Classification_of_genes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now you \"C\" me DataScience Final Project \n",
    "link to video: https://youtu.be/lhodcPtmywc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RNA and hMeDIP Clustering and Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Information obtained from data cleaning\n",
    "\n",
    "In the three previous notebooks I have cleaned up all of the data and created dataframes that contain info about all of the expressed genes. There is one dataframe for each data set (Human ARPE-19 cells treated with vitamin C, Rat Schwann cells treated with vitamin C, and Rat Schwann cells treated with cAMP). The columns (not necessarily in this order) are as follows: \n",
    "\n",
    "1. Ensembl ID: this is just the ID for each gene, will be used as the index\n",
    "2. Change: meaning the change in transcription in response to either vitamin C or cAMP, this will be the label\n",
    "3. Name: This is the gene name. I kept these so that we can look at the clusters later, but this will not be included in the analysis.\n",
    "4. Biotype: Most of these are protein coding, so this will be changed to a binary classification of either protein coding or not protein coding\n",
    "5. Chromosome: Should not be a confounding variable, but I thought I should include it for just in case.\n",
    "6. Length: Entire transcript length, from transcription start site (TSS) to transcription termination site (TTS)\n",
    "7. Exonic Length: Length of the coding transcript, exons only\n",
    "8. TSS_Downregulated: Number of peaks that decrease in 5hmC enrichment that are located in that gene's TSS\n",
    "9. Downstream_Promoter_Downregulated: Number of peaks that decrease in 5hmC enrichment that are located in that gene's downstream promoter (250 bp downstream to 3,000 bp downstream)\n",
    "10. Gene_Body_Downregulated: Number of peaks that decrease in 5hmC enrichment that are located in that gene body (from 3,000 bp downstream of the TSS to the TTS)\n",
    "11. Upstream_Promoter_Downregulated: Number of peaks that decrease in 5hmC enrichment that are located in that gene's upstream promoter (250 bp upstream to 3,000 bp upstream)\n",
    "12. TSS_Nondifferential: Number of peaks that do not change in 5hmC enrichment that are located in that gene's TSS\n",
    "13. Downstream_Promoter_Nondifferential: Number of peaks that do not change in 5hmC enrichment that are located in that gene's downstream promoter (250 bp downstream to 3,000 bp downstream)\n",
    "14. Gene_Body_Nondifferential: Number of peaks that do not change in 5hmC enrichment that are located in that gene body (from 3,000 bp downstream of the TSS to the TTS)\n",
    "15. Upstream_Promoter_Nondifferential: Number of peaks that do not change in 5hmC enrichment that are located in that gene's upstream promoter (250 bp upstream to 3,000 bp upstream)\n",
    "16. TSS_Upregulated: Number of peaks that increase in 5hmC enrichment that are located in that gene's TSS\n",
    "17. Downstream_Promoter_Upregulated: Number of peaks that increae in 5hmC enrichment that are located in that gene's downstream promoter (250 bp downstream to 3,000 bp downstream)\n",
    "18. Gene_Body_Upregulated: Number of peaks that increase in 5hmC enrichment that are located in that gene body (from 3,000 bp downstream of the TSS to the TTS)\n",
    "19. Upstream_Promoter_Upregulated: Number of peaks that increase in 5hmC enrichment that are located in that gene's upstream promoter (250 bp upstream to 3,000 bp upstream)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook will contain classification steps for these datasets\n",
    "\n",
    "For this notebook, I will include information about the machine learning classification. I plan to split one dataset (ARPE) into test and train sets to see how well I can classify the test genes. Whichever one works best will be trained on all of the genes from the ARPE dataset and then used to classify the genes in the schwann cell dataset. I predict that this will not be particularly successful on predicting changes in the Schwann cells, simply because it is another cell type and another species (human vs. rat). The cAMP dataset is also likely to not have great prediction because it is treated with cAMP, which affects three transcription factors (CREM, CREM, ATF1). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "from sklearn import tree, svm, metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10, 10)\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_data_path = \"Output_Tables_and_Results/\"\n",
    "output_path = \"Classification_Output/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ARPE_Intermediate_Files',\n",
       " 'SC_VitC_Combined_Cleaned_Data.csv',\n",
       " 'SC_cAMP_Combined_Cleaned_Data.pkl',\n",
       " '.DS_Store',\n",
       " 'ARPE_Combined_Cleaned_Data.csv',\n",
       " 'ARPE_Combined_Cleaned_Data.pkl',\n",
       " 'SC-VitC_Intermediate_Files',\n",
       " 'SC-cAMP_Intermediate_Files',\n",
       " 'SC_cAMP_Combined_Cleaned_Data.csv',\n",
       " 'SC_VitC_Combined_Cleaned_Data.pkl']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(\"Output_Tables_and_Results/\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARPE_cleaned_dataset = pd.read_pickle(cleaned_data_path+\"ARPE_Combined_Cleaned_Data.pkl\")\n",
    "SC_VitC_cleaned_dataset = pd.read_pickle(cleaned_data_path+\"SC_VitC_Combined_Cleaned_Data.pkl\")\n",
    "SC_cAMP_cleaned_dataset = pd.read_pickle(cleaned_data_path+\"SC_cAMP_Combined_Cleaned_Data.pkl\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Change</th>\n",
       "      <th>Name</th>\n",
       "      <th>Biotype</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Length</th>\n",
       "      <th>Exonic Length</th>\n",
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       "      <th>Downstream_Promoter_Downregulated</th>\n",
       "      <th>Gene_Body_Downregulated</th>\n",
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       "      <th>TSS_Upregulated</th>\n",
       "      <th>Downstream_Promoter_Upregulated</th>\n",
       "      <th>Gene_Body_Upregulated</th>\n",
       "      <th>Upstream_Promoter_Upregulated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
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       "      <th>ENSG00000000003</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>X</td>\n",
       "      <td>9996.0</td>\n",
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       "    <tr>\n",
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       "      <td>Nondifferential</td>\n",
       "      <td>DPM1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>20</td>\n",
       "      <td>23689.0</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000000457</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>SCYL3</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>44322.0</td>\n",
       "      <td>6364.0</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000000460</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>C1orf112</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>58672.0</td>\n",
       "      <td>4355.0</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000000971</th>\n",
       "      <td>Upregulated</td>\n",
       "      <td>CFH</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>95627.0</td>\n",
       "      <td>4127.0</td>\n",
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      "text/plain": [
       "                          Change      Name         Biotype Chr   Length  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000000003    Downregulated    TSPAN6  protein_coding   X   9996.0   \n",
       "ENSG00000000419  Nondifferential      DPM1  protein_coding  20  23689.0   \n",
       "ENSG00000000457  Nondifferential     SCYL3  protein_coding   1  44322.0   \n",
       "ENSG00000000460  Nondifferential  C1orf112  protein_coding   1  58672.0   \n",
       "ENSG00000000971      Upregulated       CFH  protein_coding   1  95627.0   \n",
       "\n",
       "                 Exonic Length  TSS_Downregulated  \\\n",
       "Ensembl_ID                                          \n",
       "ENSG00000000003         3796.0                0.0   \n",
       "ENSG00000000419         1161.0                0.0   \n",
       "ENSG00000000457         6364.0                0.0   \n",
       "ENSG00000000460         4355.0                0.0   \n",
       "ENSG00000000971         4127.0                0.0   \n",
       "\n",
       "                 Downstream_Promoter_Downregulated  Gene_Body_Downregulated  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSG00000000003                                0.0                      0.0   \n",
       "ENSG00000000419                                0.0                      0.0   \n",
       "ENSG00000000457                                0.0                      0.0   \n",
       "ENSG00000000460                                0.0                      0.0   \n",
       "ENSG00000000971                                0.0                      0.0   \n",
       "\n",
       "                 Upstream_Promoter_Downregulated  TSS_Nondifferential  \\\n",
       "Ensembl_ID                                                              \n",
       "ENSG00000000003                              0.0                  1.0   \n",
       "ENSG00000000419                              0.0                  0.0   \n",
       "ENSG00000000457                              0.0                  0.0   \n",
       "ENSG00000000460                              0.0                  0.0   \n",
       "ENSG00000000971                              0.0                  0.0   \n",
       "\n",
       "                 Downstream_Promoter_Nondifferential  \\\n",
       "Ensembl_ID                                             \n",
       "ENSG00000000003                                  0.0   \n",
       "ENSG00000000419                                  0.0   \n",
       "ENSG00000000457                                  0.0   \n",
       "ENSG00000000460                                  0.0   \n",
       "ENSG00000000971                                  1.0   \n",
       "\n",
       "                 Gene_Body_Nondifferential  Upstream_Promoter_Nondifferential  \\\n",
       "Ensembl_ID                                                                      \n",
       "ENSG00000000003                        0.0                                0.0   \n",
       "ENSG00000000419                        0.0                                0.0   \n",
       "ENSG00000000457                        1.0                                0.0   \n",
       "ENSG00000000460                        4.0                                0.0   \n",
       "ENSG00000000971                        1.0                                0.0   \n",
       "\n",
       "                 TSS_Upregulated  Downstream_Promoter_Upregulated  \\\n",
       "Ensembl_ID                                                          \n",
       "ENSG00000000003              0.0                              0.0   \n",
       "ENSG00000000419              0.0                              0.0   \n",
       "ENSG00000000457              0.0                              0.0   \n",
       "ENSG00000000460              0.0                              0.0   \n",
       "ENSG00000000971              0.0                              0.0   \n",
       "\n",
       "                 Gene_Body_Upregulated  Upstream_Promoter_Upregulated  \n",
       "Ensembl_ID                                                             \n",
       "ENSG00000000003                    0.0                            0.0  \n",
       "ENSG00000000419                    1.0                            0.0  \n",
       "ENSG00000000457                    1.0                            0.0  \n",
       "ENSG00000000460                    2.0                            0.0  \n",
       "ENSG00000000971                    0.0                            0.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is going to be to split the data into numpy arrays. Unfortunately, I am also going to have to create dummy variables for the categorical variables that I want to include. I also need to change the \"Change\" to numbers. I think I will use 0 for downregulated, 1 for nondifferential and 2 for upregulated. Biotype will be split into \"protein_coding\" vs everything else. For chromosomes, I am just going to split it into nuclear chromosomes vs mitochondrial chromosome and patches.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['X', '20', '1', '6', '7', '12', '11', '4', '17', '2', '16', '3',\n",
       "       '19', '9', '13', '14', '8', 'Y', '5', '10', '22', '18', '15', '21',\n",
       "       'MT', 'KI270711.1', 'GL000219.1', 'KI270713.1', 'GL000195.1'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset[\"Chr\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_change(change):\n",
    "    if change == \"Downregulated\":\n",
    "        return 0\n",
    "    elif change == \"Nondifferential\":\n",
    "        return 1\n",
    "    elif change == \"Upregulated\":\n",
    "        return 2\n",
    "    else:\n",
    "        return \"Remove this one from the dataset\"\n",
    "\n",
    "ARPE_cleaned_dataset[\"Numeric_Change\"] = ARPE_cleaned_dataset[\"Change\"].apply(get_change)\n",
    "\n",
    "def get_biotype(biotype):\n",
    "    if biotype == \"protein_coding\":\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "ARPE_cleaned_dataset[\"Numeric_Biotype\"] = ARPE_cleaned_dataset[\"Biotype\"].apply(get_biotype)\n",
    "\n",
    "def get_chromosome(Chr):\n",
    "    if Chr == \"X\": \n",
    "        return 0\n",
    "    elif Chr == \"Y\": \n",
    "        return 0\n",
    "    elif Chr == \"1\": \n",
    "        return 0\n",
    "    elif Chr == \"2\": \n",
    "        return 0\n",
    "    elif Chr == \"3\": \n",
    "        return 0\n",
    "    elif Chr == \"4\": \n",
    "        return 0\n",
    "    elif Chr == \"5\": \n",
    "        return 0\n",
    "    elif Chr == \"6\": \n",
    "        return 0\n",
    "    elif Chr == \"7\": \n",
    "        return 0\n",
    "    elif Chr == \"8\": \n",
    "        return 0\n",
    "    elif Chr == \"9\": \n",
    "        return 0\n",
    "    elif Chr == \"10\": \n",
    "        return 0\n",
    "    elif Chr == \"11\": \n",
    "        return 0\n",
    "    elif Chr == \"12\": \n",
    "        return 0\n",
    "    elif Chr == \"13\": \n",
    "        return 0\n",
    "    elif Chr == \"14\": \n",
    "        return 0\n",
    "    elif Chr == \"15\": \n",
    "        return 0\n",
    "    elif Chr == \"16\": \n",
    "        return 0\n",
    "    elif Chr == \"17\": \n",
    "        return 0\n",
    "    elif Chr == \"18\": \n",
    "        return 0\n",
    "    elif Chr == \"19\": \n",
    "        return 0\n",
    "    elif Chr == \"20\": \n",
    "        return 0\n",
    "    elif Chr == \"21\": \n",
    "        return 0\n",
    "    elif Chr == \"22\": \n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "ARPE_cleaned_dataset[\"Numeric_Chr\"] = ARPE_cleaned_dataset[\"Chr\"].apply(get_chromosome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>Exonic Length</th>\n",
       "      <th>TSS_Downregulated</th>\n",
       "      <th>Downstream_Promoter_Downregulated</th>\n",
       "      <th>Gene_Body_Downregulated</th>\n",
       "      <th>Upstream_Promoter_Downregulated</th>\n",
       "      <th>...</th>\n",
       "      <th>Downstream_Promoter_Nondifferential</th>\n",
       "      <th>Gene_Body_Nondifferential</th>\n",
       "      <th>Upstream_Promoter_Nondifferential</th>\n",
       "      <th>TSS_Upregulated</th>\n",
       "      <th>Downstream_Promoter_Upregulated</th>\n",
       "      <th>Gene_Body_Upregulated</th>\n",
       "      <th>Upstream_Promoter_Upregulated</th>\n",
       "      <th>Numeric_Change</th>\n",
       "      <th>Numeric_Biotype</th>\n",
       "      <th>Numeric_Chr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000000003</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>X</td>\n",
       "      <td>9996.0</td>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000419</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>DPM1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>20</td>\n",
       "      <td>23689.0</td>\n",
       "      <td>1161.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000457</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>SCYL3</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>44322.0</td>\n",
       "      <td>6364.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000460</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>C1orf112</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>58672.0</td>\n",
       "      <td>4355.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000971</th>\n",
       "      <td>Upregulated</td>\n",
       "      <td>CFH</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>95627.0</td>\n",
       "      <td>4127.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Change      Name         Biotype Chr   Length  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000000003    Downregulated    TSPAN6  protein_coding   X   9996.0   \n",
       "ENSG00000000419  Nondifferential      DPM1  protein_coding  20  23689.0   \n",
       "ENSG00000000457  Nondifferential     SCYL3  protein_coding   1  44322.0   \n",
       "ENSG00000000460  Nondifferential  C1orf112  protein_coding   1  58672.0   \n",
       "ENSG00000000971      Upregulated       CFH  protein_coding   1  95627.0   \n",
       "\n",
       "                 Exonic Length  TSS_Downregulated  \\\n",
       "Ensembl_ID                                          \n",
       "ENSG00000000003         3796.0                0.0   \n",
       "ENSG00000000419         1161.0                0.0   \n",
       "ENSG00000000457         6364.0                0.0   \n",
       "ENSG00000000460         4355.0                0.0   \n",
       "ENSG00000000971         4127.0                0.0   \n",
       "\n",
       "                 Downstream_Promoter_Downregulated  Gene_Body_Downregulated  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSG00000000003                                0.0                      0.0   \n",
       "ENSG00000000419                                0.0                      0.0   \n",
       "ENSG00000000457                                0.0                      0.0   \n",
       "ENSG00000000460                                0.0                      0.0   \n",
       "ENSG00000000971                                0.0                      0.0   \n",
       "\n",
       "                 Upstream_Promoter_Downregulated     ...       \\\n",
       "Ensembl_ID                                           ...        \n",
       "ENSG00000000003                              0.0     ...        \n",
       "ENSG00000000419                              0.0     ...        \n",
       "ENSG00000000457                              0.0     ...        \n",
       "ENSG00000000460                              0.0     ...        \n",
       "ENSG00000000971                              0.0     ...        \n",
       "\n",
       "                 Downstream_Promoter_Nondifferential  \\\n",
       "Ensembl_ID                                             \n",
       "ENSG00000000003                                  0.0   \n",
       "ENSG00000000419                                  0.0   \n",
       "ENSG00000000457                                  0.0   \n",
       "ENSG00000000460                                  0.0   \n",
       "ENSG00000000971                                  1.0   \n",
       "\n",
       "                 Gene_Body_Nondifferential  Upstream_Promoter_Nondifferential  \\\n",
       "Ensembl_ID                                                                      \n",
       "ENSG00000000003                        0.0                                0.0   \n",
       "ENSG00000000419                        0.0                                0.0   \n",
       "ENSG00000000457                        1.0                                0.0   \n",
       "ENSG00000000460                        4.0                                0.0   \n",
       "ENSG00000000971                        1.0                                0.0   \n",
       "\n",
       "                 TSS_Upregulated  Downstream_Promoter_Upregulated  \\\n",
       "Ensembl_ID                                                          \n",
       "ENSG00000000003              0.0                              0.0   \n",
       "ENSG00000000419              0.0                              0.0   \n",
       "ENSG00000000457              0.0                              0.0   \n",
       "ENSG00000000460              0.0                              0.0   \n",
       "ENSG00000000971              0.0                              0.0   \n",
       "\n",
       "                 Gene_Body_Upregulated  Upstream_Promoter_Upregulated  \\\n",
       "Ensembl_ID                                                              \n",
       "ENSG00000000003                    0.0                            0.0   \n",
       "ENSG00000000419                    1.0                            0.0   \n",
       "ENSG00000000457                    1.0                            0.0   \n",
       "ENSG00000000460                    2.0                            0.0   \n",
       "ENSG00000000971                    0.0                            0.0   \n",
       "\n",
       "                 Numeric_Change  Numeric_Biotype  Numeric_Chr  \n",
       "Ensembl_ID                                                     \n",
       "ENSG00000000003               0                1            0  \n",
       "ENSG00000000419               1                1            0  \n",
       "ENSG00000000457               1                1            0  \n",
       "ENSG00000000460               1                1            0  \n",
       "ENSG00000000971               2                1            0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Length</th>\n",
       "      <th>Exonic Length</th>\n",
       "      <th>TSS_Downregulated</th>\n",
       "      <th>Downstream_Promoter_Downregulated</th>\n",
       "      <th>Gene_Body_Downregulated</th>\n",
       "      <th>Upstream_Promoter_Downregulated</th>\n",
       "      <th>TSS_Nondifferential</th>\n",
       "      <th>Downstream_Promoter_Nondifferential</th>\n",
       "      <th>Gene_Body_Nondifferential</th>\n",
       "      <th>Upstream_Promoter_Nondifferential</th>\n",
       "      <th>TSS_Upregulated</th>\n",
       "      <th>Downstream_Promoter_Upregulated</th>\n",
       "      <th>Gene_Body_Upregulated</th>\n",
       "      <th>Upstream_Promoter_Upregulated</th>\n",
       "      <th>Numeric_Change</th>\n",
       "      <th>Numeric_Biotype</th>\n",
       "      <th>Numeric_Chr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.459900e+04</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "      <td>14599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.280085e+04</td>\n",
       "      <td>3476.985684</td>\n",
       "      <td>0.002671</td>\n",
       "      <td>0.010823</td>\n",
       "      <td>0.136105</td>\n",
       "      <td>0.012604</td>\n",
       "      <td>0.049387</td>\n",
       "      <td>0.205425</td>\n",
       "      <td>1.644702</td>\n",
       "      <td>0.224741</td>\n",
       "      <td>0.110419</td>\n",
       "      <td>0.342147</td>\n",
       "      <td>1.900267</td>\n",
       "      <td>0.330708</td>\n",
       "      <td>1.126379</td>\n",
       "      <td>0.769094</td>\n",
       "      <td>0.002055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.150453e+05</td>\n",
       "      <td>2869.609131</td>\n",
       "      <td>0.051618</td>\n",
       "      <td>0.104787</td>\n",
       "      <td>0.558936</td>\n",
       "      <td>0.111560</td>\n",
       "      <td>0.234009</td>\n",
       "      <td>0.495142</td>\n",
       "      <td>3.781105</td>\n",
       "      <td>0.530842</td>\n",
       "      <td>0.371804</td>\n",
       "      <td>0.728718</td>\n",
       "      <td>4.845714</td>\n",
       "      <td>0.730941</td>\n",
       "      <td>0.551861</td>\n",
       "      <td>0.421427</td>\n",
       "      <td>0.045286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.014000e+03</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.629700e+04</td>\n",
       "      <td>2794.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.702300e+04</td>\n",
       "      <td>4676.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.092292e+06</td>\n",
       "      <td>34526.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Length  Exonic Length  TSS_Downregulated  \\\n",
       "count  1.459900e+04   14599.000000       14599.000000   \n",
       "mean   6.280085e+04    3476.985684           0.002671   \n",
       "std    1.150453e+05    2869.609131           0.051618   \n",
       "min    4.800000e+01      48.000000           0.000000   \n",
       "25%    9.014000e+03    1460.000000           0.000000   \n",
       "50%    2.629700e+04    2794.000000           0.000000   \n",
       "75%    6.702300e+04    4676.000000           0.000000   \n",
       "max    2.092292e+06   34526.000000           1.000000   \n",
       "\n",
       "       Downstream_Promoter_Downregulated  Gene_Body_Downregulated  \\\n",
       "count                       14599.000000             14599.000000   \n",
       "mean                            0.010823                 0.136105   \n",
       "std                             0.104787                 0.558936   \n",
       "min                             0.000000                 0.000000   \n",
       "25%                             0.000000                 0.000000   \n",
       "50%                             0.000000                 0.000000   \n",
       "75%                             0.000000                 0.000000   \n",
       "max                             2.000000                15.000000   \n",
       "\n",
       "       Upstream_Promoter_Downregulated  TSS_Nondifferential  \\\n",
       "count                     14599.000000         14599.000000   \n",
       "mean                          0.012604             0.049387   \n",
       "std                           0.111560             0.234009   \n",
       "min                           0.000000             0.000000   \n",
       "25%                           0.000000             0.000000   \n",
       "50%                           0.000000             0.000000   \n",
       "75%                           0.000000             0.000000   \n",
       "max                           1.000000             4.000000   \n",
       "\n",
       "       Downstream_Promoter_Nondifferential  Gene_Body_Nondifferential  \\\n",
       "count                         14599.000000               14599.000000   \n",
       "mean                              0.205425                   1.644702   \n",
       "std                               0.495142                   3.781105   \n",
       "min                               0.000000                   0.000000   \n",
       "25%                               0.000000                   0.000000   \n",
       "50%                               0.000000                   0.000000   \n",
       "75%                               0.000000                   2.000000   \n",
       "max                               7.000000                  66.000000   \n",
       "\n",
       "       Upstream_Promoter_Nondifferential  TSS_Upregulated  \\\n",
       "count                       14599.000000     14599.000000   \n",
       "mean                            0.224741         0.110419   \n",
       "std                             0.530842         0.371804   \n",
       "min                             0.000000         0.000000   \n",
       "25%                             0.000000         0.000000   \n",
       "50%                             0.000000         0.000000   \n",
       "75%                             0.000000         0.000000   \n",
       "max                             8.000000         6.000000   \n",
       "\n",
       "       Downstream_Promoter_Upregulated  Gene_Body_Upregulated  \\\n",
       "count                     14599.000000           14599.000000   \n",
       "mean                          0.342147               1.900267   \n",
       "std                           0.728718               4.845714   \n",
       "min                           0.000000               0.000000   \n",
       "25%                           0.000000               0.000000   \n",
       "50%                           0.000000               0.000000   \n",
       "75%                           0.000000               2.000000   \n",
       "max                          11.000000             106.000000   \n",
       "\n",
       "       Upstream_Promoter_Upregulated  Numeric_Change  Numeric_Biotype  \\\n",
       "count                   14599.000000    14599.000000     14599.000000   \n",
       "mean                        0.330708        1.126379         0.769094   \n",
       "std                         0.730941        0.551861         0.421427   \n",
       "min                         0.000000        0.000000         0.000000   \n",
       "25%                         0.000000        1.000000         1.000000   \n",
       "50%                         0.000000        1.000000         1.000000   \n",
       "75%                         0.000000        1.000000         1.000000   \n",
       "max                        11.000000        2.000000         1.000000   \n",
       "\n",
       "        Numeric_Chr  \n",
       "count  14599.000000  \n",
       "mean       0.002055  \n",
       "std        0.045286  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Change', 'Name', 'Biotype', 'Chr', 'Length', 'Exonic Length',\n",
       "       'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
       "       'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
       "       'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
       "       'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
       "       'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
       "       'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated',\n",
       "       'Numeric_Change', 'Numeric_Biotype', 'Numeric_Chr'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARPE_X = np.array(ARPE_cleaned_dataset[['Length', 'Exonic Length',\n",
    "                     'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "                     'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "                     'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "                     'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "                     'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "                     'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated','Numeric_Biotype', 'Numeric_Chr']])\\\n",
    "\n",
    "ARPE_y = np.array(ARPE_cleaned_dataset[[\"Numeric_Change\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_ARPE_X = scale(ARPE_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(scaled_ARPE_X, ARPE_y, random_state=1, test_size=0.8) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2919\n",
      "2919\n",
      "11680\n",
      "11680\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train))\n",
    "print(len(y_train))\n",
    "print(len(X_test))\n",
    "print(len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I am going to try to figure out a good value for C in using an SVM. I recall from the previous ones we run, a bad value of C doesn't decrease your accuracy that much. I am going to only use the training data because it takes too long to run with all ~15,000 genes. I will run a test-train split and a cross-val once I find a good value of C. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## It turns out that this gives annoying warnings if your y value is a 2-D array. The .ravel() function makes it a 1-D array.\n",
    "## I have put pound signs through a few parts and saved the results in csv format so that if you run the whole notebook\n",
    "## with the restart and run all option, it will not take more than half an hour to complete. \n",
    "\n",
    "# Cs = np.arange(1,502,5)\n",
    "# Accuracies = np.zeros(Cs.shape[0])\n",
    "# for i,C in enumerate(Cs): \n",
    "#     model_numbers = svm.SVC(kernel='rbf', C = C,gamma='scale')\n",
    "#     scores = cross_val_score(estimator = model_numbers, X = X_train, y = y_train.ravel(), cv=5, scoring='accuracy')    \n",
    "#     Accuracies[i]  = scores.mean()\n",
    "        \n",
    "# plt.plot(Cs,Accuracies)\n",
    "# plt.show()\n",
    "# ARPE_SVM_Cs = pd.DataFrame({\"C_value\": Cs, \"Accuracy\": Accuracies}) \n",
    "# ARPE_SVM_Cs.to_csv(output_path+\"ARPE_SVM_C_values.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "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>C_value</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.689622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>0.684481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>0.680374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>0.677635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>0.674901</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C_value  Accuracy\n",
       "0        1  0.689622\n",
       "1        6  0.684481\n",
       "2       11  0.680374\n",
       "3       16  0.677635\n",
       "4       21  0.674901"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_SVM_Cs = pd.read_csv(output_path+\"ARPE_SVM_C_values.csv\", index_col = 0)\n",
    "ARPE_SVM_Cs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(ARPE_SVM_Cs[[\"C_value\"]], ARPE_SVM_Cs[[\"Accuracy\"]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the low numbers give the highest accuracy. I am going to repeat this step with values of C between 0 and 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## It looks like the low numbers are the best ones. I am going to try this again with numbers between 0 and 5.\n",
    "\n",
    "# Cs = np.arange(0.1,5,0.1)\n",
    "# Accuracies = np.zeros(Cs.shape[0])\n",
    "# for i,C in enumerate(Cs): \n",
    "#     model_numbers = svm.SVC(kernel='rbf', C = C,gamma='scale')\n",
    "#     scores = cross_val_score(estimator = model_numbers, X = X_train, y = y_train.ravel(), cv=5, scoring='accuracy')    \n",
    "#     Accuracies[i]  = scores.mean()\n",
    "        \n",
    "# plt.plot(Cs,Accuracies)\n",
    "# plt.show()\n",
    "# ARPE_SVM_Cs = pd.DataFrame({\"C_value\": Cs, \"Accuracy\": Accuracies}) \n",
    "# ARPE_SVM_Cs.to_csv(output_path+\"ARPE_SVM_C_values_Concentrated.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Accuracy is: 0.6920195443108341 for C = 0.7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ARPE_SVM_Cs_concentrated = pd.read_csv(output_path+\"ARPE_SVM_C_values_Concentrated.csv\", index_col = 0)\n",
    "#ARPE_SVM_Cs_concentrated.head()\n",
    "max_accuracies = max(ARPE_SVM_Cs_concentrated[\"Accuracy\"])\n",
    "for i,num in enumerate( ARPE_SVM_Cs_concentrated[\"Accuracy\"]):\n",
    "    if num == max_accuracies:\n",
    "        max_c = i*0.1+0.1\n",
    "        print (\"Maximum Accuracy is:\", max_accuracies, \"for C =\", round(max_c, 1))\n",
    "        #This is only going to get the first one, but all after 6 give the same result\n",
    "        break\n",
    "\n",
    "plt.plot(ARPE_SVM_Cs_concentrated[[\"C_value\"]], ARPE_SVM_Cs_concentrated[[\"Accuracy\"]])\n",
    "plt.show()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though I did a cross validation using only 1/5 of the genes, I think this value of C should be ok. This is the first time I have seen a value of C below 1 be the highest. The maximum that I got was 0.7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6842465753424658\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1121   30]\n",
      " [   0 7726  189]\n",
      " [   0 2348  266]]\n"
     ]
    }
   ],
   "source": [
    "# Now to see how training on the 1/5 and testing on the 80% performs\n",
    "\n",
    "model_svm = svm.SVC(kernel='rbf',C=0.7,gamma='auto')\n",
    "model_svm.fit(X_train, y_train.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(X_test)\n",
    "\n",
    "ARPE_test_train_accuracy = metrics.accuracy_score(y_true = y_test.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_test_train_accuracy)\n",
    "\n",
    "\n",
    "ARPE_SVM_confusion = metrics.confusion_matrix(y_true = y_test.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_SVM_confusion)\n",
    "#y_pred = model_numbers.predict(X_test)\n",
    "\n",
    "#print(metrics.confusion_matrix(y_true = y_test, y_pred = y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-fold cross validation Accuracy =  0.6885420679937685\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model_numbers = svm.SVC(kernel='rbf', C = 0.7, gamma='scale')\n",
    "scores = cross_val_score(estimator = model_numbers, X = scaled_ARPE_X, y = ARPE_y.ravel(), cv=5, scoring='accuracy')    \n",
    "scores.mean()\n",
    "print('5-fold cross validation Accuracy = ', scores.mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6885420679937685, 0.6842465753424658)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_SVM_accuracy_scores = ( scores.mean(), ARPE_test_train_accuracy)\n",
    "ARPE_SVM_accuracy_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the confusion matrix, this is total crap. It didn't predict that a single thing was downregulated. Looking at the test data, we had 67.77% nondifferential, 9.85% downregulated and 22.38% upregulated. Bayesian statistics would put guessing any random one correctly at 51.9%. Going up to ~69% accuracy is an improvement, but we have a 0% accuracy of predicting downregulated genes. I think we can only do predictions on ones that have at least one 5hmC peak. If they are all 0, they are probably predicted as nondifferential.\n",
    "\n",
    "I looked at the data, and it looks like a large proportion of the genes have 0 peaks in the promoter and either 0 or 1 peak in the gene body. I think I am going to filter this for genes that have either 1 peaks in the promoter or 2+ peaks in the gene body (or both). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Change</th>\n",
       "      <th>Name</th>\n",
       "      <th>Biotype</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Length</th>\n",
       "      <th>Exonic Length</th>\n",
       "      <th>TSS_Downregulated</th>\n",
       "      <th>Downstream_Promoter_Downregulated</th>\n",
       "      <th>Gene_Body_Downregulated</th>\n",
       "      <th>Upstream_Promoter_Downregulated</th>\n",
       "      <th>...</th>\n",
       "      <th>Downstream_Promoter_Nondifferential</th>\n",
       "      <th>Gene_Body_Nondifferential</th>\n",
       "      <th>Upstream_Promoter_Nondifferential</th>\n",
       "      <th>TSS_Upregulated</th>\n",
       "      <th>Downstream_Promoter_Upregulated</th>\n",
       "      <th>Gene_Body_Upregulated</th>\n",
       "      <th>Upstream_Promoter_Upregulated</th>\n",
       "      <th>Numeric_Change</th>\n",
       "      <th>Numeric_Biotype</th>\n",
       "      <th>Numeric_Chr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
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       "  <tbody>\n",
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       "      <th>ENSG00000000003</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>X</td>\n",
       "      <td>9996.0</td>\n",
       "      <td>3796.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000000419</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>DPM1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>20</td>\n",
       "      <td>23689.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000457</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>SCYL3</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>44322.0</td>\n",
       "      <td>6364.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000460</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>C1orf112</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>58672.0</td>\n",
       "      <td>4355.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>ENSG00000000971</th>\n",
       "      <td>Upregulated</td>\n",
       "      <td>CFH</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Change      Name         Biotype Chr   Length  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000000003    Downregulated    TSPAN6  protein_coding   X   9996.0   \n",
       "ENSG00000000419  Nondifferential      DPM1  protein_coding  20  23689.0   \n",
       "ENSG00000000457  Nondifferential     SCYL3  protein_coding   1  44322.0   \n",
       "ENSG00000000460  Nondifferential  C1orf112  protein_coding   1  58672.0   \n",
       "ENSG00000000971      Upregulated       CFH  protein_coding   1  95627.0   \n",
       "\n",
       "                 Exonic Length  TSS_Downregulated  \\\n",
       "Ensembl_ID                                          \n",
       "ENSG00000000003         3796.0                0.0   \n",
       "ENSG00000000419         1161.0                0.0   \n",
       "ENSG00000000457         6364.0                0.0   \n",
       "ENSG00000000460         4355.0                0.0   \n",
       "ENSG00000000971         4127.0                0.0   \n",
       "\n",
       "                 Downstream_Promoter_Downregulated  Gene_Body_Downregulated  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSG00000000003                                0.0                      0.0   \n",
       "ENSG00000000419                                0.0                      0.0   \n",
       "ENSG00000000457                                0.0                      0.0   \n",
       "ENSG00000000460                                0.0                      0.0   \n",
       "ENSG00000000971                                0.0                      0.0   \n",
       "\n",
       "                 Upstream_Promoter_Downregulated     ...       \\\n",
       "Ensembl_ID                                           ...        \n",
       "ENSG00000000003                              0.0     ...        \n",
       "ENSG00000000419                              0.0     ...        \n",
       "ENSG00000000457                              0.0     ...        \n",
       "ENSG00000000460                              0.0     ...        \n",
       "ENSG00000000971                              0.0     ...        \n",
       "\n",
       "                 Downstream_Promoter_Nondifferential  \\\n",
       "Ensembl_ID                                             \n",
       "ENSG00000000003                                  0.0   \n",
       "ENSG00000000419                                  0.0   \n",
       "ENSG00000000457                                  0.0   \n",
       "ENSG00000000460                                  0.0   \n",
       "ENSG00000000971                                  1.0   \n",
       "\n",
       "                 Gene_Body_Nondifferential  Upstream_Promoter_Nondifferential  \\\n",
       "Ensembl_ID                                                                      \n",
       "ENSG00000000003                        0.0                                0.0   \n",
       "ENSG00000000419                        0.0                                0.0   \n",
       "ENSG00000000457                        1.0                                0.0   \n",
       "ENSG00000000460                        4.0                                0.0   \n",
       "ENSG00000000971                        1.0                                0.0   \n",
       "\n",
       "                 TSS_Upregulated  Downstream_Promoter_Upregulated  \\\n",
       "Ensembl_ID                                                          \n",
       "ENSG00000000003              0.0                              0.0   \n",
       "ENSG00000000419              0.0                              0.0   \n",
       "ENSG00000000457              0.0                              0.0   \n",
       "ENSG00000000460              0.0                              0.0   \n",
       "ENSG00000000971              0.0                              0.0   \n",
       "\n",
       "                 Gene_Body_Upregulated  Upstream_Promoter_Upregulated  \\\n",
       "Ensembl_ID                                                              \n",
       "ENSG00000000003                    0.0                            0.0   \n",
       "ENSG00000000419                    1.0                            0.0   \n",
       "ENSG00000000457                    1.0                            0.0   \n",
       "ENSG00000000460                    2.0                            0.0   \n",
       "ENSG00000000971                    0.0                            0.0   \n",
       "\n",
       "                 Numeric_Change  Numeric_Biotype  Numeric_Chr  \n",
       "Ensembl_ID                                                     \n",
       "ENSG00000000003               0                1            0  \n",
       "ENSG00000000419               1                1            0  \n",
       "ENSG00000000457               1                1            0  \n",
       "ENSG00000000460               1                1            0  \n",
       "ENSG00000000971               2                1            0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Change', 'Name', 'Biotype', 'Chr', 'Length', 'Exonic Length',\n",
       "       'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
       "       'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
       "       'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
       "       'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
       "       'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
       "       'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated',\n",
       "       'Numeric_Change', 'Numeric_Biotype', 'Numeric_Chr'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARPE_cleaned_dataset[\"Gene_Body_Counts\"] = (ARPE_cleaned_dataset[\"Gene_Body_Downregulated\"]+ \n",
    "                                            ARPE_cleaned_dataset[\"Gene_Body_Nondifferential\"] + ARPE_cleaned_dataset[\"Gene_Body_Upregulated\"])\n",
    "\n",
    "ARPE_cleaned_dataset[\"Promoter_Counts\"] = (ARPE_cleaned_dataset[\"TSS_Downregulated\"]+ ARPE_cleaned_dataset[\"Downstream_Promoter_Downregulated\"]+ \n",
    "                                           ARPE_cleaned_dataset[\"Upstream_Promoter_Downregulated\"]+ ARPE_cleaned_dataset[\"TSS_Nondifferential\"]+ \n",
    "                                           ARPE_cleaned_dataset[\"Downstream_Promoter_Nondifferential\"]+ ARPE_cleaned_dataset[\"Upstream_Promoter_Nondifferential\"]+ \n",
    "                                           ARPE_cleaned_dataset[\"TSS_Upregulated\"]+ ARPE_cleaned_dataset[\"Downstream_Promoter_Upregulated\"]+ \n",
    "                                           ARPE_cleaned_dataset[\"Upstream_Promoter_Upregulated\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <th>ENSG00000000457</th>\n",
       "      <td>Nondifferential</td>\n",
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       "      <td>Nondifferential</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Change      Name         Biotype Chr   Length  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000000003    Downregulated    TSPAN6  protein_coding   X   9996.0   \n",
       "ENSG00000000419  Nondifferential      DPM1  protein_coding  20  23689.0   \n",
       "ENSG00000000457  Nondifferential     SCYL3  protein_coding   1  44322.0   \n",
       "ENSG00000000460  Nondifferential  C1orf112  protein_coding   1  58672.0   \n",
       "ENSG00000000971      Upregulated       CFH  protein_coding   1  95627.0   \n",
       "\n",
       "                 Exonic Length  TSS_Downregulated  \\\n",
       "Ensembl_ID                                          \n",
       "ENSG00000000003         3796.0                0.0   \n",
       "ENSG00000000419         1161.0                0.0   \n",
       "ENSG00000000457         6364.0                0.0   \n",
       "ENSG00000000460         4355.0                0.0   \n",
       "ENSG00000000971         4127.0                0.0   \n",
       "\n",
       "                 Downstream_Promoter_Downregulated  Gene_Body_Downregulated  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSG00000000003                                0.0                      0.0   \n",
       "ENSG00000000419                                0.0                      0.0   \n",
       "ENSG00000000457                                0.0                      0.0   \n",
       "ENSG00000000460                                0.0                      0.0   \n",
       "ENSG00000000971                                0.0                      0.0   \n",
       "\n",
       "                 Upstream_Promoter_Downregulated       ...         \\\n",
       "Ensembl_ID                                             ...          \n",
       "ENSG00000000003                              0.0       ...          \n",
       "ENSG00000000419                              0.0       ...          \n",
       "ENSG00000000457                              0.0       ...          \n",
       "ENSG00000000460                              0.0       ...          \n",
       "ENSG00000000971                              0.0       ...          \n",
       "\n",
       "                 Upstream_Promoter_Nondifferential  TSS_Upregulated  \\\n",
       "Ensembl_ID                                                            \n",
       "ENSG00000000003                                0.0              0.0   \n",
       "ENSG00000000419                                0.0              0.0   \n",
       "ENSG00000000457                                0.0              0.0   \n",
       "ENSG00000000460                                0.0              0.0   \n",
       "ENSG00000000971                                0.0              0.0   \n",
       "\n",
       "                 Downstream_Promoter_Upregulated  Gene_Body_Upregulated  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000000003                              0.0                    0.0   \n",
       "ENSG00000000419                              0.0                    1.0   \n",
       "ENSG00000000457                              0.0                    1.0   \n",
       "ENSG00000000460                              0.0                    2.0   \n",
       "ENSG00000000971                              0.0                    0.0   \n",
       "\n",
       "                 Upstream_Promoter_Upregulated  Numeric_Change  \\\n",
       "Ensembl_ID                                                       \n",
       "ENSG00000000003                            0.0               0   \n",
       "ENSG00000000419                            0.0               1   \n",
       "ENSG00000000457                            0.0               1   \n",
       "ENSG00000000460                            0.0               1   \n",
       "ENSG00000000971                            0.0               2   \n",
       "\n",
       "                 Numeric_Biotype  Numeric_Chr  Gene_Body_Counts  \\\n",
       "Ensembl_ID                                                        \n",
       "ENSG00000000003                1            0               0.0   \n",
       "ENSG00000000419                1            0               1.0   \n",
       "ENSG00000000457                1            0               2.0   \n",
       "ENSG00000000460                1            0               6.0   \n",
       "ENSG00000000971                1            0               1.0   \n",
       "\n",
       "                 Promoter_Counts  \n",
       "Ensembl_ID                        \n",
       "ENSG00000000003              1.0  \n",
       "ENSG00000000419              0.0  \n",
       "ENSG00000000457              0.0  \n",
       "ENSG00000000460              0.0  \n",
       "ENSG00000000971              1.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_keep_or_discard(gene, promoter):\n",
    "    if gene > 1:\n",
    "        return 1 #\"Keep\"\n",
    "    else:\n",
    "        if promoter > 0:\n",
    "            return 1 #\"Keep\"\n",
    "        else:\n",
    "            return 0 #\"Discard\"\n",
    "\n",
    "ARPE_cleaned_dataset[\"Discard_column\"] = ARPE_cleaned_dataset[\"Gene_Body_Counts\"].combine(\n",
    "    ARPE_cleaned_dataset[\"Promoter_Counts\"], func=get_keep_or_discard)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Change</th>\n",
       "      <th>Name</th>\n",
       "      <th>Biotype</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Length</th>\n",
       "      <th>Exonic Length</th>\n",
       "      <th>TSS_Downregulated</th>\n",
       "      <th>Downstream_Promoter_Downregulated</th>\n",
       "      <th>Gene_Body_Downregulated</th>\n",
       "      <th>Upstream_Promoter_Downregulated</th>\n",
       "      <th>...</th>\n",
       "      <th>TSS_Upregulated</th>\n",
       "      <th>Downstream_Promoter_Upregulated</th>\n",
       "      <th>Gene_Body_Upregulated</th>\n",
       "      <th>Upstream_Promoter_Upregulated</th>\n",
       "      <th>Numeric_Change</th>\n",
       "      <th>Numeric_Biotype</th>\n",
       "      <th>Numeric_Chr</th>\n",
       "      <th>Gene_Body_Counts</th>\n",
       "      <th>Promoter_Counts</th>\n",
       "      <th>Discard_column</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ensembl_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSG00000000003</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>TSPAN6</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>X</td>\n",
       "      <td>9996.0</td>\n",
       "      <td>3796.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000419</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>DPM1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>20</td>\n",
       "      <td>23689.0</td>\n",
       "      <td>1161.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000457</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>SCYL3</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>44322.0</td>\n",
       "      <td>6364.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000460</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>C1orf112</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>58672.0</td>\n",
       "      <td>4355.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000000971</th>\n",
       "      <td>Upregulated</td>\n",
       "      <td>CFH</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>1</td>\n",
       "      <td>95627.0</td>\n",
       "      <td>4127.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Change      Name         Biotype Chr   Length  \\\n",
       "Ensembl_ID                                                                \n",
       "ENSG00000000003    Downregulated    TSPAN6  protein_coding   X   9996.0   \n",
       "ENSG00000000419  Nondifferential      DPM1  protein_coding  20  23689.0   \n",
       "ENSG00000000457  Nondifferential     SCYL3  protein_coding   1  44322.0   \n",
       "ENSG00000000460  Nondifferential  C1orf112  protein_coding   1  58672.0   \n",
       "ENSG00000000971      Upregulated       CFH  protein_coding   1  95627.0   \n",
       "\n",
       "                 Exonic Length  TSS_Downregulated  \\\n",
       "Ensembl_ID                                          \n",
       "ENSG00000000003         3796.0                0.0   \n",
       "ENSG00000000419         1161.0                0.0   \n",
       "ENSG00000000457         6364.0                0.0   \n",
       "ENSG00000000460         4355.0                0.0   \n",
       "ENSG00000000971         4127.0                0.0   \n",
       "\n",
       "                 Downstream_Promoter_Downregulated  Gene_Body_Downregulated  \\\n",
       "Ensembl_ID                                                                    \n",
       "ENSG00000000003                                0.0                      0.0   \n",
       "ENSG00000000419                                0.0                      0.0   \n",
       "ENSG00000000457                                0.0                      0.0   \n",
       "ENSG00000000460                                0.0                      0.0   \n",
       "ENSG00000000971                                0.0                      0.0   \n",
       "\n",
       "                 Upstream_Promoter_Downregulated       ...        \\\n",
       "Ensembl_ID                                             ...         \n",
       "ENSG00000000003                              0.0       ...         \n",
       "ENSG00000000419                              0.0       ...         \n",
       "ENSG00000000457                              0.0       ...         \n",
       "ENSG00000000460                              0.0       ...         \n",
       "ENSG00000000971                              0.0       ...         \n",
       "\n",
       "                 TSS_Upregulated  Downstream_Promoter_Upregulated  \\\n",
       "Ensembl_ID                                                          \n",
       "ENSG00000000003              0.0                              0.0   \n",
       "ENSG00000000419              0.0                              0.0   \n",
       "ENSG00000000457              0.0                              0.0   \n",
       "ENSG00000000460              0.0                              0.0   \n",
       "ENSG00000000971              0.0                              0.0   \n",
       "\n",
       "                 Gene_Body_Upregulated  Upstream_Promoter_Upregulated  \\\n",
       "Ensembl_ID                                                              \n",
       "ENSG00000000003                    0.0                            0.0   \n",
       "ENSG00000000419                    1.0                            0.0   \n",
       "ENSG00000000457                    1.0                            0.0   \n",
       "ENSG00000000460                    2.0                            0.0   \n",
       "ENSG00000000971                    0.0                            0.0   \n",
       "\n",
       "                 Numeric_Change  Numeric_Biotype  Numeric_Chr  \\\n",
       "Ensembl_ID                                                      \n",
       "ENSG00000000003               0                1            0   \n",
       "ENSG00000000419               1                1            0   \n",
       "ENSG00000000457               1                1            0   \n",
       "ENSG00000000460               1                1            0   \n",
       "ENSG00000000971               2                1            0   \n",
       "\n",
       "                 Gene_Body_Counts  Promoter_Counts  Discard_column  \n",
       "Ensembl_ID                                                          \n",
       "ENSG00000000003               0.0              1.0             1.0  \n",
       "ENSG00000000419               1.0              0.0             0.0  \n",
       "ENSG00000000457               2.0              0.0             1.0  \n",
       "ENSG00000000460               6.0              0.0             1.0  \n",
       "ENSG00000000971               1.0              1.0             1.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset.head()\n",
    "# Yay! It worked. Now to subset it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Change', 'Name', 'Biotype', 'Chr', 'Length', 'Exonic Length',\n",
       "       'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
       "       'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
       "       'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
       "       'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
       "       'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
       "       'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated',\n",
       "       'Numeric_Change', 'Numeric_Biotype', 'Numeric_Chr',\n",
       "       'Gene_Body_Counts', 'Promoter_Counts', 'Discard_column'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_cleaned_dataset.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ARPE_genes_with_peaks = ARPE_cleaned_dataset[ARPE_cleaned_dataset[\"Discard_column\"]== 1][['Length', 'Exonic Length',\n",
    "       'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "       'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "       'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "       'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "       'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "       'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated',\n",
    "       'Numeric_Change', 'Numeric_Biotype', 'Numeric_Chr',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARPE_X = np.array(ARPE_genes_with_peaks[['Length', 'Exonic Length',\n",
    "                     'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "                     'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "                     'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "                     'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "                     'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "                     'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated','Numeric_Biotype', 'Numeric_Chr']])\n",
    "\n",
    "ARPE_y = np.array(ARPE_genes_with_peaks[[\"Numeric_Change\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1940\n",
      "1940\n",
      "7763\n",
      "7763\n"
     ]
    }
   ],
   "source": [
    "scaled_ARPE_X = scale(ARPE_X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_ARPE_X, ARPE_y, random_state=1, test_size=0.8) \n",
    "print(len(X_train))\n",
    "print(len(y_train))\n",
    "print(len(X_test))\n",
    "print(len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# It turns out that this gives annoying warnings if your y value is a 2-D array. The .ravel() function makes it a 1-D array.\n",
    "\n",
    "\n",
    "Cs = np.arange(1,502,5)\n",
    "Accuracies = np.zeros(Cs.shape[0])\n",
    "for i,C in enumerate(Cs): \n",
    "    model_numbers = svm.SVC(kernel='rbf', C = C,gamma='scale')\n",
    "    scores = cross_val_score(estimator = model_numbers, X = X_train, y = y_train.ravel(), cv=5, scoring='accuracy')    \n",
    "    Accuracies[i]  = scores.mean()\n",
    "        \n",
    "plt.plot(Cs,Accuracies)\n",
    "plt.show()\n",
    "ARPE_with_peaks_SVM_Cs = pd.DataFrame({\"C_value\": Cs, \"Accuracy\": Accuracies}) \n",
    "ARPE_with_peaks_SVM_Cs.to_csv(output_path+\"ARPE_genes_with_peaks_SVM_C_values.csv\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>C_value</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.631952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>0.618579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>0.617026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>0.614447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>0.610839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C_value  Accuracy\n",
       "0        1  0.631952\n",
       "1        6  0.618579\n",
       "2       11  0.617026\n",
       "3       16  0.614447\n",
       "4       21  0.610839"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARPE_with_peaks_SVM_Cs = pd.read_csv(output_path+\"ARPE_genes_with_peaks_SVM_C_values.csv\", index_col = 0)\n",
    "ARPE_with_peaks_SVM_Cs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ARPE_with_peaks_SVM_Cs[[\"C_value\"]], ARPE_with_peaks_SVM_Cs[[\"Accuracy\"]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # It looks like the low numbers are the best ones again. I am going to try this again with numbers between 0 and 5.\n",
    "\n",
    "# Cs = np.arange(0.1,5,0.1)\n",
    "# Accuracies = np.zeros(Cs.shape[0])\n",
    "# for i,C in enumerate(Cs): \n",
    "#     model_numbers = svm.SVC(kernel='rbf', C = C,gamma='scale')\n",
    "#     scores = cross_val_score(estimator = model_numbers, X = X_train, y = y_train.ravel(), cv=5, scoring='accuracy')    \n",
    "#     Accuracies[i]  = scores.mean()\n",
    "        \n",
    "# plt.plot(Cs,Accuracies)\n",
    "# plt.show()\n",
    "# ARPE_SVM_Cs = pd.DataFrame({\"C_value\": Cs, \"Accuracy\": Accuracies}) \n",
    "# ARPE_SVM_Cs.to_csv(output_path+\"ARPE_genes_with_peaks_SVM_C_values_Concentrated.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Accuracy is: 0.631433396671061 for C = 0.7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ARPE_with_peaks_SVM_Cs_concentrated = pd.read_csv(output_path+\"ARPE_genes_with_peaks_SVM_C_values_Concentrated.csv\", index_col = 0)\n",
    "#ARPE_SVM_Cs_concentrated.head()\n",
    "max_accuracies = max(ARPE_with_peaks_SVM_Cs_concentrated[\"Accuracy\"])\n",
    "for i,num in enumerate( ARPE_with_peaks_SVM_Cs_concentrated[\"Accuracy\"]):\n",
    "    if num == max_accuracies:\n",
    "        max_c = i*0.1+0.1\n",
    "        print (\"Maximum Accuracy is:\", max_accuracies, \"for C =\", round(max_c, 1))\n",
    "        #This is only going to get the first one, but all after 6 give the same result\n",
    "        break\n",
    "\n",
    "plt.plot(ARPE_with_peaks_SVM_Cs_concentrated[[\"C_value\"]], ARPE_with_peaks_SVM_Cs_concentrated[[\"Accuracy\"]])\n",
    "plt.show()        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I am going to see how it performs after removing the ones that have very few 5hmC peaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6368671905191292\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0  616   57]\n",
      " [   0 4540  311]\n",
      " [   0 1835  404]]\n"
     ]
    }
   ],
   "source": [
    "# Now to see how training on the 1/5 and testing on the 80% performs\n",
    "\n",
    "model_svm = svm.SVC(kernel='rbf',C=0.7,gamma='auto')\n",
    "model_svm.fit(X_train, y_train.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(X_test)\n",
    "\n",
    "ARPE_test_train_accuracy = metrics.accuracy_score(y_true = y_test.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_test_train_accuracy)\n",
    "\n",
    "\n",
    "ARPE_SVM_confusion = metrics.confusion_matrix(y_true = y_test.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_SVM_confusion)\n",
    "#y_pred = model_numbers.predict(X_test)\n",
    "\n",
    "#print(metrics.confusion_matrix(y_true = y_test, y_pred = y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-fold cross validation Accuracy =  0.6426902686747097\n"
     ]
    }
   ],
   "source": [
    "model_numbers = svm.SVC(kernel='rbf', C = 0.7, gamma='scale')\n",
    "scores = cross_val_score(estimator = model_numbers, X = scaled_ARPE_X, y = ARPE_y.ravel(), cv=5, scoring='accuracy')    \n",
    "scores.mean()\n",
    "print('5-fold cross validation Accuracy = ', scores.mean())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, this has even lower accuracy and still doesn't get a single one of the downregulated genes correct. I am going to try making it even more stringent (require 5 peaks in gene body) and see what happens. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_keep_or_discard(gene, promoter):\n",
    "    if gene > 5:\n",
    "        return 1 #\"Keep\"\n",
    "    else:\n",
    "        if promoter > 1:\n",
    "            return 1 #\"Keep\"\n",
    "        else:\n",
    "            return 0 #\"Discard\"\n",
    "\n",
    "ARPE_cleaned_dataset[\"Try3_column\"] = ARPE_cleaned_dataset[\"Gene_Body_Counts\"].combine(\n",
    "    ARPE_cleaned_dataset[\"Promoter_Counts\"], func=get_keep_or_discard)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARPE_try3 = ARPE_cleaned_dataset[ARPE_cleaned_dataset[\"Try3_column\"]== 1][['Length', 'Exonic Length',\n",
    "       'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "       'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "       'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "       'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "       'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "       'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated',\n",
    "       'Numeric_Change', 'Numeric_Biotype', 'Numeric_Chr',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARPE_X3 = np.array(ARPE_try3[['Length', 'Exonic Length',\n",
    "                     'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "                     'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "                     'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "                     'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "                     'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "                     'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated','Numeric_Biotype', 'Numeric_Chr']])\n",
    "\n",
    "ARPE_y3 = np.array(ARPE_try3[[\"Numeric_Change\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2816\n",
      "2816\n",
      "2816\n",
      "2816\n"
     ]
    }
   ],
   "source": [
    "scaled_ARPE_X3 = scale(ARPE_X3)\n",
    "X_train3, X_test3, y_train3, y_test3 = train_test_split(scaled_ARPE_X3, ARPE_y3, random_state=1, test_size=0.5) \n",
    "print(len(X_train3))\n",
    "print(len(y_train3))\n",
    "print(len(X_test3))\n",
    "print(len(y_test3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 50%, train on 50% Accuracy =  0.5862926136363636\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0  189   31]\n",
      " [   0 1400  165]\n",
      " [   0  780  251]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.7,gamma='auto')\n",
    "model_svm.fit(X_train3, y_train3.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(X_test3)\n",
    "\n",
    "ARPE_test_train_accuracy = metrics.accuracy_score(y_true = y_test3.ravel(), y_pred = Y_pred)\n",
    "print('Test on 50%, train on 50% Accuracy = ', ARPE_test_train_accuracy)\n",
    "\n",
    "\n",
    "ARPE_SVM_confusion = metrics.confusion_matrix(y_true = y_test3.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_SVM_confusion)\n",
    "#y_pred = model_numbers.predict(X_test)\n",
    "\n",
    "#print(metrics.confusion_matrix(y_true = y_test, y_pred = y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-fold cross validation Accuracy =  0.5861197494005854\n"
     ]
    }
   ],
   "source": [
    "model_numbers = svm.SVC(kernel='rbf', C = 0.7, gamma='scale')\n",
    "scores = cross_val_score(estimator = model_numbers, X = scaled_ARPE_X3, y = ARPE_y3.ravel(), cv=5, scoring='accuracy')    \n",
    "scores.mean()\n",
    "print('5-fold cross validation Accuracy = ', scores.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, this was not very successful. I am not surprised that the accuracy wasn't that high, but the fact that I can't get it to predict ANYTHING as downregulated is a little intriguing. I am going to try to do this again with the Schwann Cell cAMP dataset because it has roughly 1/3 of the genes up, 1/3 down and 1/3 nondifferential. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>Change</th>\n",
       "      <th>Gene_Name</th>\n",
       "      <th>Gene_Biotype</th>\n",
       "      <th>Chr</th>\n",
       "      <th>Length</th>\n",
       "      <th>Exon Length</th>\n",
       "      <th>Gene_Body_Downregulated</th>\n",
       "      <th>Upstream_Promoter_Downregulated</th>\n",
       "      <th>Downstream_Promoter_Downregulated</th>\n",
       "      <th>TSS_Downregulated</th>\n",
       "      <th>Gene_Body_Nondifferential</th>\n",
       "      <th>Upstream_Promoter_Nondifferential</th>\n",
       "      <th>Downstream_Promoter_Nondifferential</th>\n",
       "      <th>TSS_Nondifferential</th>\n",
       "      <th>Gene_Body_Upregulated</th>\n",
       "      <th>Upstream_Promoter_Upregulated</th>\n",
       "      <th>Downstream_Promoter_Upregulated</th>\n",
       "      <th>TSS_Upregulated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>2</td>\n",
       "      <td>1421</td>\n",
       "      <td>1416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>Cbln1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>19</td>\n",
       "      <td>3810</td>\n",
       "      <td>2444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000017</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>Steap1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>4</td>\n",
       "      <td>10589</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000021</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>AABR07061902.1</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>4</td>\n",
       "      <td>1202</td>\n",
       "      <td>1200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000024</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>Hebp1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>4</td>\n",
       "      <td>29515</td>\n",
       "      <td>1062</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Change       Gene_Name    Gene_Biotype Chr  \\\n",
       "Gene_ID                                                                   \n",
       "ENSRNOG00000000001  Nondifferential  AABR07013255.1      pseudogene   2   \n",
       "ENSRNOG00000000010    Downregulated           Cbln1  protein_coding  19   \n",
       "ENSRNOG00000000017    Downregulated          Steap1  protein_coding   4   \n",
       "ENSRNOG00000000021  Nondifferential  AABR07061902.1      pseudogene   4   \n",
       "ENSRNOG00000000024    Downregulated           Hebp1  protein_coding   4   \n",
       "\n",
       "                    Length  Exon Length  Gene_Body_Downregulated  \\\n",
       "Gene_ID                                                            \n",
       "ENSRNOG00000000001    1421         1416                      0.0   \n",
       "ENSRNOG00000000010    3810         2444                      0.0   \n",
       "ENSRNOG00000000017   10589         1280                      0.0   \n",
       "ENSRNOG00000000021    1202         1200                      0.0   \n",
       "ENSRNOG00000000024   29515         1062                      2.0   \n",
       "\n",
       "                    Upstream_Promoter_Downregulated  \\\n",
       "Gene_ID                                               \n",
       "ENSRNOG00000000001                              0.0   \n",
       "ENSRNOG00000000010                              0.0   \n",
       "ENSRNOG00000000017                              1.0   \n",
       "ENSRNOG00000000021                              0.0   \n",
       "ENSRNOG00000000024                              0.0   \n",
       "\n",
       "                    Downstream_Promoter_Downregulated  TSS_Downregulated  \\\n",
       "Gene_ID                                                                    \n",
       "ENSRNOG00000000001                                0.0                0.0   \n",
       "ENSRNOG00000000010                                0.0                0.0   \n",
       "ENSRNOG00000000017                                0.0                0.0   \n",
       "ENSRNOG00000000021                                0.0                0.0   \n",
       "ENSRNOG00000000024                                0.0                0.0   \n",
       "\n",
       "                    Gene_Body_Nondifferential  \\\n",
       "Gene_ID                                         \n",
       "ENSRNOG00000000001                        0.0   \n",
       "ENSRNOG00000000010                        0.0   \n",
       "ENSRNOG00000000017                        0.0   \n",
       "ENSRNOG00000000021                        0.0   \n",
       "ENSRNOG00000000024                        4.0   \n",
       "\n",
       "                    Upstream_Promoter_Nondifferential  \\\n",
       "Gene_ID                                                 \n",
       "ENSRNOG00000000001                                0.0   \n",
       "ENSRNOG00000000010                                0.0   \n",
       "ENSRNOG00000000017                                0.0   \n",
       "ENSRNOG00000000021                                0.0   \n",
       "ENSRNOG00000000024                                0.0   \n",
       "\n",
       "                    Downstream_Promoter_Nondifferential  TSS_Nondifferential  \\\n",
       "Gene_ID                                                                        \n",
       "ENSRNOG00000000001                                  0.0                  0.0   \n",
       "ENSRNOG00000000010                                  0.0                  0.0   \n",
       "ENSRNOG00000000017                                  0.0                  0.0   \n",
       "ENSRNOG00000000021                                  0.0                  0.0   \n",
       "ENSRNOG00000000024                                  0.0                  0.0   \n",
       "\n",
       "                    Gene_Body_Upregulated  Upstream_Promoter_Upregulated  \\\n",
       "Gene_ID                                                                    \n",
       "ENSRNOG00000000001                    0.0                            0.0   \n",
       "ENSRNOG00000000010                    0.0                            0.0   \n",
       "ENSRNOG00000000017                    1.0                            0.0   \n",
       "ENSRNOG00000000021                    0.0                            0.0   \n",
       "ENSRNOG00000000024                    1.0                            0.0   \n",
       "\n",
       "                    Downstream_Promoter_Upregulated  TSS_Upregulated  \n",
       "Gene_ID                                                               \n",
       "ENSRNOG00000000001                              0.0              0.0  \n",
       "ENSRNOG00000000010                              0.0              0.0  \n",
       "ENSRNOG00000000017                              0.0              0.0  \n",
       "ENSRNOG00000000021                              0.0              0.0  \n",
       "ENSRNOG00000000024                              0.0              0.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_cAMP_cleaned_dataset = pd.read_pickle(cleaned_data_path+\"SC_cAMP_Combined_Cleaned_Data.pkl\")\n",
    "SC_cAMP_cleaned_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_change(change):\n",
    "    if change == \"Downregulated\":\n",
    "        return 0\n",
    "    elif change == \"Nondifferential\":\n",
    "        return 1\n",
    "    elif change == \"Upregulated\":\n",
    "        return 2\n",
    "    else:\n",
    "        return \"Remove this one from the dataset\"\n",
    "\n",
    "SC_cAMP_cleaned_dataset[\"Numeric_Change\"] = SC_cAMP_cleaned_dataset[\"Change\"].apply(get_change)\n",
    "\n",
    "def get_biotype(biotype):\n",
    "    if biotype == \"protein_coding\":\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "SC_cAMP_cleaned_dataset[\"Numeric_Biotype\"] = SC_cAMP_cleaned_dataset[\"Gene_Biotype\"].apply(get_biotype)\n",
    "\n",
    "def get_chromosome(Chr):\n",
    "    if Chr == \"X\": \n",
    "        return 0\n",
    "    elif Chr == \"Y\": \n",
    "        return 0\n",
    "    elif Chr == \"1\": \n",
    "        return 0\n",
    "    elif Chr == \"2\": \n",
    "        return 0\n",
    "    elif Chr == \"3\": \n",
    "        return 0\n",
    "    elif Chr == \"4\": \n",
    "        return 0\n",
    "    elif Chr == \"5\": \n",
    "        return 0\n",
    "    elif Chr == \"6\": \n",
    "        return 0\n",
    "    elif Chr == \"7\": \n",
    "        return 0\n",
    "    elif Chr == \"8\": \n",
    "        return 0\n",
    "    elif Chr == \"9\": \n",
    "        return 0\n",
    "    elif Chr == \"10\": \n",
    "        return 0\n",
    "    elif Chr == \"11\": \n",
    "        return 0\n",
    "    elif Chr == \"12\": \n",
    "        return 0\n",
    "    elif Chr == \"13\": \n",
    "        return 0\n",
    "    elif Chr == \"14\": \n",
    "        return 0\n",
    "    elif Chr == \"15\": \n",
    "        return 0\n",
    "    elif Chr == \"16\": \n",
    "        return 0\n",
    "    elif Chr == \"17\": \n",
    "        return 0\n",
    "    elif Chr == \"18\": \n",
    "        return 0\n",
    "    elif Chr == \"19\": \n",
    "        return 0\n",
    "    elif Chr == \"20\": \n",
    "        return 0\n",
    "    elif Chr == \"21\": \n",
    "        return 0\n",
    "    elif Chr == \"22\": \n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "SC_cAMP_cleaned_dataset[\"Numeric_Chr\"] = SC_cAMP_cleaned_dataset[\"Chr\"].apply(get_chromosome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Change</th>\n",
       "      <th>Gene_Name</th>\n",
       "      <th>Gene_Biotype</th>\n",
       "      <th>Chr</th>\n",
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       "      <th>Downstream_Promoter_Downregulated</th>\n",
       "      <th>TSS_Downregulated</th>\n",
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       "      <th>Upstream_Promoter_Nondifferential</th>\n",
       "      <th>Downstream_Promoter_Nondifferential</th>\n",
       "      <th>TSS_Nondifferential</th>\n",
       "      <th>Gene_Body_Upregulated</th>\n",
       "      <th>Upstream_Promoter_Upregulated</th>\n",
       "      <th>Downstream_Promoter_Upregulated</th>\n",
       "      <th>TSS_Upregulated</th>\n",
       "      <th>Numeric_Change</th>\n",
       "      <th>Numeric_Biotype</th>\n",
       "      <th>Numeric_Chr</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gene_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000001</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>AABR07013255.1</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>2</td>\n",
       "      <td>1421</td>\n",
       "      <td>1416</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000010</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>Cbln1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>19</td>\n",
       "      <td>3810</td>\n",
       "      <td>2444</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000017</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>Steap1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>4</td>\n",
       "      <td>10589</td>\n",
       "      <td>1280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000021</th>\n",
       "      <td>Nondifferential</td>\n",
       "      <td>AABR07061902.1</td>\n",
       "      <td>pseudogene</td>\n",
       "      <td>4</td>\n",
       "      <td>1202</td>\n",
       "      <td>1200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSRNOG00000000024</th>\n",
       "      <td>Downregulated</td>\n",
       "      <td>Hebp1</td>\n",
       "      <td>protein_coding</td>\n",
       "      <td>4</td>\n",
       "      <td>29515</td>\n",
       "      <td>1062</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Change       Gene_Name    Gene_Biotype Chr  \\\n",
       "Gene_ID                                                                   \n",
       "ENSRNOG00000000001  Nondifferential  AABR07013255.1      pseudogene   2   \n",
       "ENSRNOG00000000010    Downregulated           Cbln1  protein_coding  19   \n",
       "ENSRNOG00000000017    Downregulated          Steap1  protein_coding   4   \n",
       "ENSRNOG00000000021  Nondifferential  AABR07061902.1      pseudogene   4   \n",
       "ENSRNOG00000000024    Downregulated           Hebp1  protein_coding   4   \n",
       "\n",
       "                    Length  Exon Length  Gene_Body_Downregulated  \\\n",
       "Gene_ID                                                            \n",
       "ENSRNOG00000000001    1421         1416                      0.0   \n",
       "ENSRNOG00000000010    3810         2444                      0.0   \n",
       "ENSRNOG00000000017   10589         1280                      0.0   \n",
       "ENSRNOG00000000021    1202         1200                      0.0   \n",
       "ENSRNOG00000000024   29515         1062                      2.0   \n",
       "\n",
       "                    Upstream_Promoter_Downregulated  \\\n",
       "Gene_ID                                               \n",
       "ENSRNOG00000000001                              0.0   \n",
       "ENSRNOG00000000010                              0.0   \n",
       "ENSRNOG00000000017                              1.0   \n",
       "ENSRNOG00000000021                              0.0   \n",
       "ENSRNOG00000000024                              0.0   \n",
       "\n",
       "                    Downstream_Promoter_Downregulated  TSS_Downregulated  \\\n",
       "Gene_ID                                                                    \n",
       "ENSRNOG00000000001                                0.0                0.0   \n",
       "ENSRNOG00000000010                                0.0                0.0   \n",
       "ENSRNOG00000000017                                0.0                0.0   \n",
       "ENSRNOG00000000021                                0.0                0.0   \n",
       "ENSRNOG00000000024                                0.0                0.0   \n",
       "\n",
       "                       ...       Upstream_Promoter_Nondifferential  \\\n",
       "Gene_ID                ...                                           \n",
       "ENSRNOG00000000001     ...                                     0.0   \n",
       "ENSRNOG00000000010     ...                                     0.0   \n",
       "ENSRNOG00000000017     ...                                     0.0   \n",
       "ENSRNOG00000000021     ...                                     0.0   \n",
       "ENSRNOG00000000024     ...                                     0.0   \n",
       "\n",
       "                    Downstream_Promoter_Nondifferential  TSS_Nondifferential  \\\n",
       "Gene_ID                                                                        \n",
       "ENSRNOG00000000001                                  0.0                  0.0   \n",
       "ENSRNOG00000000010                                  0.0                  0.0   \n",
       "ENSRNOG00000000017                                  0.0                  0.0   \n",
       "ENSRNOG00000000021                                  0.0                  0.0   \n",
       "ENSRNOG00000000024                                  0.0                  0.0   \n",
       "\n",
       "                    Gene_Body_Upregulated  Upstream_Promoter_Upregulated  \\\n",
       "Gene_ID                                                                    \n",
       "ENSRNOG00000000001                    0.0                            0.0   \n",
       "ENSRNOG00000000010                    0.0                            0.0   \n",
       "ENSRNOG00000000017                    1.0                            0.0   \n",
       "ENSRNOG00000000021                    0.0                            0.0   \n",
       "ENSRNOG00000000024                    1.0                            0.0   \n",
       "\n",
       "                    Downstream_Promoter_Upregulated  TSS_Upregulated  \\\n",
       "Gene_ID                                                                \n",
       "ENSRNOG00000000001                              0.0              0.0   \n",
       "ENSRNOG00000000010                              0.0              0.0   \n",
       "ENSRNOG00000000017                              0.0              0.0   \n",
       "ENSRNOG00000000021                              0.0              0.0   \n",
       "ENSRNOG00000000024                              0.0              0.0   \n",
       "\n",
       "                    Numeric_Change  Numeric_Biotype  Numeric_Chr  \n",
       "Gene_ID                                                           \n",
       "ENSRNOG00000000001               1                0            0  \n",
       "ENSRNOG00000000010               0                1            0  \n",
       "ENSRNOG00000000017               0                1            0  \n",
       "ENSRNOG00000000021               1                0            0  \n",
       "ENSRNOG00000000024               0                1            0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_cAMP_cleaned_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_cAMP_X = np.array(SC_cAMP_cleaned_dataset[['Length', 'Exon Length',\n",
    "                     'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "                     'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "                     'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "                     'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "                     'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "                     'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated','Numeric_Biotype', 'Numeric_Chr']])\n",
    "\n",
    "SC_cAMP_y = np.array(SC_cAMP_cleaned_dataset[[\"Numeric_Change\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_cAMP_X = scale(SC_cAMP_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(scaled_cAMP_X, SC_cAMP_y, random_state=1, test_size=0.8) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2433\n",
      "2433\n",
      "9732\n",
      "9732\n"
     ]
    }
   ],
   "source": [
    "print(len(X_train))\n",
    "print(len(y_train))\n",
    "print(len(X_test))\n",
    "print(len(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##It turns out that this gives annoying warnings if your y value is a 2-D array. The .ravel() function makes it a 1-D array.\n",
    "\n",
    "\n",
    "Cs = np.arange(1,502,5)\n",
    "Accuracies = np.zeros(Cs.shape[0])\n",
    "for i,C in enumerate(Cs): \n",
    "    model_numbers = svm.SVC(kernel='rbf', C = C,gamma='scale')\n",
    "    scores = cross_val_score(estimator = model_numbers, X = X_train, y = y_train.ravel(), cv=5, scoring='accuracy')    \n",
    "    Accuracies[i]  = scores.mean()\n",
    "        \n",
    "plt.plot(Cs,Accuracies)\n",
    "plt.show()\n",
    "SC_cAMP_SVM_Cs = pd.DataFrame({\"C_value\": Cs, \"Accuracy\": Accuracies}) \n",
    "SC_cAMP_SVM_Cs.to_csv(output_path+\"SC_cAMP_SVM_C_values.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>C_value</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.448836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>0.437321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>0.437326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>0.440200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>0.438970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C_value  Accuracy\n",
       "0        1  0.448836\n",
       "1        6  0.437321\n",
       "2       11  0.437326\n",
       "3       16  0.440200\n",
       "4       21  0.438970"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_cAMP_SVM_Cs = pd.read_csv(output_path+\"SC_cAMP_SVM_C_values.csv\", index_col = 0)\n",
    "SC_cAMP_SVM_Cs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(SC_cAMP_SVM_Cs[[\"C_value\"]], SC_cAMP_SVM_Cs[[\"Accuracy\"]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like a low value of C is still the best for the cAMP dataset too. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # It looks like the low numbers are the best ones. I am going to try this again with numbers between 0 and 5.\n",
    "\n",
    "# Cs = np.arange(0.1,5,0.1)\n",
    "# Accuracies = np.zeros(Cs.shape[0])\n",
    "# for i,C in enumerate(Cs): \n",
    "#     model_numbers = svm.SVC(kernel='rbf', C = C,gamma='scale')\n",
    "#     scores = cross_val_score(estimator = model_numbers, X = X_train, y = y_train.ravel(), cv=5, scoring='accuracy')    \n",
    "#     Accuracies[i]  = scores.mean()\n",
    "        \n",
    "# plt.plot(Cs,Accuracies)\n",
    "# plt.show()\n",
    "# ARPE_SVM_Cs = pd.DataFrame({\"C_value\": Cs, \"Accuracy\": Accuracies}) \n",
    "# ARPE_SVM_Cs.to_csv(output_path+\"SC_cAMP_SVM_C_values_Concentrated.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Accuracy is: 0.4508983913964855 for C = 0.4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SC_cAMP_SVM_Cs_concentrated = pd.read_csv(output_path+\"SC_cAMP_SVM_C_values_Concentrated.csv\", index_col = 0)\n",
    "#ARPE_SVM_Cs_concentrated.head()\n",
    "max_accuracies = max(SC_cAMP_SVM_Cs_concentrated[\"Accuracy\"])\n",
    "for i,num in enumerate( SC_cAMP_SVM_Cs_concentrated[\"Accuracy\"]):\n",
    "    if num == max_accuracies:\n",
    "        max_c = i*0.1+0.1\n",
    "        print (\"Maximum Accuracy is:\", max_accuracies, \"for C =\", round(max_c, 1))\n",
    "        #This is only going to get the first one, but all after 6 give the same result\n",
    "        break\n",
    "\n",
    "plt.plot(SC_cAMP_SVM_Cs_concentrated[[\"C_value\"]], SC_cAMP_SVM_Cs_concentrated[[\"Accuracy\"]])\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.44040279490341144\n",
      "\n",
      "Confusion Matrix:\n",
      "[[1378  333 1387]\n",
      " [ 762  505 1879]\n",
      " [ 783  302 2403]]\n"
     ]
    }
   ],
   "source": [
    "# Now to see how training on the 1/5 and testing on the 80% performs\n",
    "\n",
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(X_train, y_train.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(X_test)\n",
    "\n",
    "SC_cAMP_test_train_accuracy = metrics.accuracy_score(y_true = y_test.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_test_train_accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_SVM_confusion = metrics.confusion_matrix(y_true = y_test.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_SVM_confusion)\n",
    "#y_pred = model_numbers.predict(X_test)\n",
    "\n",
    "#print(metrics.confusion_matrix(y_true = y_test, y_pred = y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-fold cross validation Accuracy =  0.4446420559390667\n"
     ]
    }
   ],
   "source": [
    "model_numbers = svm.SVC(kernel='rbf', C = 0.4, gamma='scale')\n",
    "scores = cross_val_score(estimator = model_numbers, X = scaled_cAMP_X, y = SC_cAMP_y.ravel(), cv=5, scoring='accuracy')    \n",
    "scores.mean()\n",
    "print('5-fold cross validation Accuracy = ', scores.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4446420559390667, 0.44040279490341144)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_cAMP_SVM_accuracy_scores = ( scores.mean(), SC_cAMP_test_train_accuracy)\n",
    "SC_cAMP_SVM_accuracy_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time the accuracy is lower, but the naive baysean model would predict lower too. The theoretical 33.43% that would be achieved by chance is increased to 44% by the SVM. I am pleased that at least it gets many correct from each category, but the accuracy is still really quite low."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am moving on to the K-Nearest-Neighbors for this dataset. The first step will be to determine a good value for K to use. I have copied code and left the variable as Cs, but it is really Ks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #First, finding a good value of N to use\n",
    "\n",
    "# Cs = range(1,201,2)\n",
    "# Accuracies = np.zeros(100)\n",
    "# for i in Cs: \n",
    "#     model_numbers = KNeighborsClassifier(n_neighbors=i)\n",
    "#     scores = cross_val_score(estimator = model_numbers, X = scaled_cAMP_X, y = SC_cAMP_y.ravel(), cv=5, scoring='accuracy')    \n",
    "#     Accuracies[int((i-1)/2)]  = scores.mean()\n",
    "        \n",
    "# SC_cAMP_KNN_Ns = pd.DataFrame({\"C_value\": Cs, \"Accuracy\": Accuracies}) \n",
    "# SC_cAMP_KNN_Ns.to_csv(output_path+\"SC_cAMP_KNN_Ns.csv\")\n",
    "\n",
    "# plt.plot(Cs,Accuracies)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Accuracy is: 0.4400389889587145 for C = 81\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SC_cAMP_KNN_Ns = pd.read_csv(output_path+\"SC_cAMP_KNN_Ns.csv\", index_col = 0)\n",
    "\n",
    "max_accuracies = max(SC_cAMP_KNN_Ns[\"Accuracy\"])\n",
    "for i,num in enumerate( SC_cAMP_KNN_Ns[\"Accuracy\"]):\n",
    "    if num == max_accuracies:\n",
    "        max_c = i*2+1\n",
    "        print (\"Maximum Accuracy is:\", max_accuracies, \"for C =\", round(max_c, 1))\n",
    "        #This is only going to get the first one, but all after 6 give the same result\n",
    "        break\n",
    "\n",
    "plt.plot(SC_cAMP_KNN_Ns[[\"C_value\"]], SC_cAMP_KNN_Ns[[\"Accuracy\"]])\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5-fold cross validation Accuracy =  0.4446420559390667\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "scores = cross_val_score(estimator = model_numbers, X = scaled_cAMP_X, y = SC_cAMP_y.ravel(), cv=5, scoring='accuracy')    \n",
    "scores.mean()\n",
    "print('5-fold cross validation Accuracy = ', scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.43680641183723795\n",
      "\n",
      "Confusion Matrix:\n",
      "[[1347  589 1162]\n",
      " [ 763  866 1517]\n",
      " [ 804  646 2038]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(X_train, y_train.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(X_test)\n",
    "\n",
    "SC_cAMP_KNN_accuracy = metrics.accuracy_score(y_true = y_test.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_KNN_accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_KNN_confusion = metrics.confusion_matrix(y_true = y_test.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_KNN_confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4446420559390667, 0.43680641183723795)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SC_cAMP_KNN_accuracy_scores = ( scores.mean(), SC_cAMP_KNN_accuracy)\n",
    "SC_cAMP_KNN_accuracy_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I got almost the exact same accuracy for the KNN as I got for SVM. Although it isn't extremely accurate, it is better than drawing decisions out of a bag. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I am going to try this with the decision tree and see how this works out. The first step is going to be to determine the max depth and the min_samples_split values that give the most accurate results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/xh3HGFMDzvtEXfjji4uLq25VQ0k6BCyTT5n96lWEF58GHbsQjLk243sU8ilvtixz3Uha5iTlTQ0ZVfliE7tT2dQa17QZ7uCj4cN58N6bcccxxlSTFQRTq9weA6F9R8InHsCHYdxxjDHVYAXB1CpX2Ah36LGw5BP8nFfijmOMqQYrCKbWuf57QNce+Kcexpf+L+44xpgsWUEwtc4FAcERw+HrL/CvvBB3HGNMlqwgmNzovQNs1Rv/9FT86lVxpzHGZMEKgskJ51x0lLByOf6lGXHHMcZkwQqCyRm3xTbQdxf8zCfwK1fEHccYUwUrCCangsOPgzVr8M9PizuKMaYKVhBMTrnO3XG77Y3/+7P4b76OO44xZj2sIJicc4cMAxz+6UfijmKMWQ8rCCbnXLv2uL0Pwv/r75Qu/iTuOMaYSlhBMHXCHXQUNG3K9w/fGXcUY0wlrCCYOuFatsYNPJw1s1+2x2Mbk6esIJg64/YbTNCmLeET95Prx6771avwny+uuqExppwVBFNnXNNmtDjqxJw/Htt/WUx4zbmEl/0OP++tnG3HmPrGCoKpU832H5zTx2P7+e8QXjsGvl8Jm3QmnDwB/8XSWt+OMfVRNu9URkQGAbcABcAUVR1fSbshwDSgf+qdygOAyanFDrhcVadXp09Tv7hG0eOx/ZSb8HNewe38m1rrO/z7c/ipk6FjV4LRl4BzhNecR3jb1QTjbsC1aFlr2zKmPqryCEFECoBJwIFAL2CoiPTK0K4VcBYwO232e8BOqtoXGATcKSKF2fZp6qfafjy2Ly0lfPh2/CN3QO8dCcZOwLXviCvahGDUWCj5knDyDfh162ohvTH1VzZDRgOABaq6UFXXAlOBQzO0uwqYAKwum6GqP6pqaWqyKVB2JjHbPk09VJuPx/Y/rCT84xX4Wc/jDjiC4MyLcM2a/7StrXrjjj0d3n8L/9i9GxrdmHotm4LQBUi/XGNJal45EekHdFPVZyquLCI7i8g84F3g9FSBqLJPU8/VwuOx/edLovMFH83DnXg2wZARuKDgF+2CPQbi9j0E/9IMQns/gzGVyuYcgsswr/yaQREJgInAiEwrq+psYDsR2Ra4X0Ser6rPdCIyEhiZ6ouioqIsItdMYWFhTvvPhaRlTs+79qSz+G7sSJr9+yVaHnVitfpZ8/Zslt9wKUGjRmx01SQab7P9etv7UeezrOQL1j5yB6237kXjXn1rlDkpLHPuJS1vNrIpCEuAbmnTXYHitOlWQG9glogAdARmiMhgVZ1b1khV54vID6m2VfVJ2nqT+enEtC8pKckics0UFRWRy/5zIWmZf5a3XUfouws/PPEQq3b6Da5V6yrX997j//YM/tG7oUt33OhLWNGuA2SxD/yJ/wfXnc93140luPgmXNEm1c+cEJY595KUt3Pnzlm1y6YgzAF6ikgPYClwDDCsbKGqLgfKy6SIzALGpK4y6gEsVtVSEdkU2BpYBCxbX5+m4QgOP47w8tfxz0/DycnrbetL/4d/5M7ovEPfXQhO/j9c02ZZb8u1aEkw+mLCa8+PrjwaO6Fa6xtT31V5DiE15j8amAnMj2bpPBG5UkQGV7H67sA7IvI2MB04Q1VLKutzQz6ISaZsH4/tv19BOPEy/Csv4A4cQjBqbI1+mLuOXQlOuwCKFxPefXNO7oUwJqlcrh8hUMt8cXHGkaVakaRDwDJJy5wpr//ma8JLTsftvCfBiLN/sY4v/ozwtqvhu29wJ/yOYJe9NjhH+NIM/KNTcAdJ9BKfambOd5Y595KUNzVklOnc7c/YncomdumPx/bFn/1smX93LuF158PaNQTnX1srxQDA7XsIbvf98c8p4ex/1EqfxiSdFQSTF9yB0eOxw+kPAdHJ4/CF6YS3XgUdOhFcdCNu861rb3vORfcn9OyFv/9W/Ccf1VrfxiSVFQSTF1yr6PHYvP0a/oP38Pf/ET/tXui3K8EF43Ft29f+NgsbEYwaB63bEP7pGvyyb2p9G8YkiRUEkzfcfoOjH84TL8W/+lfcwUcTnHYBrknT3G2z1UYEoy+GVT8STroWv3ZNzrZlTL6zgmDyhmvaDHf48VDYGDfyfIJDj8UFuf8WdV17EJx8Liz6CH//bTl/V4Mx+Sqrp50aU1eC3ffH77ZPxkdQ5JLrtwvusOPwTz4U3fB20FF1un1j8oEVBJN36roYlG/3oKOg+DP8kw/hO3fD9d0llhzGxMWGjIxJcc7hTvgddN+CcMrN+CWL4o5kTJ2ygmBMGte4CcGZF0PT5oS3XY1fuTzuSMbUGSsIxlTgNm5HcOZFsGIZ4R3j8f/b8Jf4GJMEVhCMycD12CoaPvpwHivvuinuOMbUCTupbEwlgp1/Q7j0U1Y9/xiuU3eCPQbGHcmYnLIjBGPWwx12LI379I8eu73IHm9h6jcrCMashwsK2OjcK6I7qO+4Hv/9irgjGZMzVhCMqULQug3B6WNh+beEU27Ch+vijmRMTlhBMCYLrkdP3NCRMO8t/NNT445jTE5YQTAmS26PA3C/3hf/zKP4d+bEHceYWpfVVUYiMgi4BSgApqjq+EraDQGmAf1T71TeHxgPNAbWAuer6t9SbWcBnYBVqdUHqupXG/BZjMkp5xwMOx2/+BPCe24muPhmXIdOcccyptZUeYQgIgXAJOBAoBcwVER6ZWjXCjgLmJ02uwQ4RFW3B04AHqyw2rGq2jf1ZcXA5D3XuEl0PgFHePt4/Bp7XLapP7IZMhoALFDVhaq6FpgKHJqh3VXABGB12QxVfUtVy16CPA9oKiJNNjCzMbFy7TsSnHIeLF2Ef/hP9rhsU29kM2TUBVicNr0E2Dm9gYj0A7qp6jMiMqaSfo4E3lLV9F+p7hWRdcDjwNWq+ov/WSIyEhgJoKoUFRVlEblmCgsLc9p/LiQtc9LyQiWZ9z6A779czA+P3kPLX+1E80GHxxOuEvVmP+expOXNRjYFwWWYV/6DW0QCYCIworIORGQ74Hog/VbPY1V1aWqo6XHgeOCBiuuq6mRgctl2S0pKsohcM0VFReSy/1xIWuak5YXKM/t9BsO8d1g5ZSI/tO1Qq+983lD1aT/nqyTl7dy5c1btshkyWgJ0S5vuChSnTbcCegOzRGQRsAswQ0R2AhCRrsB0YLiqfly2kqouTf25EniEaGjKmMRwQUBwyrnQpm1005o9GdUkXDZHCHOAniLSA1gKHAMMK1uoqsuB8uOm1NVDY1JXGbUBngXGqeqraW0KgTaqWiIijYCDgZdq4fMYU6dci1YEo8YRjr+A8K4bCc65PLYX/Bizoao8QlDVUmA0MBOYH83SeSJypYgMrmL10cCWwKUi8nbqqwPQBJgpIv8B3iYqNHdtyAcxJi5u0y1wx42C+e/gn3w47jjG1JhL2BUSvri4uOpWNZSkMcEyScuctLyQfebwwUn4l2cSnHlR7K/frM/7OV8kKW/qHEKm88E/Y3cqG1NL3DGnwqZbEt7zB/yXufvFxZhcsYJgTC1xjRoTjBoLBQWEt1+HX7O66pWMySNWEIypRa5dB4JTxkDxZ/gHJtlNayZRrCAYU8vcdv1whx6Lf/0f+L8/G3ccY7JmBcGYHHAHDoE+A/B6N37B/LjjGJMVKwjG5IALAoKTzoG27QnvvB6/4ru4IxlTJSsIxuSIa96SYNQ4+PF7wjtvwK+zN62Z/GYFwZgcct164I4/Ez58D3/fLfjvvok7kjGVyuoFOcaYmgt22Zvw8yX45x/Hz3kF139P3MDDcN16xB3NmJ+xgmBMHQgOPx6/+/74vz6N/+eL+Nf+Dtv2IRh4GGy3Q/Q2NmNiZgXBmDri2nfEHXMq/pCh+Jf/gv/rM4S3XAFdNsXtfyhuwG9wjRrFHdM0YHYOwZg65lq0JDhwCMH4u3Anng2Av++PhONOIXxW8T+sjDmhaajsCMGYmLjCRrjd9sXvug+8/zbhC0/in3wI/9w03K/3w+03GNehU9wxTQNiBcGYmDnnYLt+FGzXD7/kE/wLT+Ffnomf9Rz024Vg4OG4LbaJO6ZpAKwgGJNHXNceuJPOwR9xPP5vz+D/8RfCN/8NW2wTnYDuu7O9gMfkjBUEY/KQa9MOd8QJ+IME/+pf8S89RXj7eGjfEXf48QT994g7oqmHrCAYk8dc02a4fQ/G730gvPUa4XOP4SffQPjZQtzhx+MCuy7E1J6sCoKIDAJuAQqAKao6vpJ2Q4BpQP/UO5X3B8YDjYG1wPmq+rdU2x2B+4BmwHPA2apqzwo2JgMXFMCOvyboszN+6mT8Xx7Hf76Y4JRzcU2bxx3P1BNV/nohIgXAJOBAoBcwVER6ZWjXCjgLmJ02uwQ4RFW3B04AHkxbdjswEuiZ+hpUw89gTIPhCgtxx47CDR0J784lHH8hvuTLuGOZeiKbI4QBwAJVXQggIlOBQ4H3K7S7CpgAjCmboapvpS2fBzQVkSZAW6C1qv471ecDwGHA8zX8HMY0GM453D4H4zt2IbxjAuG1YwhGjcP1/MXvacZUSzYFoQuwOG16CbBzegMR6Qd0U9VnRGQMmR0JvKWqa0SkS6qf9D67ZFpJREYSHUmgqhQVFWURuWYKCwtz2n8uJC1z0vJCHmfec39Kt9iKZddcwLqbL6H1qAtpts9vgTzOvB5Jy5y0vNnIpiBkeshK+Vi/iATARGBEZR2IyHbA9cDAbPpMp6qTgcllbUpKSqpOXENFRUXksv9cSFrmpOWFPM/cpAX+wuvhzutZces1rPxgHu7IE2jfYZP8zVyJvN7PGSQpb+fOnbNql80lCkuAbmnTXYHitOlWQG9glogsAnYBZojITgAi0hWYDgxX1Y/T+uy6nj6NMVlyLVoSnHUZbu+D8C88SXjbNYQ//hB3LJNA2RwhzAF6ikgPYClwDDCsbKGqLgfKj5tEZBYwJnWVURvgWWCcqr6ats7nIrJSRHYhOgk9HLi1Fj6PMQ2SKyzEDTudsHN3/J8n8+3YkfhR43DtO8YdzSRIlUcIqloKjAZmAvOjWTpPRK4UkcFVrD4a2BK4VETeTn11SC0bBUwBFgAfYyeUjdlgwV4HEZxzBeF3JYTXnof/4L24I5kEcd4n6tJ/X1ycu5GlJI0Jlkla5qTlhWRmbvO/1Xxz5bnw9ee4YacT7HlA3JGqlLT9nKS8qXMIVb50w+5UNqYeKuzUlWDcBMLJN+AfnERY/BnuqJNwBRv2HCRfWgqLF+I/mof/6H345ENo0w63zfa4bfpAz164Jk1r6VOYumYFwZh6yjVvSfC73+On3RO9qe2LJQQjz8c1b5l1H371Klj4AX7B+1EBWPgBrF0TLWzfEbdtH/w3X+Ffeho/czoUFMIWW+O26YPb9lew2Va4QvsxkxT2L2VMPeYKCnDHnBqdbH7kDsLrzicYfSluk8yXIfqVy+Gj938qAJ99DGEIzkHXzXC77x/dALdlL1ybtj+tt2Z1tN5/38HP/w/+6T/jZzwCTZpCz+1w2/4qOoLoupk9fymPWUEwpgEI9jwAv0kXwjuui+5sPv1C2OZXUPJl9IO/rAB8kbpftLAR9OiJG3RkVAA23wbXvEWl/bsmTaH3DrjeOwDgv18BH7yH/+9/oiIx7Y3oRqOWrWDr7VNHEH2gQyd7n3QesYJgTAPhtu5NcNFNhLdeRfiHy6D1xrDsm2hh8xawxba43faJCsCmPTfo/c6uZWvYcTfcjrsB4L8twf/3P1B2BPHGv6IC0bYoOnLYtg9+0GEb/iHNBrGCYEwD4tp3JBh3A37aPbB6VXQSuGcv6LxpTodyXNsi3G77wG774L2HL4t/Gl5653X4119ZufQTOPLEnGUwVbOCYEwD45o1xw0fHd/2nYOOXXAdu8BeB+HDEP/oFFb9ZTrB9gNwW20XW7aGzs7uGGNi5YIAd8RwCjbpTHj/rfiyq5hMnbOCYIyJnWvSlFajLoSvivHPTI07ToNlBcEYkxea9OmP+/V++JnT8Z9+XPUKptZZQTDG5A131EnQaiPC+/8Y3RVt6pQVBGNM3nAtWhIMOx0Wf4J/YXrccRocKwjGmLzidtgVdtwN//RU/OdLql7B1BorCMaYvBMMPQ0aNyF84FZ8GMYdp8GwgmCMyTtuo41xR58MC+bj/2GvSqkrVhCMMXlRCMqeAAANf0lEQVTJ7boP9OqHf/wB/DdfxR2nQbCCYIzJS845guPPADzhQ38iYS/zSqSsHl0hIoOAW4ACYIqqjq+k3RBgGtA/9U7ldsBjQH/gPlUdndZ2FtAJWJWaNVBV7dcAY0w5V7QJ7vDh+KmT8bNn4XbZO+5I9VqVBUFECoBJwP7AEmCOiMxQ1fcrtGsFnAXMTpu9GrgU6J36quhYVZ1bw+zGmAbA7X0gfs7L+KlT8L364Vq3iTtSvZXNkNEAYIGqLlTVtcBU4NAM7a4CJhAVAQBU9QdV/Wf6PGOMqQ4XFBAMHw1rVuGn3hV3nHotmyGjLsDitOklwM7pDUSkH9BNVZ8RkTHV2P69IrIOeBy4WlV/MUgoIiOBkQCqSlFRUTW6r57CwsKc9p8LScuctLxgmevKejMXFfG9nMgPj9xFy31/S9Od96zbcBkkcR9XJZuCkOl1RuU/uEUkACYCI6q57WNVdWlqqOlx4HjggYqNVHUyMLlsuyUlJdXcTPaKiorIZf+5kLTMScsLlrmuVJXZ7zEIXn6R5XdMYGWn7tV6N3QuJGkfd+6c+ZWpFWUzZLQE6JY23RUoTptuRXR+YJaILAJ2AWaIyE7r61RVl6b+XAk8QjQ0ZYwxGbnCRgQn/A6WL8M/dl/cceqlbI4Q5gA9RaQHsBQ4BhhWtlBVlwPlx02pq4fGrO9ksYgUAm1UtUREGgEHAy/V6BMYYxoMt1lP3MBDoyei9t8jei+zqTVVHiGoaikwGpgJzI9m6TwRuVJEBle1fuqo4WZghIgsEZFeQBNgpoj8B3ibqNDY2SJjTJXcIcOgQyfCByfh19jLdGqTS9jNHr64uLjqVjWUpDHBMknLnLS8YJnrSnUy+w/eJbzxYtzAwwiOOinHyTJL0j5OnUPIdD74Z+xOZWNM4ritt8ftOQj/4gz8Jx/GHafesIJgjEkkd+QJsNHG0XuYS/8Xd5x6wQqCMSaRXPMWBMeNgqWf4v/yeNxx6gUrCMaYxHJ9BuD674F/RvHFn8UdJ/GsIBhjEs0dcyo0axYNHYXr4o6TaFYQjDGJ5lq3wR19Kiz8AP+3Z+OOk2hZPf7aGGPymdv5N/jXX8ZPf5Bw9Src5lvBZj1jf7xF0lhBMMYknnOO4LhRhJOuxT/18E8PW+vYBddjK+ixVfRn181whY3ijJrXrCAYY+oF17Y9BZdOxP/4Ayz6CP/Jh9HXe2/Cv/8eFYnCRtB985+KxOZbQ9EmOFflPVsNghUEY0y94pq3gF59cb36AkSv3vz2a/zCD+GTD/CffIR/ZSb89emoSLRsXX4E4Xr0jP7eolWsnyEuVhCMMfWacw7adcC16wD9dwfAl5ZC8adRkVj0IX7hh/j33vjpvc3tO0L7Trh27aFt9OXadYC2RbBxu3o77GQFwRjT4LjCQui+Ba77FsCBANFQ06cLomGmzz6Gkq/wixfCyuXR8vKVHWzUlm836UTYeuOoYLTrgGvbHlIFxDVvEcvn2lBWEIwxhtRQ07Z9fvFIbb92DXxbEg07ffs1fPM1fPs1buUy/KKP4K1/Q2kpP3tMaLMW0LYI17k77qiTcBu3q9PPUlNWEIwxZj1c4ybQsUt0xVLa/I1TTzv1YQgrlsG3UaHwqYLhv/0a/5+5+A/nEZx5cXR+Is9ZQTDGmA3gggDatI2+Nt/6Z0XDL/2U8NarCG8YhxtxFsGA+N8FvT52p7IxxuSI67IpwcU3wWZb4u+6kfDJh6IjijyV1RGCiAwCbgEKgCmqOr6SdkOAaUB/VZ0rIu2Ax4D+wH2qOjqt7Y7AfUAz4DngbFVN1Nt6jDGmKq7VRgTnXoV/+A78s4r/fAnBSefgmjSNO9ovVHmEICIFwCSiU/G9gKGp12BWbNcKOAuYnTZ7NXApMCZD17cDI4Geqa9B1Q1vjDFJ4Aob4YaPxsnJ8NZrhBPGRieo80w2Q0YDgAWqulBV1wJTgUMztLsKmEBUBABQ1R9U9Z/p8wBEpBPQWlX/nToqeAA4rIafwRhj8p5zjmD/Qwl+dyl8/QXhNefhF34Qd6yfyaYgdAEWp00vSc0rJyL9gG6q+kyW2+2S6qfSPo0xpj5y2+9IMHYCNGlKeMNFhK/9Pe5I5bI5h5DpIR/lY/0iEgATgRHV2O56+0wnIiOJhpZQVYqKiqqxmeopLCzMaf+5kLTMScsLlrmuJC3zBuUtKiK88V6W3XAx/7t7Ik2+K6HlsadFVyzFKJuCsAToljbdFShOm24F9AZmiQhAR2CGiAxW1bnr6bPrevosp6qTgcmpSV9SUpJF5JopSl1XnCRJy5y0vGCZ60rSMtdGXn/mxbg/T+bHJx7kx4UfEpx8Lq5ps1pK+JPOnTtn1S6bgjAH6CkiPYClwDHAsLKFqrocKC+TIjILGLOeYoCqfi4iK0VkF6KT0MOBW7NKbIwx9YQrbATHnQGdN8U/OoXw+gsJRl8SPTcpBlUen6hqKTAamAnMj2bpPBG5UkQGV7W+iCwCbgZGiMiStCuURgFTgAXAx8DzNfsIxhiTXM45gn0PJjj7Mvjm6+hk84L348lS/nS/ZPDFxRlHlmpF0g5ZIXmZk5YXLHNdSVrmXOT1XywhvPVq+PYr3PFnEuy2b630mxoyqvKlD3ansjHG5AnXsSvBRTdAz+3w995C+Ni9+HBdnW3fCoIxxuQR16IVwVmX4fY6CD9zevRa0FU/1sm2rSAYY0yecYWFBMeejht2Orz3BuH4C/DLvs35du1pp8YYk6eCvQ/Cd+xC+LdnoWXuX+tpBcEYY/KY27YPBRVe2pMrNmRkjDEGsIJgjDEmxQqCMcYYwAqCMcaYFCsIxhhjACsIxhhjUqwgGGOMAawgGGOMSUnc007jDmCMMQlV75526nL5JSJv5HobDT1z0vJaZstcX/KShaQVBGOMMTliBcEYYwxgBaGiyXEHqIGkZU5aXrDMdSVpmZOWt0pJO6lsjDEmR+wIwRhjDNAA34cgIt2AB4COQAhMVtVbKrTZC3gK+CQ16wlVvbIuc1YkIouAlcA6oFRVd6qw3AG3AAcBPwIjVPXNus6ZyrI18GjarM2B36vqH9La7EXM+1hE7gEOBr5S1d6peW2Jsm8GLAJEVb/LsO4JwCWpyatV9f4YM98AHAKsBT4GTlTVZRnWXcR6vofqOPPlwKnA16lmF6nqcxnWHUT0fV0ATFHV8THlfRTYOtWkDbBMVftmWHcRMezj2tLgCgJQCpynqm+KSCvgDRF5UVXfr9DuFVU9OIZ867O3qpZUsuxAoGfqa2fg9tSfdU5VPwD6AohIAbAUmJ6hadz7+D7gNqJfEMqMBf6qquNFZGxq+sL0lVJF4zJgJ6J7Y94QkRmZCkcdZX4RGKeqpSJyPTCuYuY06/seypX7+GVmgImqemNlK6W+dyYB+wNLgDmp/Vzx/2ptu48KeVX16LRcNwHL17N+HPu4VjS4ISNV/bzsN2dVXQnMB7rEm6pWHAo8oKpeVV8D2ohIp7hDAfsCH6vqp3EHqUhVXwYqvqj2UKDst/37gcMyrHoA8KKqfpsqAi8Cg3IWNE2mzKr6gqqWpiZfA7rWRZZsVbKfszEAWKCqC1V1LTCV6N8np9aXN3UkLsCfc50jDg3xCKGciGwG9ANmZ1i8q4i8AxQDY1R1Xl1my8ADL4iIB+5U1YpXOHQBFqdNL0nN+7yO8lXmGCr/z5Nv+xhgE1X9HKJfHkSkQ4Y2le3rfHASPx+uS1fV91BdGy0iw4G5REftFY+wMu3nWI560+wBfKmqH1WyPN/2cbU0uCOEMiLSEngcOEdVV1RY/Cawqar2AW4FnqzrfBn8WlV3IBoaOlNE9qywPNOdiLFeQiYijYHBwLQMi/NxH2cr7/Y1gIhcTDQk+nAlTar6HqpLtwNbEA0tfg7clKFNPu7noaz/6CCf9nG1NciCICKNiIrBw6r6RMXlqrpCVb9P/f05oJGIFNVxzIqZilN/fkU0Hj+gQpMlQLe06a5Ev3nH6UDgTVX9suKCfNzHKV+WDbWl/vwqQ5u829epk9wHA8eqasYfmll8D9UZVf1SVdepagjcVUmWvNrPIlIIHEHlR2B5tY9rosEVhNQY4N3AfFW9uZI2HVPtEJEBRPvpm7pL+Ys8LVInwBGRFsBA4L0KzWYAw0XEicguwPKyoY8YVfrbVL7t4zQzgBNSfz+B6EqoimYCA0VkYxHZmOjfY2Yd5fuF1JU4FwKDVfXHStpk8z1UZyqc3zq8kixzgJ4i0iN1tHkM0b9PXPYD/quqSzItzLd9XBMN7sY0EdkdeAV4l+iyU4CLgO4AqnqHiIwGRhEdfq8CzlXVf8UQFwAR2ZyfrtIpBB5R1WtE5HQoz+yIrowYRHTZ6YmqOjeWwICINCca/91cVZen5qXnjX0fi8ifgb2AIuBLoiuHngSU6PvhM+AoVf1WRHYCTlfVU1LrnkT0fQNwjareG2PmcUATfiqor6nq6SLSmehSzYMq+x6KMfNeRMNFnujy3tNS52zKM6fWPQj4A9Flp/fUReZMeVX1bhG5j2jf3pHWNi/2cW1pcAXBGGNMZg1uyMgYY0xmVhCMMcYAVhCMMcakWEEwxhgDWEEwxhiTYgXBGGMMYAXBGGNMihUEY4wxAPw/u5WOIRqLHJ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cs = range(2,20,1)\n",
    "Accuracies = np.zeros(len(Cs))\n",
    "for i,C in enumerate(Cs): \n",
    "    model_numbers = tree.DecisionTreeClassifier(min_samples_split=15, max_depth=C)\n",
    "    scores = cross_val_score(estimator = model_numbers, X = scaled_cAMP_X, y = SC_cAMP_y.ravel(), cv=5, scoring='accuracy')    \n",
    "    Accuracies[i]  = scores.mean()\n",
    "\n",
    "SC_cAMP_DT_depth = pd.DataFrame({\"C_value\": Cs, \"Accuracy\": Accuracies}) \n",
    "SC_cAMP_DT_depth.to_csv(output_path+\"SC_cAMP_D_Tree_depths.csv\")\n",
    "        \n",
    "plt.plot(Cs,Accuracies)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Accuracy is: 0.4466997338726301 for Depth = 4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SC_cAMP_DT_depth = pd.read_csv(output_path+\"SC_cAMP_D_Tree_depths.csv\", index_col = 0)\n",
    "\n",
    "max_accuracies = max(SC_cAMP_DT_depth[\"Accuracy\"])\n",
    "for i,num in enumerate( SC_cAMP_DT_depth[\"Accuracy\"]):\n",
    "    if num == max_accuracies:\n",
    "        max_c = i+2\n",
    "        print (\"Maximum Accuracy is:\", max_accuracies, \"for Depth =\", round(max_c, 1))\n",
    "        #This is only going to get the first one, but all after 6 give the same result\n",
    "        break\n",
    "\n",
    "plt.plot(SC_cAMP_DT_depth[[\"C_value\"]], SC_cAMP_DT_depth[[\"Accuracy\"]])\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cs = range(5,1001,5)\n",
    "Accuracies = np.zeros(len(Cs))\n",
    "for i,C in enumerate(Cs): \n",
    "    model_numbers = tree.DecisionTreeClassifier(min_samples_split=C, max_depth=4)\n",
    "    scores = cross_val_score(estimator = model_numbers, X = scaled_cAMP_X, y = SC_cAMP_y.ravel(), cv=5, scoring='accuracy')    \n",
    "    Accuracies[i]  = scores.mean()\n",
    "\n",
    "SC_cAMP_DT_min_split = pd.DataFrame({\"C_value\": Cs, \"Accuracy\": Accuracies}) \n",
    "SC_cAMP_DT_min_split.to_csv(output_path+\"SC_cAMP_D_Tree_min_split.csv\")\n",
    "\n",
    "plt.plot(Cs,Accuracies)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum Accuracy is: 0.4480968817340668 for Depth = 720\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SC_cAMP_DT_min_split = pd.read_csv(output_path+\"SC_cAMP_D_Tree_min_split.csv\", index_col = 0)\n",
    "\n",
    "max_accuracies = max(SC_cAMP_DT_min_split[\"Accuracy\"])\n",
    "for i,num in enumerate( SC_cAMP_DT_min_split[\"Accuracy\"]):\n",
    "    if num == max_accuracies:\n",
    "        max_c = i*5+5\n",
    "        print (\"Maximum Accuracy is:\", max_accuracies, \"for Depth =\", round(max_c, 1))\n",
    "        #This is only going to get the first one, but all after 6 give the same result\n",
    "        break\n",
    "\n",
    "plt.plot(SC_cAMP_DT_min_split[[\"C_value\"]], SC_cAMP_DT_min_split[[\"Accuracy\"]])\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now I have a max depth of 4 and a min samples split value of 720. That seems shallow and like an awful lot of samples for a minimum split, but that is what the runs above have shown. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data =  0.4603370324702014\n",
      "Accuracy on test data =  0.4394780106863954\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(scaled_cAMP_X, SC_cAMP_y.ravel(), random_state=1, test_size=0.8) \n",
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 4, min_samples_split = 720)\n",
    "decisionTree = decisionTree.fit(X_train, y_train )\n",
    "y_pred_train = decisionTree.predict(X_train)\n",
    "# predict with the training data\n",
    "# measure accuracy\n",
    "print('Accuracy on training data = ', metrics.accuracy_score(y_true = y_train, y_pred = y_pred_train))\n",
    "\n",
    "# predict with the test data\n",
    "y_pred = decisionTree.predict(X_test)\n",
    "# measure accuracy\n",
    "print('Accuracy on test data = ', metrics.accuracy_score(y_true = y_test, y_pred = y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data =  0.4603370324702014\n",
      "Accuracy on test data =  0.4394780106863954\n"
     ]
    }
   ],
   "source": [
    "## This is trying the same thing, just with data that isn't scaled. \n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(SC_cAMP_X, SC_cAMP_y.ravel(), random_state=1, test_size=0.8) \n",
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 4, min_samples_split = 720)\n",
    "decisionTree = decisionTree.fit(X_train, y_train )\n",
    "y_pred_train = decisionTree.predict(X_train)\n",
    "# predict with the training data\n",
    "# measure accuracy\n",
    "print('Accuracy on training data = ', metrics.accuracy_score(y_true = y_train, y_pred = y_pred_train))\n",
    "\n",
    "# predict with the test data\n",
    "y_pred = decisionTree.predict(X_test)\n",
    "# measure accuracy\n",
    "print('Accuracy on test data = ', metrics.accuracy_score(y_true = y_test, y_pred = y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import Image, display  \n",
    "import pydotplus \n",
    "from scipy import misc\n",
    "\n",
    "def renderTree(my_tree, features):\n",
    "    # hacky solution of writing to files and reading again\n",
    "    # necessary due to library bugs\n",
    "    filename = \"temp.dot\"\n",
    "    with open(filename, 'w') as f:\n",
    "        f = tree.export_graphviz(my_tree, \n",
    "                                 out_file=f, \n",
    "                                 feature_names=features, \n",
    "                                 class_names=[\"Downregulated\", \"Nondifferential\", \"Upregulated\"],  \n",
    "                                 filled=True, \n",
    "                                 rounded=True,\n",
    "                                 special_characters=True)\n",
    "  \n",
    "    dot_data = \"\"\n",
    "    with open(filename, 'r') as f:\n",
    "        dot_data = f.read()\n",
    "\n",
    "    graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "    image_name = output_path+\"SC_cAMP_Decision_Tree.png\"\n",
    "    graph.write_png(image_name)  \n",
    "    display(Image(filename=image_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#used_features = list(all_of_the_columns.iloc[:,2:-1])\n",
    "used_features = list(SC_cAMP_cleaned_dataset[['Length', 'Exon Length',\n",
    "                     'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "                     'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "                     'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "                     'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "                     'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "                     'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated','Numeric_Biotype', 'Numeric_Chr']].columns.values)\n",
    "\n",
    "\n",
    "renderTree(decisionTree, used_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.4446420559390667, 0.44040279490341144)\n",
      "(0.4446420559390667, 0.43680641183723795)\n"
     ]
    }
   ],
   "source": [
    "print(SC_cAMP_SVM_accuracy_scores)\n",
    "print(SC_cAMP_KNN_accuracy_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, the results are in for the SC cAMP dataset. If I had used a naive Bayes model, I would have achieved 33.43%. Using SVM, I got 44.46% using the 5-fold cross validation and 44.04% accuracy on the one time test-train split using 80% for testing. Using KNN, I got 43.97% using the 5-fold cross validation and 43.68% accuracy on the one time test-train split using 80% for testing. Using the decision tree I got 46.03% accuracy on the training data and 43.95% accuracy on the test data. I am quite impressed that all three models gave me almost the exact same accuracy. Now that I have determined good parameters to use, I would like to see how a model trained on the SC cAMP dataset performs when tested on the other two datasets. I will try to run it back the other direction as well. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARPE_cleaned_dataset = pd.read_pickle(cleaned_data_path+\"ARPE_Combined_Cleaned_Data.pkl\")\n",
    "SC_VitC_cleaned_dataset = pd.read_pickle(cleaned_data_path+\"SC_VitC_Combined_Cleaned_Data.pkl\")\n",
    "SC_cAMP_cleaned_dataset = pd.read_pickle(cleaned_data_path+\"SC_cAMP_Combined_Cleaned_Data.pkl\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_change(change):\n",
    "    if change == \"Downregulated\":\n",
    "        return 0\n",
    "    elif change == \"Nondifferential\":\n",
    "        return 1\n",
    "    elif change == \"Upregulated\":\n",
    "        return 2\n",
    "    else:\n",
    "        return \"Remove this one from the dataset\"\n",
    "\n",
    "ARPE_cleaned_dataset[\"Numeric_Change\"] = ARPE_cleaned_dataset[\"Change\"].apply(get_change)\n",
    "\n",
    "def get_biotype(biotype):\n",
    "    if biotype == \"protein_coding\":\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "ARPE_cleaned_dataset[\"Numeric_Biotype\"] = ARPE_cleaned_dataset[\"Biotype\"].apply(get_biotype)\n",
    "\n",
    "def get_chromosome(Chr):\n",
    "    if Chr == \"X\": \n",
    "        return 0\n",
    "    elif Chr == \"Y\": \n",
    "        return 0\n",
    "    elif Chr == \"1\": \n",
    "        return 0\n",
    "    elif Chr == \"2\": \n",
    "        return 0\n",
    "    elif Chr == \"3\": \n",
    "        return 0\n",
    "    elif Chr == \"4\": \n",
    "        return 0\n",
    "    elif Chr == \"5\": \n",
    "        return 0\n",
    "    elif Chr == \"6\": \n",
    "        return 0\n",
    "    elif Chr == \"7\": \n",
    "        return 0\n",
    "    elif Chr == \"8\": \n",
    "        return 0\n",
    "    elif Chr == \"9\": \n",
    "        return 0\n",
    "    elif Chr == \"10\": \n",
    "        return 0\n",
    "    elif Chr == \"11\": \n",
    "        return 0\n",
    "    elif Chr == \"12\": \n",
    "        return 0\n",
    "    elif Chr == \"13\": \n",
    "        return 0\n",
    "    elif Chr == \"14\": \n",
    "        return 0\n",
    "    elif Chr == \"15\": \n",
    "        return 0\n",
    "    elif Chr == \"16\": \n",
    "        return 0\n",
    "    elif Chr == \"17\": \n",
    "        return 0\n",
    "    elif Chr == \"18\": \n",
    "        return 0\n",
    "    elif Chr == \"19\": \n",
    "        return 0\n",
    "    elif Chr == \"20\": \n",
    "        return 0\n",
    "    elif Chr == \"21\": \n",
    "        return 0\n",
    "    elif Chr == \"22\": \n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "ARPE_cleaned_dataset[\"Numeric_Chr\"] = ARPE_cleaned_dataset[\"Chr\"].apply(get_chromosome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARPE_X = np.array(ARPE_cleaned_dataset[['Length', 'Exonic Length',\n",
    "                     'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "                     'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "                     'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "                     'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "                     'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "                     'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated','Numeric_Biotype', 'Numeric_Chr']])\n",
    "\n",
    "ARPE_y = np.array(ARPE_cleaned_dataset[[\"Numeric_Change\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14599\n",
      "14599\n",
      "14599\n"
     ]
    }
   ],
   "source": [
    "scaled_ARPE_X = scale(ARPE_X)\n",
    "#ARPE_X_train, ARPE_X_test, ARPE_y_train, ARPE_y_test = train_test_split(scaled_ARPE_X, ARPE_y, random_state=1, test_size=0.8) \n",
    "print(len(ARPE_X))\n",
    "print(len(scaled_ARPE_X))\n",
    "print(len(ARPE_y))\n",
    "# print(len(ARPE_X_train))\n",
    "# print(len(ARPE_y_train))\n",
    "# print(len(ARPE_X_test))\n",
    "# print(len(ARPE_y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_change(change):\n",
    "    if change == \"Downregulated\":\n",
    "        return 0\n",
    "    elif change == \"Nondifferential\":\n",
    "        return 1\n",
    "    elif change == \"Upregulated\":\n",
    "        return 2\n",
    "    else:\n",
    "        return \"Remove this one from the dataset\"\n",
    "\n",
    "SC_VitC_cleaned_dataset[\"Numeric_Change\"] = SC_VitC_cleaned_dataset[\"Change\"].apply(get_change)\n",
    "\n",
    "def get_biotype(biotype):\n",
    "    if biotype == \"protein_coding\":\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "SC_VitC_cleaned_dataset[\"Numeric_Biotype\"] = SC_VitC_cleaned_dataset[\"Gene_Biotype\"].apply(get_biotype)\n",
    "\n",
    "def get_chromosome(Chr):\n",
    "    if Chr == \"X\": \n",
    "        return 0\n",
    "    elif Chr == \"Y\": \n",
    "        return 0\n",
    "    elif Chr == \"1\": \n",
    "        return 0\n",
    "    elif Chr == \"2\": \n",
    "        return 0\n",
    "    elif Chr == \"3\": \n",
    "        return 0\n",
    "    elif Chr == \"4\": \n",
    "        return 0\n",
    "    elif Chr == \"5\": \n",
    "        return 0\n",
    "    elif Chr == \"6\": \n",
    "        return 0\n",
    "    elif Chr == \"7\": \n",
    "        return 0\n",
    "    elif Chr == \"8\": \n",
    "        return 0\n",
    "    elif Chr == \"9\": \n",
    "        return 0\n",
    "    elif Chr == \"10\": \n",
    "        return 0\n",
    "    elif Chr == \"11\": \n",
    "        return 0\n",
    "    elif Chr == \"12\": \n",
    "        return 0\n",
    "    elif Chr == \"13\": \n",
    "        return 0\n",
    "    elif Chr == \"14\": \n",
    "        return 0\n",
    "    elif Chr == \"15\": \n",
    "        return 0\n",
    "    elif Chr == \"16\": \n",
    "        return 0\n",
    "    elif Chr == \"17\": \n",
    "        return 0\n",
    "    elif Chr == \"18\": \n",
    "        return 0\n",
    "    elif Chr == \"19\": \n",
    "        return 0\n",
    "    elif Chr == \"20\": \n",
    "        return 0\n",
    "    elif Chr == \"21\": \n",
    "        return 0\n",
    "    elif Chr == \"22\": \n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "SC_VitC_cleaned_dataset[\"Numeric_Chr\"] = SC_VitC_cleaned_dataset[\"Chr\"].apply(get_chromosome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_VitC_X = np.array(SC_VitC_cleaned_dataset[['Length', 'Exon Length',\n",
    "                     'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "                     'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "                     'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "                     'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "                     'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "                     'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated','Numeric_Biotype', 'Numeric_Chr']])\n",
    "\n",
    "SC_VitC_y = np.array(SC_VitC_cleaned_dataset[[\"Numeric_Change\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12152\n",
      "12152\n",
      "12152\n"
     ]
    }
   ],
   "source": [
    "scaled_SC_VitC_X = scale(SC_VitC_X)\n",
    "print(len(SC_VitC_X))\n",
    "print(len(scaled_SC_VitC_X))\n",
    "print(len(SC_VitC_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "SC_cAMP_cleaned_dataset[\"Numeric_Change\"] = SC_cAMP_cleaned_dataset[\"Change\"].apply(get_change)\n",
    "\n",
    "SC_cAMP_cleaned_dataset[\"Numeric_Biotype\"] = SC_cAMP_cleaned_dataset[\"Gene_Biotype\"].apply(get_biotype)\n",
    "\n",
    "SC_cAMP_cleaned_dataset[\"Numeric_Chr\"] = SC_cAMP_cleaned_dataset[\"Chr\"].apply(get_chromosome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12165\n",
      "12165\n",
      "12165\n"
     ]
    }
   ],
   "source": [
    "SC_cAMP_X = np.array(SC_cAMP_cleaned_dataset[['Length', 'Exon Length',\n",
    "                     'TSS_Downregulated', 'Downstream_Promoter_Downregulated',\n",
    "                     'Gene_Body_Downregulated', 'Upstream_Promoter_Downregulated',\n",
    "                     'TSS_Nondifferential', 'Downstream_Promoter_Nondifferential',\n",
    "                     'Gene_Body_Nondifferential', 'Upstream_Promoter_Nondifferential',\n",
    "                     'TSS_Upregulated', 'Downstream_Promoter_Upregulated',\n",
    "                     'Gene_Body_Upregulated', 'Upstream_Promoter_Upregulated','Numeric_Biotype', 'Numeric_Chr']])\n",
    "\n",
    "SC_cAMP_y = np.array(SC_cAMP_cleaned_dataset[[\"Numeric_Change\"]])\n",
    "scaled_SC_cAMP_X = scale(SC_cAMP_X)\n",
    "print(len(SC_cAMP_X))\n",
    "print(len(scaled_SC_cAMP_X))\n",
    "print(len(SC_cAMP_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14599\n",
      "14599\n",
      "14599\n",
      "\n",
      "\n",
      "12152\n",
      "12152\n",
      "12152\n",
      "\n",
      "\n",
      "12165\n",
      "12165\n",
      "12165\n"
     ]
    }
   ],
   "source": [
    "print(len(ARPE_X))\n",
    "print(len(scaled_ARPE_X))\n",
    "print(len(ARPE_y))\n",
    "print(\"\\n\")\n",
    "print(len(SC_VitC_X))\n",
    "print(len(scaled_SC_VitC_X))\n",
    "print(len(SC_VitC_y))\n",
    "print(\"\\n\")\n",
    "print(len(SC_cAMP_X))\n",
    "print(len(scaled_SC_cAMP_X))\n",
    "print(len(SC_cAMP_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First is SVM, which requires the scaled X data.\n",
    "I accidentally left the print statements as Test on 80% train on 20%, but they are really trained on one dataset and tested on all 3 datasets. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.45869297163995065\n",
      "\n",
      "Confusion Matrix:\n",
      "[[1774  286 1807]\n",
      " [ 936  572 2445]\n",
      " [ 840  271 3234]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(scaled_SC_cAMP_X, SC_cAMP_y.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(scaled_SC_cAMP_X)\n",
    "\n",
    "SC_cAMP_vs_SC_cAMP_SVM_Accuracy = metrics.accuracy_score(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_vs_SC_cAMP_SVM_Accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_vs_SC_cAMP_SVM_Confusion = metrics.confusion_matrix(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_vs_SC_cAMP_SVM_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.2900198643742722\n",
      "\n",
      "Confusion Matrix:\n",
      "[[ 401  319  697]\n",
      " [2595 2274 5051]\n",
      " [1420  283 1559]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(scaled_SC_cAMP_X, SC_cAMP_y.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(scaled_ARPE_X)\n",
    "\n",
    "SC_cAMP_vs_ARPE_SVM_Accuracy = metrics.accuracy_score(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_vs_ARPE_SVM_Accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_vs_ARPE_SVM_Confusion = metrics.confusion_matrix(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_vs_ARPE_SVM_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.18120473996050032\n",
      "\n",
      "Confusion Matrix:\n",
      "[[ 410   52 1336]\n",
      " [2329  932 5109]\n",
      " [1039   85  860]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(scaled_SC_cAMP_X, SC_cAMP_y.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(scaled_SC_VitC_X)\n",
    "\n",
    "SC_cAMP_vs_SC_VitC_SVM_Accuracy = metrics.accuracy_score(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_vs_SC_VitC_SVM_Accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_vs_SC_VitC_SVM_Confusion = metrics.confusion_matrix(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_vs_SC_VitC_SVM_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6933015141540487\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1792    6]\n",
      " [   0 8347   23]\n",
      " [   0 1906   78]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(scaled_SC_VitC_X, SC_VitC_y.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(scaled_SC_VitC_X)\n",
    "\n",
    "SC_VitC_vs_SC_VitC_SVM_Accuracy = metrics.accuracy_score(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_VitC_vs_SC_VitC_SVM_Accuracy)\n",
    "\n",
    "\n",
    "SC_VitC_vs_SC_VitC_SVM_Confusion = metrics.confusion_matrix(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_VitC_vs_SC_VitC_SVM_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.3260172626387176\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 3834   33]\n",
      " [   0 3930   23]\n",
      " [   0 4309   36]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(scaled_SC_VitC_X, SC_VitC_y.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(scaled_SC_cAMP_X)\n",
    "\n",
    "SC_VitC_vs_SC_cAMP_SVM_Accuracy = metrics.accuracy_score(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_VitC_vs_SC_cAMP_SVM_Accuracy)\n",
    "\n",
    "\n",
    "SC_VitC_vs_SC_cAMP_SVM_Confusion = metrics.confusion_matrix(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_VitC_vs_SC_cAMP_SVM_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6793616001095966\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1412    5]\n",
      " [   0 9872   48]\n",
      " [   0 3216   46]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(scaled_SC_VitC_X, SC_VitC_y.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(scaled_ARPE_X)\n",
    "\n",
    "SC_VitC_vs_ARPE_SVM_Accuracy = metrics.accuracy_score(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_VitC_vs_ARPE_SVM_Accuracy)\n",
    "\n",
    "\n",
    "SC_VitC_vs_ARPE_SVM_Confusion = metrics.confusion_matrix(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_VitC_vs_ARPE_SVM_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.7056647715596959\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1364   53]\n",
      " [   0 9718  202]\n",
      " [   0 2678  584]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(scaled_ARPE_X, ARPE_y.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(scaled_ARPE_X)\n",
    "\n",
    "ARPE_vs_ARPE_SVM_Accuracy = metrics.accuracy_score(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_vs_ARPE_SVM_Accuracy)\n",
    "\n",
    "\n",
    "ARPE_vs_ARPE_SVM_Confusion = metrics.confusion_matrix(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_vs_ARPE_SVM_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6840849242922976\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1740   58]\n",
      " [   0 8010  360]\n",
      " [   0 1681  303]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(scaled_ARPE_X, ARPE_y.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(scaled_SC_VitC_X)\n",
    "\n",
    "ARPE_vs_SC_VitC_SVM_Accuracy = metrics.accuracy_score(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_vs_SC_VitC_SVM_Accuracy)\n",
    "\n",
    "\n",
    "ARPE_vs_SC_VitC_SVM_Confusion = metrics.confusion_matrix(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_vs_SC_VitC_SVM_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.33177147554459513\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 3498  369]\n",
      " [   0 3836  117]\n",
      " [   0 4145  200]]\n"
     ]
    }
   ],
   "source": [
    "model_svm = svm.SVC(kernel='rbf',C=0.4,gamma='auto')\n",
    "model_svm.fit(scaled_ARPE_X, ARPE_y.ravel())\n",
    "\n",
    "Y_pred = model_svm.predict(scaled_SC_cAMP_X)\n",
    "\n",
    "ARPE_vs_SC_cAMP_SVM_Accuracy = metrics.accuracy_score(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_vs_SC_cAMP_SVM_Accuracy)\n",
    "\n",
    "\n",
    "ARPE_vs_SC_cAMP_SVM_Confusion = metrics.confusion_matrix(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_vs_SC_cAMP_SVM_Confusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now to try this with the K-Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.46609124537607893\n",
      "\n",
      "Confusion Matrix:\n",
      "[[1762  726 1379]\n",
      " [ 904 1291 1758]\n",
      " [ 859  869 2617]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(scaled_SC_cAMP_X, SC_cAMP_y.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(scaled_SC_cAMP_X)\n",
    "\n",
    "SC_cAMP_vs_SC_cAMP_KNN_Accuracy = metrics.accuracy_score(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_vs_SC_cAMP_KNN_Accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_vs_SC_cAMP_KNN_Confusion = metrics.confusion_matrix(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_vs_SC_cAMP_KNN_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.36632646071648745\n",
      "\n",
      "Confusion Matrix:\n",
      "[[ 363  499  555]\n",
      " [2238 3557 4125]\n",
      " [1259  575 1428]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(scaled_SC_cAMP_X, SC_cAMP_y.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(scaled_ARPE_X)\n",
    "\n",
    "SC_cAMP_vs_ARPE_KNN_Accuracy = metrics.accuracy_score(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_vs_ARPE_KNN_Accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_vs_ARPE_KNN_Confusion = metrics.confusion_matrix(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_vs_ARPE_KNN_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.26357801184990126\n",
      "\n",
      "Confusion Matrix:\n",
      "[[ 384  291 1123]\n",
      " [2207 2017 4146]\n",
      " [ 971  211  802]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(scaled_SC_cAMP_X, SC_cAMP_y.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(scaled_SC_VitC_X)\n",
    "\n",
    "SC_cAMP_vs_SC_VitC_KNN_Accuracy = metrics.accuracy_score(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_vs_SC_VitC_KNN_Accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_vs_SC_VitC_KNN_Confusion = metrics.confusion_matrix(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_vs_SC_VitC_KNN_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.69050362080316\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1784   14]\n",
      " [   0 8315   55]\n",
      " [   0 1908   76]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(scaled_SC_VitC_X, SC_VitC_y.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(scaled_SC_VitC_X)\n",
    "\n",
    "SC_VitC_vs_SC_VitC_KNN_Accuracy = metrics.accuracy_score(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_VitC_vs_SC_VitC_KNN_Accuracy)\n",
    "\n",
    "\n",
    "SC_VitC_vs_SC_VitC_KNN_Confusion = metrics.confusion_matrix(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_VitC_vs_SC_VitC_KNN_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.3263460748047678\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 3810   57]\n",
      " [   0 3929   24]\n",
      " [   0 4304   41]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(scaled_SC_VitC_X, SC_VitC_y.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(scaled_SC_cAMP_X)\n",
    "\n",
    "SC_VitC_vs_SC_cAMP_KNN_Accuracy = metrics.accuracy_score(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_VitC_vs_SC_cAMP_KNN_Accuracy)\n",
    "\n",
    "\n",
    "SC_VitC_vs_SC_cAMP_KNN_Confusion = metrics.confusion_matrix(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_VitC_vs_SC_cAMP_KNN_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6806630591136379\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1412    5]\n",
      " [   0 9830   90]\n",
      " [   0 3155  107]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(scaled_SC_VitC_X, SC_VitC_y.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(scaled_ARPE_X)\n",
    "\n",
    "SC_VitC_vs_ARPE_KNN_Accuracy = metrics.accuracy_score(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_VitC_vs_ARPE_KNN_Accuracy)\n",
    "\n",
    "\n",
    "SC_VitC_vs_ARPE_KNN_Confusion = metrics.confusion_matrix(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_VitC_vs_ARPE_KNN_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6930611685731899\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1367   50]\n",
      " [   0 9654  266]\n",
      " [   0 2798  464]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(scaled_ARPE_X, ARPE_y.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(scaled_ARPE_X)\n",
    "\n",
    "ARPE_vs_ARPE_KNN_Accuracy = metrics.accuracy_score(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_vs_ARPE_KNN_Accuracy)\n",
    "\n",
    "\n",
    "ARPE_vs_ARPE_KNN_Confusion = metrics.confusion_matrix(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_vs_ARPE_KNN_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6842495062541145\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1748   50]\n",
      " [   0 8070  300]\n",
      " [   0 1739  245]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(scaled_ARPE_X, ARPE_y.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(scaled_SC_VitC_X)\n",
    "\n",
    "ARPE_vs_SC_VitC_KNN_Accuracy = metrics.accuracy_score(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_vs_SC_VitC_KNN_Accuracy)\n",
    "\n",
    "\n",
    "ARPE_vs_SC_VitC_KNN_Confusion = metrics.confusion_matrix(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_vs_SC_VitC_KNN_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.3297163995067818\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 3534  333]\n",
      " [   0 3852  101]\n",
      " [   0 4186  159]]\n"
     ]
    }
   ],
   "source": [
    "model_KNN = KNeighborsClassifier(n_neighbors=81)\n",
    "model_KNN.fit(scaled_ARPE_X, ARPE_y.ravel())\n",
    "\n",
    "Y_pred = model_KNN.predict(scaled_SC_cAMP_X)\n",
    "\n",
    "ARPE_vs_SC_cAMP_KNN_Accuracy = metrics.accuracy_score(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_vs_SC_cAMP_KNN_Accuracy)\n",
    "\n",
    "\n",
    "ARPE_vs_SC_cAMP_KNN_Confusion = metrics.confusion_matrix(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_vs_SC_cAMP_KNN_Confusion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the decision trees. These ones don't require scaled data. I honestly don't know how comparable the datasets are, so I am not sure how this will workout. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6955270908966368\n",
      "\n",
      "Confusion Matrix:\n",
      "[[  18 1296  103]\n",
      " [  14 9256  650]\n",
      " [   0 2382  880]]\n"
     ]
    }
   ],
   "source": [
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 6, min_samples_split = 380)\n",
    "decisionTree = decisionTree.fit(ARPE_X, ARPE_y.ravel())\n",
    "\n",
    "Y_pred = decisionTree.predict(ARPE_X)\n",
    "\n",
    "ARPE_vs_ARPE_DTree_Accuracy = metrics.accuracy_score(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_vs_ARPE_DTree_Accuracy)\n",
    "\n",
    "\n",
    "ARPE_vs_ARPE_DTree_Confusion = metrics.confusion_matrix(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_vs_ARPE_DTree_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.3301274147143444\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   1 3402  464]\n",
      " [   2 3797  154]\n",
      " [   0 4127  218]]\n"
     ]
    }
   ],
   "source": [
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 6, min_samples_split = 380)\n",
    "decisionTree = decisionTree.fit(ARPE_X, ARPE_y.ravel())\n",
    "\n",
    "Y_pred = decisionTree.predict(SC_cAMP_X)\n",
    "\n",
    "ARPE_vs_SC_cAMP_DTree_Accuracy = metrics.accuracy_score(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_vs_SC_cAMP_DTree_Accuracy)\n",
    "\n",
    "\n",
    "ARPE_vs_SC_cAMP_DTree_Confusion = metrics.confusion_matrix(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_vs_SC_cAMP_DTree_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6784891375905201\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1734   64]\n",
      " [   2 7905  463]\n",
      " [   0 1644  340]]\n"
     ]
    }
   ],
   "source": [
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 6, min_samples_split = 380)\n",
    "decisionTree = decisionTree.fit(ARPE_X, ARPE_y.ravel())\n",
    "\n",
    "Y_pred = decisionTree.predict(SC_VitC_X)\n",
    "\n",
    "ARPE_vs_SC_VitC_DTree_Accuracy = metrics.accuracy_score(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', ARPE_vs_SC_VitC_DTree_Accuracy)\n",
    "\n",
    "\n",
    "ARPE_vs_SC_VitC_DTree_Confusion = metrics.confusion_matrix(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(ARPE_vs_SC_VitC_DTree_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6931369321922317\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1739   59]\n",
      " [   0 8115  255]\n",
      " [   0 1676  308]]\n"
     ]
    }
   ],
   "source": [
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 6, min_samples_split = 380)\n",
    "decisionTree = decisionTree.fit(SC_VitC_X, SC_VitC_y.ravel())\n",
    "\n",
    "Y_pred = decisionTree.predict(SC_VitC_X)\n",
    "\n",
    "SC_VitC_vs_SC_VitC_DTree_Accuracy = metrics.accuracy_score(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_VitC_vs_SC_VitC_DTree_Accuracy)\n",
    "\n",
    "\n",
    "SC_VitC_vs_SC_VitC_DTree_Confusion = metrics.confusion_matrix(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_VitC_vs_SC_VitC_DTree_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.6779231454209192\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 1376   41]\n",
      " [   0 9617  303]\n",
      " [   0 2982  280]]\n"
     ]
    }
   ],
   "source": [
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 6, min_samples_split = 380)\n",
    "decisionTree = decisionTree.fit(SC_VitC_X, SC_VitC_y.ravel())\n",
    "\n",
    "Y_pred = decisionTree.predict(ARPE_X)\n",
    "\n",
    "SC_VitC_vs_ARPE_DTree_Accuracy = metrics.accuracy_score(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_VitC_vs_ARPE_DTree_Accuracy)\n",
    "\n",
    "\n",
    "SC_VitC_vs_ARPE_DTree_Confusion = metrics.confusion_matrix(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_VitC_vs_ARPE_DTree_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.3301274147143444\n",
      "\n",
      "Confusion Matrix:\n",
      "[[   0 3491  376]\n",
      " [   0 3830  123]\n",
      " [   0 4159  186]]\n"
     ]
    }
   ],
   "source": [
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 6, min_samples_split = 380)\n",
    "decisionTree = decisionTree.fit(SC_VitC_X, SC_VitC_y.ravel())\n",
    "\n",
    "Y_pred = decisionTree.predict(SC_cAMP_X)\n",
    "\n",
    "SC_VitC_vs_SC_cAMP_DTree_Accuracy = metrics.accuracy_score(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_VitC_vs_SC_cAMP_DTree_Accuracy)\n",
    "\n",
    "\n",
    "SC_VitC_vs_SC_cAMP_DTree_Confusion = metrics.confusion_matrix(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_VitC_vs_SC_cAMP_DTree_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.46083025071927664\n",
      "\n",
      "Confusion Matrix:\n",
      "[[2011  146 1710]\n",
      " [1117  591 2245]\n",
      " [1053  288 3004]]\n"
     ]
    }
   ],
   "source": [
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 6, min_samples_split = 380)\n",
    "decisionTree = decisionTree.fit(SC_cAMP_X, SC_cAMP_y.ravel())\n",
    "\n",
    "Y_pred = decisionTree.predict(SC_cAMP_X)\n",
    "\n",
    "SC_cAMP_vs_SC_cAMP_DTree_Accuracy = metrics.accuracy_score(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_vs_SC_cAMP_DTree_Accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_vs_SC_cAMP_DTree_Confusion = metrics.confusion_matrix(y_true = SC_cAMP_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_vs_SC_cAMP_DTree_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.18054641211323239\n",
      "\n",
      "Confusion Matrix:\n",
      "[[ 476  113 1209]\n",
      " [2693  900 4777]\n",
      " [1124   42  818]]\n"
     ]
    }
   ],
   "source": [
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 6, min_samples_split = 380)\n",
    "decisionTree = decisionTree.fit(SC_cAMP_X, SC_cAMP_y.ravel())\n",
    "\n",
    "Y_pred = decisionTree.predict(SC_VitC_X)\n",
    "\n",
    "SC_cAMP_vs_SC_VitC_DTree_Accuracy = metrics.accuracy_score(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_vs_SC_VitC_DTree_Accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_vs_SC_VitC_DTree_Confusion = metrics.confusion_matrix(y_true = SC_VitC_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_vs_SC_VitC_DTree_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on 80%, train on 20% Accuracy =  0.2004246866223714\n",
      "\n",
      "Confusion Matrix:\n",
      "[[ 496  176  745]\n",
      " [3459  989 5472]\n",
      " [1753   68 1441]]\n"
     ]
    }
   ],
   "source": [
    "decisionTree = tree.DecisionTreeClassifier(max_depth = 6, min_samples_split = 380)\n",
    "decisionTree = decisionTree.fit(SC_cAMP_X, SC_cAMP_y.ravel())\n",
    "\n",
    "Y_pred = decisionTree.predict(ARPE_X)\n",
    "\n",
    "SC_cAMP_vs_ARPE_DTree_Accuracy = metrics.accuracy_score(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('Test on 80%, train on 20% Accuracy = ', SC_cAMP_vs_ARPE_DTree_Accuracy)\n",
    "\n",
    "\n",
    "SC_cAMP_vs_ARPE_DTree_Confusion = metrics.confusion_matrix(y_true = ARPE_y.ravel(), y_pred = Y_pred)\n",
    "print('\\nConfusion Matrix:')\n",
    "print(SC_cAMP_vs_ARPE_DTree_Confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14599\n",
      "14599\n",
      "14599\n",
      "\n",
      "\n",
      "12152\n",
      "12152\n",
      "12152\n",
      "\n",
      "\n",
      "12165\n",
      "12165\n",
      "12165\n"
     ]
    }
   ],
   "source": [
    "print(len(ARPE_X))\n",
    "print(len(scaled_ARPE_X))\n",
    "print(len(ARPE_y))\n",
    "print(\"\\n\")\n",
    "print(len(SC_VitC_X))\n",
    "print(len(scaled_SC_VitC_X))\n",
    "print(len(SC_VitC_y))\n",
    "print(\"\\n\")\n",
    "print(len(SC_cAMP_X))\n",
    "print(len(scaled_SC_cAMP_X))\n",
    "print(len(SC_cAMP_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>tested_SC_cAMP</th>\n",
       "      <th>tested_SC_VitC</th>\n",
       "      <th>tested_ARPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trained On</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_cAMP</th>\n",
       "      <td>0.458693</td>\n",
       "      <td>0.181205</td>\n",
       "      <td>0.290020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_VitC</th>\n",
       "      <td>0.326017</td>\n",
       "      <td>0.693302</td>\n",
       "      <td>0.679362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARPE</th>\n",
       "      <td>0.331771</td>\n",
       "      <td>0.684085</td>\n",
       "      <td>0.705665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tested_SC_cAMP  tested_SC_VitC  tested_ARPE\n",
       "Trained On                                             \n",
       "SC_cAMP           0.458693        0.181205     0.290020\n",
       "SC_VitC           0.326017        0.693302     0.679362\n",
       "ARPE              0.331771        0.684085     0.705665"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVM_tested_on_cAMP = (SC_cAMP_vs_SC_cAMP_SVM_Accuracy, SC_VitC_vs_SC_cAMP_SVM_Accuracy, ARPE_vs_SC_cAMP_SVM_Accuracy)\n",
    "SVM_tested_on_SC_VitC = (SC_cAMP_vs_SC_VitC_SVM_Accuracy, SC_VitC_vs_SC_VitC_SVM_Accuracy, ARPE_vs_SC_VitC_SVM_Accuracy)\n",
    "SVM_tested_on_ARPE = (SC_cAMP_vs_ARPE_SVM_Accuracy, SC_VitC_vs_ARPE_SVM_Accuracy, ARPE_vs_ARPE_SVM_Accuracy)\n",
    "list_of_trained_on = (\"SC_cAMP\", \"SC_VitC\", \"ARPE\")\n",
    "SVM_accuracy = pd.DataFrame({\"Trained On\":list_of_trained_on, \"tested_SC_cAMP\": SVM_tested_on_cAMP, \n",
    "             \"tested_SC_VitC\": SVM_tested_on_SC_VitC, \"tested_ARPE\": SVM_tested_on_ARPE }).set_index(\"Trained On\")\n",
    "SVM_accuracy.to_csv(output_path+\"SVM_accuracy_table.csv\")\n",
    "SVM_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>tested_SC_cAMP</th>\n",
       "      <th>tested_SC_VitC</th>\n",
       "      <th>tested_ARPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trained On</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_cAMP</th>\n",
       "      <td>0.466091</td>\n",
       "      <td>0.263578</td>\n",
       "      <td>0.366326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_VitC</th>\n",
       "      <td>0.326346</td>\n",
       "      <td>0.690504</td>\n",
       "      <td>0.680663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARPE</th>\n",
       "      <td>0.329716</td>\n",
       "      <td>0.684250</td>\n",
       "      <td>0.693061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tested_SC_cAMP  tested_SC_VitC  tested_ARPE\n",
       "Trained On                                             \n",
       "SC_cAMP           0.466091        0.263578     0.366326\n",
       "SC_VitC           0.326346        0.690504     0.680663\n",
       "ARPE              0.329716        0.684250     0.693061"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_tested_on_cAMP = (SC_cAMP_vs_SC_cAMP_KNN_Accuracy, SC_VitC_vs_SC_cAMP_KNN_Accuracy, ARPE_vs_SC_cAMP_KNN_Accuracy)\n",
    "KNN_tested_on_SC_VitC = (SC_cAMP_vs_SC_VitC_KNN_Accuracy, SC_VitC_vs_SC_VitC_KNN_Accuracy, ARPE_vs_SC_VitC_KNN_Accuracy)\n",
    "KNN_tested_on_ARPE = (SC_cAMP_vs_ARPE_KNN_Accuracy, SC_VitC_vs_ARPE_KNN_Accuracy, ARPE_vs_ARPE_KNN_Accuracy)\n",
    "list_of_trained_on = (\"SC_cAMP\", \"SC_VitC\", \"ARPE\")\n",
    "KNN_accuracy = pd.DataFrame({\"Trained On\":list_of_trained_on, \"tested_SC_cAMP\": KNN_tested_on_cAMP, \n",
    "             \"tested_SC_VitC\": KNN_tested_on_SC_VitC, \"tested_ARPE\": KNN_tested_on_ARPE }).set_index(\"Trained On\")\n",
    "KNN_accuracy.to_csv(output_path+\"KNN_accuracy_table.csv\")\n",
    "KNN_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>tested_SC_cAMP</th>\n",
       "      <th>tested_SC_VitC</th>\n",
       "      <th>tested_ARPE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trained On</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SC_cAMP</th>\n",
       "      <td>0.460830</td>\n",
       "      <td>0.180546</td>\n",
       "      <td>0.200425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SC_VitC</th>\n",
       "      <td>0.330127</td>\n",
       "      <td>0.693137</td>\n",
       "      <td>0.677923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARPE</th>\n",
       "      <td>0.330127</td>\n",
       "      <td>0.678489</td>\n",
       "      <td>0.695527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            tested_SC_cAMP  tested_SC_VitC  tested_ARPE\n",
       "Trained On                                             \n",
       "SC_cAMP           0.460830        0.180546     0.200425\n",
       "SC_VitC           0.330127        0.693137     0.677923\n",
       "ARPE              0.330127        0.678489     0.695527"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTree_tested_on_cAMP = (SC_cAMP_vs_SC_cAMP_DTree_Accuracy, SC_VitC_vs_SC_cAMP_DTree_Accuracy, ARPE_vs_SC_cAMP_DTree_Accuracy)\n",
    "DTree_tested_on_SC_VitC = (SC_cAMP_vs_SC_VitC_DTree_Accuracy, SC_VitC_vs_SC_VitC_DTree_Accuracy, ARPE_vs_SC_VitC_DTree_Accuracy)\n",
    "DTree_tested_on_ARPE = (SC_cAMP_vs_ARPE_DTree_Accuracy, SC_VitC_vs_ARPE_DTree_Accuracy, ARPE_vs_ARPE_DTree_Accuracy)\n",
    "list_of_trained_on = (\"SC_cAMP\", \"SC_VitC\", \"ARPE\")\n",
    "DTree_accuracy = pd.DataFrame({\"Trained On\":list_of_trained_on, \"tested_SC_cAMP\": DTree_tested_on_cAMP, \n",
    "             \"tested_SC_VitC\": DTree_tested_on_SC_VitC, \"tested_ARPE\": DTree_tested_on_ARPE }).set_index(\"Trained On\")\n",
    "DTree_accuracy.to_csv(output_path+\"DTree_accuracy_table.csv\")\n",
    "DTree_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14599\n",
      "12152\n",
      "12165\n"
     ]
    }
   ],
   "source": [
    "# Now I have to determine the naive bayes accuracy\n",
    "# This is simple for something like this. It is the proportion of each output possibility squared, all summed\n",
    "# for example, if you have 3 variables and exactly 1/3 of each, then you have (1/3)**2+(1/3)**2+(1/3)**2=0.33\n",
    "# This is the lowest possible naive bayes acuracy and it only increases if you have a higher percentage of one of the possible outcomes\n",
    "print(len(ARPE_y))\n",
    "print(len(SC_VitC_y))\n",
    "print(len(SC_cAMP_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "NBayes_ARPE = ((list(ARPE_y.ravel()).count(0)/len(ARPE_y))**2)+((list(ARPE_y.ravel()).count(1)/len(ARPE_y))**2)+((list(ARPE_y.ravel()).count(2)/len(ARPE_y))**2)\n",
    "NBayes_SC_VitC = ((list(SC_VitC_y.ravel()).count(0)/len(SC_VitC_y))**2)+((list(SC_VitC_y.ravel()).count(1)/len(SC_VitC_y))**2)+((list(SC_VitC_y.ravel()).count(2)/len(SC_VitC_y))**2)\n",
    "NBayes_SC_cAMP = ((list(SC_cAMP_y.ravel()).count(0)/len(SC_cAMP_y))**2)+((list(SC_cAMP_y.ravel()).count(1)/len(SC_cAMP_y))**2)+((list(SC_cAMP_y.ravel()).count(2)/len(SC_cAMP_y))**2)\n",
    "list_of_datasets = ( \"SC_cAMP\", \"SC_VitC\", \"ARPE\")\n",
    "NBayes_values = ( NBayes_SC_cAMP, NBayes_SC_VitC, NBayes_ARPE)\n",
    "NBayes_df = pd.DataFrame({\"Dataset\": list_of_datasets, \"Naive Bayes Accuracy\":NBayes_values}).set_index(\"Dataset\")\n",
    "NBayes_df.to_csv(output_path+\"Naive_Bayes_Accuracies.csv\")\n",
    "# print(NBayes_ARPE)\n",
    "# print(NBayes_SC_VitC)\n",
    "# print(NBayes_SC_cAMP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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