From 2998a6e7f816f703e08f6020b77c28f9a629849f Mon Sep 17 00:00:00 2001
From: Yacine Haddad <yhaddad@cern.ch>
Date: Fri, 2 Feb 2018 11:29:30 +0100
Subject: [PATCH] Delete boosted_bin_optimisation.ipynb

---
 examples/boosted_bin_optimisation.ipynb | 973 ------------------------
 1 file changed, 973 deletions(-)
 delete mode 100644 examples/boosted_bin_optimisation.ipynb

diff --git a/examples/boosted_bin_optimisation.ipynb b/examples/boosted_bin_optimisation.ipynb
deleted file mode 100644
index ded2078..0000000
--- a/examples/boosted_bin_optimisation.ipynb
+++ /dev/null
@@ -1,973 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy  as np\n",
-    "import pandas as pd\n",
-    "from scipy.special import logit, expit\n",
-    "np.set_printoptions(precision=3)\n",
-    "\n",
-    "%matplotlib inline\n",
-    "plt.style.use('physics')\n",
-    "\n",
-    "plt.rcParams['axes.grid'       ]  = False\n",
-    "plt.rcParams['xtick.labelsize' ]  = 14\n",
-    "plt.rcParams['ytick.labelsize' ]  = 14\n",
-    "plt.rcParams['axes.labelsize'  ]  = 14\n",
-    "plt.rcParams['legend.fancybox' ]  = False\n",
-    "\n",
-    "pd.options.mode.chained_assignment = None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index([u'Y', u'Z', u'avg_et', u'avg_pt', u'cr_region', u'diff_et',\n",
-       "       u'dijet_LeadJPt', u'dijet_Mjj', u'dijet_SubJPt', u'dijet_Zep',\n",
-       "       u'dijet_abs_dEta', u'dijet_dipho_dphi', u'dijet_dipho_dphi_trunc',\n",
-       "       u'dijet_dphi', u'dijet_leadEta', u'dijet_minDRJetPho', u'dijet_mva',\n",
-       "       u'dijet_subleadEta', u'dipho_PToM', u'dipho_cosphi', u'dipho_leadEt',\n",
-       "       u'dipho_leadEta', u'dipho_leadIDMVA', u'dipho_leadPt',\n",
-       "       u'dipho_lead_elveto', u'dipho_lead_sieie', u'dipho_mass', u'dipho_mva',\n",
-       "       u'dipho_subleadEt', u'dipho_subleadEta', u'dipho_subleadIDMVA',\n",
-       "       u'dipho_subleadPt', u'dipho_sublead_elveto', u'dipho_sublead_sieie',\n",
-       "       u'eta_cat', u'fail_id', u'isvbf', u'leadPho_PToM', u'lead_eta_cat',\n",
-       "       u'm_sideband', u'max_eta', u'max_id', u'min_eta', u'min_id',\n",
-       "       u'n_jet_30', u'pass_id', u'sample', u'sublPho_PToM', u'subl_eta_cat',\n",
-       "       u'weight', u'weight_bins'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_hdf('../../data-driven/data/hgg-double-fake-trees-training-2017.h5')\n",
-    "df.columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def vbf_presel(data):\n",
-    "    return (\n",
-    "        (data[\"leadPho_PToM\"       ]> (1/3.0))&\n",
-    "        (data[\"sublPho_PToM\"       ]> (1/4.0))&\n",
-    "        (data[\"dijet_LeadJPt\"      ]> 30     )& \n",
-    "        (data[\"dijet_SubJPt\"       ]> 20     )&\n",
-    "        (data[\"dijet_Mjj\"          ]> 250    )&\n",
-    "        (data[\"dipho_mass\"         ]> 100    )&\n",
-    "        (data[\"dipho_mass\"         ]< 180    ))\n",
-    "\n",
-    "df = df[vbf_presel(df)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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FOvCbIZZETCYzJJOIZ3/5AgBAMokwmUK/QOxOQalOtFhgTYKn/pEwdumDMAtmTzcIYDCM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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11324ddd0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(4,4))\n",
-    "\n",
-    "plt.hist(df[df.Y==1].dijet_mva,bins=100, range=[-1,1], \n",
-    "         alpha=0.4, weights=df[df.Y==1].weight, \n",
-    "         histtype='stepfilled',lw=1, normed=1)\n",
-    "plt.hist(df[df.Y==0].dijet_mva,bins=100, range=[-1,1], \n",
-    "         alpha=0.4, weights=df[df.Y==0].weight, \n",
-    "         histtype='stepfilled',lw=1, normed=1)\n",
-    "# plt.yscale('log')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.interpolate import interp1d\n",
-    "    \n",
-    "class empirical_cdf(object):\n",
-    "    def __init__(self, x, weights=None):\n",
-    "        if weights is None:\n",
-    "            weights = np.ones(x.shape[0])\n",
-    "        self._data = np.column_stack([x,weights])\n",
-    "        print self._data\n",
-    "        self._data = self._data[self._data[:,0].argsort()]\n",
-    "        self._data = np.column_stack([x,weights])\n",
-    "        x = self._data[:,0]\n",
-    "        y = self._data[:,1].cumsum()/self._data[:,1].sum()\n",
-    "        self.fcn = interp1d(x,y, bounds_error=False)\n",
-    "    def fcn(self):\n",
-    "        return self.fcn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'X' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-3-ca6cae9e3909>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mempirical_cdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mW\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfcn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "f = empirical_cdf(X[Y==0],weights=W[Y==0])\n",
-    "f.fcn([0.5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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7B9G250cwiYlhvzEME/1W5JzUmjlzAQC99u4Jjbj0OVLR4YlxvKbgW5YP1u8P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\nQrE1VMQoFIqtoSJGoVBszf8H79YeBAk2qboAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11390ee50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "plt.figure(figsize=(4,4))\n",
-    "# plt.plot(xb,Fb)\n",
-    "t = np.linspace(-1,1,1000)\n",
-    "plt.plot(t,f.fcn(t), '-')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from scipy.interpolate import interp1d\n",
-    "\n",
-    "class StepFunction(object):\n",
-    "    def __init__(self, x, y, ival=0., sorted=False, side='left'):\n",
-    "        if side.lower() not in ['right', 'left']:\n",
-    "            msg = \"side can take the values 'right' or 'left'\"\n",
-    "            raise ValueError(msg)\n",
-    "        self.side = side\n",
-    "\n",
-    "        _x = np.asarray(x)\n",
-    "        _y = np.asarray(y)\n",
-    "\n",
-    "        if _x.shape != _y.shape:\n",
-    "            msg = \"x and y do not have the same shape\"\n",
-    "            raise ValueError(msg)\n",
-    "        if len(_x.shape) != 1:\n",
-    "            msg = 'x and y must be 1-dimensional'\n",
-    "            raise ValueError(msg)\n",
-    "\n",
-    "        self.x = np.r_[-np.inf, _x]\n",
-    "        self.y = np.r_[ival, _y]\n",
-    "\n",
-    "        if not sorted:\n",
-    "            asort = np.argsort(self.x)\n",
-    "            self.x = np.take(self.x, asort, 0)\n",
-    "            self.y = np.take(self.y, asort, 0)\n",
-    "        self.n = self.x.shape[0]\n",
-    "\n",
-    "    def __call__(self, time):\n",
-    "\n",
-    "        tind = np.searchsorted(self.x, time, self.side) - 1\n",
-    "        return self.y[tind]\n",
-    "\n",
-    "class _ecdf(StepFunction):\n",
-    "    def __init__(self, x, weights=None, side='right'):\n",
-    "        step = True\n",
-    "        if step: \n",
-    "            x = np.array(x, copy=True)\n",
-    "            x.sort()\n",
-    "            nobs  = weights.sum()\n",
-    "            shape = len(x)\n",
-    "            y = np.linspace(1./nobs,1,shape)\n",
-    "            super(_ecdf, self).__init__(x, y, side=side, sorted=True)\n",
-    "        else:\n",
-    "            return interp1d(x,y,drop_errors=False,fill_values=ival)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'X' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-19-b6835a4a40e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mecdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ecdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mFs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mFb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "ecdf = _ecdf(X[Y==1])\n",
-    "plt.figure(figsize=(4,4))\n",
-    "plt.plot(xs,Fs)\n",
-    "plt.plot(xb,Fb)\n",
-    "\n",
-    "plt.plot([0.2,0.8],ecdf([0.2,0.8]),'ro')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn import tree\n",
-    "\n",
-    "clf = tree.DecisionTreeClassifier(max_depth=10, min_weight_fraction_leaf=0.1)\n",
-    "clf = clf.fit(X[:, None], Y, sample_weight=W)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pydotplus \n",
-    "dot_data = tree.export_graphviz(clf, out_file=None) \n",
-    "graph = pydotplus.graph_from_dot_data(dot_data) \n",
-    "graph.write_pdf(\"tree.pdf\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "with open(\"tree.dot\", 'w') as f:\n",
-    "    f = tree.export_graphviz(clf, out_file=f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "os.unlink('tree.dot')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pydotplus \n",
-    "dot_data = tree.export_graphviz(clf, out_file=None) \n",
-    "graph = pydotplus.graph_from_dot_data(dot_data) \n",
-    "graph.write_png(\"tree_b.png\") "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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zFle77bbbYqxVpHv++uuv3Xjeeb/++qs99thjFpVZZVFZPt7uaO/B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\nQACBFBRQCS8FEcgASUH0gEsp6yl37ty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Kly9vHTt2dJ8rV65sOm/ChAk2fPhwN3aVKlWs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pL6yy/LJOD07NnTzVub9ckmscbz8Iiv0/NUi3meKDEJ42GWqi/9ZiWCeF4W8Wb6e23btm38XAe5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J6n4EWCFx+5pDpFzz33nId9JKzkAw880KNuezJvvGTyCc3XX399nw+eOFX7NiWvt7HX+Zq1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\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ACZAACZAACZBAuRDgCpxqvJM9evSQNm3aSKNGjcxf8NJYqZLJ5FkwXTHto4McF7ByKCok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\ngARIgASqn8D/AwzZuUcdgGfGAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import Image\n",
-    "Image(filename='tree_b.png')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.tree import _tree\n",
-    "\n",
-    "def tree_to_code(tree, feature_names):\n",
-    "    tree_ = tree.tree_\n",
-    "    feature_name = [\n",
-    "        feature_names[i] if i != _tree.TREE_UNDEFINED else \"undefined!\"\n",
-    "        for i in tree_.feature\n",
-    "    ]\n",
-    "    print \"def tree({}):\".format(\", \".join(feature_names))\n",
-    "\n",
-    "    def recurse(node, depth):\n",
-    "        indent = \"  \" * depth\n",
-    "        if tree_.feature[node] != _tree.TREE_UNDEFINED:\n",
-    "            name = feature_name[node]\n",
-    "            threshold = tree_.threshold[node]\n",
-    "            print \"{}if {} <= {}:\".format(indent, name, threshold)\n",
-    "            recurse(tree_.children_left[node], depth + 1)\n",
-    "            print \"{}else:  # if {} > {}\".format(indent, name, threshold)\n",
-    "            recurse(tree_.children_right[node], depth + 1)\n",
-    "        else:\n",
-    "            print \"{}return {}\".format(indent, tree_.value[node])\n",
-    "\n",
-    "    recurse(0, 1)\n",
-    "    \n",
-    "def get_boundaries(tree):\n",
-    "    tree_ = tree.tree_\n",
-    "    cuts  = []\n",
-    "    value = []\n",
-    "    w = []\n",
-    "    def recurse(node, depth):\n",
-    "        if tree_.feature[node] != _tree.TREE_UNDEFINED:\n",
-    "            cuts.append(tree_.threshold[node])\n",
-    "            value.append(tree_.value[node])\n",
-    "            w.append(tree_.weighted_n_node_samples[node])\n",
-    "    \n",
-    "            recurse(tree_.children_left [node], depth + 1)\n",
-    "            recurse(tree_.children_right[node], depth + 1)\n",
-    "    recurse(0, 1)\n",
-    "    \n",
-    "    return np.array(cuts), np.array(value), np.array(w)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.886  0.578  0.321  0.125  0.045  0.209  0.447  0.749]\n",
-      "[[[  1.000e+06   9.876e+01]]\n",
-      "\n",
-      " [[  8.990e+05   5.052e+01]]\n",
-      "\n",
-      " [[  6.613e+05   2.019e+01]]\n",
-      "\n",
-      " [[  4.526e+05   8.582e+00]]\n",
-      "\n",
-      " [[  2.389e+05   2.796e+00]]\n",
-      "\n",
-      " [[  2.137e+05   5.786e+00]]\n",
-      "\n",
-      " [[  2.087e+05   1.161e+01]]\n",
-      "\n",
-      " [[  2.377e+05   3.033e+01]]]\n",
-      "[ 1000133.156   899075.767   661359.66    452627.57    238923.55\n",
-      "   213704.019   208732.09    237716.107]\n"
-     ]
-    }
-   ],
-   "source": [
-    "cuts_, value_, w_ =  get_boundaries(clf)\n",
-    "print cuts_\n",
-    "print value_\n",
-    "print w_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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CMf+XCljvPlchfDY3qbd1JhCAx+1GOBzGi1u3JR0uzCYWKIqcFJRmFir1mCLv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\nioiITIkFioiITOn/AHddjQ/urQfKAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110876e90>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(6,4))\n",
-    "plt.hist(X[Y==1],bins=100, range=[0,1], alpha=0.4, weights=W[Y==1], histtype='stepfilled',lw=1, normed=True)\n",
-    "plt.hist(X[Y==0],bins=100, range=[0,1], alpha=0.4, weights=W[Y==0], histtype='stepfilled',lw=1, normed=True)\n",
-    "# plt.yscale('log')\n",
-    "\n",
-    "for i,x in enumerate(cuts_):\n",
-    "    plt.axvline(x, ls='--',c='k',label=i)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5,  0.5])"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.model_selection import cross_val_score\n",
-    "\n",
-    "cross_val_score(clf,X[:, None], Y, cv=10, fit_params={\"sample_weight\":W})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
-    "\n",
-    "rdf = RandomForestClassifier(max_depth=10, min_weight_fraction_leaf=0.1, n_estimators=500)\n",
-    "rdf = rdf.fit(X[:, None], Y, sample_weight=W)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "def evaluate_sklearn(cls, vals, coef=1):\n",
-    "    scale = 1.0 / cls.n_estimators\n",
-    "    ret = np.zeros(vals.shape[0])\n",
-    "\n",
-    "    learning_rate = cls.learning_rate\n",
-    "    for itree, t in enumerate(cls.estimators_[:, 0]):\n",
-    "        r = t.predict(vals)\n",
-    "        ret += r * scale\n",
-    "    return 2.0/(1.0 + np.exp(-coef/learning_rate * ret)) - 1\n",
-    "\n",
-    "def boosted_bounds(cls):\n",
-    "    cuts_  = np.empty([cls.n_estimators,cls.max_depth], dtype=np.ndarray)\n",
-    "    for i, t in enumerate(cls.estimators_[:, 0]):\n",
-    "        tree_ = t.tree_\n",
-    "        def recurse(node, depth):\n",
-    "            if tree_.feature[node] != _tree.TREE_UNDEFINED:\n",
-    "                cuts_[i,depth-1] = tree_.threshold[node]\n",
-    "                if tree_.threshold[node]==None: print 'none stuff : ', i , depth-1\n",
-    "                recurse(tree_.children_left [node], depth + 1)\n",
-    "                recurse(tree_.children_right[node], depth + 1)\n",
-    "        recurse(0, 1)\n",
-    "    cuts_ = np.nan_to_num(np.array(cuts_, dtype=np.float))\n",
-    "    return cuts_\n",
-    "\n",
-    "def get_boundaries(cls):\n",
-    "    cuts_  = np.empty([cls.n_estimators,cls.max_depth], dtype=np.ndarray)\n",
-    "    for i, t in enumerate(cls.estimators_):\n",
-    "        tree_ = t.tree_\n",
-    "        cuts = []\n",
-    "        def recurse(node, depth):\n",
-    "            if tree_.feature[node] != _tree.TREE_UNDEFINED:\n",
-    "                cuts_[i,depth-1] = tree_.threshold[node]\n",
-    "                recurse(tree_.children_left [node], depth + 1)\n",
-    "                recurse(tree_.children_right[node], depth + 1)\n",
-    "        recurse(0, 1)\n",
-    "        \n",
-    "    cuts_ = np.nan_to_num(np.array(cuts_, dtype=np.float))\n",
-    "    return cuts_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "average : [ 0.883  0.575  0.673  0.36   0.141  0.027  0.     0.     0.     0.   ]\n",
-      "std     : [ 0.005  0.058  0.157  0.14   0.072  0.053  0.     0.     0.     0.   ]\n"
-     ]
-    }
-   ],
-   "source": [
-    "cuts = get_boundaries(rdf)\n",
-    "\n",
-    "print 'average :', np.mean(cuts, axis=0)\n",
-    "print 'std     :', np.std(cuts, axis=0)\n",
-    "\n",
-    "m_cuts = np.mean(cuts, axis=0)\n",
-    "e_cuts = np.std(cuts, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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m+CJCrYm7YJktyoKl1XKGWNfHCzzyZ8yB2+X0vUcRAKrrGjBq6myUH9rvS++c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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1147d3090>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(4,4))\n",
-    "plt.hist(X[Y==1],bins=100, range=[0,1], alpha=0.4, weights=W[Y==1], histtype='stepfilled',lw=1, normed=True)\n",
-    "plt.hist(X[Y==0],bins=100, range=[0,1], alpha=0.4, weights=W[Y==0], histtype='stepfilled',lw=1, normed=True)\n",
-    "# plt.yscale('log')\n",
-    "\n",
-    "for i,x in enumerate(m_cuts):\n",
-    "    plt.axvline(x, ls='--',c='k',alpha=0.5, label=i)\n",
-    "    plt.axvspan(x - e_cuts[i]/2, x + e_cuts[i]/2,facecolor='k',alpha=0.2)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import GradientBoostingClassifier\n",
-    "\n",
-    "gmb = GradientBoostingClassifier(max_depth=4, min_weight_fraction_leaf=0.1, n_estimators=40)\n",
-    "gmb = gmb.fit(X[:, None], Y, sample_weight=W)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1148a3b10>]"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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PH26wzzfffJP0ej2pVCry8vKiyZMnU35+foO/g+YWECcnCxcJ7NzZaBXGWBtlywsEJERE\ndsz1diUgIACAeC3s7Y4dA4YPB15/HXj11dYcGWPsfjX3/r4bfHcrG+LbBzLGAA5Wm9JqgW7deGUA\nY50dB6uN9e3LwcpYZ8fBamN9+wI//wxUVtp7JIwxe+FgtbE+fYTHu7xXNmOsA+FgtTG+fSBjjIPV\nxnhlAGOMg9XG+IyVMcbBamPduwNOThysjHVmHKw2JpEIH2DxVABjnRcHawvo2xcoLARqauw9EsaY\nPXCwtoA+fYD6eqCgwN4jYYzZAwdrC+CVAYx1bhysLYBXBjDWuXGwtgDT1VccrIx1ThysLcDbG5DL\neSqAsc6Kg7UFSKVA7958xspYZ8XB2kL69BFuxFJXZ++RMMZaGwdrC+nbFzAagcuX7T0Sxlhr42Bt\nIbwygLHOi4O1hfDKAMY6Lw7WFsIXCTDWeXGwthBfX2F1AJ+xMtb5cLC2ELkceOABDlbGOiMO1hbU\nt68wFUBk75EwxloTB2sL6ttX+LbWq1ftPRLGWGviYG1BvDKAsc6Jg7UF8VpWxjonDtYWpNcLjydO\n2HccjLHWZXWwnj9/HmFhYVCr1XBzc8PcuXNRUVFhVdukpCTo9XoolUro9XokJydb1DEajVi5ciW8\nvLygUqkwevRoHD9+XFSnpKQECxcuxIgRI6BUKiGRSBrtc8eOHRg0aBAUCgW8vb2xYsUKVFdXW9Qj\nIrzzzjsYOHAglEol3N3dER4ejhobfK9Knz6Aiwvwr3/d964YY+2IVcF648YNhISEoLS0FKmpqUhM\nTMTu3bsRHR3dbNu0tDQ899xzePLJJ7F//36Eh4fj2WefxZ49e0T1XnnlFWzZsgXx8fHYs2cPnJ2d\nMXbsWPznP/8x17ly5QpSUlLg5uaG4cOHN9pnUlISpk2bhtGjR2Pv3r1YtGgRtmzZgtjYWIu68+fP\nR1xcHKKionDgwAF88MEH8PX1RZ0N7p7SpQsQFAT88IPwVS2MsU6CrLBx40ZSKpVUVFRkLtu9ezcB\noNzc3Cbb6nQ6mjRpkqhswoQJFBgYaH5+5coVkslklJiYaC6rqKggT09Pmjdvnrmsrq7O/POGDRuo\nseH37t2bIiIiRGWbNm0iAHTixAlz2eHDhwkAZWRkNHkMJnq9nvR6vVV1TRYvJgKI/v3vu2rGGGtl\n9/L+boxVZ6wZGRkICQmBh4eHuSwiIgJqtRr79u1rtF1BQQHy8/Mxbdo0UXlUVBROnTqFwsJCAEBm\nZiZqa2sxdepUcx2VSoXIyEjR/rt0aX64165dw4ULFxAaGioqNz1PT083l23duhX9+vXD+PHjm93v\nvRo6VHjk6QDGOg+rgjUvLw86nU5UJpPJ0L9/f+Tn5zfZDoBFW/1/P9Uxtc3Ly4Orq6souE31CgsL\nUVVVZc0wAQBSqRQAIJfLReUKhQIAcPr0aXPZkSNHMHDgQLz22mvo3r075HI5goODkZOTY3V/zRky\nRHi8Y7qYMdaByaypZDAY0K1bN4tyjUaD0tLSJtsBsGir0WgAwNy2qf0TEQwGAxwdHa0ZKjQaDdzc\n3JCTk4MXXnjBXG4Ky9vHe/XqVWRmZuLUqVN45513oFQq8dprryE0NBTnzp2zCHpA+JDt6NGjDfbt\n7e0NHx8fUVn//oBazcHKWFtx6dIlXG7gRsmVlZVwcHCwSR9WBWt7s2DBAqxfvx7BwcGIjIzEyZMn\nsWLFCkilUtF0Qn19PcrLy5GWloaAgAAAwEMPPYRevXrhb3/7G9atW2exb4PBgFGjRjXYb3x8PNas\nWSMqM32A9a9/CR9gWTGbwRhrQR9++CHWrl3b4Gtubm426cOqYNVoNCgrK7MoNxgM6NevX5PtAKCs\nrAze3t6idgCg1Wqb3b9EIjHvx1rLli3DxYsXMWvWLMyYMQMKhQLr1q1DQkICevToIRqfRCIxhyoA\nuLu7Y+DAgaIpgzuP6c4VDSa3H+Pthg4Fvv1W+KoW00UDjDH7mDlzJsaNG2dRPn369NY9Y9XpdBZz\nqXV1dTh79iwiIiKabAcIc6mBgYHm8jvnXnU6Ha5fv46SkhK4u7uL6vn6+lo9DWCiUCjw0UcfISEh\nAZcvX0avXr1w69YtLF++HMHBweZ6AQEB5rHcjohw69atBvft4OCAkSNH3tV4bp9n5WBlzL58fHws\npuwA4QNzW7HqH6ZhYWHIzs5GSUmJuWzv3r0oLy9HeHh4o+38/PwwYMAA7Ny5U1SekpKCgIAA+Pr6\nAhA+sZdKpaJ6VVVVSE9Pb3L/zdFqtRg0aBBcXFyQmJgIrVaLKVOmmF+PiIhAUVERTp06ZS4rLi7G\nyZMnMWzYsHvu906mlQE8z8pYJ2HNmiyDwUA9e/akkSNHUkZGBiUnJ1P37t1p4sSJonozZswgqVQq\nKtu1axcBoOXLl1N2djYtW7aMAFBaWpqo3ksvvUTOzs60detWysrKovDwcOratSsVFBSI6qWmplJq\naipFRUURAPPz06dPm+t8+eWXlJiYSFlZWZSWlkbR0dEklUot+iwvL6c+ffpQ3759aceOHZSenk4P\nPfQQubu709WrVy1+D/e6zq22lkilInrssbtuyhhrJbZcx2pVsBIRnTlzhsaNG0cqlYq0Wi3FxsbS\nzZs3RXViYmIaXLS/bds28vf3J7lcTv7+/rR9+3aLOjU1NRQXF0eenp6kVCopODiYjh07ZjlgoMEt\nPj7eXCczM5OCgoLIycmJ1Go1hYSE0OHDhxs8rsLCQpo8eTI5OzuTk5MTjR8/nvLz8xusez+/+FGj\niDQaovr6e2rOGGthtgxWCRHfhtlapg+5GvtgqykLFgBvvSXc+Lp3b1uPjDF2v+7n/X0nXvzTSvgK\nLMY6Dw7WVsJXYDHWeXCwthKdDnB05GBlrDPgYG0lMhnw4IPCVADPajPWsXGwtqIhQ4Dr14HbbjHL\nGOuAOFhbEV8owFjnwMHaijhYGescOFhbkV4PKBS85Iqxjo6DtRU5OACDBglnrPwBFmMdFwdrKxs6\nFCgpARq4zy5jrIPgYG1lfAUWYx0fB2sr4yuwGOv4OFhbWWAgIJdzsDLWkXGwtjK5HBg4kKcCGOvI\nOFjtYMgQ4OpV4Oef7T0SxlhL4GC1A75QgLGOjYPVDjhYGevYOFjtYOBA4Qqsf/7T3iNhjLUEDlY7\nUCiAhx8G/vEPoJFv2WaMtWMcrHYydqwQqkeP2nskjDFb42C1k8ceEx6zsuw7DsaY7XGw2smQIUC3\nbhysjHVEHKx2IpUCISFAbi5gMNh7NIwxW+JgtaOxY4H6euDwYXuPhDFmSxysdjR2rPD49df2HQdj\nzLY4WO2ob1/Ax4fnWRnraDhY7UgiEc5az5wBLl2y92gYY7bCwWpnPB3AWMfDwWpnpvWsHKyMdRwc\nrHbWvbtw8+usLP6CQcY6CquD9fz58wgLC4NarYabmxvmzp2LiooKq9omJSVBr9dDqVRCr9cjOTnZ\noo7RaMTKlSvh5eUFlUqF0aNH4/gdt38qKSnBwoULMWLECCiVSkgkkkb73LFjBwYNGgSFQgFvb2+s\nWLEC1dXVjda/ceMGevToAYlEgu+++86q47KVsWOF+7Pm5bVqt4yxFmJVsN64cQMhISEoLS1Famoq\nEhMTsXv3bkRHRzfbNi0tDc899xyefPJJ7N+/H+Hh4Xj22WexZ88eUb1XXnkFW7ZsQXx8PPbs2QNn\nZ2eMHTsW//nPf8x1rly5gpSUFLi5uWH48OGN9pmUlIRp06Zh9OjR2Lt3LxYtWoQtW7YgNja20Tar\nVq2y4jfRMkzzrLw6gLEOgqywceNGUiqVVFRUZC7bvXs3AaDc3Nwm2+p0Opo0aZKobMKECRQYGGh+\nfuXKFZLJZJSYmGguq6ioIE9PT5o3b565rK6uzvzzhg0bqLHh9+7dmyIiIkRlmzZtIgB04sQJi/rH\njx8nlUpFH3zwAQGgo0ePNrhfvV5Per2+iaO9N7/+SiSTEU2YYPNdM8asZMv3t1VnrBkZGQgJCYGH\nh4e5LCIiAmq1Gvv27Wu0XUFBAfLz8zFt2jRReVRUFE6dOoXCwkIAQGZmJmprazF16lRzHZVKhcjI\nSNH+u3RpfrjXrl3DhQsXEBoaKio3PU9PTxeV19fXY86cOVi0aBH69u3b7P5bgrMzMHy4cAWW0WiX\nITDGbMiqYM3Ly4NOpxOVyWQy9O/fH/n5+U22A2DRVq/XA4C5bV5eHlxdXUXBbapXWFiIqqoqa4YJ\nAJBKpQAAuVwuKlcoFACA06dPi8q3bt2KoqIivPrqq1b30RLGjgVu3gS+/96uw2CM2YDMmkoGgwHd\nunWzKNdoNCgtLW2yHQCLthqNBgDMbZvaPxHBYDDA0dHRmqFCo9HAzc0NOTk5eOGFF8zlOTk5oj4B\noLi4GCtXrsQHH3wAlUpl1f6NRiOONnITVW9vb/j4+Fi1nzuNHQusXSvMs44adU+7YIxZ4dKlS7h8\n+bJFeWVlJRwcHGzSh1XB2t4sWLAA69evR3BwMCIjI3Hy5EmsWLECUqlUNJ2wZMkSDBs2DL///e+t\n3rfBYMCoRpIvPj4ea9asuacxDx8OqNVCsP7xj/e0C8aYFT788EOsXbu2wdfc3Nxs0odVwarRaFBW\nVmZRbjAY0K9fvybbAUBZWRm8vb1F7QBAq9U2u3+JRGLej7WWLVuGixcvYtasWZgxYwYUCgXWrVuH\nhIQE9OjRAwDw3XffISUlBd9++6257/LycvPjzZs34ezs3OAx3bmiweT2Y7xbDg7AmDHAwYNAebkQ\nsowx25s5cybGjRtnUT59+vTWPWPV6XQWc6l1dXU4e/YsIiIimmwHCHOpgYGB5vI75151Oh2uX7+O\nkpISuLu7i+r5+vpaPQ1golAo8NFHHyEhIQGXL19Gr169cOvWLSxfvhzBwcEAgH//+9+ora3FyJEj\nLdo//vjjCAgIwKlTpyxec3BwaLCNLYwdC2RkAN9+C4wf3yJdMNbp+fj4NDhlZ+10oDWs+vAqLCwM\n2dnZKCkpMZft3bsX5eXlCA8Pb7Sdn58fBgwYgJ07d4rKU1JSEBAQAF9fXwDCJ/ZSqVRUr6qqCunp\n6U3uvzlarRaDBg2Ci4sLEhMTodVqMWXKFADAE088gezsbNG2efNmAMDbb7+Njz/++J77vVe8npWx\nDsKaNVkGg4F69uxJI0eOpIyMDEpOTqbu3bvTxIkTRfVmzJhBUqlUVLZr1y4CQMuXL6fs7GxatmwZ\nAaC0tDRRvZdeeomcnZ1p69atlJWVReHh4dS1a1cqKCgQ1UtNTaXU1FSKiooiAObnp0+fNtf58ssv\nKTExkbKysigtLY2io6NJKpVa9Hmn7Oxsu6xjNamvJ+renagFu2CMNcKW72+rgpWI6MyZMzRu3DhS\nqVSk1WopNjaWbt68KaoTExPT4KL9bdu2kb+/P8nlcvL396ft27db1KmpqaG4uDjy9PQkpVJJwcHB\ndOzYMcsBAw1u8fHx5jqZmZkUFBRETk5OpFarKSQkhA4fPtzsMdo7WImIXniBCCC67f8TjLFWYMv3\nt4SIb/1hrYCAAACWa2FtKSsLePxxID4euMcFBoyxe2DL9zff3aqNefRRwN0d2LmT73bFWHvFwdrG\nyGTAU08B//43cPKkvUfDGLsXHKxt0DPPCI93LKZgjLUTHKxt0MMPAz16ALt28XQAY+0RB2sbJJUK\n0wHnzwM//GDv0TDG7hYHaxvF0wGMtV8crG3UyJGAtzdPBzDWHnGwtlFdugBPPw0UFPA9WhlrbzhY\n2zCeDmCsfeJgbcN+9zugVy9hOqC+3t6jYYxZi4O1DZNIhOmAy5eBRr60gDHWBnGwtnE8HcBY+8PB\n2sYFBQF9+wKffQbU1dl7NIwxa3CwtnESiXDW+ssvwD/+Ye/RMMaswcHaDvB0AGPtCwdrOxAYCAwY\nIEwHGI32Hg1jrDkcrO2ARAI89xxQUgJ8/rm9R8MYaw4HazsxaxYglwN/+5u9R8IYaw4Hazvh4QFM\nnSp8NfaPP9p7NIyxpnCwtiPz5gmPb79t33EwxprGwdqO/O53wEMPAUlJgMFg79EwxhrDwdrOzJsH\nVFUBH31k75EwxhrDwdrOPP208C2u77zDV2Ix1lZxsLYzCgUwezZw4QLw1Vf2Hg1jrCEcrO1QbKzw\nvVi89IrZ13iQAAAgAElEQVSxtomDtR3y8QEiI4Uz1rNn7T0axtidOFjbqfnzhcd33rHvOBhjljhY\n26kxY4R7CHz8MVBebu/RMMZux8HaTkkkwtKrX38V1rUyxtoODtZ2LCoK6NpV+BCLvyKbsbbD6mA9\nf/48wsLCoFar4ebmhrlz56KiosKqtklJSdDr9VAqldDr9UhOTraoYzQasXLlSnh5eUGlUmH06NE4\nfvy4qE5JSQkWLlyIESNGQKlUQiKRNNrnjh07MGjQICgUCnh7e2PFihWorq4W1Xn//fcxfvx4eHl5\nQa1WY8iQIfjkk09A7SSl1GrgD38ATp8GDhyw92gYY2ZkhbKyMvLx8aHhw4dTRkYGJSUlkYeHB02e\nPLnZtrt37yYAtHTpUjp06BAtWbKEAFB6erqo3vz580mtVtN7771HBw8epPHjx1O3bt2osLDQXOeH\nH34gd3d3Cg8PpzFjxlBjw//0008JAM2dO5cOHDhACQkJpFKpKCYmRlTPx8eHZs2aRbt376asrCxa\nunQpSSQS+uMf/9jgfvV6Pen1+maPuTX95z9ECgXRkCFE9fX2Hg1j7Zct399WBevGjRtJqVRSUVGR\nucwUmLm5uU221el0NGnSJFHZhAkTKDAw0Pz8ypUrJJPJKDEx0VxWUVFBnp6eNG/ePHNZXV2d+ecN\nGzY0Gqy9e/emiIgIUdmmTZsIAJ04ccJcVlxcbNF25syZpFarqba21uK1thisRESLFhEBRJ99Zu+R\nMNZ+2fL9bdVUQEZGBkJCQuDh4WEui4iIgFqtxr59+xptV1BQgPz8fEybNk1UHhUVhVOnTqGwsBAA\nkJmZidraWkydOtVcR6VSITIyUrT/Ll2aH+61a9dw4cIFhIaGispNz9PT081l7u7uFu2HDh2K8vJy\n/Prrr8321VasWCFMC6xaBdTW2ns0jDGZNZXy8vIQHR0tbiiToX///sjPz2+yHQDodDpRuV6vBwDk\n5+fD19cXeXl5cHV1FQW3qd7WrVtRVVUFR0dHa4YKqVQKAJDL5aJyhUIBADh9+nST7b/55ht4enqi\nW7duDb5uNBpx9OjRBl/z9vaGj4+PVeO0JXd3YNEiYP16YYXA88+3+hAYazcuXbqEy5cvW5RXVlbC\nwcHBJn1YFawGg6HBoNFoNCgtLW2yHQCLthqNBgDMbZvaPxHBYDBYHawajQZubm7IycnBCy+8YC7P\nyckR9dmQQ4cOYefOnXjzzTcb/WDMYDBg1KhRDb4WHx+PNWvWWDVOW3vlFWF1wJo1wLRpwj0FGGOW\nPvzwQ6xdu7bB19zc3GzSh1XB2t4sWLAA69evR3BwMCIjI3Hy5EmsWLECUqm00emE8+fPY+rUqXjs\nscfw8ssvN7pvjUaDPXv2NPiat7e3TcZ/L7p2BeLigOXLgQ8++O2m2IwxsZkzZ2LcuHEW5dOnT2/d\nM1aNRoOysjKLcoPBgH79+jXZDgDKyspEoWM6k9Vqtc3uXyKRmPdjrWXLluHixYuYNWsWZsyYAYVC\ngXXr1iEhIQE9evSwqP/zzz8jNDQUPj4+SEtLM08nNMTBwQEjR468q/G0lnnzgL/+FXjtNWEZlpOT\nvUfEWNvj4+PT4JSdSqWyWR9WfXil0+ks5lLr6upw9uxZi/nTO9sBsGh759yrTqfD9evXUVJSYlHP\n19fX6mkAE4VCgY8++gglJSX48ccfUVxcjOeffx4lJSUIDg4W1S0tLUVoaCikUin2798PZ2fnu+qr\nLVGpgNWrgaIi4K237D0axjovq4I1LCwM2dnZouDbu3cvysvLER4e3mg7Pz8/DBgwADt37hSVp6Sk\nICAgAL6+vgBgDrbb61VVVSE9Pb3J/TdHq9Vi0KBBcHFxQWJiIrRaLaZMmWJ+vaKiAmFhYSgtLcWB\nAwcsPjxrj2bOBPz8gI0b+etbGLMXq6YCYmNj8dZbb2HixIlYvXo1DAYDFi9ejIkTJ2LYsGHmejNn\nzsS2bdtQe9uan3Xr1uHpp59GXFwcnnjiCezfvx/p6elIS0sz1+nZsyfmzJmDV199FXK5HH369MHm\nzZtRWVmJpUuXisby2WefAQBOnToleq7X682rDTIyMnD+/HkEBATg119/xRdffIHk5GSkpqbCxcXF\nvK/f//73+P777/Huu++iuLgYxcXF5teCgoLMKwnaE7kcWLsWiI4GEhKA11+394gY64SsXfB65swZ\nGjduHKlUKtJqtRQbG0s3b94U1YmJiWlw0f62bdvI39+f5HI5+fv70/bt2y3q1NTUUFxcHHl6epJS\nqaTg4GA6duyYRT0ADW7x8fHmOpmZmRQUFEROTk6kVqspJCSEDh8+bPW+ANDFixct6rfVCwTuVFtL\npNcTqVREV6/aezSMtQ+2fH9LiNrJhfFtQEBAAIDm18K2BZ9/DkyeLKxp/fhje4+GsbbPlu9vvrtV\nBxUZCYSGAp98Ahw6ZO/RMNa5cLB2UBIJ8O67gKMjMGcOcOuWvUfEWOfBwdqB9e4tfJB17hx/iMVY\na+Jg7eBefhl48EHgT38C/ruQgjHWwjhYOzgHB+ES17o6YPZsoL7e3iNirOPjYO0Efvc7YMEC4OhR\nYOtWe4+GsY6Pg7WTWL8e8PERbtTy88/2Hg1jHRsHayfh7Ay8/bbwra4LFth7NIx1bBysnciECcBT\nTwG7dwNffGHv0TDWcXGwdjJbtgDdugGzZgGXLtl7NIx1TBysnUyPHsLVWNevA888A9TU2HtEjHU8\nHKyd0MSJwNKlwiqB5cvtPRrGOh4O1k7q9deBhx8WvnFg9257j4axjoWDtZNycAB27BC+4fUPfxAu\ne2WM2QYHayfWsyeQkgKUlwurBaqq7D0ixjoGDtZO7rHHhBu1nDjB3+zKmK1wsDKsXAmMGwd89BHf\nFJsxW+BgZejSBUhKEi55jY0FsrLsPSLG2jcOVgYAcHMDvvwScHISvn3g++/tPSLG2i8OVmY2cCCw\nb59wi8GwMODMGXuPiLH2iYOViQQHA6mpgMEgfGfWlSv2HhFj7Q8HK7Pw5JPCB1n/+Y/woVZpqb1H\nxFj7wsHKGhQdDbz5JnD6tBC0lZX2HhFj7QcHK2vUkiW/3VPgqaf4m14ZsxYHK2vSxo3AjBnA/v3C\ntEBZmb1HxFjbx8HKmiSRCF9GuGAB8M03wCOPAL/8Yu9RMda2cbCyZnXpItwFa8MG4dLXUaP4pi2M\nNYWDlVlFIhG+iPDDD4XVAsHBwPHj9h4VY20TByu7KzNmAJ9/Dty8CTz6KF/+ylhDrA7W8+fPIyws\nDGq1Gm5ubpg7dy4qKiqsapuUlAS9Xg+lUgm9Xo/k5GSLOkajEStXroSXlxdUKhVGjx6N43ecEpWU\nlGDhwoUYMWIElEolJBJJo33u2LEDgwYNgkKhgLe3N1asWIHq6mqLerm5uRg9ejRUKhW8vLywatUq\n1NbWWnVcnVVEBHDwICCTCVdovfsuQGTvUTHWhpAVysrKyMfHh4YPH04ZGRmUlJREHh4eNHny5Gbb\n7t69mwDQ0qVL6dChQ7RkyRICQOnp6aJ68+fPJ7VaTe+99x4dPHiQxo8fT926daPCwkJznR9++IHc\n3d0pPDycxowZQ40N/9NPPyUANHfuXDpw4AAlJCSQSqWimJgYUb2LFy+Si4sLhYWF0cGDB+ndd98l\nJycnWrx4cYP71ev1pNfrmz3mzuLkSaLevYkAoueeI6qosPeIGLt3tnx/WxWsGzduJKVSSUVFReYy\nU2Dm5uY22Van09GkSZNEZRMmTKDAwEDz8ytXrpBMJqPExERzWUVFBXl6etK8efPMZXV1deafN2zY\n0Giw9u7dmyIiIkRlmzZtIgB04sQJc9mLL75IXl5eVFVVJarn4OBAv/zyi8V+OVgtlZYSTZgghOvA\ngURnz9p7RIzdG1u+v62aCsjIyEBISAg8PDzMZREREVCr1di3b1+j7QoKCpCfn49p06aJyqOionDq\n1CkUFhYCADIzM1FbW4upU6ea66hUKkRGRor236VL88O9du0aLly4gNDQUFG56Xl6errouCIjI6FU\nKs1l06dPh9FoxIEDB5rtiwEaDfDFF8AbbwhXaQ0bJszBMtaZWRWseXl50Ol0ojKZTIb+/fsjPz+/\nyXYALNrq9XoAMLfNy8uDq6urKLhN9QoLC1F1F98ZIpVKAQByuVxUrlAoAACnT58GAFRWVqKwsNBi\nbJ6entBqtU0eFxPr0gVYsQLIzAQUCmDyZGDZMoCnqllnJbOmksFgQLdu3SzKNRoNSpu4Q4fBYAAA\ni7YajQYAzG2b2j8RwWAwwNHR0ZqhQqPRwM3NDTk5OXjhhRfM5Tk5ORZ9NjS25o7LaDTi6NGjDb7m\n7e0NHx8fq8bZET32GPCvfwFTpgj3Gfj6a2F51uDB9h4ZY7+5dOkSLl++bFFeWVkJBwcHm/RhVbC2\nNwsWLMD69esRHByMyMhInDx5EitWrIBUKrVqOqEpBoMBo0aNavC1+Ph4rFmz5r723955ewP/+7/C\n92ht3Aj87nfA8uXAqlXAbTMujNnNhx9+iLVr1zb4mpubm036sCpYNRoNyhq4SNxgMKBfv35NtgOA\nsrIyeHt7i9oBgFarbXb/EonEvB9rLVu2DBcvXsSsWbMwY8YMKBQKrFu3DgkJCejRoweA385UG+vX\nNLaGjmnPnj0Nvnb7MXZmcjnw+uvCjVtmzBB+3r1bOHtt5P9JjLWamTNnYty4cRbl06dPb90zVp1O\nZzHnWFdXh7NnzyIiIqLJdoAwlxoYGGguv3PuVafT4fr16ygpKYG7u7uonq+vr9XTACYKhQIfffQR\nEhIScPnyZfTq1Qu3bt3C8uXLERwcDABwcnLCAw88YHFcRUVFKC0ttZh7NXFwcMDIkSPvajydVVAQ\ncOwYkJAgnME+/DAwf74QtGq1vUfHOisfH58Gp+xUKpXN+rDq38VhYWHIzs5GSUmJuWzv3r0oLy9H\neHh4o+38/PwwYMAA7Ny5U1SekpKCgIAA+Pr6AhA+sZdKpaJ6VVVVSE9Pb3L/zdFqtRg0aBBcXFyQ\nmJgIrVaLKVOmiI4rPT0dt267H15KSgpkMpnFqgJ2bxwchA+2/u//gJEjgS1bgP79hbPXujp7j46x\nFmLNmiyDwUA9e/akkSNHUkZGBiUnJ1P37t1p4sSJonozZswgqVQqKtu1axcBoOXLl1N2djYtW7aM\nAFBaWpqo3ksvvUTOzs60detWysrKovDwcOratSsVFBSI6qWmplJqaipFRUURAPPz06dPm+t8+eWX\nlJiYSFlZWZSWlkbR0dEklUot+vzpp5/I2dmZnnzyScrKyqL33nuP1Go1LVy4sMHfA69jvT91dUTv\nvkvk7i6sew0MJNq/n6i+3t4jY8wOFwgQEZ05c4bGjRtHKpWKtFotxcbG0s2bN0V1YmJiGly0v23b\nNvL39ye5XE7+/v60fft2izo1NTUUFxdHnp6epFQqKTg4mI4dO2Y5YKDBLT4+3lwnMzOTgoKCyMnJ\nidRqNYWEhNDhw4cbPK6cnBwKDg4mpVJJnp6etGLFCqqpqWmwLgerbdy4QfTqq0RKpRCwjz9O9H//\nZ+9Rsc7Olu9vCRFf5W2tgIAAAL+thWX359IlYPVqYPt24XlUlLCC4LbpeMZajS3f33x3K2Y3Pj7A\nJ58Ia1/HjgWSkoSv4I6IAI4csffoGLt3HKzM7gYPFq7ayskRrtrat0+43+vo0cCXX/Kds1j7w8HK\n2oyHHhLWu+blCetfc3KEb4gNDBRWE/DXcLP2goOVtTkDBgjLsS5cAF55BbhyBVi4EPDyAp59Vriy\ni89iWVvGwcraLG9v4eKCX34RPuB66CEgOVn45oIBA4A//1n4mhjG2hoOVtbmOToCzz0nfEtsXh6w\neDFw/bqwgsDXV7jwYPNmYZUBY20BBytrV3Q6YNMmYXogLQ2YNg04dUoI2wceEO5FsHkzcP68vUfK\nOjNex3oXeB1r21RVBXz1FbBrF7B3L2D6Kra+fYEnnhC2//kfwIaXgrMOyJbvbw7Wu8DB2vZVVQEH\nDghBu3//b3OwCgUwZoxwz9gxY4ChQ4W7cDFmYsv3d4e8HyvrvBwdgchIYSMC/v3v30L2f/9X+HZZ\nU70RI4S1smPGAMOH8x23mO1wsLIOSyIR5mR1OmDRIqCyUriN4TffAN9+K1zdlZ0trjts2G/bgw/y\n9AG7NzwVcBd4KqBjMRqBH34QgvbYMSA3F7h48bfXpVIhbAMDxZufn/A9X6xj4akAxmzAwUFYG/vQ\nQ7+VXb8OHD8uhGxurhC8O3aI26lUQuD27w/06yd8SGZ6dHUVzn5Z58bBythtXF2B0FBhMykvF9bP\nnjoFnDwpPOblCQF8p27dhDNaPz+gVy/xY8+eQNeuHLydAQcrY81Qqy3PbAFhWddPPwHnzgnrZk2P\nFy8CP/4I1Ndb7kupBDw9f9t69AC6dxd+vv2xe3ee323POFgZu0dOTsCgQcJ2J6NRuBKsoEAI2oIC\n4OefgatXhUt0CwuFed2GwtdErQbc3AB3d+HRtLm6CmfGLi7CGXDXruKfnZ15KZm9cbAy1gIcHIDe\nvYWtMXV1wLVrQFGRELhFRZY/X7smPD95Erjtq9mapVQKYWvanJyEJWYq1W+b6bmTk7CZfr79daXS\ncpPLf9scHHhqoyEcrIzZiVT62z/7GzrrvVNlJVBSInzAduOGsP36q+XjnVtpKXD5stC+srLps+R7\n4eAghKxCId6USuHRwUHYZDJhu/PnxjZTcJsem9pM+zNtUqnl4+1bQ3Xq62232oODlbF2QqUSbjrz\n3y83vidEwjSFKWQrK4W5YtOjaausBKqrhbPkOzejUXitpua3rbracquoEELdaBS22lrxY1tc6KnX\n22Y/HKyMdSISyW//jO/Wzb5jqa//LXQb22pqfnusqbEMZ9NWVyeU3b6Zyk2vNfVzbS2Qmmq7Y+Ng\nZYzZRZcuv00btAX//Kft9sXXjzDGmI1xsDLGmI1xsDLGmI1xsDLGmI1xsDLGmI1xsDLGmI1xsDLG\nmI1xsDLGmI1xsN4Fo9GI4uJiXLLDF9hfunQJa9as4b65b+67hdj0/U1WOnfuHI0fP56cnJzI1dWV\nXnzxRSovL7eq7aeffko6nY4UCgXpdDpKSkqyqFNTU0Ovvvoq9ejRgxwdHenhhx+m3NxcUZ3i4mJa\nsGABDR8+nBQKBTU1/L///e/04IMPkpOTE/Xo0YOeeeYZ+umnn0R1ysvLKS4ujvr06UNKpZL8/Pxo\n2bJldPPmzQb32atXLwJAR44cseq4benIkSPcN/fNfbcgW76/rTpjvXHjBkJCQlBaWorU1FQkJiZi\n9+7diI6ObrZtWloannvuOTz55JPYv38/wsPD8eyzz2LPnj2ieq+88gq2bNmC+Ph47NmzB87Ozhg7\ndiz+Y/r+YgBXrlxBSkoK3NzcMHz48Eb7/Pvf/45Zs2YhJCQE6enp2LRpE44fP47Q0FBUVlaa68XG\nxuLtt9/GggULkJGRgQULFuDtt9/GCy+8YM2vhTHGGmZN+m7cuJGUSiUVFRWZy3bv3k0ALM4q76TT\n6WjSpEmisgkTJlBgYKD5+ZUrV0gmk1FiYqK5rKKigjw9PWnevHnmsrq6OvPPGzZsaPSM9dFHH6VH\nHnlEVPb1118TAMrOziYi4QxZLpdTfHy8qN7q1atJLpdTdXW1xX75jJX75r47bt+tfsaakZGBkJAQ\neHh4mMsiIiKgVquxb9++RtsVFBQgPz8f06ZNE5VHRUXh1KlTKCwsBABkZmaitrYWU6dONddRqVSI\njIwU7b+LlTdLNBqNcHZ2FpV17doVAFD/35tR1tfXo7a2tsF69ba+YSVjrFOxKqny8vKg0+lEZTKZ\nDP3790d+fn6T7QBYtNX/96aHprZ5eXlwdXUVBbepXmFhIaqqqqwZptns2bNx4MABpKSk4Ndff8W5\nc+ewbNkyBAUFYcyYMQAAhUKBmJgYbNmyBUeOHEF5eTn++c9/IjExES+++CLk/N0WjLF7ZNVtAw0G\nA7o1cPNGjUaD0tLSJtsBsGir0WgAwNy2qf0TEQwGAxwdHa0ZKgAgOjoa1dXViImJgdFoBAAMGTIE\nX331FWSy3w75/fffx5w5cxAcHCxq+9e//rXB/RYVFQEAJk+eDKVSafG6g4MDHBwcrB7n3TDNDU+f\nPh2qVv6WOe6b++5IfRuNRnMu3M6WKxE65P1Y09PT8fLLL2PJkiUIDQ1FUVER1q1bh/DwcHzzzTfm\nkF61ahW++OILvP322wgMDMSJEyfwxz/+EfPnz8fbb79tsV8nJydUV1fj6tWrDfbr5uZmcdZtKw4O\nDnBzc2ux4Oa+ue/O0rfBYMC1a9ca7d/b2/u++7AqWDUaDcrKyizKDQYD+vXr12Q7ACgrKxMN1nQm\nq9Vqm92/RCIx78caRITZs2fj+eefxxtvvGEuHzFiBPz8/LBt2zbMmTMHp0+fxsaNG7Fr1y5MmTIF\nADBmzBi4uLggJiYG8+fPx4ABA0T7LikpwaVLl3D58uUG+/b29oaPj4/VY2WMtb7WeA9bFaw6nc5i\nLrWurg5nz55FREREk+0AYS41MDDQXH7n3KtOp8P169dRUlICd3d3UT1fX9+7mgYoKSlBcXExhg4d\nKir39fWFq6srzp07BwA4ffo0AFjUMz0/d+6cRbACgI+PD4cnY+1Ya7yHrfrwKiwsDNnZ2SgpKTGX\n7d27F+Xl5QgPD2+0nZ+fHwYMGICdO3eKylNSUhAQEADf/34rWmhoKKRSqaheVVUV0tPTm9x/Q9zd\n3eHk5ITc3FxReUFBAa5fvw4/Pz8AQK9evQDAot7x48fNY2eMsXtizZosg8FAPXv2pJEjR1JGRgYl\nJydT9+7daeLEiaJ6M2bMIKlUKirbtWsXAaDly5dTdnY2LVu2jABQWlqaqN5LL71Ezs7OtHXrVsrK\nyqLw8HDq2rUrFRQUiOqlpqZSamoqRUVFEQDz89OnT5vrLF26lKRSKcXFxVFWVhYlJyeTTqcjDw8P\nKikpISKi2tpaGjFiBLm6utJbb71Fhw4dor/+9a+k0Wjoscceo/r6evP+7ueqs/uRnZ1NACy2gIAA\nm/d17tw5io2NpaCgIJLJZOTr69tgvf3791NQUBApFArq1asX/eUvf2mVvuPj4xv8Xbz00kv31Xdq\naipFRkaSj48POTo6kl6vp4SEBKqpqRHVs/VxW9NvSx0zkbAOPTg4mFxdXUkul5Ofnx8tWrSISktL\nRfVsfdzW9NuSx307o9FIAwcOJACUkpIieu3777+nhx9+mBwdHalHjx60cuVKMhqNVu/b6ktaz5w5\nQ+PGjSOVSkVarZZiY2MtLv2MiYlpcNH+tm3byN/fn+RyOfn7+9P27dst6tTU1FBcXBx5enqSUqmk\n4OBgOnbsmOWAG/iFAxAt9K+pqaE333yT9Ho9qVQq8vLyosmTJ1N+fr5oX8XFxTR37lzy8/MjpVJJ\nffr0oYULF4r+yGVlZeTj40PDhw+njIwMSkpKIg8PD5o8ebK1v7p7ZgrWv//973T06FHz9uOPP9q8\nry+++IJ69uxJkyZNosGDBzcYbt999x05ODhQTEwMHTp0iDZs2EBSqZS2bNnS4n3Hx8eTXC4X/R6O\nHj1KFy9evK++hw8fTs888wylpKTQoUOH6LXXXiOlUknR0dHmOi1x3Nb021LHTET0wQcf0MqVK+mL\nL76g7Oxs2rJlC7m6ulJISIi5TksctzX9tuRx3y4hIYE8PT0tgvXixYvk4uJCYWFhdPDgQXr33XfJ\nycmJFi9ebPW+rQ7Wzup+rjq7X6ZgPXr0aIv2QyS+qi02NrbBcBs/fjwNHTpUdDY/f/580mq1Fmd4\ntu47Pj6eFArFPffRmOLiYouy9evXEwC6evUqEbXMcVvTb0sdc2Pef/99AkCFhYVE1HJ/7+b6bY3j\nvnTpEjk7O9O2bdssgvXFF18kLy8vqqqqMpdt2rSJHBwc6JdffrFq/3x3q2bc61Vn7U1zV7VVV1fj\n66+/xjPPPAOJRGIuj4qKQmlpKY4cOdJifbek2z8sNTF9gPnzzz+32HE31689mFbpGI3GFv17N9Vv\na3n55ZcRERFhvmDodhkZGYiMjBStVZ8+fTqMRiMOHDhg1f45WJtxr1ed2dLEiRMhlUrRvXt3zJ49\nu8mLMlrKTz/9hJqammavomtJNTU16N69O2QyGfr27Ys//elPqKurs3k/33zzDeRyOfr06dOqx317\nvyYtfcx1dXW4desWcnNzsXbtWoSFhbXKcTfWr0lLHvdXX32FzMxMvPnmmxavVVZWorCw0OK4PT09\nodVqrT7uDnmBgC3d61VnttC1a1e88sorePTRR6FWq5GTk4MNGzbg6NGjyM3NhUKhaNH+b9fYVXTO\nzs6QSqUt/rswvbmCgoJQX1+PvXv3YuXKlbhw4QLef/99m/WTl5eHxMREzJ49Gy4uLq123Hf2C7TO\nMbu6uuLGjRsAhNU5u3btAtDyf+/G+gVa9rhv3bqFefPmIT4+Hj169EBBQYHo9caOG7i79zwHaxsW\nFBSEoKAg8/NHH30UQ4YMQWhoKFJSUvD888/bb3Ct7NlnnxU9HzduHLp27YoNGzYgLi4OvXv3vu8+\nrl27hsjISPMbu7U01m9rHPPhw4dRWVmJkydP4rXXXsOECRNw8ODB+97vvfYrlUpb9LjfeOMNyOVy\nLFiw4H4PoUk8FdCMpq4KM80NtabHH38cWq0W33//fav2e/tVdLe7efMm6urq7PK7eOaZZ0BE5rXH\n9+PmzZsYP348ampq8NVXX8HJyQlAyx93Y/02xpbHDACDBw/GqFGjEBsbi88++wzZ2dn4/PPPW/y4\nG+u3MbY47sLCQvz5z3/G+vXrUVFRgbKyMvz6668AhCmAGzdumM9U7/c9z8HajKauOrtzHqY13f6B\nQmvo06cP5HK5xe+isTuYtab7/V1UV1dj4sSJKCgowIEDB+Dl5WV+rSWPu6l+m9MSf/8hQ4ZAIpHg\n/CmmH1oAAAKpSURBVPnzrfr3vr3f5tzPcV+8eBHV1dV46qmnoNFooNFo8OCDDwIAZs6ciZ49e8LJ\nyQkPPPCAxXEXFRWhtLTU6uPmYG3GvV511lIOHDiA0tJSPPTQQ63ar0KhQEhICHbt2gUiMpenpKRA\no9Fg5MiRrToeU98SiQTDhg27533U1dVh6tSp+P7775GRkQF/f3/R6y113M312xhbHHNj/vnPf4KI\n0Lt371b9e9/eb2NscdyDBw9Gdna2aEtJSQEArF69Gvv37wcgvOfT09Nx69YtUf8ymQyhoaHWdWaz\nhWEdlLVXnbWEqKgoWrVqFX3++eeUlZVFr7/+Orm4uNDgwYMb/IaD+1FRUWG+iu3xxx8nd3d383PT\n1W9HjhwhmUxGf/jDHyg7O5s2btxIMpmMNm/e3OJ9BwUF0ebNm2n//v20b98+io2NpS5dutCLL754\nX33HxsYSAFq/fr3FgvQbN2602HFb029LHTMRUWhoKG3cuJH27dtHBw8epD//+c/k7u5OgwYNMv+3\n1RLHbU2/LXncd7p48aLFOtaffvqJnJ2d6cknn6SsrCx67733SK1W08KFC63eLwerFay56qwlvPHG\nGxQYGEjOzs4kk8moV69etGjRIiorK7N5X6b/wBraPv74Y3O9jIwMGjx4MMnlcvL19aWEhIRW6fvp\np58mPz8/cnR0JKVSSQ8++CBt2bJFdHHBvfD19W20b9PX+BDZ/rit6beljpmIaNWqVTRw4EBSq9Wk\nVqspMDCQ4uPjzaFuYuvjtqbfljzuOzUUrEREOTk5FBwcTEqlkjw9PWnFihV3dVGEhOi283zGGGP3\njedYGWPMxjhYGWPMxjhYGWPMxjhYGWPMxjhYGWPMxjhYGWPMxjhYGWPMxjhYGWPMxjhYGWPMxjhY\nGWPMxv4/yDboT71jofoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x114693e50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(4,4))\n",
-    "plt.plot(np.arange(gmb.n_estimators) + 1, \n",
-    "         gmb.train_score_, 'b-', label='Deviance')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 0.886  0.578  0.749  0.447]\n",
-      " [ 0.886  0.578  0.749  0.447]\n",
-      " [ 0.88   0.507  0.741  0.379]\n",
-      " [ 0.88   0.578  0.748  0.447]\n",
-      " [ 0.887  0.578  0.765  0.383]\n",
-      " [ 0.879  0.507  0.693  0.321]\n",
-      " [ 0.887  0.674  0.321  0.492]\n",
-      " [ 0.875  0.507  0.693  0.329]\n",
-      " [ 0.875  0.507  0.693  0.321]\n",
-      " [ 0.866  0.507  0.693  0.381]\n",
-      " [ 0.887  0.578  0.765  0.383]\n",
-      " [ 0.866  0.492  0.674  0.321]\n",
-      " [ 0.866  0.492  0.693  0.321]\n",
-      " [ 0.85   0.492  0.674  0.329]\n",
-      " [ 0.85   0.492  0.674  0.28 ]\n",
-      " [ 0.85   0.507  0.674  0.321]\n",
-      " [ 0.85   0.492  0.674  0.321]\n",
-      " [ 0.85   0.507  0.674  0.33 ]\n",
-      " [ 0.85   0.492  0.674  0.241]\n",
-      " [ 0.85   0.578  0.716  0.383]\n",
-      " [ 0.85   0.507  0.693  0.291]\n",
-      " [ 0.887  0.578  0.765  0.401]\n",
-      " [ 0.85   0.492  0.633  0.321]\n",
-      " [ 0.85   0.578  0.716  0.401]\n",
-      " [ 0.813  0.383  0.633  0.225]\n",
-      " [ 0.813  0.492  0.633  0.225]\n",
-      " [ 0.813  0.578  0.241  0.401]\n",
-      " [ 0.813  0.383  0.633  0.163]\n",
-      " [ 0.813  0.674  0.53   0.383]\n",
-      " [ 0.813  0.578  0.125  0.401]\n",
-      " [ 0.813  0.578  0.209  0.401]\n",
-      " [ 0.813  0.674  0.53   0.383]\n",
-      " [ 0.813  0.507  0.642  0.163]\n",
-      " [ 0.813  0.669  0.53   0.383]\n",
-      " [ 0.813  0.545  0.401  0.284]\n",
-      " [ 0.741  0.887  0.61   0.475]\n",
-      " [ 0.674  0.887  0.383  0.045]\n",
-      " [ 0.674  0.887  0.33   0.063]\n",
-      " [ 0.578  0.887  0.716  0.163]\n",
-      " [ 0.693  0.833  0.383  0.045]]\n",
-      "\n",
-      "[[  8.560e-15   9.723e-02   3.096e-01   8.291e-02]\n",
-      " [  2.941e-02   8.798e-02   2.776e-01   7.881e-02]\n",
-      " [  4.309e-02   8.171e-02   1.431e-01   8.462e-02]\n",
-      " [  5.020e-02   7.390e-02   2.039e-01   6.099e-02]\n",
-      " [  5.361e-02   6.495e-02   2.041e-01   6.210e-02]\n",
-      " [  5.478e-02   6.064e-02   1.043e-01   6.482e-02]\n",
-      " [  5.451e-02   5.316e-02  -3.688e-01   3.738e-02]\n",
-      " [  5.324e-02   5.069e-02   7.227e-02   5.251e-02]\n",
-      " [  5.138e-02   4.580e-02   6.492e-02   4.737e-02]\n",
-      " [  4.910e-02   4.298e-02   4.189e-02   4.119e-02]\n",
-      " [  4.657e-02   3.556e-02   1.090e-01   3.368e-02]\n",
-      " [  4.389e-02   3.516e-02   2.811e-02   3.540e-02]\n",
-      " [  4.118e-02   3.175e-02   2.525e-02   3.192e-02]\n",
-      " [  3.848e-02   3.043e-02   4.275e-03   2.878e-02]\n",
-      " [  3.583e-02   2.746e-02   3.829e-03   2.820e-02]\n",
-      " [  3.326e-02   2.478e-02   7.209e-03   2.290e-02]\n",
-      " [  3.079e-02   2.237e-02   3.258e-03   2.287e-02]\n",
-      " [  2.844e-02   2.018e-02   5.982e-03   1.844e-02]\n",
-      " [  2.621e-02   1.821e-02   2.769e-03   1.852e-02]\n",
-      " [  2.411e-02   1.642e-02   1.932e-02   1.396e-02]\n",
-      " [  2.214e-02   1.481e-02   4.915e-03   1.481e-02]\n",
-      " [  2.030e-02   1.161e-02   3.621e-02   1.083e-02]\n",
-      " [  1.858e-02   1.209e-02   2.783e-03   1.226e-02]\n",
-      " [  1.700e-02   1.090e-02   1.508e-02   9.345e-03]\n",
-      " [  1.550e-02   1.105e-02  -1.782e-02   1.095e-02]\n",
-      " [  1.415e-02   9.962e-03  -7.500e-03   9.161e-03]\n",
-      " [  1.291e-02   8.980e-03  -5.437e-02   6.931e-03]\n",
-      " [  1.174e-02   8.092e-03  -1.116e-02   8.674e-03]\n",
-      " [  1.069e-02   7.292e-03  -4.249e-02   6.583e-03]\n",
-      " [  9.701e-03   6.570e-03  -4.134e-02   5.789e-03]\n",
-      " [  8.822e-03   5.920e-03  -3.725e-02   4.567e-03]\n",
-      " [  8.008e-03   5.334e-03  -3.140e-02   4.595e-03]\n",
-      " [  7.259e-03   4.805e-03   4.293e-03   4.726e-03]\n",
-      " [  6.594e-03   4.330e-03  -2.637e-02   3.596e-03]\n",
-      " [  5.973e-03   3.900e-03  -2.675e-02   3.649e-03]\n",
-      " [  5.416e-03  -1.287e-02  -1.116e-02   5.270e-04]\n",
-      " [  4.921e-03  -1.011e-02  -1.354e-02   4.203e-03]\n",
-      " [  4.470e-03  -9.102e-03  -1.221e-02   4.219e-03]\n",
-      " [  4.057e-03  -6.922e-03   1.731e-02   4.017e-03]\n",
-      " [  3.679e-03  -8.001e-03  -9.330e-03   3.201e-03]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "['__class__', '__delattr__', '__doc__', '__format__', \n",
-    "'__getattribute__', '__getstate__', '__hash__', '__init__', \n",
-    "'__new__', '__pyx_vtable__', '__reduce__', '__reduce_ex__', \n",
-    "'__repr__', '__setattr__', '__setstate__', '__sizeof__', \n",
-    "'__str__', '__subclasshook__', 'apply', 'capacity', 'children_left', \n",
-    "'children_right', 'compute_feature_importances', 'decision_path', \n",
-    "'feature', 'impurity', 'max_depth', 'max_n_classes', 'n_classes', \n",
-    "'n_features', 'n_node_samples', 'n_outputs', 'node_count', 'predict', \n",
-    "'threshold', 'value', 'weighted_n_node_samples']\n",
-    "\"\"\"\n",
-    "\n",
-    "def boosted_bounds(cls):\n",
-    "    cuts_  = np.empty([cls.n_estimators,cls.max_depth], dtype=np.ndarray)\n",
-    "    bwei_  = np.empty([cls.n_estimators,cls.max_depth], dtype=np.ndarray)\n",
-    "    for i, t in enumerate(cls.estimators_[:, 0]):\n",
-    "        tree_ = t.tree_\n",
-    "        def recurse(node, depth):\n",
-    "            if tree_.feature[node] != _tree.TREE_UNDEFINED:\n",
-    "                cuts_[i,depth-1] = tree_.threshold[node]\n",
-    "                bwei_[i,depth-1] = tree_.value[node]\n",
-    "                if tree_.threshold[node]==None: print 'none stuff : ', i , depth-1\n",
-    "                recurse(tree_.children_left [node], depth + 1)\n",
-    "                recurse(tree_.children_right[node], depth + 1)\n",
-    "        recurse(0, 1)\n",
-    "    cuts_ = np.nan_to_num(np.array(cuts_, dtype=np.float))\n",
-    "    bwei_ = np.nan_to_num(np.array(bwei_, dtype=np.float))\n",
-    "    return cuts_, bwei_\n",
-    "\n",
-    "cuts,bwei = boosted_bounds(gmb)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  8.560e-15,   9.723e-02,   3.096e-01,   8.291e-02],\n",
-       "       [  2.941e-02,   8.798e-02,   2.776e-01,   7.881e-02],\n",
-       "       [  4.309e-02,   8.171e-02,   1.431e-01,   8.462e-02],\n",
-       "       [  5.020e-02,   7.390e-02,   2.039e-01,   6.099e-02],\n",
-       "       [  5.361e-02,   6.495e-02,   2.041e-01,   6.210e-02],\n",
-       "       [  5.478e-02,   6.064e-02,   1.043e-01,   6.482e-02],\n",
-       "       [  5.451e-02,   5.316e-02,  -3.688e-01,   3.738e-02],\n",
-       "       [  5.324e-02,   5.069e-02,   7.227e-02,   5.251e-02],\n",
-       "       [  5.138e-02,   4.580e-02,   6.492e-02,   4.737e-02],\n",
-       "       [  4.910e-02,   4.298e-02,   4.189e-02,   4.119e-02],\n",
-       "       [  4.657e-02,   3.556e-02,   1.090e-01,   3.368e-02],\n",
-       "       [  4.389e-02,   3.516e-02,   2.811e-02,   3.540e-02],\n",
-       "       [  4.118e-02,   3.175e-02,   2.525e-02,   3.192e-02],\n",
-       "       [  3.848e-02,   3.043e-02,   4.275e-03,   2.878e-02],\n",
-       "       [  3.583e-02,   2.746e-02,   3.829e-03,   2.820e-02],\n",
-       "       [  3.326e-02,   2.478e-02,   7.209e-03,   2.290e-02],\n",
-       "       [  3.079e-02,   2.237e-02,   3.258e-03,   2.287e-02],\n",
-       "       [  2.844e-02,   2.018e-02,   5.982e-03,   1.844e-02],\n",
-       "       [  2.621e-02,   1.821e-02,   2.769e-03,   1.852e-02],\n",
-       "       [  2.411e-02,   1.642e-02,   1.932e-02,   1.396e-02],\n",
-       "       [  2.214e-02,   1.481e-02,   4.915e-03,   1.481e-02],\n",
-       "       [  2.030e-02,   1.161e-02,   3.621e-02,   1.083e-02],\n",
-       "       [  1.858e-02,   1.209e-02,   2.783e-03,   1.226e-02],\n",
-       "       [  1.700e-02,   1.090e-02,   1.508e-02,   9.345e-03],\n",
-       "       [  1.550e-02,   1.105e-02,  -1.782e-02,   1.095e-02],\n",
-       "       [  1.415e-02,   9.962e-03,  -7.500e-03,   9.161e-03],\n",
-       "       [  1.291e-02,   8.980e-03,  -5.437e-02,   6.931e-03],\n",
-       "       [  1.174e-02,   8.092e-03,  -1.116e-02,   8.674e-03],\n",
-       "       [  1.069e-02,   7.292e-03,  -4.249e-02,   6.583e-03],\n",
-       "       [  9.701e-03,   6.570e-03,  -4.134e-02,   5.789e-03],\n",
-       "       [  8.822e-03,   5.920e-03,  -3.725e-02,   4.567e-03],\n",
-       "       [  8.008e-03,   5.334e-03,  -3.140e-02,   4.595e-03],\n",
-       "       [  7.259e-03,   4.805e-03,   4.293e-03,   4.726e-03],\n",
-       "       [  6.594e-03,   4.330e-03,  -2.637e-02,   3.596e-03],\n",
-       "       [  5.973e-03,   3.900e-03,  -2.675e-02,   3.649e-03],\n",
-       "       [  5.416e-03,  -1.287e-02,  -1.116e-02,   5.270e-04],\n",
-       "       [  4.921e-03,  -1.011e-02,  -1.354e-02,   4.203e-03],\n",
-       "       [  4.470e-03,  -9.102e-03,  -1.221e-02,   4.219e-03],\n",
-       "       [  4.057e-03,  -6.922e-03,   1.731e-02,   4.017e-03],\n",
-       "       [  3.679e-03,  -8.001e-03,  -9.330e-03,   3.201e-03]])"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bwei/np.sum(bwei, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "average : [ 0.021  0.013  0.025  0.009]\n",
-      "std     : [-0.016 -0.016  0.064 -0.01 ]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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LJ0Y/7zSGpVOlpaUAgKysrCjXJHrkVRmUUxnUyuKJ0c87tbAIIbpBgUUI0Q0KLEKIblBg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\nblBgEUJ0gwKLEKIb/x/1pr2Wr8KlOwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1160e7b10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print 'average :', np.mean(bwei*cuts, axis=0)/np.sum(bwei, axis=0)\n",
-    "print 'std     :', np.std (bwei*cuts, axis=0)/np.sum(bwei, axis=0)\n",
-    "\n",
-    "m_cuts = np.mean(cuts, axis=0)\n",
-    "e_cuts = np.std (cuts, axis=0)\n",
-    "\n",
-    "plt.figure(figsize=(4,4))\n",
-    "plt.hist(X[Y==1],bins=100, range=[0,1], alpha=0.4, weights=W[Y==1], histtype='stepfilled',lw=1, normed=True)\n",
-    "plt.hist(X[Y==0],bins=100, range=[0,1], alpha=0.4, weights=W[Y==0], histtype='stepfilled',lw=1, normed=True)\n",
-    "# plt.yscale('log')\n",
-    "\n",
-    "for i,x in enumerate(m_cuts):\n",
-    "    plt.axvline(x, ls='--',c='k',alpha=0.5, label=i)\n",
-    "    plt.axvspan(x - e_cuts[i]/2, x + e_cuts[i]/2,facecolor='k',alpha=0.2)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "average : [ 0.828  0.581  0.602  0.32 ]\n",
-      "std     : [ 0.067  0.129  0.167  0.11 ]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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LJ0Y/7zSGpVOlpaUAgKysrCjXJHrkVRmUUxnUyuKJ0c87tbAIIbpBgUUI0Q0KLEKIblBg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\nblBgEUJ0gwKLEKIb/x/1pr2Wr8KlOwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1162c81d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print 'average :', np.mean(cuts, axis=0)\n",
-    "print 'std     :', np.std (cuts, axis=0)\n",
-    "\n",
-    "m_cuts = np.mean(cuts, axis=0)\n",
-    "e_cuts = np.std (cuts, axis=0)\n",
-    "\n",
-    "plt.figure(figsize=(4,4))\n",
-    "plt.hist(X[Y==1],bins=100, range=[0,1], alpha=0.4, weights=W[Y==1], histtype='stepfilled',lw=1, normed=True)\n",
-    "plt.hist(X[Y==0],bins=100, range=[0,1], alpha=0.4, weights=W[Y==0], histtype='stepfilled',lw=1, normed=True)\n",
-    "# plt.yscale('log')\n",
-    "\n",
-    "for i,x in enumerate(m_cuts):\n",
-    "    plt.axvline(x, ls='--',c='k',alpha=0.5, label=i)\n",
-    "    plt.axvspan(x - e_cuts[i]/2, x + e_cuts[i]/2,facecolor='k',alpha=0.2)\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 2",
-   "language": "python",
-   "name": "python2"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}