From 6d38d96f16f64a300c1567e9b4654d7efc31dbb5 Mon Sep 17 00:00:00 2001 From: Oscar Araque Date: Fri, 23 Feb 2018 15:48:59 +0100 Subject: [PATCH] adapted some calls to new scikit version --- ml1/2_4_Preprocessing.ipynb | 32 +- ml1/2_5_1_kNN_Model.ipynb | 77 +- ml1/2_5_2_Decision_Tree_Model.ipynb | 153 ++-- ml1/2_6_Model_Tuning.ipynb | 1089 +++++++++++++-------------- ml1/2_7_Model_Persistence.ipynb | 14 +- 5 files changed, 629 insertions(+), 736 deletions(-) diff --git a/ml1/2_4_Preprocessing.ipynb b/ml1/2_4_Preprocessing.ipynb index f386904..1c985b0 100644 --- a/ml1/2_4_Preprocessing.ipynb +++ b/ml1/2_4_Preprocessing.ipynb @@ -56,9 +56,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "from sklearn import datasets\n", @@ -83,13 +81,11 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "from sklearn.cross_validation import train_test_split\n", + "from sklearn.model_selection import train_test_split\n", "x_iris, y_iris = iris.data, iris.target\n", "# Test set will be the 25% taken randomly\n", "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)" @@ -98,9 +94,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -118,9 +112,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -191,9 +183,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# Standardize the features\n", @@ -206,9 +196,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -306,9 +294,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1+" + "version": "3.6.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/ml1/2_5_1_kNN_Model.ipynb b/ml1/2_5_1_kNN_Model.ipynb index 6323f58..ba4b905 100644 --- a/ml1/2_5_1_kNN_Model.ipynb +++ b/ml1/2_5_1_kNN_Model.ipynb @@ -71,10 +71,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "# library for displaying plots\n", @@ -86,10 +84,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "## First, we repeat the load and preprocessing steps\n", @@ -99,7 +95,7 @@ "iris = datasets.load_iris()\n", "\n", "# Training and test spliting\n", - "from sklearn.cross_validation import train_test_split\n", + "from sklearn.model_selection import train_test_split\n", "\n", "x_iris, y_iris = iris.data, iris.target\n", "\n", @@ -139,10 +135,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "data": { @@ -152,7 +146,7 @@ " weights='uniform')" ] }, - "execution_count": 5, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -170,10 +164,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -198,9 +190,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -221,9 +211,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -251,9 +239,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -309,9 +295,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -349,9 +333,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -369,9 +351,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "We see we classify well all the 'setosa' and 'versicolor' samples. " ] @@ -392,10 +372,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -407,7 +385,7 @@ } ], "source": [ - "from sklearn.cross_validation import cross_val_score, KFold\n", + "from sklearn.model_selection import cross_val_score, KFold\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "\n", @@ -418,8 +396,7 @@ "])\n", "\n", "# create a k-fold cross validation iterator of k=10 folds\n", - "\n", - "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "cv = KFold(10, shuffle=True, random_state=33)\n", "\n", "# by default the score used is the one returned by score method of the estimator (accuracy)\n", "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", @@ -437,10 +414,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -481,9 +456,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -568,9 +541,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1+" + "version": "3.6.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/ml1/2_5_2_Decision_Tree_Model.ipynb b/ml1/2_5_2_Decision_Tree_Model.ipynb index a0246a8..9e56206 100644 --- a/ml1/2_5_2_Decision_Tree_Model.ipynb +++ b/ml1/2_5_2_Decision_Tree_Model.ipynb @@ -72,10 +72,8 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, + "execution_count": 1, + "metadata": {}, "outputs": [], "source": [ "# library for displaying plots\n", @@ -90,7 +88,7 @@ "iris = datasets.load_iris()\n", "\n", "# Training and test spliting\n", - "from sklearn.cross_validation import train_test_split\n", + "from sklearn.model_selection import train_test_split\n", "\n", "x_iris, y_iris = iris.data, iris.target\n", "# Test set will be the 25% taken randomly\n", @@ -126,21 +124,20 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n", - " max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=1, splitter='best')" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -163,10 +160,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, + "execution_count": 3, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -197,10 +192,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -226,10 +219,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -249,10 +240,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -283,49 +272,19 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: Not built with libexpat. Table formatting is not available.\n", - "in label of node 0\n", - "in label of node 1\n", - "in label of node 2\n", - "in label of node 3\n", - "in label of node 4\n", - "in label of node 5\n", - "in label of node 6\n", - "in label of node 7\n", - "in label of node 8\n", - "\n", - "Warning: Not built with libexpat. Table formatting is not available.\n", - "in label of node 0\n", - "in label of node 1\n", - "in label of node 2\n", - "in label of node 3\n", - "in label of node 4\n", - "in label of node 5\n", - "in label of node 6\n", - "in label of node 7\n", - "in label of node 8\n", - "\n" + "ename": "ModuleNotFoundError", + "evalue": "No module named 'pydotplus'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexternals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msix\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpydotplus\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpydot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdot_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'pydotplus'" ] - }, - { - "data": { - "image/png": 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Qo0cPaDQaAIBEIkGPHj0AoHLf9ZepPyFc8f51YojwAjPzuXPnMgDMxcWFLVq0iK1evZp1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\nmDJlCjQaDaZOnco7lsmgZq4HxcXF6NO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- "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ @@ -357,16 +316,14 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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YVMYmcLdsgaefVn/1rKBpEB0NOYqYw/Dh8OGHSjhERCg3kd4dNGWy4K/XDuav\n1w5u17zzrSwq0yw6Y2Ux27jV1ioB/NRTjsHcfSxf+Qe9UqMJCfJj14ESZjyxknvutfL002rs/PyU\nALApWQMy4vHz83E6bvp1BwdyjjBj4SrmzbPUW4tTp2xGCHsrMD9fCZmWKm0tGddmi8p0BrRlURlX\naR6Kd25k8ysvYTEuBjahWEH/BGJRcXIFH//+nHXv5Prsom1dVOZ4i1i4Ov6macsJCw1gYEY8BoOh\nfvs/bjiz/nNrispox/cAPpVSDnCxv8sUldm0LY91G7MY1DeB2OgQft6ay+JX1xMdY+XttxuOu/lm\nOHIEbrwR3nxTrTI1GhWTJDJSfV+6tOH4CX/358kHrmZQ3/azHFtTVEY7tgfHMa6daUxt2rivr4WA\nQGk3FjfdpBZshYTAsWMqlhMbCyUlahHXUY23GBenCAApCeGUVhxrtp6ALf4QF6fWftisxQl/9+fy\nMUN4+b2NJCYYyDtsxmqVdE/xq1fajjcm0CZFZYQQAcDVQA/98VLKR4+rd50QrtI8qO1PYTXFAbcA\nL6IyWxQBVehdP1LmOk0h0RZwZ0GSo9/YlR85v/go7yy+qs36KoRYBmQCMUKIbGCOlPL1NrtgG+L6\nyR+waXsecXHw4jIID1Mvc7iE4iJHip/yNaelwZo1cMklajGQ8j8rIaE/Pu+wuU1cdBaLleIjx7CY\nG6y8ZA9cp6uPq436ueG3HKY+9oXdWFRVwcKFiv4ZEGBvJUydqgT4vHkqzlZSAvPmVdplEXVGA9XH\nHxytRWWtD2D8pQPqFTWgXS1CcM8dtAKoQKm/dc0ce9zoKN6xqzQPYcnp2vbvaIgFDEf4+tJ9+CVk\nr/8SrMOBJIRPEQPG39curh9PF6kY0j+JnftL6Jse2wa9BSlll89JXXrkGAv+s55N2/PsVnVOngx+\n/hAQAUeLFKUzRtMYb7tNCYD9+5VWmZSkNEdbwH7qVLj7bujWTWmTVqvnLfHXP9zMU6//RFxUMEJb\nGa4vL9kadMZxPd50HTFRwVw6NoNN2/OYNGkrsbFKAEyerAL7kZEQFtaY8mkyqf2gAvv6cXVFA7XF\nH/THhYfDQw/5sGB6g3vWUXFrT7gjBFKklG2ZWaxDeMeu0jxUZO3Utieh3EChCJ94Tr34EqJ7DyR1\n5OXUlqtkXIGR8ViMNdRVHWlzQeCpIhXn3fIWQggsFisffPEHqd0inNahPdmx4utdzHpyFbV1FlJT\n7V/iiAgRghe2AAAgAElEQVTFDjnvPDXZT5igJvwhQ5QL6LPPlKUwdaryIRfprIW0NKVRTpgAvXvD\nfdN8PB78e/W/m1m37DaiIk78rLCtKeH46JRzGTU0jb/P/oQndMyu8nLlDnIkWVitDdtsqSFcWXX6\n+ENhoQO9tNyHT1++sUPSRjuDO0JggxBigFYK0uPoKN6xqzQPEWl9Mdc9BWSgKKL7kJZwdq94FQwx\nGHyOMmD8FAB++88/2y1jqKeKVLzxxBVt1MMTBzar697JFpYta/yyl5aqCR8Uw8THR/mOd+5UKR2W\nLgV/f3jvPRsDCKZMhPAYKClV1sLQodrEkWf2eB2HbvFhhIUEeLTN1sD37rVt0m6J2cTsfb+z6DnZ\nYB3f/RWjP8wh1tc9hsEFwK2RMcycodZ4FJdAoD/UaJTPmJiGmEBlZcO2ylIBRsm0idAtBg6XgjRZ\n8Zm1Hl9fPxJQaw9mPbyKgADJxIlWUpIVffTx+8d1GgEATaeN2I7i7fsCtwshDqDcQQKQXX3JuLPs\noLbJHQk2BpASEmOAL8B6NVbr/9i29HKEEFhN37VbxtCmUt8eD1I0oTH5n1/y9MMX2u1ztu1khM3q\nGjJEMXmuv175cePiFOvHalWCABTDxNHfe/31SgBUVQmkFBisVswWmJAPrwAfvAHffQoFpRBthFsn\nf8T8B8/nsnGtWxj07/cUYSG1WwTX3vNfzj27p13hoL9ff6arU7skco1GkuIE6enKpZaeDomxglyj\n0W0hAPDPlFRuro3j4ZwcSozVRBrVRDcmLIxTTUG8aiwitExtuzMqnuGBoZAMTxw6xEqjlUP5KmB6\nvsHQ6NpCQFSkD0aj4JpxZzH+0gGdjv7blCVwSbv1wg20Re6gbkPGEpacTkXWTiLS+gJwcM37QDz2\ni8TSgBCUZWAEopAy2O4YZwvEHJlH7i5XdwVXqW9bgj0Hy+y+WyxWtu+2zznf1jRCZ+hWerjN2i6u\nqOVQUTU94kOIiwh0ub9noC+F+RZKS5Wf+KmnFGMkOwuuN8MXoQ2JwyIi7F1FYWGKEiqtYCqSWKyS\nBFS+lQnAEuB/RgjJh2oU4+ITk4WrH1/F1en+TvvlLgyaZOoTDn36R0Flef0+gWjT37YjkOLvT362\ntLeOSyQppzROi15iNpFrNJLir/bZPtsm7HKzmU3V1XyFetOrgYuqqvihqop3UEFRgHuKi+gXHESf\noCAOScnv2vG/AwetVsrNZkrMivffUJPAosXwNjL+UqeEqg5FsxRRIcTbUsqbm9vWqk4od9CnrqyL\ntqKI6tlB5trdKCNHpYiGK4GlKEtgFPAxcDFgQOkEfsCP2KyFQDGcTf161T9UK8rKmJWTTQ8hOCQl\nV8dE819jBbGxkJtnJibah+pq0W5Lw2147u2NPPfWRmqNZoICVF+llPj5+XDjZQOYqcsg2lqKaHNw\nRhEtOGOwp5q3w8flZcwsyCYpTpBfLJmfmMoVkdH1+x3HKz0okO3mGuJioagYgkxq1L8ELvGHq66D\njz5Srh77OrMgLYBZTfwrUGrDtcB3qJwrdwHRwBHgBeA64AyDgcdPOYVBwa1L6wDwafkRLo2Mcrkt\ncfNvjc7x5Ng6jmtbjSk0jGtirKCgpPG4gv3Y7rVaMQhBujbOj3dPBWBadhYRAmr8ICkW8ksg0AhV\nAsx+EKe5iqKMUImaKf4UEsKP1dV0B7JQM0Jvg4FDUvKP+AT+F1HGSy83JOxrDzpwm1BEgX4OjfoA\nnrYrhfbXbrBnB/mhbqkhT5BaJJaBooXWAleg0hz1RTHkFmj/owkQ+SzuHl8vAErMJmblZLNWSgZK\nyVrggspSnn9B7zawMHcuzJ7TwPBpj1qjd998FnfffBbzX/rBbsI/kVFiNjGzIFvzHUvNd5zNOaFh\nxPr6OR2vi47VKK3wcIPGPh24ELAa4YP3ITlS+YInT4SIGOUiusmoJvwDwKM0cKsBzgJ6oxJw5QJf\noZ6gbUCWlPVaamvxTGFhIyHgbNuJgCsiozknNExp9qf4N3ID6cc2SUoyoH6ctwGjsrMwCMELwD/8\nsHtHJ00Eq4QXddvunwifG5WKuKG6mp+BJNRMsRYYaLWqdgsLkMd8unaheSHELOBBIEgIUWnbjFJs\n/u2pDnQU79ieHfQmjVNEJ9NgBEZpx9hqtPZAlT7YDQzm4W5RXBnd8ILlGo30EIKBmjYUAsTH2rsN\nEhIU3zgy0kpOQSVPv/lTu9YavXhMb7bvLrTbFhYaQEpCOL6+BhdndU005zt2Nl7dUdrPIZSGFwr0\nByK1zw8Y4f4ilVJ3vwVS8tX68v8Bm4GDKOGxCXu14gDKlpybnMJVh/NIE4IsTSM9Hj+2M3xTWcG3\nlZUUmEw8lJtbv73KYsFXtKuO1a6I9fVzOvnnGo2Um831Y7sJ6In9W54gBIFCcJqUJDm8o/ExKkBs\nx8iLUa68bkApSgBsBlId2k01GBh9+RncN21Lq2J47YGmsog+DjwuhHhcSjmrrTrQUbxje3bQWTRO\nEZ2HWhphQr2+x7Qzt6Gmhh5APlDKyDB7clOKvz+HNE1jIGoyKHLIQlhYqGhm+QVWDmSXsezTrQ5u\nhbatNTp70bfs2FNEn/RYkLDrQAkZvWKpPFrHY/ePZfRZXTZRZCM05zt2Nl5ZKK09ClX6Lgi4CZXo\nqhL4E7APlVlxIrAYGIe9LalWkygMRE1ATwEzDAb6hwTzXb9+jXzTrUGinx8Dg4JZWVHBwOAGemiI\nwYe5oSdPpThH155RG9seKOGsf8sLpcSAGvNyh3e0uAwsVnj3XcX62r9fBfM/1drphlICLCguib7d\n/VYr2R9sZta9o+mXkdCui7+OF01ZAjZH3n91n+shpWzsWOwC0AdrFTtoFJIEpMmEerW7o4z1v6Fc\nP6CG+3q1T+SDNKGya+dye0wopwba87Fjff14MDmZ0Xl5pApBjpTcHB7NlMlHiIhUeeSjouDhhyEk\nCD5ds7fRwpPYWPjqu31U15jsAsngmRWFCbGhLJw5joxearHYnoOlLHp1Aw/eNZK/z/7shBICsb5+\nzE9M5b677X3Htok31tePx7unkpmTTbSUFKBMXgPKd1+BmiR8UZP8TmyOQDisHXcHynbUa4NJKCEx\nHjUxFAD+NLh+nGmwrUG/oGD6BQVzVXQ0fiew5t8UHF1724CRQjBKSpJQ4ziMhpjMmIgI+gYGclVR\nEWEmWb/wr7ISpj+g1nVMnAiffAxHyiHYCAtR5Q9tE34meuewshBeA/oaLWQ++x1rPryz0woAaDom\nsEj7HwgMAbai3o2BwC8oRadLwTFNRMqwcVhNdahJPw04TGSvEMoPCpB3amdtQ9XTeRnhczcjZrwE\nwICI5Vz/uk8jAQBKE3ksL48UFGPg/5JTuCkujov+Ppbr7v2AK65UGQjj4uDwYfjt9wMcrbbXQoqK\n4Ok3N/DByp/Iz7eSJHwosVrBX5Cc7NvqfOMHco7UCwBQlaj2ZR0hLdkz9U2FEBegyDAG4FUp5QKP\nNNxCuPId29wGI8LD+K5fP+7Yt5+c2hp8sScJD0MlDknStlUC92jfS4FrUEFevTZYLAQTpeQxITgg\nFUvoKiE84vpxhjG7djYZWPu2T98m9rqPzja2eji69gYCvYQgKTSUtVVVhKAifMFAGbChooJvKyo4\nMziY2SkpvFFUxBpjOW+/rVYOA8TGQEm+utkEIBulCAzU/nqgdw7D+zQ4jlN9O3/69qbcQWMAhBDL\ngcG2xWJCiP7AI+3SOw+icZqItWR/fyHK0F+L7dUtPzBMC1Fnoob3EFCH8L2XgTfeT1hSDwAyLxjM\nqe8eanQdO00ETVM4nMcFUZEMHZjMJWNOZcWKPXZpCKZNg+uuUxpHbKwqWOHvb8tOqRaI3TdRLSha\n8rQkPd3Y6rQRp/aMYdaT33DZ2AwAPv1mN6f2iKbOaG51TEAIYQCeA8ailOVNQogVUspdrWq4lXDU\nvB3dBvcmJrKjtoanURqQTavfiXokfFEBwAXADGAj9q4fgHOAngYDOVLyRPdURoQrwRNsMHDMavWY\n68cZ3uqlTMk3SooBuCZKsWQ+OlLmMdZFZx1bGxxde9tQitjuqiqWoawyR/rHMmD8sWMYrVYujori\n47xySksbsoWWlioB4Kj9n4tyCB9C7xxW1h7acdnmzhkM1sMddlCGfrWwlHKHEMIzKgXtp1U0ThMR\nAsRpfw4BYVEDcgZK7o/Bx38MZ/z1DuL6ntXsdZxpImlCBSEjgXOG9mDTH3saBYnPPhtWfiWoq5Xc\nd5/yQ+qLj8THqNwlrU0bYcPi2X/mreVbefUD5dUbMqAbD989Cj9fHz545prjbs8BZwF7pZRZAEKI\n94DLgU4xUYBzt8GI/HxSUIyemaiXOAlF6WwgA6sJPwH7p6anEDzUsyf9g4Mb+fnbatJ3RHeNXfRd\nVRWrMxosxIeCkhm3exezPXOZTj22eteeLeh+RXQ0P5aWUkFj+kciKrCbCKyrqmJcRASxRrhvonrn\nirQFfaEO50WjBH4hyrIYjrL8boyO5qqyMnVtPwPzZnbOYLAe7giBbUKIV4B3tO83ot6FVqM9tYrG\naSKqUW6eozQKCFul1i21XkDKPMJTert1HWeaiJ7+d8ZpiY3SEBQWwoYNUF4hiY2FRYsU/1xffMRU\nC34OucdbQzkLCvDlHzecaZc62gZbsfJWIBn149mQi5o8Og2cCevumkWQD7yMKqQdgnrhHX39hdg/\nNYeB/sHBHvfztwRSwsajRzkrNBSATdVH8WDG+E4/tpdHR9dbYCn+/pSZzXxQWkoE9vSPJ1Bxmv9p\n//ceO8btcXFUAD5GMOSr9iqAcuzHuxTl9vFHScDpaWmMCFOU48lJSYqRtmhMpxcA4J4QuB2lDE3W\nvn+Hyq3sCbSbVuGYJsJiPoS0CJBGGngch1GxfntiX4+xN7idDsKZJmLzAZuB6IgghFDlB20xgcBA\neP996l1EW7aodLb6VLYTJ4LZDFMmG0ju5ttqytmmbXk89dpPjYrKrP/vnU2c5Xk4FpVpj2VzJWYT\n5WZzI2GdJyVWGhyBEqUm+OPAKAGsNDw1xULwRBv5+VuCRampTMvOotJiRSKJ9PFlcWqq3TEdsRq8\nPaBfGaxfeGdFuYICUS6gRNTEb+caqqrir7W1GITgO507dxgqhjAMFV/IASxSMsNgIEtz+10e1bBA\nzaYImLuAAAA3hICUshbFbHuqDa7frlpFTMZgBtw0FWNlGf7h0fz+7nLMtatRBiHAzUAE6tXehJoK\nkhFY7dqpqzrCvm25nGY2Eevrx57aGr6vrCTOz4+zw8K4PDqaJH8/vqyo4MaAAJL8/Xi/rJSBh0qp\nrjGRlubP/AVGCgqgrAwee0ytSLS5f0ymxsXH4+JgaN8MbrnqdA7mljtdUFZ65Bg79hSBgP69G9eo\n1WP6/NXMuWc0A/ok4GPwOJMkD0WdtiFF29YIeiEA6sVsS+jjAEYpGSkEvTRhfVtcHF8UFbGBBj/v\n6SitTz/hP6n5+nccU7RhmwXQWXB6cDDf9OlLpUXFkcJ9GldwzczMJDMzs/773Llz3W3erbHVj+vA\nqirODgtzt/0WwzHG83j3VC6PjibXaKzn9fug1nOU0JjNlYJyCaU7WIinGAzcmZLCGcHB9XEdaJx6\nojOgJcK9KYroB1LKa3WJ5OzQ3gnkWps76PAv37D17YWaq6cbwqcQKa2ocM75KOOwEmX8ZaBY3WrJ\nj2ISKxT8spo9yxZQHABPVhs5IziYH6ur6aa1JIHhYSGsr6tWVYmKlWsnMR6K73iLq87vR0GBldJS\nWL0aPv9cFZgoKm5w/xw+rASBYyrbDaY9fPn9blKSfSkpwY4dtOLrXcxcuJKISNW2QQiemHGBS/ZQ\nWEgAY5opSN4KbXETcIqWDiQfxa+9oSUNeRJ7amuYnp3FJ0CmbcWolMzQfPkAbxQXky8lQ1F0gXLU\nzdjonhOlZES4MvszwyM65kZc4MOyMq6JjualoiKn+yfEx3viMm6Nrf59LVjxiSeu6xIlZhM7jh1j\nRk620uC1sc3MyWZEeBjBBgMFqADwrcAqVEznTOwtvFxgUHAwrzlYiLlSMjY8vNFk35kmfxtaItyb\nsgRs7p+2TCTXIo1x43HmDqqrOsK2pYvB6o++PrDwOQd8RyIM3bAas1CZYa7Anhw4goNff0iPUSoF\n855lC9hgqmOgSWUTGl9dbWdSjgR+qKu2Y/9MmQLPPq9YBpMm/s4Dfx/J5Ht/wGSWvPCCsgBuvLGx\n+8eWpKy4WGWm/PhjqS0oM9uxgwAeXLSKJU9bddeUzHpylUv20NmDU5j3/HdcOPoUu0yTAzIS6j+3\nVFuUUlqEEHej3jdbwH+nWye3EVaUlTEzJ5tEVODXlrMnRQgifX3rX+gnuqcyOiebGCkpRC0Gsmk7\n44GFTjJFdhYcsyqLtdrq2dTUenS2sbVp/xFSEotD2keNkAGK/nE71I+/LfGZ3hGc7u/P5EOHiJSy\n3vVzGNqM0ttZ0BRFVAuLcB7wnZRybxtcv100xpqyAgyGBCyE4Zj584y/3sGRA9vZ/5UFFQZMx/5R\n6o3BUEVNmXJUpPj4MtCkCqxVoKSW/uh4oMZh+XlSEhQUQJ8+yu1TZ7Qwamgvtu/fT3q6cgElJ9uf\nk5amVik++4ygW5KB4cMt/Pijo4tIsGNPERHhgSTEG0hPt9hds65WuGQPbf5D3c+2XQ2pI4QQvN96\nZhAAUsqvUCZVh8PGBFqn8/OOQWmDB6QkWFdj2RZUXFpcwvOFBewHl0H+zoZbYtW6j0nxCQQaWkfz\nbQqdZWzt8gJpHdKP1SFtrMrMZopp8P+vBS7SvtssvAnAPqOxnga6FrhMSj7r08fpWqATCe4EhlOB\nl7Xi0r+iAsPfSym3tPbi7aVVBEUnYrUWojyB9kVkwlN6ExgZz/6v3kcxhg7h8ChhtVoIilaZ/3It\n5vq9ESgTUn90EVDrsPw8P1/RPPfvV26fp9/YQEQElFeobYmJ6hhHxlBUFJjNBsrKcFqhKDfPxF1z\nPuHBu0ZTWGRtdE2BdMke+uDZv3j6Z+60cMYEikCZuLE0aNA22BgeN8bFsrSkhMzCQo/m+GlrjNm1\nkzg/P/4UEsKfQkI5KzTUaVygq8NxXF9EafanoFJ6XBMdXZ8bSu/ntyWF11t4C1CuP9u2TCBdW9tx\nosOdwPAcACFEECqXwnQUr98jT1V7aBUBYVH0vfof/PH+0+hCfFpMAMKSepA68mKyv78IlSJsGNi8\n/AbBwBun17ODeo+fydnL5tMzAPKqjZg009EWExAowpFt4VdpKVgtKiNhZQn4C1iiuYrmzVPHxcWp\nSX7SRK2SUSlERggemePD4/ePA1S20dAQyaRJFlXZqBKmT4e0NAv3TfuOB+/KZMrkNYRHWCkrs8UE\nzncZHC4uq2bBy+spLKnm7UVXsudgKb/9ns/1l/Rvw5HoGDjSdm3UQNtksePYMacpnGN9/ZicmMSN\nsbGdMgjoCj+epnIS/Xz0KF9XVjIrN5cIHx++7tN+KcvbA47j2hfF5LoLmArcGRdXf1we9uRwR+Ut\nG8X37ypWnyfRrBAQQjyESpQTiqLR3A9838b98jgiuvfGJ6A3ljpbvu0z8PU/r74QTL9rJ5M68nIq\nsnYSHJuMxVQLQHhKbzt6aNKQ84jOOJOxGZ/Ta/pq5hw8iEHKeh/jaOAWwGiE6w7DR8C9wIJ8lVp4\nicYCKi+Ha66B3ZuhIk9NTP0A8uGuAAOXXjyUy8/LqGcA2YrJ5ORXMP+VVbz9trl+WXtCvKBf73h+\neO9vbPgth+Ijxxg5JLXJ5HPT/rWKay86jWff2ghAr+5RTJzz+QkpBPS03XgpycaeGpiZl8cFkZEu\nJ/jOwP0/Hhw2GtlUfZSfq4/yR00NGUGBnBXS+joFnQ36cU2QkiyUCjcNuCkmpt6N44y2fWN0NCPL\nyojT1oUI7ZxM20KvLmL1eQLuuIOuQrGqPgfWAT9KKevatFdtgKDoRKQ1DxUaanAH2dw8oCwCW1qI\nphAQFsUpA1M4LTiYPCRmf3ghVtUitVoVvbOkRCWbKgRmA5ECZvlBrL8KAksJ3bpB0VG1MuEMlAn6\nBJBfZ2XtB7/x1ru/Mm/mOC4b14eYqGBiooLpnhhORTl2y9qzckzs2FNIVl55fdHtxa/90GRuobKK\nGi4dm8Hz72wCwNfXgE8b+pE7GjZf/zeVlbyWm8tAzczXBxBPlBd+yB+/Myg4mHsTEniie2rzJ3Rh\n6BeGGa1WDhoVY8/Rj++4gMzm8nOk+doWenUVq88TcMcdNFgIEY6yBsYB/xZCFEkpu1RFElc1hW1a\n/muv3tvk+fpFKLG+fvCqijDgb19ZavJkmDwF/Pxg5gwQRsU5utKhYMWUKfDkkzbGEFxmhBiU0PgJ\nGHjMpLTU+asZMaSB4RMTFcysCaOZNOkbUlIUc+jWW+GxF9chBFo5O5rNLRQc6MeRihqElm3ytx35\nhIWe2KZvrK8fY8PDmdvEiu4TAaszMth4tJr/HTnCc4WF9AwIYHhoGONjOk9xc09Cb6k1tcjI0aJz\nRvPtalafJ+COO6g/ivk4GpVNNAcPuIOEENegEtH1BYa2R2rqbkPGEpMx2K7urztoahFKnI4JlJWl\n1gS8/LKanINDINSoAlGOBSv0jKHYWJhxGBb6+3CKQTCw1gxoxSmcZCHs1zuelGQ/7r/fRGKisgi+\n+lIQ4C/sGEJN5Rb6v3tGccfMFWTllXPlXe9ReqSGl+d1qrLSbYKmVnSfKOgXFEwP/wDSAgL4ufoo\nH5WV8ePRox0mBO6485kOuW6HY3Pzh3gaX4w4/nPccQfNRzGCngE2SSlNx38Zp9iOqtL2sofacwsB\nYVFuT/7gPNGYfhFKvpYHKCZGFSR/7jl7rv9RVCAqvwnGUHEJhKOWqh212mupzrIQdk8Mp6RE4ufX\n4BI6ckQihHQ7t9CAjAT+++y17M8uQwLpqVH4+Z54DBJncOYaOJHw5927MErJEI0d9L/ep9Ynl/PC\nC0e44w5qE/VQSrkbQNj8EZ0UTWUFLTebSTCpuqPBYRAe7sDjj4Vuh1Vd2hAHxpDBAA88oGifoSFw\nj/Rh/iyVhTxz/mpSfQ1km61OsxDGRAXzr2njuG/a6vrSdTYW0bSpq4iPN1BUZOWx+xqf++U658s9\nDuYcAeDC0e4lyuvqOJHN/qXp6SfsvTUFfcGo41H0Tna4Ywmc1HCVFXRH9THm5eViAu4zwqJSMPs7\npHooUZzaXf4QGAu15YLSEomvn6KHBgYqauj/PezDitdvrGfzjBiS2mz1sMvP61PPGLIdt+LrXUgJ\nRpN0mTVy9foDLu9VIFotBFrr5jtpXQftiC86ugNtAMeCUQPGT6HbkLEd3a0ugTYVAkKI1aiFmfWb\nUOl1ZkspP23La3sKznzIDyYn81heHt+hCk1MRgVzZxgbeP/FxWA2wnx/eK4+ICyZMtmAxSKZNUsS\nHQ2VFQbmTz/fjs5pYwI1B/1xpUeOMXvxap5a0nRgePGDf/bsD9QYHeLm86Lt0RFxPHfQuGDUNrYv\nG0VMxmCvReAG2lQISCnHeaqt1iaQOx7omUAAaYEBfJyRUZ9B0OYiSpISf+BU1PKzR4FJRvhrHjwP\nPA4QL0hPV2p5ejokd/PljitHsS+7jFNSo/nzyHS7ibyl9YNzCipJTDR4rOgMtCyBXGd08zlzE3hd\nBy1CpxTwjQtGDcRqiiVn/aeccsEtHdq3roCmsoh+ipPsoTZIKS/zYD+anTBak0DuePBxeRkzC7JJ\nihPkZkmkUXKqMNSzgmwrS/darWSgVh0cEHCKHyTHQl4JWI1qNWopIB0Cwjm5Jh5+6msS4lUKid37\ni5k79VxWfL2rnuPfkvrB3RPDKSiwuh0YdgetSDfcaeDMTQCckK6Dgi3rmtyfOGh0q9rvjAIebAWj\nDqEy/oSgqBhl7Fv5Pt1HXEpAWBRl+7ZRsnsTsRlDiT5FCQuvIqDQlCXwZFteWAhxBfAsKn3LZ0KI\nLVLKC9vyms2hxGxiZkE2i56TpKcrps20ibDSaCWfBlYQqLQMtsRVPf1gsW4NwKSJcIURFqSmIQ1w\n393ZJMYK8isEJpOFF1/SH7uVS8ZmMHvxahYtNrvF8XcGZ8Hi1hSdaQqedPM5FpXxpHHqyk0gpUSa\nV2A1qQlj+7IrickYjPFoBRVZO4lI62u3aLCrTBZFO9a73imEnRA4kYrKBIRFEddvCIVbLkIVkMwB\n/gZyJTVlBWx98zFKd28FUtj/1fvEZJxOyrALTkhFoCVoKoto02pFKyGl/BiVjbnTINdoJCnO3n3T\nLQYO5cNQ7FPT2hJSbQJSHdYAJMcJ/hXQs34hyjmhio648epuvPLh2kYMonUbszziynEWLHaEK3aQ\nDe4EhtvKzQfwxA7PWXmVuXsRwj7PqxApWE35KM5Wd6AUKcP5/YOnKdyyAdskkjryYvpdO7lLBRwH\n3vSg28e6svK6YhyvruoIxTs24ZAMBGmpofzQTk0ANOwr3T2M0r3bwbpBpxyMPmljCO4sFuuNcm+f\nhqrOBoCUslcb9qtDkOLvT26WPdf+cKmqMOW4snSv1co21L48B5dPSSn0P6VhArbREQOGpvLYi40Z\nRKPPSuON5b96xJXTXFC5rdlBjZrsIGT/8Am///cFlcdDx+2yGA+gsqDEAVlABtK8i8ItxSjejLIO\nsr+/iKTBY5xYEl1jsijasYGjBQexmIz123pfeHuz53lKwHs6hmezxnz8g7AYa+rTvdSUFWA6VgUi\nBRWZW6WdEQEYKNm9icYJ35MRohKpqyBo8EmtzyPWleHRymI6vA7MQZWXHIOqzXDCJpmRRsm0icoC\nyCkFkxHOMxjI0a0s3VNbg5GGWrQW2xqAGKgsEyxMcr4CtXePGG689HQmTdxKXKwSADdeejpDBya3\nmyunrdlBHeXm07tsCrZ+zx/vP4NKxFFCw0jZBIABqEMlwt2NqkAbiSo30gOVTjyCwm3fofLD6ieQ\nbghS9m0AACAASURBVJ1+stjx3pNYjLWU7d1MyvBLKNiylsi0vp6+TJMC3pMxPJs1JmUk0lyIwa8n\nVksWQhjw8euF1XIIq6kWFYmLR5WIMQMWEk/PpHj7Iuzzg+YhLRJ9BUGz0WiXR6yrwtOVxWwIklJ+\nI4QQWkH4R4QQvwL/19KOdgY48/PmGo2cKgysNFo5lK+mg0whuCMlxa683OZjx+gB/EBDLdo/GeHP\nplgm9U5scqHO3KnncsnYDNZtzGL0WWkMHZgMuOfK8TS+2XCAPQdLqTM2VKKacvuwVrXZEW6+w798\nw9Z3nsQgorBaSkGaUI+2QGn8q4H/AYtQL30Ban12IEogCFQ2+QZLAC7i0JqPtf26GhSmg/j4d+4i\nI+UHt3POrDf54fFb6X3R7fQcex2/vDC91e12hICvqzrCtncWIS1PoFIxfoHVZASuQfIwZovNOTEd\nWIES+Nu0/zUAhKemU5k9DFVVOI+w7r04ejgbaVmLbVyF6FKp0DwKd4RAnRDCAOzVCsDkodJKtwpC\niCeAS1Eq2X7gdillZWvbdQeuGCO2hWH5qBjANuCglJgsVruJ/YzgYHLA7rgC4IbY2GZXan6yehcP\nzV9Nmq/BLksouL8+wBOYtfBramrNbNicww2X9OfztXsZ1LfraUJ1VUfY+uZ8QGAlEKXV+6Lo7POB\naOBslGa4ngZtcLjWghn1OAfhaAkoQeGPMoBTgUMInxgsxpp2uLOWw+AXoP77B1JbUYJfcDh1laWt\nbrcjBPwvL81GWiyoEiYBqDEKR43byyit34IaH33h0ESgiu3vLESNocQmFKpy9oIhHr2FJy3xJy2l\n1B23zmQgGJUW/0xUec5bPXDtVUA/KeUgYC8wywNtNgs9Y8RcuwWraR3bly2hxGwi1tePB5OTGQac\njnr1HwEeO5xHibkhZVK0r5KdmcBg7b9+uyuUHjnGQ/NXs7bOzOZqI2vrzDw0fzWlR455+C6bxy87\n8lny8AVEhAUy9Y7hrHjpeg5oqSO6Ekr3bkZp8j8Be4BXUXHNBSjK4D5UBnQrymcM6uXPAF5CuYQq\nUITeNajieWu0bUtROsptKFdSMtJSSkVOW1Ra9Rzi+5+N6VgVvcbewPoFd7Lukb+QdOZ5Hd2t40bh\ntvVUZu8DfkRZcuWoCh0VqPHep/0PATagxm0iatwPoer8vYwSGD+jSslox1tLtONAKQWKUlpX1fXe\ngdbCndxBmwA0a+BeKWWVJy4spfxa9/UnlBhvczhbWGLwSSXXWEusrx/9g4NJF4JXpKQHypnwrkO+\n+VyjkT4GAyut1np30PlOCpDvqa1h87Fj9fnNd+wtIk7YT0XOsoS2BwL91dAHBfhSUHKUqPBAikqr\n27UPnkDxjp+w99uPA+5ATfJ6X76tmux4tDC/dmwSilLY0+H4dJT7oBtq6d9GbFbEruWjSTx9ZKeN\nC/QcOx4fP38SB2US1+9srGYjBt+ulUDu8C/fsPXtJ2gI6m5C+fxDUOOiH6seqEl/KCoOdDnKkrMp\nbo6B4R4oAXEBiu+SBbyIj+9jnT7e0xZo1hIQQgwRQmxHvTnbhRBbhRBnergfd6DS7rc51MKSLNTt\ngK24jI31YytFF4ASAM7yzTu6jfKdHPNwbjbnH9jFIrP6P37/Hu6Z+QnWWjMZwPu4zhLaHhg7oicV\nVbVMGD+Ei+5Yytl/efW4Fqd1BtRVHSF/8w9AGQ3jmY8y/feiH2PlNrgTNZFkotwG+dr2cOCgk+Or\nEYZCDP690E8iNiZJZ8VPiyfUf/bx88cvKNRuW2eHzVrH+jnKQrPx8HJR8RpbsUiw1QFv4PDlohIe\nV6CEgL4SuP74UvDxRRVK3AX0bVRk6mSBOzGB14CJUsrvAYSKoLxOw1vhEu5wjoUQswGTlHLZcfa9\nRXBVXCZ26wrAvXzzzR2zp7aGtytL7YrITJpYzXtGuIIGj7Sfvw+POckS2h64a/wQAvx9uSizN2PP\n7kmd0UKAf9dKJV1TVoCPby/M5lko5103lIvAB+XIG4PSAvfS7axMEk8fzW+vPALSH/gXkAXCypC7\nZnGstIBdy0cD3bCaDiJ8YhGGK+hz1V3sWv4f7ILDnXSyqKsspba8BIvJSEXOHmwL/s21x7AYu04x\nwAZrPRMlrMegYjsqYK8mdltlb1tMYDSqJFME8A9tWzjK8jNgVzccM6kjLyOqV3+2L5vktMjUyQR3\nhIDFJgAApJQ/CCHM7jTeHOdYCHEbalTPba4tT/KOnRaX0YQAuJdvvqljNh87RrzDArK4GKjIV98H\nAumBfsz41yWM/lOPFt9Ha3DFhPf58rUbAQjw9yXA35cL71havw06/6rSBquuL0qbWw2GOzH49sJq\nfADFZj6ET8BtpI26ksi0vpx+y0y2Lf1/9s47vsmqe+Dfm3QXWrpooaUFylYqICKIMvR9edGfgAOV\nqSKvvoiL4UBQmYriAJw4UEHEgVtEFBVQUEEFZAiCjO4CHbR0pknu74+bpEln2ibp4Pl+Pvm0efKM\nm5znuefcc8895zl0uiLMZkgY/zAR3VU9qqgLLqsQi+7bMgRvv8AqK9I1Jk4f3Enqjq8pPnOKQ5++\naNvu5RdIlxF3NGDLaofjaP0mIBJ0/0efyQvwDmjJyb0/cmLzF0AvlCvnDZRbx6okDMBylBIAob+b\n3rfN5Wz6MUASmTDItiK8LkWmmhvOKIGtQohXgfdQpsVNwBYhRB+AumYSFEIMR8V1DXKmZrGrcwfV\nVFzGmXzzVe3TOyCAU8fKLQrLUjYKWAatUnJ+l9b1+AZ141RWARmn8ykuMbL/8CmkJed0foGBomLH\nekGNPXdQZaO67tfdw0EHyz0daU6zWe7VVZer6p6oa0U6TxNz8ZXEXHwlGXu2ENVrSEM3p85UPlp/\nkMgEVTYrtFMCwbHdLQEe/sCdWMM/y1JCPGZ37P1EJgy0HV/+Wo1Vnp7CGSVwgeXv3HLbe6OUQo1W\nfBW8gIrd2mTJR/WrlHJqHc/lcirUFK4FXfz8mRgUxl1Ts2yLwi7xDeQ2UcK8aorFeIKtO0+wbsNf\npJ86y4IXyjKDtAj04aH/1aE2XQNTWQftVYPlXpcHvyl1FiEde7Lv3Scpzs3koqnPcDb9OGdOHKDd\ngKZTPrQmxWv/eUluNrnJhxySwzUFpd1YcCY6aKg7LiylbLQlrKqqKVwbFsbEMrE4QkUHdVTRQScX\n9vPoYrDKuOHK87jhyvPYsOUIVw1xvQgaYv1H+Q66qVju7mLvmsXE9L+Ko9+sBiCwdTv2vDWvSSkB\nqFnx2n9e3spvSkq7oXEmOihSCLFSCPG15X0PIcRk9zetYbCvKbzLbGaLlDycnOSwTsBZuvj5c1No\nGF381ArTsJAAenWPajAFYE/fnm25f/G3TJz5KQCHj2fx/vr9rjh1g6z/KI9vyxBaxXU/JzuC0oJc\n2vS5XNUwBXR6L4Su2WZ60agnztwZbwPfoKbWQa3ImeauBjU0tprClvf2NYWbEzOf+JbB/eI4mZkP\nQMd2IbzxYf0LRUkpv5NSmi1vf0WF52h4EL2PH4aCXFtyn5zjB/DyD6z3eYUQS4QQB4UQe4QQHwsh\nPB/brOFynFEC4VLKD1FLLpFSqsxMzRT7msJQ+TqB5kB2bhEjruiKTqe6Ci8vHXrXW4seW/+hUUa3\n6+7mj1dnUZiZxi/P3cnedxbRY7RL7LZGMcrTcC3OTAwXCCHCsAQdCyH6o1ZiNEucWSfQHAjw8yYn\ntwhrkahd+9Np2cI5RdcY139olBHcrisX3/cCBaeSQUoCI2PR6etfrKehVvlruBdn7owZwBdAvBBi\nO2oh7ej6XlgIsQC1vtuMWuVxq5SyUSzDdGadQFPnsXsGcdusz0lMPcO1d75PVk4Rry5ybuLQXes/\nXF1Z7FzFVFpC0k+fknN0HwhBSHwCsZeOQm9JLAcuWQNyG/B+PZuq0QgQ1jjxancSwguVjEUAf0sp\naz9LWvGcLaSU+Zb/7wF6SCnvrGJfad/Oq9xQY/jNlfe6/JzlMb44xO3XqA1Go5mjSdlIID42BG8v\nxxXDMQOfc3gvhEBKWW0eecv6j2dR6z+qTV1ZXq7gHtmea+x+8zG8fANoe9EwANJ+34SxKJ/ekxcC\nsOHuimmTrbKtxSivj5Sy0pGAEELOnVsWUf61C4rKaDjHg+cbHZT7/Pnza3xmnaksdgOwUUp5QAjx\nCNBHCLGorovErFgVgIVALHMOGp6huMTI6k//5Le9aQgB/S6IZsKoBPx8622JN+r1H+cCZ9OPMWjO\nGtv7sC59+PHxCU4d66pRnqsXd2o4R10WeDozE/iolPKsJWfQFahcva/UsY0OCCEWCSGSUAk+mnSR\nmqbG9EUbOXw8i0mje3Hr9b04fDyLaQs31vu8UsrOUso4KWUfy0tTAB4mOKYLOccP2N6fOXGA4Hb1\nTw5ot8p/pDOr/DWaBk7lDrL8/T/gdSnlV0KIRc6cvKahpZTyEeARIcRDwD2orF+V4uqapec6fx/P\n4oc1ZWUhLunTjssnrHLYp7HnDtKonNzkw/y69E78Q9SjV5RzksDWsfz0xC0IAdx9tK6n1kZ5zRBn\nlECqJXfQv4GnhBC+OFljuBZFq9eiavvNq2oHbXjpWs7v0ppd+9Ppc76qbrD7QDoJXSMd9mnsuYM0\nKueiqc+45byNeZW/Rt1xRgnciKq+8IyU8owQog1qSFgvhBCdpJT/WN5eAxys7zk1nGff36e45s73\niY5U631ST+YRHxvKv25ejRCCTasmNnALNepKY0xzrdF4cSZ3UCHwid37dFRS7vrypBCiC2pCOBFo\nOlUvmgHvPHttQzdBQ0OjEdBgQdlSynqvNdCoOzENUM1MQ0Oj8aGtzNFoVFQWw66hoeFGpJSN/qWa\nqdi8ebOsC9px9T/OIgdNrs3wOFfKVpNr4znOGbk2ufyydQ1Z1I5z7XGupql87+Z+nKtpKt+7uR9X\nHU1OCWhoaGhouA5NCWhoaGicwziVQK6hEUI0/kaeI8gaklHVBk2ujQtXyVaTa+OiJrk2CSWgoaGh\noeEeNHeQhoaGxjmMpgQ0NDQ0zmE0JaChoaFxDtPklIAQYoEQ4k8hxG4hxEYhhFPZsoQQS4QQB4UQ\ne4QQHwshnMqbIIQYLYTYL4QwCSH6OLH/cCHEISHEYUuKbKcQQqwUQpwUQuyteW+H42KEED8IIQ4I\nIfYJIZwqkSaE8BVC7LD8jvuEEHNrPsp91FWulmPdLltNrnWnOT6zzUquNa0ma2wvoIXd//cArzh5\n3L8AneX/J4HFTh7XFegM/IAqqVfdvjrgHyAO8Ab2AN2cvM6lQC9gby1/jyigl/W3Af6uxTUDLH/1\nqMLh/ZqaXD0hW02uDSPbxvzMNie5NrmRgKxjWUop5XdSSuu+vwIxTh73t5TyCKogTk30A45IKROl\nqsP8PjDKyetsA3Kc2bfccRlSyj2W//NRKbmjnTy20PKvLyqPVIOFitVVrpZj3S1bTa71oDk+s81J\nrk1OCYBLylLeBnzt2lYBSpjJdu9TcFLArkAI0R5lnexwcn+dEGI3kAFsklL+5r7WOdUeV5QbdYds\nNbnWE+2ZrUhjkWujVAJCiE1CiL12r32WvyMApJSPSCljgXdRw0unjrPsMwcolVKurc1xjR0hRAvg\nI+C+cpZXlUgpzVLK3igL62IhRA83t7FOcnXmWMs+zU62TUGuoD2ztaUxybVRppKWdSxLWdNxQohb\ngauAy+t4vZpIBWLt3sdYtrkVIYQX6oZ6R0r5eW2Pl1LmCSE2oyrI/eXq9tldp87lRhtYtppca76W\n9sw6SWOTa6McCVSHEKKT3Vuny1IKIYajymKOlFKW1PXyNXz+G9BJCBEnhPABxgBf1PL8dVm6/ybw\nl5RyudMXEiJcCBFs+d8fVUP6UB2u7RLqKlfLse6WrSbXetCMn9nmIVdXzC578oXSoHtRs/ifA22c\nPO4IqozlLsvrZSePuwblMyxCldX8uob9h6Nm/I8As2rxvdYCaUAJkARMcvK4gYDJ8nvstny34U4c\n19Oy7x7L7zmnKcrVU7LV5Op52TbmZ7Y5yVXLHaShoaFxDtPk3EEaGhoaGq5DUwIaGhoa5zCaEtDQ\n0NA4h2nQEFEhhC/wI+BjactHUsr5DdkmDQ0NjXOJBp8YFkIESCkLhRB6YDtwr5RyZ4M2SkNDQ+Mc\nocHdQbKR5TnR0NDQOJdo8BXDQggd8AcQD7wkK8mHIbSapY0GqdUYbra4SraaXBsXNcm1MYwEnMqH\nMXfuXNtr8+bNlS56mDt3bq0WSdRmf3eeu7Huv3nzZoff3U3ybxTftTGcuyH3bwi51ve7NOdzuKoN\nztDgIwErsoZ8GPPmzfN4m851hgwZwpAhQ2zv58/X5uw1NJobDToSaIx5TjQ0NDTOJRp6JNAGWGWZ\nF9ABH0gpN9T1ZPZWq6v3d+e5m8P+7qQxfdfG1BZP7O8KUrbPcGq/88KSnd63uZ+jrsfHDHzO9v+Q\nIUOcGr03eIioMwghpH076ysgDeewv6EAhBBIF08Ml7//NNm6n/JyBdfKVnteG466PLMNPRLQ0NBo\nQvj7+2cUFxdH1rSfEC6zFTScoF3bUH7+8JY6HaspAQ0NDacpLi6ObAreg3ON+ijdBg8R1dDQ0NBo\nODQloKGhoXEOU6M7SAjRF7gMaIuq1LMfVek+x81t09DQ0NBwM1WOBIQQk4QQu4CHAX9U+bVTwKXA\nd0KIVUKI2KqO19DQ0NBo/FQ3EggABkopiyr7UAjRC+iMqq+p0QQ5k1fMycx8/Hy9aNcmGJ1Oi+ho\nLmiy1XCWKpWAlPKl6g6UUu5xfXM03E1efgmrPvmTz787RGmpmbBW/hQbjGTmFNKnRxtuvu4CLunT\nrl7XEELEAKuBSMAMvC6lfN4FzdeoBk/ItrmRmJhIhw4dMBqN6HTn5hSpM3MCHYB7gPb2+0spR9b3\n4lpn4XmmPLKe64d35+OXbiS4pZ/DZ3sPneSTbw6SlJbLmKvPr89ljMAMKeUeIUQL4A8hxLdSSi0l\niBtxVrb3D/R824xGI3PnPs769d8TFRXB0qUL6dGj0lyRHkVKaV1Q1dBNaTCcUX2fASeAF4Bn7V6u\nwNpZnAcMAO4SQnRz0bk1KmHtsuu5fniPCp0EQEK3SObdN6S+CgApZYZ1pCilzAcOAtH1OqlGjXhC\ntlWRnJzM2LGTufTS/2PBgsUYjUaHz6dMmcayZT+yd+88Nm0axIABl5OSkuKwj5SSY8eOsX//fkpL\nS+vUjqeeeoqYmBiCgoLo3r27LePwk08+SadOnYiIiGDMmDGcOXMGgMGDBwPQqlUrgoKC2LFjB1JK\nFi1aRPv27YmKiuLWW28lLy8PgJKSEiZOnEh4eDghISFcfPHFnD59GoC3336bHj16EBQURKdOnXjt\ntdfq9B08jTOLxYrdZZ1LKTOADMv/+UIIa2ehWYwe4OA/p0nOyMNkMtu2XTm4s0uvIYRoD/QCdrj0\nxBrV4gnZWsnOzqZv38vIyroZk2kUu3cv49ixRN5+ewWgOvfVq9+itDQRCEfKyykt3cX69euZMmUK\nACaTieuvn8i33/6AXt+SyMgAtm37hqioKKfbcfjwYV566SX++OMPIiMjSUpKwmQy8fzzz/PFF1/w\n008/ER4ezr333svUqVNZu3YtP/74Ix07diQvL8+24OrNN99k9erVbN26lYiICCZOnMg999zDqlWr\nWLVqFXl5eaSmpuLj48OePXvw9/cHIDIykg0bNtC+fXt++uknhg8fTr9+/ejVq5drf3AX44wSWC6E\nmAt8C5RYN0opd7myIVpn4VlmPvEtB4+epmuHMIRl0lAgXNpRWFxBHwH3WUYEFbBPET5kyBA6ebvs\n8ucszsh2y5YtbNmyxSXX27hxI4WFF2AyLQCgsHAIa9ZE8MYbL+LlpboYvd6L0tKyGBMhimyfAaxY\n8SqbNqVTVHQC8KW4eDaTJ9/LV1996HQ79Ho9BoOB/fv3ExYWRmysCl589dVXeemll2jTpg0Ajz32\nGHFxcaxZs8bmBrK6hQDWrl3LjBkziIuLA2Dx4sX07NmTt956C29vb7Kysjh8+DA9e/akd+/etutf\neeWVtv8vu+wyhg0bxk8//eRRJVAXuTqjBHoCE4HLUX57UCUgL6/Vlaqhtp3FeWHJDNAmuOrF7r/S\n+WFN9blG6tNRCCG8UDJ9R0r5eVX7la8TkbL9izpdT6MMZ2TryloRqvO096nLCp9Pnz6d5ctHUFg4\nAy+vfbRosZNrr33Fts+uXQcoLLwWUK4so3Ese/eOrVU74uPjWbZsGfPmzePAgQMMHz6cZ599lsTE\nRK699lrbxK+UEm9vb06ePFlpuoW0tDSbAgCIi4ujtLSUkydPMnHiRFJSUhgzZgy5ublMmDCBxx9/\nHL1ez9dff82CBQs4fPgwZrOZoqIiEhISavUd6ktd5OqMErgB6CilNNS5ZdVQl87C01kJs3IKSc7I\no11UEGEhAR69trvoc14bDh/PokuHsCr3qWdH8Sbwl5RyeV3bqFE3nJGtKxk+fDgBAbMpKpqDyXQR\nAQHLuemmyQ6W/uOPz6VDh3asX/8NbdqE89hjPxMWVta+hISu+Puvp6hoCuCDl9endO/etdZtGTNm\nDGPGjCE/P5877riDhx56iNjYWN58800GDBhQYf+kpIoR7m3btiUxMdH2PjExEW9vbyIjI9HpdDz6\n6KM8+uijJCUlceWVV9K1a1fGjx/P6NGjWbNmDaNGjUKn03Httdc2iQlnZ5TAfqAVaqGYO2jUncXn\n3x1iznObiIrSkZFh5vEZ/2bUv5r+3PX1w3twzZT3iQgNxMdHbxsOb1o1sd7nFkIMBMYD+4QQu1Gm\n4Wwp5cZ6n7yRcuREFrv/yqB3jyg6t6/Y+XrSkHCnbCsjJCSEXbu2MWvWfJKSVjJs2JXMmjXTYR8h\nBLffPpnbb59c6TmmTr2TDRs2s21bF7y8WhEcXMLKld/Wqh2HDx8mNTWVgQMH4uPjg7+/P2azmSlT\npjB79mxWrVpFbGwsp0+f5pdffmHkyJFERESg0+k4evQonTsrd9nYsWNZsmQJw4cPJzw8nDlz5jBm\nzBh0Oh1btmwhPDycHj160KJFC7y9vW1uKIPBQHh4ODqdjq+//ppvv/2Wnj171u1H9SDOKIFWwCEh\nxG84zgm4IkS0UXcWWTmFzHluE88+ZyQ+Ho4ehZkzNnHphcrX6MnRgas7kQee/JZljw6nW8dwly8k\nklJuB/QuPWkj5rFlP7D2yz+JiIDTp2HciAtYMK3MW+ppQ8Kdsq2K6Oho3nmn7tEw3t7ebNz4CQcO\nHKCwsJCEhAT8/CpGOVVHSUkJs2bN4tChQ3h7e3PJJZfw2muvERkZiZSSYcOGkZ6eTuvWrbnpppsY\nOXIk/v7+zJkzh4EDB2I0Gtm4cSO33XYb6enpDBo0iJKSEoYPH87zz6vYmIyMDKZMmUJqaiotWrRg\nzJgxTJgwAZ1Ox/PPP88NN9yAwWBgxIgRjBo1qs6/hyepsaiMEGJwZdullFvd0qLK29AgRSr2HMzg\n/qc/ZsWrZZ6wKXf4MGrohbz6/m+VPtTusPjc0YmM+t/7fP7qmGr3OReLyjgjP/t9snOLuHLyal56\nCZuhMHUqLJtzlW1h1uDxK8sZEl5sfXey24yHmmRbn6IylclMo+ERQpC8bbrbisokAelSymLLSf1R\ni7uaPe2igsjIMHP0aNkDnpZuYsV7O3luqanC6GDbH0ku76yrG43UpxM5r3MEd8/bwL8GdsTXp8xo\nd1cYYVPAGWVbfp8Rl3cnIkLdHwCJiSAEPPP2N2Q9K/jfmH5ERelsn8fHQ1SkjuSMPLcpAU22GrXB\nGSWwDrjE7r3Jsu0it7SoEREWEsDjM/7NzBmbiIrUkXHSzJSxF/HF1j+IjzcBZQ/1/sOn3NJZJ2fk\nuaUTKTYY8fHR8+NvZRNgrg4RbezYW/RAtfI7ciKLn35P4unXf2TZcjPx8bBtGyxauA+TWe0fFgZL\nl8KTT4Kfn4niYpj72A6kxMGQyDhptl3THWiy1agNzigBL/vIICmlQQjh48Y2NSpG/asbl14Y69BZ\nvPbBbw4PdXqGiaNJOURGCpd31u2igkhNNTpcLzXNWO9O5LnZ/6nX8U2d8hZ9dRb78lW/2nz+pUZl\n7W/YAF99BRGt4dQp+N//lBLw9oa5cyEyEk6ehBYtYPzV/Zg54zebIfH4jH+7dR7pXJetRu1wRgmc\nFkKMlFJ+ASCEGAVkurdZjYuwkACHh9Z+dJCaZsRsNrP6y+2kpJa6xeIzmyXTpkGbNpCert7Xl+mL\nNjLvviG2FANn8opZ+OKPPDt7WL3P3dipzMU2Y3rlFntpqYm1X/7p4PO/914wmagwD/Cv/ufz8Tf7\nHbbfdZeJKwd3ZvzIBI8FEpzLstWoPc4ogSnAu0KIFy3vU1CLx85ZRv2rGz06RdjcA8+/IImPL+W9\n9+CuuyAm2ovTpyVPzKy9xVd+YjI5I4927bx58ikDGRkQFQWzHvSu9wjj4NFMhxwzrYL8OHDEXVHA\njYvKXGxtovSMHHIh06ftJCREkJMjWXz/vzmecsbB5x8fr6z90FDHbRERYDJLYqK9iI832rZHt/Wi\noKiUzu3DKpWXOwIJzmXZatSeGpWAlPIo0N+yqpeqVvSeS1hdCcGtILiV2dYZjB0Ln38O+QUS+wCK\n8r7nqh76yiYmL70wlowMM1lZ0K2b60YYZrPkTF4xrYJUZ5GTV4zRLs9Mc6ayCf+Mk2bCQvwRAnx9\nBEIoAfbuEcXp044jBINBuYDst50+DcMvi2fjj387bM/KokpZuSt09FyWrUbtqVIJCCEmAGullGao\n2PkLIeKBNlLKbe5tYuPC3pUQFgYTJzp2Bvn5MHeuCW9vmDN3E/mFJTz+8lZCQgWnT5vQ6wTR0V6V\nhpZWNjG59d3JFSanXeFTvmPMhVwz5X3+b2gXAL7afJh7bu5X79+nKVDZhP+s/w1i8Yofmb/AYmqr\nogAAIABJREFUZJvUnTN3E5++PA4B3HefsvZPnoTSUvDygjvvhNatlQLoe3404aGBPDxlMDNnbK1R\nVu6K+oJzW7Yatae6kUAYsFsI8QfwB3AaldijEzAYNS8wy+0tbCCqGqaXdyVMnw533w1t2+hJSTXh\n5QXLl8OZM9Ai0Mz8Fzaj10v0ejCb4YUXJPHxhgoPfXVRQOUnp13hNhh9ZQ8SukXy865kAF57fITH\n0gw0Bsr/pskZeQQGSsdJ3UDJ7r8y8PWDoiIoLASjEfR6Je9TpyQJnTrQbXg4b677g/uf/piMDKVQ\nzu8SWa2s3BX1BZpsNWpHdZXFllvmAS4HBgIJqELzB4GJUkqXlJUUQqwErgZOSik9m22pCqobppd3\nJcTFgbeXnv/dMIRHln6PTgcBAZCbC1nZZvR6eOIJyMiADz5QESSHDinfvv1DX5WLwupKKD85XVcK\nCg0EBqjgri4dwirtHOz3qSuNUa5QUblbf9Ps3CKysk2W8E4oLoZZs0wUF5dSVAQvv4xt5LdsGcTH\nmzh6FKZPO8FPvx+3hY0q5f5jjYvBapJ3XXBWthr1o2XLluzbt4/27dvX+RwdOnRg5cqVXH65y/Jw\n1plq5wSklCZgk+XlLt5CFaxZ7cZrOE3lkSPf0irIj/M7t7a5Eu677xtaBAryCySPTB1MVk4ROp21\ng7BGhiir8bHHlHWZnq7mDdq1U/+XGgzk5hWTlVNYqYvCHaGEkx/+gh6dIhh2WTwJXSMJ8Fe5mxNT\nz/DL7hS+/OEw40acb3Ml1INGJVeoXrkXFJUSFOQY3hnUEvYePmWbGD50SEVo2VvvQcEmDAZqbdGH\nhQRw3bDzuOsu+3QT59VL3s7Ktuu/63wJDeDs2bMN3QSX4kx0kFuRUm4TQsTVvKdnqGyY3qKlidnL\nviT3jAoP/WN/GkajGW8fMObC/Od/IDRUT1iYY2fQqpVyCz3/fJlimDYNliyBr7+Gt9+G+a+s59Qp\naeuQXO32Kc/7y0fzwy/HeffzfUzf9w1n8orx8tIRHxvC5QM6sHTOf2gdFljv6zQ2udbkgy8tNZGb\nqyx++7DP+JhWfPqteh8VpZS3vfWel6fcfLW16LNyCvnk2wMOI495cw9w3y396yx3T8m2LhiNRh6f\nO5fv168nIiqKhUuXNorykpVhMpnQ6xtn6it3tK3BlUBjo7Jhel4evPOOkawsmHbfN5QazQ6x4NOm\nwROLTdx5p2NnkJurJhOtGXPj45UleeQIvP8+PPUU+PmV2iYhrR2Su+PILx/QgcsHdHDrNRobNfng\nf/jlOMHBjrKKiICCYiPBwTBjhhohmExqhBceDmfPKtmDmheKifbm9Gnp1AjO2h77eiOumBNoKNkm\nJycz78EHyUhO5tJhw3hg9myHVNLTpkzh4HvvMa+wkH379nH5gAH8fuAAMTExtn2klBw/fpzCwkK6\ndu2Kt3ftKgwtWbKE3377jXXr1tm23XfffQghWLBgAdOnT+frr79Gr9dz6623smDBAoQQrFq1itdf\nf51+/fqxevVqpk6dyi233MLkyZPZs2cPPj4+XHHFFbz33nsA6HQ6/vnnHzp27EhxcTFz5szh448/\nJjc3l549e7Jp0yZ8fX354osvmD17NmlpafTq1YuXX36Zbt0qRn8ZDAYefPBB1q1bhxCCG264gSVL\nluDt7c3WrVuZMGEC99xzD0uXLmXYsGGsWrWqtuKpFk0JlMPeLRMRIUhJLeWBB5RV36oVBAUJhM7R\n4m/TRoUMguoUwsMhNRV8fEBK5UeePl3NH6Snq/38/akwCenOfDKNFU9VFqtu5fXE+z/m511JhIc7\nyiozEwb3i+PtT/5g/nyjzWJ/9BEdOdnw+BNmevVS5/L20jNv6tWc36W1UzJ0x5xAbXBlZbHs7Gwu\n69uXm7OyGGUysWz3bhKPHWPF228DqnN/a/VqEktLCQcul5JdpaUVyktOvP56fvj2W1rq9QRERvLN\ntm21Ki85ZswYFixYQEFBAYGBgZjNZtatW8dnn33GrbfeSlRUFMeOHSM/P5+rr76a2NhYbr/9dgB2\n7NjBuHHjOHXqFAaDgdtuu43//Oc/bNmyBYPBwO+//267jn0hmpkzZ3Lw4EF+/fVXIiMj2bFjBzqd\njsOHDzNu3Di++OILBg8ezHPPPceIESM4ePCgg3IEWLRoETt37mTv3r0AjBw5kkWLFtnqd2RkZHDm\nzBmSkpIwm6sP9XVLZTEhhC9wPdDefn8p5YJaXameeLKymNUts//wKe6c+wVxcSpP0HvvQWaWCSEc\nLX5rx962rQol/O47tW35csf5ASmVJfnMM8pNZO96uOsuE4H+jau2ois7iqrwZGUx68rr1pZUD2az\nZPdf6fy8K8kmiz174MEHy47Z9/cp7r15AI8+sp3QMMjOgkfuGkr2mSIee3QHERFKWSy+fxiDL27v\ndFs8NQdUFa6sLLZx40YuKCxkgUk9J0MKC4lYs4YX33jD1uF56fUU2RWPLxLCoTN8dcUK0jdt4kRR\nEb7A7OJi7p08mQ+/+srpdsTGxtKnTx8+/fRTJkyYwPfff09gYCDt27dnw4YN5Obm4uvri5+fH9Om\nTeO1116zKYHo6GimTp0KgJ+fH97e3iQmJpKamkp0dDSXXFKWPs2+JOVbb73Fzp07bcqqf//+AHz4\n4YdcffXVtonf+++/n+XLl/Pzzz8zaNAgh3avXbuWl156yVZkZ+7cuUyZMsUmE71ez/z5850aGbmr\nstjnQC4qTLSkhn3rirC8qsTTlcXCQgIYfHF7Ft8/jJkzNhEeJkhJK+Wpp2D/ftXZBwdDVqbKGrni\nFS+Sko088AA2t4L9aCE6GkaNgpVv6Llm6AVs2vGnQxI668rSxoQLOooa5eopkjPyaNVKR95ZE9a+\nqFUrHRt/Okrr1koGP/ygFHdkpOrYTSZ4/eMtZGQoGRcXQ4kBHn3ue0JDBcUlkvyzavuufWm1Xujl\niTkgTyCEqKa4ZFl5yRHLlzOjsJB9Xl7sbNGCV6691rbPgV27uLawEOs657FGI2MtlnFtGDt2LO+9\n9x4TJkzgvffeY9y4cSQmJlJaWmqrMSylREppq0EM0K6do1H59NNP88gjj9CvXz9CQ0OZMWMGkyZN\nctgnMzOTkpISOnbsWKEd5UtUCiFo164dqample5r35a4uDjS0tJs7yMiImrtGqsNOif2iZFS3iSl\nXCKlfNb6clUDhBBrgZ+BLkKIJCHEpJqOcQVZOYXsOZhBVk5hpZ8fOZHFhxsO0KNTBFvfnczUsUMJ\naqlcOD/+CDqdWhhmNMG1w87j/knD0OtVPplXX1XzCEePqnMdPao6lUGDIDxcMOjiOLKzhcPn1a0s\ndRcmk5mMzHxSM/JsL1fRUHKtikB/b7KyTSxbBqtXqyiurGwTCV1ac+qUGgEsXw7PPQfvvAMvvgi+\nvmpUoNfDK6+oEN9XXgGdXq0Kf+UV+GAdvPwKvPvlnxw5kVXrdoWFBNCre5TLFYA7ZVue4cOHsy8g\ngDl6PZ8B1wQEMPnmmx0s/bmPP849y5bxzciRGCZP5ufdux3KS3ZNSGC9vz/WANZPvbzo2r17rdty\nww03sGXLFlJTU/n0008ZP3487dq1w8/Pj6ysLLKzs8nJyeHMmTM29ws4ungAWrduzWuvvUZqaior\nVqxg6tSpHDt2zGGf8PBw/Pz8OGp9kO0oX6IS1LyJ/RxIVfsmJibStm3bKtvmapwZCfwshOgppdzn\njgZIKce547zVUdNy/coqRQ26KI7cvPIuHHjgAVi69ABhrQIccsxMn64+DwtTCmH6dNXRp6YZadu6\nZYO6AgDe+mg3S9/6lYiQAISl+pQrSxA2hFyro6ColDZROuLjlU/VOjHs5+eNv7/q7CMjK871/P47\nFXIHhYerVcMOuYPCYfdfGZWWlvQ07pZteUJCQti2axfzZ81iZVISVw4bxsxZjutIhRBMvv12Jlvc\nL+W5c+pUNm/YQJdt22jl5UVJcDDfrlxZ67aEh4czePBgJk2aRMeOHenSRYU6Dxs2jOnTp7Nw4UJa\ntGjB8ePHSUlJqeCasfLRRx8xYMAAoqOjadWqFTqdzlao3v47TZo0iRkzZrB69WoiIyPZuXMnF154\nITfeeCNPPfUUmzdv5rLLLmPZsmX4+flVWud47NixLFq0iL59+wKwcOFCJk70XHq26tJG7EON7LyA\nSUKIYyh3kABkY1oAVBtqChU8ciKrQtbIu+76E6PR7NAZhIVBUJAK/2zRAk6eznfIJxMXp0IHc3OV\nK+H999UEcFionoKi0gZ3Baxct5uta28lJNjfo9etCa+7t7jlvEHFRaRXMhHb6cNjUCKYOVPywgsV\n53r69lUjAPvtmZkqbYRD7qBM6PtJEl4bTrul/bXhzb8OsK1LV0LLTUBi/W13u/6a0dHRvPbOO3U+\n3tvbm082bqxXeUkr48aN45ZbbuHpp5+2bVu9ejUPPfQQPXr0ID8/n44dO/LQQw9VeY7ffvuNadOm\nkZeXR2RkJM8//7xtcZi9Zf7MM88we/ZsLrroIgoKCrjgggv45ptv6NKlC2vWrOHuu++2RQd9+eWX\nttGR/TkeeeQRzp49S0JCAkIIbrzxRubMmVOn714XqiwvWVOMt5QysbrPXYkry0tWVTLymQevp1f3\nKN5ct5vXP96C/f08YQIMvvB8Ptq4n5dfVvnkly5Vvv/cXOUTvvHK8/lgw368vLCNIMxm5VJYuLAs\nFvyRR3S8unCUbeFZQ3HjPetYu/R6vLyq9gg2RHnJjN59XHV6B/YUFvDfrMOcLbWLyPKGlWFdOGEo\n4aGMJAICJTn5EB0uyMiUlJohsjWkZyj3n3Xdh9kMocGQm69GAJmZMLpFCBPCI4jxUSt2UwwGYnx8\nCPfy/GT/9f8c4YP4TnhV4UaI2r2rwjatvGTTxi3lJa2dvBDiHSmlw9hECPEOTTSddE2heRFhAZwu\nlyEy8zTERAYRHKwmhI1Gyo0U4Kv1+wlqpTJMGgyq0zCbVUcz6yEID4PMLDAZzDz9yHqSTJJFs/7N\nyH+7r+B4Zbz2/h8AxLYN5sZ71nH5JR3w8S5bfHLHmAs92h5PEePjQ36BYP5iWbY462FBTBsfEotL\nEMWSoBJBgZTcaIxkfOdwso1GfsrL43FzGno9BHpDkQCzCZ5v1ZGAUB3HDQZywkp5PiODv87kcsRs\nRicE8UJwQkoWt4tlVGioR77jCkuccpyPD9f9c4R/BQXhI8qU/JTWrT3SDo2mhTNzAufZvxFC6IEG\n7Snq4zKIBJ4MjWbG3Um0DhecypQ8FRVN5KM7AbjMWAqlcNdUCI9QCoBS+L+fsnn1rGDCLZL16x39\nwWFhUJSmEow55J55CGbnQDtgTDo8DcwFNhWWkg4Mmv81oe/8zfkBAR6zGIsyVDxrO8vL9PlRiiyf\nCcBrm92SeDe4DdzN4eIidhcW0jsggC5+Za6ucC9vnoyK5aGH7eWuIjIeTk7iRyBBSrYAI05mMKBl\nCzr6+RHv54feR7DsJWlT+vfdBUdLSujp70+klxdzk9TxbaSkK7BFShKkZC8wJDmJgUEtPSLfArOK\nNov28SHax4dSKSmValujCNHSaJRUNyfwMDAb8BdCWEMLBGAAXvNA29yGMIMoluhSQUhJvtHEnsIC\n2/B9YlgYq3OzKC1Rsf03hIZQaDYzJyKa+W+lYCy3TiArE14E7jXBo4/aVwCDl4FEIBR4DGgFnACO\nAaXA4hMnSPagxTgzSoXJfXkmhxGtQhw++/JMjtuv704eTUninbwsWofDqWMwMSiMhTFloXfl5S7M\nym3TXggSpOR9AXd6Q6twuDHpHzBAnBAEtZEOSj84FF5NTSUTlWq3FJVVsQTogMq0iOVvnBCkGAwe\nUQLNWbYa7qM6d9BiYLEQYrGU8mEPtsmtZBpLHSy/vUD/lBTihSAVmN02mnX52bz8ip27Z2oOf2af\nIRUYHtyKL86cYfpUiAqDk1mgM0AsMMEIq0ogNxsMJTDMCD8BG4AhoK6F0qJ3Ar8ACWazxy1GgOdP\nnqzQUVS2ralwuLiId/KyeMk+emtqFhOLI+ji51+p3IckJ/FZ166csIwA7vSGZxyOh2kGyfTMcjUj\nsuAfIB0YCnwFXAdsB46j5Jxg+ZsopW2ewFM0N9lquBdn3EHrhBDlZ+tygUQppdENbXIr9pYfqIe1\nM/CGlPgCl6am0CZGR3y8+jw+HuLC4M10yWZg3pkzhAK5BjCkQzGqyMIs4BDwUCmMyIIC4CqgDUoB\nWK/VFhiHsiAbwmL8Pi+XH/LyyCgt5ZGUFNv2syZTlROJTYHdhYW0Dq8kbLOwkC5+/pXKPdpy3Oy2\n0VydmkJI+ePD4L10CDXAzKkQGgZpWfC2ASJQrzggEDXSu0YIDFJymRB0FIJEywjPU4q9ucpWw704\nowReBvqgDBsB9AT2A8FCiDullN+6sX0uJ8bHx2b5BaI66yQgB+iN6owTM6WD5XcyS1l4jwO/ojqQ\nz4AbAG+UtW+1/IYC96A6iLbASRwtw2wheDI2lllJSeyV0uMWY5S3Nwn+AXyTm0tCQJnPPFCnZ36L\naLdf3130Dgjg1LGKYZu9O6oILKvcrbJYIuCIt+RlcwppmRIzan/7409lKflmA34G8E5XD4C1kMZe\nlKuvADgtBK926MD5Aep6DREd1Fxlq+FenFECacBkKeUBACFED2AB8CDwCVAvJSCEGA4sQ61eXiml\nfKo+56uJcC9vrg8N5aqsLNoByZbtD6M6eoOUXBMYwl1Tc4gIVx0DBngU1bG3AZ5ATfK2BXxwtOhj\nUD7/dNQPdwdqJBCKUgCL28UyMiQUKZU7Is7DFuN5/gGc5x/AdaGheDcj67CLnz8Tg8K4a2qWTW5X\nBwazu7DQ9vnidrEMSkokDEj1xuI6UpFi06eCyQAzpkJbi8UvDMr6OQnKjUSZS28FkAEEAdcCJikd\nJvgbIjS0ucpWw704owS6WBUAgJTyLyFENynlsfouZxZC6FBzqleg+szfhBCfSykP1evE1ZBpLOXj\n7GybRb8X1UlvRHXclwBfZOWwEQhMU1bedSjLvy/QBQhHWYSPAA/haOkfAW6zfJkg4AvADNwQGcX4\niHBb5zAqNJSBQS09bjEOPXSw2kiRH7rVfql+ZXhauQMsjIllYnEEuwsL2eafy1c5uezLySUZGB8W\nRt/AFuiEQAeEhztO9rYMA306/GGAE+mwB5gG/Gk590GUjBNQk7+jgWEoWb8APKTTeWwCuCo8JVuN\n5oUzSuCAEOIV4H3L+5uAvyzZReub8awfcMRuTcL7wCiUe90tVOYbbo+y3tujXEQtKfPjY9luXT69\nFUfXz1PAANSoIBW4HxiBUh4jgGUdO1YZAhru5e3xTmN1R9XzvZ2pVraODlERSR/nZLssjLAhlLsV\na1jow0lJDor+4qwsPsrOZquUtAE6lXP95GWpCfvtQABqmPsLjobC5ShD4TgwBnXzZ6JGgw0xAVwe\nT8i2qXLVVVcxduzYOqdjqM3x9b2Wp3FGCdwKTEUZRqCek/tRz8DQel4/mjKPDEAKSjG4jfK+4b0o\nBbAH+A8qhDMNKnx+EhVX7zCZi1IaAkho1Yph3t68kJnJJyiF8Ey7WIYEBbvz69SadpaO6sezZ9nU\ntWyh2iP+0fz770O4aLG6x5W7PbsLCyvIKgI1MrNue9UAk6aqNR6ZWeBlifAah5Jp+Yn7UNQoMdty\nnqtRCiESuM7i5mvIUQB4TLZNkg0bNnjs+Ppey9PUqASklEXAs5ZXefJd3iIXk2ksdXC5hHt5s7hd\nrM0ff0xKSqVkGmWW3xKU37crKp6/CHgGOI2jcvgb5fMHOJ6Xx2YpmdM2mvMDAxosZYCzSAk78/Pp\n16IFAL8V5OPCbAAeV+729A4IIBlHWZ0Gztpt6wH4GmBeuprI/xnHEV5muePTgLeB1qiorwVxcXTz\n96fQbG50snazbOuE0Whk4cK5bNq0ntato3jiicZTXrIxl5P0BM4UlRkIzEMZvvZFZSom0a49qSgD\nzEqMZVsF7OsJJJw9yyUtW9Z48s+zs3k4OYn25Zbwl/fHbz97lmcSE22W34PAK6iOwAT4C0ErITht\nNtPf0sgUYEjLlvx89qyaNLTG+6el8uN55zWqTqEyno2NZUZSInkmMxJJK70Xz8XGOuzj6aIyQ4YM\nwRVJNLr4+TM+LIz+WVk2Wf0nKJhLg1oyKCWFENTI7i2gIypEuPwIbzSObj4JPImKBooEYn19HVYk\nNyYaQrbJyck8+uiDpKcnM2jQMB56yLG85L33TuG3395j7NhCjh3bx5AhA9i1y/3lJadNm4aUkr17\n9zJx4kRuu+22SstJzps3jwceeIDVq1cTFBTEjBkzuOeeezAajeh0OoYOHepw/BtvvEH//v1ZuXIl\nISEhvPTSSwwfPhzAYV+A119/naVLl5KSkkJsbCxr1qyhV69ePPXUU7z++uucOnWK2NhYFi1axDXX\nXFMfMbinshiwEpiOKipjqn2zquU3oJMlWV06ytU6trId7TuLjM9rrj5lXRxU1RJ+e398d3//SkM5\nn7aEctqfYxBwS9u2XBYURKHZTEZBAQmWkm+eXiFaHy4ICOD7bt3Js1SDCqrEEqpHUZk6KXdQETeu\nYGG7WCZGRPDm6dN8lpVFUv5ZHsvLBVTYJ6gO/XKUknBY4IVy/eiByMBA0gsKWE9ZSPF1QjS4/786\n3CzbCmRnZzNwYF+GDs1i0CATn366mxMnjvH6628DqnN/++3VvPdeKcHB0KeP5OjRiuUlx427nh9+\n+JaAAD0tW0by3Xf1Ly/54Ycf8tlnnznUDgDHcpKlpaW89tprfPPNN+zdu5eAgABGjx5dbR7/nTt3\nMmnSJLKysnj11VeZPHlypQVj1q1bx4IFC/j888/p06cPx44dsym3Tp06sX37diIjI1m3bh0TJkzg\n6NGjREZGOv2dy1MXuTpTVCZXSvm1lPKUlDLL+qpzK+2QUpqAu1FhpgeA96WUB11xbtsEsOW9fQdt\nz+fZ2Vzz99/4o1xAXSx/bwwNJdbXt8I5Ouh0XNiiBV38/B3mF6DhVojWho+yswGVbGzFqVOszcpi\nbVaW7b2LsCl3IYQPSrm7tG5kprGUPYUFZBpLq93+eXY2bwOjLYr6V+Cw5e88oBfKpzkQ6CEE/VE+\n/+uE4OnYOD7q3IVnY+O4Tgju0Okajf+/Mjwk2wps3LiR9u0LmTTJxMCBMH9+IatXr8FoLFtL6uWl\np8SuLqHB4FhecsWKFfzzzybefbeIt9/Op1evRO66a3Kt2mFfXhKwlZfs16+iJ9JaTlKn0+Hr68u6\ndeu47777aNOmDcHBwcwqVw+hPHFxcdx2220IIbjllltIT0/nVCW/8cqVK3nwwQfp00ett+3YsaOt\nitn1119v6/BvuOEGOnfuzM6dO2v1nV2BMyOBzUKIp1FrAmxilFJWzEdbB6SUG1Hud5dS2QRw+Q7a\nOlr4REquR6V3sFl72dlMjIio9hzl5xc8vUK0LhRaOkNrsjF3IKU0CSGsyt0aIuoS5Q5Vu/nstx+1\nzPX4oyZ7rYv37BV6W1T4rh9qiGtCPRBjGkk4b23xhGwro7zFXH7+QQjBtGnTefTR5Vx3XSEnTnhx\n5EgLrrUrL7l//y4uuaQQ6+M5dKiRp5+uf3nJ8ePHV7pf+XKSaWlpDtvKf14e+xGKv79yC+bn59O6\nXKbW5ORk4q2xyOVYvXo1S5cu5cSJEwAUFBSQmZlZ7XXdgTNK4GLL37522yRqJN1ocaaDto4WAqWk\nPY5hofYpBYakpVZ5jqbSQVi5OTwcgLtaR+Knc2YgWDfcpdyrcvN1D/CvsH0QUAjsQC3y60o5lx9q\ncj8dJfttUqr/T51kfES4w3UbIpy3tnhKtuUZPnw4s2YFsHJlEV27mvj00wAmTbrJwdKfP/9xYmM7\n8N1364mIaMOvvz7mUF6yW7cE1q71Z+TIIry9Yft2L7p2rVt5yfvvv99WXnLHjh2V7ldecbVp04YU\nu1QbSUlJ5Q+pE+3atau0/GRSUhJ33HEHmzdvtlUb6927t62IvSdxJjqovmGgDUZNHbR1tFCACgO1\npRQAjkrJWykpJEnJ7Ohozg+oOuKnKXQQ5Rl66CAR3t5cHBjIxYEt6NeiRaW+48ZGZes84oRgd2Fh\nhe3tgDzKrP9XKJvsTQPepCwHUHvUPXARTWdepyo8LduQkBB+/nkXjz02i+3bk7jhhmE88EDF8pL/\n/e/t/Pe/lZeXnDp1Kt9/v4Fbb91Gy5ZemM3BfP+968pL1sSNN97I8uXLueqqqwgICGDJkiW1vnZl\n/Pe//2XmzJkMHDiQPn36cPToUXx8fCgoKECn0xEeHo7ZbGbVqlXs37/fJdesLc5EB0WiMiW0lVJe\naUkbMUBKWXsJNQDVddDW0cJ1yUkES0l/1GxmEpYcQdaIn9SmEfFTG37pcR4pBgM78vP5Li+Ph1NS\nCNbr+a6bZ4vc1Jaq3Hy9AwKYX257Msp/ad3WHeX+uap1a1adOoXVzrSuBWlP05jXqYmGkG10dDQr\nV9avvOTnn7uvvGRN2Q1uv/12jhw5QkJCAsHBwdx7771s3brVVle4puPtP7f/f/To0WRnZzNu3DjS\n0tJo374977zzDhdccAEzZ86kf//+6PV6br75Zi699NK6fN16U2V5SdsOQnyNiqabI6W8QAjhBeyW\nUvb0RAMtbXAoaefqEoTWtQQBOh27Cwt5KyWFPRb/KkBvnY7FnTrRKyDQpddtSNIMBnYU5PNLfj5/\nFRXRysuLfoGB3BtZ5ussX4awsZSXtPr+7V109nMC1vUfJikJQE38WkNFx4eFsbBdbKX7dtbpHM7X\nVKlJtlp5yZrZuHEjd955J8ePH2/opjiFW8pL2hEupfzQUmQGKaVRCOHZmSc3Yz9aCPXyqmBRNnXL\nsDL6/nWAXgEB3BsZyZJ2sTUf0Iioys1Xfjso95HBbOa4weBQbayqfZvCvE5NNGXZNhQlfpD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LLhKcZP/Rz7v7OGnIrOLpK+GF4ALJD0MTO7vdEFS7qK8Lnfa4TXaE43sy2NLqfZ1HNibsTQVFb6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\nx3GcLsY7AcdxnC7m/6QDJ23UAGkPAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -390,10 +347,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -423,6 +378,15 @@ " }\n", "}\n" ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -439,10 +403,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, + "execution_count": 10, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -492,10 +454,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -532,10 +492,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -577,9 +535,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -591,7 +547,7 @@ } ], "source": [ - "from sklearn.cross_validation import cross_val_score, KFold\n", + "from sklearn.model_selection import cross_val_score, KFold\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "\n", @@ -602,8 +558,7 @@ "])\n", "\n", "# create a k-fold cross validation iterator of k=10 folds\n", - "\n", - "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "cv = KFold(10, shuffle=True, random_state=33)\n", "\n", "# by default the score used is the one returned by score method of the estimator (accuracy)\n", "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", @@ -622,9 +577,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -693,9 +646,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/ml1/2_6_Model_Tuning.ipynb b/ml1/2_6_Model_Tuning.ipynb index c312d8d..6f417a0 100644 --- a/ml1/2_6_Model_Tuning.ipynb +++ b/ml1/2_6_Model_Tuning.ipynb @@ -71,9 +71,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# library for displaying plots\n", @@ -88,7 +86,7 @@ "iris = datasets.load_iris()\n", "\n", "# Training and test spliting\n", - "from sklearn.cross_validation import train_test_split\n", + "from sklearn.model_selection import train_test_split\n", "\n", "x_iris, y_iris = iris.data, iris.target\n", "# Test set will be the 25% taken randomly\n", @@ -120,20 +118,18 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean score: 0.947 (+/- 0.022)\n" + "Mean score: 0.940 (+/- 0.021)\n" ] } ], "source": [ - "from sklearn.cross_validation import cross_val_score, KFold\n", + "from sklearn.model_selection import cross_val_score, KFold\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.tree import DecisionTreeClassifier\n", @@ -149,7 +145,7 @@ "model.fit(x_train, y_train) \n", "\n", "# create a k-fold cross validation iterator of k=10 folds\n", - "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "cv = KFold(10, shuffle=True, random_state=33)\n", "\n", "# by default the score used is the one returned by score method of the estimator (accuracy)\n", "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", @@ -184,15 +180,14 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ds': DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", - " max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best'),\n", " 'scaler': StandardScaler(copy=True, with_mean=True, with_std=True)}" @@ -210,9 +205,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -220,7 +213,8 @@ "[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)),\n", " ('ds',\n", " DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n", - " max_features=None, max_leaf_nodes=None, min_samples_leaf=1,\n", + " max_features=None, max_leaf_nodes=None,\n", + " min_impurity_split=1e-07, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best'))]" ] @@ -244,14 +238,12 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "dict_keys(['ds__max_depth', 'scaler__with_mean', 'ds__random_state', 'ds__max_features', 'ds__max_leaf_nodes', 'ds', 'ds__min_weight_fraction_leaf', 'ds__splitter', 'ds__presort', 'ds__min_samples_split', 'steps', 'ds__class_weight', 'scaler__copy', 'scaler', 'ds__criterion', 'scaler__with_std', 'ds__min_samples_leaf'])" + "dict_keys(['steps', 'scaler', 'ds', 'scaler__copy', 'scaler__with_mean', 'scaler__with_std', 'ds__class_weight', 'ds__criterion', 'ds__max_depth', 'ds__max_features', 'ds__max_leaf_nodes', 'ds__min_impurity_split', 'ds__min_samples_leaf', 'ds__min_samples_split', 'ds__min_weight_fraction_leaf', 'ds__presort', 'ds__random_state', 'ds__splitter'])" ] }, "execution_count": 5, @@ -273,18 +265,16 @@ { "cell_type": "code", "execution_count": 6, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", " max_depth=None, max_features=None, max_leaf_nodes=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", - " splitter='best'))])" + " min_impurity_split=1e-07, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=None, splitter='best'))])" ] }, "execution_count": 6, @@ -306,18 +296,16 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Pipeline(steps=[('scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", " max_depth=None, max_features=None, max_leaf_nodes=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", - " splitter='best'))])" + " min_impurity_split=1e-07, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=None, splitter='best'))])" ] }, "execution_count": 7, @@ -343,15 +331,13 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 0.01834862 0.01910853 0.06605168 0.89649117]\n" + "[ 0.01834862 0.01910853 0.05728223 0.90526062]\n" ] } ], @@ -366,15 +352,13 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[ 0.01834862 0.01910853 0.06605168 0.89649117]\n" + "[ 0.01834862 0.01910853 0.05728223 0.90526062]\n" ] } ], @@ -406,23 +390,22 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'ds': DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", " max_depth=None, max_features=None, max_leaf_nodes=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", - " splitter='best'),\n", + " min_impurity_split=1e-07, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=None, splitter='best'),\n", " 'ds__class_weight': 'balanced',\n", " 'ds__criterion': 'gini',\n", " 'ds__max_depth': None,\n", " 'ds__max_features': None,\n", " 'ds__max_leaf_nodes': None,\n", + " 'ds__min_impurity_split': 1e-07,\n", " 'ds__min_samples_leaf': 1,\n", " 'ds__min_samples_split': 2,\n", " 'ds__min_weight_fraction_leaf': 0.0,\n", @@ -437,9 +420,9 @@ " StandardScaler(copy=True, with_mean=True, with_std=True)),\n", " ('ds', DecisionTreeClassifier(class_weight='balanced', criterion='gini',\n", " max_depth=None, max_features=None, max_leaf_nodes=None,\n", - " min_samples_leaf=1, min_samples_split=2,\n", - " min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n", - " splitter='best'))]}" + " min_impurity_split=1e-07, min_samples_leaf=1,\n", + " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", + " presort=False, random_state=None, splitter='best'))]}" ] }, "execution_count": 10, @@ -484,9 +467,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -498,7 +479,7 @@ } ], "source": [ - "from sklearn.grid_search import GridSearchCV\n", + "from sklearn.model_selection import GridSearchCV\n", "from sklearn.tree import DecisionTreeClassifier\n", "import numpy as np\n", "\n", @@ -525,28 +506,28 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.946 (+/-0.075) for {'max_depth': 3}\n", - "0.938 (+/-0.050) for {'max_depth': 4}\n", - "0.938 (+/-0.050) for {'max_depth': 5}\n", - "0.929 (+/-0.025) for {'max_depth': 6}\n", + "0.929 (+/-0.024) for {'max_depth': 4}\n", + "0.946 (+/-0.075) for {'max_depth': 5}\n", + "0.929 (+/-0.024) for {'max_depth': 6}\n", "0.946 (+/-0.075) for {'max_depth': 7}\n", - "0.938 (+/-0.050) for {'max_depth': 8}\n", - "0.938 (+/-0.050) for {'max_depth': 9}\n" + "0.946 (+/-0.075) for {'max_depth': 8}\n", + "0.929 (+/-0.024) for {'max_depth': 9}\n" ] } ], "source": [ "# We print the score for each value of max_depth\n", - "for params, mean_score, scores in gs.grid_scores_:\n", - " print(\"%0.3f (+/-%0.03f) for %r\" % (mean_score, scores.std() * 2, params))" + "for i, max_depth in enumerate(gs.cv_results_['params']):\n", + " print(\"%0.3f (+/-%0.03f) for %r\" % (gs.cv_results_['mean_test_score'][i],\n", + " gs.cv_results_['std_test_score'][i] * 2,\n", + " max_depth))" ] }, { @@ -559,9 +540,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -582,7 +561,7 @@ "model.fit(x_train, y_train) \n", "\n", "# create a k-fold cross validation iterator of k=10 folds\n", - "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "cv = KFold(10, shuffle=True, random_state=33)\n", "\n", "# by default the score used is the one returned by score method of the estimator (accuracy)\n", "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", @@ -603,9 +582,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -619,25 +596,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n", - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n", - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n", - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n", - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n", - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n", - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n", - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", - " 'precision', 'predicted', average, warn_for)\n", - "/usr/local/lib/python3.5/dist-packages/sklearn/metrics/classification.py:1074: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", + "/opt/conda/lib/python3.6/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n", " 'precision', 'predicted', average, warn_for)\n" ] }, @@ -647,234 +610,234 @@ "text": [ "Best parameters set found on development set:\n", "\n", - "{'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 7, 'splitter': 'random', 'criterion': 'entropy'}\n", + "{'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", "\n", "Grid scores on development set:\n", "\n", - "0.964 (+/-0.092) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.950 (+/-0.117) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.123) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.947 (+/-0.076) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.123) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.959 (+/-0.111) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.935 (+/-0.123) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.123) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.960 (+/-0.083) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.123) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.935 (+/-0.074) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.953 (+/-0.126) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.964 (+/-0.092) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.954 (+/-0.111) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.943 (+/-0.115) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.123) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.954 (+/-0.099) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.953 (+/-0.126) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.940 (+/-0.121) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.942 (+/-0.150) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.953 (+/-0.126) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.954 (+/-0.112) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.953 (+/-0.126) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.945 (+/-0.102) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.954 (+/-0.112) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.926 (+/-0.146) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.953 (+/-0.126) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.943 (+/-0.113) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.953 (+/-0.126) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.878 (+/-0.345) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.957 (+/-0.120) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.935 (+/-0.107) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.957 (+/-0.120) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.953 (+/-0.126) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.957 (+/-0.120) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.939 (+/-0.118) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.123) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.919 (+/-0.148) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.953 (+/-0.126) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.939 (+/-0.142) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.953 (+/-0.126) for {'max_leaf_nodes': 20, 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{'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.954 (+/-0.112) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.955 (+/-0.097) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.974 (+/-0.081) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.947 (+/-0.175) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.950 (+/-0.117) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.935 (+/-0.075) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.954 (+/-0.129) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.940 (+/-0.142) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.934 (+/-0.155) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.927 (+/-0.112) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.934 (+/-0.184) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.932 (+/-0.136) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.968 (+/-0.082) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.903 (+/-0.240) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.939 (+/-0.179) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.975 (+/-0.079) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.923 (+/-0.094) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.967 (+/-0.083) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.944 (+/-0.115) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.938 (+/-0.177) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.964 (+/-0.092) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.950 (+/-0.117) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.895 (+/-0.229) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.944 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.930 (+/-0.199) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.953 (+/-0.126) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.949 (+/-0.116) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.922 (+/-0.177) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.959 (+/-0.067) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.933 (+/-0.136) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.933 (+/-0.125) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.943 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.939 (+/-0.117) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.946 (+/-0.123) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.918 (+/-0.155) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.945 (+/-0.123) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.931 (+/-0.153) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.944 (+/-0.113) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.957 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.972 (+/-0.069) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.968 (+/-0.082) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.950 (+/-0.118) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.955 (+/-0.111) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", "\n", "Detailed classification report:\n", "\n", @@ -884,244 +847,258 @@ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 8\n", - " 1 1.00 1.00 1.00 11\n", - " 2 1.00 1.00 1.00 19\n", + " 1 0.92 1.00 0.96 11\n", + " 2 1.00 0.95 0.97 19\n", "\n", - "avg / total 1.00 1.00 1.00 38\n", + "avg / total 0.98 0.97 0.97 38\n", "\n", "\n", "# Tuning hyper-parameters for recall\n", - "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Best parameters set found on development set:\n", "\n", - "{'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", + "{'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n", "\n", "Grid scores on development set:\n", "\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.911 (+/-0.146) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.159) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.946 (+/-0.116) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.902 (+/-0.148) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.137) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.973 (+/-0.083) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.920 (+/-0.153) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.929 (+/-0.155) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.938 (+/-0.164) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.938 (+/-0.141) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.938 (+/-0.110) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.929 (+/-0.155) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.159) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.920 (+/-0.187) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.159) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.929 (+/-0.175) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.159) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.938 (+/-0.137) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.964 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.159) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.938 (+/-0.137) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.920 (+/-0.168) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.938 (+/-0.141) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.955 (+/-0.146) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.159) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.902 (+/-0.263) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.955 (+/-0.115) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.938 (+/-0.141) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.920 (+/-0.147) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.929 (+/-0.155) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.955 (+/-0.141) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.159) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.929 (+/-0.193) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.159) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.929 (+/-0.109) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.159) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.946 (+/-0.145) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.911 (+/-0.137) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.920 (+/-0.168) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.955 (+/-0.115) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.911 (+/-0.159) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.946 (+/-0.145) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.117) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.137) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.920 (+/-0.190) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 3, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.884 (+/-0.175) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.911 (+/-0.173) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.955 (+/-0.089) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.946 (+/-0.145) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 4, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.173) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.955 (+/-0.121) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.955 (+/-0.091) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.929 (+/-0.159) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 5, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.920 (+/-0.128) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.964 (+/-0.166) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.197) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.911 (+/-0.173) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 6, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.911 (+/-0.179) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.911 (+/-0.179) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.929 (+/-0.136) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.920 (+/-0.168) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 7, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.946 (+/-0.114) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.929 (+/-0.155) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.137) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.929 (+/-0.155) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 8, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.929 (+/-0.155) for {'max_leaf_nodes': None, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.946 (+/-0.167) for {'max_leaf_nodes': 5, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.929 (+/-0.131) for {'max_leaf_nodes': 10, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.116) for {'max_leaf_nodes': 20, 'class_weight': 'balanced', 'max_depth': 9, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.955 (+/-0.115) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.893 (+/-0.213) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.902 (+/-0.151) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.946 (+/-0.120) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 3, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.893 (+/-0.170) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 3, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.946 (+/-0.139) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.938 (+/-0.117) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.902 (+/-0.169) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 4, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.920 (+/-0.115) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 4, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.929 (+/-0.131) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 5, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.946 (+/-0.120) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 5, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.138) for 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'class_weight': None, 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 7, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.955 (+/-0.115) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 7, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 8, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.929 (+/-0.155) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 8, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 8, 'splitter': 'best', 'criterion': 'gini'}\n", - "0.938 (+/-0.137) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 8, 'splitter': 'random', 'criterion': 'gini'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 8, 'splitter': 'best', 'criterion': 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"0.929 (+/-0.132) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 3, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.920 (+/-0.123) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 3, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 3, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.964 (+/-0.087) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 3, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 4, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.929 (+/-0.103) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 4, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 4, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.955 (+/-0.123) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 4, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 4, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.964 (+/-0.119) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 4, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 4, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.082) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 4, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 5, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.911 (+/-0.173) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 5, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 5, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.920 (+/-0.219) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 5, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 5, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.946 (+/-0.114) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 5, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 5, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.946 (+/-0.114) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 5, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 6, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.955 (+/-0.115) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 6, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 6, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.141) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 6, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 6, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.946 (+/-0.145) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 6, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 6, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.115) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 6, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 7, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.920 (+/-0.161) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 7, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 7, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.911 (+/-0.257) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 7, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 7, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.183) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 7, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 7, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.141) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 7, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 8, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.920 (+/-0.169) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 8, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 8, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.117) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 8, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 8, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 8, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.929 (+/-0.132) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 8, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.911 (+/-0.179) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 8, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 9, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.911 (+/-0.137) for {'max_leaf_nodes': None, 'class_weight': None, 'max_depth': 9, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.946 (+/-0.140) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 9, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.920 (+/-0.147) for {'max_leaf_nodes': 5, 'class_weight': None, 'max_depth': 9, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 9, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.955 (+/-0.115) for {'max_leaf_nodes': 10, 'class_weight': None, 'max_depth': 9, 'splitter': 'random', 'criterion': 'entropy'}\n", - "0.938 (+/-0.138) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 9, 'splitter': 'best', 'criterion': 'entropy'}\n", - "0.920 (+/-0.181) for {'max_leaf_nodes': 20, 'class_weight': None, 'max_depth': 9, 'splitter': 'random', 'criterion': 'entropy'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.893 (+/-0.215) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.955 (+/-0.092) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.938 (+/-0.138) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.920 (+/-0.241) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.946 (+/-0.120) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.866 (+/-0.268) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.884 (+/-0.218) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.911 (+/-0.179) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.973 (+/-0.081) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.929 (+/-0.155) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.893 (+/-0.177) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.938 (+/-0.137) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.920 (+/-0.162) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.920 (+/-0.187) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.929 (+/-0.104) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.911 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'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.902 (+/-0.148) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.929 (+/-0.158) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.938 (+/-0.159) for {'class_weight': 'balanced', 'criterion': 'gini', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.946 (+/-0.113) for {'class_weight': 'balanced', 'criterion': 'gini', 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'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.938 (+/-0.159) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.946 (+/-0.120) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.920 (+/-0.147) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.920 (+/-0.127) for {'class_weight': None, 'criterion': 'gini', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.955 (+/-0.115) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.893 (+/-0.213) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.875 (+/-0.216) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.929 (+/-0.196) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.911 (+/-0.173) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 3, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.938 (+/-0.163) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.938 (+/-0.115) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 4, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.920 (+/-0.187) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.920 (+/-0.187) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.929 (+/-0.131) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.929 (+/-0.155) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 5, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.920 (+/-0.127) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.938 (+/-0.159) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.946 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 6, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.938 (+/-0.137) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.902 (+/-0.179) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.929 (+/-0.175) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.902 (+/-0.148) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 7, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.929 (+/-0.132) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.955 (+/-0.146) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.955 (+/-0.169) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.964 (+/-0.121) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.929 (+/-0.136) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 8, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'best'}\n", + "0.920 (+/-0.147) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': None, 'splitter': 'random'}\n", + "0.946 (+/-0.140) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'best'}\n", + "0.938 (+/-0.137) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 5, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'best'}\n", + "0.929 (+/-0.168) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 10, 'splitter': 'random'}\n", + "0.938 (+/-0.138) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'best'}\n", + "0.946 (+/-0.120) for {'class_weight': None, 'criterion': 'entropy', 'max_depth': 9, 'max_leaf_nodes': 20, 'splitter': 'random'}\n", "\n", "Detailed classification report:\n", "\n", @@ -1131,13 +1108,21 @@ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 8\n", - " 1 1.00 1.00 1.00 11\n", - " 2 1.00 1.00 1.00 19\n", + " 1 1.00 0.64 0.78 11\n", + " 2 0.83 1.00 0.90 19\n", "\n", - "avg / total 1.00 1.00 1.00 38\n", + "avg / total 0.91 0.89 0.89 38\n", "\n", "\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:667: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n", + " DeprecationWarning)\n" + ] } ], "source": [ @@ -1197,15 +1182,13 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Mean score: 0.940 (+/- 0.021)\n" + "Mean score: 0.907 (+/- 0.015)\n" ] } ], @@ -1221,7 +1204,7 @@ "model.fit(x_train, y_train) \n", "\n", "# create a k-fold cross validation iterator of k=10 folds\n", - "cv = KFold(x_iris.shape[0], 10, shuffle=True, random_state=33)\n", + "cv = KFold(10, shuffle=True, random_state=33)\n", "\n", "# by default the score used is the one returned by score method of the estimator (accuracy)\n", "scores = cross_val_score(model, x_iris, y_iris, cv=cv)\n", @@ -1288,9 +1271,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1+" + "version": "3.6.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/ml1/2_7_Model_Persistence.ipynb b/ml1/2_7_Model_Persistence.ipynb index 5913267..726abcf 100644 --- a/ml1/2_7_Model_Persistence.ipynb +++ b/ml1/2_7_Model_Persistence.ipynb @@ -56,9 +56,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -79,7 +77,7 @@ "iris = datasets.load_iris()\n", "\n", "# Training and test spliting\n", - "from sklearn.cross_validation import train_test_split\n", + "from sklearn.model_selection import train_test_split\n", "x_iris, y_iris = iris.data, iris.target\n", "# Test set will be the 25% taken randomly\n", "x_train, x_test, y_train, y_test = train_test_split(x_iris, y_iris, test_size=0.25, random_state=33)\n", @@ -109,9 +107,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -196,9 +192,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1+" + "version": "3.6.3" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 }