"\n",
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"Here we will discuss some of the most popular dimensionality\n",
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"reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE).\n",
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"Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data.\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"## Preprocessing our data\n",
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"\n",
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"Before we proceed however, we will discuss how to preprocess our\n",
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"data. Till now and in connection with our previous examples we have not met so many cases\n",
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"where we are too sensitive to the scaling of our data. Normally the\n",
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"data may need a rescaling and/or may be sensitive to extreme\n",
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"values. Scaling the data renders our inputs much more suitable for the\n",
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"algorithms we want to employ.\n",
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"\n",
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"**Scikit-Learn** has several functions which allow us to rescale the\n",
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"data, normally resulting in much better results in terms of various\n",
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"accuracy scores. The **StandardScaler** function in **Scikit-Learn**\n",
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"ensures that for each feature/predictor we study the mean value is\n",
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"zero and the variance is one (every column in the design/feature\n",
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"matrix). This scaling has the drawback that it does not ensure that\n",
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"we have a particular maximum or minimum in our data set. Another\n",
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"function included in **Scikit-Learn** is the **MinMaxScaler** which\n",
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"ensures that all features are exactly between $0$ and $1$. The\n",
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"\n",
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"## More preprocessing\n",
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"\n",
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"\n",
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"The **Normalizer** scales each data\n",
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"point such that the feature vector has a euclidean length of one. In other words, it\n",
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"projects a data point on the circle (or sphere in the case of higher dimensions) with a\n",
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"radius of 1. This means every data point is scaled by a different number (by the\n",
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"inverse of it’s length).\n",
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"This normalization is often used when only the direction (or angle) of the data matters,\n",
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"not the length of the feature vector.\n",
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"\n",
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"The **RobustScaler** works similarly to the StandardScaler in that it\n",
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"ensures statistical properties for each feature that guarantee that\n",
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"they are on the same scale. However, the RobustScaler uses the median\n",
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"and quartiles, instead of mean and variance. This makes the\n",
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"RobustScaler ignore data points that are very different from the rest\n",
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"(like measurement errors). These odd data points are also called\n",
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"outliers, and might often lead to trouble for other scaling\n",
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"techniques.\n",
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"\n",
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"\n",
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"\n",
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"## Simple preprocessing examples, Franke function and regression"
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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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"metadata": {},
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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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"MSE before scaling: 0.01\n",
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"R2 score before scaling 0.93\n",
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"Feature min values before scaling:\n",
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" [1.00000000e+00 1.56868531e-04 4.25016115e-04 2.46077359e-08\n",
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" 6.66716535e-08 1.80638698e-07 3.86017938e-12 1.04586843e-11\n",
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" 2.83365271e-11 7.67743576e-11 6.05540668e-16 1.64063844e-15\n",
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" 4.44510938e-15 1.20434807e-14 3.26303392e-14 9.49902749e-20\n",
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" 2.57364542e-19 6.97297777e-19 1.88924312e-18 5.11867337e-18\n",
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" 1.38684200e-17]\n",
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"Feature max values before scaling:\n",
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" [1. 0.99980229 0.9996158 0.99960462 0.99941816 0.99923174\n",
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" 0.99940699 0.99922057 0.99903418 0.99884783 0.99920939 0.99902301\n",
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" 0.99883666 0.99865035 0.99846407 0.99901184 0.99882549 0.99863918\n",
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" 0.99845291 0.99826666 0.99808046]\n",
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"Feature min values after scaling:\n",
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" [ 0. -1.78024859 -1.70920112 -1.1515339 -1.1300959 -1.11033121\n",
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" -0.90340545 -0.8959348 -0.88900131 -0.88255214 -0.76338933 -0.76113708\n",
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" -0.75906691 -0.7571601 -0.75539963 -0.67113296 -0.67101229 -0.6709573\n",
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" -0.67096075 -0.67101596 -0.67111677]\n",
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"Feature max values after scaling:\n",
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" [0. 1.74136774 1.71581105 2.27322963 2.24138196 2.20981521\n",
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" 2.70775213 2.67747156 2.64735409 2.61738276 3.08600037 3.05801994\n",
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" 3.03002682 3.00201047 2.97396141 3.42593492 3.39990873 3.37378657\n",
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" 3.34756305 3.32123338 3.29479332]\n",
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"MSE after scaling: 0.00\n",
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"R2 score for scaled data: 0.97\n"
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]
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}
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],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"# Common imports\n",
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import sklearn.linear_model as skl\n",
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"from sklearn.metrics import mean_squared_error\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import MinMaxScaler, StandardScaler, Normalizer\n",
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"from sklearn.svm import SVR\n",
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"\n",
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"# Where to save the figures and data files\n",
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"PROJECT_ROOT_DIR = \"Results\"\n",
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"FIGURE_ID = \"Results/FigureFiles\"\n",
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"DATA_ID = \"DataFiles/\"\n",
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"\n",
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"if not os.path.exists(PROJECT_ROOT_DIR):\n",
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" os.mkdir(PROJECT_ROOT_DIR)\n",
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"\n",
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"if not os.path.exists(FIGURE_ID):\n",
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" os.makedirs(FIGURE_ID)\n",
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"\n",
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"if not os.path.exists(DATA_ID):\n",
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" os.makedirs(DATA_ID)\n",
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"\n",
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"def image_path(fig_id):\n",
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" return os.path.join(FIGURE_ID, fig_id)\n",
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"\n",
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"def data_path(dat_id):\n",
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" return os.path.join(DATA_ID, dat_id)\n",
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"\n",
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"def save_fig(fig_id):\n",
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" plt.savefig(image_path(fig_id) + \".png\", format='png')\n",
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"\n",
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"\n",
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"def FrankeFunction(x,y):\n",
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"\tterm1 = 0.75*np.exp(-(0.25*(9*x-2)**2) - 0.25*((9*y-2)**2))\n",
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"\tterm2 = 0.75*np.exp(-((9*x+1)**2)/49.0 - 0.1*(9*y+1))\n",
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"\tterm3 = 0.5*np.exp(-(9*x-7)**2/4.0 - 0.25*((9*y-3)**2))\n",
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"\tterm4 = -0.2*np.exp(-(9*x-4)**2 - (9*y-7)**2)\n",
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"\treturn term1 + term2 + term3 + term4\n",
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"\n",
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"\n",
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"def create_X(x, y, n ):\n",
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"\tif len(x.shape) > 1:\n",
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"\t\tx = np.ravel(x)\n",
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"\t\ty = np.ravel(y)\n",
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"\n",
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"\tN = len(x)\n",
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"\tl = int((n+1)*(n+2)/2)\t\t# Number of elements in beta\n",
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"\tX = np.ones((N,l))\n",
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"\n",
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"\tfor i in range(1,n+1):\n",
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"\t\tq = int((i)*(i+1)/2)\n",
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"\t\tfor k in range(i+1):\n",
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"\t\t\tX[:,q+k] = (x**(i-k))*(y**k)\n",
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"\n",
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"\treturn X\n",
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"\n",
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"\n",
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"# Making meshgrid of datapoints and compute Franke's function\n",
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"n = 5\n",
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"N = 1000\n",
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"x = np.sort(np.random.uniform(0, 1, N))\n",
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"y = np.sort(np.random.uniform(0, 1, N))\n",
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"z = FrankeFunction(x, y)\n",
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"X = create_X(x, y, n=n) \n",
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"# split in training and test data\n",
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"X_train, X_test, y_train, y_test = train_test_split(X,z,test_size=0.2)\n",
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"\n",
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"\n",
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"svm = SVR(gamma='auto',C=10.0)\n",
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"svm.fit(X_train, y_train)\n",
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"\n",
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"# The mean squared error and R2 score\n",
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"print(\"MSE before scaling: {:.2f}\".format(mean_squared_error(svm.predict(X_test), y_test)))\n",
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"print(\"R2 score before scaling {:.2f}\".format(svm.score(X_test,y_test)))\n",
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"\n",
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"scaler = StandardScaler()\n",
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"scaler.fit(X_train)\n",
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"X_train_scaled = scaler.transform(X_train)\n",
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"X_test_scaled = scaler.transform(X_test)\n",
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"\n",
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"print(\"Feature min values before scaling:\\n {}\".format(X_train.min(axis=0)))\n",
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"print(\"Feature max values before scaling:\\n {}\".format(X_train.max(axis=0)))\n",
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"\n",
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"print(\"Feature min values after scaling:\\n {}\".format(X_train_scaled.min(axis=0)))\n",
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"print(\"Feature max values after scaling:\\n {}\".format(X_train_scaled.max(axis=0)))\n",
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"\n",
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"svm = SVR(gamma='auto',C=10.0)\n",
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"svm.fit(X_train_scaled, y_train)\n",
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"\n",
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"print(\"MSE after scaling: {:.2f}\".format(mean_squared_error(svm.predict(X_test_scaled), y_test)))\n",
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"print(\"R2 score for scaled data: {:.2f}\".format(svm.score(X_test_scaled,y_test)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Simple preprocessing examples, breast cancer data and classification, Support Vector Machines\n",
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"\n",
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"We show here how we can use a simple regression case on the breast\n",
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"cancer data using support vector machines (SVM) as algorithm for\n",
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"classification."
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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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"metadata": {},
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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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"(426, 30)\n",
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"(143, 30)\n",
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"Test set accuracy: 0.63\n",
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"Feature min values before scaling:\n",
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" [6.981e+00 9.710e+00 4.379e+01 1.435e+02 5.263e-02 1.938e-02 0.000e+00\n",
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" 0.000e+00 1.060e-01 4.996e-02 1.115e-01 3.628e-01 7.570e-01 7.228e+00\n",
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" 1.713e-03 2.252e-03 0.000e+00 0.000e+00 7.882e-03 8.948e-04 7.930e+00\n",
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" 1.202e+01 5.041e+01 1.852e+02 7.117e-02 2.729e-02 0.000e+00 0.000e+00\n",
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" 1.565e-01 5.504e-02]\n",
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"Feature max values before scaling:\n",
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" [2.811e+01 3.381e+01 1.885e+02 2.501e+03 1.447e-01 3.114e-01 4.268e-01\n",
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" 2.012e-01 3.040e-01 9.744e-02 2.873e+00 4.885e+00 2.198e+01 5.422e+02\n",
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" 2.333e-02 1.064e-01 3.960e-01 5.279e-02 6.146e-02 2.984e-02 3.604e+01\n",
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" 4.954e+01 2.512e+02 4.254e+03 2.226e-01 1.058e+00 1.252e+00 2.903e-01\n",
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" 6.638e-01 2.075e-01]\n",
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"Feature min values before scaling:\n",
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" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
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" 0. 0. 0. 0. 0. 0.]\n",
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"Feature max values before scaling:\n",
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" [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
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" 1. 1. 1. 1. 1. 1.]\n",
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"Test set accuracy scaled data with Min-Max scaling: 0.97\n",
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"Test set accuracy scaled data with Standar Scaler: 0.96\n"
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]
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},
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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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"/usr/local/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
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" \"avoid this warning.\", FutureWarning)\n",
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"/usr/local/lib/python3.7/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
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" \"avoid this warning.\", FutureWarning)\n"
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]
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split \n",
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"from sklearn.datasets import load_breast_cancer\n",
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"from sklearn.svm import SVC\n",
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"cancer = load_breast_cancer()\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
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"print(X_train.shape)\n",
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"print(X_test.shape)\n",
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"\n",
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"svm = SVC(C=100)\n",
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"svm.fit(X_train, y_train)\n",
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"print(\"Test set accuracy: {:.2f}\".format(svm.score(X_test,y_test)))\n",
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"\n",
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"from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
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"scaler = MinMaxScaler()\n",
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"scaler.fit(X_train)\n",
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"X_train_scaled = scaler.transform(X_train)\n",
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"X_test_scaled = scaler.transform(X_test)\n",
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"\n",
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"print(\"Feature min values before scaling:\\n {}\".format(X_train.min(axis=0)))\n",
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"print(\"Feature max values before scaling:\\n {}\".format(X_train.max(axis=0)))\n",
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"\n",
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"print(\"Feature min values before scaling:\\n {}\".format(X_train_scaled.min(axis=0)))\n",
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"print(\"Feature max values before scaling:\\n {}\".format(X_train_scaled.max(axis=0)))\n",
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"\n",
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"\n",
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"svm.fit(X_train_scaled, y_train)\n",
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"print(\"Test set accuracy scaled data with Min-Max scaling: {:.2f}\".format(svm.score(X_test_scaled,y_test)))\n",
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"\n",
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"scaler = StandardScaler()\n",
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"scaler.fit(X_train)\n",
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"X_train_scaled = scaler.transform(X_train)\n",
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"X_test_scaled = scaler.transform(X_test)\n",
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"\n",
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"svm.fit(X_train_scaled, y_train)\n",
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"print(\"Test set accuracy scaled data with Standar Scaler: {:.2f}\".format(svm.score(X_test_scaled,y_test)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## More on Cancer Data, now with Logistic Regression"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split \n",
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"from sklearn.datasets import load_breast_cancer\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"cancer = load_breast_cancer()\n",
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"\n",
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"# Set up training data\n",
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"X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
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"logreg = LogisticRegression()\n",
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"logreg.fit(X_train, y_train)\n",
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"print(\"Test set accuracy: {:.2f}\".format(logreg.score(X_test,y_test)))\n",
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"\n",
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"# Scale data\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"scaler = StandardScaler()\n",
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"scaler.fit(X_train)\n",
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"X_train_scaled = scaler.transform(X_train)\n",
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"X_test_scaled = scaler.transform(X_test)\n",
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"logreg.fit(X_train_scaled, y_train)\n",
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"print(\"Test set accuracy scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Why should we think of reducing the dimensionality\n",
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"\n",
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"In addition to the plot of the features, we study now also the covariance (or rather the correlation matrix).\n",
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"We use also **Pandas** to compute the correlation matrix."
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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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"metadata": {},
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"outputs": [
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{
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"data": {
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\n",
|
||
"text/plain": [
|
||
"<Figure size 720x1440 with 30 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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\n",
|
||
"text/plain": [
|
||
"<Figure size 432x288 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[ 1.33026664e+01 5.69238112e+00 2.83259341e+00 1.99126621e+00\n",
|
||
" 1.69095941e+00 1.19614372e+00 7.13037918e-01 5.77804918e-01\n",
|
||
" 5.14549644e-01 4.30579019e-01 3.58977964e-01 -1.91269450e-01\n",
|
||
" 2.86195387e-01 -1.59310961e-01 -1.38483045e-01 2.44786221e-01\n",
|
||
" -8.79318674e-02 1.91811329e-01 1.70896266e-01 -7.17683028e-02\n",
|
||
" 1.39860289e-01 1.18721828e-01 -4.83194721e-02 8.92983892e-02\n",
|
||
" 6.81402316e-02 6.01968016e-02 3.31819744e-02 1.56432255e-02\n",
|
||
" -1.61028757e-02 -6.50565973e-03]\n",
|
||
"(426, 30)\n",
|
||
"(143, 30)\n",
|
||
"Test set accuracy from Logistic Regression: 0.96\n",
|
||
"Test set accuracy scaled data: 0.96\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
|
||
" FutureWarning)\n",
|
||
"/usr/local/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
|
||
" FutureWarning)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"from sklearn.model_selection import train_test_split \n",
|
||
"from sklearn.datasets import load_breast_cancer\n",
|
||
"from sklearn.linear_model import LogisticRegression\n",
|
||
"cancer = load_breast_cancer()\n",
|
||
"import pandas as pd\n",
|
||
"# Making a data frame\n",
|
||
"cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(15,2,figsize=(10,20))\n",
|
||
"malignant = cancer.data[cancer.target == 0]\n",
|
||
"benign = cancer.data[cancer.target == 1]\n",
|
||
"ax = axes.ravel()\n",
|
||
"\n",
|
||
"for i in range(30):\n",
|
||
" _, bins = np.histogram(cancer.data[:,i], bins =50)\n",
|
||
" ax[i].hist(malignant[:,i], bins = bins, alpha = 0.5)\n",
|
||
" ax[i].hist(benign[:,i], bins = bins, alpha = 0.5)\n",
|
||
" ax[i].set_title(cancer.feature_names[i])\n",
|
||
" ax[i].set_yticks(())\n",
|
||
"ax[0].set_xlabel(\"Feature magnitude\")\n",
|
||
"ax[0].set_ylabel(\"Frequency\")\n",
|
||
"ax[0].legend([\"Malignant\", \"Benign\"], loc =\"best\")\n",
|
||
"fig.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"import seaborn as sns\n",
|
||
"correlation_matrix = cancerpd.corr().round(1)\n",
|
||
"# use the heatmap function from seaborn to plot the correlation matrix\n",
|
||
"# annot = True to print the values inside the square\n",
|
||
"sns.heatmap(data=correlation_matrix, annot=True)\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"#print eigvalues of correlation matrix\n",
|
||
"EigValues, EigVectors = np.linalg.eig(correlation_matrix)\n",
|
||
"print(EigValues)\n",
|
||
"\n",
|
||
"#split into train and test and then scale thereafter\n",
|
||
"X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
|
||
"print(X_train.shape)\n",
|
||
"print(X_test.shape)\n",
|
||
"\n",
|
||
"logreg = LogisticRegression()\n",
|
||
"logreg.fit(X_train, y_train)\n",
|
||
"print(\"Test set accuracy from Logistic Regression: {:.2f}\".format(logreg.score(X_test,y_test)))\n",
|
||
"\n",
|
||
"from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
|
||
"scaler = StandardScaler()\n",
|
||
"scaler.fit(X_train)\n",
|
||
"X_train_scaled = scaler.transform(X_train)\n",
|
||
"X_test_scaled = scaler.transform(X_test)\n",
|
||
"\n",
|
||
"logreg.fit(X_train_scaled, y_train)\n",
|
||
"print(\"Test set accuracy scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Getting started with PCA\n",
|
||
"\n",
|
||
"This material is being finalized, not yet ready."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Now add PCA\n",
|
||
"from sklearn.decomposition import PCA\n",
|
||
"pca = PCA(n_components = 2)\n",
|
||
"pca.fit(X_train_scaled)\n",
|
||
"\n",
|
||
"X_pca = pca.transform(X_train_scaled)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Principal Component Analysis\n",
|
||
"Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm.\n",
|
||
"First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it.\n",
|
||
"\n",
|
||
"The following Python code uses NumPy’s **svd()** function to obtain all the principal components of the\n",
|
||
"training set, then extracts the first two principal components"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"X_centered = X - X.mean(axis=0)\n",
|
||
"U, s, V = np.linalg.svd(X_centered)\n",
|
||
"c1 = V.T[:, 0]\n",
|
||
"c2 = V.T[:, 1]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering\n",
|
||
"the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t\n",
|
||
"forget to center the data first.\n",
|
||
"\n",
|
||
"Once you have identified all the principal components, you can reduce the dimensionality of the dataset\n",
|
||
"down to $d$ dimensions by projecting it onto the hyperplane defined by the first $d$ principal components.\n",
|
||
"Selecting this hyperplane ensures that the projection will preserve as much variance as possible."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"W2 = V.T[:, :2]\n",
|
||
"X2D = X_centered.dot(W2)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<!-- !split -->\n",
|
||
"## PCA and scikit-learn\n",
|
||
"\n",
|
||
"Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The\n",
|
||
"following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note\n",
|
||
"that it automatically takes care of centering the data):"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.decomposition import PCA\n",
|
||
"pca = PCA(n_components = 2)\n",
|
||
"X2D = pca.fit_transform(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"After fitting the PCA transformer to the dataset, you can access the principal components using the\n",
|
||
"components variable (note that it contains the PCs as horizontal vectors, so, for example, the first\n",
|
||
"principal component is equal to"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"pca.components_.T[:, 0])."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Another very useful piece of information is the explained variance ratio of each principal component,\n",
|
||
"available via the $explained\\_variance\\_ratio$ variable. It indicates the proportion of the dataset’s\n",
|
||
"variance that lies along the axis of each principal component. \n",
|
||
"More material to come here.\n",
|
||
"\n",
|
||
"## More on the PCA\n",
|
||
"Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to\n",
|
||
"choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%).\n",
|
||
"Unless, of course, you are reducing dimensionality for data visualization — in that case you will\n",
|
||
"generally want to reduce the dimensionality down to 2 or 3.\n",
|
||
"The following code computes PCA without reducing dimensionality, then computes the minimum number\n",
|
||
"of dimensions required to preserve 95% of the training set’s variance:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"pca = PCA()\n",
|
||
"pca.fit(X)\n",
|
||
"cumsum = np.cumsum(pca.explained_variance_ratio_)\n",
|
||
"d = np.argmax(cumsum >= 0.95) + 1"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"You could then set $n\\_components=d$ and run PCA again. However, there is a much better option: instead\n",
|
||
"of specifying the number of principal components you want to preserve, you can set $n\\_components$ to be\n",
|
||
"a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"pca = PCA(n_components=0.95)\n",
|
||
"X_reduced = pca.fit_transform(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Incremental PCA\n",
|
||
"One problem with the preceding implementation of PCA is that it requires the whole training set to fit in\n",
|
||
"memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have\n",
|
||
"been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch\n",
|
||
"at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new\n",
|
||
"instances arrive).\n",
|
||
"\n",
|
||
"## Randomized PCA\n",
|
||
"\n",
|
||
"Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic\n",
|
||
"algorithm that quickly finds an approximation of the first d principal components. Its computational\n",
|
||
"complexity is $O(m \\times d^2)+O(d^3)$, instead of $O(m \\times n^2) + O(n^3)$, so it is dramatically faster than the\n",
|
||
"previous algorithms when $d$ is much smaller than $n$.\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"## Kernel PCA\n",
|
||
"\n",
|
||
"The kernel trick is a mathematical technique that implicitly maps instances into a\n",
|
||
"very high-dimensional space (called the feature space), enabling nonlinear classification and regression\n",
|
||
"with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature\n",
|
||
"space corresponds to a complex nonlinear decision boundary in the original space.\n",
|
||
"It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear\n",
|
||
"projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at\n",
|
||
"preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a\n",
|
||
"twisted manifold.\n",
|
||
"For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"from sklearn.decomposition import KernelPCA\n",
|
||
"rbf_pca = KernelPCA(n_components = 2, kernel=\"rbf\", gamma=0.04)\n",
|
||
"X_reduced = rbf_pca.fit_transform(X)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## LLE\n",
|
||
"\n",
|
||
"Locally Linear Embedding (LLE) is another very powerful nonlinear dimensionality reduction\n",
|
||
"(NLDR) technique. It is a Manifold Learning technique that does not rely on projections like the previous\n",
|
||
"algorithms. In a nutshell, LLE works by first measuring how each training instance linearly relates to its\n",
|
||
"closest neighbors (c.n.), and then looking for a low-dimensional representation of the training set where\n",
|
||
"these local relationships are best preserved (more details shortly). \n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"## Other techniques\n",
|
||
"\n",
|
||
"\n",
|
||
"There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn.\n",
|
||
"\n",
|
||
"Here are some of the most popular:\n",
|
||
"* **Multidimensional Scaling (MDS)** reduces dimensionality while trying to preserve the distances between the instances.\n",
|
||
"\n",
|
||
"* **Isomap** creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances.\n",
|
||
"\n",
|
||
"* **t-Distributed Stochastic Neighbor Embedding** (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D).\n",
|
||
"\n",
|
||
"* Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"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": "3.7.4"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|