testing_framework.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-6fbd0ad95a8b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mdata_keeper\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_data_keeper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/home/vlad/diploma/data_keeper.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 82\u001b[1;33m \u001b[0mDATA_KEEPER\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDataKeeper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     83\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mget_data_keeper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mDATA_KEEPER\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/vlad/diploma/data_keeper.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m             \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstderr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdevnull\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"w\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m             \u001b[0mmatrix\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'apr15.snps.matrix'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'\\t'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'#snp_pos'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_common_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'-'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drug_results\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'drugs_effect.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Strain'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mset_index\u001b[1;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[0;32m   2211\u001b[0m             \u001b[0mframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2212\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2213\u001b[1;33m             \u001b[0mframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2215\u001b[0m         \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mcopy\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m   2034\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2035\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdeep\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2036\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2037\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2038\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mcopy\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m   2703\u001b[0m             \u001b[0mnew_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2704\u001b[0m         return self.apply('copy', axes=new_axes, deep=deep,\n\u001b[1;32m-> 2705\u001b[1;33m                           ref_items=new_axes[0], do_integrity_check=False)\n\u001b[0m\u001b[0;32m   2706\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2707\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mas_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitems\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2383\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_empty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxes\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2384\u001b[0m         bm = self.__class__(result_blocks, axes or self.axes,\n\u001b[1;32m-> 2385\u001b[1;33m                             do_integrity_check=do_integrity_check)\n\u001b[0m\u001b[0;32m   2386\u001b[0m         \u001b[0mbm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2387\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mbm\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, blocks, axes, do_integrity_check, fastpath)\u001b[0m\n\u001b[0;32m   2035\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2036\u001b[0m         \u001b[1;31m# we have a duplicate items index, setup the block maps\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2037\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2038\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_ref_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdo_refs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2039\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mproperties.pyx\u001b[0m in \u001b[0;36mpandas.lib.cache_readonly.__get__ (pandas/lib.c:30438)\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m/usr/lib/python2.7/dist-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mis_unique\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    409\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mcache_readonly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mallow_setting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    410\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mis_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 411\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from data_keeper import get_data_keeper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from testing import test_models_with_drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "get_data_keeper().get_possible_drugs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import wrappers\n",
    "from wrappers import GridSearchCVWrapper\n",
    "from wrappers import XGBoostClassifierFeatureImportances as XGB\n",
    "from wrappers import MatrixCleaningWrapper\n",
    "from wrappers import SparseWrapper\n",
    "from wrappers import ModelFeatureSelectionWrapper\n",
    "from wrappers import ModelBasedFeatureImportanceGetter\n",
    "from wrappers import AsMatrixWrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from frn import FeatureRelevanceNetworkWrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_complete_linear_model():\n",
    "    inner_model = LogisticRegressionCV(Cs=[10 ** i for i in xrange(-4, 4)], solver='liblinear')\n",
    "    outer_model = GridSearchCV(inner_model, {'penalty': ['l1', 'l2']})\n",
    "    return MatrixCleaningWrapper(SparseWrapper(outer_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_complete_tree_based_model():\n",
    "    cv_params = {'inner_model__inner_model__n_estimators': [1],#, 5, 10, 20, 50, 100], \n",
    "                 'feature_selection_threshold_coef': [0.1]}#, 1, 3, 10, 30, 100, 300]}\n",
    "    return MatrixCleaningWrapper(FeatureRelevanceNetworkWrapper(XGB(n_estimators=100), ModelBasedFeatureImportanceGetter(XGB())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from generate_subsets_for_common_x import get_ready_generator\n",
    "GENERATOR_FOLDER = '/media/vlad/01D198E892261920/vlad/vlad/diploma'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import complex_features_inserting\n",
    "complex_features_inserting = reload(complex_features_inserting)\n",
    "from complex_features_inserting import ExtenderStrategy, \\\n",
    "                                       MinSizePreFilter, \\\n",
    "                                       SimplePriorityGetter, \\\n",
    "                                       BayesBasedPriorityGetter, \\\n",
    "                                       ComplexFeaturesAdderWrapper, \\\n",
    "                                       AndBasedSimpleFeaturesIndexesGetter, \\\n",
    "                                       MinSimpleFeaturesIndexGetter\n",
    "\n",
    "def get_simple_feature_adder_wrapper(inner_model, max_features_to_add):\n",
    "    #generator, matrix_before_generating = get_ready_generator()\n",
    "    #priority_getter = SimplePriorityGetter()\n",
    "    #pre_filter = MinSizePreFilter(min_size=1)\n",
    "    #simple_features_indexes_getter = MinSimpleFeaturesIndexGetter(generator, 1000)\n",
    "    #extender_strategy = ExtenderStrategy(max_features=1000, \n",
    "                                         priority_getter=priority_getter, \n",
    "                                         pre_filter=pre_filter,\n",
    "                                         generator=get_ready_generator()[0],\n",
    "                                         simple_features_indexes_getter=simple_features_indexes_getter,\n",
    "                                         )\n",
    "    feature_selector = AsMatrixWrapper(ModelFeatureSelectionWrapper(inner_model))\n",
    "    return feature_selector\n",
    "    return ComplexFeaturesAdderWrapper(inner_model=feature_selector, \n",
    "                                       matrix_before_generating=matrix_before_generating.as_matrix(),\n",
    "                                       features_names=list(matrix_before_generating.columns.values),\n",
    "                                       extender_strategy=extender_strategy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "simple_feature_adder_wrapper = get_simple_feature_adder_wrapper(XGB(), 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from testing import test_models_with_drugs\n",
    "test_models_with_drugs([('test', simple_feature_adder_wrapper)], ['ETHI: Ethionamide/ Prothionamide '], as_indexes=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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