diff --git a/carl/__init__.py b/carl/__init__.py index 35afba5..dcaa76d 100644 --- a/carl/__init__.py +++ b/carl/__init__.py @@ -10,18 +10,13 @@ [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.47798.svg)](http://dx.doi.org/10.5281/zenodo.47798) -## Documentation - -* Illustrative examples complementing the online reference - can be found under the - [`examples/`](https://github.com/diana-hep/carl/tree/master/examples) - directory. - -* Extended details regarding likelihood-free inference with calibrated - classifiers can be found in the companion paper _"Approximating Likelihood - Ratios with Calibrated Discriminative Classifiers", Kyle Cranmer, Juan Pavez, - Gilles Louppe._ - [http://arxiv.org/abs/1506.02169](http://arxiv.org/abs/1506.02169) +## Likelihood-free inference with classifiers + +Extended details regarding likelihood-free inference with calibrated +classifiers can be found in the companion paper _"Approximating Likelihood +Ratios with Calibrated Discriminative Classifiers", Kyle Cranmer, Juan Pavez, +Gilles Louppe._ +[http://arxiv.org/abs/1506.02169](http://arxiv.org/abs/1506.02169) ## Installation diff --git a/carl/learning/calibration.py b/carl/learning/calibration.py index 19950a6..dbe37cf 100644 --- a/carl/learning/calibration.py +++ b/carl/learning/calibration.py @@ -32,7 +32,7 @@ class CalibratedClassifierCV(BaseEstimator, ClassifierMixin): probabilities for each of the folds are then averaged for prediction. """ - def __init__(self, base_estimator, method="histogram", cv=1): + def __init__(self, base_estimator, method="histogram", bins="auto", cv=1): """Constructor. Parameters @@ -47,6 +47,9 @@ def __init__(self, base_estimator, method="histogram", cv=1): `"histogram"`, `"kde"`, `"isotonic"`, `"interpolated-isotonic"` and `"sigmoid"`. + * `bins` [int, default="auto"]: + The number of bins, if `method` is `"histogram"`. + * `cv` [integer, cross-validation generator, iterable or `"prefit"`]: Determines the cross-validation splitting strategy. Possible inputs for cv are: @@ -61,6 +64,7 @@ def __init__(self, base_estimator, method="histogram", cv=1): """ self.base_estimator = base_estimator self.method = method + self.bins = bins self.cv = cv def fit(self, X, y): @@ -93,7 +97,7 @@ def fit(self, X, y): # Calibrator if self.method == "histogram": - base_calibrator = HistogramCalibrator() + base_calibrator = HistogramCalibrator(bins=self.bins) elif self.method == "kde": base_calibrator = KernelDensityCalibrator() elif self.method == "isotonic": diff --git a/ci/templates/html.mako b/ci/templates/html.mako index 70e3e85..09ad0e0 100644 --- a/ci/templates/html.mako +++ b/ci/templates/html.mako @@ -361,6 +361,8 @@ %> diff --git a/examples/Diagnostics for approximate likelihood ratios.ipynb b/examples/Diagnostics for approximate likelihood ratios.ipynb index 49b45b1..6728921 100644 --- a/examples/Diagnostics for approximate likelihood ratios.ipynb +++ b/examples/Diagnostics for approximate likelihood ratios.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Diagnostics for approximate likelihood ratios\n", + "# Diagnostics for approximate likelihood ratios\n", "\n", "Kyle Cranmer, Juan Pavez, Gilles Louppe, March 2016.\n", "\n", @@ -23,23 +23,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Couldn't import dot_parser, loading of dot files will not be possible.\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", - "#plt.set_cmap(\"viridis\")\n", "\n", "import numpy as np\n", "import theano\n", @@ -53,7 +44,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Create model and generate artificial dataset" + "## Create model and generate artificial data" ] }, { @@ -71,8 +62,6 @@ "from carl.distributions import LinearTransform\n", "from sklearn.datasets import make_sparse_spd_matrix\n", "\n", - "import pdb\n", - "\n", "# Parameters\n", "true_A = 1.\n", "A = theano.shared(true_A, name=\"A\")\n", @@ -100,6 +89,7 @@ " Exponential(inverse_scale=3.0),\n", " Exponential(inverse_scale=0.5)]), R))\n", "p1 = p1s[0]\n", + "\n", "# Draw data\n", "X_true = p0.rvs(500, random_state=314) " ] @@ -108,7 +98,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Known likelihood setup" + "## Known likelihood setup" ] }, { @@ -129,7 +119,9 @@ "source": [ "# Minimize the exact LR\n", "from scipy.optimize import minimize\n", + "\n", "p1 = p1s[2]\n", + "\n", "def nll_exact(theta, X):\n", " A.set_value(theta[0])\n", " return (p0.nll(X) - p1.nll(X)).sum()\n", @@ -188,7 +180,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Likelihood-free setup\n", + "## Likelihood-free setup\n", "Here we create the data to train a parametrized classifier" ] }, @@ -205,36 +197,25 @@ "\n", "bounds = [(-3, 3), (-3, 3)]\n", "\n", - "clf_parameters = [(1000,100000),(1000000,500),(1000000,100000)]\n", + "clf_parameters = [(1000, 100000), (1000000, 500), (1000000, 100000)]\n", "X = [0]*3*3\n", "y = [0]*3*3\n", "\n", - "\n", - "\n", "for k,(param,p1) in enumerate(product(clf_parameters,p1s)):\n", " X[k], y[k] = make_parameterized_classification(\n", " p0, p1,\n", " param[0], \n", " [(A, np.linspace(bounds[0][0],bounds[0][1], num=30))],\n", - " random_state=0)\n" + " random_state=0)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/juanpavez/Library/Python/2.7/lib/python/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module has been deprecated in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", - " \"This module will be removed in 0.20.\", DeprecationWarning)\n" - ] - } - ], + "outputs": [], "source": [ "# Train parameterized classifier\n", "from carl.learning import as_classifier\n", @@ -243,9 +224,10 @@ "from sklearn.neural_network import MLPRegressor\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.model_selection import RandomizedSearchCV\n", + "\n", "clfs = []\n", - "for k,_ in enumerate(product(clf_parameters,p1s)):\n", + "\n", + "for k, _ in enumerate(product(clf_parameters,p1s)):\n", " clfs.append(ParameterizedClassifier(\n", " make_pipeline(StandardScaler(), \n", " as_classifier(MLPRegressor(learning_rate=\"adaptive\", \n", @@ -279,9 +261,9 @@ " return wrapper\n", "\n", "def objective(theta, random_state=0, n_samples=100000, clf=clfs[0],p1=p1s[0]): \n", - " \n", " # Set parameter values \n", " A.set_value(theta[0])\n", + " \n", " # Fit ratio\n", " ratio = ClassifierRatio(CalibratedClassifierCV(\n", " base_estimator=clf, \n", @@ -313,40 +295,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "*Optimization completed:\n", - " -Maximum number of iterations reached.\n", - "*Optimization completed:\n", - " -Maximum number of iterations reached.\n", - "*Optimization completed:\n", - " -Maximum number of iterations reached.\n", - "*Optimization completed:\n", - " -Maximum number of iterations reached.\n", - "*Optimization completed:\n", - " -Maximum number of iterations reached.\n", - "*Optimization completed:\n", - " -Maximum number of iterations reached.\n", - "*Optimization completed:\n", - " -Maximum number of iterations reached.\n", - "*Optimization completed:\n", - " -Maximum number of iterations reached.\n", - "*Optimization completed:\n", - " -Maximum number of iterations reached.\n" - ] - } - ], + "outputs": [], "source": [ "from GPyOpt.methods import BayesianOptimization\n", + "\n", "solvers = []\n", - "for k,(param,p1) in enumerate(product(clf_parameters,p1s)):\n", + "\n", + "for k, (param, p1) in enumerate(product(clf_parameters,p1s)):\n", " clf = clfs[k]\n", " n_samples = param[1]\n", " bounds = [(-3, 3)]\n", @@ -379,7 +338,8 @@ ], "source": [ "approx_MLEs = []\n", - "for k,_ in enumerate(product(clf_parameters,p1s)):\n", + "\n", + "for k, _ in enumerate(product(clf_parameters,p1s)):\n", " solver = solvers[k]\n", " approx_MLE = solver.x_opt\n", " approx_MLEs.append(approx_MLE)\n", @@ -459,7 +419,7 @@ "rs = []\n", "solver = solvers[0]\n", "\n", - "for k,_ in enumerate(product(clf_parameters,p1s)):\n", + "for k, _ in enumerate(product(clf_parameters,p1s)):\n", " def gp_objective(theta):\n", " theta = theta.reshape(1, -1)\n", " return solvers[k].model.predict(theta)[0][0]\n", @@ -496,27 +456,18 @@ " nll_gp, var_gp = solvers[k].model.predict(As.reshape(-1, 1))\n", " nll_gp = 2. * (nll_gp - rs[k].fun) * len(X_true)\n", " gp_ratios.append(nll_gp)\n", + " \n", " # STD\n", " std_gp = np.sqrt(4*var_gp*len(X_true)*len(X_true))\n", " std_gp[np.isnan(std_gp)] = 0.\n", " gp_std.append(std_gp)\n", + " \n", " # 95% CI\n", " q1_gp, q2_gp = solvers[k].model.predict_quantiles(As.reshape(-1, 1))\n", " q1_gp = 2. * (q1_gp - rs[k].fun) * len(X_true)\n", " q2_gp = 2. * (q2_gp - rs[k].fun) * len(X_true)\n", " gp_q1.append(q1_gp)\n", - " gp_q2.append(q2_gp)\n", - "\n", - " #nll_approx = np.zeros(n_points)\n", - "\n", - " #approx = [objective([a]) for a in np.linspace(*bounds[0], n_points)]\n", - " #approx = [objective([a],n_samples=n_samples,clf=clf,p1=p1) for a \n", - " # in np.linspace(bounds[0][0],bounds[0][1], n_points)]\n", - "\n", - " #approx = np.array(approx)\n", - " #approx = 2. * (approx - approx.min()) * len(X_true)\n", - " #nll_approx = approx\n", - " #approx_ratios.append(nll_approx)\n" + " gp_q2.append(q2_gp)" ] }, { @@ -566,7 +517,8 @@ ], "source": [ "bounds = [(true_A - 0.30, true_A + 0.30)]\n", - "for k,_ in enumerate(clf_parameters):\n", + "\n", + "for k, _ in enumerate(clf_parameters):\n", " fig = plt.figure()\n", " ax = fig.add_subplot(1,1,1)\n", " ax.plot(As, nll, label=\"Exact\")\n", @@ -624,9 +576,11 @@ "outputs": [], "source": [ "from sklearn.metrics import roc_curve, auc\n", - "def makeROC(predictions,targetdata):\n", + "\n", + "def makeROC(predictions ,targetdata):\n", " fpr, tpr, _ = roc_curve(targetdata.ravel(),predictions.ravel())\n", " roc_auc = auc(fpr, tpr)\n", + " \n", " return fpr,tpr,roc_auc" ] }, @@ -669,8 +623,6 @@ } ], "source": [ - "#fig = plt.figure(figsize=(15,15))\n", - "\n", "# I obtain data from r*p1 by resampling data from p1 using r as weights\n", "def weight_data(x0,x1,weights):\n", " x1_len = x1.shape[0]\n", @@ -767,15 +719,6 @@ "trained, well calibrated case is almost identical with the exact likelihood\n", "ratio, confirming the quality of the approximation." ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/Likelihood ratios of mixtures of normals.ipynb b/examples/Likelihood ratios of mixtures of normals.ipynb index 98aa8be..095fcdf 100644 --- a/examples/Likelihood ratios of mixtures of normals.ipynb +++ b/examples/Likelihood ratios of mixtures of normals.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -90,7 +90,7 @@ "data": { "image/png": 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b7yXoz+l2h6SUU9Lk7qyWLmWld2e3Xwz7WvTtCzNmwJG+w6g+9w9r8XCl1EUc\nSu4iEikim0Vkq4g8n8f+hiKyTERSReSpXPt2i8h6EYkVEe2c7AhjYOlSfj3Uia5d7Q7G+TRtCqVK\nwaJDDUiuHQxTdJUmpXK7anIXEQ/gU6AXEAIMEZFGuQ47BjwKvJfHJTKAcGNMK2NMu+uMt3jYsQPj\n5cVvsTXp3NnuYJyPCAwaBJMnw+7+Q+Cjj3TEqlK5ONJybwdsM8bsMcakAZOAfjkPMMYcNcasAfIa\nAy8Ovo7KsnQpx0M6Uz1IqFjR7mCcU2QkxMVBXM3ucOoULF5sd0hKORVHkm51YF+O5/sztznKAHNF\nZJWIPJSf4IqtJUtYX1pLMlfi4wO33AIzZ1eGJ56wWu9KqWxFMStkJ2PMQREJxEry8caYPCflHj16\ndPbj8PBwwsPDiyA8J7R0KTOrPEyXAXYH4twGDIB77qnAmc/vpdTLL8P27RAcbHdY+RITE3fJOrC6\n6Le6nOjoaKKjox061pHkfgComeN5UOY2hxhjDmb+M0FEpmGVea6a3IutY8cw+/fz34TmPKY9Za4o\nKAiaNEnhuykBPPzQQ/DhhzB2rN1h5UtS0rlLpmpOSFhjUzTK2eVu9I4ZM+ayxzpSllkFBItILRHx\nBgYDM65wvGQ/EPEVEb/Mx6WBCGCjA69ZfC1bxumm7SlZ2ouaNa9+eHE3aNAh3nsP0h99EiZNgv37\n7Q5JKadw1eRujDkPjALmAJuAScaYeBEZISLDAUSksojsA54E/i0iezOTemVgiYjEAjHA78aYOYX1\nZtzC0qVsrqj92x3VuHEKNWrAlIWV4MEH4e237Q5JKafgUM3dGBMFNMy1bVyOx4eBGnmcmgwU85nI\n82nJEub6jKZLH7sDcR3//Kf1N/jPZ5AmjeFf/7JmGVOqGNMuis7kzBlYt44ft7bXlns+REZa/5y1\npjLcf7+23pVCk7tzWbGC1PpNSUwrQ6Pcw8TUZYnAv/8No0eDefY5mDgRdu60OyylbKULZDuT6Gi2\nVg0nvNGFxbCVYwYOhLfegmnLKtN2wBC87hvB+hcutOC1e6EqbrTl7kyio4k6cwPdu9sdiOvx8IA3\n3oAXX4SNvYZQYetmav99NnsB8dx9yZVyd5rcnUVqKmb1ar7e3Ilu3ewOxjX17g0VKsDcpdU5MPJN\nanz4JGTodMmqeNKyjLNYsYLUeiGcOV6WevXsDsZ1xMfvuOj5bbeVZvToWvSbfieBv46j4q/jODrw\n4UKPI/f8kj6SAAAduUlEQVRIUy0DKbtpcncW0dFsqXwD3VpqvT0/UlIyLhrhGRgI9ert5Otv61Lx\nhXE0GBHOiRv6XeEKBSP3SFMdZarspsndWSxcyOz0Z7QkUwAiItbzxRd16ds3hHIDH6bGu6P4+9kX\nLjnOlVrbrhSrcg5ac3cGqamYlSv58q/OmtwLgJ/fWYYNgzffhL/vfQGfvduo9r+ZlxyX1dp2hZuu\nrhSrcg6a3J3BypWcqd0Y8S9LrVp2B+MeBg2y7qVOmu7Dzjd+otFX/2fNGqlUMaFlGRtl/dSuN/En\ndnu1pkGDBL78cgk1alw8Y1h8/K5LZg5UV+bpCa+8Yg1Y7dixKTJ0GE2GDLEW9fDxsTu8PCUnW98/\nu3YFkJgIlSpB/froF766JprcbZT1U7vy5uf4JuMpOncO5PDhs7RufXEiX716m00RuraaNWHECGv0\n6jtvD6bJ0d3w8MPwzTdOc9c6IaEEv/8O0dGwaxfUrQv+/uXZvh2OHIFNm6BsWQgNrcZ991k3jJVy\nhJZlbCZnUym9aSUTdnemTRu7o3E/t98ONWrA51/UhO++g7Vr4T//sTss4uNhyBAYObIJR47Ao4/C\nggXw/ffw0ks7mTQJ5s+HQ4es9b9Pn/bkjjvg44/h9Gm7o1euQJO7zfzWLeFY1aaUrOSv66UWAhFr\n1OratSV54t8JLHzqdVJff5PYf79DfPyuIo/nxAl4+mno2hVat4bvvovjn/+Etm3BK4/f0SLQsiWM\nHLmPyZMhIQEGD4YtW3yLPHblWjS526zsirmsCuhJhw52R+K+SpeGwYMXM2FCbWKP38LOT+bS9IsP\nqbxudZHFkJFh/XBo3NhK8Js2wbPPQunSjo+grVgRXnvNWjL2lVeC+frrwotXuT6tudus7Iq5TE3/\nD2Fhdkfi3gIDT/HBB1ZiLPN+Czzem0a/RyM52KozJwp58vxVq6yyizHw229WK/16dO8O5cpt4fXX\nm5KQAM8/7zS3EJQT0Za7jUokHcd7/w5+/TuMlrqkSaFr2hRefx2eeQb+d7ojP9/1HLVeH0b5P34o\nlNc7fNhaHKpvX/jHP2D58utP7FmCgs6yZAn8+CO89FLBXFO5F22526jCupXsqdmVJv4lnLV3ntsJ\nC4P334fnnoNu3XrQ/vPbqP9oJCUP7CSh/80F8hppaTBtWiXuvhvuuQc2bwZ//wK5dLasOXVeesmL\n555rwOHDR3nwwSQdtaqyaXK3UcXYFczx7qklmSLWsiWMHQsjRzYlI6MM/xy3gsZj7iA0di6E/Q7l\ny1/TdY2xujR+/DGULu3FG29sombNVJYvt/YX5JQBWXPqBAbC55/DsGE18PdP1/+WVDYty9jFGCrE\nruCHg5rc7RAcDA8//CcpKTDo8apMuG8eyTXrQIsWMGtWvq5lDKxY4c+DD8IXX1g18DvvXEhoaEiR\nTBlQpQp88gl89VVQ9heJUprc7bJ1Kxnphr8yGukUvzbx8UnnzTetm6xvvFuCW3eMZdWo7zGjRln1\nlIMHr3j+0aNWK715c/jhh2oMHmyt8GfHl3WdOvDUU7sZOBD27y/611fOR8sydvnf/4irfANh9UV7\nOtisSxcrIU+deox/TO5OUsoGPlv3KuH1m3J40GOkjnqG8z6lSUmBHTtg40ZrgNHGjdCvH/zf/8G5\nc/FUqlQwU0TknqPe2nb1KSjatTtJyZLQvz8sXQolSxZIOMpFacvdLlFRzEyL0JKMkyhRAm68MZE1\na2D2Yj+2D3+XF3quZtvv8ZRvV4+54a/zzIPHmTrV6nb42mtWb5gff4QePQq2K+KFevqFv5SUdIfO\nfe45ay6aZ54puHiUa3IouYtIpIhsFpGtIvJ8HvsbisgyEUkVkafyc26xdOYMZuFCvjnQTwcvOaEG\nDWDUKPhwWh1uTJhE4Ib5PH7zDhbur8fUao/x2h0b6dEDSpWyO9JLicDXX8Mff8Cvv9odjbLTVZO7\niHgAnwK9gBBgiIg0ynXYMeBR4L1rOLf4iY4msUYLKjbwoWxZu4NRV9WkCXz7LaxfDwEBEBlp1XG+\n+soabupkAgJg0iSrb/2uop9hQTkJR1ru7YBtxpg9xpg0YBJw0bplxpijxpg1QO7fjlc9t1iaOZOF\nfjfTvn2S3ZGo/KhRA159FfbsgZdftnrV1KwJ/fpRdf5sPJJP2h1htvbt4Z//tOahOafrehRLjiT3\n6sC+HM/3Z25zxPWc656MwcycyWe7bqZdO+dr9SkHeHrCTTdZdY+9e2HgQKounEPzm2tQ95lbKRc1\nEe9U+6dufPJJaz6al1+2OxJlB6fqLTN69Ojsx+Hh4YSHh9sWS6HZvJm0M+fZH9CUoKC1dkejrpe/\nP9x9N2sDm1DFpx4B0dMp/+dPPL5yHmcWTeN4j4Gc6NqX82XLFXloIlY1qVUr6NnTuvGrXFt0dDTR\n0dEOHetIcj8A5FwaKChzmyPydW7O5O62Zs5kQ42b6dNV+z+6m/NlAjh2y30cu+U+5k/7hoE+JQmY\nN5Wa7z1KcotOHAuqxbwTSaT5B2SfU9irbFWqZCX4e++F2Fhd7MPV5W70jhkz5rLHOpLcVwHBIlIL\nOAgMBoZc4ficWSu/57q/mTOZcPxp+t8Cqal2B6OuJmspxJwcmUbgrI8vib0Hk9j7TjxOJ+O/dBZB\nEz6k4cyfSAlpy/HuA0nqdiurUwpula28+scHBHgTEdGMIUOsScx++01nkCwurprcjTHnRWQUMAer\nRv+1MSZeREZYu814EakMrAbKABki8jjQxBiTnNe5hfZunF1iIhlr1jLFszvvdYJ58+wOSF1N1lKI\nOSUkrMnXNTJ8/TjecxCz0zO4qVtf/JdFETD/F6p/9i8CKlRFTh7leMQdpJe7vmZ1Vv/4vGJ94w3o\n2BE++8zq5qncn0M1d2NMFNAw17ZxOR4fBmo4em6x9fvv7K7Xg/AmvpQoYXcwKrdrHRmaH8bHl6Tu\nt5HU/TbkbCrbxr7EjRtXUO3zFznVtjvH+j6AnD9fYK+XxdsbfvrJSvBdu1pTJij35lQ3VN3er7/y\n87nbGTjQ7kBUXvJq+Rbm4uSmpA/bGoUS3HswHsknKT/3Z6p8+yaP7YwnZcd6jvZ7gLO1C25YSP36\n1nTHQ4ZYC4j46kp9bk2TexFZOX8FrebO4xO+4XOPtURFmUK/maZcR4ZfWY7e+hBHb32IlT98wK1J\nR2g4Ipwz9ZpyZNCjSIbjy/HllPvXSKVKUL16PZ56KoAvviiIyJWz0uReRErMi2ZX1c40rVeBoKAK\nQOG2ClXhKezyzbHA6hy452n+/serlJs3lSrfvcUj+3eQfHQ/R/s9yPmACg5fK69fIyNGxPLss634\n9Ve47bYCCVk5IU3uRaTK0vl8bwZoX2M3UFTlG+NdksTed5LY+07WffkG/XduoumtwSRGDiUgqME1\nX3fv3m08+mgpHnywHikp8dSvL7qCkxvSWSGLQmoqFVYtY/zhfnTqZHcwyhUdDKrH7jHfs2nqZs77\n+fPAuBep8++hlNq6Pt/XSknJoGvXRgwdWoL/+7/mHDum8xO4I03uRWHuXPYHNKJOWGWnnElQuY70\nCpX5+5E3+fTJ/3C6YSuCH+t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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -125,22 +125,17 @@ }, "outputs": [], "source": [ - "from sklearn.ensemble import ExtraTreesRegressor\n", "from sklearn.model_selection import StratifiedShuffleSplit\n", "from sklearn.neural_network import MLPRegressor\n", - "from sklearn.linear_model import ElasticNetCV\n", "from carl.ratios import ClassifierRatio\n", "from carl.ratios import DecomposedRatio\n", "from carl.learning import CalibratedClassifierCV\n", "\n", - "# clf = ElasticNetCV() # use 100 and 50 bins\n", + "n_samples = 200000\n", "clf = MLPRegressor(tol=1e-05, activation=\"logistic\", \n", " hidden_layer_sizes=(10, 10), learning_rate_init=1e-07, \n", " learning_rate=\"constant\", algorithm=\"l-bfgs\", random_state=1, \n", - " max_iter=75) # use 15 and 12 bins\n", - "# clf = ExtraTreesRegressor(n_estimators=250, max_leaf_nodes=15) # use 15 and 15 bins\n", - "\n", - "n_samples = 200000\n", + " max_iter=75) \n", "\n", "# No calibration\n", "cc_none = ClassifierRatio(base_estimator=clf, random_state=1)\n", @@ -165,6 +160,7 @@ "metadata": {}, "source": [ "Note: `CalibratedClassifierRatio` takes three arguments for controlling its execution:\n", + "\n", "- `base_estimator` specifying the classifier to be used (note commented `ExtraTreesRegressor`),\n", "- `calibration` specifying the calibration algorithm (`\"kde\"`, `\"histogram\"`, or a user-defined distribution-like object),\n", "- `cv` specifying how to allocate data for training and calibration.\n", @@ -175,16 +171,16 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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d5cuc8BuE4efl9z3Xt05fmpdvzsh1I61UnXhSEvJCZJE5++Yw0tsSglN/v0HB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\nkhRvXIrDVT3Y4OTEBq2J1pp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CKdUaeFtr3d3ataRGxuQzRpOL/xzLAbMBV2BT8uliX1m7\noJyitTYBI7GcYXQMWJGPA7458DLQRil1KPm90MnadYn0yZG8EELYMDmSF0IIGyYhL4QQNkxCXggh\nbJiEvBBC2DAJeSGEsGES8kIIYcMk5IUQwoZJyAshhA37f79Wt4IMtKKtAAAAAElFTkSuQmCC\n", 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lNzzWZQPg85wP7Su2562/3pLDKnMRCXkhMsm8g/No83wbXPO78uXKO0QWsaV7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\n7gFzb69YGVAolCHx3mAwJD9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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -215,26 +211,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { + "image/png": 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R6TsSCg4zSwfqnHPhLhrPSaexl7FyZRm1tY5QKBePZyFmbwCFhMPJJCcv57zz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\nLddOH1yrKhL3E9K1qkSk31N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"text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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g6ONqamrYtWsXRUWruOeeBbjd51BWtoP09Kuorl5EevpVVFTMxeudSiiUwuDB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\nv/zlI5zgEJxrUs0F0sjJyTl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- "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -245,67 +231,7 @@ "plt.scatter(-p0.nll(reals.reshape(-1, 1)) + p1.nll(reals.reshape(-1, 1)), \n", " cc_none.classifier_.predict_proba(reals.reshape(-1, 1))[:, 0], alpha=0.5)\n", "plt.xlabel(\"r(x)\")\n", - "plt.ylabel(\"s(x)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us inspect the individual pair-wise classifiers, their calibrating distributions, and the resulting estimate of $(1+r(x))^{-1}$ for the decomposed case." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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ILWtDFLFEaSzRxJo8Jh9NHNHizDuPsdKMZtKSBGlFYlQrkqQtydGptIhJpWVM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\nbYhQSkq0J+Hs2To3xRNPwKhRobYq7NmyBS65ROt8W+KxI1m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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for n, (j, i) in enumerate([(0, 1), (0, 2), (1, 2)]):\n", - " try:\n", - " c = cc_decomposed.ratios_[(j, i)].classifier_\n", - " except:\n", - " c = cc_decomposed.ratios_[(i, j)].classifier_\n", - " \n", - " ax1 = plt.subplot(3, 1, n+1)\n", - " \n", - " h0 = c.calibrators_[0][0]\n", - " h1 = c.calibrators_[0][1]\n", - " r = np.linspace(0, 1, 100)\n", - " \n", - " ax1.plot(r, h0.pdf(r.reshape(-1, 1)), label=r\"$p_{%d}(s_{%d%d}(x))$\" % (i, i, j))\n", - " ax1.plot(r, h1.pdf(r.reshape(-1, 1)), label=r\"$p_{%d}(s_{%d%d}(x))$\" % (j, i, j))\n", - " ax1.legend(prop={\"size\": 8}, frameon=False, loc=\"upper left\") \n", - "\n", - " ax2 = ax1.twinx()\n", - " s = h1.pdf(r.reshape(-1, 1)) / (h0.pdf(r.reshape(-1, 1)) + h1.pdf(r.reshape(-1, 1)))\n", - " ax2.plot(r, s, \"r\", label=r\"$1 / (1 + r(s_{%d%d}(x)))$\" % (i, j))\n", - " ax2.set_yticks([0, 0.5, 1.0])\n", - " ax2.legend(prop={\"size\": 8}, frameon=False, loc=\"upper right\") \n", - " \n", - " if n == 1:\n", - " ax1.set_ylabel(r\"$\\hat{p}(\\hat{s}_{c,c'}(x))$\")\n", - " ax2.set_ylabel(r\"$1 / (1 + r(\\hat{s}_{c,c'}(x)))$\")\n", - " \n", - " if n < 2:\n", - " plt.xticks([])\n", - "\n", - " plt.xlim(-.1, 1)\n", - " \n", - "#plt.savefig(\"fig1b.pdf\")\n", + "plt.ylabel(\"s(x)\")\n", "plt.show()" ] }, @@ -313,21 +239,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we inspect the distribution of the exact $\\log {r}(x)$ and approximate $\\log \\hat{r}(x)$ " + "Now we inspect the distribution of the exact $\\log {r}(x)$ and approximate $\\log \\hat{r}(x)$." ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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1dvx142lVO786R1XPqjzX6znANA565pdnWP73cizKwhd3flHsycDHyydvPeko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\nhkVllZWTxfSN0/MKbm11uRN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SD7Pt2dfp/NREYj1X0bxhEH6efvz3zv8S5BPk6BCFEAIApRRa60q1vCx3nYDWemQZy1cD\nqysTjLO5PvMlqD2e0QNh+BfDOXL2iCQBIUSN4lQVw87WKsgTP+pbutGtMfh4+Dg6HCGEAGzbSsip\nkoAQQohLK7hgdqbWQUIIIaohp0kC0jpICCGsY8vWQVIcJIQQ1Ywti4OcJgkIIURNs3zXcnYm7ASg\nff323NnuTgdHdCGnKQ4SQoia5uHvHiY9O5307HQmr57s6HBK5TR3As7YRFQIISprSq8pKKX4aMdH\nNtunNBEVQggXJk1EhRBC2IQkASGEcCJbt4KPD3h6msdLL9n385yqOMgZ6wTWroW8PDiZCWdTgIaO\njkgIUZPFxUH//rB8Obz3HuzYceE6tqwTcJo7gYIk4EzuuQc6dIDERDhzBn76ydERCSFcgcVi7gLc\ny7hMDw8Pr3mdxZxRu3bw4ovm+XszHBuLEELYg9PcCQghhKh6kgSEEMKFOU0SkAHkhBDCOjKAnBBC\nuDDpLCaEEMImJAkIIYQLkyQghBAuTJKAEEK4MEkCQgjhwpwmCUgTUSGEsI40EXUAiwXenQ9fvwce\nHvDJJ9C8uWbFnhWkZqUC0Ce0D8F1gx0cqRCippM5hh2gUSOYPgda1YEHHzvNT7+fpXHubsZ/NZ5B\nrQaxLmYdscmxDLtiGAAzr59Ju/rtHBy1EEJcnCQBK1ks0O4K6NhAs713S2bs88fnCIxsP5K3b32b\nrNwsvtr7FVprFkUvYs2hNZIEhBCFzmWf4+1f3wbg2pBr6digo4MjMiQJVECOexILO5xh4MCiZZ5u\nnoV3Aetj1zsoMiGEM6rnU4+pvaby18m/iEmO4et9X/Pd3d85OixAkoAQQtidu8WdmeEzAfjuwHe8\n/svrjg2oGKdpHSSEEKLqSRIQQggXJsVB5ZCWnVbYHPS992DNGjMh9IwZ5qcQQlQ3kgSs1LZeW278\n6EYAWtRuR8F0yK+/DoMGQc+ejotNCFG9ZWfDggWQkVH6xPL25DRJoGCieWebbL7A0mFLS12+bFkV\nByKEcJhzWee475v7SM5IJis3ixua38DAVqaZYNt6bXGzuFVov5s3w+zZMHw41KsHY8defP2oqCib\njbDgVElACCGc2dGUo6w9tJYFgxaw++Ru3vz1TT7e8THHU48zM3wmD3V/qML7btEC5syxbl3pMexE\nmjSB668HNzfw96/6WzkhRNXy8/RjYKuBDGw1kMeueQyA6T9OJyUzxcGRVYy0DqqkZcvgzBk4dQq8\nvSEpydERCSGE9eROoJLc3c0DzNASQghRnUgSsLEDByAxEZIkIQghLiEjA3JzIT3dcTFIErChvn3h\n0UfhRFf4LA6m9gZfX0dHJYSoCtl52WTkZKBQeLl7XXL9o0ehdWvQ2ry+6y47B1gGSQI29O675ufk\n1bDgRcjJqfi+oo9Hs/bQWgCCfIIY1XEUSikbRCmEsLWWgS15cNWDzF4/mzydxy8TfqFro64X3SY5\nGZo1g7/+qqIgyyBJwEk9vfZpLMpCmH8YT699mtjkWPy9/Wkd1JobW9zo6PCEEMWM7zqe8V3HAzDi\nixEMXDIQX09fUjJTrLorcCRJAnbi5Wmajrq7m/a/FelUNqHLBAa1HkS7+u2IPh7NoTOHmBk1k1OP\nn7J9wEIIm3h/0PvEp8QDpjlpYK3AEu8nJ5s6gKNHHRHdhSQJ2MmUKXBrPVMkdO21ldvXhK4TADiV\ndoqVe1faIDohhL34evpyedDlpb539izccw/QE+59Dvr1q9LQSiVJwE4CAuDKK82YIEII16U1PPkk\nxMSY1kBeXtDzevjuPUdHZtg9CSilfIB3gExgndZ6ib0/09loDVlZ5rmbm3kIIaqPb/Z9w9+Jf5Nw\nLqFC27/wAnzyiXl+fV1YfcaGwVVSVbRmHwJ8prW+H7itCj7PqVgsEBICfn6muWivXo6OSAhRXnd+\ndid7T+8lNSuVf1//7wrtY+RI82jd2sbBVVK57wSUUguBgcAJrXXHYssHAK9jEstCrfWL+W81BQpG\n1MmtXLjVj5sb/P23eX7sGHS9eKsxIaqd+JR4rll0DeeyzgEwvfd0Hu31qIOjsr1X+r+Cj0fNmzik\nIsVBi4E3gciCBUopC/AW0BeIB7YqpVZqrfcAcRQlApdp6P5zzM+4KTe83b0Z03kM7hZ3vL0hJQVC\nQ806AwbA/PkODbNayM3L5cM/PiQ9O531setZtquoqdWqkau4+fKbHRidOJ56HD9PP7ZM2MKSP5ew\n40TNGEXxcNJhdiXsAiBXW3f9euJEUasff38IC7NXdLZT7iSgtd6glAo5b3F3YL/WOgZAKbUUGAzs\nAVYAbymlbgW+rmS81cKojqP4IPoD/jr5F//d818uD7qc60KuIyAADh6EtDSIjTVJYP16s82dd0JE\nhGPjdla/Hv2V6WumM6ztMAJrBbJ5/GZ6Nu3J/V/fT2xyrKPDE4CHxYP6vvWp41Wn1PePpRxj7+m9\nANTzqUf7+u2rMrwKGfPlGLJzswmoFcCYTmPwcrt0e/9bbjHNP729Yc8emDfPVAQ7M1tVDDfBXPEX\nOIJJDGit04Bxl9pB8fkEnHlyGWtc1eQqrmpyFQB/JvxZ4r369c3P0FDYtcskhC1b4OOPqzjIaiAl\nM4X0nHQS0xMJCwjj7VvfdnRIooLGfDmGhHMJ+Hv788uRXzj9+Gl8PZ17TJXs3Gxe6f8KVwdfbfU2\n587Bl19CmzbwzDOwapVZ/vjjtonJlpPJFHCaJqKuOKlM8+bmZ0KCuWq47z7zeuhQx8XkLLJyswid\nG4qbMk2phl0xzMERicrIyMngjZvf4LqQ66jzfB2ri1ec1dGjsH27eV6rlhk37PxRXZ5+2vafe/4F\nsjNNKnMUaFbsddP8ZcIK114Lzz8PmZmwbRssXAjc4eioHCs3L5e07DTSZzhweEVRIXtO7WHBbwsA\nGN5+eJlFRNXZI4/AoUPQqBFs3AirV0OPHo6OqmIqmgQUJSt5twIt8+sKjgHDgRHl2aGzzzFsT+7u\nMGaMee7vD0uXmuFlU1Ph88+L1uva2zHxCWGtG8NuZMuRLWyL38amuE0s27WMLg27cPDMQUeHVmkn\nThTV4R0+DE89BbffDnfcYer3PD3N/2zt2vaPxaFzDCullgDhQJBSKhaI0FovVkpNAn6gqIno7vLs\n1xWLg0oTEgI//ADpvrD6GWitoH17M9Lg4H+4keqWSt/IvgAMaTOEB7s/6OCIhSgSXDeYdweZ4XT/\nTvybFXtWAPBE7yfo1dT5O8nsTNjJZ7s+A7ig0UFEBPz6q2nx06IFXGWq/fj0UzMeEJhEEBBg/zgd\nOsew1npkGctXA6srGogr3wkU16OHuZoY9Cnc9xQMyu9YMmsW5OUF8Mv9v3A67TS/HfuNFXtWSBIQ\nDqG1puP8jsQkxZCrcwkPDb9gnRaBLfjX1f+q+uAq4Z2t7xCTHEP3xt2Z3GMy3Rp1K3wvNxceeADu\nvbfkNt7e5lGVHHonYC9yJ3Bxbm6wfDns2dMZgP4PaMyNlxCOsTNhJ8lPmEvgWu61HByN7dzS8lZu\nqD0RgIP7ITjY/P9VZn4QW3PonYBwjEmT4PL8gQkXLYLFiyChFbz2Gtx2mxmSwt0d6tWr2P7PZZ0r\nbM5ay70WnRp2slHk9rV011J2JuzEx8OHiPAIq3p0Rh+PZuH2hQB4unny7+v/TV3vupfcLk/nFRYR\nuCk3gusGVy74GsBRlb5nM8+SmZMJQF3vuni6eVZqf1u3wogRkHAVpPwNlt+gVSs4c8bUBXh7m9Y/\nd99ti+idiySBaqJuXfjHP8zzli1h9lKIzYBPv4J//QsuuwySkmDTpooNTRERFcHnf31OQ7+G7Dq5\ni7kD5hIWEMZlPpfRrn47234ZG5nWexrf7vsWgFc2v8LQK4bSvUn3S273yY5PiD0bS7/m/Xhr61uE\nBYTRo2kP/L39aRnYsszt5m+bz/Q10wnwDiDhXAJfj/iavmF9bfZ9hHVSMlNo9lozPNw8yMrNom/z\nvrxx8xsANPJrhJul5AiNc3+Zy4a4DQCE+begd8YLpKWZ9666ypTx//UXdOwIPrdBK3948kZzUeUK\nnOZrSp2A9bp1gwcCIHkD/Phs0fJ+/cwk92B6LRZUVnl7m1ZHYAaw+i1xLXluafhcvpXDDV8jsFYg\np9JOsWDgAkZ0GMHLG18m8g8zKsgvR34h6YkkvN2ruNDTCmEBYUzqMQmAj3Z8dMn1/078mzydx5mM\nM/QO7s2kHpNIz0lncfRiFkcv5q+TfxE/NZ7anrWZs3kOp9NOAzCg5QD6NO9DYnoik7pP4tkbnmXY\n8mGcyXCioSBdSGZuJu4Wd04+dpIDiQe46eOb6Pl+T1KyUnjimieYfu30Euu/+9sCGhwdD6mN+CJw\nNO+/+QL9+pm2/hs3goeHKe+fPh0S65i7aWdPAFInUI3U86nH7Utvx83iRp7OI0/nkZtnKtK+GvFV\niXXXHFzDMz8/A5iexpO7T67w5952m+nM4uFhbmcnTjStFpZGReMz8Xba1+3Ntu1ZvHnXCwxoOQCA\nUP9QAB675jEeu+YxAPxm+zF6xWjcLe70bNqTyT0qHpMjrdq/in98/g8a+jUE4B/tzG3V49c8zuPX\nmO6cl718Gdm52RxPPc6zPz/LjGtnsPvUbiZ/N5lJ3SexNX4rHep3KNxnUkYSJ1JP4OXuhb+3f9V/\nKRdz6MwhYpNjScpIAky/mmDflux54G88POCVTa9wPPX4BdvlZMPvn9/EC4+1Yn38aH78Ebp0Me9l\nZ0NMUiwvbp5NgtJsOLy+xO/YWUmdQDWyZOgSkjKSsCgLbsoNi7IQkxzD0OUXdguOOhxFWEAYozuN\nxsPiUa7u6gVWrID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EzSVJQAg7qOVRi4jwiAuW33z5zczbNo/oE9Fk5mTSKqgV3u7evND3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\nAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -356,7 +282,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -373,16 +299,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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EA0hun3wurXUEgNb6IpDLepHMCakWwhn3Daz47gR//mk6jRD/NnjEDXT9Icxr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\nODl+CQOb17V6gQfn+O6eRdo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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -413,7 +339,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -453,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -461,10 +387,10 @@ { "data": { "text/plain": [ - "(0.050114731288578115, 0.050111494213692112)" + "(0.050114731275088642, 0.050060529983498012)" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -479,16 +405,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -506,16 +432,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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BDc65rQBm9howCVhXr80kIAfAObfUzDqbWXfnXAu6aN7IXb0iOgO9oPGHZTTn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\nGXQpkVAAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/examples/Parameterized inference from multidimensional data.ipynb b/examples/Parameterized inference from multidimensional data.ipynb index d92e56d..44ddeba 100644 --- a/examples/Parameterized inference from multidimensional data.ipynb +++ b/examples/Parameterized inference from multidimensional data.ipynb @@ -24,21 +24,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n",