merge_datasets_random_forests.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn import decomposition\n",
    "import os\n",
    "import scipy.stats as sc\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "df_co = pd.read_csv('Transformed Data/Transformed_Co', index_col = 0)\n",
    "df_pt = pd.read_csv('Transformed Data/Transformed_Pt' , index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "#pca1 = decomposition.PCA(n_components=50)\n",
    "#df_co_pca = pca1.fit_transform(df_co)\n",
    "#df_pt_pca = pca1.fit_transform(df_pt)\n",
    "\n",
    "df_co_pca= df_co\n",
    "df_pt_pca= df_pt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_co_len = df_co_pca.shape[0]\n",
    "df_pt_len = df_pt_pca.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [],
   "source": [
    "df_co_pca = pd.DataFrame(df_co_pca)\n",
    "df_pt_pca = pd.DataFrame(df_pt_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "X = pd.concat([df_co_pca, df_pt_pca])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [
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     "execution_count": 7,
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   ],
   "source": [
    "y1 = pd.Series([0]*df_co_len)\n",
    "y1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     "execution_count": 8,
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   ],
   "source": [
    "y2 = pd.Series([1]*df_pt_len, index = range(df_co_len-1,(df_co_len + df_pt_len)-1))\n",
    "y2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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     "execution_count": 9,
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   "source": [
    "y = pd.concat([y1,y2]) \n",
    "y.shape"
   ]
  },
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      "0.974025974026 40 51\n"
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    },
    {
     "data": {
      "text/plain": [
       "'print(X_train.shape)\\nprint(X_test.shape)\\nprint(y_train.shape)\\nprint(y_test.shape)'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "max_est = 0\n",
    "max_acc = 0\n",
    "curr_max_acc = 0\n",
    "curr_max_est =0\n",
    "\n",
    "for i in range (0,100):\n",
    "    \n",
    "    X_train, X_test, y_train1, y_test1 = train_test_split(X, y, random_state=i)\n",
    "    y_train = pd.DataFrame(y_train1)\n",
    "    y_test = pd.DataFrame(y_test1)\n",
    "    \n",
    "    scaler = MinMaxScaler().fit(X_train)\n",
    "    X_train = scaler.transform(X_train)\n",
    "    X_test = scaler.transform(X_test)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    for n in [40,50,100,150,200,250,300]:\n",
    "\n",
    "            clf = RandomForestClassifier(n_estimators = int(n), n_jobs=3)\n",
    "            clf.fit(X_train, np.ravel(y_train))\n",
    "            if max_acc < clf.score(X_test, np.ravel(y_test)):\n",
    "                max_acc = clf.score(X_test, np.ravel(y_test))\n",
    "                max_est = n\n",
    "    if(curr_max_acc < max_acc):\n",
    "            curr_max_acc = max_acc\n",
    "            curr_max_est = max_est\n",
    "            n_rs = i\n",
    "    print(\"{} done\".format(i))\n",
    "print(curr_max_acc,curr_max_est, n_rs) \n",
    "    \n",
    "    \n",
    "    \n",
    "\"\"\"print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\"\"\"\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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