3b-model-basic-comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "import pickle\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": [
    "## Loading processed query, library and found_matches data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\training_query_library_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "# load training dataset, for making these see notebook 1b\n",
    "\n",
    "outfile = os.path.join(path_data, 'training_query_library_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        training_query_library_s2v_2dec = pickle.load(inf)\n",
    "        old_and_unique_documents_query_s2v_2dec, old_and_unique_documents_library_s2v_2dec = training_query_library_s2v_2dec\n",
    "else:\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        training_query_library_s2v_2dec = (old_and_unique_documents_query_s2v_2dec, old_and_unique_documents_library_s2v_2dec)\n",
    "        pickle.dump(training_query_library_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\testing_query_library_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "#loading test dataset\n",
    "\n",
    "outfile = os.path.join(path_data, 'testing_query_library_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        testing_query_library_s2v_2dec = pickle.load(inf)\n",
    "        new_and_unique2_documents_query_s2v_2dec, new_and_unique2_documents_library_s2v_2dec = testing_query_library_s2v_2dec\n",
    "else:\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        testing_query_library_s2v_2dec = (new_and_unique2_documents_query_s2v_2dec, new_and_unique2_documents_library_s2v_2dec)\n",
    "        pickle.dump(testing_query_library_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\old_and_unique_found_matches_s2v_2dec.pickle\n",
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\new_and_unique2_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "# load/make found_matches for both query and test set\n",
    "\n",
    "# define models\n",
    "path_models = os.path.join(path_data, \"trained_models\")\n",
    "model_file2 = os.path.join(path_models, \"spec2vec_library_testing_4000removed_2dec.model\")\n",
    "\n",
    "# check if output exists, if not: query models\n",
    "outfile = os.path.join(path_data, 'old_and_unique_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        old_and_unique_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    # load model\n",
    "    model2 = gensim.models.Word2Vec.load(model_file2)\n",
    "    print(model2)\n",
    "    old_and_unique_found_matches_s2v_2dec = library_matching(old_and_unique_documents_query_s2v_2dec,\n",
    "                                                             old_and_unique_documents_library_s2v_2dec,\n",
    "                                                             model2,\n",
    "                                                             presearch_based_on=[\"spec2vec-top200\"],\n",
    "                                                             ignore_non_annotated=True,\n",
    "                                                             intensity_weighting_power=0.5,\n",
    "                                                             allowed_missing_percentage=100,\n",
    "                                                             cosine_tol=0.005,\n",
    "                                                             mass_tolerance=1.0)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(old_and_unique_found_matches_s2v_2dec, outf)\n",
    "\n",
    "outfile = os.path.join(path_data, 'new_and_unique2_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        new_and_unique2_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    # load model\n",
    "    model2 = gensim.models.Word2Vec.load(model_file2)\n",
    "    print(model2)\n",
    "    new_and_unique2_found_matches_s2v_2dec = library_matching(new_and_unique2_documents_query_s2v_2dec,\n",
    "                                                             new_and_unique2_documents_library_s2v_2dec,\n",
    "                                                             model2,\n",
    "                                                             presearch_based_on=[\"spec2vec-top200\"],\n",
    "                                                             ignore_non_annotated=True,\n",
    "                                                             intensity_weighting_power=0.5,\n",
    "                                                             allowed_missing_percentage=100,\n",
    "                                                             cosine_tol=0.005,\n",
    "                                                             mass_tolerance=1.0)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(new_and_unique2_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max parent mass: 13418.370894192036\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get max parent mass in dataset\n",
    "all_q_masses = [doc._obj.get(\"parent_mass\") for doc in old_and_unique_documents_query_s2v_2dec]\n",
    "all_l_masses = [doc._obj.get(\"parent_mass\") for doc in old_and_unique_documents_library_s2v_2dec]\n",
    "max_parent_mass = max(all_q_masses + all_l_masses)\n",
    "print(\"Max parent mass:\", max_parent_mass)\n",
    "plt.hist(all_q_masses + all_l_masses, bins = 200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get additional features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem, DataStructs\n",
    "\n",
    "def find_info_matches(matches, documents_library, documents_query,\n",
    "                      add_label = True, add_tanimoto_sim = True, add_cols = False,\n",
    "                      add_num_matches_transform = True, add_mass_transform = True,\n",
    "                      max_parent_mass = False, add_mass_similarity = True):\n",
    "    '''\n",
    "    To each df in matches, add more info like tanimoto scores vs query etc.\n",
    "    \n",
    "    A matching inchikey gets label 1 and non-matching 0.\n",
    "\n",
    "    matches: list of pandas DataFrames, library matching result of query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    documents_query: list of SpectrumDocuments, spectra in query set.\n",
    "        Indices should correspond to indices of matches.\n",
    "    bins: list of int/float, the cutoff for the mass change, will result in boolean columns,\n",
    "        i.e. if a cutoff is 2 there will be a column with 1 (true) or 0 (false) for if the mass change\n",
    "        is within 2\n",
    "    calc_change: bool, instead of bins add a percentage change of the parent mass difference\n",
    "    add_label: bool, add a label for inchikey match or not\n",
    "    add_tanimoto_sim: bool, add tanimoto similarity or not\n",
    "    add_cols: bool/list of str, add other info present in metadata such as parent_mass, adduct\n",
    "        default: False\n",
    "    add_num_matches_transform: bool, transform cosine and mod_cosine matches to a number 0-1.\n",
    "        both matches are transformed to between 0-1 by doing 1-0.93^num_matches\n",
    "    add_mass_transform: bool, add transform of the parent masses to a fraction of the maximal parent mass\n",
    "    max_parent_mass: int/float, the maximum parent mass in the dataset, default = False\n",
    "    add_mass_similarity: bool, add similarity of parent mass to the query as a scaled number from 0-1\n",
    "        where The similarity in dalton is calculated and transformed into a value 0 - 1 by doing\n",
    "        1 - base_num^diff_in_dalton\n",
    "    \n",
    "    Output\n",
    "    matches_with_info: list of pandas DataFrames, library matching result of query\n",
    "        on library with matching labels\n",
    "    '''\n",
    "    matches_with_info = []\n",
    "    if add_mass_transform and not max_parent_mass:\n",
    "        print(\"If you want to transform the masses, please provide a max_parent_mass\")\n",
    "        return\n",
    "    else:\n",
    "        print('Max parent mass:', max_parent_mass)\n",
    "\n",
    "    for query_id in range(len(documents_query)):\n",
    "        match = matches[query_id].copy()\n",
    "        if add_label:\n",
    "            query_inchi = documents_query[query_id]._obj.get(\"inchikey\")[:14]\n",
    "            match = find_inchikey_match(match, documents_library, query_inchi)\n",
    "        if add_cols:\n",
    "            match = find_basic_info(match, documents_library, add_cols)\n",
    "        if add_tanimoto_sim:\n",
    "            query_smiles = documents_query[query_id]._obj.get(\"smiles\")\n",
    "            match = find_tanimoto_sim(match, documents_library, query_smiles)\n",
    "        if add_num_matches_transform:\n",
    "            match = transform_num_matches(match)\n",
    "        if add_mass_transform:\n",
    "            #add parent_mass if its not there already\n",
    "            match = find_basic_info(match, documents_library, add_cols = ['parent_mass'])\n",
    "            match['parent_mass'] = [cur_pm/max_parent_mass for cur_pm in match['parent_mass']]\n",
    "        if add_mass_similarity:\n",
    "            if 'mass_match' in match:\n",
    "                match.drop(['mass_match'], axis=1, inplace=True)\n",
    "            q_mass = documents_query[query_id]._obj.get(\"parent_mass\")\n",
    "            match = find_mass_similarity(match, documents_library, q_mass, base_num = 0.8)\n",
    "        \n",
    "        matches_with_info.append(match)\n",
    "    return matches_with_info\n",
    "\n",
    "def find_inchikey_match(matches, documents_library, query_inchi):\n",
    "    '''To each match in the matches df, add label for matching inchikey (1) or non-matching (0)\n",
    "\n",
    "    matches: pandas DataFrame, library matching result of 1 query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    query_inchi: str, first 14 symbols of query inchikey\n",
    "    df: pandas DataFrame, library matching result of query on library with matching labels\n",
    "    '''\n",
    "    matches_with_labels = []\n",
    "    df = matches.copy()\n",
    "    library_ids = df.index.values\n",
    "    labels = []\n",
    "    for lib_id in library_ids:\n",
    "        lib_inchi = documents_library[lib_id]._obj.get(\"inchikey\")[:14]\n",
    "        lab = 0\n",
    "        if query_inchi == lib_inchi:\n",
    "            lab = 1\n",
    "        labels.append(lab)\n",
    "    df['label'] = labels\n",
    "    return df\n",
    "\n",
    "def find_mass_matches(matches, documents_library, query_mass, bins = [2, 5], calc_change = False):\n",
    "    '''\n",
    "    To each match in matches df, add the mass change of query to match in bins or a percentage change\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",
    "    bins: list of int/float, the cutoff for the mass change, will result in boolean columns,\n",
    "        i.e. if a cutoff is 2 there will be a column with 1 (true) or 0 (false) for if the mass change\n",
    "        is within 2\n",
    "    calc_change: bool, instead of bins add a percentage change of the parent mass difference\n",
    "    df: pandas DataFrame, library matching result of 1 query on library with mass matches\n",
    "    '''\n",
    "    range_bins = range(len(bins)) #calc once\n",
    "    df = matches.copy()\n",
    "    library_ids = df.index.values\n",
    "    masses = [[] for _ in range_bins] #initialise\n",
    "    mass_changes = []\n",
    "    for lib_id in library_ids:\n",
    "        lib_mass = documents_library[lib_id]._obj.get(\"parent_mass\")\n",
    "        if calc_change: #calculate a percentage change in parent mass instead of discrete bins\n",
    "            perc_change = abs(lib_mass - query_mass) / query_mass * 100\n",
    "            mass_changes.append(perc_change)\n",
    "        else:\n",
    "            for bin_i in range_bins:\n",
    "                cutoff = bins[bin_i]\n",
    "                lab = 0\n",
    "                if abs(query_mass - lib_mass) < cutoff:\n",
    "                    lab = 1\n",
    "                masses[bin_i].append(lab)\n",
    "    #add to df\n",
    "    if calc_change:\n",
    "        df['perc_mass_change'] = mass_changes\n",
    "    else:\n",
    "        for bin_i in range_bins:\n",
    "            df['mass_match_' + str(bins[bin_i])] = masses[bin_i]\n",
    "    return df\n",
    "\n",
    "def find_basic_info(matches, documents_library, add_cols = ['parent_mass']):\n",
    "    '''\n",
    "    To each match in matches df, add the info from add_cols entries\n",
    "\n",
    "    matches: pandas DataFrame, library matching result of 1 query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    df: pandas DataFrame, library matching result of 1 query on library with added info\n",
    "    '''\n",
    "    df = matches.copy()\n",
    "    library_ids = df.index.values\n",
    "    if add_cols:\n",
    "        for col in add_cols:\n",
    "            col_data = []\n",
    "            for lib_id in library_ids:\n",
    "                lib_data = documents_library[lib_id]._obj.get(col)\n",
    "                col_data.append(lib_data)\n",
    "            df[col] = col_data\n",
    "    return df\n",
    "\n",
    "def find_tanimoto_sim(matches, documents_library, query_smiles):\n",
    "    '''To each match in matches df, add the tanimoto similarity between query and match\n",
    "\n",
    "    matches: pandas DataFrame, library matching result of 1 query on library\n",
    "    documents_library: list of SpectrumDocuments, spectra in library\n",
    "    df: pandas DataFrame, library matching result of 1 query on library with tanimoto similarities\n",
    "    '''\n",
    "    df = matches.copy()\n",
    "    sims = []\n",
    "    library_ids = df.index.values\n",
    "    \n",
    "    if not query_smiles or query_smiles == \"None\": # check that query smiles exist\n",
    "        df['similarity'] = [0] * len(library_ids) #default to all 0 if it doesnt exist\n",
    "        return df\n",
    "    ms_q = Chem.MolFromSmiles(query_smiles)\n",
    "    if not ms_q: #in case something is wrong with smiles\n",
    "        df['similarity'] = [0] * len(library_ids) #default to all 0 if it doesnt exist\n",
    "        return df\n",
    "    \n",
    "    fp_q = Chem.RDKFingerprint(ms_q)\n",
    "    for lib_id in library_ids:\n",
    "        smiles_lib = documents_library[lib_id]._obj.get(\"smiles\")\n",
    "        if smiles_lib and smiles_lib != \"None\":\n",
    "            ms_lib = Chem.MolFromSmiles(smiles_lib)\n",
    "            if ms_lib:\n",
    "                fp_lib = Chem.RDKFingerprint(ms_lib)\n",
    "                score = DataStructs.FingerprintSimilarity(fp_q, fp_lib)\n",
    "            else: #in case something is wrong with smiles\n",
    "                score = 0\n",
    "        else: #in case it doesnt have smiles\n",
    "            score = 0\n",
    "        sims.append(score)\n",
    "    df['similarity'] = sims\n",
    "    return df\n",
    "\n",
    "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\n",
    "\n",
    "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": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get top20\n",
    "old_and_unique_found_matches_s2v_2dec_top20 = []\n",
    "\n",
    "for matches in old_and_unique_found_matches_s2v_2dec:\n",
    "    old_and_unique_found_matches_s2v_2dec_top20.append(\n",
    "    matches.sort_values(\"s2v_score\", ascending=False).iloc[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_prep_training_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'nn_prep_training_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_prep_training_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_prep_training_found_matches_s2v_2dec = find_info_matches(old_and_unique_found_matches_s2v_2dec_top20,\n",
    "                                                                old_and_unique_documents_library_s2v_2dec,\n",
    "                                                                old_and_unique_documents_query_s2v_2dec,\n",
    "                                                                max_parent_mass=max_parent_mass)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_prep_training_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get top20\n",
    "new_and_unique2_found_matches_s2v_2dec_top20 = []\n",
    "\n",
    "for matches in new_and_unique2_found_matches_s2v_2dec:\n",
    "    new_and_unique2_found_matches_s2v_2dec_top20.append(\n",
    "    matches.sort_values(\"s2v_score\", ascending=False).iloc[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_prep_testing_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'nn_prep_testing_found_matches_s2v_2dec.pickle')\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_prep_testing_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_prep_testing_found_matches_s2v_2dec = find_info_matches(new_and_unique2_found_matches_s2v_2dec_top20,\n",
    "                                                                new_and_unique2_documents_library_s2v_2dec,\n",
    "                                                                new_and_unique2_documents_query_s2v_2dec,\n",
    "                                                                max_parent_mass=max_parent_mass)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_prep_testing_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>similarity</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>mass_sim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22101</th>\n",
       "      <td>0.928457</td>\n",
       "      <td>0.663299</td>\n",
       "      <td>0.928457</td>\n",
       "      <td>0.663299</td>\n",
       "      <td>0.909346</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>0.999777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20538</th>\n",
       "      <td>0.856661</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.856661</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.859138</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22454</th>\n",
       "      <td>0.857507</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.857507</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.674490</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034743</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.389183</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.584506</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.034744</td>\n",
       "      <td>0.999554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>0.336160</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.337487</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.303197</td>\n",
       "      <td>0</td>\n",
       "      <td>0.581808</td>\n",
       "      <td>0.037051</td>\n",
       "      <td>0.000997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "22101      0.928457        0.663299          0.928457            0.663299   \n",
       "20538      0.856661        0.549896          0.856661            0.549896   \n",
       "22454      0.857507        0.549896          0.857507            0.549896   \n",
       "1366       0.389183        0.549896          0.389183            0.549896   \n",
       "5998       0.336160        0.070000          0.337487            0.195643   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass  mass_sim  \n",
       "22101   0.909346      1    1.000000     0.034743  0.999777  \n",
       "20538   0.859138      1    1.000000     0.034743  1.000000  \n",
       "22454   0.674490      1    1.000000     0.034743  1.000000  \n",
       "1366    0.584506      1    1.000000     0.034744  0.999554  \n",
       "5998    0.303197      0    0.581808     0.037051  0.000997  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_prep_testing_found_matches_s2v_2dec[2].iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add matches together for each query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add all the found matches together in one big df\n",
    "nn_training_found_matches_s2v_2dec = nn_prep_training_found_matches_s2v_2dec[0].append(\n",
    "    nn_prep_training_found_matches_s2v_2dec[1:])\n",
    "nn_training_found_matches_s2v_2dec = nn_training_found_matches_s2v_2dec.sample(frac=1)\n",
    "\n",
    "nn_testing_full_found_matches_s2v_2dec = nn_prep_testing_found_matches_s2v_2dec[0].append(\n",
    "    nn_prep_testing_found_matches_s2v_2dec[1:])\n",
    "nn_testing_full_found_matches_s2v_2dec = nn_testing_full_found_matches_s2v_2dec.sample(frac=1)\n",
    "\n",
    "\n",
    "# take 500 (randomly chosen) query matches for validation set\n",
    "np.random.seed(42)\n",
    "first_half = list(np.random.choice(range(0,1000), 250, replace=False))\n",
    "np.random.seed(42)\n",
    "second_half = list(np.random.choice(range(1000,2000), 250, replace=False))\n",
    "val_set = first_half + second_half\n",
    "nn_val_found_matches_s2v_2dec = pd.DataFrame()\n",
    "nn_testing_found_matches_s2v_2dec = pd.DataFrame()\n",
    "\n",
    "for i in range(len(nn_prep_testing_found_matches_s2v_2dec)):\n",
    "    if i in val_set:\n",
    "        nn_val_found_matches_s2v_2dec = nn_val_found_matches_s2v_2dec.append(nn_prep_testing_found_matches_s2v_2dec[i])\n",
    "    else:\n",
    "        nn_testing_found_matches_s2v_2dec = nn_testing_found_matches_s2v_2dec.append(nn_prep_testing_found_matches_s2v_2dec[i])\n",
    "\n",
    "nn_val_found_matches_s2v_2dec = nn_val_found_matches_s2v_2dec.sample(frac=1)\n",
    "nn_testing_found_matches_s2v_2dec = nn_testing_found_matches_s2v_2dec.sample(frac=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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(nn_training_found_matches_s2v_2dec['similarity'],\n",
    "         alpha=0.5, bins = np.arange(0,1,0.05), label = 'training set')\n",
    "plt.hist(nn_testing_found_matches_s2v_2dec['similarity'],\n",
    "         alpha=0.5, bins = np.arange(0,1,0.05), label = 'test subset')\n",
    "plt.hist(nn_val_found_matches_s2v_2dec['similarity'],\n",
    "         alpha=0.5, bins = np.arange(0,1,0.05), label = 'validation set')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = nn_training_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "y_train = nn_training_found_matches_s2v_2dec['similarity']\n",
    "X_test = nn_testing_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "y_test = nn_testing_found_matches_s2v_2dec['similarity']\n",
    "X_val = nn_val_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "y_val = nn_val_found_matches_s2v_2dec['similarity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE 0.049800809392068046\n",
      "Validation MSE 0.05047421650180994\n",
      "Variable: s2v_score            Importance: 0.38\n",
      "Variable: mass_sim             Importance: 0.23\n",
      "Variable: parent_mass          Importance: 0.21\n",
      "Variable: mod_cosine_matches   Importance: 0.14\n",
      "Variable: cosine_matches       Importance: 0.03\n",
      "Variable: cosine_score         Importance: 0.0\n",
      "Variable: mod_cosine_score     Importance: 0.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train rf using optimised parameters from below\n",
    "num_est = 250\n",
    "rf = RandomForestRegressor(n_estimators = num_est, random_state = 42, max_depth = 5)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# predict on train\n",
    "rf_train_predictions = rf.predict(X_train)\n",
    "mse_train_rf = mean_squared_error(y_train, rf_train_predictions)\n",
    "print('Training MSE', mse_train_rf)\n",
    "\n",
    "# predict on test\n",
    "rf_predictions = rf.predict(X_val)\n",
    "mse_rf = mean_squared_error(y_val, rf_predictions)\n",
    "print('Validation MSE', mse_rf)\n",
    "\n",
    "# get feature importances\n",
    "importances = list(rf.feature_importances_)\n",
    "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(X_train.columns, importances)]\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimising RF parameters\n",
    "- training and val loss seem very far apart -> overfitting in some way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find good max_depth -> 5 (see plot)\n",
    "to_try = range(1, 21)\n",
    "num_est = 40\n",
    "\n",
    "train_mse = []\n",
    "val_mse = []\n",
    "for try_maxd in to_try:\n",
    "    rf2 = RandomForestRegressor(n_estimators = num_est, random_state = 42, max_depth = try_maxd)\n",
    "    rf2.fit(X_train, y_train)\n",
    "\n",
    "    # predict on train\n",
    "    rf_train_predictions2 = rf2.predict(X_train)\n",
    "    mse_train_rf2 = mean_squared_error(y_train, rf_train_predictions2)\n",
    "#     print('Training MSE', mse_train_rf2)\n",
    "    train_mse.append(mse_train_rf2)\n",
    "    # predict on test\n",
    "    rf_predictions2 = rf2.predict(X_val)\n",
    "    mse_rf2 = mean_squared_error(y_val, rf_predictions2)\n",
    "#     print('Validation MSE', mse_rf2)\n",
    "    val_mse.append(mse_rf2)\n",
    "\n",
    "plt.plot(to_try, train_mse, \"b\", label=\"Train MSE\")\n",
    "plt.plot(to_try, val_mse, \"r\", label=\"Val MSE\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.xlabel(\"Tree depth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 9.832176208496094\n",
      "100 19.46282410621643\n",
      "250 59.83411455154419\n",
      "500 132.64440441131592\n",
      "1000 308.5162515640259\n",
      "1500 184.54904007911682\n"
     ]
    },
    {
     "data": {
      "image/png": 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kFCEErFYrJEmCSqVqsW1hYSFqamo8VNn1a486hRBQq9Xw8/Nr9X2sjwFDRDctq9UKHx8faLWt7+q0Wi00Go0Hqrox7VWn3W6H1WqFv79/m5/DITIiumlJktSmcKHWabVaSJJ0Tc9hwBDRTetahnOoddf6fjLaiYgUYrFYMG7cOABAcXExNBoNjEYjAGDHjh3w9fVt9rlfffUVPvroIyxZsqTN2xswYADCwsKwZcsWed6wYcPgcDiwd+9eVFdX44UXXsC3334LIQSCg4OxceNGBAYGIjw8HL/61a/k5z3yyCOYMWPGtb5kFwwYIiKFGI1GfPLJJwCAFStWIDAwEFOnTpWX2+32Zofw+vXrh379+l3zNisrK3Hu3Dl06dIFeXl5LsvWrVsHs9mMPXv2AADy8/Pl7fv5+cm1uguHyIiIPGjmzJlYtGgRRo8ejeTkZOTm5mLUqFEYPnw4Ro0ahfz8fADAwYMH8dRTTwFwhtPs2bMxevRoxMTEIDU1tdn1P/zww9i2bRsAID09HQkJCfKyoqIidO7cWZ7u0aMHdDqdAq/SiT0YIvpFWLAgGCdO+DS7XKVSQQhxTevs3bsWixdXXHMt33//Pf75z39Co9Hg0qVL2Lx5M7RaLfbv34/XXnsN7777bqPn5Ofn48MPP4TVakVsbCyeeuop+Pg0fj0jR47ErFmzMHXqVHzyySd4++238a9//QsAMG7cODzxxBPYsWMHBg4ciDFjxiAyMhKA84y7YcOGyeuZMWMGHnnkkWt+bfW1GjD/+c9/cM8998jTBQUFCAsLk6d37NiBkSNH3lARRES/JA899JB8qnFFRQVmzpyJ06dPQ6VSoba2tsnnDB06FDqdDoGBgTCbzSguLnbZF9cxGAzQ6/XIyMhAz549XU4r7tOnD/7zn/8gKysLn332GUaOHImtW7eiZ8+eigyRtRowq1evdgmYP/7xj0hLS5OnN23axIAhIq/XWk9Dq9XCbrd7pJaAgAD58euvv47Y2Fikpqbi7NmzGD16dJPPqT+UpdFo4HA4ml3/qFGj8PLLLyMlJaXRssDAQIwYMQIjRoyAWq3Gnj170LNnzxt4Nc1r9RhMa13Ga+1SEhHRVZcuXZKPi2zatMkt63zwwQeRmJiI3/zmNy7zs7OzcfHiRQCAzWbDyZMn0bVrV7dssymt9mBaO++5redFHz16FGlpaZAkCUOHDnU58AQ4gyotLQ25ubnQ6XRITEyUxwanT58OPz8/qNVqaDQaLFu2DIDzbImUlBQUFxcjJCQEs2bNQlBQEI4dO4aNGzfKZ2hMmDABffr0aVOdRESeNG3aNMycORNr167Fvffe65Z1BgUFYfr06Y3m//DDD5g7dy6EEPK+uG4EquExmLi4OLz88ss3VIdKtNIFefrpp7F+/Xq5p/LMM8/gb3/7mzw9adIkbNiwocWNSJKE559/HvPmzYPJZMLcuXPx/PPPuyRnTk4Odu3ahblz5yIvLw/r16/Hq6++CsAZMEuXLkVwcLDLet9//30EBQUhISEB6enpqKysxPjx43H69Gno9XoYjUacOXMGycnJWLNmTatvRkFBgcu02WxGSUlJq89rb6zTvVine7VnnZcvX3YZjmqJJ4fIbkR71tnU+9nUcaA6rfZgrFYrHn/8cZd5Dadbk5+fj86dOyM0NBQAEBsbi+zsbJeAOXz4MAYPHgyVSoVevXqhqqoKZWVlMBgMza43OzsbixYtAgAMGTIEixYtwvjx43HLLbfIbcLDw1FbW4va2tomz7ggIiJltBowb7/99g1vxGKxwGQyydMmk6nRF4AsFgvMZrNLG4vFIgdMcnIyAOe3UuPj4wEA5eXl8nKDwYCKisYH8b744gvccsstTYZLZmYmMjMzAQDLli1z2T7g/KTQcJ43Yp3uxTrdqz3rLCwsvKZrkf1crlvWXnXqdLpr+rdstcqQkJAm51dWViIoKKhNG2lqFK7hsZuW2ixZsgRGoxHl5eV45ZVXEBYWht69e7e63bNnz2Ljxo344x//2OTy+Ph4OawANOrGcwjCvVine7HO1tXU1LT5ysMcImtdTU1No3/LlobIWj2LLCsrC0ePHpWnT506hWnTpuGZZ57B888/3+i4RVNMJhNKS0vl6dLS0kZDXyaTyaXw+m3qrt2j1+sRExMjf9NVr9ejrKwMAFBWVuZyjKa0tBTLly/H9OnTXb65SkREntFqwGzfvh0dO3aUp9euXYu+ffti+fLl6Nu3L957771WN9K9e3ecP38eRUVFsNvtOHjwIKKjo13aREdHY//+/RBC4OTJkwgICIDBYIDVakV1dTUA5/GgY8eOISIiQn5OVlYWAGcQxsTEAACqqqqwbNky/O53v3O5eBsREXlOq0NkJSUl8g69pKQEZ86cwfz58xEUFIQnn3wSSUlJrW5Eo9Fg8uTJSE5OhiRJiIuLQ3h4OHbv3g0AGD58OKKiopCTk4OkpCT4+voiMTERgPM4y/LlywEADocDAwcORP/+/QEACQkJSElJwd69e2E2mzF79mwAwK5du3DhwgX861//ki+RMG/ePOj1+mt8e4iI6Hq1GjBqtRp2ux2+vr44efIkwsLC5GMvOp0ONputTRu64447cMcdd7jMGz58uPxYpVLh2WefbfS80NBQvP76602us0OHDliwYEGj+Y899hgee+yxNtVFRKSU0aNHY8aMGS5feHz33Xfx/fffY+nSpc0+Z/78+Y2upDx69Gj8+OOPyMnJkedNnjwZn332GfLy8iBJEhYuXIgDBw5ApVJBp9Nh9erViIiIwIABAxAUFAS12jlodffdd1/TbQCuV6sB07t3b/zjH//AkCFD8PHHH+POO++Ul507d85l+IyIiK565JFHkJGR4RIwGRkZmD9//nWtT6/X48svv8Sdd96J8vJyFBUVycu2bt2KCxcuIDMzE2q1GgUFBS7fWfnwww/l49me0uoxmEmTJuH06dOYP38+dDqdyzfw9+/ff133KyAi+iUYOXIkMjMzUVNTA8B5ZmthYSHuuusuzJkzBw8++CDi4uLkwwCtGTVqFNLT0wEAH3/8MR588EF5WWFhIUJDQ+VeSlhYWLt3AFrtwUiShOnTp0MIAZVKhcuXL+Py5csAgPvvv1/xAomI3CF4wQL4nDjR7PLruVx/be/eqFi8uNnlRqMR/fv3x6effor7778fGRkZGDVqFFQqFV566SUYDAY4HA6MGzcOJ06caPXrFwMHDsSLL74Ih8OBjIwM/PnPf8abb74JwHkfmEcffRRffPEFBg4ciMcee8zlElljxoyRw2fMmDGYMmXKNb3W69FqwDR1PZuG/vnPf7qlGCKim01CQgIyMjLkgHnjjTcAANu2bcPGjRvhcDhQWFiIvLy8VgNGo9FgwIAB2Lp1K6xWK8LDw+VlYWFh2L9/Pw4cOIADBw5g3LhxWL16NQYNGgSgfYbIWg2YiIgI1NbWYsiQIRg0aJDHCyQicoeWehqAcl9gfOCBB/CnP/0JX3/9NaxWK/r27YszZ85gzZo12LFjBzp27IiZM2fCarW2aX0JCQmYOHEifv/73zdaptPpcN999+G+++5DSEgI/v3vf8sB0x5aPQbz+uuvY/bs2aisrMSCBQuwdOlSHDhwAHa7HWq1Wu5yERFRY4GBgbjnnnswe/Zs+Rj2pUuX4O/vj+DgYBQXF2Pfvn1tXt/dd9+N//f//l+jK9J//fXXuHDhAgDnoY0TJ04oein+tmjTBW0iIiIwYcIEPPnkkzh27Bg+/fRTpKamYsGCBfIl9YmIqGkJCQl49tln8de//hUAcPvtt6NPnz6Ii4tDRESE/CXxtlCpVJg6dWqj+SUlJXjhhRfkr470798fEydOlJfXPwZz2223YeXKlTfwitpYa2uX66+voKAAn376KQ4cOIBOnTph2rRp6NSpk5L1eRQv168s1ulerLN1vFy/e7n9cv2VlZX4/PPPkZWVBavVikGDBuFPf/rTz+IqrkRE1H5aDZj/+7//Q6dOnTBo0CD06tULAHDhwgV5rA8A7xZJRESNtBowHTt2hM1mw549e7Bnz55Gy1UqlVvuGUNERDeXVgNm1apVnqiDiMjtrvWLk9Sya30/eY4xEd206i7WSzeu7qsp1+LncX9QIqLr4OfnB6vVipqamkZ30W1Ip9PJ1wzzZu1RpxACarUafn5+1/Q8BgwR3bRUKhX8/f3b1Janfbsfh8iIiEgRDBgiIlIEA4aIiBTBgCEiIkUwYIiISBEMGCIiUgQDhoiIFMGAISIiRTBgiIhIEQwYIiJSBAOGiIgUwYAhIiJFMGCIiEgRDBgiIlIEA4aIiBTBgCEiIkUwYIiISBEMGCIiUgQDhoiIFMGAISIiRTBgiIhIEVpPbejo0aNIS0uDJEkYOnQoEhISXJYLIZCWlobc3FzodDokJiYiMjISADB9+nT4+flBrVZDo9Fg2bJlAIDKykqkpKSguLgYISEhmDVrFoKCggAAW7Zswd69e6FWqzFp0iT079/fUy+ViIjgoYCRJAmpqamYN28eTCYT5s6di+joaHTt2lVuk5ubiwsXLmDlypXIy8vDunXr8Oqrr8rLFy5ciODgYJf1pqeno2/fvkhISEB6ejrS09Mxfvx4/PTTTzh48CDeeOMNlJWVYcmSJfjLX/4CtZodNiIiT/HIHjc/Px+dO3dGaGgotFotYmNjkZ2d7dLm8OHDGDx4MFQqFXr16oWqqiqUlZW1uN7s7GwMGTIEADBkyBB5ndnZ2YiNjYWPjw86deqEzp07Iz8/X5kXR0RETfJID8ZiscBkMsnTJpMJeXl5jdqYzWaXNhaLBQaDAQCQnJwMABg2bBji4+MBAOXl5fJyg8GAiooKeV09e/aU12U0GmGxWBrVlZmZiczMTADAsmXLXLYPAFqtttE8b8Q63Yt1uhfrdK+fS52AhwJGCNFonkqlanObJUuWwGg0ory8HK+88grCwsLQu3fva9peU+Lj4+WwAoCSkhKX5WazudE8b8Q63Yt1uhfrdC9vqzMsLKzZZR4ZIjOZTCgtLZWnS0tL5Z5H/Tb137T6bYxGIwBAr9cjJiZGHu7S6/XyMFpZWZl8jKbh9iwWi7wOIiLyDI8ETPfu3XH+/HkUFRXBbrfj4MGDiI6OdmkTHR2N/fv3QwiBkydPIiAgAAaDAVarFdXV1QAAq9WKY8eOISIiQn5OVlYWACArKwsxMTHy/IMHD6K2thZFRUU4f/48evTo4YmXSkREV3hkiEyj0WDy5MlITk6GJEmIi4tDeHg4du/eDQAYPnw4oqKikJOTg6SkJPj6+iIxMRGA8zjL8uXLAQAOhwMDBw6UTzlOSEhASkoK9u7dC7PZjNmzZwMAwsPDcc8992D27NlQq9V45plneAYZEZGHqURbD1j8AhQUFLhMe9tYZ3NYp3uxTvdine7lbXW2+zEYIiL65WHAEBGRIhgwRESkCAYMEREpggFDRESKYMAQEZEiGDBERKQIBgwRESmCAUNERIpgwBARkSIYMEREpAgGDBERKYIBQ0REimDAEBGRIhgwRESkCAYMEREpggFDRESKYMAQEZEiGDBERKQIBgwRESmCAUNERIpgwBARkSIYMEREpAgGDBERKYIBQ0REimDAEBGRIhgwRESkCAYMEREpggFDRESKYMAQEZEiGDBERKQIBgwRESmCAUNERIpgwBARkSIYMEREpAgGDBERKULrqQ0dPXoUaWlpkCQJQ4cORUJCgstyIQTS0tKQm5sLnU6HxMREREZGysslScKcOXNgNBoxZ84cAMAPP/yAd999F1arFSEhIUhKSkJAQADsdjtWr16N06dPQ5IkDB48GI8++qinXioREcFDPRhJkpCamoqXX34ZKSkpOHDgAH766SeXNrm5ubhw4QJWrlyJKVOmYN26dS7Ld+7ciS5durjMW7NmDZ588kmsWLECd911F7Zu3QoAOHToEOx2O1asWIFly5YhMzMTRUVFyr5IIiJy4ZGAyc/PR+fOnREaGgqtVovY2FhkZ2e7tDl8+DAGDx4MlUqFXr16oaqqCmVlZQCA0tJS5OTkYOjQoS7PKSgowG233QYA+PWvf40vvvhCXma1WuFwOGCz2aDVahEQEKDwq2yZJAEZGX5Yty4QZ89q2rUWIiJP8MgQmcVigclkkqdNJhPy8vIatTGbzS5tLBYLDAYD1q9fj/Hjx6O6utrlOeHh4Th8+DBiYmJw6NAhlJaWAgDuvvtuHD58GFOmTIHNZsPTTz+NoKCgRnVlZmYiMzMTALBs2TKX7QOAVqttNO965OaqMHOmBocOOfN84UI97rhDwqOPSkhIkNCr142t3111Ko11uhfrdC/W6X4eCRghRKN5KpWqTW2OHDkCvV6PyMhIHD9+3GX5tGnTkJaWho8++gjR0dHQap0vJz8/H2q1GmvWrEFVVRUWLFiAvn37IjQ01OX58fHxiI+Pl6dLSkpclpvN5kbzroXFosJrrwVj48YAmEwS3nijDAMG2LBrlx+2b/fH/Pm+mD8fuO22WowcWY2RI63o1ct+zdu50To9hXW6F+t0L9Z5fcLCwppd5pGAMZlMcu8CcA55GQyGRm3qv2l1bQ4dOoTDhw8jNzcXNpsN1dXVWLlyJZKSktClSxfMmzcPgHO4LCcnBwDw+eefo3///tBqtdDr9bj11ltx6tSpRgGjFIcD2LgxAK+9FoxLl1SYPLkKv//9Jej1zhCdOrUKU6dW4dw5NT7+2B87d/phxYoOWL48GD161GLECCtGjqzG7bfb0SCHiYh+NjxyDKZ79+44f/48ioqKYLfbcfDgQURHR7u0iY6Oxv79+yGEwMmTJxEQEACDwYAnnngCq1evxqpVqzBz5kz06dMHSUlJAIDy8nIAzpMINm/ejGHDhgFwJvw333wDIQSsVivy8vIanSCglOxsH4wYYcbcuR1x22212L27GIsXV8jhUl+XLhKefbYKmzeX4siRQiQnX0RoqIS33w7C/fd3wr33dkJycgfk5vqgiQ4eEZFX80gPRqPRYPLkyUhOToYkSYiLi0N4eDh2794NABg+fDiioqKQk5ODpKQk+Pr6IjExsdX1HjhwAP/+978BAHfddRfi4uIAAA888ADeeecd/P73v4cQAnFxcfjf//1f5V4ggKIiNZKTg/HRRwH4n/9x4J13LBg1ytrmHkhoqISJEy9j4sTLKC1V49//9sOOHX5YuzYI77zTAV262K/0bKy4804b1PwGExF5OZVo6uDHL1RBQYHLdFvGOmtrgb/9LRBvvNEBNpsKU6ZUIimpEoGB7nlbL15UYfduP+zc6Y+sLB1sNhVCQx148EHnMNqAATaEhnrXmGxzvG3suDms071Yp3t5W53tfgzmZpad7YvFi/W47z4r/vSnckRGOty6/o4dBcaOrcbYsdW4dEmFPXucPZt//MMf69cHwmRyICEBGDpUh9jYGvj4uHXzRETXjQFzg2Jjbdi+vRhRUbWKb6tDB4GEhGokJFTj8mUV9u7VYedOP/zzn/5ITTWhY0cJw4c7ezaDBtVAp1O8JLoJCOHsidfWqlBTA9hsqis/9R87l9XWXn1cN7+2FqipUTXzPMDPTwNJ0sPHR1z5AXx8BHx96343ntdUO9fnuM7Tap3tOXTsXRgwbuCJcGkoIEDgoYeseOghK4KCtNi8+RK2b/fHrl1+2LQpAB06SBg2zIoRI6z4zW+s8Pf3eInUBLsdjXbCdTvrhjv4hjvu+jt4m815bLO8PBg2W/12dUHR9M7euR7nuuvPF8J9pyvW3/HrdAJqtRpWqx9qa1Ww252vw53bq88ZNPUDCFcCrP5j1xCrm9ehQ10Qth52NxqKPj6A5hfwfWsGzE3Azw8YPrwGw4fXwGYDPv9chx07/LBrlz82bw5AQICE++6rwf33W2EwSFCrAbVaXPmNZqavr41KJaDRNN3G4XB+WvbEqdeShGZ33E3t4OvvuH181LBYAlv9ZO7cUbvuuF138I23L0nue/EajYCvbwB8fZ07SeePc6det0Pz9RXQ6yV5R6rTXW3n41M3fXVZXTvn9NWdqU5XfxvCZZv12/n6Nu5FNHXMwOGA/F7VvYf1fzt/Gs+7+vjqe1vXruG8upBuql1trQpVVWqXbTgcdUEI2O1Xt2W3K/MfVqNxDbu2hmJQkAZCdKwXZjfeAzQaJXTt6t7hfYAH+V1cz0F+b9BcnbW1wH/+44udO/3x8cd+KCnxno9MGo1wCSW1GnIwqVTOUGo4XdfGOe2cJ0lNf0p3505Bpbq6g63/ybxuB+vjA5cdd8OdcFM77rp11d/Bt7Tjdt3BOx/z5A73aq5OSUKrYddSAF4NqsbzmgvF+iFbf57zQ4oWVqsDNltdELouvx6jRlXjr38tu67n8iD/L5SPDzB4sA2DB9uQnFyOb7/VwmpVQQgVJMn5h+NwOH83nNd4GpCkq/OcPw2nXecJATgcV6f9/QNQWXnZZV7DNs7tXJ1XNy1Jqnq1Xp2nUrX+ybytO27n84DOnQ2orCyVn6PVeqbXRd5JrQZ0OueHCKf2/UzeUmAL4RyGbdgrtNub7ynabEBIiKRIrQyYXwiNBujT59ovQ+NOZrMfSkoq27WGtjCbgZISduzp50elgjw05ry+b/v+P+Y5F0REpAgGDBERKYIBQ0REimDAEBGRIhgwRESkCAYMEREpggFDRESKYMAQEZEieKkYIiJSBHswLZgzZ057l9AmrNO9WKd7sU73+rnUCTBgiIhIIQwYIiJSBAOmBfHx8e1dQpuwTvdine7FOt3r51InwIP8RESkEPZgiIhIEQwYIiJSBG841oSjR48iLS0NkiRh6NChSEhIaLdaSkpKsGrVKly8eBEqlQrx8fEYMWIEKisrkZKSguLiYoSEhGDWrFkICgoCAGzZsgV79+6FWq3GpEmT0L9/f4/VK0kS5syZA6PRiDlz5nhlnVVVVVi9ejXOnj0LlUqFadOmISwszOvq3L59O/bu3QuVSoXw8HAkJibCZrO1e53vvPMOcnJyoNfrsWLFCgC4rn/n77//HqtWrYLNZkNUVBQmTZoElRtvHdpUne+99x6OHDkCrVaL0NBQJCYmIjAw0OvqrLN161a8//77WLduHYKDg9u1zusiyIXD4RAzZswQFy5cELW1teIPf/iDOHv2bLvVY7FYxKlTp4QQQly+fFkkJSWJs2fPivfee09s2bJFCCHEli1bxHvvvSeEEOLs2bPiD3/4g7DZbKKwsFDMmDFDOBwOj9W7bds28eabb4qlS5cKIYRX1vnWW2+JzMxMIYQQtbW1orKy0uvqLC0tFYmJiaKmpkYIIcSKFSvEvn37vKLO48ePi1OnTonZs2fL866nrjlz5ojvvvtOSJIkkpOTRU5OjuJ1Hj16VNjtdrlmb61TCCGKi4vFK6+8IqZNmybKy8vbvc7rwSGyBvLz89G5c2eEhoZCq9UiNjYW2dnZ7VaPwWBAZGQkAMDf3x9dunSBxWJBdnY2hgwZAgAYMmSIXGN2djZiY2Ph4+ODTp06oXPnzsjPz/dIraWlpcjJycHQoUPled5W5+XLl/Htt9/ivvvuAwBotVoEBgZ6XZ2Aszdos9ngcDhgs9lgMBi8os7evXvLvZM611pXWVkZqqur0atXL6hUKgwePNjtf2dN1dmvXz9oNBoAQK9evWCxWLyyTgDYsGEDnnzySZdeSHvWeT04RNaAxWKByWSSp00mE/Ly8tqxoquKiopw+vRp9OjRA+Xl5TAYDACcIVRRUQHAWX/Pnj3l5xiNRvmPSGnr16/H+PHjUV1dLc/ztjqLiooQHByMd955Bz/++CMiIyMxceJEr6vTaDTi4YcfxrRp0+Dr64t+/fqhX79+XldnnWutS6PRNPo782S9ALB3717ExsZ6ZZ2HDx+G0WhEt27dXOZ7W52tYQ+mAdHEWdvtPo4JwGq1YsWKFZg4cSICAgKabddU/Z5w5MgR6PV6ubfVmvaq0+Fw4PTp0xg+fDj+/Oc/Q6fTIT09vdn27VVnZWUlsrOzsWrVKqxZswZWqxX79+9vtn171dma5upq73o3b94MjUaDQYMGtVhPe9RZU1ODzZs3Y9y4cW2up73fz+awB9OAyWRCaWmpPF1aWip/MmsvdrsdK1aswKBBgzBgwAAAgF6vR1lZGQwGA8rKyuQDgA3rt1gsMBqNitf43Xff4fDhw8jNzYXNZkN1dTVWrlzpdXWaTCaYTCb5U+Ddd9+N9PR0r6vz66+/RqdOneQ6BgwYgJMnT3pdnXWuta6m/s48Ve+nn36KI0eOYMGCBfKHR2+qs7CwEEVFRXjhhRfkbb700ktYunSpV9XZFuzBNNC9e3ecP38eRUVFsNvtOHjwIKKjo9utHiEEVq9ejS5duuChhx6S50dHRyMrKwsAkJWVhZiYGHn+wYMHUVtbi6KiIpw/fx49evRQvM4nnngCq1evxqpVqzBz5kz06dMHSUlJXldnx44dYTKZUFBQAMC5I+/atavX1Wk2m5GXl4eamhoIIfD111+jS5cuXldnnWuty2AwwN/fHydPnoQQAvv37/fI39nRo0eRkZGBl156CTqdzqV+b6kzIiIC69atw6pVq7Bq1SqYTCa89tpr6Nixo1fV2Rb8Jn8TcnJysGHDBkiShLi4OPz2t79tt1r++9//YsGCBYiIiJA/bf3ud79Dz549kZKSgpKSEpjNZsyePVs+ULh582bs27cParUaEydORFRUlEdrPn78OLZt24Y5c+bg0qVLXlfnDz/8gNWrV8Nut6NTp05ITEyEEMLr6ty0aRMOHjwIjUaDbt26YerUqbBare1e55tvvokTJ07g0qVL0Ov1GDt2LGJiYq65rlOnTuGdd96BzWZD//79MXnyZLcORzdV55YtW2C32+XaevbsiSlTpnhdnXUnoQDA9OnTsXTpUrlX2F51Xg8GDBERKYJDZEREpAgGDBERKYIBQ0REimDAEBGRIhgwRESkCAYMkZdYu3YtPvroo/Yug8htGDBE9UyfPh3PPfccrFarPG/Pnj1YtGiRW7fz6aefYv78+S7zpkyZgtGjR7t1O3XGjh2LCxcuKLJuouYwYIgacDgc2LlzZ3uX4TUcDkd7l0A/U7wWGVEDo0aNQkZGBu6//375ZlQtOXnyJP7+97/jp59+QkhICCZOnIjbb78dgLOn8tFHH6GiogIdOnTA448/jltuuQXvvvsu7HY7JkyYAI1Gg/Xr18uXBXn88cdx/PhxvPXWW3jwwQexbds2qNVqPPvss9BqtdiwYQMqKirw8MMPy1eZyM/PR1paGs6dOwdfX18MGDAATz/9NLRaLRYuXAgA8rWtpk2bhtjYWGRmZiIjIwOVlZX41a9+heeee06+ftXYsWMxefJk7Ny5Ew6HA2+//TY2bNiAzz//HLW1tQgJCUFSUhIiIiKU+Cegm4VH7z5D5OUSExPFV199JV5//XXxwQcfCCGEyMzMFAsXLmyyfWlpqZg0aZI4cuSIcDgc4quvvhKTJk0S5eXlorq6Wjz11FPi3LlzQgjnzePOnDkjhBBi3759Yt68eS7revvtt+VtfvPNN2LcuHHiww8/FLW1teKTTz4RkydPFm+++aa4fPmyOHPmjHjiiSfEhQsXhBBCnDp1Snz33XfCbreLwsJCMXPmTLF9+3Z53WPGjBHnz5+Xp7/++msxefJkcerUKWGz2URqaqpYsGCBS/vFixeLS5cuiZqaGpGbmytefPFFUVlZKSRJEmfPnhUWi+UG32262XGIjKgJY8eOxccffyzf16Q5+/fvR1RUFO644w6o1Wr8+te/Rvfu3ZGTkwPAeauHM2fOyDcMCw8Pb3MNGo0Gv/3tb6HVanHvvffi0qVLGDFiBPz9/REeHo6uXbvixx9/BABERkaiV69e0Gg06NSpE+Lj43HixIlm1/3ZZ58hLi4OkZGR8PHxwRNPPIGTJ0+iqKhIbvPoo48iKCgIvr6+0Gq1sFqtOHfuHIQQ6Nq1a7tfZZy8H4fIiJoQERGBO++8E+np6ejSpUuz7UpKSnDo0CEcOXJEnudwOHD77bfDz88PM2fOxLZt27B69WrceuuteOqpp1pcX30dOnSAWu38DOjr6wvAeVn8Or6+vvLJCAUFBfj73/+OU6dOyXfBbOnePGVlZbjlllvkaT8/PwQFBcFisaBTp04A4HIDqz59+uD+++9HamoqSkpKcNddd2HChAkt3puIiAFD1IyxY8fipZdecrlNQkMmkwmDBg3C1KlTm1zev39/9O/fHzabDf/4xz+wZs0aLF682O21rlu3Dt26dcPzzz8Pf39/7NixA4cOHWq2vcFgQElJiTxttVpRWVnpcg+RhlfiHTFiBEaMGIHy8nKkpKRg69atePzxx93+WujmwSEyomZ07twZ99xzDz7++ONm2wwaNAhHjhzB0aNHIUkSbDYbjh8/jtLSUly8eBGHDx+G1WqFVquFn5+f3CPp2LEjLBYL7Ha7W2qtrq5GQEAA/Pz8cO7cOezevdtluV6vR2FhoTw9cOBA7Nu3Dz/88ANqa2vxwQcfoEePHnLvpaH8/Hzk5eXBbrdDp9PBx8dHfi1EzWEPhqgFo0ePxmeffdbscrPZjBdffBHvv/8+/vKXv0CtVqNHjx547rnnIITAtm3b8NZbb0GlUqFbt2549tlnATiHnLp27YrnnnsOarUaqampN1TnhAkTsHbtWmRkZOCWW25BbGwsvvnmG3n5mDFjsGrVKthsNkyZMgWxsbEYN24cVqxYgcrKStx6662YOXNms+uvrq7Ghg0bUFhYCF9fX/Tr1w+jRo26oZrp5sf7wRARkSLYxyUiIkUwYIiISBEMGCIiUgQDhoiIFMGAISIiRTBgiIhIEQwYIiJSBAOGiIgU8f8B7dbNERTVJG4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# find good nodes\n",
    "import time\n",
    "to_try = [50,100,250,500,1000,1500]\n",
    "\n",
    "train_mse = []\n",
    "val_mse = []\n",
    "for try_ns in to_try:\n",
    "    start = time.time()\n",
    "    rf2 = RandomForestRegressor(n_estimators = try_ns, random_state = 42, max_depth = 5)\n",
    "    rf2.fit(X_train, y_train)\n",
    "    end = time.time()\n",
    "    elapsed = end-start\n",
    "    print(try_ns, elapsed)\n",
    "    # predict on train\n",
    "    rf_train_predictions2 = rf2.predict(X_train)\n",
    "    mse_train_rf2 = mean_squared_error(y_train, rf_train_predictions2)\n",
    "#     print('Training MSE', mse_train_rf2)\n",
    "    train_mse.append(mse_train_rf2)\n",
    "    # predict on test\n",
    "    rf_predictions2 = rf2.predict(X_val)\n",
    "    mse_rf2 = mean_squared_error(y_val, rf_predictions2)\n",
    "#     print('Validation MSE', mse_rf2)\n",
    "    val_mse.append(mse_rf2)\n",
    "\n",
    "plt.plot(to_try, train_mse, \"b\", label=\"Train MSE\")\n",
    "plt.plot(to_try, val_mse, \"r\", label=\"Val MSE\")\n",
    "plt.legend()\n",
    "plt.ylabel(\"MSE\")\n",
    "plt.xlabel(\"N estimators\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_rf_predict_found_matches_s2v_2dec = []\n",
    "for ID in range(len(nn_prep_testing_found_matches_s2v_2dec)):\n",
    "    current_match = nn_prep_testing_found_matches_s2v_2dec[ID].copy()\n",
    "    predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "    predictions = rf.predict(predict_on)\n",
    "    current_match['rf_predictions'] = predictions\n",
    "    nn_rf_predict_found_matches_s2v_2dec.append(current_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>s2v_score</th>\n",
       "      <th>label</th>\n",
       "      <th>similarity</th>\n",
       "      <th>parent_mass</th>\n",
       "      <th>mass_sim</th>\n",
       "      <th>rf_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18625</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.306735</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.069334</td>\n",
       "      <td>2.747835e-68</td>\n",
       "      <td>0.358800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18631</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.303958</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.034521</td>\n",
       "      <td>5.104856e-23</td>\n",
       "      <td>0.311354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7299</th>\n",
       "      <td>0.355064</td>\n",
       "      <td>0.1351</td>\n",
       "      <td>0.355068</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.292372</td>\n",
       "      <td>0</td>\n",
       "      <td>0.414251</td>\n",
       "      <td>0.015286</td>\n",
       "      <td>1.897516e-03</td>\n",
       "      <td>0.182476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18626</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.290061</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.068143</td>\n",
       "      <td>9.705796e-67</td>\n",
       "      <td>0.358800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18627</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.274596</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.067774</td>\n",
       "      <td>2.933035e-66</td>\n",
       "      <td>0.358800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92968</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.119211</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64763</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120513</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18628</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.262923</td>\n",
       "      <td>0</td>\n",
       "      <td>0.323934</td>\n",
       "      <td>0.033253</td>\n",
       "      <td>2.280202e-21</td>\n",
       "      <td>0.311354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58180</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255365</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120513</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19223</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.169412</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.252203</td>\n",
       "      <td>0</td>\n",
       "      <td>0.267964</td>\n",
       "      <td>0.018184</td>\n",
       "      <td>8.961955e-02</td>\n",
       "      <td>0.258783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90526</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.252028</td>\n",
       "      <td>0</td>\n",
       "      <td>0.117347</td>\n",
       "      <td>0.013497</td>\n",
       "      <td>8.968766e-06</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19527</th>\n",
       "      <td>0.392147</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.392147</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.245704</td>\n",
       "      <td>0</td>\n",
       "      <td>0.306294</td>\n",
       "      <td>0.020657</td>\n",
       "      <td>5.461553e-05</td>\n",
       "      <td>0.295133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82058</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.000822</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.242076</td>\n",
       "      <td>0</td>\n",
       "      <td>0.248347</td>\n",
       "      <td>0.014165</td>\n",
       "      <td>6.627308e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24340</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.240455</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069481</td>\n",
       "      <td>0.010738</td>\n",
       "      <td>2.317539e-09</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68124</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.079702</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.235435</td>\n",
       "      <td>0</td>\n",
       "      <td>0.266990</td>\n",
       "      <td>0.024001</td>\n",
       "      <td>2.442311e-09</td>\n",
       "      <td>0.293433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6978</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.234369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.156800</td>\n",
       "      <td>0.017067</td>\n",
       "      <td>3.939133e-01</td>\n",
       "      <td>0.184220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58178</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.233790</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120513</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58181</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.233335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.120513</td>\n",
       "      <td>0.014083</td>\n",
       "      <td>5.186006e-05</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90527</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.230278</td>\n",
       "      <td>0</td>\n",
       "      <td>0.117347</td>\n",
       "      <td>0.013497</td>\n",
       "      <td>8.968766e-06</td>\n",
       "      <td>0.181458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65686</th>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.229389</td>\n",
       "      <td>0</td>\n",
       "      <td>0.336756</td>\n",
       "      <td>0.027499</td>\n",
       "      <td>6.912263e-14</td>\n",
       "      <td>0.297047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "18625      0.093364          0.0700          0.093364            0.070000   \n",
       "18631      0.093364          0.0700          0.093364            0.070000   \n",
       "7299       0.355064          0.1351          0.355068            0.195643   \n",
       "18626      0.093364          0.0700          0.093364            0.070000   \n",
       "18627      0.093364          0.0700          0.093364            0.070000   \n",
       "92968      0.000000          0.0000          0.000000            0.000000   \n",
       "64763      0.000000          0.0000          0.000000            0.000000   \n",
       "18628      0.093364          0.0700          0.093364            0.070000   \n",
       "58180      0.000000          0.0000          0.000000            0.000000   \n",
       "19223      0.000000          0.0000          0.169412            0.070000   \n",
       "90526      0.000000          0.0000          0.000000            0.000000   \n",
       "19527      0.392147          0.0700          0.392147            0.070000   \n",
       "82058      0.000002          0.0700          0.000822            0.135100   \n",
       "24340      0.000000          0.0000          0.000000            0.000000   \n",
       "68124      0.000000          0.0000          0.079702            0.070000   \n",
       "6978       0.000000          0.0000          0.000043            0.070000   \n",
       "58178      0.000000          0.0000          0.000000            0.000000   \n",
       "58181      0.000000          0.0000          0.000000            0.000000   \n",
       "90527      0.000000          0.0000          0.000172            0.195643   \n",
       "65686      0.093364          0.0700          0.093364            0.070000   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass      mass_sim  rf_predictions  \n",
       "18625   0.306735      0    0.323934     0.069334  2.747835e-68        0.358800  \n",
       "18631   0.303958      0    0.323934     0.034521  5.104856e-23        0.311354  \n",
       "7299    0.292372      0    0.414251     0.015286  1.897516e-03        0.182476  \n",
       "18626   0.290061      0    0.323934     0.068143  9.705796e-67        0.358800  \n",
       "18627   0.274596      0    0.323934     0.067774  2.933035e-66        0.358800  \n",
       "92968   0.272369      0    0.119211     0.014083  5.186006e-05        0.181458  \n",
       "64763   0.272369      0    0.120513     0.014083  5.186006e-05        0.181458  \n",
       "18628   0.262923      0    0.323934     0.033253  2.280202e-21        0.311354  \n",
       "58180   0.255365      0    0.120513     0.014083  5.186006e-05        0.181458  \n",
       "19223   0.252203      0    0.267964     0.018184  8.961955e-02        0.258783  \n",
       "90526   0.252028      0    0.117347     0.013497  8.968766e-06        0.181458  \n",
       "19527   0.245704      0    0.306294     0.020657  5.461553e-05        0.295133  \n",
       "82058   0.242076      0    0.248347     0.014165  6.627308e-05        0.181458  \n",
       "24340   0.240455      0    0.069481     0.010738  2.317539e-09        0.181458  \n",
       "68124   0.235435      0    0.266990     0.024001  2.442311e-09        0.293433  \n",
       "6978    0.234369      0    0.156800     0.017067  3.939133e-01        0.184220  \n",
       "58178   0.233790      0    0.120513     0.014083  5.186006e-05        0.181458  \n",
       "58181   0.233335      0    0.120513     0.014083  5.186006e-05        0.181458  \n",
       "90527   0.230278      0    0.117347     0.013497  8.968766e-06        0.181458  \n",
       "65686   0.229389      0    0.336756     0.027499  6.912263e-14        0.297047  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_rf_predict_found_matches_s2v_2dec[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training NN\n",
    "The best model used was optimised in notebook 3c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#nn function\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Dense\n",
    "\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, history, accuracy, loss)\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",
    "    Returns:\n",
    "    model: keras sequential\n",
    "    history: dict, training statistics\n",
    "    accuracy: float, accuracy on test set\n",
    "    loss, float, loss on test set\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('\\nLoading existing model')\n",
    "        nn_model = load_model(save_name)\n",
    "        with open(save_name + '_train_hist.pickle', 'rb') as hist_inf:\n",
    "            history = pickle.load(hist_inf)\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",
    "        hist = nn_model.fit(X_train, y_train, epochs = model_epochs, batch_size = model_batch_size)\n",
    "        history = hist.history\n",
    "    \n",
    "    #training set\n",
    "    print('Training loss: {:.4f}\\n'.format(history['loss'][-1]))\n",
    "    \n",
    "    #test set    \n",
    "    loss, accuracy = nn_model.evaluate(X_test, y_test)\n",
    "    print('Test accuracy: {:.2f}'.format(accuracy*100))\n",
    "    print('Test loss: {:.4f}'.format(loss))\n",
    "    \n",
    "    if save_name and not os.path.exists(save_name):\n",
    "        print('Saving model at:', save_name)\n",
    "        nn_model.save(save_name)\n",
    "        with open(save_name + '_train_hist.pickle', 'wb') as hist_outf:\n",
    "            pickle.dump(history, hist_outf)\n",
    "    \n",
    "    return nn_model, history, accuracy, loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading existing model\n",
      "Training loss: 0.0493\n",
      "\n",
      "30000/30000 [==============================] - 2s 54us/sample - loss: 0.0502 - accuracy: 0.1053\n",
      "Test accuracy: 10.53\n",
      "Test loss: 0.0502\n"
     ]
    }
   ],
   "source": [
    "test_layers = [10,24,1]\n",
    "#model_name = os.path.join(path_data, 'nn_2000_queries_top20_1')\n",
    "model_name = os.path.join(path_data, 'nn_2000_queries_top20_layers_opt_1') #this one tested best in notebook 3\n",
    "\n",
    "# X_tanimoto_top20 = nn_training_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "# y_tanimoto_top20 = nn_training_found_matches_s2v_2dec['similarity']\n",
    "# X_tanimoto_top20_test = nn_testing_found_matches_s2v_2dec.drop(['similarity', 'label'], axis = 1)\n",
    "# y_tanimoto_top20_test = nn_testing_found_matches_s2v_2dec['similarity']\n",
    "\n",
    "nn_2000_queries_top20_1 = train_nn(X_train, y_train, X_test, y_test, layers = test_layers,\n",
    "         model_loss = 'mean_squared_error', activations = 'relu', last_activation = None,\n",
    "         model_epochs = 50, model_batch_size = 16, save_name = model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from notebook 3c\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "\n",
    "best_model_name = os.path.join(path_data, 'nn_2000_queries_trimming_simple_10.hdf5')\n",
    "best_model = load_model(best_model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30000/30000 [==============================] - 1s 43us/sample - loss: 0.0488 - mae: 0.1706\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.04878429824113846, 0.17055081]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying test set on model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_tested_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'nn_tested_found_matches_s2v_2dec.pickle')\n",
    "#query and library of the test data can be found (as a tuple of (query, library)) in testing_query_library_s2v_2dec.pickle\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_tested_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_tested_found_matches_s2v_2dec = []\n",
    "    for ID in range(len(nn_prep_testing_found_matches_s2v_2dec)):\n",
    "        current_match = nn_prep_testing_found_matches_s2v_2dec[ID].copy()\n",
    "        predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "        predictions = nn_2000_queries_top20_1[0].predict(predict_on)\n",
    "        current_match['sim_predictions'] = predictions\n",
    "        nn_tested_found_matches_s2v_2dec.append(current_match)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_tested_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joris\\Documents\\eScience_data\\data\\nn_best_model_found_matches_s2v_2dec.pickle\n"
     ]
    }
   ],
   "source": [
    "outfile = os.path.join(path_data, 'nn_best_model_found_matches_s2v_2dec.pickle')\n",
    "#query and library of the test data can be found (as a tuple of (query, library)) in testing_query_library_s2v_2dec.pickle\n",
    "print(outfile)\n",
    "if os.path.exists(outfile):\n",
    "    with open(outfile, 'rb') as inf:\n",
    "        nn_best_model_found_matches_s2v_2dec = pickle.load(inf)\n",
    "else:\n",
    "    nn_best_model_found_matches_s2v_2dec = []\n",
    "    for ID in range(len(nn_prep_testing_found_matches_s2v_2dec)):\n",
    "        current_match = nn_prep_testing_found_matches_s2v_2dec[ID].copy()\n",
    "        predict_on = current_match.drop(['label', 'similarity'], axis = 1)\n",
    "        predictions = best_model.predict(predict_on)\n",
    "        current_match['sim_predictions'] = predictions\n",
    "        nn_best_model_found_matches_s2v_2dec.append(current_match)\n",
    "    with open(outfile, 'wb') as outf:\n",
    "        pickle.dump(nn_best_model_found_matches_s2v_2dec, outf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "      <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>similarity</th>\n",
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       "      <th>1639</th>\n",
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       "      <td>0.109013</td>\n",
       "      <td>1.326203e-39</td>\n",
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       "      <th>3005</th>\n",
       "      <td>0.000169</td>\n",
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       "      <td>0.000169</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.471357</td>\n",
       "      <td>0</td>\n",
       "      <td>0.483736</td>\n",
       "      <td>0.093364</td>\n",
       "      <td>2.963293e-19</td>\n",
       "      <td>0.440107</td>\n",
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       "      <th>4364</th>\n",
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       "      <td>0.016285</td>\n",
       "      <td>0.549896</td>\n",
       "      <td>0.456992</td>\n",
       "      <td>0</td>\n",
       "      <td>0.635606</td>\n",
       "      <td>0.073968</td>\n",
       "      <td>2.023195e-07</td>\n",
       "      <td>0.477650</td>\n",
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       "    <tr>\n",
       "      <th>26600</th>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.022880</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.439868</td>\n",
       "      <td>0</td>\n",
       "      <td>0.670978</td>\n",
       "      <td>0.085979</td>\n",
       "      <td>1.187375e-09</td>\n",
       "      <td>0.442850</td>\n",
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       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>0.001208</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.439756</td>\n",
       "      <td>0</td>\n",
       "      <td>0.383291</td>\n",
       "      <td>0.066811</td>\n",
       "      <td>9.974635e-17</td>\n",
       "      <td>0.449107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "1639       0.000052        0.251948          0.000107            0.440418   \n",
       "3005       0.000169        0.070000          0.000169            0.070000   \n",
       "4364       0.016219        0.353010          0.016285            0.549896   \n",
       "26600      0.000075        0.135100          0.022880            0.195643   \n",
       "6633       0.001208        0.070000          0.001212            0.135100   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass      mass_sim  \\\n",
       "1639    0.481348      0    0.618732     0.109013  1.326203e-39   \n",
       "3005    0.471357      0    0.483736     0.093364  2.963293e-19   \n",
       "4364    0.456992      0    0.635606     0.073968  2.023195e-07   \n",
       "26600   0.439868      0    0.670978     0.085979  1.187375e-09   \n",
       "6633    0.439756      0    0.383291     0.066811  9.974635e-17   \n",
       "\n",
       "       sim_predictions  \n",
       "1639          0.450711  \n",
       "3005          0.440107  \n",
       "4364          0.477650  \n",
       "26600         0.442850  \n",
       "6633          0.449107  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_tested_found_matches_s2v_2dec[0].iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "      <th>label</th>\n",
       "      <th>similarity</th>\n",
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       "      <th>1639</th>\n",
       "      <td>0.000052</td>\n",
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       "      <td>0.000107</td>\n",
       "      <td>0.440418</td>\n",
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       "      <td>1.326203e-39</td>\n",
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       "      <th>3005</th>\n",
       "      <td>0.000169</td>\n",
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       "      <td>2.963293e-19</td>\n",
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       "      <td>0.073968</td>\n",
       "      <td>2.023195e-07</td>\n",
       "      <td>0.514977</td>\n",
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       "      <th>26600</th>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.022880</td>\n",
       "      <td>0.195643</td>\n",
       "      <td>0.439868</td>\n",
       "      <td>0</td>\n",
       "      <td>0.670978</td>\n",
       "      <td>0.085979</td>\n",
       "      <td>1.187375e-09</td>\n",
       "      <td>0.469464</td>\n",
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       "    <tr>\n",
       "      <th>6633</th>\n",
       "      <td>0.001208</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.135100</td>\n",
       "      <td>0.439756</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "1639       0.000052        0.251948          0.000107            0.440418   \n",
       "3005       0.000169        0.070000          0.000169            0.070000   \n",
       "4364       0.016219        0.353010          0.016285            0.549896   \n",
       "26600      0.000075        0.135100          0.022880            0.195643   \n",
       "6633       0.001208        0.070000          0.001212            0.135100   \n",
       "\n",
       "       s2v_score  label  similarity  parent_mass      mass_sim  \\\n",
       "1639    0.481348      0    0.618732     0.109013  1.326203e-39   \n",
       "3005    0.471357      0    0.483736     0.093364  2.963293e-19   \n",
       "4364    0.456992      0    0.635606     0.073968  2.023195e-07   \n",
       "26600   0.439868      0    0.670978     0.085979  1.187375e-09   \n",
       "6633    0.439756      0    0.383291     0.066811  9.974635e-17   \n",
       "\n",
       "       sim_predictions  \n",
       "1639          0.482210  \n",
       "3005          0.500722  \n",
       "4364          0.514977  \n",
       "26600         0.469464  \n",
       "6633          0.430324  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_best_model_found_matches_s2v_2dec[0].iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combine with rf score\n",
    "nn_scores_rf_nn_found_matches_s2v_2dec = []\n",
    "for ID in range(len(nn_best_model_found_matches_s2v_2dec)):\n",
    "    current_match = nn_best_model_found_matches_s2v_2dec[ID].copy()\n",
    "    current_match[\"rf_predictions\"] = nn_rf_predict_found_matches_s2v_2dec[ID][\"rf_predictions\"]\n",
    "    nn_scores_rf_nn_found_matches_s2v_2dec.append(current_match)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>s2v_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22101</th>\n",
       "      <td>0.928457</td>\n",
       "      <td>15</td>\n",
       "      <td>0.928457</td>\n",
       "      <td>15</td>\n",
       "      <td>0.909346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20538</th>\n",
       "      <td>0.856661</td>\n",
       "      <td>11</td>\n",
       "      <td>0.856661</td>\n",
       "      <td>11</td>\n",
       "      <td>0.859138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22454</th>\n",
       "      <td>0.857507</td>\n",
       "      <td>11</td>\n",
       "      <td>0.857507</td>\n",
       "      <td>11</td>\n",
       "      <td>0.674490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1366</th>\n",
       "      <td>0.389183</td>\n",
       "      <td>11</td>\n",
       "      <td>0.389183</td>\n",
       "      <td>11</td>\n",
       "      <td>0.584506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5998</th>\n",
       "      <td>0.336160</td>\n",
       "      <td>1</td>\n",
       "      <td>0.337487</td>\n",
       "      <td>3</td>\n",
       "      <td>0.303197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11071</th>\n",
       "      <td>0.009762</td>\n",
       "      <td>1</td>\n",
       "      <td>0.079068</td>\n",
       "      <td>3</td>\n",
       "      <td>0.178408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84725</th>\n",
       "      <td>0.000692</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002880</td>\n",
       "      <td>4</td>\n",
       "      <td>0.178206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7971</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.178070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19586</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.178032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72734</th>\n",
       "      <td>0.086645</td>\n",
       "      <td>3</td>\n",
       "      <td>0.086645</td>\n",
       "      <td>3</td>\n",
       "      <td>0.177984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cosine_score  cosine_matches  mod_cosine_score  mod_cosine_matches  \\\n",
       "22101      0.928457              15          0.928457                  15   \n",
       "20538      0.856661              11          0.856661                  11   \n",
       "22454      0.857507              11          0.857507                  11   \n",
       "1366       0.389183              11          0.389183                  11   \n",
       "5998       0.336160               1          0.337487                   3   \n",
       "...             ...             ...               ...                 ...   \n",
       "11071      0.009762               1          0.079068                   3   \n",
       "84725      0.000692               1          0.002880                   4   \n",
       "7971       0.000000               0          0.000000                   0   \n",
       "19586      0.000000               0          0.000000                   0   \n",
       "72734      0.086645               3          0.086645                   3   \n",
       "\n",
       "       s2v_score  \n",
       "22101   0.909346  \n",
       "20538   0.859138  \n",
       "22454   0.674490  \n",
       "1366    0.584506  \n",
       "5998    0.303197  \n",
       "...          ...  \n",
       "11071   0.178408  \n",
       "84725   0.178206  \n",
       "7971    0.178070  \n",
       "19586   0.178032  \n",
       "72734   0.177984  \n",
       "\n",
       "[200 rows x 5 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nn_scores_rf_nn_found_matches_s2v_2dec[2]\n",
    "new_and_unique2_found_matches_s2v_2dec[2].sort_values(\"s2v_score\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making well known performance plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing if top hit is above 0.6 similarity\n",
    "#look at top20\n",
    "test_tan_nn_top20 = []\n",
    "test_sim_mcos = []\n",
    "test_sim_s2v = []\n",
    "ideal_case_top20 = []\n",
    "test_tan_rf_top20 = []\n",
    "test_nn_rf_top20 = []\n",
    "\n",
    "pred_thresh = 0.6\n",
    "mass_sim_thresh = 0.000705\n",
    "restr_nn = False # do mass+spec2vec restricitons on nn models\n",
    "highest_pred = False # take highest prediction instead of first prediction over threshold\n",
    "threshold = 0.4\n",
    "topn = 20 #only look at top 20\n",
    "sim_threshold = 0.6\n",
    "mc_matches_thresh = 0.35 # mod cosine matches threshold corresponds to min match 6\n",
    "\n",
    "for ID in range(len(nn_scores_rf_nn_found_matches_s2v_2dec)):\n",
    "    if ID in val_set:\n",
    "        continue\n",
    "    # mass + mod cosine\n",
    "    current_match = nn_scores_rf_nn_found_matches_s2v_2dec[ID]\\\n",
    "                        .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",
    "                                  & (current_match['mod_cosine_matches'] > mc_matches_thresh)]\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.append(best_bet)\n",
    "\n",
    "    # Scenario 1: mass + Spec2Vec + threshold\n",
    "    current_match = nn_scores_rf_nn_found_matches_s2v_2dec[ID]\\\n",
    "                        .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.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['sim_predictions'] >= pred_thresh)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['sim_predictions'] >= pred_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",
    "        if highest_pred:\n",
    "            #look at highest predicted above prediction threshold\n",
    "            sim = df_select.iloc[np.argmax(df_select['sim_predictions'])]['similarity']\n",
    "        else:\n",
    "            #look at similarity of top hit\n",
    "            sim = df_select.iloc[0]['similarity']\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_tan_nn_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",
    "    # Scenario RF: filter with trained random forest model\n",
    "    if not restr_nn:\n",
    "        df_select = current_match[(current_match['rf_predictions'] >= pred_thresh)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['rf_predictions'] >= pred_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",
    "        if highest_pred:\n",
    "            #look at highest predicted above prediction threshold\n",
    "            sim = df_select.iloc[np.argmax(df_select['rf_predictions'])]['similarity']\n",
    "        else:\n",
    "            #look at similarity of top hit\n",
    "            sim = df_select.iloc[0]['similarity']\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_tan_rf_top20.append(best_bet)\n",
    "    \n",
    "    # Scenario NN+RF: filter with both models\n",
    "    if not restr_nn:\n",
    "        df_select = current_match[(current_match['rf_predictions'] >= pred_thresh)\n",
    "                                      & (current_match['sim_predictions'] >= pred_thresh)]\n",
    "    else:\n",
    "        df_select = current_match[(current_match['mass_sim'] >= mass_sim_thresh)\n",
    "                                      & (current_match['rf_predictions'] >= pred_thresh)\n",
    "                                      & (current_match['s2v_score'] > threshold)\n",
    "                                      & (current_match['sim_predictions'] >= pred_thresh)]\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",
    "        if highest_pred:\n",
    "            #look at highest predicted above prediction threshold\n",
    "            sim = df_select.iloc[np.argmax(df_select['rf_predictions'])]['similarity']\n",
    "        else:\n",
    "            #look at similarity of top hit\n",
    "            sim = df_select.iloc[0]['similarity']\n",
    "        best_bet = 1 * (sim >= sim_threshold)\n",
    "    else:\n",
    "        best_bet = -1 # meaning: not found\n",
    "    test_nn_rf_top20.append(best_bet)\n",
    "    \n",
    "    \n",
    "test_tan_nn_top20_arr = np.array(test_tan_nn_top20)\n",
    "test_sim_mcos_arr = np.array(test_sim_mcos)\n",
    "test_sim_s2v_arr = np.array(test_sim_s2v)\n",
    "ideal_case_top20_arr = np.array(ideal_case_top20)\n",
    "test_tan_rf_top20_arr = np.array(test_tan_rf_top20)\n",
    "test_nn_rf_top20_arr = np.array(test_nn_rf_top20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
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     "data": {
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       "Text(0.5, 1.0, 'Compare if top hit is >= 0.6 tanimoto similarity\\n(2000 query dataset)')"
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     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from math import log\n",
    "from IPython.display import set_matplotlib_formats\n",
    "set_matplotlib_formats('svg')\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "da_diff = log(mass_sim_thresh, 0.93)\n",
    "\n",
    "if restr_nn:\n",
    "    ylabels = ['mass < {:.1f}Da + mod cosine > {} + min match 6'.format(da_diff, threshold),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {}'.format(da_diff, threshold),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {} + NN tanimoto > {}'.format(da_diff, threshold, pred_thresh),\n",
    "               'ideal case Spec2Vec > {}'.format(threshold),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {} + RF tanimoto > {}'.format(da_diff, threshold, pred_thresh),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {} + RF tanimoto > {} + NN tanimoto > {}'.format(da_diff, threshold, pred_thresh, pred_thresh),]\n",
    "else:\n",
    "    ylabels = ['mass < {:.1f}Da + mod cosine > {} + min match 6'.format(da_diff, threshold),\n",
    "               'mass < {:.1f}Da + Spec2Vec > {}'.format(da_diff, threshold),\n",
    "               'NN tanimoto > {}'.format(pred_thresh),\n",
    "               'ideal case Spec2Vec > {}'.format(threshold),\n",
    "               'RF tanimoto > {}'.format(pred_thresh),\n",
    "               'RF tanimoto > {} + NN tanimoto > {}'.format(pred_thresh, pred_thresh),]\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 == 1), np.sum(test_sim_mcos_arr == 0), np.sum(test_sim_mcos_arr == -1)],\n",
    "                 [np.sum(test_sim_s2v_arr == 1), np.sum(test_sim_s2v_arr == 0), np.sum(test_sim_s2v_arr == -1)],\n",
    "                 [np.sum(test_tan_nn_top20_arr == 1), np.sum(test_tan_nn_top20_arr == 0), np.sum(test_tan_nn_top20_arr == -1)],\n",
    "                 [np.sum(ideal_case_top20_arr == 1), np.sum(ideal_case_top20_arr == 0), np.sum(ideal_case_top20_arr == -1)],\n",
    "                 [np.sum(test_tan_rf_top20_arr == 1), np.sum(test_tan_rf_top20_arr == 0), np.sum(test_tan_rf_top20_arr == -1)],\n",
    "                 [np.sum(test_nn_rf_top20_arr == 1), np.sum(test_nn_rf_top20_arr == 0), np.sum(test_nn_rf_top20_arr == -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', figsize=(6, 4))\n",
    "\n",
    "for p in ax.patches:\n",
    "    width, height = p.get_width(), p.get_height()\n",
    "    x, y = p.get_xy()\n",
    "    if width != 0:\n",
    "        ax.text(x+width/2, \n",
    "                y+height/2, \n",
    "                '{:.0f}'.format(width), \n",
    "                horizontalalignment='center', \n",
    "                verticalalignment='center', size=10)\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 >= {} tanimoto similarity\\n(2000 query dataset)'.format(sim_threshold))\n",
    "#plt.figure(figsize=(5,5))\n",
    "# plt.savefig(os.path.join(path_data, \"Plots\", \"Performance_plot_ML_2000_queries.svg\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Redo some additional plots from earlier notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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))"
   ]
  }
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