2-library-matching-improvement.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import gensim\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "ROOT = os.path.dirname(os.getcwd())\n",
    "#path_data = os.path.join(ROOT, 'data')\n",
    "path_data = 'C:\\\\Users\\\\joris\\\\Documents\\\\eScience_data\\\\data'\n",
    "sys.path.insert(0, ROOT)\n",
    "sys.path.insert(0, \"C:\\\\Users\\\\joris\\\\Documents\\\\eScience_data\\\\spec2vec_gnps_data_analysis\\\\custom_functions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import pre-processed dataset \"AllPositive\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import pickle\n",
    "\n",
    "#loading data as pickled object goes a lot quicker\n",
    "# outfile = os.path.join(path_data, 'gnps_positive_ionmode_cleaned_by_matchms_and_lookups.pickle')\n",
    "\n",
    "# print(outfile)\n",
    "# start = time.time()\n",
    "# with open(outfile, 'rb') as inf:\n",
    "#         spectrums = pickle.load(inf)\n",
    "# end = time.time()\n",
    "# print('loading took {:.2f} s'.format(end-start))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import spec2vec matches of 1,000 spectra where their inchikeys occur >=5 times in the library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\found_matches_s2v.pickle\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\documents_query_s2v.pickle\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\documents_library_s2v.pickle\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "outfile = os.path.join(path_data, 'found_matches_s2v.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        found_matches_s2v = pickle.load(inf)\n",
    "else:\n",
    "    print('error')\n",
    "\n",
    "#contains info on the query/library\n",
    "outfile = os.path.join(path_data, 'documents_query_s2v.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        documents_query_s2v = pickle.load(inf)\n",
    "\n",
    "outfile = os.path.join(path_data, 'documents_library_s2v.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        documents_library_s2v = pickle.load(inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\new_documents_query_s2v.pickle\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\new_documents_library_s2v.pickle\n"
     ]
    }
   ],
   "source": [
    "#and another set of 1,000 different spectra\n",
    "#contains info on the query/library\n",
    "outfile = os.path.join(path_data, 'new_documents_query_s2v.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        new_documents_query_s2v = pickle.load(inf)\n",
    "\n",
    "outfile = os.path.join(path_data, 'new_documents_library_s2v.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        new_documents_library_s2v = pickle.load(inf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\rfreg_all_match_perc_pmass.pickle\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\found_matches_s2v_match_perc_pmass_sim.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'rfreg_all_match_perc_pmass.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        rfreg_all_match_perc_pmass = pickle.load(inf)\n",
    "\n",
    "outfile = os.path.join(path_data, 'found_matches_s2v_match_perc_pmass_sim.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        found_matches_s2v_match_perc_pmass_sim = pickle.load(inf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate pairwise tanimoto similarity between top 20 results for each query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\similarity_matrices_test_set1.pickle\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "from rdkit import Chem, DataStructs\n",
    "\n",
    "outfile = os.path.join(path_data, 'similarity_matrices_test_set1.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        similarity_matrices = pickle.load(inf)\n",
    "else:\n",
    "    #for each matches df make a similarity matrix based on tanimoto\n",
    "    similarity_matrices = []\n",
    "    topn = 20\n",
    "\n",
    "    for ID in range(len(found_matches_s2v)):\n",
    "        df = found_matches_s2v[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "        library_ids = df.index.values\n",
    "        rows = []\n",
    "        for lib_id_i in library_ids:\n",
    "            smiles_i = documents_library_s2v[lib_id_i]._obj.get(\"smiles\")\n",
    "\n",
    "            if not smiles_i or smiles_i == \"None\": # check that lib_id_i smiles exist\n",
    "                rows.append([0] * len(library_ids)) #default to all 0 if it doesnt exist\n",
    "                continue\n",
    "            ms_i = Chem.MolFromSmiles(smiles_i)\n",
    "            if not ms_i: #in case something is wrong with smiles\n",
    "                rows.append([0] * len(library_ids)) #default to all 0 if it doesnt exist\n",
    "                continue\n",
    "            fp_i = Chem.RDKFingerprint(ms_i)\n",
    "\n",
    "            row = []\n",
    "            for lib_id_j in library_ids:\n",
    "                smiles_j = documents_library_s2v[lib_id_j]._obj.get(\"smiles\")\n",
    "\n",
    "                if smiles_j and smiles_j != \"None\":\n",
    "                    ms_j = Chem.MolFromSmiles(smiles_j)\n",
    "                    if ms_j:\n",
    "                        fp_j = Chem.RDKFingerprint(ms_j)\n",
    "                        score = DataStructs.FingerprintSimilarity(fp_i, fp_j)\n",
    "                    else: #in case something is wrong with smiles\n",
    "                        score = 0\n",
    "                else: #in case it doesnt have smiles\n",
    "                    score = 0\n",
    "                row.append(score)\n",
    "\n",
    "            rows.append(row)\n",
    "        similarity_matrices.append(pd.DataFrame(rows))\n",
    "    #write to file    \n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(similarity_matrices, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\similarity_matrices_corrected_test_set1.pickle\n"
     ]
    }
   ],
   "source": [
    "#corrected version where true matches get a slightly higher value in the diagonal\n",
    "import pickle\n",
    "cor_val = 1.001\n",
    "outfile = os.path.join(path_data, 'similarity_matrices_corrected_test_set1.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        similarity_matrices_cor = pickle.load(inf)\n",
    "else:\n",
    "    #for each matches df make a similarity matrix based on tanimoto\n",
    "    similarity_matrices_cor = []\n",
    "    topn = 20\n",
    "\n",
    "    for ID in range(len(found_matches_s2v)):\n",
    "        df = found_matches_s2v[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "        library_ids = df.index.values\n",
    "        rows = []\n",
    "        for lib_id_i in library_ids:\n",
    "            smiles_i = documents_library_s2v[lib_id_i]._obj.get(\"smiles\")\n",
    "\n",
    "            if not smiles_i or smiles_i == \"None\": # check that lib_id_i smiles exist\n",
    "                rows.append([0] * len(library_ids)) #default to all 0 if it doesnt exist\n",
    "                continue\n",
    "            ms_i = Chem.MolFromSmiles(smiles_i)\n",
    "            if not ms_i: #in case something is wrong with smiles\n",
    "                rows.append([0] * len(library_ids)) #default to all 0 if it doesnt exist\n",
    "                continue\n",
    "            fp_i = Chem.RDKFingerprint(ms_i)\n",
    "\n",
    "            row = []\n",
    "            for lib_id_j in library_ids:\n",
    "                if lib_id_i == lib_id_j: #if diagonal see if its a true match and give it a slightly higher value for plotting\n",
    "                    df_select = found_matches_s2v_match_perc_pmass_sim[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "                    if df_select.loc[lib_id_j]['label'] == 1:\n",
    "                        score = cor_val\n",
    "                    else:\n",
    "                        score = 1\n",
    "                    \n",
    "                else:\n",
    "                    smiles_j = documents_library_s2v[lib_id_j]._obj.get(\"smiles\")\n",
    "\n",
    "                    if smiles_j and smiles_j != \"None\":\n",
    "                        ms_j = Chem.MolFromSmiles(smiles_j)\n",
    "                        if ms_j:\n",
    "                            fp_j = Chem.RDKFingerprint(ms_j)\n",
    "                            score = DataStructs.FingerprintSimilarity(fp_i, fp_j)\n",
    "                        else: #in case something is wrong with smiles\n",
    "                            score = 0\n",
    "                    else: #in case it doesnt have smiles\n",
    "                        score = 0\n",
    "                row.append(score)\n",
    "\n",
    "            rows.append(row)\n",
    "        similarity_matrices_cor.append(pd.DataFrame(rows))\n",
    "    #write to file\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(similarity_matrices_cor, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <td>1.000000</td>\n",
       "      <td>0.201183</td>\n",
       "      <td>0.402556</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.988293</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.996071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.493651</td>\n",
       "      <td>0.483254</td>\n",
       "      <td>0.446927</td>\n",
       "      <td>0.446927</td>\n",
       "      <td>0.493651</td>\n",
       "      <td>0.493651</td>\n",
       "      <td>0.483254</td>\n",
       "      <td>0.989268</td>\n",
       "      <td>0.989268</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989268</td>\n",
       "      <td>0.199024</td>\n",
       "      <td>0.402416</td>\n",
       "      <td>0.993171</td>\n",
       "      <td>0.989268</td>\n",
       "      <td>0.299512</td>\n",
       "      <td>0.999024</td>\n",
       "      <td>0.993171</td>\n",
       "      <td>0.299512</td>\n",
       "      <td>0.993171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.493216</td>\n",
       "      <td>0.482759</td>\n",
       "      <td>0.446228</td>\n",
       "      <td>0.446228</td>\n",
       "      <td>0.493216</td>\n",
       "      <td>0.493216</td>\n",
       "      <td>0.482759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989268</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.201183</td>\n",
       "      <td>0.402556</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.988293</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.996071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.207053</td>\n",
       "      <td>0.210956</td>\n",
       "      <td>0.223181</td>\n",
       "      <td>0.223181</td>\n",
       "      <td>0.207053</td>\n",
       "      <td>0.207053</td>\n",
       "      <td>0.210956</td>\n",
       "      <td>0.201183</td>\n",
       "      <td>0.201183</td>\n",
       "      <td>0.199024</td>\n",
       "      <td>0.201183</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.155129</td>\n",
       "      <td>0.200393</td>\n",
       "      <td>0.201183</td>\n",
       "      <td>0.664495</td>\n",
       "      <td>0.199219</td>\n",
       "      <td>0.200393</td>\n",
       "      <td>0.664495</td>\n",
       "      <td>0.200393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.366868</td>\n",
       "      <td>0.359407</td>\n",
       "      <td>0.349554</td>\n",
       "      <td>0.349554</td>\n",
       "      <td>0.366868</td>\n",
       "      <td>0.366868</td>\n",
       "      <td>0.359407</td>\n",
       "      <td>0.402556</td>\n",
       "      <td>0.402556</td>\n",
       "      <td>0.402416</td>\n",
       "      <td>0.402556</td>\n",
       "      <td>0.155129</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.401530</td>\n",
       "      <td>0.402556</td>\n",
       "      <td>0.194222</td>\n",
       "      <td>0.402672</td>\n",
       "      <td>0.401530</td>\n",
       "      <td>0.194222</td>\n",
       "      <td>0.401530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.483587</td>\n",
       "      <td>0.447115</td>\n",
       "      <td>0.447115</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.483587</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.993171</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.200393</td>\n",
       "      <td>0.401530</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>0.992195</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.493216</td>\n",
       "      <td>0.482759</td>\n",
       "      <td>0.446228</td>\n",
       "      <td>0.446228</td>\n",
       "      <td>0.493216</td>\n",
       "      <td>0.493216</td>\n",
       "      <td>0.482759</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989268</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.201183</td>\n",
       "      <td>0.402556</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.988293</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.996071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.287611</td>\n",
       "      <td>0.268889</td>\n",
       "      <td>0.280702</td>\n",
       "      <td>0.280702</td>\n",
       "      <td>0.287611</td>\n",
       "      <td>0.287611</td>\n",
       "      <td>0.268889</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.299512</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.664495</td>\n",
       "      <td>0.194222</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.299805</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.494043</td>\n",
       "      <td>0.483639</td>\n",
       "      <td>0.447284</td>\n",
       "      <td>0.447284</td>\n",
       "      <td>0.494043</td>\n",
       "      <td>0.494043</td>\n",
       "      <td>0.483639</td>\n",
       "      <td>0.988293</td>\n",
       "      <td>0.988293</td>\n",
       "      <td>0.999024</td>\n",
       "      <td>0.988293</td>\n",
       "      <td>0.199219</td>\n",
       "      <td>0.402672</td>\n",
       "      <td>0.992195</td>\n",
       "      <td>0.988293</td>\n",
       "      <td>0.299805</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992195</td>\n",
       "      <td>0.299805</td>\n",
       "      <td>0.992195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.483587</td>\n",
       "      <td>0.447115</td>\n",
       "      <td>0.447115</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.483587</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.993171</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.200393</td>\n",
       "      <td>0.401530</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>0.992195</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.287611</td>\n",
       "      <td>0.268889</td>\n",
       "      <td>0.280702</td>\n",
       "      <td>0.280702</td>\n",
       "      <td>0.287611</td>\n",
       "      <td>0.287611</td>\n",
       "      <td>0.268889</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.299512</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>0.664495</td>\n",
       "      <td>0.194222</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>0.300197</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.299805</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.483587</td>\n",
       "      <td>0.447115</td>\n",
       "      <td>0.447115</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.494024</td>\n",
       "      <td>0.483587</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.993171</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.200393</td>\n",
       "      <td>0.401530</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996071</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>0.992195</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.301572</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6  \\\n",
       "0   1.000000  0.949309  0.919487  0.919487  1.000000  1.000000  0.949309   \n",
       "1   0.949309  1.000000  0.943713  0.943713  0.949309  0.949309  1.000000   \n",
       "2   0.919487  0.943713  1.001000  1.000000  0.919487  0.919487  0.943713   \n",
       "3   0.919487  0.943713  1.000000  1.001000  0.919487  0.919487  0.943713   \n",
       "4   1.000000  0.949309  0.919487  0.919487  1.000000  1.000000  0.949309   \n",
       "5   1.000000  0.949309  0.919487  0.919487  1.000000  1.000000  0.949309   \n",
       "6   0.949309  1.000000  0.943713  0.943713  0.949309  0.949309  1.000000   \n",
       "7   0.493216  0.482759  0.446228  0.446228  0.493216  0.493216  0.482759   \n",
       "8   0.493216  0.482759  0.446228  0.446228  0.493216  0.493216  0.482759   \n",
       "9   0.493651  0.483254  0.446927  0.446927  0.493651  0.493651  0.483254   \n",
       "10  0.493216  0.482759  0.446228  0.446228  0.493216  0.493216  0.482759   \n",
       "11  0.207053  0.210956  0.223181  0.223181  0.207053  0.207053  0.210956   \n",
       "12  0.366868  0.359407  0.349554  0.349554  0.366868  0.366868  0.359407   \n",
       "13  0.494024  0.483587  0.447115  0.447115  0.494024  0.494024  0.483587   \n",
       "14  0.493216  0.482759  0.446228  0.446228  0.493216  0.493216  0.482759   \n",
       "15  0.287611  0.268889  0.280702  0.280702  0.287611  0.287611  0.268889   \n",
       "16  0.494043  0.483639  0.447284  0.447284  0.494043  0.494043  0.483639   \n",
       "17  0.494024  0.483587  0.447115  0.447115  0.494024  0.494024  0.483587   \n",
       "18  0.287611  0.268889  0.280702  0.280702  0.287611  0.287611  0.268889   \n",
       "19  0.494024  0.483587  0.447115  0.447115  0.494024  0.494024  0.483587   \n",
       "\n",
       "           7         8         9        10        11        12        13  \\\n",
       "0   0.493216  0.493216  0.493651  0.493216  0.207053  0.366868  0.494024   \n",
       "1   0.482759  0.482759  0.483254  0.482759  0.210956  0.359407  0.483587   \n",
       "2   0.446228  0.446228  0.446927  0.446228  0.223181  0.349554  0.447115   \n",
       "3   0.446228  0.446228  0.446927  0.446228  0.223181  0.349554  0.447115   \n",
       "4   0.493216  0.493216  0.493651  0.493216  0.207053  0.366868  0.494024   \n",
       "5   0.493216  0.493216  0.493651  0.493216  0.207053  0.366868  0.494024   \n",
       "6   0.482759  0.482759  0.483254  0.482759  0.210956  0.359407  0.483587   \n",
       "7   1.000000  1.000000  0.989268  1.000000  0.201183  0.402556  0.996071   \n",
       "8   1.000000  1.000000  0.989268  1.000000  0.201183  0.402556  0.996071   \n",
       "9   0.989268  0.989268  1.000000  0.989268  0.199024  0.402416  0.993171   \n",
       "10  1.000000  1.000000  0.989268  1.000000  0.201183  0.402556  0.996071   \n",
       "11  0.201183  0.201183  0.199024  0.201183  1.000000  0.155129  0.200393   \n",
       "12  0.402556  0.402556  0.402416  0.402556  0.155129  1.000000  0.401530   \n",
       "13  0.996071  0.996071  0.993171  0.996071  0.200393  0.401530  1.000000   \n",
       "14  1.000000  1.000000  0.989268  1.000000  0.201183  0.402556  0.996071   \n",
       "15  0.300197  0.300197  0.299512  0.300197  0.664495  0.194222  0.301572   \n",
       "16  0.988293  0.988293  0.999024  0.988293  0.199219  0.402672  0.992195   \n",
       "17  0.996071  0.996071  0.993171  0.996071  0.200393  0.401530  1.000000   \n",
       "18  0.300197  0.300197  0.299512  0.300197  0.664495  0.194222  0.301572   \n",
       "19  0.996071  0.996071  0.993171  0.996071  0.200393  0.401530  1.000000   \n",
       "\n",
       "          14        15        16        17        18        19  \n",
       "0   0.493216  0.287611  0.494043  0.494024  0.287611  0.494024  \n",
       "1   0.482759  0.268889  0.483639  0.483587  0.268889  0.483587  \n",
       "2   0.446228  0.280702  0.447284  0.447115  0.280702  0.447115  \n",
       "3   0.446228  0.280702  0.447284  0.447115  0.280702  0.447115  \n",
       "4   0.493216  0.287611  0.494043  0.494024  0.287611  0.494024  \n",
       "5   0.493216  0.287611  0.494043  0.494024  0.287611  0.494024  \n",
       "6   0.482759  0.268889  0.483639  0.483587  0.268889  0.483587  \n",
       "7   1.000000  0.300197  0.988293  0.996071  0.300197  0.996071  \n",
       "8   1.000000  0.300197  0.988293  0.996071  0.300197  0.996071  \n",
       "9   0.989268  0.299512  0.999024  0.993171  0.299512  0.993171  \n",
       "10  1.000000  0.300197  0.988293  0.996071  0.300197  0.996071  \n",
       "11  0.201183  0.664495  0.199219  0.200393  0.664495  0.200393  \n",
       "12  0.402556  0.194222  0.402672  0.401530  0.194222  0.401530  \n",
       "13  0.996071  0.301572  0.992195  1.000000  0.301572  1.000000  \n",
       "14  1.000000  0.300197  0.988293  0.996071  0.300197  0.996071  \n",
       "15  0.300197  1.000000  0.299805  0.301572  1.000000  0.301572  \n",
       "16  0.988293  0.299805  1.000000  0.992195  0.299805  0.992195  \n",
       "17  0.996071  0.301572  0.992195  1.000000  0.301572  1.000000  \n",
       "18  0.300197  1.000000  0.299805  0.301572  1.000000  0.301572  \n",
       "19  0.996071  0.301572  0.992195  1.000000  0.301572  1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rows = similarity_matrices_cor[9]\n",
    "df_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "\n",
    "x = 100000\n",
    "inferno = cm.get_cmap('inferno', x)\n",
    "green = np.array([50/256, 205/256, 50/256, 1])\n",
    "new_colours = np.vstack((inferno(np.linspace(0, 1, x)), green))\n",
    "new_cmap = ListedColormap(new_colours, name='inferno-green')\n",
    "plt.imshow(df_rows, cmap=new_cmap, vmin=0, vmax=cor_val)\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, 5, figsize=(14,10))\n",
    "for i, ax in enumerate(axs.flat):\n",
    "    im = ax.imshow(similarity_matrices[i], cmap='inferno', vmin = 0, vmax = 1)\n",
    "\n",
    "fig.subplots_adjust(right=0.95)\n",
    "cbar_ax = fig.add_axes([0.97, 0.15, 0.02, 0.69])\n",
    "fig.colorbar(im, cax=cbar_ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(4, 5, figsize=(14,10))\n",
    "for i, ax in enumerate(axs.flat):\n",
    "    im = ax.imshow(similarity_matrices_cor[i], cmap=new_cmap, vmax=cor_val, vmin=0)\n",
    "\n",
    "fig.subplots_adjust(right=0.95)\n",
    "cbar_ax = fig.add_axes([0.97, 0.15, 0.02, 0.69])\n",
    "fig.colorbar(im, cax=cbar_ax)\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for each best true hit, how many connections > 0.6 does it have?\n",
    "topn = 20\n",
    "ID=0\n",
    "cutoff = 0.6\n",
    "amount_sims = []\n",
    "for ID in range(len(found_matches_s2v_match_perc_pmass_sim)):\n",
    "    df_select = found_matches_s2v_match_perc_pmass_sim[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    true_positives = [i for i, lab in enumerate(df_select['label']) if lab == 1]\n",
    "    try:\n",
    "        true_pos = true_positives[0] #take top hit\n",
    "    except IndexError:\n",
    "        amount_sim = -1 #true positive can't be found in topn\n",
    "    else:\n",
    "        amount_sim = len(similarity_matrices[ID][similarity_matrices[ID][true_pos] > cutoff])\n",
    "    #report max cases\n",
    "    #if amount_sim == 20:\n",
    "        #print(ID)\n",
    "    amount_sims.append(amount_sim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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.hist(amount_sims, bins = range(-1, max(amount_sims)+1))\n",
    "plt.xlabel('Connections above 0.6')\n",
    "plt.title('Amount of connections above 0.6 for each best true match')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 0.2403846153846154\n",
      "9 0.9194865810968494\n",
      "16 0.8412371134020619\n",
      "22 0.7375\n",
      "26 0.3267909715407262\n",
      "40 0.18181818181818182\n",
      "42 0.45215869311551926\n",
      "46 0.9715639810426541\n",
      "48 0.5869191049913941\n",
      "49 0.054187192118226604\n",
      "51 0.9177489177489178\n",
      "52 0.3597014925373134\n",
      "57 0.5076645626690712\n",
      "61 0.047409040793825796\n",
      "65 0.2460431654676259\n",
      "66 0.9473684210526315\n",
      "74 0.24050632911392406\n",
      "79 0.04966139954853273\n",
      "85 0.9792122538293216\n",
      "86 0.14160401002506265\n",
      "88 0.8095238095238095\n",
      "89 0.10071942446043165\n",
      "92 1.0\n",
      "95 0.9693530079455165\n",
      "96 0.4790697674418605\n",
      "97 0.29707112970711297\n",
      "98 1.0\n",
      "104 1.0\n",
      "105 0.9194476409666283\n",
      "115 0.6427406199021207\n",
      "116 0.7189655172413794\n",
      "119 0.8018494055482166\n",
      "128 0.9015486725663717\n",
      "132 0.9971374045801527\n",
      "135 0.9919839679358717\n",
      "136 0.3629081947011707\n",
      "138 0.9522709776751347\n",
      "145 0.4662613981762918\n",
      "147 0.20631067961165048\n",
      "150 0.5722145804676754\n",
      "153 1.0\n",
      "154 0.9902755267423015\n",
      "157 0.4552090245520902\n",
      "158 0.5613249776186213\n",
      "166 0.15494296577946767\n",
      "167 0.20911528150134048\n",
      "169 0.8957345971563981\n",
      "172 0.6984732824427481\n",
      "173 1.0\n",
      "177 0.7392\n",
      "178 0.9556485355648535\n",
      "179 0.9776785714285714\n",
      "181 0.5395387149917628\n",
      "187 0.15145985401459855\n",
      "188 0.6585014409221902\n",
      "189 1.0\n",
      "190 0.34830019243104554\n",
      "200 0.9283236994219654\n",
      "205 1.0\n",
      "208 0.9372666168782674\n",
      "210 0.8950617283950617\n",
      "211 0.17761033369214208\n",
      "215 0.06642066420664207\n",
      "219 0.9273827534039334\n",
      "220 0.8218623481781376\n",
      "222 0.9897959183673469\n",
      "236 1.0\n",
      "238 0.9992526158445441\n",
      "245 0.8629032258064516\n",
      "246 0.36191860465116277\n",
      "247 0.125\n",
      "252 0.3630831643002028\n",
      "253 0.15942028985507245\n",
      "255 0.10022271714922049\n",
      "256 0.3566084788029925\n",
      "258 0.9653641207815276\n",
      "268 0.1719022687609075\n",
      "269 0.4274041483343809\n",
      "270 0.2654320987654321\n",
      "281 0.49364791288566245\n",
      "290 0.42732376793512167\n",
      "293 0.2657689581856839\n",
      "301 0.47232472324723246\n",
      "306 0.038202247191011236\n",
      "317 0.9784226190476191\n",
      "322 0.1435523114355231\n",
      "323 0.0871559633027523\n",
      "324 0.19402985074626866\n",
      "329 0.34036144578313254\n",
      "331 0.5769230769230769\n",
      "333 1.0\n",
      "334 1.0\n",
      "336 0.9557171183079973\n",
      "345 0.7995391705069125\n",
      "350 1.0\n",
      "353 0.1407035175879397\n",
      "357 1.0\n",
      "360 1.0\n",
      "370 0.6124794745484401\n",
      "372 1.0\n",
      "373 0.9345029239766082\n",
      "375 0.972318339100346\n",
      "379 0.32246633593196317\n",
      "384 1.0\n",
      "391 0.8395061728395061\n",
      "394 0.018072289156626505\n",
      "395 0.5766871165644172\n",
      "400 0.69377990430622\n",
      "401 0.9867924528301887\n",
      "405 0.9974424552429667\n",
      "409 0.4033970276008493\n",
      "415 0.36893203883495146\n",
      "418 0.5477621910487642\n",
      "419 0.43243243243243246\n",
      "420 0.9273827534039334\n",
      "435 0.6408296943231441\n",
      "440 0.9584158415841584\n",
      "443 1.0\n",
      "445 0.08130081300813008\n",
      "453 0.635048231511254\n",
      "458 0.8491803278688524\n",
      "461 0.41338854382332646\n",
      "462 1.0\n",
      "469 0.9138655462184874\n",
      "481 0.8721017202692596\n",
      "486 0.8960739030023095\n",
      "487 0.4740061162079511\n",
      "490 0.350253807106599\n",
      "493 0.8571428571428571\n",
      "494 0.489261744966443\n",
      "499 0.3112033195020747\n",
      "503 0.8572448251249107\n",
      "511 0.9394618834080718\n",
      "516 0.15359477124183007\n",
      "522 0.6331877729257642\n",
      "525 0.6177703269069572\n",
      "532 0.5324165029469549\n",
      "534 0.3512396694214876\n",
      "535 0.5241995532390171\n",
      "558 0.3150326797385621\n",
      "563 0.7165354330708661\n",
      "565 0.05741626794258373\n",
      "566 0.2613430127041742\n",
      "568 0.29668552950687144\n",
      "576 0.0463768115942029\n",
      "579 0.2703751617076326\n",
      "580 0.9438502673796791\n",
      "582 0.3287292817679558\n",
      "588 0.5586206896551724\n",
      "589 0.1993006993006993\n",
      "596 0.9435146443514645\n",
      "598 0.10556621880998081\n",
      "602 0.1721311475409836\n",
      "605 0.8219584569732937\n",
      "607 0.18028846153846154\n",
      "609 0.4076858813700919\n",
      "610 0.4942459115687462\n",
      "611 0.967816091954023\n",
      "612 0.3689759036144578\n",
      "619 0.8827977315689981\n",
      "620 0.026804123711340205\n",
      "622 0.9284064665127021\n",
      "625 1.0\n",
      "626 0.04181494661921708\n",
      "629 0.8644859813084113\n",
      "632 0.1797752808988764\n",
      "641 0.9511627906976744\n",
      "645 0.9781491002570694\n",
      "647 0.9842696629213483\n",
      "648 0.34718498659517427\n",
      "649 0.93058568329718\n",
      "653 0.25\n",
      "655 0.05343511450381679\n",
      "662 0.2937365010799136\n",
      "668 0.7551669316375199\n",
      "672 1.0\n",
      "677 0.9178617992177314\n",
      "683 0.7732962447844228\n",
      "684 0.9924433249370277\n",
      "689 0.43007915567282323\n",
      "690 0.12531969309462915\n",
      "691 0.7715019255455713\n",
      "694 0.5153681200857756\n",
      "696 0.9775280898876404\n",
      "701 0.6756756756756757\n",
      "703 0.9243119266055045\n",
      "706 0.3875968992248062\n",
      "713 0.5422116527942925\n",
      "717 0.7146562905317769\n",
      "720 0.8851063829787233\n",
      "727 0.32114882506527415\n",
      "729 0.7581552305961755\n",
      "740 0.8625954198473282\n",
      "744 0.6756756756756757\n",
      "748 0.1832627118644068\n",
      "749 1.0\n",
      "750 0.25152439024390244\n",
      "751 0.980110497237569\n",
      "754 0.5976447497546614\n",
      "761 0.9961240310077519\n",
      "763 1.0\n",
      "764 0.9988610478359908\n",
      "766 0.8933002481389578\n",
      "767 1.0\n",
      "769 0.8678414096916299\n",
      "772 0.3664688427299703\n",
      "774 0.9071428571428571\n",
      "775 0.6590450571620713\n",
      "779 0.98\n",
      "784 0.56\n",
      "790 0.8919431279620853\n",
      "793 1.0\n",
      "801 0.9162946428571429\n",
      "803 0.9548693586698337\n",
      "804 0.8308270676691729\n",
      "810 0.9774590163934426\n",
      "811 0.9288888888888889\n",
      "812 0.7322515212981744\n",
      "813 0.8521939953810623\n",
      "824 0.8333333333333334\n",
      "827 1.0\n",
      "829 0.9116022099447514\n",
      "832 0.9550398839738942\n",
      "836 1.0\n",
      "844 1.0\n",
      "849 0.6119402985074627\n",
      "859 0.8489535941765242\n",
      "866 1.0\n",
      "870 0.24938875305623473\n",
      "876 0.8778625954198473\n",
      "877 0.10326086956521739\n",
      "878 1.0\n",
      "883 0.7666098807495741\n",
      "887 0.7144060657118787\n",
      "894 1.0\n",
      "909 0.36666666666666664\n",
      "914 0.8053691275167785\n",
      "921 0.872\n",
      "925 0.0779896013864818\n",
      "927 0.4711111111111111\n",
      "928 0.991556091676719\n",
      "936 1.0\n",
      "938 0.2596685082872928\n",
      "942 0.10862619808306709\n",
      "943 0.9807355516637478\n",
      "946 0.6861313868613139\n",
      "950 0.38184791496320525\n",
      "951 0.1797593772116065\n",
      "956 0.29136690647482016\n",
      "965 0.7724288840262582\n",
      "966 0.34188817598533455\n",
      "970 0.3177570093457944\n",
      "971 0.5047759000734754\n",
      "975 0.04861111111111111\n",
      "976 0.4301675977653631\n",
      "977 0.9536516853932584\n",
      "978 0.6675461741424802\n",
      "980 0.7714285714285715\n",
      "982 0.9801071155317521\n",
      "988 0.06308851224105462\n",
      "989 0.47214854111405835\n",
      "993 1.0\n",
      "999 0.3439878234398782\n",
      "263\n"
     ]
    }
   ],
   "source": [
    "#if top hit is not true match, what is the average similarity of top hit above a threshold?\n",
    "topn = 20\n",
    "threshold = 0.4\n",
    "false_m_simils_thresh = []\n",
    "ids_top_false_hits_thresh = []\n",
    "for ID in range(len(found_matches_s2v_match_perc_pmass_sim)):\n",
    "    df_select = found_matches_s2v_match_perc_pmass_sim[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    df_select = df_select[(df_select['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        top_hit = df_select.iloc[0]\n",
    "        if top_hit['label'] == 0:\n",
    "            false_m_simil = top_hit['similarity']\n",
    "            print(ID,false_m_simil)\n",
    "            ids_top_false_hits_thresh.append(ID)\n",
    "            false_m_simils_thresh.append(false_m_simil)\n",
    "print(len(false_m_simils_thresh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJsAAACMCAYAAABrstuvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAGbklEQVR4nO3dS4hcVR7H8e/P10IZNW3HB2rsWYSBOChI4wMH1IWSBCUILhKGkQmCKOPGxYArHzvBlYIPggRxYdz4XMTXTlEDdos6zjAjwQeGCEm0iTgKEvm7uKdjJVZ1305O/vW4vw9c0nXOPVWnyI9bj1v/exQRmGU4adgTsO5w2CyNw2ZpHDZL47BZGofN0pwy7An0Mz09HTMzM8Oehh2D+fn5AxGxul/fsmGTtB24GdgXEX/u0y/gUWAj8CPw94j4sPStL30nA09HxMNtJjwzM8Pc3FybXW3ESPpqUF+bl9FngPVL9G8A1pbtTuDJ8qAnA4+X/nXAFknr2k3ZJtGyYYuIt4HvlthlE/BsNHYBZ0u6ALgS2B0Rn0fEz8DzZV/rqBofEC4Evu65vae0DWq3jqrxAUF92mKJ9v53It1J8zLMmjVrKkxrNDRvaZfXhXPUNY5se4CLe25fBOxdor2viNgWEbMRMbt6dd8PM2MpIo7Y+rV1IWhQJ2yvArercTVwMCK+AT4A1kr6o6TTgM1lX+uoNl997ACuB6Yl7QEeAE4FiIingJ00X3vspvnqY2vpOyTpHuANmq8+tkfEv0/Ac7AxsWzYImLLMv0B/GNA306aMJr5dFVtU1NTSBq4AUv2S2JqamrIz+LEGMnTVeNsYWHhuN/wt/0EO258ZLM0DpulcdgsjcNmaRw2S+OwWRqHzdI4bJbGYbM0DpulcdgsjcNmaRw2S+OwWZpWYZO0XtL/JO2WdF+f/n9K+qhsn0r6RdJU6ftS0r9KnyuPO6zNz8IXi41vpCli+UDSqxHxn8V9IuIR4JGy/y3AvRHRW2t6Q0QcqDpzGzttjmwrLTbeAuyoMTmbLG3C1rrYWNLpNJdqeKGnOYA3Jc2X2lDrqDY/C19JsfEtwLtHvYReGxF7JZ0LvCXpv+WSDkc+yIQWKdtv2hzZVlJsvJmjXkIjYm/5dx/wEs3L8u9MapGy/aZN2FoVG0s6C7gOeKWn7QxJf1j8G7gJ+LTGxG38tKkb7VtsLOmu0v9U2fVW4M2I+H/P8POAl0q10CnAcxHxes0nYONDo3ididnZ2RjXiwFKqlLKN4r/L21Imo+I2X59rhutLB44Ex486/jvYwI5bJXpoe/rHNkerDOfUeJzo5bGYbM0DpulcdgsjcNmaRw2S+OwWRqHzdI4bJbGYbM0DpulcdgsjcNmaRw2S1OrSPl6SQd7CpXvbzvWuqNKkXLxTkTcfIxjrQNORJFyrbE2YWoWKV8j6WNJr0m6dIVjrQNqFSl/CFwSET9I2gi8DKxtObZ5EBcpT7wqRcoR8X1E/FD+3gmcKmm6zdie+3CR8oSrUqQs6XyV4lBJV5b7/bbNWOuOWkXKtwF3SzoE/ARsLoveejVlO8xFypW5SHlwkbLPIFgah83SOGyWxmGzNA6bpXHYLI3DZmkcNkvjsFkah83SOGyWxpc5PQHKD2CO2apVqyrNZLQ4bJUtdwJ9nE+yHy+/jFoah83SOGyWxmGzNLUq4v8q6ZOyvSfp8p4+L9ttQL2K+C+A6yJiQdIGYBtwVU+/l+22OhXxEfFeRCyUm7toSvbMjlB12e7iDuC1nttettuAyst2S7qBJmx/6Wn2st0GVFy2W9JlwNPApoj4drHdy3bboloV8WuAF4G/RcRnPe1ettsOq1URfz9wDvBEOQl9qBSqetluO8wV8ckm/US8K+JtJDhslsZhszQOm6Vx2CyNw2ZpHDZL47BZGofN0jhslsZhszQOm6Vx2CyNw2ZpHDZL47BZmlpFypL0WOn/RNIVbcdadywbtp4i5Q3AOmCLpHVH7baBZn3RtTQVUk+uYKx1RK1luzcBz0ZjF3C2pAtajp1oko7Y+rUd78UDx0WtIuVB+3R+2e6IaLV1QZuwtSlSHrTPipbtljQnaW7//v0tpmXjplaR8qB9vGy3HValSLncvr18Kr0aOBgR37Qcax1Rq0h5J7AR2A38CGxdauxyjzk/P39A0lfH+JxG3TQwyZcPu2RQx0gWKU8ySXODingnnc8gWBqHzdI4bPm2DXsCw+L3bJbGRzZL47AlkbRd0j5Jnb0YosOW5xlg/bAnMUwOW5Jy0ervhj2PYXLYLI3DZmkcNkvjsFkahy2JpB3A+8CfJO2RdMew55TNZxAsjY9slsZhszQOm6Vx2CyNw2ZpHDZL47BZGofN0vwKe73mz0xY/hwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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>avg_similarity_false_top_hit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>263.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.629614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.327008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.018072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.342938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.698473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.949266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       avg_similarity_false_top_hit\n",
       "count                    263.000000\n",
       "mean                       0.629614\n",
       "std                        0.327008\n",
       "min                        0.018072\n",
       "25%                        0.342938\n",
       "50%                        0.698473\n",
       "75%                        0.949266\n",
       "max                        1.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(2,2))\n",
    "plt.boxplot(false_m_simils_thresh)\n",
    "plt.show()\n",
    "plt.close()\n",
    "pd.DataFrame(false_m_simils_thresh, columns = ['avg_similarity_false_top_hit']).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1300x900 with 21 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#same plot but only the ones where the top hit is not the true hit\n",
    "plt.style.use('default')\n",
    "fig, axs = plt.subplots(4, 5, figsize=(13,9))\n",
    "for i, ax in enumerate(axs.flat):\n",
    "    im = ax.imshow(similarity_matrices_cor[ids_top_false_hits_thresh[i]], cmap=new_cmap, vmax=cor_val, vmin=0)\n",
    "\n",
    "fig.subplots_adjust(right=0.95)\n",
    "cbar_ax = fig.add_axes([0.97, 0.15, 0.02, 0.69])\n",
    "fig.colorbar(im, cax=cbar_ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>mass_match</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>perc_mass_change</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>63644</th>\n",
       "      <td>4.746049e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>0.474605</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.480207</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.240385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64360</th>\n",
       "      <td>4.674166e-01</td>\n",
       "      <td>6</td>\n",
       "      <td>0.467438</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.463698</td>\n",
       "      <td>1</td>\n",
       "      <td>0.678008</td>\n",
       "      <td>117.192724</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66640</th>\n",
       "      <td>3.027054e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.302705</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.442751</td>\n",
       "      <td>0</td>\n",
       "      <td>57.630672</td>\n",
       "      <td>185.992724</td>\n",
       "      <td>0.026567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64027</th>\n",
       "      <td>8.221855e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0.822186</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.438960</td>\n",
       "      <td>0</td>\n",
       "      <td>72.885850</td>\n",
       "      <td>203.992724</td>\n",
       "      <td>0.056711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63322</th>\n",
       "      <td>2.663150e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.266315</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.379661</td>\n",
       "      <td>0</td>\n",
       "      <td>160.179369</td>\n",
       "      <td>306.992724</td>\n",
       "      <td>0.071233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63919</th>\n",
       "      <td>8.439887e-03</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012535</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.365428</td>\n",
       "      <td>0</td>\n",
       "      <td>22.882767</td>\n",
       "      <td>144.992724</td>\n",
       "      <td>0.021818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64380</th>\n",
       "      <td>8.442114e-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0.844212</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.354063</td>\n",
       "      <td>0</td>\n",
       "      <td>108.650768</td>\n",
       "      <td>246.192724</td>\n",
       "      <td>0.112540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62877</th>\n",
       "      <td>9.540146e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0.009540</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.353165</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>117.992724</td>\n",
       "      <td>0.252427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66700</th>\n",
       "      <td>1.447068e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0.022203</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.352218</td>\n",
       "      <td>0</td>\n",
       "      <td>38.137945</td>\n",
       "      <td>162.992724</td>\n",
       "      <td>0.068720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63089</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.004762</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.341212</td>\n",
       "      <td>0</td>\n",
       "      <td>24.577787</td>\n",
       "      <td>146.992724</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62599</th>\n",
       "      <td>2.806973e-05</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008937</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.339830</td>\n",
       "      <td>0</td>\n",
       "      <td>11.017629</td>\n",
       "      <td>130.992724</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62765</th>\n",
       "      <td>1.187142e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.120306</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.327784</td>\n",
       "      <td>0</td>\n",
       "      <td>29.662846</td>\n",
       "      <td>152.992724</td>\n",
       "      <td>0.168831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66749</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.007860</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.326186</td>\n",
       "      <td>0</td>\n",
       "      <td>17.797708</td>\n",
       "      <td>138.992724</td>\n",
       "      <td>0.038117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62495</th>\n",
       "      <td>1.802696e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.180271</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.324013</td>\n",
       "      <td>0</td>\n",
       "      <td>37.290435</td>\n",
       "      <td>161.992724</td>\n",
       "      <td>0.030879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66696</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009914</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.320751</td>\n",
       "      <td>0</td>\n",
       "      <td>149.161740</td>\n",
       "      <td>293.992724</td>\n",
       "      <td>0.031469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64065</th>\n",
       "      <td>1.087738e-05</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002915</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.319085</td>\n",
       "      <td>0</td>\n",
       "      <td>52.545613</td>\n",
       "      <td>179.992724</td>\n",
       "      <td>0.030812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66691</th>\n",
       "      <td>1.024550e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.102456</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.317690</td>\n",
       "      <td>0</td>\n",
       "      <td>121.193914</td>\n",
       "      <td>260.992724</td>\n",
       "      <td>0.071233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67341</th>\n",
       "      <td>1.024550e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.102456</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.317690</td>\n",
       "      <td>0</td>\n",
       "      <td>121.193914</td>\n",
       "      <td>260.992724</td>\n",
       "      <td>0.071233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62626</th>\n",
       "      <td>6.302874e-01</td>\n",
       "      <td>4</td>\n",
       "      <td>0.630287</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.316583</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.211864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63355</th>\n",
       "      <td>2.407918e-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0.240792</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314746</td>\n",
       "      <td>0</td>\n",
       "      <td>76.275890</td>\n",
       "      <td>207.992724</td>\n",
       "      <td>0.068720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64178</th>\n",
       "      <td>5.638382e-01</td>\n",
       "      <td>8</td>\n",
       "      <td>0.563838</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.286218</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62740</th>\n",
       "      <td>1.685722e-03</td>\n",
       "      <td>3</td>\n",
       "      <td>0.001686</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.245037</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>117.992724</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62627</th>\n",
       "      <td>4.274009e-05</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.242911</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.211864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93932</th>\n",
       "      <td>7.629720e-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000763</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.242149</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069496</td>\n",
       "      <td>118.074724</td>\n",
       "      <td>0.252427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64046</th>\n",
       "      <td>8.891573e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>0.008406</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.231553</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>118.992724</td>\n",
       "      <td>0.211864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63428</th>\n",
       "      <td>1.855486e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002237</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.227307</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>118.992724</td>\n",
       "      <td>0.233645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63646</th>\n",
       "      <td>4.281891e-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.226793</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.240385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62742</th>\n",
       "      <td>4.294387e-03</td>\n",
       "      <td>2</td>\n",
       "      <td>0.004294</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.219405</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>117.992724</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92895</th>\n",
       "      <td>2.734385e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002734</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.216312</td>\n",
       "      <td>0</td>\n",
       "      <td>0.779709</td>\n",
       "      <td>117.072724</td>\n",
       "      <td>0.292135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62741</th>\n",
       "      <td>2.065324e-04</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.211284</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>117.992724</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62628</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.209123</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.211864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92693</th>\n",
       "      <td>6.136944e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.204812</td>\n",
       "      <td>0</td>\n",
       "      <td>0.762759</td>\n",
       "      <td>117.092724</td>\n",
       "      <td>0.213483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64180</th>\n",
       "      <td>1.157076e-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000125</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.192268</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62629</th>\n",
       "      <td>5.233428e-05</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000144</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.183699</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.211864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64049</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.182039</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>118.992724</td>\n",
       "      <td>0.211864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93603</th>\n",
       "      <td>5.375520e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0.005376</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.179211</td>\n",
       "      <td>0</td>\n",
       "      <td>0.773777</td>\n",
       "      <td>117.079724</td>\n",
       "      <td>0.213483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62682</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.172337</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>117.992724</td>\n",
       "      <td>0.015873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63648</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.171086</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.240385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62586</th>\n",
       "      <td>4.802263e-02</td>\n",
       "      <td>2</td>\n",
       "      <td>0.048050</td>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.168876</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.213483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64181</th>\n",
       "      <td>1.796546e-05</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.163996</td>\n",
       "      <td>0</td>\n",
       "      <td>0.847510</td>\n",
       "      <td>116.992724</td>\n",
       "      <td>0.238095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "63644  4.746049e-01               4          0.474605                   4   \n",
       "64360  4.674166e-01               6          0.467438                   7   \n",
       "66640  3.027054e-01               1          0.302705                   1   \n",
       "64027  8.221855e-01               2          0.822186                   2   \n",
       "63322  2.663150e-01               1          0.266315                   1   \n",
       "63919  8.439887e-03               2          0.012535                   2   \n",
       "64380  8.442114e-01               2          0.844212                   3   \n",
       "62877  9.540146e-03               1          0.009540                   1   \n",
       "66700  1.447068e-02               1          0.022203                   2   \n",
       "63089  0.000000e+00               0          0.004762                   2   \n",
       "62599  2.806973e-05               2          0.008937                   4   \n",
       "62765  1.187142e-01               1          0.120306                   3   \n",
       "66749  0.000000e+00               0          0.007860                   1   \n",
       "62495  1.802696e-01               1          0.180271                   4   \n",
       "66696  0.000000e+00               0          0.009914                   2   \n",
       "64065  1.087738e-05               2          0.002915                   4   \n",
       "66691  1.024550e-01               1          0.102456                   2   \n",
       "67341  1.024550e-01               1          0.102456                   2   \n",
       "62626  6.302874e-01               4          0.630287                   4   \n",
       "63355  2.407918e-01               3          0.240792                   3   \n",
       "64178  5.638382e-01               8          0.563838                   8   \n",
       "62740  1.685722e-03               3          0.001686                   3   \n",
       "62627  4.274009e-05               2          0.000043                   2   \n",
       "93932  7.629720e-04               1          0.000763                   1   \n",
       "64046  8.891573e-08               1          0.008406                   3   \n",
       "63428  1.855486e-06               1          0.002237                   2   \n",
       "63646  4.281891e-05               1          0.000043                   1   \n",
       "62742  4.294387e-03               2          0.004294                   2   \n",
       "92895  2.734385e-03               1          0.002734                   1   \n",
       "62741  2.065324e-04               2          0.000207                   2   \n",
       "62628  0.000000e+00               0          0.000023                   1   \n",
       "92693  6.136944e-03               1          0.006150                   2   \n",
       "64180  1.157076e-04               1          0.000125                   2   \n",
       "62629  5.233428e-05               2          0.000144                   4   \n",
       "64049  0.000000e+00               0          0.000122                   3   \n",
       "93603  5.375520e-03               1          0.005376                   1   \n",
       "62682  0.000000e+00               0          0.000000                   0   \n",
       "63648  0.000000e+00               0          0.000000                   0   \n",
       "62586  4.802263e-02               2          0.048050                   4   \n",
       "64181  1.796546e-05               2          0.000033                   3   \n",
       "\n",
       "       mass_match  s2v_score  label  perc_mass_change  parent_mass  similarity  \n",
       "63644         1.0   0.480207      0          0.847510   116.992724    0.240385  \n",
       "64360         1.0   0.463698      1          0.678008   117.192724    1.000000  \n",
       "66640         0.0   0.442751      0         57.630672   185.992724    0.026567  \n",
       "64027         0.0   0.438960      0         72.885850   203.992724    0.056711  \n",
       "63322         0.0   0.379661      0        160.179369   306.992724    0.071233  \n",
       "63919         0.0   0.365428      0         22.882767   144.992724    0.021818  \n",
       "64380         0.0   0.354063      0        108.650768   246.192724    0.112540  \n",
       "62877         1.0   0.353165      0          0.000000   117.992724    0.252427  \n",
       "66700         0.0   0.352218      0         38.137945   162.992724    0.068720  \n",
       "63089         0.0   0.341212      0         24.577787   146.992724    0.200000  \n",
       "62599         0.0   0.339830      0         11.017629   130.992724    0.200000  \n",
       "62765         0.0   0.327784      0         29.662846   152.992724    0.168831  \n",
       "66749         0.0   0.326186      0         17.797708   138.992724    0.038117  \n",
       "62495         0.0   0.324013      0         37.290435   161.992724    0.030879  \n",
       "66696         0.0   0.320751      0        149.161740   293.992724    0.031469  \n",
       "64065         0.0   0.319085      0         52.545613   179.992724    0.030812  \n",
       "66691         0.0   0.317690      0        121.193914   260.992724    0.071233  \n",
       "67341         0.0   0.317690      0        121.193914   260.992724    0.071233  \n",
       "62626         1.0   0.316583      0          0.847510   116.992724    0.211864  \n",
       "63355         0.0   0.314746      0         76.275890   207.992724    0.068720  \n",
       "64178         1.0   0.286218      0          0.847510   116.992724    0.238095  \n",
       "62740         1.0   0.245037      1          0.000000   117.992724    1.000000  \n",
       "62627         1.0   0.242911      0          0.847510   116.992724    0.211864  \n",
       "93932         1.0   0.242149      0          0.069496   118.074724    0.252427  \n",
       "64046         1.0   0.231553      0          0.847510   118.992724    0.211864  \n",
       "63428         1.0   0.227307      0          0.847510   118.992724    0.233645  \n",
       "63646         1.0   0.226793      0          0.847510   116.992724    0.240385  \n",
       "62742         1.0   0.219405      1          0.000000   117.992724    1.000000  \n",
       "92895         1.0   0.216312      0          0.779709   117.072724    0.292135  \n",
       "62741         1.0   0.211284      1          0.000000   117.992724    1.000000  \n",
       "62628         1.0   0.209123      0          0.847510   116.992724    0.211864  \n",
       "92693         1.0   0.204812      0          0.762759   117.092724    0.213483  \n",
       "64180         1.0   0.192268      0          0.847510   116.992724    0.238095  \n",
       "62629         1.0   0.183699      0          0.847510   116.992724    0.211864  \n",
       "64049         1.0   0.182039      0          0.847510   118.992724    0.211864  \n",
       "93603         1.0   0.179211      0          0.773777   117.079724    0.213483  \n",
       "62682         1.0   0.172337      0          0.000000   117.992724    0.015873  \n",
       "63648         1.0   0.171086      0          0.847510   116.992724    0.240385  \n",
       "62586         1.0   0.168876      0          0.847510   116.992724    0.213483  \n",
       "64181         1.0   0.163996      0          0.847510   116.992724    0.238095  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "found_matches_s2v_match_perc_pmass_sim[8].sort_values(by=['s2v_score'], ascending = False).iloc[:40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#visualise tanimoto score of query vs match per position\n",
    "topn = 20\n",
    "all_similarities = []\n",
    "for ID in range(len(found_matches_s2v_match_perc_pmass_sim)):\n",
    "    df_select = found_matches_s2v_match_perc_pmass_sim[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    simil = df_select['similarity']\n",
    "    all_similarities.append(list(simil))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_pos = list(zip(*all_similarities))\n",
    "#print(per_pos)\n",
    "means = list(map(np.mean, per_pos))\n",
    "stdevs = list(map(np.std, per_pos))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 20 artists>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(means)), means, yerr = stdevs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "found_matches_s2v_info_connections = []\n",
    "cutoff = 0.6\n",
    "connections = [] #list of lists, just the connections column for each query\n",
    "for ID in range(len(found_matches_s2v_match_perc_pmass_sim)):\n",
    "    df_select = found_matches_s2v_match_perc_pmass_sim[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    sim_matrix_select = similarity_matrices[ID]\n",
    "    amount_sims = []\n",
    "    for inner_ID in range(df_select.shape[0]):\n",
    "        amount_sim = len(sim_matrix_select[sim_matrix_select[inner_ID] > cutoff])\n",
    "        amount_sims.append(amount_sim)\n",
    "    new_df = df_select.copy()\n",
    "    new_df['connections'] = amount_sims\n",
    "    connections.append(amount_sims)\n",
    "    found_matches_s2v_info_connections.append(new_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        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>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>mass_match</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>perc_mass_change</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>similarity</th>\n",
       "      <th>connections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77858</th>\n",
       "      <td>0.653871</td>\n",
       "      <td>103</td>\n",
       "      <td>0.653871</td>\n",
       "      <td>103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.915145</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>269.104724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77859</th>\n",
       "      <td>0.815921</td>\n",
       "      <td>79</td>\n",
       "      <td>0.815921</td>\n",
       "      <td>79</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.859299</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>269.104724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69919</th>\n",
       "      <td>0.896335</td>\n",
       "      <td>55</td>\n",
       "      <td>0.896365</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.853750</td>\n",
       "      <td>0</td>\n",
       "      <td>6.692933</td>\n",
       "      <td>287.115724</td>\n",
       "      <td>0.288419</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81380</th>\n",
       "      <td>0.969064</td>\n",
       "      <td>42</td>\n",
       "      <td>0.969064</td>\n",
       "      <td>42</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.851007</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370488</td>\n",
       "      <td>268.107724</td>\n",
       "      <td>0.161074</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71576</th>\n",
       "      <td>0.987751</td>\n",
       "      <td>51</td>\n",
       "      <td>0.986732</td>\n",
       "      <td>48</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.842918</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370488</td>\n",
       "      <td>268.107724</td>\n",
       "      <td>0.198387</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86964</th>\n",
       "      <td>0.876170</td>\n",
       "      <td>34</td>\n",
       "      <td>0.879715</td>\n",
       "      <td>34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.836468</td>\n",
       "      <td>0</td>\n",
       "      <td>0.741719</td>\n",
       "      <td>271.100724</td>\n",
       "      <td>0.123853</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71464</th>\n",
       "      <td>0.953082</td>\n",
       "      <td>71</td>\n",
       "      <td>0.954121</td>\n",
       "      <td>69</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.830731</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370488</td>\n",
       "      <td>270.101724</td>\n",
       "      <td>0.180196</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69918</th>\n",
       "      <td>0.806747</td>\n",
       "      <td>51</td>\n",
       "      <td>0.807096</td>\n",
       "      <td>55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.818196</td>\n",
       "      <td>0</td>\n",
       "      <td>6.692933</td>\n",
       "      <td>287.115724</td>\n",
       "      <td>0.288419</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70602</th>\n",
       "      <td>0.932597</td>\n",
       "      <td>37</td>\n",
       "      <td>0.932548</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.809579</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370488</td>\n",
       "      <td>268.107724</td>\n",
       "      <td>0.206022</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86963</th>\n",
       "      <td>0.648677</td>\n",
       "      <td>28</td>\n",
       "      <td>0.653825</td>\n",
       "      <td>30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.780006</td>\n",
       "      <td>0</td>\n",
       "      <td>0.741719</td>\n",
       "      <td>271.100724</td>\n",
       "      <td>0.123853</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81379</th>\n",
       "      <td>0.577208</td>\n",
       "      <td>31</td>\n",
       "      <td>0.577208</td>\n",
       "      <td>31</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.769058</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370488</td>\n",
       "      <td>268.107724</td>\n",
       "      <td>0.161074</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71463</th>\n",
       "      <td>0.650513</td>\n",
       "      <td>58</td>\n",
       "      <td>0.652129</td>\n",
       "      <td>60</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.768410</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370488</td>\n",
       "      <td>270.101724</td>\n",
       "      <td>0.180196</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70167</th>\n",
       "      <td>0.947166</td>\n",
       "      <td>38</td>\n",
       "      <td>0.947166</td>\n",
       "      <td>38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.768369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.379778</td>\n",
       "      <td>268.082724</td>\n",
       "      <td>0.228860</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71575</th>\n",
       "      <td>0.585219</td>\n",
       "      <td>43</td>\n",
       "      <td>0.584769</td>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.763262</td>\n",
       "      <td>0</td>\n",
       "      <td>0.370488</td>\n",
       "      <td>268.107724</td>\n",
       "      <td>0.198387</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88678</th>\n",
       "      <td>0.761851</td>\n",
       "      <td>53</td>\n",
       "      <td>0.761870</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.761568</td>\n",
       "      <td>0</td>\n",
       "      <td>28.994289</td>\n",
       "      <td>191.079724</td>\n",
       "      <td>0.125654</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79811</th>\n",
       "      <td>0.839948</td>\n",
       "      <td>64</td>\n",
       "      <td>0.841598</td>\n",
       "      <td>65</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.760367</td>\n",
       "      <td>0</td>\n",
       "      <td>7.428335</td>\n",
       "      <td>289.094724</td>\n",
       "      <td>0.400389</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72451</th>\n",
       "      <td>0.789930</td>\n",
       "      <td>67</td>\n",
       "      <td>0.794361</td>\n",
       "      <td>71</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.753755</td>\n",
       "      <td>0</td>\n",
       "      <td>0.735773</td>\n",
       "      <td>271.084724</td>\n",
       "      <td>0.281731</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71281</th>\n",
       "      <td>0.966700</td>\n",
       "      <td>33</td>\n",
       "      <td>0.966707</td>\n",
       "      <td>34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.750169</td>\n",
       "      <td>0</td>\n",
       "      <td>0.384237</td>\n",
       "      <td>268.070724</td>\n",
       "      <td>0.247879</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69904</th>\n",
       "      <td>0.863105</td>\n",
       "      <td>54</td>\n",
       "      <td>0.863105</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.742703</td>\n",
       "      <td>0</td>\n",
       "      <td>1.492727</td>\n",
       "      <td>273.121724</td>\n",
       "      <td>0.211799</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79810</th>\n",
       "      <td>0.860947</td>\n",
       "      <td>55</td>\n",
       "      <td>0.862813</td>\n",
       "      <td>56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.741840</td>\n",
       "      <td>0</td>\n",
       "      <td>7.428335</td>\n",
       "      <td>289.094724</td>\n",
       "      <td>0.400389</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "77858      0.653871             103          0.653871                 103   \n",
       "77859      0.815921              79          0.815921                  79   \n",
       "69919      0.896335              55          0.896365                  56   \n",
       "81380      0.969064              42          0.969064                  42   \n",
       "71576      0.987751              51          0.986732                  48   \n",
       "86964      0.876170              34          0.879715                  34   \n",
       "71464      0.953082              71          0.954121                  69   \n",
       "69918      0.806747              51          0.807096                  55   \n",
       "70602      0.932597              37          0.932548                  35   \n",
       "86963      0.648677              28          0.653825                  30   \n",
       "81379      0.577208              31          0.577208                  31   \n",
       "71463      0.650513              58          0.652129                  60   \n",
       "70167      0.947166              38          0.947166                  38   \n",
       "71575      0.585219              43          0.584769                  40   \n",
       "88678      0.761851              53          0.761870                  54   \n",
       "79811      0.839948              64          0.841598                  65   \n",
       "72451      0.789930              67          0.794361                  71   \n",
       "71281      0.966700              33          0.966707                  34   \n",
       "69904      0.863105              54          0.863105                  54   \n",
       "79810      0.860947              55          0.862813                  56   \n",
       "\n",
       "       mass_match  s2v_score  label  perc_mass_change  parent_mass  \\\n",
       "77858         1.0   0.915145      1          0.000000   269.104724   \n",
       "77859         1.0   0.859299      1          0.000000   269.104724   \n",
       "69919         0.0   0.853750      0          6.692933   287.115724   \n",
       "81380         1.0   0.851007      0          0.370488   268.107724   \n",
       "71576         1.0   0.842918      0          0.370488   268.107724   \n",
       "86964         0.0   0.836468      0          0.741719   271.100724   \n",
       "71464         1.0   0.830731      0          0.370488   270.101724   \n",
       "69918         0.0   0.818196      0          6.692933   287.115724   \n",
       "70602         1.0   0.809579      0          0.370488   268.107724   \n",
       "86963         0.0   0.780006      0          0.741719   271.100724   \n",
       "81379         1.0   0.769058      0          0.370488   268.107724   \n",
       "71463         1.0   0.768410      0          0.370488   270.101724   \n",
       "70167         0.0   0.768369      0          0.379778   268.082724   \n",
       "71575         1.0   0.763262      0          0.370488   268.107724   \n",
       "88678         0.0   0.761568      0         28.994289   191.079724   \n",
       "79811         0.0   0.760367      0          7.428335   289.094724   \n",
       "72451         0.0   0.753755      0          0.735773   271.084724   \n",
       "71281         0.0   0.750169      0          0.384237   268.070724   \n",
       "69904         0.0   0.742703      0          1.492727   273.121724   \n",
       "79810         0.0   0.741840      0          7.428335   289.094724   \n",
       "\n",
       "       similarity  connections  \n",
       "77858    1.000000            2  \n",
       "77859    1.000000            2  \n",
       "69919    0.288419            2  \n",
       "81380    0.161074            2  \n",
       "71576    0.198387            3  \n",
       "86964    0.123853            2  \n",
       "71464    0.180196            3  \n",
       "69918    0.288419            2  \n",
       "70602    0.206022            5  \n",
       "86963    0.123853            2  \n",
       "81379    0.161074            2  \n",
       "71463    0.180196            3  \n",
       "70167    0.228860            1  \n",
       "71575    0.198387            3  \n",
       "88678    0.125654            1  \n",
       "79811    0.400389            2  \n",
       "72451    0.281731            1  \n",
       "71281    0.247879            1  \n",
       "69904    0.211799            1  \n",
       "79810    0.400389            2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "found_matches_s2v_info_connections[13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make new score which is s2v_score * factor^connections\n",
    "factor = 1.04\n",
    "cor_scores = []\n",
    "found_matches_s2v_info_new_score = []\n",
    "for ID in range(len(found_matches_s2v_info_connections)):\n",
    "    cur_connections = connections[ID]\n",
    "    cur_df = found_matches_s2v_info_connections[ID].copy()\n",
    "    s2v_scores = cur_df['s2v_score']\n",
    "    cor_score = []\n",
    "    for con, s2v_sc in zip(cur_connections, s2v_scores):\n",
    "        new_score = s2v_sc * (factor ** con)\n",
    "        cor_score.append(new_score)\n",
    "    cor_scores.append(cor_score)\n",
    "    cur_df['new_score'] = cor_score\n",
    "    found_matches_s2v_info_new_score.append(cur_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>mass_match</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>perc_mass_change</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>similarity</th>\n",
       "      <th>connections</th>\n",
       "      <th>new_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61637</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>38</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>286.084724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1.423312</td>\n",
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       "    <tr>\n",
       "      <th>62117</th>\n",
       "      <td>0.710039</td>\n",
       "      <td>30</td>\n",
       "      <td>0.710039</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.882587</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>286.084724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1.256196</td>\n",
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       "    <tr>\n",
       "      <th>61638</th>\n",
       "      <td>0.710039</td>\n",
       "      <td>30</td>\n",
       "      <td>0.710039</td>\n",
       "      <td>30</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.882587</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>286.084724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1.256196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62116</th>\n",
       "      <td>0.461954</td>\n",
       "      <td>17</td>\n",
       "      <td>0.461954</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.854334</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>286.084724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1.215984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61636</th>\n",
       "      <td>0.461954</td>\n",
       "      <td>17</td>\n",
       "      <td>0.461954</td>\n",
       "      <td>17</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.854334</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>286.084724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1.215984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61639</th>\n",
       "      <td>0.536247</td>\n",
       "      <td>24</td>\n",
       "      <td>0.536247</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.723554</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>286.084724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1.029843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62118</th>\n",
       "      <td>0.536247</td>\n",
       "      <td>24</td>\n",
       "      <td>0.536247</td>\n",
       "      <td>24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.723554</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>286.084724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>1.029843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62115</th>\n",
       "      <td>0.178749</td>\n",
       "      <td>7</td>\n",
       "      <td>0.178749</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.684594</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>286.084724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.974391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61635</th>\n",
       "      <td>0.178749</td>\n",
       "      <td>7</td>\n",
       "      <td>0.178749</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.684594</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>286.084724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.974391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12123</th>\n",
       "      <td>0.369597</td>\n",
       "      <td>18</td>\n",
       "      <td>0.369597</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.559346</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>286.083724</td>\n",
       "      <td>0.405584</td>\n",
       "      <td>1</td>\n",
       "      <td>0.581720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90396</th>\n",
       "      <td>0.472262</td>\n",
       "      <td>8</td>\n",
       "      <td>0.473603</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.526375</td>\n",
       "      <td>0</td>\n",
       "      <td>84.611648</td>\n",
       "      <td>528.145724</td>\n",
       "      <td>0.136449</td>\n",
       "      <td>4</td>\n",
       "      <td>0.615785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62046</th>\n",
       "      <td>0.450947</td>\n",
       "      <td>6</td>\n",
       "      <td>0.450947</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.520307</td>\n",
       "      <td>0</td>\n",
       "      <td>37.771328</td>\n",
       "      <td>178.026724</td>\n",
       "      <td>0.176846</td>\n",
       "      <td>4</td>\n",
       "      <td>0.608685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61566</th>\n",
       "      <td>0.450947</td>\n",
       "      <td>6</td>\n",
       "      <td>0.450947</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.520307</td>\n",
       "      <td>0</td>\n",
       "      <td>37.771328</td>\n",
       "      <td>178.026724</td>\n",
       "      <td>0.176846</td>\n",
       "      <td>4</td>\n",
       "      <td>0.608685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90417</th>\n",
       "      <td>0.468345</td>\n",
       "      <td>7</td>\n",
       "      <td>0.468345</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.508896</td>\n",
       "      <td>0</td>\n",
       "      <td>82.879294</td>\n",
       "      <td>523.189724</td>\n",
       "      <td>0.136449</td>\n",
       "      <td>4</td>\n",
       "      <td>0.595336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61565</th>\n",
       "      <td>0.503404</td>\n",
       "      <td>7</td>\n",
       "      <td>0.503404</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.499939</td>\n",
       "      <td>0</td>\n",
       "      <td>37.771328</td>\n",
       "      <td>178.026724</td>\n",
       "      <td>0.176846</td>\n",
       "      <td>4</td>\n",
       "      <td>0.584857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62045</th>\n",
       "      <td>0.503404</td>\n",
       "      <td>7</td>\n",
       "      <td>0.503404</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.499939</td>\n",
       "      <td>0</td>\n",
       "      <td>37.771328</td>\n",
       "      <td>178.026724</td>\n",
       "      <td>0.176846</td>\n",
       "      <td>4</td>\n",
       "      <td>0.584857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90404</th>\n",
       "      <td>0.437076</td>\n",
       "      <td>9</td>\n",
       "      <td>0.437076</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.494515</td>\n",
       "      <td>0</td>\n",
       "      <td>90.195309</td>\n",
       "      <td>544.119724</td>\n",
       "      <td>0.136449</td>\n",
       "      <td>4</td>\n",
       "      <td>0.578512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23356</th>\n",
       "      <td>0.526231</td>\n",
       "      <td>10</td>\n",
       "      <td>0.532023</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.490670</td>\n",
       "      <td>0</td>\n",
       "      <td>42.657643</td>\n",
       "      <td>164.047724</td>\n",
       "      <td>0.386458</td>\n",
       "      <td>1</td>\n",
       "      <td>0.510297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90399</th>\n",
       "      <td>0.396851</td>\n",
       "      <td>7</td>\n",
       "      <td>0.396851</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.490585</td>\n",
       "      <td>0</td>\n",
       "      <td>84.611648</td>\n",
       "      <td>528.145724</td>\n",
       "      <td>0.136449</td>\n",
       "      <td>4</td>\n",
       "      <td>0.573915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10143</th>\n",
       "      <td>0.354913</td>\n",
       "      <td>7</td>\n",
       "      <td>0.359343</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.486659</td>\n",
       "      <td>0</td>\n",
       "      <td>25.212461</td>\n",
       "      <td>358.213724</td>\n",
       "      <td>0.192719</td>\n",
       "      <td>1</td>\n",
       "      <td>0.506125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "61637      1.000000              38          1.000000                  38   \n",
       "62117      0.710039              30          0.710039                  30   \n",
       "61638      0.710039              30          0.710039                  30   \n",
       "62116      0.461954              17          0.461954                  17   \n",
       "61636      0.461954              17          0.461954                  17   \n",
       "61639      0.536247              24          0.536247                  24   \n",
       "62118      0.536247              24          0.536247                  24   \n",
       "62115      0.178749               7          0.178749                   7   \n",
       "61635      0.178749               7          0.178749                   7   \n",
       "12123      0.369597              18          0.369597                  18   \n",
       "90396      0.472262               8          0.473603                   9   \n",
       "62046      0.450947               6          0.450947                   6   \n",
       "61566      0.450947               6          0.450947                   6   \n",
       "90417      0.468345               7          0.468345                   7   \n",
       "61565      0.503404               7          0.503404                   7   \n",
       "62045      0.503404               7          0.503404                   7   \n",
       "90404      0.437076               9          0.437076                   9   \n",
       "23356      0.526231              10          0.532023                  10   \n",
       "90399      0.396851               7          0.396851                   7   \n",
       "10143      0.354913               7          0.359343                   8   \n",
       "\n",
       "       mass_match  s2v_score  label  perc_mass_change  parent_mass  \\\n",
       "61637         1.0   1.000000      1          0.000000   286.084724   \n",
       "62117         1.0   0.882587      1          0.000000   286.084724   \n",
       "61638         1.0   0.882587      1          0.000000   286.084724   \n",
       "62116         1.0   0.854334      1          0.000000   286.084724   \n",
       "61636         1.0   0.854334      1          0.000000   286.084724   \n",
       "61639         1.0   0.723554      1          0.000000   286.084724   \n",
       "62118         1.0   0.723554      1          0.000000   286.084724   \n",
       "62115         1.0   0.684594      1          0.000000   286.084724   \n",
       "61635         1.0   0.684594      1          0.000000   286.084724   \n",
       "12123         1.0   0.559346      0          0.000350   286.083724   \n",
       "90396         0.0   0.526375      0         84.611648   528.145724   \n",
       "62046         0.0   0.520307      0         37.771328   178.026724   \n",
       "61566         0.0   0.520307      0         37.771328   178.026724   \n",
       "90417         0.0   0.508896      0         82.879294   523.189724   \n",
       "61565         0.0   0.499939      0         37.771328   178.026724   \n",
       "62045         0.0   0.499939      0         37.771328   178.026724   \n",
       "90404         0.0   0.494515      0         90.195309   544.119724   \n",
       "23356         0.0   0.490670      0         42.657643   164.047724   \n",
       "90399         0.0   0.490585      0         84.611648   528.145724   \n",
       "10143         0.0   0.486659      0         25.212461   358.213724   \n",
       "\n",
       "       similarity  connections  new_score  \n",
       "61637    1.000000            9   1.423312  \n",
       "62117    1.000000            9   1.256196  \n",
       "61638    1.000000            9   1.256196  \n",
       "62116    1.000000            9   1.215984  \n",
       "61636    1.000000            9   1.215984  \n",
       "61639    1.000000            9   1.029843  \n",
       "62118    1.000000            9   1.029843  \n",
       "62115    1.000000            9   0.974391  \n",
       "61635    1.000000            9   0.974391  \n",
       "12123    0.405584            1   0.581720  \n",
       "90396    0.136449            4   0.615785  \n",
       "62046    0.176846            4   0.608685  \n",
       "61566    0.176846            4   0.608685  \n",
       "90417    0.136449            4   0.595336  \n",
       "61565    0.176846            4   0.584857  \n",
       "62045    0.176846            4   0.584857  \n",
       "90404    0.136449            4   0.578512  \n",
       "23356    0.386458            1   0.510297  \n",
       "90399    0.136449            4   0.573915  \n",
       "10143    0.192719            1   0.506125  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "found_matches_s2v_info_new_score[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing new_score + mass\n",
    "#look at top20\n",
    "test_m_s2v_top20 = []\n",
    "test_m_new_score_top20 = []\n",
    "threshold = 0.4\n",
    "topn = 20 #only look at top 20\n",
    "\n",
    "for ID in range(len(documents_query_s2v)):\n",
    "    inchikey_query = documents_query_s2v[ID]._obj.get(\"inchikey\")[:14]\n",
    "    #select top20\n",
    "    current_match = found_matches_s2v[ID].sort_values(by=['s2v_score'], ascending=False).iloc[:topn]\n",
    "\n",
    "    # Scenario 2: mass + Spec2Vec + threshold\n",
    "    df_select = current_match[(current_match['mass_match'] == 1)\n",
    "                                  & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        best_bet = 1 * (inchikey_match == inchikey_query)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_m_s2v_top20.append(best_bet)\n",
    "    \n",
    "    # Scenario 2: mass + new_score + threshold\n",
    "    current_match_new = found_matches_s2v_info_new_score[ID].sort_values(by=['new_score'], ascending=False).iloc[:topn]\n",
    "    df_select = current_match_new[(current_match_new['mass_match'] == 1)\n",
    "                                  & (current_match_new['new_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        best_bet = 1 * (inchikey_match == inchikey_query)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_m_new_score_top20.append(best_bet)\n",
    "\n",
    "\n",
    "#make arrays from lists\n",
    "test_m_s2v_arr_top20 = np.array(test_m_s2v_top20)\n",
    "test_m_new_score_arr_top20 = np.array(test_m_new_score_top20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "\n",
    "ylabels = ['mass + Spec2Vec',\n",
    "           'mass + new_score',]\n",
    "xlabels = ['true matches', 'false matches', 'no matches found']\n",
    "          #'mass + mod.cosine \\n + as backup: Spec2Vec + mod.cosine']\n",
    "data = np.array([[np.sum(test_m_s2v_arr_top20 == 1), np.sum(test_m_s2v_arr_top20 == 0), np.sum(test_m_s2v_arr_top20 == -1)],\n",
    "                 [np.sum(test_m_new_score_arr_top20 == 1), np.sum(test_m_new_score_arr_top20 == 0), np.sum(test_m_new_score_arr_top20 == -1)]])\n",
    "\n",
    "dataframe = pd.DataFrame(data, columns=xlabels, index=ylabels)\n",
    "\n",
    "ax = dataframe.sort_values(by=['false matches'], ascending=True).plot.barh(stacked=True, edgecolor='none')\n",
    "\n",
    "horiz_offset = 1.03\n",
    "vert_offset = 1.\n",
    "ax.legend(bbox_to_anchor=(horiz_offset, vert_offset))\n",
    "plt.xlabel('Number of spectra')\n",
    "plt.title('Comparing new_score vs spec2vec looking at top 20\\n(>5 matching inchikey in library)')\n",
    "#plt.figure(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prep data for NN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1U1bJXnntlHUkMb1OzPTp0+XxeKwTdCUpFAqppqbGKii5ublKTEyMGNPe3q7Gxkbl5+dLunT+S3d3t86cOWON+fzzz9Xd3W3NAwAA7G3UOzE9PT1qbm62bre0tKihoUFut1tTp07V8uXL5ff7lZmZqYyMDPn9fk2ePFmLFy+WJCUnJ2vp0qXy+XxKSUmR2+2Wz+eT1+u1TvbNysrS3Llz9eKLL2rt2rWSpJdeekl33333kCf1AgAA+xl1ifniiy/07LPPWrcPHDggSVqyZInWr1+vlStXqre3V3v37tX58+c1c+ZMlZeXW58UkqTi4mIlJiZq165d1sXutmzZooSEP24Mbdy4Ufv379f27dslSfPnz1dpaek1BwUAAPHFEY7zN9VaW1sjPnodCw6HQ5mZmfry/gWSpMSXj8R0/olkIGtTU5Mt3n+1U147ZZXslddOWSV75bVDVpfLFfWJvXx3EgAAMBIlBgAAGIkSAwAAjESJAQAARqLEAAAAI1FiAACAkSgxAADASJQYAABgJEoMAAAwEiUGAAAYiRIDAACMRIkBAABGosQAAAAjUWIAAICRKDEAAMBIlBgAAGAkSgwAADASJQYAABiJEgMAAIxEiQEAAEaixAAAACNRYgAAgJEoMQAAwEiUGAAAYCRKTAz0rX1AfWsfGO9lAABgK5QYAABgJEoMAAAwEiUGAAAYiRIDAACMRIkBAABGosQAAAAjUWIAAICRKDEAAMBIlBgAAGAkSgwAADASJQYAABiJEgMAAIxEiQEAAEaixAAAACM5Yz1hX1+fDh48qN/+9rcKBAJKS0vTfffdp+985ztKSLjUmcLhsA4ePKjjx4+rq6tLeXl5Ki0tVXZ2tjVPMBiUz+dTVVWVent7NXv2bJWVlWnKlCmxXjIAADBQzHdiDh8+rHfffVelpaXatWuXHn30UR05ckS//vWvI8a8/fbbKikp0Y4dO+TxeLRt2zZduHDBGlNZWanTp09r06ZN2rp1q3p6erRz50719/fHeskAAMBAMS8xdXV1WrBgge6++25Nnz5d3/zmN1VYWKgvvvhC0qVdmKNHj6qoqEgLFy6U1+vV+vXrdfHiRZ08eVKS1N3drRMnTmjNmjUqLCxUTk6ONmzYoMbGRlVXV8d6yQAAwEAxfzvp9ttv17vvvqtz585pxowZamhoUG1trYqLiyVJLS0tCgQCmjNnjvUcl8ulWbNmqba2VsuWLVN9fb36+vpUWFhojUlPT5fX61VdXZ3mzp076HWDwaCCwaB12+FwKCkpyfo5loabL9avMxEMZIrHbEOxU147ZZXslddOWSV75bVT1mjEvMSsXLlS3d3d+t73vqeEhAT19/dr9erVWrx4sSQpEAhIklJTUyOel5qaqra2NmuM0+mU2+0eNGbg+Vfy+/06dOiQdTsnJ0cVFRWaNm1ajJJdXWZm5g17rRstIyNjvJdwQ9kpr52ySvbKa6eskr3y2inrSGJeYt5//3399re/1caNG5Wdna2GhgZVVlZaJ/gOuLJFhsPhq8490piioiKtWLFi0Pytra0KhUKjTDEyh8Mx5C9QU1NTTF9nIhjI2tzcHNUxMp2d8topq2SvvHbKKtkrrx2yOp3OqDcgYl5iXn31Va1cuVL33nuvJMnr9aq1tVW/+tWvdN9998nj8UiS9cmlAZ2dndbujMfjUSgUUldXV8RuTGdnpwoKCoZ8XZfLJZfLNeRjN+pAx+svlHQpWzznu5Kd8topq2SvvHbKKtkrr52yjiTmJ/ZevHjR+ii19SIJCdYf9vTp0+XxeCJO0A2FQqqpqbEKSm5urhITEyPGtLe3q7GxUfn5+bFeMgAAMFDMd2Lmz5+vt956S1OnTlVWVpYaGhr03//93/r2t78t6dJW2PLly+X3+5WZmamMjAz5/X5NnjzZOm8mOTlZS5culc/nU0pKitxut3w+n7xeb8TJvgAAwL5iXmJKSkr0+uuva+/evero6FB6erqWLVumBx980BqzcuVK9fb2au/evTp//rxmzpyp8vJy69NEklRcXKzExETt2rXLutjdli1bBu3yAAAAe3KE4/xNtdbW1oiPXseCw+FQZmamvrx/QcT9iS8fienrTAQDWZuammzx/qud8topq2SvvHbKKtkrrx2yulyuqE/sZVsDAAAYiRIDAACMRIkBAABGosQAAAAjUWIAAICRKDEAAMBIlBgAAGAkSgwAADASJQYAABiJEgMAAIxEiQEAAEaixAAAACNRYgAAgJEoMQAAwEiUGAAAYCRKDAAAMBIlBgAAGIkSAwAAjESJAQAARqLEAAAAI1FiAACAkSgxAADASJQYAABgJEoMAAAwEiUGAAAYiRIDAACMRIkBAABGosQAAAAjUWIAAICRKDEAAMBIlJgY6lv7wHgvAQAA26DEAAAAI1FiAACAkSgxAADASJQYAABgJEoMAAAwEiUGAAAYiRIDAACMRIkBAABGco7FpL///e/16quv6pNPPlFvb68yMzP1T//0T8rNzZUkhcNhHTx4UMePH1dXV5fy8vJUWlqq7Oxsa45gMCifz6eqqir19vZq9uzZKisr05QpU8ZiyQAAwDAx34np6urSU089JafTqR/84Ad67rnntGbNGiUnJ1tjDh8+rLffflslJSXasWOHPB6Ptm3bpgsXLlhjKisrdfr0aW3atElbt25VT0+Pdu7cqf7+/lgvGQAAGCjmJebw4cOaMmWK1q1bp5kzZ2r69Om66667lJGRIenSLszRo0dVVFSkhQsXyuv1av369bp48aJOnjwpSeru7taJEye0Zs0aFRYWKicnRxs2bFBjY6Oqq6tjvWQAAGCgmL+d9MEHH2jOnDl67rnnVFNTo/T0dP3lX/6l/uIv/kKS1NLSokAgoDlz5ljPcblcmjVrlmpra7Vs2TLV19err69PhYWF1pj09HR5vV7V1dVp7ty5g143GAwqGAxatx0Oh5KSkqyfY2mk+WL9WuNtIE+85RqOnfLaKatkr7x2yirZK6+dskYj5iWmpaVF7777ru6//34VFRXpzJkzeuWVV+RyubRkyRIFAgFJUmpqasTzUlNT1dbWJkkKBAJyOp1yu92Dxgw8/0p+v1+HDh2ybufk5KiiokLTpk2LXbgoZGZm3tDXu1EGdtLswk557ZRVsldeO2WV7JXXTllHEvMS09/fr9tuu00PP/ywpEtl4ssvv9Q777yjJUuWWOOubJHhcPiqc480pqioSCtWrBg0f2trq0Kh0KgyXI3D4Rj2F6ipqSmmrzXeBrI2NzdHdYxMZ6e8dsoq2SuvnbJK9sprh6xOpzPqDYiYl5i0tDRlZWVF3JeVlaVTp05Jkjwej6RLuy1paWnWmM7OTmt3xuPxKBQKqaurK2I3prOzUwUFBUO+rsvlksvlGvKxG3mg4/WXKhwOx222odgpr52ySvbKa6eskr3y2inrSGJ+Ym9BQYHOnTsXcd+5c+esVjV9+nR5PJ6IE3RDoZBqamqsgpKbm6vExMSIMe3t7WpsbFR+fn6slwwAAAwU852Y+++/X0899ZTeeustLVq0SGfOnNHx48f1j//4j5IubYUtX75cfr9fmZmZysjIkN/v1+TJk7V48WJJUnJyspYuXSqfz6eUlBS53W75fD55vd6Ik30BAIB9xbzEzJw5U5s3b9Zrr72mN998U9OnT1dxcbH+/M//3BqzcuVK9fb2au/evTp//rxmzpyp8vJy69NEklRcXKzExETt2rXLutjdli1blJDARYYBAMAYXbF3/vz5mj9//rCPOxwOrVq1SqtWrRp2zKRJk1RSUqKSkpKxWCIAADAc2xoAAMBIlBgAAGAkSgwAADASJQYAABiJEgMAAIxEiQEAAEaixAAAACNRYmKsb+0D470EAABsgRIDAACMRIkBAABGosQAAAAjUWIAAICRKDEAAMBIlBgAAGAkSgwAADASJQYAABiJEgMAAIxEiQEAAEaixAAAACNRYgAAgJEoMWOAL4EEAGDsUWIAAICRKDEAAMBIlJhr9OX9C8Z7CQAA2BolBgAAGIkSAwAAjESJAQAARqLEjBE+Zg0AwNiixAAAACNRYgAAgJEoMQAAwEiUGAAAYCRKDAAAMBIlBgAAGIkSAwAAjESJAQAARqLEAAAAI1FiAACAkSgxY4ivHgAAYOw4x/oF/H6/fvGLX2j58uV67LHHJEnhcFgHDx7U8ePH1dXVpby8PJWWlio7O9t6XjAYlM/nU1VVlXp7ezV79myVlZVpypQpY71kAABggDHdiTlz5oyOHTumb3zjGxH3Hz58WG+//bZKSkq0Y8cOeTwebdu2TRcuXLDGVFZW6vTp09q0aZO2bt2qnp4e7dy5U/39/WO5ZAAAYIgxKzE9PT3as2ePHn/8cd18883W/eFwWEePHlVRUZEWLlwor9er9evX6+LFizp58qQkqbu7WydOnNCaNWtUWFionJwcbdiwQY2Njaqurh6rJQMAAIOM2dtJe/fu1bx581RYWKi33nrLur+lpUWBQEBz5syx7nO5XJo1a5Zqa2u1bNky1dfXq6+vT4WFhdaY9PR0eb1e1dXVae7cuYNeLxgMKhgMWrcdDoeSkpKsn2NpNPPF+rVvtIH1m54jWnbKa6eskr3y2imrZK+8dsoajTEpMVVVVTp79qx27Ngx6LFAICBJSk1Njbg/NTVVbW1t1hin0ym32z1ozMDzr+T3+3Xo0CHrdk5OjioqKjRt2rTrSDK8L6Mcl5mZOSavf6NlZGSM9xJuKDvltVNWyV557ZRVsldeO2UdScxLTFtbmyorK1VeXq5JkyYNO+7KFhkOh68690hjioqKtGLFikHzt7a2KhQKXXXu0RhNA25qaorpa99oDodDGRkZam5ujuoYmc5Oee2UVbJXXjtlleyV1w5ZnU5n1BsQMS8x9fX16ujo0L/+679a9/X39+uzzz7Tr3/9a+3evVvSpd2WtLQ0a0xnZ6e1O+PxeBQKhdTV1RWxG9PZ2amCgoIhX9flcsnlcg352Hge6Hj5JQuHw3GTJRp2ymunrJK98topq2SvvHbKOpKYl5i77rpL//7v/x5x309/+lPNmDFDK1eu1J/8yZ/I4/GourpaOTk5kqRQKKSamho98sgjkqTc3FwlJiaqurpaixYtkiS1t7ersbHRGmOKgWvFJL58ZJxXAgBAfIl5iUlKSpLX6424b/LkyUpJSbHuX758ufx+vzIzM5WRkSG/36/Jkydr8eLFkqTk5GQtXbpUPp9PKSkpcrvd8vl88nq9ESf7AgAA+xrzi90NZeXKlert7dXevXt1/vx5zZw5U+Xl5daniSSpuLhYiYmJ2rVrl3Wxuy1btighgYsMAwCAG1RifvSjH0XcdjgcWrVqlVatWjXscyZNmqSSkhKVlJSM8eoAAICJ2NYAAABGosQAAAAjUWIAAICRKDE3yMBHrQEAQGxQYgAAgJEoMQAAwEiUGAAAYCRKDAAAMBIlBgAAGIkSAwAAjESJAQAARqLEAAAAI1FiAACAkSgxAADASJSYG4ivHgAAIHYoMQAAwEiUmBuM3RgAAGKDEgMAAIxEiQEAAEZyjvcC7Ojyt5QSXz4yjisBAMBc7MQAAAAjUWIAAICRKDEAAMBIlBgAAGAkSsw447oxAABcG0oMAAAwEiUGAAAYiRIDAACMRIkBAABGosQAAAAjUWIAAICRKDEAAMBIlBgAAGAkSgwAADASJQYAABiJEjMB8NUDAACMHiUGAAAYiRIzQbAbAwDA6FBiAACAkZyxntDv9+v06dP6+uuvNWnSJOXn5+vRRx/VjBkzrDHhcFgHDx7U8ePH1dXVpby8PJWWlio7O9saEwwG5fP5VFVVpd7eXs2ePVtlZWWaMmVKrJcMAAAMFPOdmJqaGv3VX/2Vtm/frh/+8Ifq7+/Xtm3b1NPTY405fPiw3n77bZWUlGjHjh3yeDzatm2bLly4YI2prKzU6dOntWnTJm3dulU9PT3auXOn+vv7Y71kAABgoJiXmPLyct13333Kzs7WrbfeqnXr1qmtrU319fWSLu3CHD16VEVFRVq4cKG8Xq/Wr1+vixcv6uTJk5Kk7u5unThxQmvWrFFhYaFycnK0YcMGNTY2qrq6OtZLBgAABor520lX6u7uliS53W5JUktLiwKBgObMmWONcblcmjVrlmpra7Vs2TLV19err69PhYWF1pj09HR5vV7V1dVp7ty5g14nGAwqGAxatx0Oh5KSkqyfYynW8431vNdjYE0TcW1jwU557ZRVsldeO2WV7JXXTlmjMaYlJhwO62c/+5luv/12eb1eSVIgEJAkpaamRoxNTU1VW1ubNcbpdFrF5/IxA8+/kt/v16FDh6zbOTk5qqio0LRp02KUJtKXYzBnZmbmGMwaGxkZGeO9hBvKTnntlFWyV147ZZXslddOWUcypiVm3759amxs1NatWwc9dmWLDIfDV51vpDFFRUVasWLFoPlbW1sVCoWiXXJUxqoBNzU1jcm818PhcCgjI0PNzc1RHSPT2SmvnbJK9sprp6ySvfLaIavT6Yx6A2LMSsz+/fv14Ycf6tlnn434RJHH45F0abclLS3Nur+zs9PanfF4PAqFQurq6orYjens7FRBQcGQr+dyueRyuYZ8zJQDPZHXGQ6HJ/T6Ys1Oee2UVbJXXjtlleyV105ZRxLzE3vD4bD27dunU6dO6emnn9b06dMjHp8+fbo8Hk/ECbqhUEg1NTVWQcnNzVViYmLEmPb2djU2Nio/Pz/WS54wuOAdAADRi/lOzL59+3Ty5Ek9+eSTSkpKss5hSU5O1qRJk+RwOLR8+XL5/X5lZmYqIyNDfr9fkydP1uLFi62xS5culc/nU0pKitxut3w+n7xeb8TJvgAAwL5iXmLeeecdSdKPfvSjiPvXrVun++67T5K0cuVK9fb2au/evTp//rxmzpyp8vJy69NEklRcXKzExETt2rXLutjdli1blJDARYYBAMAYlJg33njjqmMcDodWrVqlVatWDTtm0qRJKikpUUlJSSyXBwAA4gTbGhPM5efFcI4MAADDG/OL3WH0KC8AAFwdOzEAAMBIlBiDsWMDALAzSoyhhiswFBsAgF1QYgxBOQEAIBIn9hpgoMBQZAAA+CN2Yia40RQXyg4AwE4oMQAAwEiUGAAAYCRKTBzoW/tAzN9Cutp8vGUFABhvlBiMKFT2N+O9BAAAhkSJiVND7c5c6+7Jl/cvGHZOAADGCyXGcKMtFZd/gulqPwMAMJFRYuLc1UoJhQUAYCpKDIYVTcEZzRgKEwAgligxAADASJQYm7h8F+RadkQu/5RSrHZoAAC4HpQYXDNOBgYAjCdKDCyxKCLDzUHJAQDEGt9ijZi58gTea/nyysSXj8R+YQCAuMRODG4odmQAALFCicENF81JxpQdAMDVUGIwocTy6xIAAPGNEoNxEc0nmoZ6fKTzbSg7AGAvlBiMO74WAQBwLSgxMM5QbzcNtUPD9WsAIL7xEWsYaywKSqjsb/SlJOfe/xvzuQEAsUWJQdwZ7dciXO3aNH1rH+D6NQAwAfF2EmxvqNJz+XdFAQAmJnZiYAtXuyLw9VyvhqsNA8D4YCcGRojVSbpjfaIvJxIDwI1DiUHcu9ZiEc11arheDQCMH0oMMEqxKkWj/YJMyhEARKLEwHaupQxE83UI13LRvoF5r3VN0b4OAMQjTuwFbqCrFZ2hTg6mmADA0CgxwBgYzXVohnvecM+N5ro1XNsGgB3wdhIwxq5nJyWat7Cu9lbVcGsY6Vo47P4AMAElBogT0Vzr5spzcL68f8Go56XgAJgoJvzbSb/5zW905MgRBQIBZWVl6bHHHtMdd9wx3ssCxlUsv/l7uB2Zod6SunxnJ/HlI8Ne6G+ot9N4iwtArE3onZj3339flZWV+s53vqOKigrdcccd+rd/+ze1tbWN99KACWW03xcVi3mH2+GJ9i2vywvRlXMNNfZq62eHCLAfRzgcDo/3Iobzgx/8QDk5OVq7dq113/e+9z396Z/+qR5++OGo5mhtbVUwGIzpuhwOB9+tA9wAl+/2XMu4y3d++tY+oOy3P1BTU5PC4XBcf12Ew+FQZmamlTXe2SmvHbK6XC5NmzYtqrET9u2kUCik+vp6/e3f/m3E/YWFhaqtrR00PhgMRpQVh8OhpKQkOZ2xj+hwOOS4rSDm8wK4ws5/UUI0/60NN27nv1g/JtxWoOaNj8ghyfH/b185ZkDiU7vV93++a/08nGjGXDn+amOjGXM1DodD0qW/DOL1L7rL2SmvHbKO5u/tCVtiOjs71d/fr9TU1Ij7U1NTFQgEBo33+/06dOiQdfvee+/Vpk2blJaWNjYLfP7nYzMvgIkhmv/GR/v/A2Mx5wimTp0as7lMYKe8dso6kgl9Toz0x9Z5tfuKiopUWVlp/W/t2rUxfxtpwIULF7RlyxZduHBhTOafSOyUVbJXXjtlleyV105ZJXvltVPWaEzYnZhbbrlFCQkJg3ZdOjo6Bu3OSJe21lwu1w1ZWzgc1tmzZ+N2K+9ydsoq2SuvnbJK9sprp6ySvfLaKWs0JuxOjNPpVG5urqqrqyPur66uVkEB56MAAGB3E3YnRpJWrFihPXv2KDc3V/n5+Tp27Jja2tq0bNmy8V4aAAAYZxO6xCxatEh/+MMf9Oabb6q9vV3Z2dn6/ve/H/VHr8aKy+XSgw8+eMPevhpPdsoq2SuvnbJK9sprp6ySvfLaKWs0JvR1YgAAAIYzYc+JAQAAGAklBgAAGIkSAwAAjESJAQAARprQn06aiH7zm9/oyJEjCgQCysrK0mOPPaY77rhjvJd1Xd54442Ir2yQLn29w8svvyzp0sWVDh48qOPHj6urq0t5eXkqLS1Vdnb2eCx31GpqanTkyBGdPXtW7e3t2rx5s+655x7r8WjyBYNB+Xw+VVVVqbe3V7Nnz1ZZWZmmTJkyHpGGdbWsL7zwgt57772I5+Tl5Wn79u3WbVOy+v1+nT59Wl9//bUmTZqk/Px8Pfroo5oxY4Y1Jp6ObTR54+X4vvPOO3rnnXfU2toqScrKytKDDz6oefPmSYqv4ypdPW+8HNexwKeTRuH999/Xnj17VFZWpoKCAh07dkzHjx/Xrl27jP4eizfeeEOnTp3SU089Zd2XkJCgW265RZL0q1/9Sn6/X+vWrVNmZqbeeustffbZZ9q9e7eSkpLGa9lR+/jjj1VbW6ucnBz9x3/8x6C/2KPJ9/LLL+vDDz/UunXrlJKSogMHDqirq0sVFRVKSJg4G5pXy/rCCy+oo6ND69ats+5zOp1yu93WbVOybt++Xffee69uu+029fX16Ze//KUaGxv13HPP6aabbpIUX8c2mrzxcnw/+OADJSQkKCMjQ5L03nvv6ciRI/rxj3+s7OzsuDqu0tXzxstxHRNhRO373/9++KWXXoq477vf/W745z//+TitKDZef/318ObNm4d8rL+/P7x27dqw3++37uvt7Q0XFxeH33nnnRu0wth56KGHwqdOnbJuR5Pv/Pnz4dWrV4erqqqsMb/73e/Cq1atCn/88cc3aumjdmXWcDgc/s///M9wRUXFsM8xNWs4HA53dHSEH3roofCnn34aDofj+9iGw4PzhsPxfXwfe+yx8PHjx+P+uA4YyBsOx/dxvV5xXM9iKxQKqb6+XnPmzIm4v7CwULW1teO0qthpbm7W448/rvXr12v37t363//9X0lSS0uLAoFARG6Xy6VZs2bFRe5o8tXX16uvr0+FhYXWmPT0dHm9XtXV1d3wNV+vmpoalZWVadOmTfqv//ovdXR0WI+ZnLW7u1uSrH+dxvuxvTLvgHg7vv39/aqqqtLFixeVn58f98f1yrwD4u24xgrnxESps7NT/f39g758MjU1ddCXVJomLy9P69ev14wZMxQIBPTWW2/phz/8oZ577jkr21C529raxmG1sRVNvkAgMGjrdmCMacd+3rx5+rM/+zNNnTpVLS0tev3117V161bt3LlTLpfL2KzhcFg/+9nPdPvtt8vr9UqK72M7VF4pvo5vY2OjysvLFQwGddNNN2nz5s3Kysqyikq8Hdfh8krxdVxjjRIzSg6HI6r7TDJw8pgkeb1e5efna8OGDXrvvfeUl5cnaXDGcJydSnUt+Uz8M1i0aJH1s9fr1W233aZ169bpo48+0sKFC4d93kTPum/fPjU2Nmrr1q2DHovHYztc3ng6vjNmzNBPfvITnT9/XqdOndILL7ygZ5991no83o7rcHmzsrLi6rjGGm8nRemWW25RQkLCoFbb0dEx6F8Eprvpppvk9XrV1NQkj8cjSYNyd3Z2xkXuaPJ5PB6FQiF1dXUNGjPwfFOlpaVp2rRpampqkmRm1v379+vDDz/UM888E/FJjHg9tsPlHYrJx9fpdCojI0O33XabHn74Yd166606evRo3B7X4fIOxeTjGmuUmCg5nU7l5uaquro64v7q6moVFBSM06rGRjAY1Ndff620tDRNnz5dHo8nIncoFFJNTU1c5I4mX25urhITEyPGtLe3q7GxMeI9axP94Q9/0O9+9zulpaVJMitrOBzWvn37dOrUKT399NOaPn16xOPxdmyvlncoJh/fK4XDYQWDwbg7rsMZyDuUeDqu14u3k0ZhxYoV2rNnj3Jzc5Wfn69jx46pra1Ny5YtG++lXZcDBw5owYIFmjp1qjo6OvTmm2/qwoULWrJkiRwOh5YvXy6/36/MzExlZGTI7/dr8uTJWrx48XgvPSo9PT1qbm62bre0tKihoUFut1tTp069ar7k5GQtXbpUPp9PKSkpcrvd8vl88nq9ESfSTQQjZXW73XrjjTf0zW9+Ux6PR62trfrFL36hlJQU62PYJmXdt2+fTp48qSeffFJJSUnWv8yTk5M1adKkqH534ylvT09P3Bzf1157TfPmzdOUKVPU09OjqqoqffrppyovL4+74yqNnDeejutY4DoxozRwsbv29nZlZ2eruLhYs2bNGu9lXZfdu3frs88+U2dnp2655Rbl5eVp9erV1kll4f9/Yaljx47p/PnzmjlzpkpLSyNOKJzIPv3004j30gcsWbJE69evjypfb2+vXn31VZ08eTLiQlIT7fpAI2Vdu3atfvKTn+js2bM6f/680tLSdOedd+rv//7vI3KYknXVqlVD3r9u3Trdd999kqL73Y2XvL29vXFzfH/605/qf/7nf9Te3q7k5GR94xvf0MqVK62/kOPpuEoj542n4zoWKDEAAMBInBMDAACMRIkBAABGosQAAAAjUWIAAICRKDEAAMBIlBgAAGAkSgwAADASJQYAABiJEgMAAIxEiQEAAEaixAAAACNRYgAAgJH+H9ebW2yo+9aRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualise mod_cosine_matches for all top20 results\n",
    "mod_cosine_matches = [elem for ID in range(len(found_matches_s2v_info_new_score)) for elem in list(found_matches_s2v_info_new_score[ID]['mod_cosine_matches'])]\n",
    "plt.hist(mod_cosine_matches, bins = max(mod_cosine_matches)+1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualise cosine_matches for all top20 results\n",
    "cosine_matches = [elem for ID in range(len(found_matches_s2v_info_new_score)) for elem in list(found_matches_s2v_info_new_score[ID]['cosine_matches'])]\n",
    "plt.hist(cosine_matches, bins = max(cosine_matches)+1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x182596d8a48>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#testing scaling\n",
    "x = 15\n",
    "base = 0.8\n",
    "dist = [(base**i) for i in range(x)]\n",
    "plt.scatter(range(x), dist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transform_num_matches(input_df, exp = 0.93):\n",
    "    '''Transform the cosine_matches and mod_cosine_matches to between 0-1\n",
    "    \n",
    "    input_df: pandas DataFrame, spec2vec matches for one query\n",
    "    exp: int, the base for the exponential, default: 0.93\n",
    "    \n",
    "    Both matches are transformed to between 0-1 by doing 1-0.93^num_matches\n",
    "    '''\n",
    "    df = input_df.copy() #otherwise it edits the df outside the function\n",
    "    df['cosine_matches'] = [(1-0.93**i) for i in df['cosine_matches']]\n",
    "    df['mod_cosine_matches'] = [(1-0.93**i) for i in df['mod_cosine_matches']]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_mass_similarity(matches, documents_library, query_mass, base_num = 0.8):\n",
    "    '''\n",
    "    To each match in matches df, add a scaled value for how similar the parent_mass is to the query\n",
    "\n",
    "    matches: pandas DataFrame, library matching result of 1 query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    query_mass: float, parent mass of query\n",
    "    base_num: float, the base for the exponent\n",
    "    df: pandas DataFrame, library matching result of 1 query on library with mass sims\n",
    "    \n",
    "    The similarity in dalton is calculated and transformed into a value 0 - 1 by doing\n",
    "    1 - base_num^diff_in_dalton\n",
    "    '''\n",
    "    df = matches.copy()\n",
    "    library_ids = df.index.values\n",
    "    scaled_mass_sims = []\n",
    "    for lib_id in library_ids:\n",
    "        lib_mass = documents_library[lib_id]._obj.get(\"parent_mass\")\n",
    "        mass_diff = abs(lib_mass - query_mass)\n",
    "        scaled_mass_sim = base_num ** mass_diff\n",
    "        scaled_mass_sims.append(scaled_mass_sim)\n",
    "\n",
    "    #add to df\n",
    "    df['mass_sim'] = scaled_mass_sims\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1913.4927235480093"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get max parent mass to make it relative\n",
    "all_parent_masses = [elem for ID in range(len(found_matches_s2v_match_perc_pmass_sim)) for elem in list(found_matches_s2v_match_perc_pmass_sim[ID]['parent_mass'])]\n",
    "max_parent_mass = max(all_parent_masses)\n",
    "max_parent_mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preprocess for neural network, remove mass_match and perc_mass_change, make number of matches relative\n",
    "#add new transform of mass difference\n",
    "nn_prep_found_matches_s2v_match_perc_pmass_sim = []\n",
    "for ID in range(len(found_matches_s2v_match_perc_pmass_sim)):\n",
    "    current = found_matches_s2v_match_perc_pmass_sim[ID].copy()\n",
    "    current = transform_num_matches(current)\n",
    "    current['parent_mass'] = [cur_pm/max_parent_mass for cur_pm in current['parent_mass']]\n",
    "    current.drop(['mass_match', 'perc_mass_change'], axis=1, inplace=True)\n",
    "    q_mass = documents_query_s2v[ID]._obj.get(\"parent_mass\")\n",
    "    current = find_mass_similarity(current, documents_library_s2v, q_mass, base_num = 0.8)\n",
    "    nn_prep_found_matches_s2v_match_perc_pmass_sim.append(current)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
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       "...             ...             ...               ...                 ...   \n",
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       "\n",
       "       s2v_score  parent_mass  mass_sim  sim_label  \n",
       "65637   1.000000     0.099886  1.000000          1  \n",
       "40198   1.000000     0.412700  1.000000          1  \n",
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       "...          ...          ...       ...        ...  \n",
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       "30366  -0.245904     0.296578  0.831858          0  \n",
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       "90987  -0.275764     0.220614  0.981212          0  \n",
       "\n",
       "[239233 rows x 8 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finalising data for nn, add label 1 if similarity is above cutoff\n",
    "sim_cutoff = 0.6\n",
    "nn_found_matches_s2v_match_perc_pmass_sim = nn_prep_found_matches_s2v_match_perc_pmass_sim[0]\\\n",
    "    .append(nn_prep_found_matches_s2v_match_perc_pmass_sim[1:])\\\n",
    "    .drop(['label'], axis = 1)\n",
    "simlabs = [1 if sim >= sim_cutoff else 0 for sim in nn_found_matches_s2v_match_perc_pmass_sim['similarity']]\n",
    "nn_found_matches_s2v_match_perc_pmass_sim['sim_label'] = simlabs\n",
    "nn_found_matches_s2v_match_perc_pmass_sim.drop(['similarity'], axis = 1, inplace = True)\n",
    "nn_found_matches_s2v_match_perc_pmass_sim.sort_values(by='s2v_score', ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "46003      0.960780        0.195643          0.960780            0.195643   \n",
       "38195      0.049197        0.070000          0.971344            0.195643   \n",
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       "32721      0.000000        0.000000          0.961388            0.070000   \n",
       "56286      0.000000        0.000000          0.961388            0.070000   \n",
       "45044      0.000000        0.000000          0.961388            0.070000   \n",
       "\n",
       "       s2v_score  label  parent_mass  similarity      mass_sim  \n",
       "46003   0.887800      1     0.265661    1.000000  1.000000e+00  \n",
       "38195   0.858902      0     0.272986    0.774278  4.382372e-02  \n",
       "48062   0.848561      0     0.269826    0.907563  1.689367e-01  \n",
       "45969   0.844523      0     0.243687    0.774278  8.418306e-05  \n",
       "32755   0.811812      0     0.272986    0.774278  4.382372e-02  \n",
       "33654   0.799576      1     0.265661    1.000000  1.000000e+00  \n",
       "33166   0.790485      0     0.243687    0.774278  8.418306e-05  \n",
       "30807   0.760594      0     0.289792    0.170132  3.351044e-05  \n",
       "56270   0.747925      0     0.333740    0.170132  2.374814e-13  \n",
       "34114   0.739805      0     0.258336    0.774278  4.382372e-02  \n",
       "56863   0.735438      0     0.348389    0.170132  4.561891e-16  \n",
       "45979   0.732793      0     0.257283    0.944606  2.795346e-02  \n",
       "53958   0.730020      0     0.340030    0.175758  1.618902e-14  \n",
       "57799   0.728967      0     0.325381    0.175758  8.427624e-12  \n",
       "33190   0.727989      0     0.258336    0.774278  4.382372e-02  \n",
       "38480   0.726993      0     0.340030    0.175758  1.618902e-14  \n",
       "38277   0.724805      0     0.281433    0.175758  1.189202e-03  \n",
       "32721   0.724702      0     0.295964    0.797844  2.402553e-06  \n",
       "56286   0.724587      0     0.348389    0.170132  4.561891e-16  \n",
       "45044   0.721622      0     0.325381    0.175758  8.427624e-12  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_prep_found_matches_s2v_match_perc_pmass_sim[0].sort_values(by=['s2v_score'], ascending=False).iloc[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\new_found_matches_s2v_match_perc_pmass_sim.pickle\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
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      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "68321      1.000000        0.516018          1.000000            0.516018   \n",
       "7990       1.000000        0.999989          1.000000            0.999989   \n",
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       "56994      0.000000        0.000000          0.000000            0.000000   \n",
       "\n",
       "       s2v_score  parent_mass  mass_sim  sim_label  \n",
       "68321   1.000000     0.188767  1.000000          1  \n",
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       "\n",
       "[241054 rows x 8 columns]"
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     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading second test set to test nn output\n",
    "outfile = os.path.join(path_data, 'new_found_matches_s2v_match_perc_pmass_sim.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        new_found_matches_s2v_match_perc_pmass_sim = pickle.load(inf)\n",
    "\n",
    "#preprocess for neural network, remove mass_match, make number of matches and parent_mass relative and let s2v_score start at 0\n",
    "nn_prep_new_found_matches_s2v_match_perc_pmass_sim = []\n",
    "for ID in range(len(new_found_matches_s2v_match_perc_pmass_sim)):\n",
    "    current = new_found_matches_s2v_match_perc_pmass_sim[ID].copy()\n",
    "    current = transform_num_matches(current)\n",
    "    current['parent_mass'] = [cur_pm/max_parent_mass for cur_pm in current['parent_mass']]\n",
    "    current.drop(['mass_match', 'perc_mass_change'], axis=1, inplace=True)\n",
    "    q_mass = new_documents_query_s2v[ID]._obj.get(\"parent_mass\")\n",
    "    current = find_mass_similarity(current, new_documents_library_s2v, q_mass, base_num = 0.8)\n",
    "    nn_prep_new_found_matches_s2v_match_perc_pmass_sim.append(current)\n",
    "\n",
    "#finalising data for nn, add label 1 if similarity is above cutoff\n",
    "sim_cutoff = 0.6\n",
    "nn_new_found_matches_s2v_match_perc_pmass_sim = nn_prep_new_found_matches_s2v_match_perc_pmass_sim[0]\\\n",
    "    .append(nn_prep_new_found_matches_s2v_match_perc_pmass_sim[1:])\\\n",
    "    .drop(['label'], axis = 1)\n",
    "simlabs = [1 if sim >= sim_cutoff else 0 for sim in nn_new_found_matches_s2v_match_perc_pmass_sim['similarity']]\n",
    "nn_new_found_matches_s2v_match_perc_pmass_sim['sim_label'] = simlabs\n",
    "nn_new_found_matches_s2v_match_perc_pmass_sim.drop(['similarity'], axis = 1, inplace = True)\n",
    "nn_new_found_matches_s2v_match_perc_pmass_sim.sort_values(by='s2v_score', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NN for learning similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 239233 samples\n",
      "Epoch 1/30\n",
      "239233/239233 [==============================] - 51s 213us/sample - loss: 0.1873 - accuracy: 0.9356\n",
      "Epoch 2/30\n",
      "239233/239233 [==============================] - 39s 164us/sample - loss: 0.1669 - accuracy: 0.9419\n",
      "Epoch 3/30\n",
      "239233/239233 [==============================] - 39s 161us/sample - loss: 0.1611 - accuracy: 0.9435\n",
      "Epoch 4/30\n",
      "239233/239233 [==============================] - 39s 163us/sample - loss: 0.1574 - accuracy: 0.9446\n",
      "Epoch 5/30\n",
      "239233/239233 [==============================] - 39s 165us/sample - loss: 0.1555 - accuracy: 0.9454\n",
      "Epoch 6/30\n",
      "239233/239233 [==============================] - 40s 167us/sample - loss: 0.1540 - accuracy: 0.9459\n",
      "Epoch 7/30\n",
      "239233/239233 [==============================] - 40s 166us/sample - loss: 0.1523 - accuracy: 0.9463\n",
      "Epoch 8/30\n",
      "239233/239233 [==============================] - 40s 169us/sample - loss: 0.1508 - accuracy: 0.9468\n",
      "Epoch 9/30\n",
      "239233/239233 [==============================] - 46s 192us/sample - loss: 0.1495 - accuracy: 0.9473\n",
      "Epoch 10/30\n",
      "239233/239233 [==============================] - 40s 166us/sample - loss: 0.1487 - accuracy: 0.9476\n",
      "Epoch 11/30\n",
      "239233/239233 [==============================] - 39s 165us/sample - loss: 0.1479 - accuracy: 0.9478\n",
      "Epoch 12/30\n",
      "239233/239233 [==============================] - 42s 175us/sample - loss: 0.1474 - accuracy: 0.9479\n",
      "Epoch 13/30\n",
      "239233/239233 [==============================] - 37s 156us/sample - loss: 0.1467 - accuracy: 0.9481\n",
      "Epoch 14/30\n",
      "239233/239233 [==============================] - 31s 131us/sample - loss: 0.1460 - accuracy: 0.9485\n",
      "Epoch 15/30\n",
      "239233/239233 [==============================] - 31s 131us/sample - loss: 0.1454 - accuracy: 0.9487\n",
      "Epoch 16/30\n",
      "239233/239233 [==============================] - 35s 147us/sample - loss: 0.1452 - accuracy: 0.9483\n",
      "Epoch 17/30\n",
      "239233/239233 [==============================] - 32s 133us/sample - loss: 0.1448 - accuracy: 0.9489\n",
      "Epoch 18/30\n",
      "239233/239233 [==============================] - 32s 133us/sample - loss: 0.1439 - accuracy: 0.9490\n",
      "Epoch 19/30\n",
      "239233/239233 [==============================] - 33s 138us/sample - loss: 0.1436 - accuracy: 0.9491\n",
      "Epoch 20/30\n",
      "239233/239233 [==============================] - 32s 135us/sample - loss: 0.1436 - accuracy: 0.9490\n",
      "Epoch 21/30\n",
      "239233/239233 [==============================] - 33s 136us/sample - loss: 0.1429 - accuracy: 0.9493\n",
      "Epoch 22/30\n",
      "239233/239233 [==============================] - 39s 164us/sample - loss: 0.1423 - accuracy: 0.9494\n",
      "Epoch 23/30\n",
      "239233/239233 [==============================] - 33s 136us/sample - loss: 0.1424 - accuracy: 0.9493\n",
      "Epoch 24/30\n",
      "239233/239233 [==============================] - 35s 148us/sample - loss: 0.1417 - accuracy: 0.9497\n",
      "Epoch 25/30\n",
      "239233/239233 [==============================] - 32s 133us/sample - loss: 0.1414 - accuracy: 0.9497\n",
      "Epoch 26/30\n",
      "239233/239233 [==============================] - 40s 168us/sample - loss: 0.1412 - accuracy: 0.9496\n",
      "Epoch 27/30\n",
      "239233/239233 [==============================] - 40s 165us/sample - loss: 0.1410 - accuracy: 0.9499\n",
      "Epoch 28/30\n",
      "239233/239233 [==============================] - 41s 173us/sample - loss: 0.1407 - accuracy: 0.9496\n",
      "Epoch 29/30\n",
      "239233/239233 [==============================] - 41s 173us/sample - loss: 0.1401 - accuracy: 0.9499\n",
      "Epoch 30/30\n",
      "239233/239233 [==============================] - 41s 172us/sample - loss: 0.1401 - accuracy: 0.9500\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1829787ae88>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#from tensorflow import keras\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "model_name = os.path.join(path_data, 'first_deep_nn_similarity')\n",
    "\n",
    "# split into input (X) and output (y) variables\n",
    "X = nn_found_matches_s2v_match_perc_pmass_sim.drop('sim_label', axis = 1)\n",
    "y = nn_found_matches_s2v_match_perc_pmass_sim['sim_label']\n",
    "if os.path.exists(model_name):\n",
    "    model = load_model(model_name)\n",
    "else:\n",
    "    # define the keras model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(12, input_dim=X.shape[1], activation='relu'))\n",
    "    model.add(Dense(12, activation='relu'))\n",
    "    model.add(Dense(16, activation='relu'))\n",
    "    model.add(Dense(12, activation='relu'))\n",
    "    model.add(Dense(8, activation='relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    # compile the keras model\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    # fit the keras model on the dataset\n",
    "    model.fit(X, y, epochs=30, batch_size = 16)\n",
    "    \n",
    "    model.save(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "241054/241054 [==============================] - 11s 46us/sample - loss: 0.1446 - accuracy: 0.9488\n",
      "Accuracy: 94.88\n"
     ]
    }
   ],
   "source": [
    "X_test = nn_new_found_matches_s2v_match_perc_pmass_sim.drop(['sim_label'], axis = 1)\n",
    "y_test = nn_new_found_matches_s2v_match_perc_pmass_sim['sim_label']\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print('Accuracy: %.2f' % (accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make class predictions with the model\n",
    "predictions_test = model.predict_classes(X_test)\n",
    "# # summarize the first 5 cases\n",
    "# for i in range(5):\n",
    "#     print('%s => %d (expected %d)' % (X_test.iloc[i].tolist(), predictions_test[i], y_test.iloc[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_new_testset_tested = []\n",
    "for ID in range(len(nn_prep_new_found_matches_s2v_match_perc_pmass_sim)):\n",
    "    current_match = nn_prep_new_found_matches_s2v_match_perc_pmass_sim[ID].copy()\n",
    "    simlabs = [1 if sim >= sim_cutoff else 0 for sim in current_match['similarity']]\n",
    "    predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "    predictions = model.predict_classes(predict_on)\n",
    "    current_match['predictions'] = predictions\n",
    "    correct_guess = [pred == lab for pred, lab in zip(predictions, simlabs)]\n",
    "    current_match['correct_guess'] = correct_guess\n",
    "    nn_new_testset_tested.append(current_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>similarity</th>\n",
       "      <th>mass_sim</th>\n",
       "      <th>predictions</th>\n",
       "      <th>correct_guess</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>46341</th>\n",
       "      <td>0.792853</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.792853</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.838697</td>\n",
       "      <td>0</td>\n",
       "      <td>0.433620</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52743</th>\n",
       "      <td>0.766401</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.766401</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.589020</td>\n",
       "      <td>0</td>\n",
       "      <td>0.471248</td>\n",
       "      <td>0.901186</td>\n",
       "      <td>1.053123e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34539</th>\n",
       "      <td>0.241939</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.955195</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.585604</td>\n",
       "      <td>0</td>\n",
       "      <td>0.435727</td>\n",
       "      <td>0.859649</td>\n",
       "      <td>4.066856e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47562</th>\n",
       "      <td>0.010289</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.010289</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.584888</td>\n",
       "      <td>1</td>\n",
       "      <td>0.433620</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35469</th>\n",
       "      <td>0.243206</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.243206</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.565561</td>\n",
       "      <td>0</td>\n",
       "      <td>0.435727</td>\n",
       "      <td>0.859649</td>\n",
       "      <td>4.066856e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33568</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.560947</td>\n",
       "      <td>0</td>\n",
       "      <td>0.432567</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.378614e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52554</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.551094</td>\n",
       "      <td>0</td>\n",
       "      <td>0.433620</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47158</th>\n",
       "      <td>0.285278</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.285278</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.550833</td>\n",
       "      <td>0</td>\n",
       "      <td>0.446163</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.722366e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47558</th>\n",
       "      <td>0.032219</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.032219</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.548738</td>\n",
       "      <td>0</td>\n",
       "      <td>0.433620</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38527</th>\n",
       "      <td>0.013670</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.013693</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.545742</td>\n",
       "      <td>0</td>\n",
       "      <td>0.432567</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.378614e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52973</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.544748</td>\n",
       "      <td>1</td>\n",
       "      <td>0.433620</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38523</th>\n",
       "      <td>0.036623</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.040348</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.540438</td>\n",
       "      <td>0</td>\n",
       "      <td>0.418971</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.920947e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45757</th>\n",
       "      <td>0.766454</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.766454</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.538008</td>\n",
       "      <td>0</td>\n",
       "      <td>0.471248</td>\n",
       "      <td>0.901186</td>\n",
       "      <td>1.053123e-07</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51823</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.537810</td>\n",
       "      <td>1</td>\n",
       "      <td>0.433620</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43816</th>\n",
       "      <td>0.010825</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.384554</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.536827</td>\n",
       "      <td>0</td>\n",
       "      <td>0.449323</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.225025e-03</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37029</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.534970</td>\n",
       "      <td>0</td>\n",
       "      <td>0.477568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.086787e-09</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37404</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.531860</td>\n",
       "      <td>0</td>\n",
       "      <td>0.433620</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38017</th>\n",
       "      <td>0.000935</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.000935</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.531845</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448270</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.920519e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32638</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.531173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.417917</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.225025e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37798</th>\n",
       "      <td>0.029850</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.033895</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.526315</td>\n",
       "      <td>0</td>\n",
       "      <td>0.462919</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>3.689215e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "46341      0.792853        0.195643          0.792853            0.195643   \n",
       "52743      0.766401        0.070000          0.766401            0.070000   \n",
       "34539      0.241939        0.070000          0.955195            0.135100   \n",
       "47562      0.010289        0.070000          0.010289            0.070000   \n",
       "35469      0.243206        0.070000          0.243206            0.070000   \n",
       "33568      0.000000        0.000000          0.000000            0.000000   \n",
       "52554      0.000000        0.000000          0.000000            0.000000   \n",
       "47158      0.285278        0.135100          0.285278            0.135100   \n",
       "47558      0.032219        0.070000          0.032219            0.070000   \n",
       "38527      0.013670        0.070000          0.013693            0.135100   \n",
       "52973      0.000008        0.070000          0.000008            0.070000   \n",
       "38523      0.036623        0.070000          0.040348            0.135100   \n",
       "45757      0.766454        0.135100          0.766454            0.135100   \n",
       "51823      0.000000        0.000000          0.000000            0.000000   \n",
       "43816      0.010825        0.070000          0.384554            0.195643   \n",
       "37029      0.000000        0.000000          0.000000            0.000000   \n",
       "37404      0.000000        0.000000          0.000000            0.000000   \n",
       "38017      0.000935        0.070000          0.000935            0.070000   \n",
       "32638      0.000000        0.000000          0.000000            0.000000   \n",
       "37798      0.029850        0.070000          0.033895            0.195643   \n",
       "\n",
       "       s2v_score  label  parent_mass  similarity      mass_sim  predictions  \\\n",
       "46341   0.838697      0     0.433620    0.947368  1.000000e+00            1   \n",
       "52743   0.589020      0     0.471248    0.901186  1.053123e-07            0   \n",
       "34539   0.585604      0     0.435727    0.859649  4.066856e-01            0   \n",
       "47562   0.584888      1     0.433620    1.000000  1.000000e+00            1   \n",
       "35469   0.565561      0     0.435727    0.859649  4.066856e-01            0   \n",
       "33568   0.560947      0     0.432567    1.000000  6.378614e-01            0   \n",
       "52554   0.551094      0     0.433620    0.947368  1.000000e+00            1   \n",
       "47158   0.550833      0     0.446163    1.000000  4.722366e-03            0   \n",
       "47558   0.548738      0     0.433620    0.947368  1.000000e+00            1   \n",
       "38527   0.545742      0     0.432567    1.000000  6.378614e-01            0   \n",
       "52973   0.544748      1     0.433620    1.000000  1.000000e+00            1   \n",
       "38523   0.540438      0     0.418971    0.947368  1.920947e-03            1   \n",
       "45757   0.538008      0     0.471248    0.901186  1.053123e-07            0   \n",
       "51823   0.537810      1     0.433620    1.000000  1.000000e+00            1   \n",
       "43816   0.536827      0     0.449323    0.947368  1.225025e-03            0   \n",
       "37029   0.534970      0     0.477568    1.000000  7.086787e-09            0   \n",
       "37404   0.531860      0     0.433620    0.947368  1.000000e+00            1   \n",
       "38017   0.531845      0     0.448270    0.947368  1.920519e-03            1   \n",
       "32638   0.531173      0     0.417917    1.000000  1.225025e-03            1   \n",
       "37798   0.526315      0     0.462919    0.947368  3.689215e-06            1   \n",
       "\n",
       "      correct_guess  \n",
       "46341        [True]  \n",
       "52743       [False]  \n",
       "34539       [False]  \n",
       "47562        [True]  \n",
       "35469       [False]  \n",
       "33568       [False]  \n",
       "52554        [True]  \n",
       "47158       [False]  \n",
       "47558        [True]  \n",
       "38527       [False]  \n",
       "52973        [True]  \n",
       "38523        [True]  \n",
       "45757       [False]  \n",
       "51823        [True]  \n",
       "43816       [False]  \n",
       "37029       [False]  \n",
       "37404        [True]  \n",
       "38017        [True]  \n",
       "32638        [True]  \n",
       "37798        [True]  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_new_testset_tested[0].sort_values(by=['s2v_score'], ascending=False).iloc[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.9473684210526315\n",
      "6 1.0\n",
      "7 0.9564315352697096\n",
      "8 0.9243641231593038\n",
      "9 0.9606986899563319\n",
      "10 0.9859550561797753\n",
      "11 0.1043360433604336\n",
      "20 0.5894179894179894\n",
      "24 0.47648261758691207\n",
      "27 0.9954022988505747\n",
      "28 0.9834905660377359\n",
      "33 0.380143696930111\n",
      "36 0.9022633744855967\n",
      "38 0.9722863741339491\n",
      "41 0.2834101382488479\n",
      "43 1.0\n",
      "44 1.0\n",
      "50 0.41708542713567837\n",
      "64 0.25815808556925307\n",
      "65 1.0\n",
      "66 0.9410609037328095\n",
      "68 0.20887728459530025\n",
      "77 0.3350189633375474\n",
      "79 0.0840751730959446\n",
      "82 0.9281481481481482\n",
      "105 0.9122340425531915\n",
      "109 1.0\n",
      "112 0.8425531914893617\n",
      "116 0.0877742946708464\n",
      "118 0.39643652561247217\n",
      "128 0.3782051282051282\n",
      "134 0.9707207207207207\n",
      "135 0.40821917808219177\n",
      "137 1.0\n",
      "138 0.9283088235294118\n",
      "142 0.13793103448275862\n",
      "143 0.1828978622327791\n",
      "144 0.9265873015873016\n",
      "149 1.0\n",
      "150 0.9493087557603687\n",
      "152 0.40606843124596514\n",
      "164 0.4371816638370119\n",
      "165 1.0\n",
      "168 0.9795918367346939\n",
      "174 0.6467625899280576\n",
      "176 1.0\n",
      "177 1.0\n",
      "178 0.7666098807495741\n",
      "183 0.9053254437869822\n",
      "186 0.9090909090909091\n",
      "188 0.9522776572668112\n",
      "189 0.10200364298724955\n",
      "198 0.8319672131147541\n",
      "199 0.8082595870206489\n",
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      "207 0.77109375\n",
      "211 0.8421052631578947\n",
      "214 0.9368318756073858\n",
      "216 0.9159663865546218\n",
      "219 0.34595959595959597\n",
      "223 0.3456221198156682\n",
      "224 0.8057054741711642\n",
      "232 0.9238237490664675\n",
      "235 0.07150050352467271\n",
      "238 0.9674418604651163\n",
      "239 1.0\n",
      "242 0.8336842105263158\n",
      "243 0.4856687898089172\n",
      "244 1.0\n",
      "260 0.40615384615384614\n",
      "261 0.9506781750924784\n",
      "262 0.778816199376947\n",
      "276 0.7628032345013477\n",
      "290 0.2736625514403292\n",
      "291 0.2611111111111111\n",
      "297 0.9232456140350878\n",
      "300 0.8245614035087719\n",
      "301 0.18537074148296592\n",
      "315 0.2124492557510149\n",
      "318 0.11235955056179775\n",
      "321 0.9216152019002375\n",
      "322 0.74\n",
      "323 0.4444444444444444\n",
      "325 0.5774134790528234\n",
      "329 0.5549348230912476\n",
      "334 0.9428263214670982\n",
      "337 0.2990720913633119\n",
      "345 0.7054108216432866\n",
      "346 0.1324200913242009\n",
      "347 0.25226860254083483\n",
      "350 0.16340425531914893\n",
      "351 0.6369426751592356\n",
      "355 1.0\n",
      "358 0.28820598006644516\n",
      "359 0.19653179190751446\n",
      "362 0.9265873015873016\n",
      "367 0.8073260073260073\n",
      "371 0.4609595484477893\n",
      "374 1.0\n",
      "375 0.7727891156462585\n",
      "376 0.3698630136986301\n",
      "381 0.995787700084246\n",
      "382 1.0\n",
      "383 0.9636363636363636\n",
      "387 1.0\n",
      "390 0.5137614678899083\n",
      "392 0.2042833607907743\n",
      "395 0.6844305120167189\n",
      "407 1.0\n",
      "418 0.11069651741293532\n",
      "419 0.1793103448275862\n",
      "420 1.0\n",
      "427 0.9867924528301887\n",
      "428 0.17761033369214208\n",
      "429 0.27204030226700254\n",
      "431 0.10424710424710425\n",
      "436 0.6943820224719102\n",
      "440 0.9860834990059643\n",
      "442 0.2375\n",
      "446 0.1748768472906404\n",
      "448 0.9752252252252253\n",
      "450 0.3722033898305085\n",
      "453 0.5877587758775877\n",
      "454 0.7724288840262582\n",
      "455 0.9985007496251874\n",
      "459 0.7106227106227107\n",
      "461 1.0\n",
      "464 0.8360927152317881\n",
      "465 0.4147783251231527\n",
      "472 0.10979228486646884\n",
      "479 0.2807017543859649\n",
      "484 0.36042402826855124\n",
      "488 0.40258939580764486\n",
      "490 0.31843575418994413\n",
      "496 0.05104408352668213\n",
      "500 0.9121621621621622\n",
      "518 0.3411910669975186\n",
      "520 0.9216152019002375\n",
      "523 0.896551724137931\n",
      "527 0.9290281329923273\n",
      "533 0.3071253071253071\n",
      "537 0.6061867935752528\n",
      "538 0.9583941605839416\n",
      "543 0.2981867024848892\n",
      "545 0.20331421287444232\n",
      "549 0.8349056603773585\n",
      "550 1.0\n",
      "552 0.12834224598930483\n",
      "556 0.9812332439678284\n",
      "560 0.1450653983353151\n",
      "561 0.9448082319925164\n",
      "567 0.8420703384207033\n",
      "575 1.0\n",
      "576 0.9054054054054054\n",
      "581 0.19\n",
      "583 0.776255707762557\n",
      "585 0.9144329896907216\n",
      "586 0.9954022988505747\n",
      "587 0.4040247678018576\n",
      "591 0.4048376830044558\n",
      "597 0.8263157894736842\n",
      "604 0.9394736842105263\n",
      "606 0.8093645484949833\n",
      "608 0.782608695652174\n",
      "611 1.0\n",
      "613 0.43243243243243246\n",
      "615 0.8123138033763655\n",
      "616 0.23599320882852293\n",
      "617 0.9129662522202486\n",
      "619 0.4602076124567474\n",
      "626 0.43118383060635224\n",
      "628 1.0\n",
      "637 0.06666666666666667\n",
      "644 0.11574697173620457\n",
      "649 0.20198675496688742\n",
      "651 0.7146840148698885\n",
      "660 0.6135216952573158\n",
      "661 0.19714964370546317\n",
      "663 0.5663265306122449\n",
      "664 0.48501362397820164\n",
      "678 1.0\n",
      "681 0.0688622754491018\n",
      "682 0.4864864864864865\n",
      "684 0.6025236593059937\n",
      "692 1.0\n",
      "695 0.7293868921775899\n",
      "696 0.93488824101069\n",
      "697 0.7913385826771654\n",
      "705 0.9713656387665198\n",
      "708 0.9385964912280702\n",
      "717 0.9584158415841584\n",
      "722 0.9557171183079973\n",
      "725 1.0\n",
      "728 0.9072426937738246\n",
      "730 0.5076645626690712\n",
      "734 0.9656964656964657\n",
      "737 1.0\n",
      "738 0.9146341463414634\n",
      "741 1.0\n",
      "742 0.6073059360730594\n",
      "746 0.09876543209876543\n",
      "749 0.1793103448275862\n",
      "750 0.8043217286914766\n",
      "751 0.8162729658792651\n",
      "752 0.08500772797527048\n",
      "754 0.6360924683072334\n",
      "759 0.15317559153175592\n",
      "764 0.594925634295713\n",
      "767 0.752411575562701\n",
      "774 0.18092428711897737\n",
      "775 0.038461538461538464\n",
      "782 0.9781491002570694\n",
      "785 0.9978046103183315\n",
      "793 0.18181818181818182\n",
      "794 1.0\n",
      "795 0.14531548757170173\n",
      "796 0.9825783972125436\n",
      "799 0.9374167776298269\n",
      "802 0.9754901960784313\n",
      "803 0.9061032863849765\n",
      "804 0.8976034858387799\n",
      "810 0.20721476510067113\n",
      "811 0.9801071155317521\n",
      "814 0.38848920863309355\n",
      "815 0.08500772797527048\n",
      "819 0.7338308457711443\n",
      "823 0.4741729541497388\n",
      "836 0.17853922452660054\n",
      "837 0.20739064856711917\n",
      "839 0.5574288724973656\n",
      "841 0.6976744186046512\n",
      "842 0.3555404486743712\n",
      "854 0.20880149812734083\n",
      "861 0.74487895716946\n",
      "865 0.8020304568527918\n",
      "871 1.0\n",
      "873 0.785140562248996\n",
      "874 0.10160427807486631\n",
      "879 0.9757207890743551\n",
      "881 0.37480798771121354\n",
      "882 0.28893905191873587\n",
      "889 0.185121107266436\n",
      "891 0.4576502732240437\n",
      "896 0.3162596783799881\n",
      "899 0.4209569957601454\n",
      "900 0.9546191247974068\n",
      "902 1.0\n",
      "903 0.1297709923664122\n",
      "908 0.19591836734693877\n",
      "916 0.9548693586698337\n",
      "920 0.3829787234042553\n",
      "925 0.9372384937238494\n",
      "926 0.9939393939393939\n",
      "932 0.7587188612099645\n",
      "933 0.08875739644970414\n",
      "934 0.6200814111261872\n",
      "940 0.2430647291941876\n",
      "943 0.06147540983606557\n",
      "946 0.9130434782608695\n",
      "951 0.6273127753303964\n",
      "955 0.9775280898876404\n",
      "963 0.23084677419354838\n",
      "965 0.4367396593673966\n",
      "976 0.3245952197378566\n",
      "977 0.9689922480620154\n",
      "980 0.9656964656964657\n",
      "982 1.0\n",
      "984 0.2574931880108992\n",
      "985 1.0\n",
      "987 0.9797297297297297\n",
      "989 0.1985731272294887\n",
      "996 0.632\n",
      "997 0.8662131519274376\n",
      "998 0.03367003367003367\n",
      "274\n"
     ]
    }
   ],
   "source": [
    "#if top hit is not true match, what is the average similarity of top hit above a threshold?\n",
    "topn = 20\n",
    "threshold = 0.4\n",
    "new_false_m_simils_thresh = []\n",
    "new_ids_top_false_hits_thresh = []\n",
    "for ID in range(len(nn_new_testset_tested)):\n",
    "    df_select = nn_new_testset_tested[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    df_select = df_select[(df_select['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        top_hit = df_select.iloc[0]\n",
    "        if top_hit['label'] == 0:\n",
    "            new_false_m_simil = top_hit['similarity']\n",
    "            print(ID, new_false_m_simil)\n",
    "            new_ids_top_false_hits_thresh.append(ID)\n",
    "            new_false_m_simils_thresh.append(new_false_m_simil)\n",
    "print(len(new_false_m_simils_thresh))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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38N1332HPnj3IzMwEADQ0NOCLL75ATk4OMjIyUFtbi59++gmHDh2CRqORMj2GSwKZTIb4+Hi0trayQkgCYbezfrETefCopVKpYDQa/Y43Go1QqVTQ6/VITEzE2rVrsXr1aly8eNEzxmAwIC0tDTqdDvPmzYNOp8OSJUtgMBikTo9owhDeiWy1Wn3K7pYvX47q6mq4XC7I5XI0NDRg48aNXmOWLVuGysrKgHNhJ/LIvdiJTKNPeCeyw+HwO76/vx9dXV1QKpVwOBw+RXcxMTFD9iyzE3n0xMXFjfcUpqQx6UR++bnB8/uh1nG73UM+H6gTubOzk53IIZLJZFCpVLDb7XzPJYFcLodSqQw+TspGh9OJ7O8I9OTJE8yYMQNRUVEBxwTrWQ7UiRzKiyZv/i5E0cgJ70TWaDQ+481mM9RqNeTy59nWarWwWCw+29RqtVKmRxL19vZi//796O3tHe+pTEmSrxZmZ2fj2rVrMJlMaG5uRmlpqU8ncklJiWd8VlYW7Ha753Muk8kEk8mETZs2ecZs2LABZrMZFRUVaGlpQUVFBSwWi89FDhpdbrcbTU1NPCUURHgn8pw5c3DgwAGUlZXh6tWrUCqV2LZtm+czLgBITU1FQUEBzp07h/LycsTFxaGgoEDyZ1xEE8mU+/oTha6npwd6vR6lpaVQKBTjPZ0ph9+Kn8bCw8Px/vvv+70wRCPHIxeRIDxyEQnCcBEJwnARCcJwEQnCcBEJwntoTEP19fW4cOECmpqa0NnZib179w7rt75paDxyTUN9fX1ISkpCXl7eeE9lSuORaxpKT09Henr6eE9jyuORi0gQhotIEIaLSBCGi0gQhotIEF4tnIacTifa2to8jx89egSbzYaoqCjeT2MU8VdOpqG7d++isLDQZ/mqVauwY8eOcZjR1MRwEQnC91xEgjBcRIIwXESCMFxEgjBcRIIwXESCMFxEgjBcRIIwXESCMFxEgjBcRIL8P7x2ozITpEuOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(2,2))\n",
    "plt.boxplot(new_false_m_simils_thresh)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing if top hit is above 0.6 similarity after filtering with nn\n",
    "#look at top20\n",
    "test_sim_mcos_top20 = []\n",
    "test_sim_s2v_top20 = []\n",
    "test_sim_filt_nn = []\n",
    "ideal_case_top20 = []\n",
    "\n",
    "mass_sim_thresh = 0.95\n",
    "threshold = 0.4\n",
    "topn = 20 #only look at top 20\n",
    "sim_threshold = 0.6\n",
    "\n",
    "for ID in range(len(nn_new_testset_tested)):\n",
    "    # mass + mod cosine\n",
    "    current_match = nn_new_testset_tested[ID].sort_values(by=['mod_cosine_score'], ascending=False).iloc[:topn]\n",
    "\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['mod_cosine_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['mod_cosine_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_mcos_top20.append(best_bet)\n",
    "\n",
    "    # Scenario 1: mass + Spec2Vec + threshold\n",
    "    current_match = nn_new_testset_tested[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    \n",
    "    #inchikey_query = documents_query_s2v[ID]._obj.get(\"inchikey\")[:14]\n",
    "    #select top20\n",
    "    #current_match = found_matches_s2v[ID].sort_values(by=['s2v_score'], ascending=False).iloc[:topn]\n",
    "\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_s2v_top20.append(best_bet)\n",
    "    \n",
    "    # Scenario 2: Spec2Vec + threshold after filtering for non-similar matches with nn\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['predictions'] == 1)\n",
    "                                  & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_filt_nn.append(best_bet)\n",
    "    \n",
    "    # ideal case: how many queries have an actual match above s2v threshold and >0.6 tanimoto\n",
    "    df_select = current_match[(current_match['s2v_score'] > threshold)]\n",
    "    best_bet = -1\n",
    "    if df_select.shape[0] > 0:\n",
    "        if any(df_select['similarity'] > 0.6):\n",
    "            best_bet = 1\n",
    "    ideal_case_top20.append(best_bet)\n",
    "\n",
    "\n",
    "#make arrays from lists\n",
    "test_sim_mcos_arr_top20 = np.array(test_sim_mcos_top20)\n",
    "test_sim_s2v_arr_top20 = np.array(test_sim_s2v_top20)\n",
    "test_sim_filt_nn_arr = np.array(test_sim_filt_nn)\n",
    "ideal_case_arr_top20 = np.array(ideal_case_top20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### plt.style.use('ggplot')\n",
    "\n",
    "ylabels = ['mass + mod cosine',\n",
    "           'mass + Spec2Vec',\n",
    "           'mass + Spec2Vec + filtering NN',\n",
    "           'ideal case Spec2Vec>0.4']\n",
    "xlabels = ['true matches', 'false matches', 'no matches found']\n",
    "          #'mass + mod.cosine \\n + as backup: Spec2Vec + mod.cosine']\n",
    "data = np.array([[np.sum(test_sim_mcos_arr_top20 == 1), np.sum(test_sim_mcos_arr_top20 == 0), np.sum(test_sim_mcos_arr_top20 == -1)],\n",
    "                 [np.sum(test_sim_s2v_arr_top20 == 1), np.sum(test_sim_s2v_arr_top20 == 0), np.sum(test_sim_s2v_arr_top20 == -1)],\n",
    "                 [np.sum(test_sim_filt_nn_arr == 1), np.sum(test_sim_filt_nn_arr == 0), np.sum(test_sim_filt_nn_arr == -1)],\n",
    "                 [np.sum(ideal_case_arr_top20 == 1), np.sum(ideal_case_arr_top20 == 0), np.sum(ideal_case_arr_top20 == -1)]])\n",
    "\n",
    "dataframe = pd.DataFrame(data, columns=xlabels, index=ylabels)\n",
    "\n",
    "#fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax = dataframe.sort_values(by=['false matches'], ascending=True).plot.barh(stacked=True, edgecolor='none', figsize=(5, 3))\n",
    "\n",
    "horiz_offset = 1.03\n",
    "vert_offset = 1.\n",
    "ax.legend(bbox_to_anchor=(horiz_offset, vert_offset))\n",
    "plt.xlabel('Number of spectra')\n",
    "plt.title('Compare if top hit is above 0.6 tanimoto similarity\\n(>5 matching inchikey in library)')\n",
    "#plt.figure(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NN for learning true hits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing if top hit is a true hit after filtering for similarity with nn\n",
    "#look at top20\n",
    "test_m_new_s2v_top20 = []\n",
    "test_m_new_filt_nn_top20 = []\n",
    "\n",
    "mass_sim_thresh = 1\n",
    "threshold = 0.4\n",
    "topn = 20 #only look at top 20\n",
    "#sim_threshold = 0.6\n",
    "\n",
    "for ID in range(len(nn_new_testset_tested)):\n",
    "    current_match = nn_new_testset_tested[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    \n",
    "    inchikey_query = new_documents_query_s2v[ID]._obj.get(\"inchikey\")[:14]\n",
    "    #select top20\n",
    "    #current_match = found_matches_s2v[ID].sort_values(by=['s2v_score'], ascending=False).iloc[:topn]\n",
    "\n",
    "    # Scenario 1: mass + Spec2Vec + threshold\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        inchikey_match = new_documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        #sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (inchikey_query == inchikey_match)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_m_new_s2v_top20.append(best_bet)\n",
    "    \n",
    "    # Scenario 2: Spec2Vec + threshold after filtering for non-similar matches with nn\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['predictions'] == 1)\n",
    "                                  & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        #sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (inchikey_query == inchikey_match)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_m_new_filt_nn_top20.append(best_bet)\n",
    "\n",
    "\n",
    "#make arrays from lists\n",
    "test_m_new_s2v_arr_top20 = np.array(test_m_new_s2v_top20)\n",
    "test_m_new_filt_nn_arr_top20 = np.array(test_m_new_filt_nn_top20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### plt.style.use('ggplot')\n",
    "\n",
    "ylabels = ['mass + Spec2Vec',\n",
    "           'mass + Spec2Vec + filtering NN']\n",
    "xlabels = ['true matches', 'false matches', 'no matches found']\n",
    "          #'mass + mod.cosine \\n + as backup: Spec2Vec + mod.cosine']\n",
    "data = np.array([[np.sum(test_m_new_s2v_arr_top20 == 1), np.sum(test_m_new_s2v_arr_top20 == 0), np.sum(test_m_new_s2v_arr_top20 == -1)],\n",
    "                 [np.sum(test_m_new_filt_nn_arr_top20 == 1), np.sum(test_m_new_filt_nn_arr_top20 == 0), np.sum(test_m_new_filt_nn_arr_top20 == -1)]])\n",
    "\n",
    "dataframe = pd.DataFrame(data, columns=xlabels, index=ylabels)\n",
    "\n",
    "#fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax = dataframe.sort_values(by=['false matches'], ascending=True).plot.barh(stacked=True, edgecolor='none', figsize=(5, 3))\n",
    "\n",
    "horiz_offset = 1.03\n",
    "vert_offset = 1.\n",
    "ax.legend(bbox_to_anchor=(horiz_offset, vert_offset))\n",
    "plt.xlabel('Number of spectra')\n",
    "plt.title('Selecting top hit\\n(>5 matching inchikey in library)')\n",
    "#plt.figure(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NN with equal true/pos labels by oversampling or taking top 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#oversample true hits to get equal ratio\n",
    "def oversample(matches, label_column):\n",
    "    '''Oversamples the 1 label so that 1 and 0 label are equal in the df\n",
    "\n",
    "    matches: pandas DataFrame, concatenation of all queries matching to a library\n",
    "    label_column: str, the column that contains the label\n",
    "    '''\n",
    "    all_true_p = matches.copy()[matches[label_column]==1]\n",
    "    len_all_true_p = all_true_p.shape[0]\n",
    "    all_false_p = matches.copy()[matches[label_column]==0]\n",
    "    len_all_false_p = all_false_p.shape[0]\n",
    "    ratio = len_all_false_p/len_all_true_p\n",
    "    decimals = ratio % 1\n",
    "    np.random.seed(42)\n",
    "    chosen_indices = np.random.choice(range(len_all_true_p), round(decimals*len_all_true_p))\n",
    "    balanced_true_p = all_true_p.iloc[chosen_indices]\n",
    "    for i in range(1,int(ratio)+1):\n",
    "        balanced_true_p = balanced_true_p.append(all_true_p)\n",
    "    matches_oversampled = balanced_true_p.append(all_false_p)\n",
    "    np.random.seed(42)\n",
    "    matches_oversampled = matches_oversampled.sample(frac=1)\n",
    "    print('New shape:', matches_oversampled.shape)\n",
    "    return matches_oversampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New shape: (433146, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(216573, 216573)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_found_matches_s2v_match_perc_pmass_sim_oversampled = oversample(nn_found_matches_s2v_match_perc_pmass_sim, 'sim_label')\n",
    "#check:\n",
    "sim_labels_oversampled = list(nn_found_matches_s2v_match_perc_pmass_sim_oversampled['sim_label'])\n",
    "sim_labels_oversampled.count(1), sim_labels_oversampled.count(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 433146 samples\n",
      "Epoch 1/30\n",
      "433146/433146 [==============================] - 59s 135us/sample - loss: 0.3534 - accuracy: 0.8562\n",
      "Epoch 2/30\n",
      "433146/433146 [==============================] - 57s 133us/sample - loss: 0.3161 - accuracy: 0.8734\n",
      "Epoch 3/30\n",
      "433146/433146 [==============================] - 58s 134us/sample - loss: 0.3061 - accuracy: 0.8762\n",
      "Epoch 4/30\n",
      "433146/433146 [==============================] - 58s 134us/sample - loss: 0.2999 - accuracy: 0.8783\n",
      "Epoch 5/30\n",
      "433146/433146 [==============================] - 59s 136us/sample - loss: 0.2956 - accuracy: 0.8798\n",
      "Epoch 6/30\n",
      "433146/433146 [==============================] - 57s 133us/sample - loss: 0.2919 - accuracy: 0.8812\n",
      "Epoch 7/30\n",
      "433146/433146 [==============================] - 57s 133us/sample - loss: 0.2887 - accuracy: 0.8824\n",
      "Epoch 8/30\n",
      "433146/433146 [==============================] - 58s 133us/sample - loss: 0.2865 - accuracy: 0.8829\n",
      "Epoch 9/30\n",
      "433146/433146 [==============================] - 58s 134us/sample - loss: 0.2842 - accuracy: 0.8841\n",
      "Epoch 10/30\n",
      "433146/433146 [==============================] - 58s 133us/sample - loss: 0.2826 - accuracy: 0.8853\n",
      "Epoch 11/30\n",
      "433146/433146 [==============================] - 59s 135us/sample - loss: 0.2813 - accuracy: 0.8850\n",
      "Epoch 12/30\n",
      "433146/433146 [==============================] - 58s 134us/sample - loss: 0.2793 - accuracy: 0.8861\n",
      "Epoch 13/30\n",
      "433146/433146 [==============================] - 59s 136us/sample - loss: 0.2782 - accuracy: 0.8867\n",
      "Epoch 14/30\n",
      "433146/433146 [==============================] - 59s 136us/sample - loss: 0.2770 - accuracy: 0.8871\n",
      "Epoch 15/30\n",
      "433146/433146 [==============================] - 58s 135us/sample - loss: 0.2759 - accuracy: 0.8878\n",
      "Epoch 16/30\n",
      "433146/433146 [==============================] - 59s 136us/sample - loss: 0.2750 - accuracy: 0.8879\n",
      "Epoch 17/30\n",
      "433146/433146 [==============================] - 64s 149us/sample - loss: 0.2744 - accuracy: 0.8887\n",
      "Epoch 18/30\n",
      "433146/433146 [==============================] - 59s 136us/sample - loss: 0.2740 - accuracy: 0.8885\n",
      "Epoch 19/30\n",
      "433146/433146 [==============================] - 60s 138us/sample - loss: 0.2732 - accuracy: 0.8886\n",
      "Epoch 20/30\n",
      "433146/433146 [==============================] - 59s 136us/sample - loss: 0.2723 - accuracy: 0.8891\n",
      "Epoch 21/30\n",
      "433146/433146 [==============================] - 61s 142us/sample - loss: 0.2721 - accuracy: 0.8892\n",
      "Epoch 22/30\n",
      "433146/433146 [==============================] - 60s 140us/sample - loss: 0.2711 - accuracy: 0.8902\n",
      "Epoch 23/30\n",
      "433146/433146 [==============================] - 54s 125us/sample - loss: 0.2706 - accuracy: 0.8905\n",
      "Epoch 24/30\n",
      "433146/433146 [==============================] - 64s 148us/sample - loss: 0.2703 - accuracy: 0.8903\n",
      "Epoch 25/30\n",
      "433146/433146 [==============================] - 59s 136us/sample - loss: 0.2699 - accuracy: 0.8904\n",
      "Epoch 26/30\n",
      "433146/433146 [==============================] - 60s 138us/sample - loss: 0.2698 - accuracy: 0.8906\n",
      "Epoch 27/30\n",
      "433146/433146 [==============================] - 59s 137us/sample - loss: 0.2689 - accuracy: 0.8910\n",
      "Epoch 28/30\n",
      "433146/433146 [==============================] - 60s 138us/sample - loss: 0.2688 - accuracy: 0.8909\n",
      "Epoch 29/30\n",
      "433146/433146 [==============================] - 60s 138us/sample - loss: 0.2680 - accuracy: 0.8918\n",
      "Epoch 30/30\n",
      "433146/433146 [==============================] - 59s 136us/sample - loss: 0.2677 - accuracy: 0.8919\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\first_deep_nn_similarity_oversampled\\assets\n"
     ]
    }
   ],
   "source": [
    "model_name_oversampled = os.path.join(path_data, 'first_deep_nn_similarity_oversampled')\n",
    "\n",
    "# split into input (X) and output (y) variables\n",
    "X = nn_found_matches_s2v_match_perc_pmass_sim_oversampled.drop('sim_label', axis = 1)\n",
    "y = nn_found_matches_s2v_match_perc_pmass_sim_oversampled['sim_label']\n",
    "if os.path.exists(model_name_oversampled):\n",
    "    model_oversampled = load_model(model_name_oversampled)\n",
    "else:\n",
    "    # define the keras model\n",
    "    model_oversampled = Sequential()\n",
    "    model_oversampled.add(Dense(12, input_dim=X.shape[1], activation='relu'))\n",
    "    model_oversampled.add(Dense(12, activation='relu'))\n",
    "    model_oversampled.add(Dense(16, activation='relu'))\n",
    "    model_oversampled.add(Dense(12, activation='relu'))\n",
    "    model_oversampled.add(Dense(8, activation='relu'))\n",
    "    model_oversampled.add(Dense(1, activation='sigmoid'))\n",
    "    # compile the keras model\n",
    "    model_oversampled.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    # fit the keras model on the dataset\n",
    "    model_oversampled.fit(X, y, epochs=30, batch_size = 16)\n",
    "    \n",
    "    model_oversampled.save(model_name_oversampled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "241054/241054 [==============================] - 9s 37us/sample - loss: 0.3322 - accuracy: 0.8576\n",
      "Accuracy: 85.76\n"
     ]
    }
   ],
   "source": [
    "X_test = nn_new_found_matches_s2v_match_perc_pmass_sim.drop(['sim_label'], axis = 1)\n",
    "y_test = nn_new_found_matches_s2v_match_perc_pmass_sim['sim_label']\n",
    "loss, accuracy = model_oversampled.evaluate(X_test, y_test)\n",
    "print('Accuracy: %.2f' % (accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_test_oversampled = model_oversampled.predict_classes(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_new_testset_oversampled_tested = []\n",
    "for ID in range(len(nn_prep_new_found_matches_s2v_match_perc_pmass_sim)):\n",
    "    current_match = nn_prep_new_found_matches_s2v_match_perc_pmass_sim[ID].copy()\n",
    "    simlabs = [1 if sim >= sim_cutoff else 0 for sim in current_match['similarity']]\n",
    "    predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "    predictions = model_oversampled.predict_classes(predict_on)\n",
    "    current_match['predictions'] = predictions\n",
    "    correct_guess = [pred == lab for pred, lab in zip(predictions, simlabs)]\n",
    "    current_match['correct_guess'] = correct_guess\n",
    "    nn_new_testset_oversampled_tested.append(current_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>similarity</th>\n",
       "      <th>mass_sim</th>\n",
       "      <th>predictions</th>\n",
       "      <th>correct_guess</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>[True]</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.285278</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.550833</td>\n",
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       "      <td>4.722366e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
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       "      <td>0.032219</td>\n",
       "      <td>0.070000</td>\n",
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       "      <td>0.070000</td>\n",
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       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
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       "      <td>[True]</td>\n",
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       "    <tr>\n",
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       "      <td>0.000935</td>\n",
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       "      <td>0.000935</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.531845</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448270</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.920519e-03</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
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       "    <tr>\n",
       "      <th>32638</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
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       "    <tr>\n",
       "      <th>37798</th>\n",
       "      <td>0.029850</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.033895</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.526315</td>\n",
       "      <td>0</td>\n",
       "      <td>0.462919</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>3.689215e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "46341      0.792853        0.195643          0.792853            0.195643   \n",
       "52743      0.766401        0.070000          0.766401            0.070000   \n",
       "34539      0.241939        0.070000          0.955195            0.135100   \n",
       "47562      0.010289        0.070000          0.010289            0.070000   \n",
       "35469      0.243206        0.070000          0.243206            0.070000   \n",
       "33568      0.000000        0.000000          0.000000            0.000000   \n",
       "52554      0.000000        0.000000          0.000000            0.000000   \n",
       "47158      0.285278        0.135100          0.285278            0.135100   \n",
       "47558      0.032219        0.070000          0.032219            0.070000   \n",
       "38527      0.013670        0.070000          0.013693            0.135100   \n",
       "52973      0.000008        0.070000          0.000008            0.070000   \n",
       "38523      0.036623        0.070000          0.040348            0.135100   \n",
       "45757      0.766454        0.135100          0.766454            0.135100   \n",
       "51823      0.000000        0.000000          0.000000            0.000000   \n",
       "43816      0.010825        0.070000          0.384554            0.195643   \n",
       "37029      0.000000        0.000000          0.000000            0.000000   \n",
       "37404      0.000000        0.000000          0.000000            0.000000   \n",
       "38017      0.000935        0.070000          0.000935            0.070000   \n",
       "32638      0.000000        0.000000          0.000000            0.000000   \n",
       "37798      0.029850        0.070000          0.033895            0.195643   \n",
       "\n",
       "       s2v_score  label  parent_mass  similarity      mass_sim  predictions  \\\n",
       "46341   0.838697      0     0.433620    0.947368  1.000000e+00            1   \n",
       "52743   0.589020      0     0.471248    0.901186  1.053123e-07            1   \n",
       "34539   0.585604      0     0.435727    0.859649  4.066856e-01            1   \n",
       "47562   0.584888      1     0.433620    1.000000  1.000000e+00            1   \n",
       "35469   0.565561      0     0.435727    0.859649  4.066856e-01            1   \n",
       "33568   0.560947      0     0.432567    1.000000  6.378614e-01            1   \n",
       "52554   0.551094      0     0.433620    0.947368  1.000000e+00            1   \n",
       "47158   0.550833      0     0.446163    1.000000  4.722366e-03            1   \n",
       "47558   0.548738      0     0.433620    0.947368  1.000000e+00            1   \n",
       "38527   0.545742      0     0.432567    1.000000  6.378614e-01            1   \n",
       "52973   0.544748      1     0.433620    1.000000  1.000000e+00            1   \n",
       "38523   0.540438      0     0.418971    0.947368  1.920947e-03            1   \n",
       "45757   0.538008      0     0.471248    0.901186  1.053123e-07            1   \n",
       "51823   0.537810      1     0.433620    1.000000  1.000000e+00            1   \n",
       "43816   0.536827      0     0.449323    0.947368  1.225025e-03            1   \n",
       "37029   0.534970      0     0.477568    1.000000  7.086787e-09            1   \n",
       "37404   0.531860      0     0.433620    0.947368  1.000000e+00            1   \n",
       "38017   0.531845      0     0.448270    0.947368  1.920519e-03            1   \n",
       "32638   0.531173      0     0.417917    1.000000  1.225025e-03            1   \n",
       "37798   0.526315      0     0.462919    0.947368  3.689215e-06            1   \n",
       "\n",
       "      correct_guess  \n",
       "46341        [True]  \n",
       "52743        [True]  \n",
       "34539        [True]  \n",
       "47562        [True]  \n",
       "35469        [True]  \n",
       "33568        [True]  \n",
       "52554        [True]  \n",
       "47158        [True]  \n",
       "47558        [True]  \n",
       "38527        [True]  \n",
       "52973        [True]  \n",
       "38523        [True]  \n",
       "45757        [True]  \n",
       "51823        [True]  \n",
       "43816        [True]  \n",
       "37029        [True]  \n",
       "37404        [True]  \n",
       "38017        [True]  \n",
       "32638        [True]  \n",
       "37798        [True]  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_new_testset_oversampled_tested[0].sort_values(by=['s2v_score'], ascending=False).iloc[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing if top hit is above 0.6 similarity after filtering with nn on oversampled data\n",
    "#look at top20\n",
    "test_sim_filt_nn_oversampled_top20 = []\n",
    "\n",
    "threshold = 0.4\n",
    "topn = 20 #only look at top 20\n",
    "sim_threshold = 0.6\n",
    "\n",
    "for ID in range(len(nn_new_testset_oversampled_tested)):\n",
    "    current_match = nn_new_testset_oversampled_tested[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    \n",
    "    # Scenario 2: Spec2Vec + threshold after filtering for non-similar matches with nn\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['predictions'] == 1)\n",
    "                                  & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_filt_nn_oversampled_top20.append(best_bet)\n",
    "    \n",
    "#     # ideal case: how many queries have an actual match above s2v threshold and >0.6 tanimoto\n",
    "#     df_select = current_match[(current_match['s2v_score'] > threshold)]\n",
    "#     best_bet = -1\n",
    "#     if df_select.shape[0] > 0:\n",
    "#         if any(df_select['similarity'] > 0.6):\n",
    "#             best_bet = 1\n",
    "#     ideal_case_top20.append(best_bet)\n",
    "\n",
    "\n",
    "#make arrays from lists\n",
    "test_sim_filt_nn_oversampled_arr = np.array(test_sim_filt_nn_oversampled_top20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### plt.style.use('ggplot')\n",
    "\n",
    "ylabels = ['mass + mod cosine',\n",
    "           'mass + Spec2Vec',\n",
    "           'mass + Spec2Vec + filtering NN',\n",
    "           'mass + Spec2Vec + filtering NN oversampled',\n",
    "           'ideal case Spec2Vec>0.4']\n",
    "xlabels = ['true matches', 'false matches', 'no matches found']\n",
    "          #'mass + mod.cosine \\n + as backup: Spec2Vec + mod.cosine']\n",
    "data = np.array([[np.sum(test_sim_mcos_arr_top20 == 1), np.sum(test_sim_mcos_arr_top20 == 0), np.sum(test_sim_mcos_arr_top20 == -1)],\n",
    "                 [np.sum(test_sim_s2v_arr_top20 == 1), np.sum(test_sim_s2v_arr_top20 == 0), np.sum(test_sim_s2v_arr_top20 == -1)],\n",
    "                 [np.sum(test_sim_filt_nn_arr == 1), np.sum(test_sim_filt_nn_arr == 0), np.sum(test_sim_filt_nn_arr == -1)],\n",
    "                 [np.sum(test_sim_filt_nn_oversampled_arr == 1), np.sum(test_sim_filt_nn_oversampled_arr == 0), np.sum(test_sim_filt_nn_oversampled_arr == -1)],\n",
    "                 [np.sum(ideal_case_arr_top20 == 1), np.sum(ideal_case_arr_top20 == 0), np.sum(ideal_case_arr_top20 == -1)]])\n",
    "\n",
    "dataframe = pd.DataFrame(data, columns=xlabels, index=ylabels)\n",
    "\n",
    "#fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax = dataframe.sort_values(by=['false matches'], ascending=True).plot.barh(stacked=True, edgecolor='none', figsize=(5, 3))\n",
    "\n",
    "horiz_offset = 1.03\n",
    "vert_offset = 1.\n",
    "ax.legend(bbox_to_anchor=(horiz_offset, vert_offset))\n",
    "plt.xlabel('Number of spectra')\n",
    "plt.title('Compare if top hit is above 0.6 tanimoto similarity\\n(>5 matching inchikey in library)')\n",
    "#plt.figure(figsize=(5,5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "#top20\n",
    "\n",
    "def make_s2v_selection(matches, topn):\n",
    "    '''Return one df with topn matches (based on s2v_score) for each query\n",
    "    \n",
    "    matches: list of pandas DataFrames, library matching result with matching labels\n",
    "    topn: int, number of top hits to select, sorted on s2v_score\n",
    "    all_selected_matches: pandas DataFrames, concatonated selection of matches for each query\n",
    "    '''\n",
    "    selected_matches = []\n",
    "    for df in matches:\n",
    "        selected_matches.append(df.sort_values(by=['s2v_score'], ascending=False).iloc[:topn])\n",
    "    all_selected_matches = selected_matches[0].append(selected_matches[1:])\n",
    "    return all_selected_matches\n",
    "\n",
    "nn_found_matches_s2v_match_perc_pmass_sim_top20 = make_s2v_selection(nn_prep_found_matches_s2v_match_perc_pmass_sim, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11017, 8983)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add simlabel\n",
    "simlabs = [1 if sim >= sim_cutoff else 0 for sim in nn_found_matches_s2v_match_perc_pmass_sim_top20['similarity']]\n",
    "nn_found_matches_s2v_match_perc_pmass_sim_top20['sim_label'] = simlabs\n",
    "nn_found_matches_s2v_match_perc_pmass_sim_top20.drop(['similarity', 'label'], axis = 1, inplace = True)\n",
    "#check label dist\n",
    "simlabs.count(0), simlabs.count(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 20000 samples\n",
      "Epoch 1/30\n",
      "20000/20000 [==============================] - 4s 178us/sample - loss: 0.5886 - accuracy: 0.6915\n",
      "Epoch 2/30\n",
      "20000/20000 [==============================] - 3s 129us/sample - loss: 0.5101 - accuracy: 0.7519\n",
      "Epoch 3/30\n",
      "20000/20000 [==============================] - 3s 132us/sample - loss: 0.4925 - accuracy: 0.7627\n",
      "Epoch 4/30\n",
      "20000/20000 [==============================] - 2s 125us/sample - loss: 0.4858 - accuracy: 0.7653\n",
      "Epoch 5/30\n",
      "20000/20000 [==============================] - 3s 128us/sample - loss: 0.4802 - accuracy: 0.7701\n",
      "Epoch 6/30\n",
      "20000/20000 [==============================] - 2s 124us/sample - loss: 0.4776 - accuracy: 0.7706\n",
      "Epoch 7/30\n",
      "20000/20000 [==============================] - 3s 135us/sample - loss: 0.4754 - accuracy: 0.7711\n",
      "Epoch 8/30\n",
      "20000/20000 [==============================] - 2s 123us/sample - loss: 0.4720 - accuracy: 0.7711\n",
      "Epoch 9/30\n",
      "20000/20000 [==============================] - 2s 120us/sample - loss: 0.4712 - accuracy: 0.7745\n",
      "Epoch 10/30\n",
      "20000/20000 [==============================] - 3s 140us/sample - loss: 0.4691 - accuracy: 0.7757\n",
      "Epoch 11/30\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4674 - accuracy: 0.7754\n",
      "Epoch 12/30\n",
      "20000/20000 [==============================] - 2s 123us/sample - loss: 0.4672 - accuracy: 0.7747\n",
      "Epoch 13/30\n",
      "20000/20000 [==============================] - 2s 124us/sample - loss: 0.4652 - accuracy: 0.7782\n",
      "Epoch 14/30\n",
      "20000/20000 [==============================] - 2s 124us/sample - loss: 0.4634 - accuracy: 0.7794\n",
      "Epoch 15/30\n",
      "20000/20000 [==============================] - 2s 121us/sample - loss: 0.4625 - accuracy: 0.7780\n",
      "Epoch 16/30\n",
      "20000/20000 [==============================] - 2s 119us/sample - loss: 0.4622 - accuracy: 0.7783\n",
      "Epoch 17/30\n",
      "20000/20000 [==============================] - 2s 120us/sample - loss: 0.4622 - accuracy: 0.7801\n",
      "Epoch 18/30\n",
      "20000/20000 [==============================] - 2s 119us/sample - loss: 0.4612 - accuracy: 0.7779\n",
      "Epoch 19/30\n",
      "20000/20000 [==============================] - 2s 120us/sample - loss: 0.4605 - accuracy: 0.7797\n",
      "Epoch 20/30\n",
      "20000/20000 [==============================] - 2s 122us/sample - loss: 0.4586 - accuracy: 0.7800\n",
      "Epoch 21/30\n",
      "20000/20000 [==============================] - 2s 121us/sample - loss: 0.4578 - accuracy: 0.7801\n",
      "Epoch 22/30\n",
      "20000/20000 [==============================] - 2s 120us/sample - loss: 0.4588 - accuracy: 0.7799\n",
      "Epoch 23/30\n",
      "20000/20000 [==============================] - 2s 124us/sample - loss: 0.4576 - accuracy: 0.7799\n",
      "Epoch 24/30\n",
      "20000/20000 [==============================] - 2s 121us/sample - loss: 0.4573 - accuracy: 0.7811\n",
      "Epoch 25/30\n",
      "20000/20000 [==============================] - 2s 120us/sample - loss: 0.4556 - accuracy: 0.7813\n",
      "Epoch 26/30\n",
      "20000/20000 [==============================] - 3s 125us/sample - loss: 0.4549 - accuracy: 0.7822\n",
      "Epoch 27/30\n",
      "20000/20000 [==============================] - 2s 120us/sample - loss: 0.4559 - accuracy: 0.7803\n",
      "Epoch 28/30\n",
      "20000/20000 [==============================] - 2s 120us/sample - loss: 0.4545 - accuracy: 0.7839\n",
      "Epoch 29/30\n",
      "20000/20000 [==============================] - 2s 123us/sample - loss: 0.4546 - accuracy: 0.7803\n",
      "Epoch 30/30\n",
      "20000/20000 [==============================] - 2s 121us/sample - loss: 0.4538 - accuracy: 0.7821\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\first_deep_nn_similarity_top20\\assets\n"
     ]
    }
   ],
   "source": [
    "model_name_top20 = os.path.join(path_data, 'first_deep_nn_similarity_top20')\n",
    "\n",
    "# split into input (X) and output (y) variables\n",
    "X = nn_found_matches_s2v_match_perc_pmass_sim_top20.drop('sim_label', axis = 1)\n",
    "y = nn_found_matches_s2v_match_perc_pmass_sim_top20['sim_label']\n",
    "if os.path.exists(model_name_top20):\n",
    "    model_top20 = load_model(model_name_top20)\n",
    "else:\n",
    "    # define the keras model\n",
    "    model_top20 = Sequential()\n",
    "    model_top20.add(Dense(12, input_dim=X.shape[1], activation='relu'))\n",
    "    model_top20.add(Dense(12, activation='relu'))\n",
    "    model_top20.add(Dense(16, activation='relu'))\n",
    "    model_top20.add(Dense(12, activation='relu'))\n",
    "    model_top20.add(Dense(8, activation='relu'))\n",
    "    model_top20.add(Dense(1, activation='sigmoid'))\n",
    "    # compile the keras model\n",
    "    model_top20.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    # fit the keras model on the dataset\n",
    "    model_top20.fit(X, y, epochs=30, batch_size = 16) #add validation set?\n",
    "    \n",
    "    model_top20.save(model_name_top20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10940, 9060)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make test set top20 to compare performance\n",
    "nn_new_found_matches_s2v_match_perc_pmass_sim_top20 = make_s2v_selection(nn_prep_new_found_matches_s2v_match_perc_pmass_sim, 20)\n",
    "#add simlabel\n",
    "simlabs = [1 if sim >= sim_cutoff else 0 for sim in nn_new_found_matches_s2v_match_perc_pmass_sim_top20['similarity']]\n",
    "nn_new_found_matches_s2v_match_perc_pmass_sim_top20['sim_label'] = simlabs\n",
    "nn_new_found_matches_s2v_match_perc_pmass_sim_top20.drop(['similarity', 'label'], axis = 1, inplace = True)\n",
    "#check label dist\n",
    "simlabs.count(0), simlabs.count(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "241054/241054 [==============================] - 9s 38us/sample - loss: 0.2575 - accuracy: 0.9241\n",
      "Accuracy: 92.41\n"
     ]
    }
   ],
   "source": [
    "#with complete test set\n",
    "X_test = nn_new_found_matches_s2v_match_perc_pmass_sim.drop(['sim_label'], axis = 1)\n",
    "y_test = nn_new_found_matches_s2v_match_perc_pmass_sim['sim_label']\n",
    "loss, accuracy = model_top20.evaluate(X_test, y_test)\n",
    "print('Accuracy: %.2f' % (accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000/20000 [==============================] - 1s 36us/sample - loss: 0.4711 - accuracy: 0.7803\n",
      "Accuracy first model: 78.03\n",
      "20000/20000 [==============================] - 1s 37us/sample - loss: 0.7995 - accuracy: 0.6364\n",
      "Accuracy oversampled model: 63.64\n",
      "20000/20000 [==============================] - 1s 36us/sample - loss: 0.4605 - accuracy: 0.7818\n",
      "Accuracy top20 model: 78.18\n"
     ]
    }
   ],
   "source": [
    "#accuracy of models with top20 testset\n",
    "\n",
    "X_test = nn_new_found_matches_s2v_match_perc_pmass_sim_top20.drop(['sim_label'], axis = 1)\n",
    "y_test = nn_new_found_matches_s2v_match_perc_pmass_sim_top20['sim_label']\n",
    "\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print('Accuracy first model: %.2f' % (accuracy*100))\n",
    "loss, accuracy = model_oversampled.evaluate(X_test, y_test)\n",
    "print('Accuracy oversampled model: %.2f' % (accuracy*100))\n",
    "loss, accuracy = model_top20.evaluate(X_test, y_test)\n",
    "print('Accuracy top20 model: %.2f' % (accuracy*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_new_testset_top20_tested = []\n",
    "for ID in range(len(nn_prep_new_found_matches_s2v_match_perc_pmass_sim)):\n",
    "    current_match = nn_prep_new_found_matches_s2v_match_perc_pmass_sim[ID].copy()\n",
    "    simlabs = [1 if sim >= sim_cutoff else 0 for sim in current_match['similarity']]\n",
    "    predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "    predictions = model_top20.predict_classes(predict_on)\n",
    "    current_match['predictions'] = predictions\n",
    "    correct_guess = [pred == lab for pred, lab in zip(predictions, simlabs)]\n",
    "    current_match['correct_guess'] = correct_guess\n",
    "    nn_new_testset_top20_tested.append(current_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cosine_score</th>\n",
       "      <th>cosine_matches</th>\n",
       "      <th>mod_cosine_score</th>\n",
       "      <th>mod_cosine_matches</th>\n",
       "      <th>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>similarity</th>\n",
       "      <th>mass_sim</th>\n",
       "      <th>predictions</th>\n",
       "      <th>correct_guess</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.573812</td>\n",
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       "      <td>5.489322e-49</td>\n",
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       "      <td>3.115975e-40</td>\n",
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       "      <td>[False]</td>\n",
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       "      <th>2242</th>\n",
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       "      <td>0.094386</td>\n",
       "      <td>3.424336e-12</td>\n",
       "      <td>0</td>\n",
       "      <td>[True]</td>\n",
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       "    <tr>\n",
       "      <th>27441</th>\n",
       "      <td>2.610575e-03</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.003749</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.359356</td>\n",
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       "      <td>0.533928</td>\n",
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       "      <td>1.366146e-41</td>\n",
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       "      <th>891</th>\n",
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       "      <th>917</th>\n",
       "      <td>2.030914e-03</td>\n",
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       "      <th>27521</th>\n",
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       "      <td>5.698447e-74</td>\n",
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       "      <td>[True]</td>\n",
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       "      <td>0.337027</td>\n",
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       "      <td>0.678333</td>\n",
       "      <td>0.120109</td>\n",
       "      <td>2.277803e-68</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.335418</td>\n",
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       "      <td>0.495764</td>\n",
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       "      <td>1.630876e-34</td>\n",
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       "      <th>70</th>\n",
       "      <td>1.937704e-02</td>\n",
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       "      <td>0.019377</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>9.232221e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.326024</td>\n",
       "      <td>0</td>\n",
       "      <td>0.356622</td>\n",
       "      <td>0.091587</td>\n",
       "      <td>1.034225e-08</td>\n",
       "      <td>1</td>\n",
       "      <td>[False]</td>\n",
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       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>9.121653e-07</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.325305</td>\n",
       "      <td>0</td>\n",
       "      <td>0.535139</td>\n",
       "      <td>0.162116</td>\n",
       "      <td>8.144410e-42</td>\n",
       "      <td>0</td>\n",
       "      <td>[True]</td>\n",
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       "      <th>1094</th>\n",
       "      <td>2.075509e-05</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.324304</td>\n",
       "      <td>0</td>\n",
       "      <td>0.386253</td>\n",
       "      <td>0.074227</td>\n",
       "      <td>3.309918e-14</td>\n",
       "      <td>1</td>\n",
       "      <td>[False]</td>\n",
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       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>2.990286e-05</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.318003</td>\n",
       "      <td>0</td>\n",
       "      <td>0.311625</td>\n",
       "      <td>0.093430</td>\n",
       "      <td>4.379585e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>[False]</td>\n",
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       "    <tr>\n",
       "      <th>4337</th>\n",
       "      <td>9.476838e-03</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.009477</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.308026</td>\n",
       "      <td>1</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.238404e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>[True]</td>\n",
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       "    <tr>\n",
       "      <th>90512</th>\n",
       "      <td>1.510168e-02</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.015102</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.292459</td>\n",
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       "      <td>[True]</td>\n",
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       "    <tr>\n",
       "      <th>2567</th>\n",
       "      <td>2.834600e-03</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.002835</td>\n",
       "      <td>0.070000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <th>975</th>\n",
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       "      <td>1.000000e+00</td>\n",
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       "      <th>10244</th>\n",
       "      <td>2.895164e-02</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.028952</td>\n",
       "      <td>0.070000</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
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       "      <th>72498</th>\n",
       "      <td>6.177593e-02</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.061776</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.225713</td>\n",
       "      <td>1</td>\n",
       "      <td>0.313745</td>\n",
       "      <td>0.464646</td>\n",
       "      <td>9.236343e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>[True]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15927</th>\n",
       "      <td>4.586321e-02</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.045863</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.222022</td>\n",
       "      <td>1</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.238404e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2813</th>\n",
       "      <td>4.724822e-04</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.000472</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.215531</td>\n",
       "      <td>1</td>\n",
       "      <td>0.313738</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.265242e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90514</th>\n",
       "      <td>4.528539e-02</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.045285</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.203941</td>\n",
       "      <td>1</td>\n",
       "      <td>0.313745</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.236343e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90511</th>\n",
       "      <td>1.956495e-03</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.192414</td>\n",
       "      <td>1</td>\n",
       "      <td>0.313745</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.236343e-01</td>\n",
       "      <td>0</td>\n",
       "      <td>[False]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "983    7.756194e-03        0.195643          0.007760            0.251948   \n",
       "986    3.000084e-05        0.070000          0.000030            0.070000   \n",
       "27265  7.032016e-03        0.195643          0.007032            0.195643   \n",
       "2242   0.000000e+00        0.000000          0.000019            0.135100   \n",
       "1075   2.745118e-05        0.251948          0.000034            0.304312   \n",
       "27441  2.610575e-03        0.135100          0.003749            0.195643   \n",
       "891    3.259607e-04        0.070000          0.000360            0.195643   \n",
       "917    2.030914e-03        0.195643          0.002108            0.398299   \n",
       "27521  0.000000e+00        0.000000          0.000132            0.070000   \n",
       "27177  0.000000e+00        0.000000          0.000097            0.135100   \n",
       "4487   6.089623e-02        0.070000          0.060896            0.070000   \n",
       "27191  7.205158e-03        0.251948          0.007205            0.251948   \n",
       "3350   0.000000e+00        0.000000          0.000191            0.135100   \n",
       "27134  0.000000e+00        0.000000          0.000000            0.000000   \n",
       "1072   2.586987e-05        0.070000          0.000323            0.135100   \n",
       "70     1.937704e-02        0.070000          0.019377            0.070000   \n",
       "90265  0.000000e+00        0.000000          0.000000            0.000000   \n",
       "897    9.121653e-07        0.070000          0.000008            0.135100   \n",
       "1094   2.075509e-05        0.135100          0.000021            0.135100   \n",
       "1077   2.990286e-05        0.135100          0.000030            0.135100   \n",
       "4337   9.476838e-03        0.070000          0.009477            0.070000   \n",
       "90512  1.510168e-02        0.070000          0.015102            0.070000   \n",
       "2567   2.834600e-03        0.070000          0.002835            0.070000   \n",
       "975    7.865920e-05        0.070000          0.000079            0.070000   \n",
       "10244  2.895164e-02        0.070000          0.028952            0.070000   \n",
       "72498  6.177593e-02        0.135100          0.061776            0.135100   \n",
       "15927  4.586321e-02        0.070000          0.045863            0.070000   \n",
       "2813   4.724822e-04        0.195643          0.000472            0.195643   \n",
       "90514  4.528539e-02        0.070000          0.045285            0.070000   \n",
       "90511  1.956495e-03        0.070000          0.001956            0.070000   \n",
       "\n",
       "       s2v_score  label  parent_mass  similarity       mass_sim  predictions  \\\n",
       "983     0.503588      0     0.582174    0.104336   1.545107e-50            1   \n",
       "986     0.458536      0     0.573812    0.104905   5.489322e-49            0   \n",
       "27265   0.409947      0     0.526604    0.156410   3.115975e-40            1   \n",
       "2242    0.375313      0     0.280055    0.089059   6.124901e-07            1   \n",
       "1075    0.374054      0     0.251730    0.094386   3.424336e-12            0   \n",
       "27441   0.359356      0     0.533928    0.150188   1.366146e-41            0   \n",
       "891     0.355948      0     0.876923    0.115809  3.401818e-105            0   \n",
       "917     0.355223      0     0.613530    0.121270   2.367869e-56            0   \n",
       "27521   0.346668      0     0.708542    0.151990   5.698447e-74            0   \n",
       "27177   0.343926      0     0.666800    0.150127   3.134197e-66            0   \n",
       "4487    0.341297      0     0.402712    0.000000   2.936501e-17            1   \n",
       "27191   0.339179      0     0.541254    0.156410   5.984288e-43            0   \n",
       "3350    0.337027      0     0.678333    0.120109   2.277803e-68            0   \n",
       "27134   0.335418      0     0.495764    0.088106   1.630876e-34            0   \n",
       "1072    0.332275      0     0.326295    0.092926   4.348135e-03            1   \n",
       "70      0.326389      1     0.313746    1.000000   9.232221e-01            1   \n",
       "90265   0.326024      0     0.356622    0.091587   1.034225e-08            1   \n",
       "897     0.325305      0     0.535139    0.162116   8.144410e-42            0   \n",
       "1094    0.324304      0     0.386253    0.074227   3.309918e-14            1   \n",
       "1077    0.318003      0     0.311625    0.093430   4.379585e-01            1   \n",
       "4337    0.308026      1     0.313744    1.000000   9.238404e-01            1   \n",
       "90512   0.292459      1     0.313745    1.000000   9.236343e-01            1   \n",
       "2567    0.281891      1     0.313747    1.000000   9.228102e-01            1   \n",
       "975     0.257389      1     0.313559    1.000000   1.000000e+00            1   \n",
       "10244   0.232616      1     0.313744    1.000000   9.238404e-01            0   \n",
       "72498   0.225713      1     0.313745    0.464646   9.236343e-01            0   \n",
       "15927   0.222022      1     0.313744    1.000000   9.238404e-01            0   \n",
       "2813    0.215531      1     0.313738    1.000000   9.265242e-01            0   \n",
       "90514   0.203941      1     0.313745    1.000000   9.236343e-01            0   \n",
       "90511   0.192414      1     0.313745    1.000000   9.236343e-01            0   \n",
       "\n",
       "      correct_guess  \n",
       "983         [False]  \n",
       "986          [True]  \n",
       "27265       [False]  \n",
       "2242        [False]  \n",
       "1075         [True]  \n",
       "27441        [True]  \n",
       "891          [True]  \n",
       "917          [True]  \n",
       "27521        [True]  \n",
       "27177        [True]  \n",
       "4487        [False]  \n",
       "27191        [True]  \n",
       "3350         [True]  \n",
       "27134        [True]  \n",
       "1072        [False]  \n",
       "70           [True]  \n",
       "90265       [False]  \n",
       "897          [True]  \n",
       "1094        [False]  \n",
       "1077        [False]  \n",
       "4337         [True]  \n",
       "90512        [True]  \n",
       "2567         [True]  \n",
       "975          [True]  \n",
       "10244       [False]  \n",
       "72498        [True]  \n",
       "15927       [False]  \n",
       "2813        [False]  \n",
       "90514       [False]  \n",
       "90511       [False]  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# investigating bad matches\n",
    "false_matches_oversampled = [i for i, score in enumerate(test_sim_filt_nn_oversampled_top20) if score == 0]\n",
    "x = 0\n",
    "print(false_matches_oversampled[x])\n",
    "nn_new_testset_oversampled_tested[false_matches_oversampled[x]].sort_values(by=['s2v_score'], ascending=False).iloc[:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying to vary number of layers for top20 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nn function\n",
    "def train_nn(X_train, y_train, X_test, y_test, layers = [12, 12, 12, 12, 12, 1],\n",
    "             model_loss = 'binary_crossentropy', activations = 'relu',\n",
    "             last_activation = 'sigmoid', model_epochs = 20, model_batch_size = 16,\n",
    "             save_name = False):\n",
    "    '''Train a keras deep NN and test on test data, returns (model, accuracy)\n",
    "    \n",
    "    X_train: matrix like object like pd.DataFrame, training set\n",
    "    y_train: list like object like np.array, training labels\n",
    "    X_test: matrix like object like pd.DataFrame, test set\n",
    "    y_test: list like object like np.array, test labels\n",
    "    layers: list of ints, the number of layers is the len of this list while the elements\n",
    "        are the amount of neurons per layer, default: [12, 12, 12, 12, 12, 1]\n",
    "    model_loss: str, loss function, default: binary_crossentropy\n",
    "    activations: str, the activation of the layers except the last one, default: relu\n",
    "    last_activation: str, activation of last layer, default: sigmoid\n",
    "    model_epochs: int, number of epochs, default: 20\n",
    "    model_batch_size: int, batch size for updating the model, default: 16\n",
    "    save_name: str, location for saving model, optional, default: False\n",
    "    \n",
    "    If save_name is not False and save_name exists this function will load existing model\n",
    "    '''\n",
    "    if os.path.exists(save_name) and save_name:\n",
    "        print('Loading existing model')\n",
    "        nn_model = load_model(save_name)\n",
    "    else:\n",
    "        # define the keras model\n",
    "        nn_model = Sequential()\n",
    "        #add first layer\n",
    "        nn_model.add(Dense(layers[0], input_dim = X_train.shape[1], activation = activations))\n",
    "        #add other layers\n",
    "        for i in range(1,len(layers)-1): #skip first and last one\n",
    "            nn_model.add(Dense(layers[i], activation = activations))\n",
    "        #add last layer\n",
    "        nn_model.add(Dense(layers[-1], activation = last_activation))\n",
    "        # compile the keras model\n",
    "        nn_model.compile(loss = model_loss, optimizer='adam', metrics=['accuracy'])\n",
    "        # fit the keras model on the dataset\n",
    "        nn_model.fit(X_train, y_train, epochs = model_epochs, batch_size = model_batch_size)\n",
    "        \n",
    "    #test set    \n",
    "    loss, accuracy = nn_model.evaluate(X_test, y_test)\n",
    "    if save_name:\n",
    "        print(save_name)\n",
    "    print('Accuracy: %.2f' % (accuracy*100))\n",
    "    \n",
    "    if save_name and not os.path.exists(save_name):\n",
    "#         #adding accuracy to the model name\n",
    "#         save_model = '{}_{:.2f}'.format(save_model, accuracy*100)\n",
    "        nn_model.save(save_name)\n",
    "    \n",
    "    return (nn_model, accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_0\n",
      "Train on 20000 samples\n",
      "Epoch 1/100\n",
      "20000/20000 [==============================] - 5s 235us/sample - loss: 0.5486 - accuracy: 0.7107\n",
      "Epoch 2/100\n",
      "20000/20000 [==============================] - 3s 146us/sample - loss: 0.4978 - accuracy: 0.7569\n",
      "Epoch 3/100\n",
      "20000/20000 [==============================] - 3s 152us/sample - loss: 0.4879 - accuracy: 0.7638\n",
      "Epoch 4/100\n",
      "20000/20000 [==============================] - 3s 169us/sample - loss: 0.4833 - accuracy: 0.7666\n",
      "Epoch 5/100\n",
      "20000/20000 [==============================] - 4s 181us/sample - loss: 0.4800 - accuracy: 0.7679\n",
      "Epoch 6/100\n",
      "20000/20000 [==============================] - 3s 158us/sample - loss: 0.4810 - accuracy: 0.7679\n",
      "Epoch 7/100\n",
      "20000/20000 [==============================] - 3s 140us/sample - loss: 0.4774 - accuracy: 0.7732\n",
      "Epoch 8/100\n",
      "20000/20000 [==============================] - 3s 141us/sample - loss: 0.4769 - accuracy: 0.7706\n",
      "Epoch 9/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4764 - accuracy: 0.7690\n",
      "Epoch 10/100\n",
      "20000/20000 [==============================] - 3s 141us/sample - loss: 0.4743 - accuracy: 0.7707\n",
      "Epoch 11/100\n",
      "20000/20000 [==============================] - 3s 136us/sample - loss: 0.4743 - accuracy: 0.7699\n",
      "Epoch 12/100\n",
      "20000/20000 [==============================] - 3s 137us/sample - loss: 0.4713 - accuracy: 0.7737\n",
      "Epoch 13/100\n",
      "20000/20000 [==============================] - 3s 140us/sample - loss: 0.4690 - accuracy: 0.7752\n",
      "Epoch 14/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4712 - accuracy: 0.7749\n",
      "Epoch 15/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4684 - accuracy: 0.7736\n",
      "Epoch 16/100\n",
      "20000/20000 [==============================] - 3s 143us/sample - loss: 0.4680 - accuracy: 0.7738\n",
      "Epoch 17/100\n",
      "20000/20000 [==============================] - 3s 140us/sample - loss: 0.4677 - accuracy: 0.7742\n",
      "Epoch 18/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4662 - accuracy: 0.7748\n",
      "Epoch 19/100\n",
      "20000/20000 [==============================] - 3s 140us/sample - loss: 0.4656 - accuracy: 0.7760\n",
      "Epoch 20/100\n",
      "20000/20000 [==============================] - 3s 136us/sample - loss: 0.4645 - accuracy: 0.7775\n",
      "Epoch 21/100\n",
      "20000/20000 [==============================] - 3s 137us/sample - loss: 0.4646 - accuracy: 0.7771\n",
      "Epoch 22/100\n",
      "20000/20000 [==============================] - 3s 147us/sample - loss: 0.4646 - accuracy: 0.7757\n",
      "Epoch 23/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4621 - accuracy: 0.7747\n",
      "Epoch 24/100\n",
      "20000/20000 [==============================] - 3s 152us/sample - loss: 0.4623 - accuracy: 0.7778\n",
      "Epoch 25/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4611 - accuracy: 0.7782\n",
      "Epoch 26/100\n",
      "20000/20000 [==============================] - 3s 137us/sample - loss: 0.4619 - accuracy: 0.7787\n",
      "Epoch 27/100\n",
      "20000/20000 [==============================] - 3s 137us/sample - loss: 0.4621 - accuracy: 0.7795\n",
      "Epoch 28/100\n",
      "20000/20000 [==============================] - 3s 155us/sample - loss: 0.4601 - accuracy: 0.7805\n",
      "Epoch 29/100\n",
      "20000/20000 [==============================] - 3s 151us/sample - loss: 0.4602 - accuracy: 0.7790\n",
      "Epoch 30/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4597 - accuracy: 0.7792\n",
      "Epoch 31/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4585 - accuracy: 0.7801\n",
      "Epoch 32/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4587 - accuracy: 0.7789\n",
      "Epoch 33/100\n",
      "20000/20000 [==============================] - 3s 144us/sample - loss: 0.4584 - accuracy: 0.7783\n",
      "Epoch 34/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4593 - accuracy: 0.7803\n",
      "Epoch 35/100\n",
      "20000/20000 [==============================] - 3s 140us/sample - loss: 0.4572 - accuracy: 0.7793\n",
      "Epoch 36/100\n",
      "20000/20000 [==============================] - 3s 141us/sample - loss: 0.4573 - accuracy: 0.7799\n",
      "Epoch 37/100\n",
      "20000/20000 [==============================] - 3s 143us/sample - loss: 0.4560 - accuracy: 0.7824\n",
      "Epoch 38/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4560 - accuracy: 0.7814\n",
      "Epoch 39/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4558 - accuracy: 0.7829\n",
      "Epoch 40/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4551 - accuracy: 0.7814\n",
      "Epoch 41/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4557 - accuracy: 0.7807\n",
      "Epoch 42/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4550 - accuracy: 0.7799\n",
      "Epoch 43/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4561 - accuracy: 0.7814\n",
      "Epoch 44/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4539 - accuracy: 0.7818\n",
      "Epoch 45/100\n",
      "20000/20000 [==============================] - 3s 143us/sample - loss: 0.4538 - accuracy: 0.7831\n",
      "Epoch 46/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4526 - accuracy: 0.7817\n",
      "Epoch 47/100\n",
      "20000/20000 [==============================] - 3s 140us/sample - loss: 0.4524 - accuracy: 0.7846\n",
      "Epoch 48/100\n",
      "20000/20000 [==============================] - 3s 156us/sample - loss: 0.4535 - accuracy: 0.7816\n",
      "Epoch 49/100\n",
      "20000/20000 [==============================] - 3s 140us/sample - loss: 0.4539 - accuracy: 0.7813\n",
      "Epoch 50/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4519 - accuracy: 0.7829\n",
      "Epoch 51/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4514 - accuracy: 0.7822\n",
      "Epoch 52/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4507 - accuracy: 0.7827\n",
      "Epoch 53/100\n",
      "20000/20000 [==============================] - 3s 141us/sample - loss: 0.4525 - accuracy: 0.7828\n",
      "Epoch 54/100\n",
      "20000/20000 [==============================] - 3s 138us/sample - loss: 0.4515 - accuracy: 0.7845\n",
      "Epoch 55/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4515 - accuracy: 0.7850\n",
      "Epoch 56/100\n",
      "20000/20000 [==============================] - 3s 150us/sample - loss: 0.4517 - accuracy: 0.7837\n",
      "Epoch 57/100\n",
      "20000/20000 [==============================] - 3s 139us/sample - loss: 0.4502 - accuracy: 0.7850\n",
      "Epoch 58/100\n",
      "20000/20000 [==============================] - 3s 159us/sample - loss: 0.4500 - accuracy: 0.7818\n",
      "Epoch 59/100\n",
      "20000/20000 [==============================] - 3s 153us/sample - loss: 0.4506 - accuracy: 0.7846\n",
      "Epoch 60/100\n",
      "20000/20000 [==============================] - 3s 141us/sample - loss: 0.4484 - accuracy: 0.7832\n",
      "Epoch 61/100\n",
      "20000/20000 [==============================] - 3s 143us/sample - loss: 0.4489 - accuracy: 0.7833\n",
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      "Epoch 74/100\n"
     ]
    },
    {
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     "text": [
      "20000/20000 [==============================] - 4s 184us/sample - loss: 0.4482 - accuracy: 0.7815\n",
      "Epoch 75/100\n",
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      "20000/20000 [==============================] - ETA: 0s - loss: 0.4435 - accuracy: 0.78 - 3s 135us/sample - loss: 0.4432 - accuracy: 0.7864\n",
      "Epoch 83/100\n",
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      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_0\n",
      "Accuracy: 77.95\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_0\\assets\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_1\n",
      "Train on 20000 samples\n",
      "Epoch 1/100\n",
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     ]
    },
    {
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     "text": [
      "20000/20000 [==============================] - 3s 133us/sample - loss: 0.4391 - accuracy: 0.7878\n",
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      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_1\n",
      "Accuracy: 78.80\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_1\\assets\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_2\n",
      "Train on 20000 samples\n",
      "Epoch 1/100\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 15/100\n",
      "20000/20000 [==============================] - 5s 227us/sample - loss: 0.4592 - accuracy: 0.7802\n",
      "Epoch 16/100\n",
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      "Epoch 18/100\n",
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      "Epoch 19/100\n",
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      "Epoch 89/100\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "20000/20000 [==============================] - 5s 225us/sample - loss: 0.4334 - accuracy: 0.7886\n",
      "Epoch 90/100\n",
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      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_2\n",
      "Accuracy: 78.53\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_2\\assets\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_3\n",
      "Train on 20000 samples\n",
      "Epoch 1/100\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Epoch 60/100\n",
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      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_3\n",
      "Accuracy: 54.70\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_3\\assets\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_4\n",
      "Train on 20000 samples\n",
      "Epoch 1/100\n",
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      "Epoch 30/100\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "20000/20000 [==============================] - 7s 349us/sample - loss: 0.4557 - accuracy: 0.7810\n",
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      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_4\n",
      "Accuracy: 79.58\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_4\\assets\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_5\n",
      "Train on 20000 samples\n",
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000/20000 [==============================] - 18s 885us/sample - loss: 0.6107 - accuracy: 0.6413\n",
      "Epoch 2/100\n",
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      "20000/20000 [==============================] - ETA: 0s - loss: 0.4437 - accuracy: 0.78 - 11s 549us/sample - loss: 0.4437 - accuracy: 0.7883\n",
      "Epoch 60/100\n",
      "20000/20000 [==============================] - 10s 514us/sample - loss: 0.4422 - accuracy: 0.7882- loss: 0.4423 - accuracy: 0.\n",
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      "Epoch 73/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000/20000 [==============================] - 8s 408us/sample - loss: 0.4392 - accuracy: 0.7889\n",
      "Epoch 74/100\n",
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      "Epoch 75/100\n",
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      "Epoch 99/100\n",
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      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_5\n",
      "Accuracy: 78.39\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_5\\assets\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_6\n",
      "Train on 20000 samples\n",
      "Epoch 1/100\n",
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      "Epoch 43/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000/20000 [==============================] - 4s 222us/sample - loss: 0.4286 - accuracy: 0.7976\n",
      "Epoch 44/100\n",
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      "Epoch 50/100\n",
      "20000/20000 [==============================] - 5s 226us/sample - loss: 0.4210 - accuracy: 0.8018\n",
      "Epoch 51/100\n",
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      "20000/20000 [==============================] - 4s 224us/sample - loss: 0.4229 - accuracy: 0.8012\n",
      "Epoch 53/100\n",
      "20000/20000 [==============================] - 4s 223us/sample - loss: 0.4187 - accuracy: 0.8019\n",
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      "Epoch 69/100\n",
      "20000/20000 [==============================] - 5s 226us/sample - loss: 0.4113 - accuracy: 0.8080\n",
      "Epoch 70/100\n",
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      "20000/20000 [==============================] - 5s 225us/sample - loss: 0.4062 - accuracy: 0.8098\n",
      "Epoch 72/100\n",
      "20000/20000 [==============================] - 5s 254us/sample - loss: 0.4105 - accuracy: 0.8061\n",
      "Epoch 73/100\n",
      "20000/20000 [==============================] - 5s 232us/sample - loss: 0.4091 - accuracy: 0.8103\n",
      "Epoch 74/100\n",
      "20000/20000 [==============================] - 5s 229us/sample - loss: 0.4045 - accuracy: 0.8108\n",
      "Epoch 75/100\n",
      "20000/20000 [==============================] - 5s 231us/sample - loss: 0.4082 - accuracy: 0.8091\n",
      "Epoch 76/100\n",
      "20000/20000 [==============================] - 5s 230us/sample - loss: 0.4052 - accuracy: 0.8115\n",
      "Epoch 77/100\n",
      "20000/20000 [==============================] - 5s 238us/sample - loss: 0.4028 - accuracy: 0.8095\n",
      "Epoch 78/100\n",
      "20000/20000 [==============================] - 5s 231us/sample - loss: 0.4028 - accuracy: 0.8105\n",
      "Epoch 79/100\n",
      "20000/20000 [==============================] - 5s 234us/sample - loss: 0.4025 - accuracy: 0.8121\n",
      "Epoch 80/100\n",
      "20000/20000 [==============================] - 5s 231us/sample - loss: 0.4039 - accuracy: 0.8112\n",
      "Epoch 81/100\n",
      "20000/20000 [==============================] - 5s 230us/sample - loss: 0.3998 - accuracy: 0.8130\n",
      "Epoch 82/100\n",
      "20000/20000 [==============================] - 5s 250us/sample - loss: 0.4038 - accuracy: 0.8096\n",
      "Epoch 83/100\n",
      "20000/20000 [==============================] - 5s 249us/sample - loss: 0.3998 - accuracy: 0.8119\n",
      "Epoch 84/100\n",
      "20000/20000 [==============================] - 5s 237us/sample - loss: 0.4008 - accuracy: 0.8149\n",
      "Epoch 85/100\n",
      "20000/20000 [==============================] - 5s 238us/sample - loss: 0.3989 - accuracy: 0.8146\n",
      "Epoch 86/100\n",
      "20000/20000 [==============================] - 5s 236us/sample - loss: 0.3992 - accuracy: 0.8133\n",
      "Epoch 87/100\n",
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      "Epoch 88/100\n",
      "20000/20000 [==============================] - 5s 237us/sample - loss: 0.3963 - accuracy: 0.8123\n",
      "Epoch 89/100\n",
      "20000/20000 [==============================] - 5s 239us/sample - loss: 0.3993 - accuracy: 0.8138\n",
      "Epoch 90/100\n",
      "20000/20000 [==============================] - 5s 237us/sample - loss: 0.3967 - accuracy: 0.8152\n",
      "Epoch 91/100\n",
      "20000/20000 [==============================] - 5s 236us/sample - loss: 0.3999 - accuracy: 0.8108\n",
      "Epoch 92/100\n",
      "20000/20000 [==============================] - 5s 233us/sample - loss: 0.3946 - accuracy: 0.8156\n",
      "Epoch 93/100\n",
      "20000/20000 [==============================] - 5s 234us/sample - loss: 0.3932 - accuracy: 0.8148\n",
      "Epoch 94/100\n",
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      "Epoch 95/100\n",
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      "Epoch 96/100\n",
      "20000/20000 [==============================] - 5s 231us/sample - loss: 0.3951 - accuracy: 0.8128\n",
      "Epoch 97/100\n",
      "20000/20000 [==============================] - 5s 231us/sample - loss: 0.3951 - accuracy: 0.8161\n",
      "Epoch 98/100\n",
      "20000/20000 [==============================] - 5s 236us/sample - loss: 0.3934 - accuracy: 0.8145\n",
      "Epoch 99/100\n",
      "20000/20000 [==============================] - 5s 232us/sample - loss: 0.3926 - accuracy: 0.8167\n",
      "Epoch 100/100\n",
      "20000/20000 [==============================] - 5s 243us/sample - loss: 0.3927 - accuracy: 0.8152\n",
      "20000/20000 [==============================] - 2s 83us/sample - loss: 0.4833 - accuracy: 0.7833\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_6\n",
      "Accuracy: 78.33\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_top20_more_layers_100epochs_6\\assets\n"
     ]
    }
   ],
   "source": [
    "layers_to_test = [[12]*10+[1],[16]*10+[1], [20]*20+[1], [20]*40+[1], [20]*30+[1], [12]*5+[24]*10+[12]*5+[1], [30]*20+[1]]\n",
    "name = 'nn_top20_more_layers_100epochs'\n",
    "epcs = 100 #epochs\n",
    "models_acc = []\n",
    "X_top20 = nn_found_matches_s2v_match_perc_pmass_sim_top20.drop('sim_label', axis = 1)\n",
    "y_top20 = nn_found_matches_s2v_match_perc_pmass_sim_top20['sim_label']\n",
    "X_test_top20 = nn_new_found_matches_s2v_match_perc_pmass_sim_top20.drop(['sim_label'], axis = 1)\n",
    "y_test_top20 = nn_new_found_matches_s2v_match_perc_pmass_sim_top20['sim_label']\n",
    "for i in range(len(layers_to_test)):\n",
    "    new_name = name + '_' + str(i)\n",
    "    save_new_name = os.path.join(path_data, new_name)\n",
    "    print(save_new_name)\n",
    "    model_acc = train_nn(X_top20, y_top20, X_test_top20, y_test_top20, layers = layers_to_test[i], model_epochs = epcs, save_name = save_new_name)\n",
    "    models_acc.append(model_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.7795 [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1]\n",
      "1 0.788 [16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 1]\n",
      "2 0.7853 [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 1]\n",
      "3 0.547 [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 1]\n",
      "4 0.7958 [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 1]\n",
      "5 0.7839 [12, 12, 12, 12, 12, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 12, 12, 12, 12, 12, 1]\n",
      "6 0.7833 [30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 1]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(models_acc)):\n",
    "    print(i, models_acc[i][1], layers_to_test[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#test highest model, number 4\n",
    "nn_new_testset_top20_tested_more_layers_4 = []\n",
    "for ID in range(len(nn_prep_new_found_matches_s2v_match_perc_pmass_sim)):\n",
    "    current_match = nn_prep_new_found_matches_s2v_match_perc_pmass_sim[ID].copy()\n",
    "    simlabs = [1 if sim >= sim_cutoff else 0 for sim in current_match['similarity']]\n",
    "    predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "    predictions = models_acc[4][0].predict_classes(predict_on)\n",
    "    current_match['predictions'] = predictions\n",
    "    correct_guess = [pred == lab for pred, lab in zip(predictions, simlabs)]\n",
    "    current_match['correct_guess'] = correct_guess\n",
    "    nn_new_testset_top20_tested_more_layers_4.append(current_match)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try vary layers for oversampled model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_0\n",
      "Train on 433146 samples\n",
      "Epoch 1/30\n",
      "433146/433146 [==============================] - 68s 157us/sample - loss: 0.3481 - accuracy: 0.8567\n",
      "Epoch 2/30\n",
      "433146/433146 [==============================] - 64s 148us/sample - loss: 0.3183 - accuracy: 0.8704\n",
      "Epoch 3/30\n",
      "433146/433146 [==============================] - 64s 147us/sample - loss: 0.3113 - accuracy: 0.8731\n",
      "Epoch 4/30\n",
      "433146/433146 [==============================] - 71s 164us/sample - loss: 0.3048 - accuracy: 0.8762\n",
      "Epoch 5/30\n",
      "433146/433146 [==============================] - 77s 178us/sample - loss: 0.2993 - accuracy: 0.8781\n",
      "Epoch 6/30\n",
      "433146/433146 [==============================] - 85s 195us/sample - loss: 0.2957 - accuracy: 0.8796\n",
      "Epoch 7/30\n",
      "433146/433146 [==============================] - 76s 176us/sample - loss: 0.2933 - accuracy: 0.8806\n",
      "Epoch 8/30\n",
      "433146/433146 [==============================] - 81s 186us/sample - loss: 0.2912 - accuracy: 0.8814\n",
      "Epoch 9/30\n",
      "433146/433146 [==============================] - 78s 181us/sample - loss: 0.2898 - accuracy: 0.8820\n",
      "Epoch 10/30\n",
      "433146/433146 [==============================] - 85s 196us/sample - loss: 0.2885 - accuracy: 0.8825\n",
      "Epoch 11/30\n",
      "433146/433146 [==============================] - 88s 203us/sample - loss: 0.2871 - accuracy: 0.8832\n",
      "Epoch 12/30\n",
      "433146/433146 [==============================] - 80s 184us/sample - loss: 0.2859 - accuracy: 0.8835\n",
      "Epoch 13/30\n",
      "433146/433146 [==============================] - 82s 189us/sample - loss: 0.2837 - accuracy: 0.8847\n",
      "Epoch 14/30\n",
      "433146/433146 [==============================] - 82s 189us/sample - loss: 0.2829 - accuracy: 0.8846\n",
      "Epoch 15/30\n",
      "433146/433146 [==============================] - 93s 216us/sample - loss: 0.2817 - accuracy: 0.8854\n",
      "Epoch 16/30\n",
      "433146/433146 [==============================] - 87s 200us/sample - loss: 0.2799 - accuracy: 0.8861\n",
      "Epoch 17/30\n",
      "433146/433146 [==============================] - 85s 195us/sample - loss: 0.2792 - accuracy: 0.8869\n",
      "Epoch 18/30\n",
      "433146/433146 [==============================] - 83s 191us/sample - loss: 0.2778 - accuracy: 0.8866\n",
      "Epoch 19/30\n",
      "433146/433146 [==============================] - 79s 183us/sample - loss: 0.2774 - accuracy: 0.8864\n",
      "Epoch 20/30\n",
      "433146/433146 [==============================] - 91s 209us/sample - loss: 0.2754 - accuracy: 0.8877\n",
      "Epoch 21/30\n",
      "433146/433146 [==============================] - 85s 196us/sample - loss: 0.2754 - accuracy: 0.8875\n",
      "Epoch 22/30\n",
      "433146/433146 [==============================] - 90s 207us/sample - loss: 0.2745 - accuracy: 0.8878\n",
      "Epoch 23/30\n",
      "433146/433146 [==============================] - 77s 178us/sample - loss: 0.2731 - accuracy: 0.8888\n",
      "Epoch 24/30\n",
      "433146/433146 [==============================] - 75s 172us/sample - loss: 0.2732 - accuracy: 0.8888\n",
      "Epoch 25/30\n",
      "433146/433146 [==============================] - 86s 199us/sample - loss: 0.2734 - accuracy: 0.8888\n",
      "Epoch 26/30\n",
      "433146/433146 [==============================] - 89s 206us/sample - loss: 0.2727 - accuracy: 0.8888\n",
      "Epoch 27/30\n",
      "433146/433146 [==============================] - 88s 204us/sample - loss: 0.2722 - accuracy: 0.8889\n",
      "Epoch 28/30\n",
      "433146/433146 [==============================] - 81s 188us/sample - loss: 0.2710 - accuracy: 0.8898\n",
      "Epoch 29/30\n",
      "433146/433146 [==============================] - 94s 217us/sample - loss: 0.2709 - accuracy: 0.8899\n",
      "Epoch 30/30\n",
      "433146/433146 [==============================] - 86s 198us/sample - loss: 0.2703 - accuracy: 0.8897\n",
      "20000/20000 [==============================] - 1s 75us/sample - loss: 0.7907 - accuracy: 0.5979\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_0\n",
      "Accuracy: 59.79\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_0\\assets\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_1\n",
      "Train on 433146 samples\n",
      "Epoch 1/30\n",
      "433146/433146 [==============================] - 110s 254us/sample - loss: 0.3492 - accuracy: 0.8560\n",
      "Epoch 2/30\n",
      "433146/433146 [==============================] - 91s 210us/sample - loss: 0.3153 - accuracy: 0.8702\n",
      "Epoch 3/30\n",
      "433146/433146 [==============================] - 93s 215us/sample - loss: 0.3041 - accuracy: 0.8752\n",
      "Epoch 4/30\n",
      "433146/433146 [==============================] - 77s 177us/sample - loss: 0.2978 - accuracy: 0.8782\n",
      "Epoch 5/30\n",
      "433146/433146 [==============================] - 68s 157us/sample - loss: 0.2927 - accuracy: 0.8803\n",
      "Epoch 6/30\n",
      "433146/433146 [==============================] - 75s 173us/sample - loss: 0.2887 - accuracy: 0.8813\n",
      "Epoch 7/30\n",
      "433146/433146 [==============================] - 69s 160us/sample - loss: 0.2848 - accuracy: 0.8830\n",
      "Epoch 8/30\n",
      "433146/433146 [==============================] - 71s 164us/sample - loss: 0.2824 - accuracy: 0.8841\n",
      "Epoch 9/30\n",
      "433146/433146 [==============================] - 77s 178us/sample - loss: 0.2800 - accuracy: 0.8854\n",
      "Epoch 10/30\n",
      "433146/433146 [==============================] - 85s 197us/sample - loss: 0.2789 - accuracy: 0.8859\n",
      "Epoch 11/30\n",
      "433146/433146 [==============================] - 88s 203us/sample - loss: 0.2772 - accuracy: 0.8868\n",
      "Epoch 12/30\n",
      "433146/433146 [==============================] - 112s 258us/sample - loss: 0.2755 - accuracy: 0.8879\n",
      "Epoch 13/30\n",
      "433146/433146 [==============================] - 108s 249us/sample - loss: 0.2743 - accuracy: 0.8886\n",
      "Epoch 14/30\n",
      "433146/433146 [==============================] - 109s 251us/sample - loss: 0.2733 - accuracy: 0.8886\n",
      "Epoch 15/30\n",
      "433146/433146 [==============================] - 105s 243us/sample - loss: 0.2728 - accuracy: 0.8890\n",
      "Epoch 16/30\n",
      "433146/433146 [==============================] - 90s 208us/sample - loss: 0.2709 - accuracy: 0.8903\n",
      "Epoch 17/30\n",
      "433146/433146 [==============================] - 80s 184us/sample - loss: 0.2702 - accuracy: 0.8909\n",
      "Epoch 18/30\n",
      "433146/433146 [==============================] - 104s 241us/sample - loss: 0.2703 - accuracy: 0.8904\n",
      "Epoch 19/30\n",
      "433146/433146 [==============================] - 103s 237us/sample - loss: 0.2693 - accuracy: 0.8910\n",
      "Epoch 20/30\n",
      "433146/433146 [==============================] - 95s 219us/sample - loss: 0.2691 - accuracy: 0.8907\n",
      "Epoch 21/30\n",
      "433146/433146 [==============================] - 90s 208us/sample - loss: 0.2686 - accuracy: 0.8911\n",
      "Epoch 22/30\n",
      "433146/433146 [==============================] - 81s 186us/sample - loss: 0.2679 - accuracy: 0.8916\n",
      "Epoch 23/30\n",
      "433146/433146 [==============================] - 87s 200us/sample - loss: 0.2671 - accuracy: 0.8919\n",
      "Epoch 24/30\n",
      "433146/433146 [==============================] - 84s 195us/sample - loss: 0.2665 - accuracy: 0.8916\n",
      "Epoch 25/30\n",
      "433146/433146 [==============================] - 86s 197us/sample - loss: 0.2666 - accuracy: 0.8918\n",
      "Epoch 26/30\n",
      "433146/433146 [==============================] - 87s 201us/sample - loss: 0.2658 - accuracy: 0.8921\n",
      "Epoch 27/30\n",
      "433146/433146 [==============================] - 85s 196us/sample - loss: 0.2655 - accuracy: 0.8924\n",
      "Epoch 28/30\n",
      "433146/433146 [==============================] - 86s 198us/sample - loss: 0.2656 - accuracy: 0.8929\n",
      "Epoch 29/30\n",
      "433146/433146 [==============================] - 86s 198us/sample - loss: 0.2647 - accuracy: 0.8930\n",
      "Epoch 30/30\n",
      "433146/433146 [==============================] - 89s 206us/sample - loss: 0.2640 - accuracy: 0.8927\n",
      "20000/20000 [==============================] - 2s 76us/sample - loss: 0.8714 - accuracy: 0.6288\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_1\n",
      "Accuracy: 62.88\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_1\\assets\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_2\n",
      "Train on 433146 samples\n",
      "Epoch 1/30\n",
      "433146/433146 [==============================] - 151s 348us/sample - loss: 0.3519 - accuracy: 0.8549\n",
      "Epoch 2/30\n",
      "433146/433146 [==============================] - 134s 310us/sample - loss: 0.3163 - accuracy: 0.8714\n",
      "Epoch 3/30\n",
      "433146/433146 [==============================] - 134s 310us/sample - loss: 0.3046 - accuracy: 0.8753\n",
      "Epoch 4/30\n",
      "433146/433146 [==============================] - 126s 292us/sample - loss: 0.2972 - accuracy: 0.8779\n",
      "Epoch 5/30\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "433146/433146 [==============================] - 126s 292us/sample - loss: 0.2933 - accuracy: 0.8799\n",
      "Epoch 6/30\n",
      "433146/433146 [==============================] - 125s 288us/sample - loss: 0.3004 - accuracy: 0.8776\n",
      "Epoch 7/30\n",
      "433146/433146 [==============================] - 132s 306us/sample - loss: 0.2992 - accuracy: 0.8780\n",
      "Epoch 8/30\n",
      "433146/433146 [==============================] - 139s 321us/sample - loss: 0.2901 - accuracy: 0.8818\n",
      "Epoch 9/30\n",
      "433146/433146 [==============================] - 158s 365us/sample - loss: 0.2855 - accuracy: 0.8833\n",
      "Epoch 10/30\n",
      "433146/433146 [==============================] - 159s 368us/sample - loss: 0.2865 - accuracy: 0.8827\n",
      "Epoch 11/30\n",
      "433146/433146 [==============================] - 158s 364us/sample - loss: 0.2810 - accuracy: 0.8854\n",
      "Epoch 12/30\n",
      "433146/433146 [==============================] - 128s 296us/sample - loss: 0.2770 - accuracy: 0.8869\n",
      "Epoch 13/30\n",
      "433146/433146 [==============================] - 106s 245us/sample - loss: 0.2738 - accuracy: 0.8884\n",
      "Epoch 14/30\n",
      "433146/433146 [==============================] - 105s 241us/sample - loss: 0.2722 - accuracy: 0.8892\n",
      "Epoch 15/30\n",
      "433146/433146 [==============================] - 147s 339us/sample - loss: 0.2733 - accuracy: 0.8882\n",
      "Epoch 16/30\n",
      "433146/433146 [==============================] - 132s 305us/sample - loss: 0.2706 - accuracy: 0.8899\n",
      "Epoch 17/30\n",
      "433146/433146 [==============================] - 103s 238us/sample - loss: 0.2684 - accuracy: 0.8911\n",
      "Epoch 18/30\n",
      "433146/433146 [==============================] - 103s 238us/sample - loss: 0.2670 - accuracy: 0.8911\n",
      "Epoch 19/30\n",
      "433146/433146 [==============================] - 103s 238us/sample - loss: 0.2658 - accuracy: 0.8920\n",
      "Epoch 20/30\n",
      "433146/433146 [==============================] - 103s 238us/sample - loss: 0.2645 - accuracy: 0.8922\n",
      "Epoch 21/30\n",
      "433146/433146 [==============================] - 103s 239us/sample - loss: 0.2648 - accuracy: 0.8926\n",
      "Epoch 22/30\n",
      "433146/433146 [==============================] - 105s 243us/sample - loss: 0.2630 - accuracy: 0.8929\n",
      "Epoch 23/30\n",
      "433146/433146 [==============================] - 95s 220us/sample - loss: 0.2626 - accuracy: 0.8932\n",
      "Epoch 24/30\n",
      "433146/433146 [==============================] - 96s 221us/sample - loss: 0.2631 - accuracy: 0.8930\n",
      "Epoch 25/30\n",
      "433146/433146 [==============================] - 100s 230us/sample - loss: 0.2614 - accuracy: 0.8940\n",
      "Epoch 26/30\n",
      "433146/433146 [==============================] - 97s 224us/sample - loss: 0.2611 - accuracy: 0.8940\n",
      "Epoch 27/30\n",
      "433146/433146 [==============================] - 94s 216us/sample - loss: 0.2606 - accuracy: 0.8936\n",
      "Epoch 28/30\n",
      "433146/433146 [==============================] - 102s 234us/sample - loss: 0.2591 - accuracy: 0.8947\n",
      "Epoch 29/30\n",
      "433146/433146 [==============================] - 105s 243us/sample - loss: 0.2688 - accuracy: 0.8906\n",
      "Epoch 30/30\n",
      "433146/433146 [==============================] - 100s 231us/sample - loss: 0.2614 - accuracy: 0.8939\n",
      "20000/20000 [==============================] - 1s 72us/sample - loss: 0.8066 - accuracy: 0.6317\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_2\n",
      "Accuracy: 63.17\n",
      "INFO:tensorflow:Assets written to: C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_2\\assets\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_oversampled_more_layers_30epochs_3\n",
      "Train on 433146 samples\n",
      "Epoch 1/30\n",
      "433146/433146 [==============================] - 173s 400us/sample - loss: 0.6932 - accuracy: 0.4992\n",
      "Epoch 2/30\n",
      "433146/433146 [==============================] - 155s 357us/sample - loss: 0.6932 - accuracy: 0.5003\n",
      "Epoch 3/30\n",
      "433146/433146 [==============================] - 156s 359us/sample - loss: 0.6932 - accuracy: 0.4976\n",
      "Epoch 4/30\n",
      "433146/433146 [==============================] - 158s 365us/sample - loss: 0.6932 - accuracy: 0.4982\n",
      "Epoch 5/30\n",
      "433146/433146 [==============================] - 155s 359us/sample - loss: 0.6932 - accuracy: 0.4991\n",
      "Epoch 6/30\n",
      "433146/433146 [==============================] - 153s 353us/sample - loss: 0.6932 - accuracy: 0.4996\n",
      "Epoch 7/30\n",
      "433146/433146 [==============================] - 153s 354us/sample - loss: 0.6932 - accuracy: 0.5006\n",
      "Epoch 8/30\n",
      "433146/433146 [==============================] - 153s 354us/sample - loss: 0.6932 - accuracy: 0.5010\n",
      "Epoch 9/30\n",
      "433146/433146 [==============================] - 153s 352us/sample - loss: 0.6932 - accuracy: 0.5001\n",
      "Epoch 10/30\n",
      "433146/433146 [==============================] - 152s 352us/sample - loss: 0.6932 - accuracy: 0.5004\n",
      "Epoch 11/30\n",
      "433146/433146 [==============================] - 153s 352us/sample - loss: 0.6932 - accuracy: 0.5002\n",
      "Epoch 12/30\n",
      "433146/433146 [==============================] - 153s 352us/sample - loss: 0.6932 - accuracy: 0.5009\n",
      "Epoch 13/30\n",
      "433146/433146 [==============================] - 153s 354us/sample - loss: 0.6932 - accuracy: 0.4996\n",
      "Epoch 14/30\n",
      "433146/433146 [==============================] - 157s 362us/sample - loss: 0.6932 - accuracy: 0.5003\n",
      "Epoch 15/30\n",
      "433146/433146 [==============================] - 150s 346us/sample - loss: 0.6932 - accuracy: 0.4999\n",
      "Epoch 16/30\n",
      "433146/433146 [==============================] - 151s 348us/sample - loss: 0.6932 - accuracy: 0.4986\n",
      "Epoch 17/30\n",
      "433146/433146 [==============================] - 150s 347us/sample - loss: 0.6932 - accuracy: 0.4988\n",
      "Epoch 18/30\n",
      "433146/433146 [==============================] - 150s 347us/sample - loss: 0.6932 - accuracy: 0.4987\n",
      "Epoch 19/30\n",
      "433146/433146 [==============================] - 152s 352us/sample - loss: 0.6932 - accuracy: 0.5008\n",
      "Epoch 20/30\n",
      "433146/433146 [==============================] - 153s 353us/sample - loss: 0.6932 - accuracy: 0.4986\n",
      "Epoch 21/30\n",
      "433146/433146 [==============================] - 152s 350us/sample - loss: 0.6932 - accuracy: 0.5010\n",
      "Epoch 22/30\n",
      "433146/433146 [==============================] - 150s 347us/sample - loss: 0.6932 - accuracy: 0.5016\n",
      "Epoch 23/30\n",
      "433146/433146 [==============================] - 175s 404us/sample - loss: 0.6932 - accuracy: 0.5027\n",
      "Epoch 24/30\n",
      "433146/433146 [==============================] - 158s 366us/sample - loss: 0.6932 - accuracy: 0.4985\n",
      "Epoch 25/30\n",
      "433146/433146 [==============================] - 148s 342us/sample - loss: 0.6932 - accuracy: 0.5010\n",
      "Epoch 26/30\n",
      "356448/433146 [=======================>......] - ETA: 26s - loss: 0.6932 - accuracy: 0.5001"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-169-59f1fa22164d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0msave_new_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msave_new_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m     \u001b[0mmodel_acc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_nn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_overs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_overs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_test_top20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test_top20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlayers_to_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_epochs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mepcs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msave_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msave_new_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m     \u001b[0mmodels_acc_overs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_acc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-153-1ffc05378b34>\u001b[0m in \u001b[0;36mtrain_nn\u001b[1;34m(X_train, y_train, X_test, y_test, layers, model_loss, activations, last_activation, model_epochs, model_batch_size, save_name)\u001b[0m\n\u001b[0;32m     37\u001b[0m         \u001b[0mnn_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_loss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'adam'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'accuracy'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[1;31m# fit the keras model on the dataset\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[0mnn_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_epochs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_batch_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m     \u001b[1;31m#test set\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    817\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m   def evaluate(self,\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m    340\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    341\u001b[0m                 \u001b[0mtraining_context\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 342\u001b[1;33m                 total_epochs=epochs)\n\u001b[0m\u001b[0;32m    343\u001b[0m             \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtraining_result\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTRAIN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    344\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[1;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m    179\u001b[0m             batch_end=step * batch_size + current_batch_size)\n\u001b[0;32m    180\u001b[0m       \u001b[0mcbks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_outs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m       \u001b[0mstep\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    182\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\contextlib.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type, value, traceback)\u001b[0m\n\u001b[0;32m    117\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m                 \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mon_batch\u001b[1;34m(self, step, mode, size)\u001b[0m\n\u001b[0;32m    786\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data_exhausted'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    787\u001b[0m           self.callbacks._call_batch_hook(\n\u001b[1;32m--> 788\u001b[1;33m               mode, 'end', step, batch_logs)\n\u001b[0m\u001b[0;32m    789\u001b[0m           \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_batch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\site-packages\\tensorflow_core\\python\\keras\\callbacks.py\u001b[0m in \u001b[0;36m_call_batch_hook\u001b[1;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[0;32m    240\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delta_ts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mt_before_callbacks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    241\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 242\u001b[1;33m     \u001b[0mdelta_t_median\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_delta_ts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mhook_name\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    243\u001b[0m     if (self._delta_t_batch > 0. and\n\u001b[0;32m    244\u001b[0m         delta_t_median > 0.95 * self._delta_t_batch and delta_t_median > 0.1):\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mmedian\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mmedian\u001b[1;34m(a, axis, out, overwrite_input, keepdims)\u001b[0m\n\u001b[0;32m   3519\u001b[0m     \"\"\"\n\u001b[0;32m   3520\u001b[0m     r, k = _ureduce(a, func=_median, axis=axis, out=out,\n\u001b[1;32m-> 3521\u001b[1;33m                     overwrite_input=overwrite_input)\n\u001b[0m\u001b[0;32m   3522\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3523\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m_ureduce\u001b[1;34m(a, func, **kwargs)\u001b[0m\n\u001b[0;32m   3427\u001b[0m         \u001b[0mkeepdim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3428\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3429\u001b[1;33m     \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3430\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3431\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m_median\u001b[1;34m(a, axis, out, overwrite_input)\u001b[0m\n\u001b[0;32m   3553\u001b[0m             \u001b[0mpart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3554\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3555\u001b[1;33m         \u001b[0mpart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3556\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3557\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mpart\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mpartition\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32mc:\\users\\joris\\documents\\miniconda3\\envs\\spec2vec_analysis\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36mpartition\u001b[1;34m(a, kth, axis, kind, order)\u001b[0m\n\u001b[0;32m    746\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    747\u001b[0m         \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"K\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 748\u001b[1;33m     \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpartition\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    749\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    750\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "layers_to_test = [[12]*10+[1],[16]*10+[1], [20]*20+[1], [20]*40+[1], [20]*30+[1], [12]*5+[24]*10+[12]*5+[1], [30]*20+[1]]\n",
    "name = 'nn_oversampled_more_layers_30epochs'\n",
    "epcs = 30 #epochs\n",
    "models_acc_overs = []\n",
    "X_overs = nn_found_matches_s2v_match_perc_pmass_sim_oversampled.drop('sim_label', axis = 1)\n",
    "y_overs = nn_found_matches_s2v_match_perc_pmass_sim_oversampled['sim_label']\n",
    "X_test_top20 = nn_new_found_matches_s2v_match_perc_pmass_sim_top20.drop(['sim_label'], axis = 1)\n",
    "y_test_top20 = nn_new_found_matches_s2v_match_perc_pmass_sim_top20['sim_label']\n",
    "for i in range(len(layers_to_test)):\n",
    "    new_name = name + '_' + str(i)\n",
    "    save_new_name = os.path.join(path_data, new_name)\n",
    "    print(save_new_name)\n",
    "    model_acc = train_nn(X_overs, y_overs, X_test_top20, y_test_top20, layers = layers_to_test[i], model_epochs = epcs, save_name = save_new_name)\n",
    "    models_acc_overs.append(model_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.59785 [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1]\n",
      "1 0.62875 [16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 1]\n",
      "2 0.6317 [20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 1]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(models_acc_overs)):\n",
    "    print(i, models_acc_overs[i][1], layers_to_test[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing if top hit is above 0.6 similarity after filtering with nn on oversampled data\n",
    "#look at top20\n",
    "test_sim_filt_nn_oversampled_top20 = []\n",
    "test_sim_filt_nn_top20_top20 = []\n",
    "test_sim_mcos_top20 = []\n",
    "test_sim_s2v_top20 = []\n",
    "test_sim_filt_nn = []\n",
    "ideal_case_top20 = []\n",
    "test_sim_filt_nn_top20_more_layers_4_top20 = []\n",
    "\n",
    "mass_sim_thresh = 0.95\n",
    "restr_nn = False # do mass+spec2vec restricitons on nn models\n",
    "threshold = 0.4\n",
    "topn = 20 #only look at top 20\n",
    "sim_threshold = 0.6\n",
    "\n",
    "for ID in range(len(nn_new_testset_top20_tested)):\n",
    "    # mass + mod cosine\n",
    "    current_match = nn_new_testset_tested[ID].sort_values(by=['mod_cosine_score'], ascending=False).iloc[:topn]\n",
    "\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['mod_cosine_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['mod_cosine_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_mcos_top20.append(best_bet)\n",
    "\n",
    "    # Scenario 1: mass + Spec2Vec + threshold\n",
    "    current_match = nn_new_testset_tested[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    \n",
    "    #inchikey_query = documents_query_s2v[ID]._obj.get(\"inchikey\")[:14]\n",
    "    #select top20\n",
    "    #current_match = found_matches_s2v[ID].sort_values(by=['s2v_score'], ascending=False).iloc[:topn]\n",
    "\n",
    "    df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                  & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_s2v_top20.append(best_bet)\n",
    "    \n",
    "    # Scenario 3: (Spec2Vec + threshold) after filtering for non-similar matches with nn\n",
    "    if not restr_nn:\n",
    "        df_select = current_match[(current_match['predictions'] == 1)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['predictions'] == 1)\n",
    "                                      & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_filt_nn.append(best_bet)\n",
    "    \n",
    "    # ideal case: how many queries have an actual match above s2v threshold and >0.6 tanimoto\n",
    "    df_select = current_match[(current_match['s2v_score'] > threshold)]\n",
    "    best_bet = -1\n",
    "    if df_select.shape[0] > 0:\n",
    "        if any(df_select['similarity'] > 0.6):\n",
    "            best_bet = 1\n",
    "    ideal_case_top20.append(best_bet)\n",
    "    \n",
    "    current_match = nn_new_testset_top20_tested[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    \n",
    "    # Scenario 4: (Spec2Vec + threshold) after filtering for non-similar matches with nn trained on top20\n",
    "    if not restr_nn:\n",
    "        df_select = current_match[(current_match['predictions'] == 1)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['predictions'] == 1)\n",
    "                                      & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_filt_nn_top20_top20.append(best_bet)\n",
    "    \n",
    "    current_match = nn_new_testset_oversampled_tested[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    \n",
    "    # Scenario 5: (Spec2Vec + threshold) after filtering for non-similar matches with nn trained on oversampled\n",
    "    if not restr_nn:\n",
    "        df_select = current_match[(current_match['predictions'] == 1)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['predictions'] == 1)\n",
    "                                      & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_filt_nn_oversampled_top20.append(best_bet)\n",
    "\n",
    "    # Scenario 6: (Spec2Vec + threshold) after filtering for non-similar matches with nn trained on top20 #4\n",
    "    current_match = nn_new_testset_top20_tested_more_layers_4[ID].sort_values(by=['s2v_score'], ascending = False).iloc[:topn]\n",
    "    if not restr_nn:\n",
    "        df_select = current_match[(current_match['predictions'] == 1)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['predictions'] == 1)\n",
    "                                      & (current_match['s2v_score'] > threshold)]\n",
    "    if df_select.shape[0] > 0:\n",
    "        #best_match_ID = df_select.sort_values(by=['s2v_score'], ascending=False).index[0]\n",
    "        #inchikey_match = documents_library_s2v[best_match_ID]._obj.get(\"inchikey\")[:14]\n",
    "        sim = df_select.iloc[0]['similarity'] #look at similarity of top hit\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_sim_filt_nn_top20_more_layers_4_top20.append(best_bet)\n",
    "    nn_new_testset_top20_tested_more_layers_4\n",
    "\n",
    "#make arrays from lists\n",
    "test_sim_filt_nn_oversampled_arr = np.array(test_sim_filt_nn_oversampled_top20)\n",
    "test_sim_filt_nn_top20_arr = np.array(test_sim_filt_nn_top20_top20)\n",
    "test_sim_mcos_arr_top20 = np.array(test_sim_mcos_top20)\n",
    "test_sim_s2v_arr_top20 = np.array(test_sim_s2v_top20)\n",
    "test_sim_filt_nn_arr = np.array(test_sim_filt_nn)\n",
    "ideal_case_arr_top20 = np.array(ideal_case_top20)\n",
    "test_sim_filt_nn_top20_more_layers_4_top20_arr = np.array(test_sim_filt_nn_top20_more_layers_4_top20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### plt.style.use('ggplot')\n",
    "if restr_nn:\n",
    "    ylabels = ['mass + mod cosine',\n",
    "               'mass + Spec2Vec',\n",
    "               'mass + Spec2Vec + NN',\n",
    "               'mass + Spec2Vec + NN oversampled',\n",
    "               'mass + Spec2Vec + NN top20',\n",
    "               'ideal case Spec2Vec>0.4',\n",
    "               'mass + Spec2Vec + NN top20 #4']\n",
    "else:\n",
    "    ylabels = ['mass + mod cosine',\n",
    "               'mass + Spec2Vec',\n",
    "               'NN',\n",
    "               'NN oversampled',\n",
    "               'NN top20',\n",
    "               'ideal case Spec2Vec>0.4',\n",
    "               'NN top20 #4']\n",
    "xlabels = ['true matches', 'false matches', 'no matches found']\n",
    "          #'mass + mod.cosine \\n + as backup: Spec2Vec + mod.cosine']\n",
    "data = np.array([[np.sum(test_sim_mcos_arr_top20 == 1), np.sum(test_sim_mcos_arr_top20 == 0), np.sum(test_sim_mcos_arr_top20 == -1)],\n",
    "                 [np.sum(test_sim_s2v_arr_top20 == 1), np.sum(test_sim_s2v_arr_top20 == 0), np.sum(test_sim_s2v_arr_top20 == -1)],\n",
    "                 [np.sum(test_sim_filt_nn_arr == 1), np.sum(test_sim_filt_nn_arr == 0), np.sum(test_sim_filt_nn_arr == -1)],\n",
    "                 [np.sum(test_sim_filt_nn_oversampled_arr == 1), np.sum(test_sim_filt_nn_oversampled_arr == 0), np.sum(test_sim_filt_nn_oversampled_arr == -1)],\n",
    "                 [np.sum(test_sim_filt_nn_top20_arr == 1), np.sum(test_sim_filt_nn_top20_arr == 0), np.sum(test_sim_filt_nn_top20_arr == -1)],\n",
    "                 [np.sum(ideal_case_arr_top20 == 1), np.sum(ideal_case_arr_top20 == 0), np.sum(ideal_case_arr_top20 == -1)],\n",
    "                 [np.sum(test_sim_filt_nn_top20_more_layers_4_top20_arr == 1), np.sum(test_sim_filt_nn_top20_more_layers_4_top20_arr == 0),\n",
    "                  np.sum(test_sim_filt_nn_top20_more_layers_4_top20_arr == -1)]])\n",
    "\n",
    "dataframe = pd.DataFrame(data, columns=xlabels, index=ylabels)\n",
    "\n",
    "#fig, ax = plt.subplots(figsize=(5, 5))\n",
    "ax = dataframe.sort_values(by=['false matches'], ascending=True).plot.barh(stacked=True, edgecolor='none', figsize=(5, 3))\n",
    "\n",
    "horiz_offset = 1.03\n",
    "vert_offset = 1.\n",
    "ax.legend(bbox_to_anchor=(horiz_offset, vert_offset))\n",
    "plt.xlabel('Number of spectra')\n",
    "plt.title('Compare if top hit is above 0.6 tanimoto similarity\\n(>5 matching inchikey in library)')\n",
    "#plt.figure(figsize=(5,5))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}