.ipynb_checkpoints/Vary elements PhOLED-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(304, 31)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import model_selection\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import pickle\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler, Normalizer, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "df = pd.read_excel('C:/Users/acc_a/OneDrive/PhOLED 300.xlsx', sheetname='Sheet2')\n",
    "df = df[list(df.columns)[1:] ]\n",
    "df.dropna(axis=0, inplace=True)\n",
    "\n",
    "X = df[list(df.columns)[:-1]]\n",
    "y = df[['cd/A']]\n",
    "\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared:  0.895240129661\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.25, random_state=1)\n",
    "\n",
    "filename = 'C:/Anaconda3/projects/oled/PhOLED 300/rforest_300.sav'\n",
    "\n",
    "# load the model from disk\n",
    "loaded_model = pickle.load(open(filename, 'rb'))\n",
    "\n",
    "print('R-squared: ', loaded_model.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### triplet host "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21, 31)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "new_df = pd.read_pickle('C:/Anaconda3/projects/oled/PhOLED 300/vary triplet host/generated1.pkl')\n",
    "\n",
    "new_X = new_df[:]\n",
    "print(new_X.shape)\n",
    "\n",
    "y_pred = loaded_model.predict(new_X)\n",
    "y_pred = [round(float(i), 2) for i in y_pred]\n",
    "\n",
    "new_X['cd/a'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[22.88, 22.88, 22.88, 22.88, 22.88, 22.88, 22.88, 25.92, 33.87, 34.01, 30.74]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.0\n",
       "1     2.1\n",
       "2     2.2\n",
       "3     2.3\n",
       "4     2.4\n",
       "5     2.5\n",
       "6     2.6\n",
       "7     2.7\n",
       "8     2.8\n",
       "9     2.9\n",
       "10    3.0\n",
       "Name: triplet_HOST, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_X['triplet_HOST']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>triplet_HOST</th>\n",
       "      <th>pred_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.60</td>\n",
       "      <td>23.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.62</td>\n",
       "      <td>23.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.64</td>\n",
       "      <td>24.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.66</td>\n",
       "      <td>24.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.68</td>\n",
       "      <td>23.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.70</td>\n",
       "      <td>26.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.72</td>\n",
       "      <td>28.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.74</td>\n",
       "      <td>31.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.76</td>\n",
       "      <td>33.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.78</td>\n",
       "      <td>33.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.80</td>\n",
       "      <td>34.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.82</td>\n",
       "      <td>34.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.84</td>\n",
       "      <td>34.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.86</td>\n",
       "      <td>34.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.88</td>\n",
       "      <td>35.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.90</td>\n",
       "      <td>34.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.92</td>\n",
       "      <td>34.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.94</td>\n",
       "      <td>34.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.96</td>\n",
       "      <td>34.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.98</td>\n",
       "      <td>34.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.00</td>\n",
       "      <td>31.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    triplet_HOST  pred_y\n",
       "0           2.60   23.60\n",
       "1           2.62   23.60\n",
       "2           2.64   24.02\n",
       "3           2.66   24.07\n",
       "4           2.68   23.95\n",
       "5           2.70   26.64\n",
       "6           2.72   28.73\n",
       "7           2.74   31.69\n",
       "8           2.76   33.56\n",
       "9           2.78   33.98\n",
       "10          2.80   34.53\n",
       "11          2.82   34.16\n",
       "12          2.84   34.41\n",
       "13          2.86   34.98\n",
       "14          2.88   35.49\n",
       "15          2.90   34.82\n",
       "16          2.92   34.68\n",
       "17          2.94   34.94\n",
       "18          2.96   34.94\n",
       "19          2.98   34.51\n",
       "20          3.00   31.41"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "host = pd.DataFrame(new_X['triplet_HOST']) \n",
    "host['pred_y'] = y_pred\n",
    "host"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### triplet ETL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21, 31)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "new_df = pd.read_pickle('C:/Anaconda3/projects/oled/PhOLED 300/vary triplet etl/generated1.pkl')\n",
    "\n",
    "new_X = new_df[:]\n",
    "print(new_X.shape)\n",
    "\n",
    "y_pred = loaded_model.predict(new_X)\n",
    "y_pred = [round(float(i), 2) for i in y_pred]\n",
    "\n",
    "new_X['cd/a'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>triplet_ETL</th>\n",
       "      <th>pred_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.60</td>\n",
       "      <td>15.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.62</td>\n",
       "      <td>15.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.64</td>\n",
       "      <td>15.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.66</td>\n",
       "      <td>31.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.68</td>\n",
       "      <td>31.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.70</td>\n",
       "      <td>31.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.72</td>\n",
       "      <td>31.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.74</td>\n",
       "      <td>31.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.76</td>\n",
       "      <td>31.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.78</td>\n",
       "      <td>31.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.80</td>\n",
       "      <td>31.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.82</td>\n",
       "      <td>31.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.84</td>\n",
       "      <td>31.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.86</td>\n",
       "      <td>31.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.88</td>\n",
       "      <td>31.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.90</td>\n",
       "      <td>31.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.92</td>\n",
       "      <td>31.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.94</td>\n",
       "      <td>31.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.96</td>\n",
       "      <td>31.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.98</td>\n",
       "      <td>31.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.00</td>\n",
       "      <td>31.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    triplet_ETL  pred_y\n",
       "0          2.60   15.37\n",
       "1          2.62   15.37\n",
       "2          2.64   15.37\n",
       "3          2.66   31.80\n",
       "4          2.68   31.80\n",
       "5          2.70   31.80\n",
       "6          2.72   31.80\n",
       "7          2.74   31.87\n",
       "8          2.76   31.79\n",
       "9          2.78   31.69\n",
       "10         2.80   31.78\n",
       "11         2.82   31.76\n",
       "12         2.84   31.76\n",
       "13         2.86   31.76\n",
       "14         2.88   31.76\n",
       "15         2.90   31.76\n",
       "16         2.92   31.76\n",
       "17         2.94   31.51\n",
       "18         2.96   31.51\n",
       "19         2.98   31.41\n",
       "20         3.00   31.41"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etl = pd.DataFrame(new_X['triplet_ETL']) \n",
    "etl['pred_y'] = y_pred\n",
    "etl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(21, 31)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "new_df = pd.read_pickle('C:/Anaconda3/projects/oled/PhOLED 300/vary triplet htl/generated1.pkl')\n",
    "\n",
    "new_X = new_df[:]\n",
    "print(new_X.shape)\n",
    "\n",
    "y_pred = loaded_model.predict(new_X)\n",
    "y_pred = [round(float(i), 2) for i in y_pred]\n",
    "\n",
    "new_X['cd/a'] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>triplet_HTL</th>\n",
       "      <th>pred_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.60</td>\n",
       "      <td>26.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.62</td>\n",
       "      <td>26.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.64</td>\n",
       "      <td>26.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.66</td>\n",
       "      <td>26.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.68</td>\n",
       "      <td>26.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.70</td>\n",
       "      <td>26.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.72</td>\n",
       "      <td>26.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.74</td>\n",
       "      <td>27.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.76</td>\n",
       "      <td>27.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.78</td>\n",
       "      <td>29.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.80</td>\n",
       "      <td>29.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.82</td>\n",
       "      <td>29.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.84</td>\n",
       "      <td>29.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2.86</td>\n",
       "      <td>31.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.88</td>\n",
       "      <td>31.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.90</td>\n",
       "      <td>31.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.92</td>\n",
       "      <td>32.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.94</td>\n",
       "      <td>31.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2.96</td>\n",
       "      <td>31.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2.98</td>\n",
       "      <td>31.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.00</td>\n",
       "      <td>31.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    triplet_HTL  pred_y\n",
       "0          2.60   26.87\n",
       "1          2.62   26.87\n",
       "2          2.64   26.87\n",
       "3          2.66   26.87\n",
       "4          2.68   26.87\n",
       "5          2.70   26.97\n",
       "6          2.72   26.97\n",
       "7          2.74   27.26\n",
       "8          2.76   27.26\n",
       "9          2.78   29.80\n",
       "10         2.80   29.92\n",
       "11         2.82   29.71\n",
       "12         2.84   29.72\n",
       "13         2.86   31.91\n",
       "14         2.88   31.96\n",
       "15         2.90   31.94\n",
       "16         2.92   32.00\n",
       "17         2.94   31.55\n",
       "18         2.96   31.49\n",
       "19         2.98   31.41\n",
       "20         3.00   31.41"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "htl = pd.DataFrame(new_X['triplet_HTL']) \n",
    "htl['pred_y'] = y_pred\n",
    "htl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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