tpot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\kogentix\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: scipy in c:\\users\\kogentix\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: scikit-learn in c:\\users\\kogentix\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: pandas in c:\\users\\kogentix\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: python-dateutil>=2 in c:\\users\\kogentix\\anaconda3\\lib\\site-packages (from pandas)\n",
      "Requirement already satisfied: pytz>=2011k in c:\\users\\kogentix\\anaconda3\\lib\\site-packages (from pandas)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\kogentix\\anaconda3\\lib\\site-packages (from python-dateutil>=2->pandas)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy scipy scikit-learn pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: deap in c:\\users\\kogentix\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: update_checker in c:\\users\\kogentix\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: tqdm in c:\\users\\kogentix\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: stopit in c:\\users\\kogentix\\anaconda3\\lib\\site-packages\n",
      "Requirement already satisfied: requests>=2.3.0 in c:\\users\\kogentix\\anaconda3\\lib\\site-packages (from update_checker)\n"
     ]
    }
   ],
   "source": [
    "!pip install deap update_checker tqdm stopit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Using the Python Library\n",
    "import numpy as np\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Booster', 'DMatrix', 'VERSION_FILE', 'XGBClassifier', 'XGBModel', 'XGBRegressor', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '__version__', 'absolute_import', 'callback', 'compat', 'core', 'cv', 'f', 'libpath', 'os', 'plot_importance', 'plot_tree', 'plotting', 'rabit', 'sklearn', 'to_graphviz', 'train', 'training']\n"
     ]
    }
   ],
   "source": [
    "print(dir(xgb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\python36.zip', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\DLLs', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\lib', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\lib\\\\site-packages', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\lib\\\\site-packages\\\\Sphinx-1.5.6-py3.6.egg', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\lib\\\\site-packages\\\\xgboost-0.6-py3.6.egg', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\lib\\\\site-packages\\\\win32\\\\lib', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\lib\\\\site-packages\\\\Pythonwin', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\lib\\\\site-packages\\\\setuptools-27.2.0-py3.6.egg', 'C:\\\\Users\\\\KOGENTIX\\\\Anaconda3\\\\lib\\\\site-packages\\\\IPython\\\\extensions', 'C:\\\\Users\\\\KOGENTIX\\\\.ipython', 'C:\\\\Users\\\\KOGENTIX\\\\xgboost\\\\python-package\\\\']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"C:\\\\Users\\\\KOGENTIX\\\\xgboost\\\\python-package\\\\\")\n",
    "print (sys.path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets, neighbors, linear_model, tree, svm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1797"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data\n",
    "y_digits = digits.target\n",
    "\n",
    "n_samples = len(X_digits)\n",
    "n_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = X_digits[:int(.9 * n_samples)]\n",
    "y_train = y_digits[:int(.9 * n_samples)]\n",
    "X_test = X_digits[int(.9 * n_samples):]\n",
    "y_test = y_digits[int(.9 * n_samples):]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 5, 0, 9])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0, 162],\n",
       "       [  1, 163],\n",
       "       [  2, 160],\n",
       "       [  3, 165],\n",
       "       [  4, 161],\n",
       "       [  5, 164],\n",
       "       [  6, 163],\n",
       "       [  7, 160],\n",
       "       [  8, 157],\n",
       "       [  9, 162]], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.itemfreq(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 2, 2, 0, 1, 7, 6, 3, 2, 1, 5, 4, 3, 3, 1, 3, 7, 1, 3, 6, 1, 4, 3,\n",
       "       1, 4, 0, 5, 3, 5, 9, 4, 1, 7, 5, 4, 4, 7, 2, 2, 5, 3, 8, 5, 9, 4, 7,\n",
       "       0, 5, 7, 7, 0, 8, 2, 5, 4, 5, 6, 7, 2, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8,\n",
       "       9, 0, 8, 2, 2, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0, 9, 8, 9, 8, 4,\n",
       "       1, 7, 7, 1, 5, 1, 0, 0, 9, 2, 7, 8, 2, 0, 1, 2, 6, 2, 2, 7, 4, 3, 4,\n",
       "       6, 6, 6, 4, 9, 1, 6, 0, 9, 5, 2, 8, 1, 0, 0, 6, 7, 6, 1, 2, 1, 7, 4,\n",
       "       6, 3, 1, 2, 9, 1, 7, 6, 8, 4, 5, 1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4,\n",
       "       4, 7, 2, 8, 2, 2, 5, 7, 9, 3, 4, 3, 2, 4, 9, 0, 8, 9, 8])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier()\n",
    "clf = clf.fit(X_train, y_train)\n",
    "clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.811111111111\n"
     ]
    }
   ],
   "source": [
    "print(clf.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xr = xgb.XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "xr=xr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.17454863,  2.31009936,  5.6211586 ,  0.13941881,  0.79184723,\n",
       "        6.69167852,  5.78008556,  2.75774169,  1.25420761,  3.88986874,\n",
       "        6.53292084,  7.64552402,  6.02077436,  2.18998647,  1.34927988,\n",
       "        3.54345942,  7.82803583,  0.77500153,  8.04337502,  5.97606659,\n",
       "        6.33106804,  3.50779319,  3.05139947,  2.53856802,  4.477005  ,\n",
       "       -0.28811425,  6.15758705,  1.73706651,  5.25047827,  8.17740345,\n",
       "        4.87318468,  1.47395992,  4.23157883,  5.02434826,  4.23819637,\n",
       "        4.44589853,  6.25152922,  2.46546197,  3.01612639,  4.97270441,\n",
       "        6.26480484,  4.78373098,  4.82396936,  7.77662086,  4.53779554,\n",
       "        2.62748647,  0.58428752,  7.43105268,  7.84978962,  6.68497181,\n",
       "       -0.43701953,  1.69745815,  1.19588113,  4.55399942,  5.44729614,\n",
       "        5.21953487,  5.15919018,  7.47001648,  5.57749367,  8.38232613,\n",
       "        0.11813346,  1.01219177,  3.26156807,  3.34898233,  3.77228022,\n",
       "        5.18798351,  5.56377363,  5.55503988,  5.69231129,  9.27871513,\n",
       "        0.23553038,  2.98534942,  2.9672277 ,  4.37331295,  4.05083179,\n",
       "        5.35860491,  4.50167513,  6.55852222,  5.99427176,  8.79152489,\n",
       "       -0.0560739 ,  8.39695549,  4.91200256,  5.08682823,  5.07829237,\n",
       "        5.55622244, -0.09274095,  8.56768799,  9.40511417,  7.96262836,\n",
       "        7.87627077,  4.28712797,  1.99141049,  5.79908609,  6.9753952 ,\n",
       "        5.89846325,  5.84728098,  2.30634046, -0.60442746,  0.28149834,\n",
       "        1.9418658 ,  1.9422158 ,  6.34814787,  7.74735546,  2.57224393,\n",
       "        0.31663024,  3.30871034,  3.34071231,  5.37906456,  5.26454973,\n",
       "        4.34845161,  4.45209503,  4.11032057,  5.4872117 ,  4.70515633,\n",
       "        4.50495052,  6.04063702,  4.17267513,  4.40274429,  7.88456392,\n",
       "        3.16085935,  4.5046258 , -0.04774338,  7.89965773,  5.53477383,\n",
       "        3.49594831,  5.54834604,  2.28996778, -0.36804682, -0.50901437,\n",
       "        2.3560257 ,  5.90113878,  4.94886303,  4.04959488,  2.13626289,\n",
       "        2.58525276,  5.28104973,  3.81460381,  5.87017059,  3.28680253,\n",
       "        2.31958008,  3.75891876,  6.53239727,  1.89289927,  7.19561815,\n",
       "        6.19357872,  8.21565628,  3.33273292,  4.72547579,  2.16792297,\n",
       "        4.58852625,  1.53387964,  5.46388054,  3.475353  ,  4.73784971,\n",
       "        6.14358616,  5.69037294,  2.51056719,  6.40560293,  5.5530138 ,\n",
       "        4.09691381,  3.43549562,  7.43306684,  2.24002838,  3.57986665,\n",
       "        2.04830194,  2.78988934,  5.89063787,  5.87044859,  5.56125641,\n",
       "        5.92334414,  3.58165717,  4.03868723,  4.50158215,  4.0555582 ,\n",
       "        8.14502335, -0.06778443,  7.20665073,  7.40505505,  7.43251753], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.76077616868\n"
     ]
    }
   ],
   "source": [
    "print(xr.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "knn = neighbors.KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96111111111111114"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train).score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "logistic = linear_model.LogisticRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93888888888888888"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic.fit(X_train, y_train).score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clfs = svm.SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44444444444444442"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfs.fit(X_train, y_train).score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, auc, roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.81111111111111112"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(clf.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32077100115074797"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr, tpr, thresholds =  roc_curve(clf.predict(X_test), y_test, pos_label=2)\n",
    "auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.44444444444444442"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(clfs.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23428571428571426"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr, tpr, thresholds =  roc_curve(clfs.predict(X_test), y_test, pos_label=2)\n",
    "auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.96111111111111114"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(knn.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21896433470507543"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fpr, tpr, thresholds =  roc_curve(knn.predict(X_test), y_test, pos_label=2)\n",
    "auc(fpr, tpr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93888888888888888"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(logistic.predict(X_test), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "! pip install scikit-mdr skrebate\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!pip install tpot\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KOGENTIX\\Anaconda3\\lib\\site-packages\\deap\\tools\\_hypervolume\\pyhv.py:33: ImportWarning: Falling back to the python version of hypervolume module. Expect this to be very slow.\n",
      "  \"module. Expect this to be very slow.\", ImportWarning)\n"
     ]
    }
   ],
   "source": [
    "from tpot import TPOTClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KOGENTIX\\Anaconda3\\lib\\importlib\\_bootstrap.py:205: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "pipeline_optimizer = TPOTClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KOGENTIX\\Anaconda3\\lib\\site-packages\\deap\\creator.py:141: RuntimeWarning: A class named 'FitnessMulti' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
      "  RuntimeWarning)\n",
      "C:\\Users\\KOGENTIX\\Anaconda3\\lib\\site-packages\\deap\\creator.py:141: RuntimeWarning: A class named 'Individual' has already been created and it will be overwritten. Consider deleting previous creation of that class or rename it.\n",
      "  RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "pipeline_optimizer = TPOTClassifier(generations=5, population_size=20, cv=5,\n",
    "                                    random_state=42, verbosity=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 0.9382234388034776\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 2 - Current best internal CV score: 0.9462601281990894\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 3 - Current best internal CV score: 0.9629851932956182\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 4 - Current best internal CV score: 0.9629851932956182\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                                                                       \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 5 - Current best internal CV score: 0.9647684149987755\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                           \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best pipeline: KNeighborsClassifier(Normalizer(input_matrix, norm=max), n_neighbors=6, p=2, weights=distance)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TPOTClassifier(config_dict={'sklearn.naive_bayes.GaussianNB': {}, 'sklearn.naive_bayes.BernoulliNB': {'alpha': [0.001, 0.01, 0.1, 1.0, 10.0, 100.0], 'fit_prior': [True, False]}, 'sklearn.naive_bayes.MultinomialNB': {'alpha': [0.001, 0.01, 0.1, 1.0, 10.0, 100.0], 'fit_prior': [True, False]}, 'sklearn.tree.DecisionT....45,\n",
       "        0.5 ,  0.55,  0.6 ,  0.65,  0.7 ,  0.75,  0.8 ,  0.85,  0.9 ,\n",
       "        0.95,  1.  ])}}}},\n",
       "        crossover_rate=0.1, cv=5, disable_update_check=False,\n",
       "        early_stop=None, generations=5, max_eval_time_mins=5,\n",
       "        max_time_mins=None, memory=None, mutation_rate=0.9, n_jobs=1,\n",
       "        offspring_size=20, periodic_checkpoint_folder=None,\n",
       "        population_size=20, random_state=42, scoring=None, subsample=1.0,\n",
       "        verbosity=2, warm_start=False)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_optimizer.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.961111111111\n"
     ]
    }
   ],
   "source": [
    "print(pipeline_optimizer.score(X_test, y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_optimizer.export('tpot_exported_pipeline.py')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os as os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\KOGENTIX'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This is the exported pipeline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import Normalizer\n",
    "\n",
    "#### NOTE: Make sure that the class is labeled 'target' in the data file\n",
    "tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)\n",
    "features = tpot_data.drop('target', axis=1).values\n",
    "training_features, testing_features, training_target, testing_target = \\\n",
    "            train_test_split(features, tpot_data['target'].values, random_state=42)\n",
    "\n",
    "#### Score on the training set was:0.9647684149987755\n",
    "exported_pipeline = make_pipeline(\n",
    "    Normalizer(norm=\"max\"),\n",
    "    KNeighborsClassifier(n_neighbors=6, p=2, weights=\"distance\")\n",
    ")\n",
    "\n",
    "exported_pipeline.fit(training_features, training_target)\n",
    "results = exported_pipeline.predict(testing_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-45-19486b893ab8>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-45-19486b893ab8>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    tpot data/mnist.csv -is , -target class -o tpot_exported_pipeline.py -g 5 -p 20 -cv 5 -s 42 -v 2\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "##tpot data/mnist.csv -is , -target class -o tpot_exported_pipeline.py -g 5 -p 20 -cv 5 -s 42 -v 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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