{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Feat's archive\n", "\n", "Feat optimizes a population of models. \n", "At the end of the run, it can be useful to explore this population to find a trade-off between objectives, \n", "such as performance and complexity. \n", "\n", "In this example, we apply Feat to a regression problem and visualize the archive of representations. \n", "\n", "Note: this code uses the Penn ML Benchmark Suite (https://github.com/EpistasisLab/penn-ml-benchmarks/) to fetch data. You can install it using `pip install pmlb`.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we import the data and create a train-test split." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pmlb import fetch_data\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import mean_squared_error as mse\n", "import numpy as np\n", "# fix the random state\n", "random_state=42\n", "dataset='690_visualizing_galaxy'\n", "X, y = fetch_data(dataset,return_X_y=True)\n", "X_t,X_v, y_t, y_v = train_test_split(X,y,train_size=0.75,test_size=0.25,random_state=random_state)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we set up a Feat instance and train the model, storing the final archive." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "FEAT version: 0.5.2.post75\n" ] }, { "data": { "text/html": [ "
FeatRegressor(backprop=False, batch_size=0, classification=False,\n",
       "              corr_delete_mutate=False, cross_rate=0.5, erc=False, fb=0.5,\n",
       "              feature_names='',\n",
       "              functions=['+', '-', '*', '/', '^2', '^3', 'sqrt', 'sin', 'cos',\n",
       "                         'exp', 'log', '^', 'logit', 'tanh', 'gauss', 'relu',\n",
       "                         'split', 'split_c', 'b2f', 'c2f', 'and', 'or', 'not',\n",
       "                         'xor', '=', '<', '<=', '>', '>=', 'if', ...],\n",
       "              gens=100, hillclimb=True, iters=10, logfile='', lr=0.1,\n",
       "              max_depth=2, max_dim=5, max_stall=0, max_time=60,\n",
       "              ml='LinearRidgeRegression', n_jobs=4, normalize=True,\n",
       "              objectives=['fitness', 'complexity'], otype='a', pop_size=100,\n",
       "              protected_groups='', random_state=42, residual_xo=False,\n",
       "              root_xo_rate=0.5, save_pop=0, scorer='', ...)
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" ], "text/plain": [ "FeatRegressor(backprop=False, batch_size=0, classification=False,\n", " corr_delete_mutate=False, cross_rate=0.5, erc=False, fb=0.5,\n", " feature_names='',\n", " functions=['+', '-', '*', '/', '^2', '^3', 'sqrt', 'sin', 'cos',\n", " 'exp', 'log', '^', 'logit', 'tanh', 'gauss', 'relu',\n", " 'split', 'split_c', 'b2f', 'c2f', 'and', 'or', 'not',\n", " 'xor', '=', '<', '<=', '>', '>=', 'if', ...],\n", " gens=100, hillclimb=True, iters=10, logfile='', lr=0.1,\n", " max_depth=2, max_dim=5, max_stall=0, max_time=60,\n", " ml='LinearRidgeRegression', n_jobs=4, normalize=True,\n", " objectives=['fitness', 'complexity'], otype='a', pop_size=100,\n", " protected_groups='', random_state=42, residual_xo=False,\n", " root_xo_rate=0.5, save_pop=0, scorer='', ...)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from feat import FeatRegressor\n", "\n", "\n", "est = FeatRegressor(\n", " pop_size=100, # population size\n", " gens=100, # maximum generations \n", " max_time=60, # max time in seconds \n", " max_depth=2, # constrain features depth \n", " max_dim=5, # constrain representation dimensionality \n", " random_state=random_state, \n", " hillclimb=True, # use stochastic hillclimbing to optimize weights \n", " iters=10, # iterations of hillclimbing\n", " n_jobs=4, # restricts to single thread \n", ") \n", "\n", "\n", "print('FEAT version:', est.__version__)\n", "est" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
FeatRegressor(backprop=False, batch_size=0, classification=False,\n",
       "              corr_delete_mutate=False, cross_rate=0.5, erc=False, fb=0.5,\n",
       "              feature_names='',\n",
       "              functions=['+', '-', '*', '/', '^2', '^3', 'sqrt', 'sin', 'cos',\n",
       "                         'exp', 'log', '^', 'logit', 'tanh', 'gauss', 'relu',\n",
       "                         'split', 'split_c', 'b2f', 'c2f', 'and', 'or', 'not',\n",
       "                         'xor', '=', '<', '<=', '>', '>=', 'if', ...],\n",
       "              gens=100, hillclimb=True, iters=10, logfile='', lr=0.1,\n",
       "              max_depth=2, max_dim=5, max_stall=0, max_time=60,\n",
       "              ml='LinearRidgeRegression', n_jobs=4, normalize=True,\n",
       "              objectives=['fitness', 'complexity'], otype='a', pop_size=100,\n",
       "              protected_groups='', random_state=42, residual_xo=False,\n",
       "              root_xo_rate=0.5, save_pop=0, scorer='', ...)
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" ], "text/plain": [ "FeatRegressor(backprop=False, batch_size=0, classification=False,\n", " corr_delete_mutate=False, cross_rate=0.5, erc=False, fb=0.5,\n", " feature_names='',\n", " functions=['+', '-', '*', '/', '^2', '^3', 'sqrt', 'sin', 'cos',\n", " 'exp', 'log', '^', 'logit', 'tanh', 'gauss', 'relu',\n", " 'split', 'split_c', 'b2f', 'c2f', 'and', 'or', 'not',\n", " 'xor', '=', '<', '<=', '>', '>=', 'if', ...],\n", " gens=100, hillclimb=True, iters=10, logfile='', lr=0.1,\n", " max_depth=2, max_dim=5, max_stall=0, max_time=60,\n", " ml='LinearRidgeRegression', n_jobs=4, normalize=True,\n", " objectives=['fitness', 'complexity'], otype='a', pop_size=100,\n", " protected_groups='', random_state=42, residual_xo=False,\n", " root_xo_rate=0.5, save_pop=0, scorer='', ...)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# train the model\n", "est.fit(X_t,y_t)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "complexity fitness validation fitness eqn\n", "1 1834.9365234375 1672.86279296875 [x_1]\n", "2 978.2460327148438 810.285400390625 [x_1][x_3]\n", "3 948.1231689453125 814.2865600585938 [x_3][x_0][x_1]\n", "4 915.998291015625 787.1356811523438 [x_3][x_2][x_0][x_1]\n", "6 915.9979858398438 787.1354370117188 [x_1][(0.5418*x_1-0.5815*x_0)][x_2][x_3]\n", "7 542.2037963867188 533.512939453125 [x_3][tanh(0.8951*x_1)]\n", "8 465.80938720703125 503.4342041015625 [x_1][tanh(0.8951*x_1)][x_3]\n", "9 428.57049560546875 431.0047912597656 [x_2][tanh(0.8951*x_1)][x_1][x_3]\n", "10 394.3541564941406 405.9887390136719 [x_3][tanh(0.8951*x_1)][x_2][x_0][x_1]\n", "12 394.3539733886719 405.9885559082031 [x_2][tanh(0.8951*x_1)][x_3][(0.5418*x_1-0.5815*x_0)][x_0]\n", "13 380.3902893066406 468.53033447265625 [x_2][tanh(0.8951*x_1)][relu(0.7307*x_3)][x_1][x_0]\n", "14 379.486328125 368.8024597167969 [x_1][tanh(0.8951*x_1)][x_2][sin(0.9585*x_3)]\n", "15 354.5791320800781 383.6985168457031 [x_3][tanh(0.8951*x_1)][x_2][sin(0.9398*x_0)][x_1]\n", "17 344.9056701660156 373.0729675292969 [x_2][tanh(0.8951*x_1)][x_3][(0.5418*x_1-0.5815*x_0)][tanh(0.8951*x_0)]\n", "18 328.8770751953125 414.16217041015625 [relu(0.7307*x_3)][tanh(0.8951*x_1)][x_2][sin(0.9398*x_0)][x_1]\n", "20 319.9519348144531 402.3149719238281 [x_2][tanh(0.8951*x_1)][relu(0.7307*x_3)][(0.5418*x_1-0.5815*x_0)][tanh(0.8951*x_0)]\n", "22 313.8825988769531 391.0963439941406 [x_2][tanh(0.8951*x_1)][relu(0.7307*x_3)][(0.5418*x_1-0.5815*(0.5418*x_1-0.5815*x_0))][tanh(0.8951*x_0)]\n", "24 313.66217041015625 378.6908874511719 [sqrt(|0.1721*relu(0.7307*x_3)|)][tanh(0.8951*x_1)][x_2][sin(0.9398*x_0)][x_1]\n", "29 308.3426208496094 386.4595031738281 [tanh(0.8951*x_1)][sqrt(|0.6298*relu(0.7307*x_3)|)][x_1][tanh(0.8951*x_0)][(x_2<=x_1)]\n", "34 304.9555969238281 392.111328125 [gauss(relu(0.7307*x_3))][tanh(0.8951*x_1)][x_2][sin(0.9398*x_0)][x_1]\n", "37 290.0493469238281 378.2729187011719 [((0.8126*x_2^3)>=x_1)][tanh(0.8951*x_1)][relu(0.7307*x_3)][(0.5418*x_1-0.5815*(0.5418*x_1-0.5815*x_0))][tanh(0.8951*x_0)]\n", "49 278.4764099121094 384.1788330078125 [gauss(relu(0.7307*x_3))][tanh(0.8951*x_1)][((0.8126*x_2^3)>=x_1)][sin(0.9398*x_0)][x_1]\n", "54 278.0923767089844 383.3207702636719 [gauss(relu(0.7307*x_3))][tanh(0.8951*x_1)][((0.8126*x_2^3)>=x_1)][sin(0.9398*x_0)][tanh(0.1940*x_1)]\n", "55 286.6203308105469 331.2903747558594 [tanh(0.8951*x_1)][relu(0.7307*x_3)][x_1][gauss(tanh(0.8951*x_0))][((0.8126*x_2^3)>=x_1)]\n", "59 277.9620056152344 388.03424072265625 [gauss(relu(0.7307*x_3))][tanh(0.8951*x_1)][((0.8126*x_2^3)>=sin(0.6267*x_1))][sin(0.9398*x_0)][x_1]\n", "114 266.8835754394531 343.7061767578125 [tanh(0.8951*x_1)][sin(0.5094*gauss(relu(0.7307*x_3)))][x_1][gauss(tanh(0.8951*x_0))][((0.8126*x_2^3)>=x_1)]\n", "153 261.151123046875 357.7651062011719 [tanh(0.8951*x_1)][((0.8496*gauss(relu(0.7307*x_3)))^2)][x_1][gauss(tanh(0.8951*sin(0.9398*x_0)))][((0.8126*x_2^3)>=x_1)]\n", "244 257.5480651855469 345.8831481933594 [tanh(0.8951*x_1)][((0.8496*sin(0.5094*gauss(relu(0.7307*x_3))))^2)][sin(0.6267*x_1)][gauss(tanh(0.8951*sin(0.9398*x_0)))][((0.8126*x_2^3)>=x_1)]\n" ] } ], "source": [ "# get the test score\n", "test_score = {}\n", "test_score['feat'] = mse(y_v,est.predict(X_v))\n", "\n", "# store the archive\n", "archive = est.cfeat_.get_archive(True)\n", "\n", "# print the archive\n", "print('complexity','fitness','validation fitness',\n", " 'eqn')\n", "order = np.argsort([a['complexity'] for a in archive])\n", "complexity = []\n", "fit_train = []\n", "fit_test = []\n", "eqn = []\n", "\n", "for o in order:\n", " model = archive[o]\n", " if model['rank'] == 1:\n", " print(model['complexity'],\n", " model['fitness'],\n", " model['fitness_v'],\n", " model['eqn'],\n", " )\n", "\n", " complexity.append(model['complexity'])\n", " fit_train.append(model['fitness'])\n", " fit_test.append(model['fitness_v'])\n", " eqn.append(model['eqn'])" ] }, { "cell_type": "markdown", "metadata": { "scrolled": true }, "source": [ "For comparison, we can fit an Elastic Net and Random Forest regression model to the same data.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "\n", "rf = RandomForestRegressor(random_state=random_state)\n", "\n", "rf.fit(X_t,y_t)\n", "\n", "test_score['rf'] = mse(y_v,rf.predict(X_v))\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import ElasticNet\n", "\n", "linest = ElasticNet()\n", "\n", "linest.fit(X_t,y_t)\n", "\n", "test_score['elasticnet'] = mse(y_v,linest.predict(X_v))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's look at the test set mean squared errors by method." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'feat': 381.70598775221976,\n", " 'rf': 347.15749506172847,\n", " 'elasticnet': 919.3515337699572}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "test_score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing the Archive\n", "\n", "Let's visualize this archive with the test scores. This gives us a sense of how increasing the representation\n", "complexity affects the quality of the model and its generalization.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "best: 55\n", "middle: 14\n", "small: 3\n", "complexity [1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 20, 22, 24, 29, 34, 37, 49, 54, 55, 59, 114, 153, 244]\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import math\n", "\n", "matplotlib.rcParams['figure.figsize'] = (10, 6)\n", "%matplotlib inline \n", "sns.set_style('white')\n", "h = plt.figure(figsize=(14,8))\n", "\n", "# plot archive points \n", "plt.plot(fit_train,complexity,'--ro',label='Train',markersize=6)\n", "plt.plot(fit_test,complexity,'--bx',label='Validation')\n", "# some models to point out\n", "best = np.argmin(np.array(fit_test))\n", "middle = np.argmin(np.abs(np.array(fit_test[:best])-test_score['rf']))\n", "small = np.argmin(np.abs(np.array(fit_test[:middle])-test_score['elasticnet']))\n", "\n", "print('best:',complexity[best])\n", "print('middle:',complexity[middle])\n", "print('small:',complexity[small])\n", "plt.plot(fit_test[best],complexity[best],'sk',markersize=16,markerfacecolor='none',label='Model Selection')\n", "\n", "# test score lines\n", "y1 = -1\n", "y2 = np.max(complexity)+1\n", "plt.plot((test_score['feat'],test_score['feat']),(y1,y2),'--k',label='FEAT Test',alpha=0.5)\n", "plt.plot((test_score['rf'],test_score['rf']),(y1,y2),'-.xg',label='RF Test',alpha=0.5)\n", "plt.plot((test_score['elasticnet'],test_score['elasticnet']),(y1,y2),'-sm',label='ElasticNet Test',alpha=0.5)\n", "\n", "print('complexity',complexity)\n", "xoff = 100\n", "for e,t,c in zip(eqn,fit_test,complexity):\n", " if c in [complexity[best],complexity[middle],complexity[small]]:\n", " t = t+xoff\n", " tax = plt.text(t,c,'$\\leftarrow'+e+'$',size=18,horizontalalignment='left',\n", " verticalalignment='center')\n", " tax.set_bbox(dict(facecolor='white', alpha=0.75, edgecolor='k'))\n", "\n", "l = plt.legend(prop={'size': 16},loc=[1.01,0.25])\n", "plt.xlabel('MSE',size=16)\n", "plt.xlim(np.min(fit_train)*.75,np.max(fit_test)*2)\n", "plt.gca().set_xscale('log')\n", "plt.gca().set_yscale('log')\n", "\n", "plt.gca().set_yticklabels('')\n", "plt.gca().set_xticklabels('')\n", "\n", "plt.ylabel('Complexity',size=18)\n", "h.tight_layout()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that ElasticNet produces a similar test score to the linear representation\n", "in Feat's archive, and that Random Forest's test score is near the representation shown in the middle.\n", "\n", "The best model, marked with a square, is selected from the validation curve (blue line).\n", "The validation curve shows how models begin to overfit as complexity grows.\n", "By visualizing the archive, we can see that some lower complexity models achieve nearly as good of a validation score.\n", "In this case it may be preferable to choose that representation instead." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, FEAT will choose the model with the lowest validation error, marked with a square above. \n", "Let's look at that model.\n", "\n", "the function `get_model()` will print a table of the learned features, optionally ordered by the magnitude of their weights." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weight\tFeature\n", "1547.33\toffset\n", "-182.50\ttanh(0.8951*x_1)\n", "37.58\trelu(0.7307*x_3)\n", "40.92\tx_1\n", "69.79\tgauss(tanh(0.8951*x_0))\n", "-17.11\t((0.8126*x_2^3)>=x_1)\n", "\n" ] } ], "source": [ "print(est.get_model(False))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 2 }