Examples/UserCoders/UserCoders.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# to install\n",
    "!pip install /Users/johnmount/Documents/work/pyvtreat/pkg/dist/vtreat-0.2.1.tar.gz\n",
    "#!pip install https://github.com/WinVector/pyvtreat/raw/master/pkg/dist/vtreat-0.2.1.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "import sklearn.linear_model\n",
    "import pandas\n",
    "import numpy\n",
    "import numpy.random\n",
    "import seaborn\n",
    "import vtreat\n",
    "import vtreat.util\n",
    "import vtreat.transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "class PolyTransform(vtreat.transform.UserTransform):\n",
    "    \"\"\"a polynomial model\"\"\"\n",
    "    def __init__(self, *, deg=5, alpha=0.1):\n",
    "        vtreat.transform.UserTransform.__init__(self, treatment='poly')\n",
    "        self.models_ = None\n",
    "        self.deg = deg\n",
    "        self.alpha = alpha\n",
    "\n",
    "    def poly_terms(self, vname, vec):\n",
    "        vec = numpy.asarray(vec)\n",
    "        r = pandas.DataFrame({'x': vec})\n",
    "        for d in range(1, self.deg+1):\n",
    "            r[vname + '_' + str(d)] = vec**d\n",
    "        return r\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        self.models_ = {}\n",
    "        self.incoming_vars_ = []\n",
    "        self.derived_vars_ = []\n",
    "        for v in X.columns:\n",
    "            if vtreat.util.can_convert_v_to_numeric(X[v]):\n",
    "                X_v = self.poly_terms(v, X[v])\n",
    "                model_v = sklearn.linear_model.Ridge(alpha=self.alpha).fit(X_v, y) \n",
    "                new_var = v + \"_poly\"\n",
    "                self.models_[v] = (model_v, [c for c in X_v.columns], new_var)\n",
    "                self.incoming_vars_.append(v)\n",
    "                self.derived_vars_.append(new_var)\n",
    "        return self\n",
    "        \n",
    "    def transform(self, X):\n",
    "        r = pandas.DataFrame()\n",
    "        for k, v in self.models_.items():\n",
    "            model_k = v[0]\n",
    "            cols_k = v[1]\n",
    "            new_var = v[2]\n",
    "            X_k = self.poly_terms(k, X[k])\n",
    "            xform_k = model_k.predict(X_k)\n",
    "            r[new_var] = xform_k\n",
    "        return r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "   x         y\n0  0 -0.188057\n1  1 -0.104672\n2  2  0.469285\n3  3  0.272010\n4  4  0.603709",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>x</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>-0.188057</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>-0.104672</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>0.469285</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>0.272010</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>0.603709</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 3
    }
   ],
   "source": [
    "d = pandas.DataFrame({'x':[i for i in range(100)]})\n",
    "d['y'] = numpy.sin(0.2*d['x']) + 0.2*numpy.random.normal(size=d.shape[0])\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "step = PolyTransform(deg=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=1.09351e-40): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "     x_poly  x\n0 -0.263258  0\n1 -0.043296  1\n2  0.218220  2\n3  0.478494  3\n4  0.707910  4",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>x_poly</th>\n      <th>x</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.263258</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.043296</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.218220</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.478494</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.707910</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 5
    }
   ],
   "source": [
    "fit = step.fit_transform(d[['x']], d['y'])\n",
    "fit['x'] = d['x']\n",
    "fit.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a16a36190>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 6
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.scatterplot(x='x', y='y', data=d)\n",
    "seaborn.lineplot(x='x', y='x_poly', data=fit, color='red', alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": [
    "transform = vtreat.NumericOutcomeTreatment(\n",
    "    outcome_name='y',\n",
    "    params = vtreat.vtreat_parameters({\n",
    "        'filter_to_recommended': False,\n",
    "        'user_transforms': [PolyTransform(deg=10)]\n",
    "    }))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=2.78226e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=3.53976e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=3.51805e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=3.04556e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=4.3458e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=3.19132e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ],
     "output_type": "stream"
    },
    {
     "data": {
      "text/plain": "vtreat.vtreat_api.NumericOutcomeTreatment(outcome_name='y', cols_to_copy=['y'], )"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 8
    }
   ],
   "source": [
    "transform.fit(d, d['y'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "  variable orig_variable   treatment  y_aware  has_range  PearsonR  \\\n0        x             x  clean_copy    False       True -0.135012   \n1   x_poly             x        poly     True       True  0.896726   \n\n   significance  vcount  default_threshold  recommended  \n0  1.804771e-01     1.0                0.5         True  \n1  1.816677e-36     1.0                0.5         True  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>variable</th>\n      <th>orig_variable</th>\n      <th>treatment</th>\n      <th>y_aware</th>\n      <th>has_range</th>\n      <th>PearsonR</th>\n      <th>significance</th>\n      <th>vcount</th>\n      <th>default_threshold</th>\n      <th>recommended</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>x</td>\n      <td>x</td>\n      <td>clean_copy</td>\n      <td>False</td>\n      <td>True</td>\n      <td>-0.135012</td>\n      <td>1.804771e-01</td>\n      <td>1.0</td>\n      <td>0.5</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x_poly</td>\n      <td>x</td>\n      <td>poly</td>\n      <td>True</td>\n      <td>True</td>\n      <td>0.896726</td>\n      <td>1.816677e-36</td>\n      <td>1.0</td>\n      <td>0.5</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 9
    }
   ],
   "source": [
    "transform.score_frame_"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "/Users/johnmount/Documents/work/pyvtreat/pkg/vtreat/vtreat_api.py:107: UserWarning: possibly called transform on same data used to fit\n",
      "(this causes over-fit, please use fit_transform() instead)\n",
      "  \"possibly called transform on same data used to fit\\n\" +\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "x2_overfit = transform.transform(d)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a1817c390>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 11
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.scatterplot(x='x', y='y', data=x2_overfit)\n",
    "seaborn.lineplot(x='x', y='x_poly', data=x2_overfit, color='red', alpha=0.5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "text": [
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=2.78226e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=4.4025e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=4.22739e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=2.92077e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=3.10173e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n",
      "/Users/johnmount/opt/anaconda3/envs/ai_academy_3_7/lib/python3.7/site-packages/sklearn/linear_model/ridge.py:147: LinAlgWarning: Ill-conditioned matrix (rcond=3.23015e-42): result may not be accurate.\n",
      "  overwrite_a=True).T\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "x2 = transform.fit_transform(d, d['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "  variable orig_variable   treatment  y_aware  has_range  PearsonR  \\\n0        x             x  clean_copy    False       True -0.135012   \n1   x_poly             x        poly     True       True  0.834335   \n\n   significance  vcount  default_threshold  recommended  \n0  1.804771e-01     1.0                0.5         True  \n1  4.312400e-27     1.0                0.5         True  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>variable</th>\n      <th>orig_variable</th>\n      <th>treatment</th>\n      <th>y_aware</th>\n      <th>has_range</th>\n      <th>PearsonR</th>\n      <th>significance</th>\n      <th>vcount</th>\n      <th>default_threshold</th>\n      <th>recommended</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>x</td>\n      <td>x</td>\n      <td>clean_copy</td>\n      <td>False</td>\n      <td>True</td>\n      <td>-0.135012</td>\n      <td>1.804771e-01</td>\n      <td>1.0</td>\n      <td>0.5</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>x_poly</td>\n      <td>x</td>\n      <td>poly</td>\n      <td>True</td>\n      <td>True</td>\n      <td>0.834335</td>\n      <td>4.312400e-27</td>\n      <td>1.0</td>\n      <td>0.5</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 13
    }
   ],
   "source": [
    "transform.score_frame_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "          y    x    x_poly\n0 -0.188057  0.0  2.332155\n1 -0.104672  1.0  1.152522\n2  0.469285  2.0 -0.175078\n3  0.272010  3.0  0.208507\n4  0.603709  4.0  0.507284",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>y</th>\n      <th>x</th>\n      <th>x_poly</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-0.188057</td>\n      <td>0.0</td>\n      <td>2.332155</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.104672</td>\n      <td>1.0</td>\n      <td>1.152522</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.469285</td>\n      <td>2.0</td>\n      <td>-0.175078</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.272010</td>\n      <td>3.0</td>\n      <td>0.208507</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.603709</td>\n      <td>4.0</td>\n      <td>0.507284</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 14
    }
   ],
   "source": [
    "x2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1a182ac7d0>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 15
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "seaborn.scatterplot(x='x', y='y', data=x2)\n",
    "seaborn.lineplot(x='x', y='x_poly', data=x2, color='red', alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}