{ "metadata": { "name": "", "signature": "sha256:7d446e8423566eba2eaf9bf3458949c1ad15be846a08309eeeedb87e0b22fed4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solutions: Model Validation Breakout" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This contains some possible solutions to the [Model Validation Breakout](04.4-Validation-Breakout.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminaries\n", "\n", "Again, we'll start with some boilerplate imports and setup" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import stats\n", "\n", "# use seaborn plotting defaults\n", "# If this causes an error, you can comment it out.\n", "import seaborn as sns\n", "sns.set()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RR Lyrae\n", "\n", "Just as we did in the previous breakout, we'll take a look at the RR Lyrae data that we'll be classifying:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from astroML.datasets import fetch_rrlyrae_combined\n", "from sklearn.cross_validation import train_test_split\n", "\n", "X, y = fetch_rrlyrae_combined()\n", "\n", "N_plot = 5000\n", "plt.scatter(X[-N_plot:, 0], X[-N_plot:, 1], c=y[-N_plot:],\n", " edgecolors='none', cmap='RdBu')\n", "plt.xlabel('u-g color')\n", "plt.ylabel('g-r color')\n", "plt.xlim(0.7, 1.4)\n", "plt.ylim(-0.2, 0.4);" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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3H3Fo9mKyMzJo9MEQg/dMUSnTtD4P/3oRz1CmSd1XGu/wnEX6qnrJUTE4l/XJ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\nfOphfVC83Bw0ZpqONhZYWWirdWXIMzWsSAFSdZRsvStuTrZv3G8XSyS4V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5eTIc/CyECf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\n8kXJJY/eCSG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dNdD/47cpuXz+bsDHQCelVDv0FUmfsHyE5pNL3x2AT4HOQGtgjKZpZS0f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\n", "text": [ "" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to fit an **SVM** classifier to this data, but adjust the SVM parameters to find the optimal model.\n", "The Support Vector Classifier, ``SVC``, has several hyperparameters which affect the final fit:\n", "\n", "- ``kernel``: can be ``'rbf'`` (radial basis function) or ``'linear'``, among others. This controls whether a linear or kernel fit is used\n", "- ``C``: the SVC penalty parameter\n", "- ``gamma``: the kernel coefficient for ``rbf``\n", "\n", "You can see more using IPython's help feature:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.svm import SVC\n", "SVC?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Classification breakout questions\n", "\n", "1. Using ``sklearn.cross_validation.cross_val_score``, explore various values for these parameters. Recall the discussion from the [previous breakout](03.2-Machine-Learning-Breakout.ipynb). What is the best **completeness** you can obtain? What is the best **precision**? \n", "\n", "2. Use the concept of **validation curves** and **learning curves** to determine how this could be improved. Would you expect more training samples to help? More features for the current samples? A more complicated model?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import cross_val_score\n", "cross_val_score?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start by working with just a subset of the data, because these things can be pretty slow:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split\n", "Xsubset, _, ysubset, _ = train_test_split(X, y, train_size=0.1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "Xsubset.shape, ysubset.shape" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "((9314, 4), (9314,))" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can get a cross-validation score for a model as follows, using the f1-score (remember that the f1-score combines the notions of precision and recall)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "model = SVC(kernel='linear', class_weight='auto')\n", "cross_val_score(model, Xsubset, ysubset, scoring='f1')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "array([ 0.17610063, 0.24242424, 0.21582734])" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "But what we really want is the trend of the f1-score with different values of the inputs:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "C_values = 10 ** np.linspace(-2, 8, 10)\n", "f1_scores = []\n", "\n", "for C in C_values:\n", " model = SVC(kernel='linear', class_weight='auto', C=C)\n", " cv = cross_val_score(model, Xsubset, ysubset, scoring='f1')\n", " f1_scores.append(cv.mean())" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.semilogx(C_values, f1_scores, '-k')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "[]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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8l9lreUGZo8FreaEVM+uyniIiIh7iqg/xRUREpHkqbhEREQ9RcYuI\niHiIiltERMRDVNwiIiIeouIWERHxEBW3iIiIh6i4RUREPETFLSIi4iH/Hz/VsJewxX7yAAAAAElF\nTkSuQmCC\n", "text": [ "" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that we can get an F1 score of arount 0.2 depending on what our $C$ value is.\n", "\n", "This is how cross-validation usually works: as a function of hyperparameters, compute the best-fit model and see how well your model does." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Photometric Redshifts\n", "\n", "We'll now do a similar validation exercise using the photometric redshift problem on SDSS dr7 quasars using ``sklearn.ensemble.RandomForestRegressor``. The parameters you should explore are\n", "``n_estimators``, ``criterion``, and ``max_depth``. You can read more about these with IPython's help functionality:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import RandomForestRegressor\n", "RandomForestRegressor?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's the code again to download the data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from astroML.datasets import fetch_sdss_specgals\n", "\n", "data = fetch_sdss_specgals()\n", "\n", "# put magnitudes in a matrix\n", "feature_names = ['modelMag_%s' % f for f in 'ugriz']\n", "X = np.vstack([data[f] for f in feature_names]).T\n", "y = data['z']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# Plot some magnitudes for the first two thousand points\n", "i, j = 0, 1\n", "N = 2000\n", "plt.scatter(X[:N, i], X[:N, j], c=y[:N],\n", " edgecolor='none', cmap='cubehelix')\n", "plt.xlabel(feature_names[i])\n", "plt.ylabel(feature_names[j])\n", "plt.colorbar(label='redshift');" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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gdmA4QS3uaOBfxpjxO5q+7ZGZtYQQjB40guM+8jFqKoJSouM4uAXdQVApcEo8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"text": [ "" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Photoz Breakout Questions\n", "\n", "Think about what you know about the random forest.\n", "\n", "1. Think about how over-fitting and under-fitting might affect the results: how do you expect ``n_estimators`` and ``max_depth`` to affect these? Can you make some learning curve plots which confirm this expectation?\n", "\n", "2. What is the best mean squared error you can find for this data? (use ``sklearn.metrics.mean_squared_error``)\n", "\n", "3. Often for photometric redshifts, one is not concerned with mean squared error, but with minimizing **catastrophic outliers**: that is, points for which the redshift is off by (say) 0.5 or more. Can you find a combination of model parameters which leads to the lowest catastrophic outlier rate? (Note that you can provide a ``scoring`` function to ``sklearn.cross_validation.cross_val_score``.\n", "\n", "4. Create some learning curves for this data. If you wanted to improve random forest photometric redshift results, would it be more fruitful to:\n", " A. Gather more training samples (i.e. more galaxies with spectroscopic redshifts)\n", " B. Gather more features (i.e. more photometric observations for each existing sample)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Over-fitting vs. Under-fitting\n", "\n", "The problem with random forests is that as ``max_depth`` increases, our tendency to over-fit the data also increases. Adding more randomized trees tends to correct for this over-fitting." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Best mean-squared error\n", "\n", "We can use cross-validation just like above to answer this." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Custom scorer\n", "\n", "We can see from the documentation of ``cross_val_score`` that we can give it a custom scoring function; we simply need to define what we want to be the score here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Learning Curves\n", "\n", "This takes a bit more work, because the learning curves are not yet built-in to the scikit-learn utilities." ] } ], "metadata": {} } ] }