ASIACCS-Eval-CameraReady.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42b0a1b0",
   "metadata": {},
   "source": [
    "# Evaluation of classifiers for ACM ASIACC paper\n",
    "\n",
    "128-feature vector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27319649",
   "metadata": {},
   "source": [
    "## Loading data and selecting features of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8327cd7c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total records: 500925\n",
      "Number of used features: 143\n",
      "No. of available unused features: 34\n"
     ]
    }
   ],
   "source": [
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "import inspect\n",
    "\n",
    "#phishing = pq.read_table('floor/phishing_2307.parquet')\n",
    "phishing = pq.read_table('floor/phishing_2311.parquet')\n",
    "benign = pq.read_table('floor/benign_2310.parquet')\n",
    "\n",
    "from transformers.drop_nontrain import drop_nontrain_table as drop_nontrain\n",
    "phishing = drop_nontrain(phishing)\n",
    "benign = drop_nontrain(benign)\n",
    "\n",
    "# realign schemas (parquet files save in nonsense orders)\n",
    "benign = benign.cast(phishing.schema)\n",
    "\n",
    "# concatentate tables\n",
    "data = pa.concat_tables([phishing, benign])\n",
    "df = data.to_pandas()\n",
    "\n",
    "print(f\"Total records: {len(df)}\")\n",
    "\n",
    "from transformers.cast_timestamp import cast_timestamp\n",
    "df = cast_timestamp(df)\n",
    "\n",
    "used_features = [\n",
    "\n",
    "    # IP  ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"ip_mean_average_rtt\",\n",
    "    \"ip_entropy\",\n",
    "    \"ip_count\",\n",
    "    #\"ip_v4_count\", \"ip_v6_count\", # Moved RDAP and RENAMED to rdap_ip\n",
    "    \n",
    "    # Newly added for ACM ASIACCS\n",
    "    \"ip_v4_ratio\",\n",
    "    #\"ip_a_aaaa_to_all_ratio\", <--- not useful for this dataset\n",
    "    \"ip_as_address_entropy\",\n",
    "    \"ip_asn_entropy\",\n",
    "    \"ip_distinct_as_count\",\n",
    "    \n",
    "    # DNS  ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"dns_A_count\",\n",
    "    \"dns_AAAA_count\",\n",
    "    \"dns_MX_count\",\n",
    "    \"dns_NS_count\",\n",
    "    #\"dns_SOA_count\", <-- REMOVED, very low effect -------------------------------------\n",
    "    \"dns_TXT_count\",\n",
    "    \"dns_CNAME_count\",\n",
    "    \"dns_soa_primary_ns_len\",\n",
    "    \"dns_soa_primary_ns_level\", # renamed\n",
    "    \"dns_soa_primary_ns_digit_count\",\n",
    "    \"dns_soa_primary_ns_entropy\",\n",
    "    \"dns_soa_email_len\",\n",
    "    \"dns_soa_email_level\", # renamed\n",
    "    \"dns_soa_email_digit_count\",\n",
    "    \"dns_soa_email_entropy\",\n",
    "    # \"dns_soa_serial\", <-- Doesn't convey any kind of comparative information\n",
    "    \"dns_soa_refresh\",\n",
    "    \"dns_soa_retry\",\n",
    "    \"dns_soa_expire\",\n",
    "    \"dns_mx_avg_len\",\n",
    "    \"dns_mx_avg_entropy\",\n",
    "    \"dns_domain_name_in_mx\",\n",
    "    \"dns_txt_spf_exists\",\n",
    "    \"dns_txt_avg_entropy\",\n",
    "    \"dns_txt_dkim_exists\",\n",
    "    \n",
    "    \"dns_zone_level\",\n",
    "    \"dns_zone_digit_count\",\n",
    "    \"dns_zone_len\",\n",
    "    \"dns_zone_entropy\",\n",
    "    \n",
    "    # Newly added for ACM ASIACCS\n",
    "    \"dns_resolved_record_types\",\n",
    "    \"dns_has_dnskey\",\n",
    "    \"dns_dnssec_score\",\n",
    "    \"dns_ttl_avg\",\n",
    "    \"dns_ttl_stdev\",\n",
    "    \"dns_ttl_low\",\n",
    "    \"dns_ttl_mid\",\n",
    "    \"dns_ttl_distinct_count\",\n",
    "    \"dns_soa_min_ttl\", # why is this not in NOMS?!\n",
    "    \"dns_txt_external_verification_score\",\n",
    "    \"dns_txt_dmarc_exists\",\n",
    "    \n",
    "    \n",
    "    # TLS  ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"tls_broken_chain\",\n",
    "    \"tls_expired_chain\",\n",
    "    \"tls_is_self_signed\", # <--- NEW (23-12-02)\n",
    "    \"tls_root_authority_hash\", # <--- NEW (23-12-02)\n",
    "    \"tls_leaf_authority_hash\", # <--- NEW (23-12-02)\n",
    "    \"tls_total_extension_count\",\n",
    "    \"tls_critical_extensions\",\n",
    "    \"tls_with_policies_crt_count\",\n",
    "    \"tls_percentage_crt_with_policies\",\n",
    "    \"tls_x509_anypolicy_crt_count\",\n",
    "    \"tls_iso_policy_crt_count\",\n",
    "    \"tls_joint_isoitu_policy_crt_count\",\n",
    "    # \"tls_iso_policy_oid\", <-- Basically a random value, doesn't make much sense when collected like this; policy OIDs need more research\n",
    "    # \"tls_isoitu_policy_oid\", <-- same\n",
    "    #\"tls_unknown_policy_crt_count\", <-- Abandoned, no useful values\n",
    "    \"tls_root_cert_validity_len\",\n",
    "    \"tls_leaf_cert_validity_len\",\n",
    "    \"tls_chain_len\",\n",
    "    #\"tls_root_cert_lifetime\", <---- NO!\n",
    "    #\"tls_leaf_cert_lifetime\", <---- NO!\n",
    "    \"tls_subject_count\",\n",
    "    \"tls_unique_SLD_count\",\n",
    "    \"tls_server_auth_crt_count\",\n",
    "    \"tls_client_auth_crt_count\",\n",
    "    \"tls_CA_certs_in_chain_ratio\",\n",
    "    \"tls_common_name_count\",\n",
    "    \n",
    "    # Newly added for ACM ASIACCS\n",
    "    #\"tls_has_tls\", , <-- REMOVED, very low effect -------------------------------------\n",
    "    \"tls_negotiated_version_id\",\n",
    "    \"tls_negotiated_cipher_id\",\n",
    "    \n",
    "    # LEX ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"lex_name_len\",\n",
    "    #\"lex_digit_count\", <-- abandoned, almost the same as \"lex_sub_digit_ratio\"\n",
    "    \"lex_has_digit\",\n",
    "    \"lex_phishing_keyword_count\",\n",
    "    #\"lex_vowel_count\", <-- moved to sub\n",
    "    #\"lex_underscore_hyphen_count\", <--- same as nonalnum\n",
    "    \"lex_consecutive_chars\",\n",
    "    \"lex_tld_len\",\n",
    "    \"lex_tld_abuse_score\", # *************************************************************\n",
    "    \"lex_tld_hash\", # *************************************************************\n",
    "    \n",
    "    \"lex_sld_len\",\n",
    "    \"lex_sld_norm_entropy\", # <-- newly added feature on 23-09-29\n",
    "    # ============================= New SLD features 23-11\n",
    "    \"lex_sld_phishing_keyword_count\",\n",
    "    #\"lex_sld_digit_count\",\n",
    "    #\"lex_sld_digit_ratio\",\n",
    "    \"lex_sld_vowel_count\",\n",
    "    \"lex_sld_vowel_ratio\",\n",
    "    \"lex_sld_consonant_count\",\n",
    "    \"lex_sld_consonant_ratio\",\n",
    "    \"lex_sld_non_alphanum_count\",\n",
    "    \"lex_sld_non_alphanum_ratio\",\n",
    "    \"lex_sld_hex_count\",\n",
    "    \"lex_sld_hex_ratio\",\n",
    "    # =============================      \n",
    "    \n",
    "    \"lex_sub_count\",\n",
    "    \"lex_stld_unique_char_count\",\n",
    "    \"lex_begins_with_digit\",\n",
    "    \"lex_www_flag\",\n",
    "    \"lex_sub_max_consonant_len\",\n",
    "    \"lex_sub_norm_entropy\",\n",
    "    \"lex_sub_digit_count\",\n",
    "    \"lex_sub_digit_ratio\",\n",
    "    \"lex_sub_consonant_ratio\",\n",
    "    \"lex_sub_non_alphanum_ratio\",\n",
    "    \"lex_sub_hex_ratio\",\n",
    "\n",
    "    #\"lex_bigram_matches\",  # OLD NAME\n",
    "    #\"lex_trigram_matches\", # OLD NAME\n",
    "    \"lex_phishing_bigram_matches\",  # NEW NAME\n",
    "    \"lex_phishing_trigram_matches\", # NEW NAME\n",
    "    \"lex_phishing_tetragram_matches\", # *************************************************************\n",
    "    \"lex_phishing_pentagram_matches\", # *************************************************************\n",
    "    \"lex_sub_vowel_count\",\n",
    "    \"lex_sub_vowel_ratio\",\n",
    "    \"lex_sub_consonant_count\",\n",
    "    \"lex_sub_non_alphanum_count\",\n",
    "    \"lex_sub_hex_count\",\n",
    "    \"lex_avg_part_len\",\n",
    "    \"lex_stdev_part_lens\",\n",
    "    \"lex_longest_part_len\",\n",
    "    \"lex_shortest_sub_len\",\n",
    "    \n",
    "    # RDAP ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"rdap_registration_period\",\n",
    "    \"rdap_domain_age\",\n",
    "    \"rdap_time_from_last_change\",\n",
    "    \"rdap_domain_active_time\",\n",
    "    \"rdap_has_dnssec\",\n",
    "    \"rdap_ip_v4_count\", \"rdap_ip_v6_count\", # <-- used for NOMS, but now prefixed with rdap_\n",
    "    \n",
    "    # Newly added for ACM ASIACCS\n",
    "    \"rdap_registrar_name_len\",\n",
    "    \"rdap_registrar_name_entropy\",\n",
    "    \"rdap_registrar_name_hash\",\n",
    "    \"rdap_registrant_name_len\",\n",
    "    \"rdap_registrant_name_entropy\",\n",
    "    \"rdap_admin_name_len\",\n",
    "    \"rdap_admin_name_entropy\",\n",
    "    \"rdap_admin_email_len\",\n",
    "    \"rdap_admin_email_entropy\",\n",
    "    \n",
    "    \"rdap_ip_shortest_v4_prefix_len\",\n",
    "    \"rdap_ip_longest_v4_prefix_len\",\n",
    "    \"rdap_ip_shortest_v6_prefix_len\",\n",
    "    \"rdap_ip_longest_v6_prefix_len\",\n",
    "    \n",
    "    \"rdap_ip_avg_admin_name_len\",\n",
    "    \"rdap_ip_avg_admin_name_entropy\",\n",
    "    \"rdap_ip_avg_admin_email_len\",\n",
    "    \"rdap_ip_avg_admin_email_entropy\",\n",
    "    \n",
    "    # GEO ===============================\n",
    "    # Used in the original NOMS paper\n",
    "    \"geo_countries_count\",\n",
    "    \"geo_continent_hash\",\n",
    "    \"geo_countries_hash\",\n",
    "    \n",
    "    # Newly added for ACM ASIACCS\n",
    "    \"geo_lat_stdev\", # <- Puvodne to pridal Adam, ale pak jsme dali pryc, ze to budem resit jinak.\n",
    "    \"geo_lon_stdev\",  #    Ale muze byt helpful pro identifikaci, jestli lokality jsou blizko sebe.    \n",
    "    #\"geo_min_lat\", # not very useful\n",
    "    #\"geo_max_lat\", # not very useful\n",
    "    #\"geo_min_lon\", # not very useful\n",
    "    #\"geo_max_lon\", # not very useful\n",
    "    \"geo_mean_lat\", # *************************************************************\n",
    "    \"geo_mean_lon\", # *************************************************************\n",
    "    #\"geo_lat_range\", # not very useful\n",
    "    #\"geo_lon_range\", # not very useful\n",
    "    \"geo_centroid_lat\", # *************************************************************\n",
    "    \"geo_centroid_lon\", # *************************************************************\n",
    "    #\"geo_estimated_area\",  # not very useful \n",
    "]\n",
    "\n",
    "\n",
    "print(\"Number of used features: \" + str(len(used_features)))\n",
    "\n",
    "# NUMBER OF AVAILABLE AND UNUSED FEATURES\n",
    "missing_columns = set(df.columns) - set(used_features) - set([\"label\"])\n",
    "print(\"No. of available unused features: \" + str(len(missing_columns)))\n",
    "\n",
    "df = df[[\"label\", *used_features]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "913738d6",
   "metadata": {},
   "source": [
    "## Subsampling entire dataset (for testing purposes only!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "28b11c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subsample = 0.15 # 1.0 means no subsample\n",
    "subsample = 1.0 # 1.0 means no subsample\n",
    "\n",
    "if subsample < 1.0:\n",
    "    df = df.sample(frac=subsample)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efb8fb7e",
   "metadata": {},
   "source": [
    "## PRNG seed generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c3e752e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prng_create_seeds(base_seed=None, n=1):\n",
    "    rng = np.random.default_rng()\n",
    "    if base_seed is not None:\n",
    "        rng = np.random.default_rng(base_seed)\n",
    "    return rng.integers(0, 65535, size=n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24a01143",
   "metadata": {},
   "source": [
    "## Supressing unwanted warnings (futures etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4b30d3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=FutureWarning, module='sklearn.*')\n",
    "warnings.filterwarnings('ignore', category=FutureWarning, module='xgboost.*')\n",
    "warnings.filterwarnings('ignore', message=\"Series.__getitem__ treating keys as positions is deprecated.*\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b579353e",
   "metadata": {},
   "source": [
    "## Shortening feature names (for paper purposes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2b10e171",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>ip_mean_average_rtt</th>\n",
       "      <th>ip_entropy</th>\n",
       "      <th>ip_count</th>\n",
       "      <th>ip_v4_ratio</th>\n",
       "      <th>ip_as_address_entropy</th>\n",
       "      <th>ip_asn_entropy</th>\n",
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       "      <th>dns_A_count</th>\n",
       "      <th>dns_AAAA_count</th>\n",
       "      <th>...</th>\n",
       "      <th>rdap_ip_avg_admin_email_ent</th>\n",
       "      <th>geo_countries_count</th>\n",
       "      <th>geo_continent_hash</th>\n",
       "      <th>geo_countries_hash</th>\n",
       "      <th>geo_lat_stdev</th>\n",
       "      <th>geo_lon_stdev</th>\n",
       "      <th>geo_mean_lat</th>\n",
       "      <th>geo_mean_lon</th>\n",
       "      <th>geo_centroid_lat</th>\n",
       "      <th>geo_centroid_lon</th>\n",
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       "      <td>-0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>55.73860</td>\n",
       "      <td>37.6068</td>\n",
       "      <td>83.60790</td>\n",
       "      <td>56.41020</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500920</th>\n",
       "      <td>benign_2310:unknown</td>\n",
       "      <td>43.488625</td>\n",
       "      <td>4.240224</td>\n",
       "      <td>16</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.106891</td>\n",
       "      <td>1.061278</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.157633</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>37.75100</td>\n",
       "      <td>-97.8220</td>\n",
       "      <td>56.62650</td>\n",
       "      <td>-146.73300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500921</th>\n",
       "      <td>benign_2310:unknown</td>\n",
       "      <td>3.996786</td>\n",
       "      <td>4.186138</td>\n",
       "      <td>14</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.900424</td>\n",
       "      <td>0.734530</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190507</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>39.10270</td>\n",
       "      <td>-94.5778</td>\n",
       "      <td>58.65405</td>\n",
       "      <td>-141.86670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500922</th>\n",
       "      <td>benign_2310:unknown</td>\n",
       "      <td>4.329750</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.149881</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>86</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>35.68930</td>\n",
       "      <td>139.6899</td>\n",
       "      <td>53.53395</td>\n",
       "      <td>209.53485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500923</th>\n",
       "      <td>benign_2310:unknown</td>\n",
       "      <td>16.280900</td>\n",
       "      <td>4.643856</td>\n",
       "      <td>10</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.043856</td>\n",
       "      <td>0.721928</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179189</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>1.19615</td>\n",
       "      <td>2.3587</td>\n",
       "      <td>51.32345</td>\n",
       "      <td>11.0482</td>\n",
       "      <td>76.38710</td>\n",
       "      <td>15.39295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500924</th>\n",
       "      <td>benign_2310:unknown</td>\n",
       "      <td>12.666600</td>\n",
       "      <td>2.918296</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.918296</td>\n",
       "      <td>1.760964</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.105152</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>38.57730</td>\n",
       "      <td>-92.2443</td>\n",
       "      <td>57.86595</td>\n",
       "      <td>-138.36645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500925 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      label  ip_mean_average_rtt  ip_entropy  ip_count  \\\n",
       "0        misp_2310:phishing             0.000000   -0.000000         1   \n",
       "1        misp_2310:phishing            -1.000000   -0.000000         0   \n",
       "2        misp_2310:phishing             0.000000   -0.000000         1   \n",
       "3        misp_2310:phishing            -1.000000   -0.000000         0   \n",
       "4        misp_2310:phishing            -1.000000   -0.000000         0   \n",
       "...                     ...                  ...         ...       ...   \n",
       "500920  benign_2310:unknown            43.488625    4.240224        16   \n",
       "500921  benign_2310:unknown             3.996786    4.186138        14   \n",
       "500922  benign_2310:unknown             4.329750    4.000000        12   \n",
       "500923  benign_2310:unknown            16.280900    4.643856        10   \n",
       "500924  benign_2310:unknown            12.666600    2.918296        10   \n",
       "\n",
       "        ip_v4_ratio  ip_as_address_entropy  ip_asn_entropy  \\\n",
       "0               1.0              -0.000000       -0.000000   \n",
       "1               0.0                    NaN             NaN   \n",
       "2               1.0              -0.000000       -0.000000   \n",
       "3               0.0                    NaN             NaN   \n",
       "4               0.0                    NaN             NaN   \n",
       "...             ...                    ...             ...   \n",
       "500920          1.0               4.106891        1.061278   \n",
       "500921          0.5               3.900424        0.734530   \n",
       "500922          1.0               2.000000       -0.000000   \n",
       "500923          0.5               3.043856        0.721928   \n",
       "500924          1.0               3.918296        1.760964   \n",
       "\n",
       "        ip_distinct_as_count  dns_A_count  dns_AAAA_count  ...  \\\n",
       "0                        1.0            1               0  ...   \n",
       "1                        NaN            0               0  ...   \n",
       "2                        1.0            1               0  ...   \n",
       "3                        NaN            0               0  ...   \n",
       "4                        NaN            0               0  ...   \n",
       "...                      ...          ...             ...  ...   \n",
       "500920                   3.0            2               0  ...   \n",
       "500921                   3.0            1               1  ...   \n",
       "500922                   1.0            2               0  ...   \n",
       "500923                   2.0            1               1  ...   \n",
       "500924                   4.0            1               0  ...   \n",
       "\n",
       "        rdap_ip_avg_admin_email_ent  geo_countries_count  geo_continent_hash  \\\n",
       "0                          0.000000                    1                   3   \n",
       "1                          0.000000                    0                   0   \n",
       "2                          0.000000                    1                   0   \n",
       "3                          0.000000                    0                   0   \n",
       "4                          0.000000                    0                   0   \n",
       "...                             ...                  ...                 ...   \n",
       "500920                     0.157633                    1                   1   \n",
       "500921                     0.190507                    1                   1   \n",
       "500922                     0.149881                    1                   5   \n",
       "500923                     0.179189                    1                   3   \n",
       "500924                     0.105152                    1                   1   \n",
       "\n",
       "        geo_countries_hash  geo_lat_stdev  geo_lon_stdev  geo_mean_lat  \\\n",
       "0                      184        0.00000         0.0000      50.45220   \n",
       "1                        0        0.00000         0.0000       0.00000   \n",
       "2                      144        0.00000         0.0000      55.73860   \n",
       "3                        0        0.00000         0.0000       0.00000   \n",
       "4                        0        0.00000         0.0000       0.00000   \n",
       "...                    ...            ...            ...           ...   \n",
       "500920                 187        0.00000         0.0000      37.75100   \n",
       "500921                 187        0.00000         0.0000      39.10270   \n",
       "500922                  86        0.00000         0.0000      35.68930   \n",
       "500923                  65        1.19615         2.3587      51.32345   \n",
       "500924                 187        0.00000         0.0000      38.57730   \n",
       "\n",
       "        geo_mean_lon  geo_centroid_lat  geo_centroid_lon  \n",
       "0            30.5287          75.67830          45.79305  \n",
       "1             0.0000           0.00000           0.00000  \n",
       "2            37.6068          83.60790          56.41020  \n",
       "3             0.0000           0.00000           0.00000  \n",
       "4             0.0000           0.00000           0.00000  \n",
       "...              ...               ...               ...  \n",
       "500920      -97.8220          56.62650        -146.73300  \n",
       "500921      -94.5778          58.65405        -141.86670  \n",
       "500922      139.6899          53.53395         209.53485  \n",
       "500923       11.0482          76.38710          15.39295  \n",
       "500924      -92.2443          57.86595        -138.36645  \n",
       "\n",
       "[500925 rows x 144 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shortening = {\n",
    "    \"lex_stld_unique_char_count\": \"lex_stld_uniq_char_cnt\",\n",
    "    \"lex_sub_max_consonant_len\": \"lex_max_conson_len\",\n",
    "    \"lex_sub_non_alphanum_count\": \"lex_sub_nonalnum_count\",\n",
    "    \"lex_sub_non_alphanum_ratio\": \"lex_sub_nonalnum_ratio\",\n",
    "    \"lex_phishing_keyword_count\": \"lex_phishing_kw_count\",\n",
    "    \"lex_sld_phishing_keyword_count\": \"lex_sld_phishing_kw_count\",\n",
    "    \n",
    "    \"dns_resolved_record_types\": \"dns_resolved_rec_types\",\n",
    "    \"dns_domain_name_in_mx\": \"dns_dn_in_mx\",\n",
    "    \"dns_txt_external_verification_score\": \"dns_txt_ext_verif_score\",\n",
    "    \"dns_zone_digit_count\": \"dns_zone_digits\",\n",
    "    \"dns_soa_primary_ns_digit_count\": \"dns_soa_prim_ns_digits\",\n",
    "    \"dns_soa_primary_ns_len\" : \"dns_soa_pri_ns_len\",\n",
    "    \"dns_soa_primary_ns_level\": \"dns_soa_pri_ns_level\",\n",
    "    \"dns_soa_primary_ns_entropy\": \"dns_soa_pri_ns_entropy\",\n",
    "    \"dns_soa_email_digit_count\": \"dns_soa_email_digits\",\n",
    "    \n",
    "    \"tls_percentage_crt_with_policies\": \"tls_percentage_with_policies\",\n",
    "    \"tls_x509_anypolicy_crt_count\": \"tls_x509_anypol_crt_count\",\n",
    "    \"tls_iso_policy_crt_count\": \"tls_iso_pol_crt_count\",\n",
    "    \"tls_joint_isoitu_policy_crt_count\": \"tls_isoitu_pol_crt_count\",\n",
    "    \n",
    "    \"rdap_ip_avg_admin_name_entropy\": \"rdap_ip_avg_admin_name_ent\",\n",
    "    \"rdap_ip_avg_admin_email_entropy\": \"rdap_ip_avg_admin_email_ent\"\n",
    "}\n",
    "\n",
    "df.rename(columns=shortening, inplace=True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a8b8ab",
   "metadata": {},
   "source": [
    "# NaNs will be replaced by -1 (best results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "838425da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handle NaNs\n",
    "df.fillna(-1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f736af",
   "metadata": {},
   "source": [
    "# Train-Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3460ca64",
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import Config\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "class_map = {\"benign_2310:unknown\": 0, \"misp_2310:phishing\": 1}\n",
    "\n",
    "labels = df['label'].apply(lambda x: class_map[x]) # y vector\n",
    "features = df.drop('label', axis=1).copy() # X matrix\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "  features,\n",
    "  labels,\n",
    "  test_size=0.3,\n",
    "  random_state=42,\n",
    "  shuffle=True, \n",
    "  stratify=labels\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31198dd",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c684da21",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import datetime\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.utils import shuffle\n",
    "import json\n",
    "\n",
    "# Filter out specific FutureWarnings from scikit-learn\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"sklearn.utils.validation\")\n",
    "\n",
    "shuffle_training_set = True\n",
    "\n",
    "def run_logistic_regression_multiple_seeds(seeds, X_train, y_train, X_test, y_test,\n",
    "                                           display_cm=False, display_graphs=False, verbose=False):\n",
    "    results = {\n",
    "        \"Accuracy\": [],\n",
    "        \"Precision\": [],\n",
    "        \"Recall\": [],\n",
    "        \"F1\": [],\n",
    "        \"FPR\": []\n",
    "    }\n",
    "\n",
    "    # Scale the data\n",
    "    scaler = StandardScaler()\n",
    "    x_train_scaled = scaler.fit_transform(X_train)\n",
    "    x_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "    for seed in seeds:\n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"LOGISTICREGRESSION({now}): Seed {seed}: {inspect.currentframe().f_code.co_name}() Running...\")\n",
    "        \n",
    "        model_x_train = x_train_scaled\n",
    "        model_y_train = y_train\n",
    "            \n",
    "        if shuffle_training_set:\n",
    "            model_x_train, model_y_train = shuffle(\n",
    "                x_train_scaled,\n",
    "                y_train,\n",
    "                random_state=seed\n",
    "            )\n",
    "            \n",
    "            # 99% sumsample\n",
    "            model_x_train, _, model_y_train, _ = train_test_split(\n",
    "                model_x_train,\n",
    "                model_y_train,\n",
    "                test_size=0.01,\n",
    "                random_state=seed,\n",
    "                shuffle=True,\n",
    "                stratify=model_y_train\n",
    "            )\n",
    "        \n",
    "        params = {\n",
    "            \"C\": 30,  # Regularization parameter (default: 1.0)\n",
    "            \"penalty\": \"l2\",  # (default: l2)\n",
    "            \"max_iter\": 1130,  # Maximum number of iterations (default: 100)\n",
    "            \"solver\": \"liblinear\",\n",
    "            \"random_state\": seed\n",
    "        }\n",
    "        \n",
    "        model = LogisticRegression(**params)\n",
    "        model.fit(model_x_train, model_y_train)\n",
    "        y_pred = model.predict(x_test_scaled)\n",
    "        \n",
    "        # Compute metrics\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        precision = precision_score(y_test, y_pred)\n",
    "        recall = recall_score(y_test, y_pred)\n",
    "        f1 = f1_score(y_test, y_pred)\n",
    "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "        fpr = fp / (fp + tn)\n",
    "\n",
    "        results[\"Accuracy\"].append(accuracy)   # Only makes sense for balanced datasets\n",
    "        results[\"Precision\"].append(precision)\n",
    "        results[\"Recall\"].append(recall)\n",
    "        results[\"F1\"].append(f1)\n",
    "        results[\"FPR\"].append(fpr)\n",
    "        \n",
    "        now = datetime.datetime.now()\n",
    "        print(f\"LR55:\\n {now}, Seed: {seed}:\\n{results}\")\n",
    "        with open(f\"_LR55_{seed}.out\", 'a') as file:\n",
    "            file.write(\"LR55 {now}, Seed: {seed}:\\n\")\n",
    "            file.write(json.dumps(results, indent=4))\n",
    "            file.write(\"\\n\")\n",
    "        \n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"LOGISTICREGRESSION({now}): Seed {seed} done.\")\n",
    "    \n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e471654",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "40f87b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import inspect\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.utils import shuffle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import copy\n",
    "import datetime\n",
    "import json\n",
    "\n",
    "# Filter out specific FutureWarnings\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"sklearn.utils.validation\")\n",
    "\n",
    "shuffle_training_set = True\n",
    "\n",
    "def run_svm_multiple_seeds(seeds, X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=False):\n",
    "    results = {\n",
    "        \"Accuracy\": [],\n",
    "        \"Precision\": [],\n",
    "        \"Recall\": [],\n",
    "        \"F1\": [],\n",
    "        \"FPR\": []\n",
    "    }\n",
    "    \n",
    "    svm_x_train = copy.deepcopy(X_train)\n",
    "    svm_x_test = copy.deepcopy(X_test)\n",
    "\n",
    "    svm_y_train = copy.deepcopy(y_train)\n",
    "    svm_y_test = copy.deepcopy(y_test)\n",
    "\n",
    "\n",
    "    subsample = 1.0  # Subsample < 1.0 for TESTING PURPOSES ONLY!\n",
    "    if subsample < 1.0:\n",
    "        svm_x_train, _, svm_y_train, _ = train_test_split(\n",
    "            svm_x_train,\n",
    "            svm_y_train,\n",
    "            test_size=1.0 - subsample,\n",
    "            random_state=42,\n",
    "            shuffle=True,\n",
    "            stratify=svm_y_train\n",
    "        )\n",
    "\n",
    "        # Check the number of samples after subsampling\n",
    "        print(f\"After Subsampling - svm_x_train: {len(svm_x_train)}, svm_y_train: {len(svm_y_train)}\")\n",
    "\n",
    "    # PREPROCESSING\n",
    "    # fill nans with 0 in X_train and X_test and y_train and y_test\n",
    "    svm_x_train = svm_x_train.fillna(0)\n",
    "    svm_x_test = svm_x_test.fillna(0)\n",
    "\n",
    "    svm_y_train = svm_y_train.fillna(0)\n",
    "    svm_y_test = svm_y_test.fillna(0)\n",
    "\n",
    "    # convert x_train to numpy array\n",
    "    svm_x_train = svm_x_train.to_numpy()\n",
    "    svm_x_test = svm_x_test.to_numpy()\n",
    "\n",
    "    svm_y_train = svm_y_train.to_numpy()\n",
    "    svm_y_test = svm_y_test.to_numpy()\n",
    "\n",
    "    # Converting False and True to 0 and 1\n",
    "    svm_x_train = np.where(svm_x_train == False, 0, svm_x_train)\n",
    "    svm_x_train = np.where(svm_x_train == True, 1, svm_x_train)\n",
    "\n",
    "    svm_x_test = np.where(svm_x_test == False, 0, svm_x_test)\n",
    "    svm_x_test = np.where(svm_x_test == True, 1, svm_x_test)\n",
    "    \n",
    "    # MinMax data scaler\n",
    "    scaler = MinMaxScaler()\n",
    "    x_train_scaled = scaler.fit_transform(svm_x_train)\n",
    "    x_test_scaled = scaler.transform(svm_x_test)\n",
    "\n",
    "    for seed in seeds:\n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"SVM({now}): Seed {seed}: {inspect.currentframe().f_code.co_name}() Running...\")\n",
    "        \n",
    "        \n",
    "        model_x_train = x_train_scaled\n",
    "        model_y_train = svm_y_train\n",
    "            \n",
    "        if shuffle_training_set:\n",
    "            model_x_train, model_y_train = shuffle(\n",
    "                x_train_scaled,\n",
    "                svm_y_train,\n",
    "                random_state=seed\n",
    "            )\n",
    "            # 99% sumsample\n",
    "            model_x_train, _, model_y_train, _ = train_test_split(\n",
    "                model_x_train,\n",
    "                model_y_train,\n",
    "                test_size=0.01,\n",
    "                random_state=seed,\n",
    "                shuffle=True,\n",
    "                stratify=model_y_train\n",
    "            )\n",
    "\n",
    "        params = {\n",
    "            \"C\": 59,\n",
    "            \"gamma\": 0.1,\n",
    "            \"kernel\": \"rbf\",  # ['rbf', 'linear', 'poly', 'sigmoid']\n",
    "            \"random_state\": seed,\n",
    "            \"random_state\": None,\n",
    "            \"shrinking\": True,\n",
    "            \"probability\": True,\n",
    "            \"verbose\": False,  # Set to False to avoid excessive output\n",
    "        }\n",
    "\n",
    "        model = SVC(**params)\n",
    "        model.fit(model_x_train, model_y_train)\n",
    "        y_pred = model.predict(x_test_scaled)\n",
    "        \n",
    "        # Compute metrics\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        precision = precision_score(y_test, y_pred)\n",
    "        recall = recall_score(y_test, y_pred)\n",
    "        f1 = f1_score(y_test, y_pred)\n",
    "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "        fpr = fp / (fp + tn)\n",
    "\n",
    "        results[\"Accuracy\"].append(accuracy)   # Only makes sense for balanced datasets\n",
    "        results[\"Precision\"].append(precision)\n",
    "        results[\"Recall\"].append(recall)\n",
    "        results[\"F1\"].append(f1)\n",
    "        results[\"FPR\"].append(fpr)\n",
    "        \n",
    "        now = datetime.datetime.now()\n",
    "        print(f\"SVM:\\n {now}, Seed: {seed}:\\n{results}\")\n",
    "        with open(\"_SVM_{seed}.out\", 'a') as file:\n",
    "            file.write(\"SVM {now}, Seed: {seed}:\\n\")\n",
    "            file.write(json.dumps(results, indent=4))\n",
    "            file.write(\"\\n\")\n",
    "        \n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"SVM({now}): Seed {seed} done.\")\n",
    "\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb573a9",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "98cc825f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "from sklearn.utils import shuffle\n",
    "import datetime\n",
    "\n",
    "shuffle_training_set = True\n",
    "\n",
    "def run_decision_tree_multiple_seeds(seeds, X_train, y_train, X_test, y_test,\n",
    "                                     display_cm=False, display_graphs=False, verbose=False):\n",
    "    results = {\n",
    "        \"Accuracy\": [],\n",
    "        \"Precision\": [],\n",
    "        \"Recall\": [],\n",
    "        \"F1\": [],\n",
    "        \"FPR\": []\n",
    "    }\n",
    "    \n",
    "    for seed in seeds:\n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"DECISIONTREE({now}): Seed {seed}: {inspect.currentframe().f_code.co_name}() Running...\")\n",
    "            \n",
    "        \n",
    "        model_x_train = X_train\n",
    "        model_y_train = y_train\n",
    "            \n",
    "        if shuffle_training_set:\n",
    "            model_x_train, model_y_train = shuffle(\n",
    "                X_train,\n",
    "                y_train,\n",
    "                random_state=seed\n",
    "            )\n",
    "            \n",
    "        params = {\n",
    "            #\"max_depth\": 30,\n",
    "            \"max_depth\": 24,\n",
    "            #\"max_leaf_nodes\": 803,\n",
    "            \"max_leaf_nodes\": 403,\n",
    "            \"min_samples_leaf\": 1,\n",
    "            \"min_samples_split\": 5,\n",
    "            \"random_state\": seed\n",
    "        }\n",
    "\n",
    "        model = DecisionTreeClassifier(**params)\n",
    "        model.fit(model_x_train, model_y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        # Compute metrics\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        precision = precision_score(y_test, y_pred)\n",
    "        recall = recall_score(y_test, y_pred)\n",
    "        f1 = f1_score(y_test, y_pred)\n",
    "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "        fpr = fp / (fp + tn)\n",
    "\n",
    "        results[\"Accuracy\"].append(accuracy)   # Only makes sense for balanced datasets\n",
    "        results[\"Precision\"].append(precision)\n",
    "        results[\"Recall\"].append(recall)\n",
    "        results[\"F1\"].append(f1)\n",
    "        results[\"FPR\"].append(fpr)\n",
    "        \n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"DECISIONTREE({now}): Seed {seed} done.\")\n",
    "    \n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fa6d98b",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "77452764",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "from sklearn.utils import shuffle\n",
    "import datetime\n",
    "\n",
    "shuffle_training_set = True\n",
    "\n",
    "def run_random_forest_multiple_seeds(seeds, X_train, y_train, X_test, y_test,\n",
    "                                     display_cm=False, display_graphs=False, verbose=False):\n",
    "    results = {\n",
    "        \"Accuracy\": [],\n",
    "        \"Precision\": [],\n",
    "        \"Recall\": [],\n",
    "        \"F1\": [],\n",
    "        \"FPR\": []\n",
    "    }\n",
    "    \n",
    "    for seed in seeds:\n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"RANDOMFOREST({now}): Seed {seed}: {inspect.currentframe().f_code.co_name}() Running...\")\n",
    "            \n",
    "        \n",
    "        model_x_train = X_train\n",
    "        model_y_train = y_train\n",
    "            \n",
    "        if shuffle_training_set:\n",
    "            model_x_train, model_y_train = shuffle(\n",
    "                X_train,\n",
    "                y_train,\n",
    "                random_state=seed\n",
    "            )\n",
    "            \n",
    "        # Random Forest parameters with variable seed\n",
    "        params = {\n",
    "            \"n_estimators\": 340,\n",
    "            \"criterion\": 'entropy',\n",
    "            \"max_depth\": 17,\n",
    "            \"min_samples_split\": 2,\n",
    "            \"min_samples_leaf\": 1,\n",
    "            \"max_leaf_nodes\": 450,\n",
    "            \"class_weight\": {0:1, 1:2},\n",
    "            \"random_state\": seed\n",
    "        }\n",
    "\n",
    "        # Create and train the model\n",
    "        model = RandomForestClassifier(**params)\n",
    "        model.fit(model_x_train, model_y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        # Compute metrics\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        precision = precision_score(y_test, y_pred)\n",
    "        recall = recall_score(y_test, y_pred)\n",
    "        f1 = f1_score(y_test, y_pred)\n",
    "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "        fpr = fp / (fp + tn)\n",
    "\n",
    "        results[\"Accuracy\"].append(accuracy) # Only makes sense for balanced datasets\n",
    "        results[\"Precision\"].append(precision)\n",
    "        results[\"Recall\"].append(recall)\n",
    "        results[\"F1\"].append(f1)\n",
    "        results[\"FPR\"].append(fpr)\n",
    "        \n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"RANDOMFOREST({now}): Seed {seed} done.\")\n",
    "    \n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c787b515",
   "metadata": {},
   "source": [
    "# AdaBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7585ebe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
    "import datetime\n",
    "\n",
    "shuffle_training_set = True\n",
    "\n",
    "def run_adaboost_multiple_seeds(seeds, X_train, y_train, X_test, y_test,\n",
    "                                display_cm=False, display_graphs=False, verbose=False):\n",
    "    results = {\n",
    "        \"Accuracy\": [],\n",
    "        \"Precision\": [],\n",
    "        \"Recall\": [],\n",
    "        \"F1\": [],\n",
    "        \"FPR\": []\n",
    "    }\n",
    "    \n",
    "    for seed in seeds:\n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"ADABOOST({now}: Seed {seed}: {inspect.currentframe().f_code.co_name}() Running...\")\n",
    "            \n",
    "        \n",
    "        model_x_train = X_train\n",
    "        model_y_train = y_train\n",
    "            \n",
    "        if shuffle_training_set:\n",
    "            model_x_train, model_y_train = shuffle(\n",
    "                X_train,\n",
    "                y_train,\n",
    "                random_state=seed\n",
    "            )\n",
    "            \n",
    "        dt_params = {\n",
    "            \"max_depth\": 3,\n",
    "            \"min_samples_leaf\": 1,\n",
    "            \"min_samples_split\": 5,\n",
    "            #\"class_weight\": {0:1, 1:1},\n",
    "            \"random_state\": seed\n",
    "        }    \n",
    "        \n",
    "        # AdaBoost parameters with variable seed\n",
    "        params = {\n",
    "            \"base_estimator\": DecisionTreeClassifier(**dt_params),  #  base estimator\n",
    "            \"n_estimators\": 320,  # Number of weak learners to train iteratively\n",
    "            \"random_state\": seed\n",
    "        }\n",
    "\n",
    "        # Create and train the model\n",
    "        model = AdaBoostClassifier(**params)\n",
    "        model.fit(model_x_train, model_y_train)\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "        # Compute metrics\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        precision = precision_score(y_test, y_pred, zero_division=1)\n",
    "        recall = recall_score(y_test, y_pred)\n",
    "        f1 = f1_score(y_test, y_pred)\n",
    "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "        fpr = fp / (fp + tn)\n",
    "\n",
    "        results[\"Accuracy\"].append(accuracy)   # Only makes sense for balanced datasets\n",
    "        results[\"Precision\"].append(precision)\n",
    "        results[\"Recall\"].append(recall)\n",
    "        results[\"F1\"].append(f1)\n",
    "        results[\"FPR\"].append(fpr)\n",
    "        \n",
    "        # Optionally display confusion matrix\n",
    "        if display_cm:\n",
    "            cm = confusion_matrix(y_test, y_pred, labels=model.classes_)\n",
    "            disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n",
    "            disp.plot(cmap=plt.cm.Blues)\n",
    "            plt.show()\n",
    "            \n",
    "        if display_graphs:\n",
    "            pass\n",
    "        \n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"ADABOOST({now}): Seed {seed} done.\")\n",
    "    \n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eac2d15",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5375765b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "# Suppress specific FutureWarnings from XGBoost\n",
    "warnings.filterwarnings('ignore', category=FutureWarning, module='xgboost.*')\n",
    "# Suppress specific FutureWarnings from Sklearn\n",
    "warnings.filterwarnings('ignore', category=FutureWarning, module='sklearn.*')\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_recall_fscore_support, confusion_matrix\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.utils import shuffle\n",
    "from xgboost import XGBClassifier\n",
    "import pickle\n",
    "import datetime\n",
    "\n",
    "shuffle_training_set = True\n",
    "\n",
    "def run_xgb_multiple_seeds(seeds, X_train, y_train, X_test, y_test,\n",
    "                           display_cm=False, display_graphs=False, verbose=False):\n",
    "    results = {\n",
    "        \"Accuracy\": [],\n",
    "        \"Weighted Accuracy\": [],\n",
    "        \"Precision\": [],\n",
    "        \"Recall\": [],\n",
    "        \"F1\": [],\n",
    "        \"FPR\": []\n",
    "    }\n",
    "\n",
    "    for seed in seeds:\n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"XGBOOST({now}): Seed {seed}: {inspect.currentframe().f_code.co_name}() Running...\")\n",
    "        \n",
    "        model_x_train = X_train\n",
    "        model_y_train = y_train\n",
    "            \n",
    "        if shuffle_training_set:\n",
    "            model_x_train, model_y_train = shuffle(\n",
    "                X_train,\n",
    "                y_train,\n",
    "                random_state=seed\n",
    "            )\n",
    "        \n",
    "            \n",
    "        params = {\n",
    "            \"objective\": \"binary:logistic\",\n",
    "            \"tree_method\": \"gpu_hist\",\n",
    "            \"sampling_method\": \"gradient_based\",\n",
    "            \"max_depth\": 8,\n",
    "            \"eta\": 0.15,\n",
    "            \"min_child_weight\": 2.0,\n",
    "            \"subsample\": 0.6,\n",
    "            \"alpha\": 0,\n",
    "            \"gamma\": 0.1,\n",
    "            \"lambda\": 1.0,\n",
    "            \"max_delta_step\": 0,\n",
    "            \"grow_policy\": \"depthwise\",\n",
    "            \"max_bin\": 512,\n",
    "            \"n_estimators\": 550,\n",
    "            \"random_state\": seed,  # Set the seed for each run\n",
    "            \n",
    "            # SCALE_POS_WEIGHT - imbalanced dataset\n",
    "            \"scale_pos_weight\": 4.5\n",
    "        }\n",
    "        model = XGBClassifier(\n",
    "            **params,\n",
    "            eval_metric=[\"error\", \"logloss\", \"auc\"]\n",
    "        )\n",
    "\n",
    "        eval_set = [(model_x_train, model_y_train), (X_test, y_test)]\n",
    "        model.fit(model_x_train, model_y_train, eval_set=eval_set, verbose=False)\n",
    "\n",
    "        # Calculate metrics\n",
    "        y_pred = model.predict(X_test)\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        precision, recall, f1, _ = precision_recall_fscore_support(y_test, y_pred, average='binary')\n",
    "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "        fpr = fp / (fp + tn)\n",
    "\n",
    "        results[\"Accuracy\"].append(accuracy) # Only makes sense for balanced datasets\n",
    "        results[\"Weighted Accuracy\"].append((tp / (tp + fn) * np.sum(y_test == 1) + tn / (tn + fp) * np.sum(y_test == 0)) / len(y_test))\n",
    "        results[\"Precision\"].append(precision)\n",
    "        results[\"Recall\"].append(recall)\n",
    "        results[\"F1\"].append(f1)\n",
    "        results[\"FPR\"].append(fpr)\n",
    "\n",
    "        # Optionally print confusion matrix\n",
    "        if display_cm:\n",
    "            print(f\"Confusion Matrix for Seed {seed}:\\n\", confusion_matrix(y_test, y_pred))\n",
    "\n",
    "        # Optionally display graphs\n",
    "        if display_graphs:\n",
    "            # Extract and plot metrics from eval_set results\n",
    "            # plot evaluation\n",
    "            results = model.evals_result()\n",
    "            trees = len(results['validation_0']['error'])\n",
    "            x_axis = range(0, trees)\n",
    "\n",
    "            def plot_metric(metric):\n",
    "                import matplotlib.pyplot as plt\n",
    "                plt.rcParams[\"figure.dpi\"] = 300\n",
    "                #\n",
    "                plt.plot(x_axis, results['validation_0'][metric], label='Training set')\n",
    "                plt.plot(x_axis, results['validation_1'][metric], label='Testing set')\n",
    "                plt.legend()\n",
    "                mnames = {'error': 'Classification Error', 'logloss': 'Log Loss', 'auc': 'AUC'}\n",
    "                mname = mnames[metric]\n",
    "                plt.ylabel(mname)\n",
    "                plt.xlabel('Number of trees')\n",
    "                plt.title('XGBoost ' + mname)\n",
    "                plt.show()\n",
    "\n",
    "            plot_metric('error')\n",
    "            plot_metric('logloss')\n",
    "            plot_metric('auc')\n",
    "            \n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(\"XGBOOST(now): Seed {seed} done.\")\n",
    "\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b6b2b92",
   "metadata": {},
   "source": [
    "# LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "63e7c67b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightgbm as lgb\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.utils import shuffle\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import datetime\n",
    "\n",
    "shuffle_training_set = True\n",
    "\n",
    "def run_lgb_multiple_seeds(seeds, X_train, y_train, X_test, y_test,\n",
    "                           display_cm=False, display_graphs=False, verbose=False):\n",
    "    results = {\n",
    "        \"Accuracy\": [],\n",
    "        \"Weighted Accuracy\": [],\n",
    "        \"Precision\": [],\n",
    "        \"Recall\": [],\n",
    "        \"F1\": [],\n",
    "        \"FPR\": []\n",
    "    }\n",
    "    \n",
    "    for seed in seeds:\n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"LIGHTGBM({now}) Seed {seed}: {inspect.currentframe().f_code.co_name}() Running...\")\n",
    "        \n",
    "        model_x_train = X_train\n",
    "        model_y_train = y_train\n",
    "            \n",
    "        if shuffle_training_set:\n",
    "            model_x_train, model_y_train = shuffle(\n",
    "                X_train,\n",
    "                y_train,\n",
    "                random_state=seed\n",
    "            )\n",
    "        \n",
    "        # LightGBM parameters with variable seed        \n",
    "        params = {\n",
    "            \"objective\": \"binary\",  # binary classification\n",
    "            \"boosting_type\": \"gbdt\",  # Gradient Boosting Decision Tree\n",
    "            \"n_estimators\": 897, # Number of boosted trees to fit, equivalent to number of boosting iterations\n",
    "            \"num_leaves\": 59,\n",
    "            \"max_depth\": 17,\n",
    "            \"learning_rate\": 0.15,\n",
    "            \"objective\": \"binary\",\n",
    "            \"min_child_samples\": 27,\n",
    "            \"colsample_bytree\": 0.9,\n",
    "            \"reg_lambda\": 0.11,\n",
    "            \"scale_pos_weight\": 6.28, # default: 1.0\n",
    "            \"subsample_for_bin\": 240000,\n",
    "            \"random_state\": seed,\n",
    "            \n",
    "            # SCALE_POS_WEIGHT - imbalanced dataset\n",
    "            \"scale_pos_weight\": 6.28\n",
    "        }\n",
    "\n",
    "        # Create and train the model\n",
    "        model = lgb.LGBMClassifier(**params)\n",
    "        model.fit(model_x_train, model_y_train)\n",
    "        \n",
    "        # Save the model if needed\n",
    "        model_filename = f\"lightgbm_model_seed_{seed}.pickle.dat\"\n",
    "        pickle.dump(model, open(model_filename, \"wb\"))\n",
    "\n",
    "        # Predictions\n",
    "        y_pred = model.predict(X_test)\n",
    "        \n",
    "         # Calculate metrics\n",
    "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
    "        fpr = fp / (fp + tn)\n",
    "       \n",
    "        # Compute metrics\n",
    "        results[\"Accuracy\"].append(accuracy_score(y_test, y_pred)) # only makes sense for balanced datasets\n",
    "        results[\"Weighted Accuracy\"].append((tp / (tp + fn) * np.sum(y_test == 1) + tn / (tn + fp) * np.sum(y_test == 0)) / len(y_test))\n",
    "        results[\"Precision\"].append(precision_score(y_test, y_pred, zero_division=1))\n",
    "        results[\"Recall\"].append(recall_score(y_test, y_pred))\n",
    "        results[\"F1\"].append(f1_score(y_test, y_pred))\n",
    "        results[\"FPR\"].append(fpr)\n",
    "\n",
    "        # Optionally display confusion matrix\n",
    "        if display_cm:\n",
    "            cm = confusion_matrix(y_test, y_pred, labels=model.classes_)\n",
    "            disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=model.classes_)\n",
    "            disp.plot(cmap=plt.cm.Blues)\n",
    "            plt.show()\n",
    "            \n",
    "        if display_graphs:\n",
    "            pass\n",
    "        \n",
    "        if verbose:\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"LIGHTGBM({now}): Seed {seed} done.\")\n",
    "\n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a585fae2",
   "metadata": {},
   "source": [
    "# Run evaluations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95e766ba",
   "metadata": {},
   "source": [
    "## Initialize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "54759041",
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_results_if_nonempty = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "35e617c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    if isinstance(my_variable, dict):\n",
    "        if len(results) > 0 and replace_results_if_nonempty:\n",
    "            results = dict()\n",
    "    else:\n",
    "        results = dict()\n",
    "except NameError:\n",
    "    results = dict()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33038e24",
   "metadata": {},
   "source": [
    "## Run"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fb7c743",
   "metadata": {},
   "source": [
    "### Serial run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "05f0f6ad",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start Time: 2024-03-02 16:41:47.573086\n",
      "XGBOOST(2024-03-02 16:41:47.573446): Seed 5849: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "XGBOOST(2024-03-02 16:42:03.272157): Seed 50721: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "XGBOOST(2024-03-02 16:42:18.969749): Seed 42897: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "XGBOOST(2024-03-02 16:42:34.620127): Seed 28761: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "XGBOOST(2024-03-02 16:42:50.242836): Seed 28377: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "XGBOOST(2024-03-02 16:43:05.896276): Seed 56268: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "XGBOOST(2024-03-02 16:43:21.583456): Seed 5632: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "XGBOOST(2024-03-02 16:43:37.229267): Seed 45702: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "XGBOOST(2024-03-02 16:43:52.840633): Seed 13203: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "XGBOOST(2024-03-02 16:44:08.416886): Seed 6171: run_xgb_multiple_seeds() Running...\n",
      "XGBOOST(now): Seed {seed} done.\n",
      "LIGHTGBM(2024-03-02 16:44:24.046446) Seed 5849: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.023636 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 13514\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:44:37.272818): Seed 5849 done.\n",
      "LIGHTGBM(2024-03-02 16:44:37.272862) Seed 50721: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.026486 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 13490\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:44:50.366212): Seed 50721 done.\n",
      "LIGHTGBM(2024-03-02 16:44:50.366252) Seed 42897: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.026869 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 13495\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:45:03.527338): Seed 42897 done.\n",
      "LIGHTGBM(2024-03-02 16:45:03.527381) Seed 28761: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.027374 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 13468\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:45:16.611841): Seed 28761 done.\n",
      "LIGHTGBM(2024-03-02 16:45:16.611884) Seed 28377: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.023794 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 13496\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:45:29.665206): Seed 28377 done.\n",
      "LIGHTGBM(2024-03-02 16:45:29.665247) Seed 56268: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.026951 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 13503\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:45:42.851927): Seed 56268 done.\n",
      "LIGHTGBM(2024-03-02 16:45:42.851969) Seed 5632: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.023255 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 13503\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:45:56.001216): Seed 5632 done.\n",
      "LIGHTGBM(2024-03-02 16:45:56.001261) Seed 45702: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.023471 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 13496\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:46:09.195740): Seed 45702 done.\n",
      "LIGHTGBM(2024-03-02 16:46:09.195784) Seed 13203: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.078026 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 13495\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:46:28.128115): Seed 13203 done.\n",
      "LIGHTGBM(2024-03-02 16:46:28.128158) Seed 6171: run_lgb_multiple_seeds() Running...\n",
      "[LightGBM] [Info] Number of positive: 47847, number of negative: 302800\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.024191 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 13491\n",
      "[LightGBM] [Info] Number of data points in the train set: 350647, number of used features: 143\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.136453 -> initscore=-1.845064\n",
      "[LightGBM] [Info] Start training from score -1.845064\n",
      "LIGHTGBM(2024-03-02 16:46:41.292048): Seed 6171 done.\n",
      "End Time: 2024-03-02 16:46:41.311014\n",
      "Time Taken: 0:04:53.737928\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "import time\n",
    "\n",
    "# Print the current date and time\n",
    "start_time = datetime.datetime.now()\n",
    "print(f\"Start Time: {start_time}\")\n",
    "\n",
    "x = 42\n",
    "ROUNDS = 10\n",
    "\n",
    "# Run the function with the desired number of seeds and store the results in the dictionary\n",
    "#results['Logistic Regression'] = run_logistic_regression_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n",
    "#results['SVM'] = run_svm_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n",
    "#results['DecisionTree'] = run_decision_tree_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n",
    "#results['RandomForest'] = run_random_forest_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n",
    "#results['AdaBoost'] = run_adaboost_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n",
    "results['XGBoost'] = run_xgb_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n",
    "results['LightGBM'] = run_lgb_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n",
    "\n",
    "# Print end time and duration\n",
    "end_time = datetime.datetime.now()\n",
    "print(f\"End Time: {end_time}\")\n",
    "time_taken = end_time - start_time\n",
    "print(f\"Time Taken: {time_taken}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "769d6d00",
   "metadata": {},
   "source": [
    "### Parallel run\n",
    "This is fast but may have **ultra-high** processor and memory demands.\n",
    "\n",
    "Set the **n_workers** accordingly to your machine's capabilities! Otherwise, the threads may crash due to insufficient memory, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "877bb45e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import concurrent.futures\n",
    "\n",
    "def train_models_in_parallel(tasks, n_workers, X_train, y_train, X_test, y_test):\n",
    "    print(\"******** PARALLEL MODEL TRAINING & VALIDATION ********\")\n",
    "    # Print the current date and time\n",
    "    start_time = datetime.datetime.now()\n",
    "    print(f\"Start Time: {start_time}\")\n",
    "    \n",
    "    # Dictionary to store results\n",
    "    results = {}\n",
    "\n",
    "    with concurrent.futures.ProcessPoolExecutor(max_workers=n_workers) as executor:\n",
    "        # Launching the model training tasks\n",
    "        future_to_model = {}\n",
    "        for name, (task, seeds) in tasks.items():\n",
    "            now = datetime.datetime.now()\n",
    "            print(f\"{now}: Starting task: {name}\")  # Verbose: indicate task start\n",
    "            future = executor.submit(task, seeds, X_train, y_train, X_test, y_test, False, False, True)\n",
    "            future_to_model[future] = name\n",
    "\n",
    "        # Collecting results as they complete\n",
    "        for future in concurrent.futures.as_completed(future_to_model):\n",
    "            model_name = future_to_model[future]\n",
    "            now = datetime.datetime.now()\n",
    "            try:\n",
    "                results[model_name] = future.result()\n",
    "                print(f\"{now}: Completed task: {model_name}\")  # Verbose: indicate task completion\n",
    "            except Exception as exc:\n",
    "                print(f'{now}: {model_name} generated an exception: {exc}')\n",
    "                \n",
    "    # Print end time and duration\n",
    "    end_time = datetime.datetime.now()\n",
    "    print(f\"End Time: {end_time}\")\n",
    "    time_taken = end_time - start_time\n",
    "    print(f\"Time Taken: {time_taken}\")\n",
    "    print(\"******************** JOB DONE ************************\")\n",
    "\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ba3535aa",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******** PARALLEL MODEL TRAINING & VALIDATION ********\n",
      "Start Time: 2023-12-07 20:25:28.957226\n",
      "2023-12-07 20:25:28.957458: Starting task: Logistic Regression\n",
      "2023-12-07 20:25:28.992645: Starting task: Logistic Regression4_b\n",
      "2023-12-07 20:25:28.992890: Starting task: Logistic Regression4_c\n",
      "2023-12-07 20:25:28.992911: Starting task: Logistic Regression4_d\n",
      "2023-12-07 20:25:28.992943: Starting task: Logistic Regression4_e\n",
      "LOGISTICREGRESSION(2023-12-07 20:25:32.370583): Seed 5849: run_logistic_regression_multiple_seeds() Running...\n",
      "LOGISTICREGRESSION(2023-12-07 20:25:33.388246): Seed 1031: run_logistic_regression_multiple_seeds() Running...\n",
      "LOGISTICREGRESSION(2023-12-07 20:25:34.253688): Seed 48400: run_logistic_regression_multiple_seeds() Running...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[29], line 22\u001b[0m, in \u001b[0;36mtrain_models_in_parallel\u001b[0;34m(tasks, n_workers, X_train, y_train, X_test, y_test)\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# Collecting results as they complete\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m future \u001b[38;5;129;01min\u001b[39;00m concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mas_completed(future_to_model):\n\u001b[1;32m     23\u001b[0m     model_name \u001b[38;5;241m=\u001b[39m future_to_model[future]\n",
      "File \u001b[0;32m/usr/lib/python3.11/concurrent/futures/_base.py:243\u001b[0m, in \u001b[0;36mas_completed\u001b[0;34m(fs, timeout)\u001b[0m\n\u001b[1;32m    239\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTimeoutError\u001b[39;00m(\n\u001b[1;32m    240\u001b[0m                 \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m (of \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m) futures unfinished\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m (\n\u001b[1;32m    241\u001b[0m                 \u001b[38;5;28mlen\u001b[39m(pending), total_futures))\n\u001b[0;32m--> 243\u001b[0m \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwait_timeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    245\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m waiter\u001b[38;5;241m.\u001b[39mlock:\n",
      "File \u001b[0;32m/usr/lib/python3.11/threading.py:622\u001b[0m, in \u001b[0;36mEvent.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    621\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m signaled:\n\u001b[0;32m--> 622\u001b[0m     signaled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cond\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    623\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m signaled\n",
      "File \u001b[0;32m/usr/lib/python3.11/threading.py:320\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    319\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 320\u001b[0m     \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    321\u001b[0m     gotit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[30], line 20\u001b[0m\n\u001b[1;32m      7\u001b[0m n_workers \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m  \u001b[38;5;66;03m# 0 uses all available processors\u001b[39;00m\n\u001b[1;32m      9\u001b[0m batch1 \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLogistic Regression\u001b[39m\u001b[38;5;124m'\u001b[39m: (run_logistic_regression_multiple_seeds, prng_create_seeds(n\u001b[38;5;241m=\u001b[39mROUNDS,base_seed\u001b[38;5;241m=\u001b[39mbase_seed)),\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLogistic Regression4_b\u001b[39m\u001b[38;5;124m'\u001b[39m: (run_logistic_regression_multiple_seeds, prng_create_seeds(n\u001b[38;5;241m=\u001b[39mROUNDS,base_seed\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m142\u001b[39m)),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[38;5;66;03m#'RandomForest': (run_random_forest_multiple_seeds, prng_create_seeds(n=ROUNDS,base_seed=base_seed)),\u001b[39;00m\n\u001b[1;32m     18\u001b[0m }\n\u001b[0;32m---> 20\u001b[0m results1 \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_models_in_parallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_workers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     21\u001b[0m results\u001b[38;5;241m.\u001b[39mupdate(results1)\n\u001b[1;32m     23\u001b[0m \u001b[38;5;66;03m# XGboost, and LightGBM are multi-threaded by design (running them in threads may cause crashes)\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;66;03m#results['AdaBoost'] = run_adaboost_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\u001b[39;00m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m#results['XGBoost'] = run_xgb_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\u001b[39;00m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;66;03m#results['LightGBM'] = run_lgb_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[29], line 12\u001b[0m, in \u001b[0;36mtrain_models_in_parallel\u001b[0;34m(tasks, n_workers, X_train, y_train, X_test, y_test)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Dictionary to store results\u001b[39;00m\n\u001b[1;32m     10\u001b[0m results \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m---> 12\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mconcurrent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfutures\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mProcessPoolExecutor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_workers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mn_workers\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m     13\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# Launching the model training tasks\u001b[39;49;00m\n\u001b[1;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfuture_to_model\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseeds\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mtasks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n",
      "File \u001b[0;32m/usr/lib/python3.11/concurrent/futures/_base.py:647\u001b[0m, in \u001b[0;36mExecutor.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m    646\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, exc_type, exc_val, exc_tb):\n\u001b[0;32m--> 647\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshutdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwait\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m    648\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m/usr/lib/python3.11/concurrent/futures/process.py:834\u001b[0m, in \u001b[0;36mProcessPoolExecutor.shutdown\u001b[0;34m(self, wait, cancel_futures)\u001b[0m\n\u001b[1;32m    831\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executor_manager_thread_wakeup\u001b[38;5;241m.\u001b[39mwakeup()\n\u001b[1;32m    833\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executor_manager_thread \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m wait:\n\u001b[0;32m--> 834\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_executor_manager_thread\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    835\u001b[0m \u001b[38;5;66;03m# To reduce the risk of opening too many files, remove references to\u001b[39;00m\n\u001b[1;32m    836\u001b[0m \u001b[38;5;66;03m# objects that use file descriptors.\u001b[39;00m\n\u001b[1;32m    837\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_executor_manager_thread \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/usr/lib/python3.11/threading.py:1112\u001b[0m, in \u001b[0;36mThread.join\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1109\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot join current thread\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1112\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_wait_for_tstate_lock\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1113\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1114\u001b[0m     \u001b[38;5;66;03m# the behavior of a negative timeout isn't documented, but\u001b[39;00m\n\u001b[1;32m   1115\u001b[0m     \u001b[38;5;66;03m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001b[39;00m\n\u001b[1;32m   1116\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wait_for_tstate_lock(timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mmax\u001b[39m(timeout, \u001b[38;5;241m0\u001b[39m))\n",
      "File \u001b[0;32m/usr/lib/python3.11/threading.py:1132\u001b[0m, in \u001b[0;36mThread._wait_for_tstate_lock\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m   1129\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m   1131\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1132\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mlock\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m   1133\u001b[0m         lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m   1134\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stop()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LOGISTICREGRESSION(2023-12-07 20:42:57.080333): Seed 51392: run_logistic_regression_multiple_seeds() Running...\n",
      "LOGISTICREGRESSION(2023-12-07 20:47:08.528947): Seed 35667: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 21:00:06.338020, Seed: 51392:\n",
      "{'Accuracy': [0.963900238225156], 'Precision': [0.9065178715833737], 'Recall': [0.8200039012971813], 'F1': [0.8610933299193445], 'FPR': [0.013361896248805597]}\n",
      "LOGISTICREGRESSION(2023-12-07 21:00:06.339713): Seed 51392 done.\n",
      "LOGISTICREGRESSION(2023-12-07 21:00:06.340639): Seed 39263: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 21:01:08.755090, Seed: 35667:\n",
      "{'Accuracy': [0.963900238225156], 'Precision': [0.9066494094806665], 'Recall': [0.8198576026528821], 'F1': [0.8610719864785269], 'FPR': [0.01333877878124711]}\n",
      "LOGISTICREGRESSION(2023-12-07 21:01:08.756667): Seed 35667 done.\n",
      "LOGISTICREGRESSION(2023-12-07 21:01:08.757432): Seed 28115: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 21:19:40.271984, Seed: 28115:\n",
      "{'Accuracy': [0.963900238225156, 0.9638603122213497], 'Precision': [0.9066494094806665, 0.9066630698678176], 'Recall': [0.8198576026528821, 0.8195162391495172], 'F1': [0.8610719864785269, 0.860889833764504], 'FPR': [0.01333877878124711, 0.013331072958727614]}\n",
      "LOGISTICREGRESSION(2023-12-07 21:19:40.291042): Seed 28115 done.\n",
      "LOGISTICREGRESSION(2023-12-07 21:19:40.291754): Seed 39466: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 21:22:54.965848, Seed: 39263:\n",
      "{'Accuracy': [0.963900238225156, 0.9639202012270592], 'Precision': [0.9065178715833737, 0.9066645098673568], 'Recall': [0.8200039012971813, 0.8200039012971813], 'F1': [0.8610933299193445, 0.8611594796681348], 'FPR': [0.013361896248805597, 0.01333877878124711]}\n",
      "LOGISTICREGRESSION(2023-12-07 21:22:54.967550): Seed 39263 done.\n",
      "LOGISTICREGRESSION(2023-12-07 21:22:54.968364): Seed 48764: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 21:41:04.906453, Seed: 48764:\n",
      "{'Accuracy': [0.963900238225156, 0.9639202012270592, 0.9638603122213497], 'Precision': [0.9065178715833737, 0.9066645098673568, 0.9063999568663396], 'Recall': [0.8200039012971813, 0.8200039012971813, 0.8198088364381156], 'F1': [0.8610933299193445, 0.8611594796681348, 0.8609325788031649], 'FPR': [0.013361896248805597, 0.01333877878124711, 0.013377307893844588]}\n",
      "LOGISTICREGRESSION(2023-12-07 21:41:04.908174): Seed 48764 done.\n",
      "LOGISTICREGRESSION(2023-12-07 21:41:04.908903): Seed 30939: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 21:44:22.230734, Seed: 39466:\n",
      "{'Accuracy': [0.963900238225156, 0.9638603122213497, 0.9638802752232529], 'Precision': [0.9066494094806665, 0.9066630698678176, 0.9063274765549207], 'Recall': [0.8198576026528821, 0.8195162391495172, 0.8200526675119477], 'F1': [0.8610719864785269, 0.860889833764504, 0.8610343061955965], 'FPR': [0.01333877878124711, 0.013331072958727614, 0.013392719538883581]}\n",
      "LOGISTICREGRESSION(2023-12-07 21:44:22.232399): Seed 39466 done.\n",
      "LOGISTICREGRESSION(2023-12-07 21:44:22.233146): Seed 51966: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 22:00:44.335158, Seed: 30939:\n",
      "{'Accuracy': [0.963900238225156, 0.9639202012270592, 0.9638603122213497, 0.9638802752232529], 'Precision': [0.9065178715833737, 0.9066645098673568, 0.9063999568663396, 0.90654659188956], 'Recall': [0.8200039012971813, 0.8200039012971813, 0.8198088364381156, 0.8198088364381156], 'F1': [0.8610933299193445, 0.8611594796681348, 0.8609325788031649, 0.8609987195902689], 'FPR': [0.013361896248805597, 0.01333877878124711, 0.013377307893844588, 0.013354190426286102]}\n",
      "LOGISTICREGRESSION(2023-12-07 22:00:44.336713): Seed 30939 done.\n",
      "LOGISTICREGRESSION(2023-12-07 22:00:44.337322): Seed 10128: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 22:16:57.344109, Seed: 51966:\n",
      "{'Accuracy': [0.963900238225156, 0.9638603122213497, 0.9638802752232529, 0.9638669665553175], 'Precision': [0.9066494094806665, 0.9066630698678176, 0.9063274765549207, 0.9065365117031604], 'Recall': [0.8198576026528821, 0.8195162391495172, 0.8200526675119477, 0.8197113040085828], 'F1': [0.8610719864785269, 0.860889833764504, 0.8610343061955965, 0.8609403810694529], 'FPR': [0.01333877878124711, 0.013331072958727614, 0.013392719538883581, 0.013354190426286102]}\n",
      "LOGISTICREGRESSION(2023-12-07 22:16:57.345331): Seed 51966 done.\n",
      "LOGISTICREGRESSION(2023-12-07 22:16:57.346495): Seed 57839: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 22:27:26.807283, Seed: 10128:\n",
      "{'Accuracy': [0.963900238225156, 0.9639202012270592, 0.9638603122213497, 0.9638802752232529, 0.9638203862175435], 'Precision': [0.9065178715833737, 0.9066645098673568, 0.9063999568663396, 0.90654659188956, 0.9062382056397261], 'Recall': [0.8200039012971813, 0.8200039012971813, 0.8198088364381156, 0.8198088364381156, 0.8196625377938165], 'F1': [0.8610933299193445, 0.8611594796681348, 0.8609325788031649, 0.8609987195902689, 0.8607789414385579], 'FPR': [0.013361896248805597, 0.01333877878124711, 0.013377307893844588, 0.013354190426286102, 0.013400425361403076]}\n",
      "LOGISTICREGRESSION(2023-12-07 22:27:26.808895): Seed 10128 done.\n",
      "LOGISTICREGRESSION(2023-12-07 22:27:26.809826): Seed 9012: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 22:38:12.986541, Seed: 57839:\n",
      "{'Accuracy': [0.963900238225156, 0.9638603122213497, 0.9638802752232529, 0.9638669665553175, 0.9638669665553175], 'Precision': [0.9066494094806665, 0.9066630698678176, 0.9063274765549207, 0.9065365117031604, 0.9066681053085887], 'Recall': [0.8198576026528821, 0.8195162391495172, 0.8200526675119477, 0.8197113040085828, 0.8195650053642837], 'F1': [0.8610719864785269, 0.860889833764504, 0.8610343061955965, 0.8609403810694529, 0.8609190102966037], 'FPR': [0.01333877878124711, 0.013331072958727614, 0.013392719538883581, 0.013354190426286102, 0.013331072958727614]}\n",
      "LOGISTICREGRESSION(2023-12-07 22:38:12.988193): Seed 57839 done.\n",
      "LOGISTICREGRESSION(2023-12-07 22:38:12.989103): Seed 6460: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 22:52:08.270092, Seed: 9012:\n",
      "{'Accuracy': [0.963900238225156, 0.9639202012270592, 0.9638603122213497, 0.9638802752232529, 0.9638203862175435, 0.9638336948854789], 'Precision': [0.9065178715833737, 0.9066645098673568, 0.9063999568663396, 0.90654659188956, 0.9062382056397261, 0.9062921227152639], 'Recall': [0.8200039012971813, 0.8200039012971813, 0.8198088364381156, 0.8198088364381156, 0.8196625377938165, 0.8197113040085828], 'F1': [0.8610933299193445, 0.8611594796681348, 0.8609325788031649, 0.8609987195902689, 0.8607789414385579, 0.8608301538934268], 'FPR': [0.013361896248805597, 0.01333877878124711, 0.013377307893844588, 0.013354190426286102, 0.013400425361403076, 0.013392719538883581]}\n",
      "LOGISTICREGRESSION(2023-12-07 22:52:08.271882): Seed 9012 done.\n",
      "LOGISTICREGRESSION(2023-12-07 22:52:08.272617): Seed 3081: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 23:06:14.528539, Seed: 6460:\n",
      "{'Accuracy': [0.963900238225156, 0.9638603122213497, 0.9638802752232529, 0.9638669665553175, 0.9638669665553175, 0.963807077549608], 'Precision': [0.9066494094806665, 0.9066630698678176, 0.9063274765549207, 0.9065365117031604, 0.9066681053085887, 0.9061842885641883], 'Recall': [0.8198576026528821, 0.8195162391495172, 0.8200526675119477, 0.8197113040085828, 0.8195650053642837, 0.81961377157905], 'F1': [0.8610719864785269, 0.860889833764504, 0.8610343061955965, 0.8609403810694529, 0.8609190102966037, 0.8607277289836889], 'FPR': [0.01333877878124711, 0.013331072958727614, 0.013392719538883581, 0.013354190426286102, 0.013331072958727614, 0.013408131183922572]}\n",
      "LOGISTICREGRESSION(2023-12-07 23:06:14.529716): Seed 6460 done.\n",
      "LOGISTICREGRESSION(2023-12-07 23:06:14.530818): Seed 8562: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 23:10:13.337892, Seed: 3081:\n",
      "{'Accuracy': [0.963900238225156, 0.9639202012270592, 0.9638603122213497, 0.9638802752232529, 0.9638203862175435, 0.9638336948854789, 0.9638736208892852], 'Precision': [0.9065178715833737, 0.9066645098673568, 0.9063999568663396, 0.90654659188956, 0.9062382056397261, 0.9062921227152639, 0.9064100490592485], 'Recall': [0.8200039012971813, 0.8200039012971813, 0.8198088364381156, 0.8198088364381156, 0.8196625377938165, 0.8197113040085828, 0.8199063688676484], 'F1': [0.8610933299193445, 0.8611594796681348, 0.8609325788031649, 0.8609987195902689, 0.8607789414385579, 0.8608301538934268, 0.8609909102547689], 'FPR': [0.013361896248805597, 0.01333877878124711, 0.013377307893844588, 0.013354190426286102, 0.013400425361403076, 0.013392719538883581, 0.013377307893844588]}\n",
      "LOGISTICREGRESSION(2023-12-07 23:10:13.339566): Seed 3081 done.\n",
      "LOGISTICREGRESSION(2023-12-07 23:10:13.340276): Seed 7647: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 23:26:25.387393, Seed: 8562:\n",
      "{'Accuracy': [0.963900238225156, 0.9638603122213497, 0.9638802752232529, 0.9638669665553175, 0.9638669665553175, 0.963807077549608, 0.9638736208892852], 'Precision': [0.9066494094806665, 0.9066630698678176, 0.9063274765549207, 0.9065365117031604, 0.9066681053085887, 0.9061842885641883, 0.9065854053179441], 'Recall': [0.8198576026528821, 0.8195162391495172, 0.8200526675119477, 0.8197113040085828, 0.8195650053642837, 0.81961377157905, 0.8197113040085828], 'F1': [0.8610719864785269, 0.860889833764504, 0.8610343061955965, 0.8609403810694529, 0.8609190102966037, 0.8607277289836889, 0.8609624298921812], 'FPR': [0.01333877878124711, 0.013331072958727614, 0.013392719538883581, 0.013354190426286102, 0.013331072958727614, 0.013408131183922572, 0.013346484603766607]}\n",
      "LOGISTICREGRESSION(2023-12-07 23:26:25.388986): Seed 8562 done.\n",
      "LOGISTICREGRESSION(2023-12-07 23:26:25.389821): Seed 18842: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 23:32:37.910442, Seed: 7647:\n",
      "{'Accuracy': [0.963900238225156, 0.9639202012270592, 0.9638603122213497, 0.9638802752232529, 0.9638203862175435, 0.9638336948854789, 0.9638736208892852, 0.9638802752232529], 'Precision': [0.9065178715833737, 0.9066645098673568, 0.9063999568663396, 0.90654659188956, 0.9062382056397261, 0.9062921227152639, 0.9064100490592485, 0.9064150943396226], 'Recall': [0.8200039012971813, 0.8200039012971813, 0.8198088364381156, 0.8198088364381156, 0.8196625377938165, 0.8197113040085828, 0.8199063688676484, 0.8199551350824149], 'F1': [0.8610933299193445, 0.8611594796681348, 0.8609325788031649, 0.8609987195902689, 0.8607789414385579, 0.8608301538934268, 0.8609909102547689, 0.8610200737402703], 'FPR': [0.013361896248805597, 0.01333877878124711, 0.013377307893844588, 0.013354190426286102, 0.013400425361403076, 0.013392719538883581, 0.013377307893844588, 0.013377307893844588]}\n",
      "LOGISTICREGRESSION(2023-12-07 23:32:37.912114): Seed 7647 done.\n",
      "LOGISTICREGRESSION(2023-12-07 23:32:37.912795): Seed 12268: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 23:48:26.863543, Seed: 18842:\n",
      "{'Accuracy': [0.963900238225156, 0.9638603122213497, 0.9638802752232529, 0.9638669665553175, 0.9638669665553175, 0.963807077549608, 0.9638736208892852, 0.9638470035534143], 'Precision': [0.9066494094806665, 0.9066630698678176, 0.9063274765549207, 0.9065365117031604, 0.9066681053085887, 0.9061842885641883, 0.9065854053179441, 0.9063898624966298], 'Recall': [0.8198576026528821, 0.8195162391495172, 0.8200526675119477, 0.8197113040085828, 0.8195650053642837, 0.81961377157905, 0.8197113040085828, 0.8197113040085828], 'F1': [0.8610719864785269, 0.860889833764504, 0.8610343061955965, 0.8609403810694529, 0.8609190102966037, 0.8607277289836889, 0.8609624298921812, 0.8608742413766612], 'FPR': [0.01333877878124711, 0.013331072958727614, 0.013392719538883581, 0.013354190426286102, 0.013331072958727614, 0.013408131183922572, 0.013346484603766607, 0.013377307893844588]}\n",
      "LOGISTICREGRESSION(2023-12-07 23:48:26.865037): Seed 18842 done.\n",
      "LOGISTICREGRESSION(2023-12-07 23:48:26.865861): Seed 38139: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-07 23:49:01.653315, Seed: 12268:\n",
      "{'Accuracy': [0.963900238225156, 0.9639202012270592, 0.9638603122213497, 0.9638802752232529, 0.9638203862175435, 0.9638336948854789, 0.9638736208892852, 0.9638802752232529, 0.9638802752232529], 'Precision': [0.9065178715833737, 0.9066645098673568, 0.9063999568663396, 0.90654659188956, 0.9062382056397261, 0.9062921227152639, 0.9064100490592485, 0.9064150943396226, 0.9066343042071198], 'Recall': [0.8200039012971813, 0.8200039012971813, 0.8198088364381156, 0.8198088364381156, 0.8196625377938165, 0.8197113040085828, 0.8199063688676484, 0.8199551350824149, 0.8197113040085828], 'F1': [0.8610933299193445, 0.8611594796681348, 0.8609325788031649, 0.8609987195902689, 0.8607789414385579, 0.8608301538934268, 0.8609909102547689, 0.8610200737402703, 0.8609844798442863], 'FPR': [0.013361896248805597, 0.01333877878124711, 0.013377307893844588, 0.013354190426286102, 0.013400425361403076, 0.013392719538883581, 0.013377307893844588, 0.013377307893844588, 0.01333877878124711]}\n",
      "LOGISTICREGRESSION(2023-12-07 23:49:01.655243): Seed 12268 done.\n",
      "LOGISTICREGRESSION(2023-12-07 23:49:01.656092): Seed 31730: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-08 00:05:59.302507, Seed: 38139:\n",
      "{'Accuracy': [0.963900238225156, 0.9638603122213497, 0.9638802752232529, 0.9638669665553175, 0.9638669665553175, 0.963807077549608, 0.9638736208892852, 0.9638470035534143, 0.9638536578873821], 'Precision': [0.9066494094806665, 0.9066630698678176, 0.9063274765549207, 0.9065365117031604, 0.9066681053085887, 0.9061842885641883, 0.9065854053179441, 0.9063898624966298, 0.9064825800884478], 'Recall': [0.8198576026528821, 0.8195162391495172, 0.8200526675119477, 0.8197113040085828, 0.8195650053642837, 0.81961377157905, 0.8197113040085828, 0.8197113040085828, 0.8196625377938165], 'F1': [0.8610719864785269, 0.860889833764504, 0.8610343061955965, 0.8609403810694529, 0.8609190102966037, 0.8607277289836889, 0.8609624298921812, 0.8608742413766612, 0.8608891620569556], 'FPR': [0.01333877878124711, 0.013331072958727614, 0.013392719538883581, 0.013354190426286102, 0.013331072958727614, 0.013408131183922572, 0.013346484603766607, 0.013377307893844588, 0.013361896248805597]}\n",
      "LOGISTICREGRESSION(2023-12-08 00:05:59.304138): Seed 38139 done.\n",
      "LOGISTICREGRESSION(2023-12-08 00:05:59.304865): Seed 45917: run_logistic_regression_multiple_seeds() Running...\n",
      "LR55:\n",
      " 2023-12-08 00:12:12.791463, Seed: 31730:\n",
      "{'Accuracy': [0.963900238225156, 0.9639202012270592, 0.9638603122213497, 0.9638802752232529, 0.9638203862175435, 0.9638336948854789, 0.9638736208892852, 0.9638802752232529, 0.9638802752232529, 0.9637871145477049], 'Precision': [0.9065178715833737, 0.9066645098673568, 0.9063999568663396, 0.90654659188956, 0.9062382056397261, 0.9062921227152639, 0.9064100490592485, 0.9064150943396226, 0.9066343042071198, 0.9062129220148851], 'Recall': [0.8200039012971813, 0.8200039012971813, 0.8198088364381156, 0.8198088364381156, 0.8196625377938165, 0.8197113040085828, 0.8199063688676484, 0.8199551350824149, 0.8197113040085828, 0.8194187067199844], 'F1': [0.8610933299193445, 0.8611594796681348, 0.8609325788031649, 0.8609987195902689, 0.8607789414385579, 0.8608301538934268, 0.8609909102547689, 0.8610200737402703, 0.8609844798442863, 0.8606330669944684], 'FPR': [0.013361896248805597, 0.01333877878124711, 0.013377307893844588, 0.013354190426286102, 0.013400425361403076, 0.013392719538883581, 0.013377307893844588, 0.013377307893844588, 0.01333877878124711, 0.013400425361403076]}\n",
      "LOGISTICREGRESSION(2023-12-08 00:12:12.793001): Seed 31730 done.\n",
      "LR55:\n",
      " 2023-12-08 00:21:10.009145, Seed: 45917:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Accuracy': [0.963900238225156, 0.9638603122213497, 0.9638802752232529, 0.9638669665553175, 0.9638669665553175, 0.963807077549608, 0.9638736208892852, 0.9638470035534143, 0.9638536578873821, 0.9638470035534143], 'Precision': [0.9066494094806665, 0.9066630698678176, 0.9063274765549207, 0.9065365117031604, 0.9066681053085887, 0.9061842885641883, 0.9065854053179441, 0.9063898624966298, 0.9064825800884478, 0.9063460397908017], 'Recall': [0.8198576026528821, 0.8195162391495172, 0.8200526675119477, 0.8197113040085828, 0.8195650053642837, 0.81961377157905, 0.8197113040085828, 0.8197113040085828, 0.8196625377938165, 0.8197600702233493], 'F1': [0.8610719864785269, 0.860889833764504, 0.8610343061955965, 0.8609403810694529, 0.8609190102966037, 0.8607277289836889, 0.8609624298921812, 0.8608742413766612, 0.8608891620569556, 0.8608813663482959], 'FPR': [0.01333877878124711, 0.013331072958727614, 0.013392719538883581, 0.013354190426286102, 0.013331072958727614, 0.013408131183922572, 0.013346484603766607, 0.013377307893844588, 0.013361896248805597, 0.013385013716364086]}\n",
      "LOGISTICREGRESSION(2023-12-08 00:21:10.011025): Seed 45917 done.\n"
     ]
    }
   ],
   "source": [
    "# Define the tasks\n",
    "\n",
    "base_seed = 42\n",
    "ROUNDS = 10\n",
    "\n",
    "\n",
    "n_workers = 3  # 0 uses all available processors\n",
    "\n",
    "batch1 = {\n",
    "    #'Logistic Regression': (run_logistic_regression_multiple_seeds, prng_create_seeds(n=ROUNDS,base_seed=base_seed)),\n",
    "    #'Logistic Regression4_b': (run_logistic_regression_multiple_seeds, prng_create_seeds(n=ROUNDS,base_seed=142)),\n",
    "    #'Logistic Regression4_c': (run_logistic_regression_multiple_seeds, prng_create_seeds(n=ROUNDS,base_seed=242)),\n",
    "    #'Logistic Regression4_d': (run_logistic_regression_multiple_seeds, prng_create_seeds(n=ROUNDS,base_seed=342)),\n",
    "    #'Logistic Regression4_e': (run_logistic_regression_multiple_seeds, prng_create_seeds(n=ROUNDS,base_seed=442)),\n",
    "    #'SVM': (run_svm_multiple_seeds, prng_create_seeds(n=ROUNDS,base_seed=base_seed)),\n",
    "    #'DecisionTree': (run_decision_tree_multiple_seeds, prng_create_seeds(n=ROUNDS,base_seed=base_seed)),\n",
    "    #'RandomForest': (run_random_forest_multiple_seeds, prng_create_seeds(n=ROUNDS,base_seed=base_seed)),\n",
    "}\n",
    "\n",
    "results1 = train_models_in_parallel(batch1, n_workers, X_train, y_train, X_test, y_test)\n",
    "results.update(results1)\n",
    "\n",
    "# XGboost, and LightGBM are multi-threaded by design (running them in threads may cause crashes)\n",
    "#results['AdaBoost'] = run_adaboost_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n",
    "results['XGBoost'] = run_xgb_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n",
    "results['LightGBM'] = run_lgb_multiple_seeds(prng_create_seeds(n=ROUNDS,base_seed=x), X_train, y_train, X_test, y_test, display_cm=False, display_graphs=False, verbose=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa2672eb",
   "metadata": {},
   "source": [
    "# Display results and produce outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d330d85",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "ce18fd3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'XGBoost': {'Accuracy': [0.9935452960513182,\n",
       "   0.9934055550379962,\n",
       "   0.993412209371964,\n",
       "   0.9934720983776734,\n",
       "   0.993412209371964,\n",
       "   0.9934720983776734,\n",
       "   0.993525333049415,\n",
       "   0.9934854070456088,\n",
       "   0.9935319873833828,\n",
       "   0.9935785677211568],\n",
       "  'Weighted Accuracy': [0.9935452960513182,\n",
       "   0.9934055550379962,\n",
       "   0.993412209371964,\n",
       "   0.9934720983776734,\n",
       "   0.993412209371964,\n",
       "   0.9934720983776734,\n",
       "   0.993525333049415,\n",
       "   0.9934854070456088,\n",
       "   0.9935319873833828,\n",
       "   0.9935785677211568],\n",
       "  'Precision': [0.9815143448683822,\n",
       "   0.9809730369201952,\n",
       "   0.9816862474084312,\n",
       "   0.9815518176885513,\n",
       "   0.9808791642026414,\n",
       "   0.9815043156596794,\n",
       "   0.981844195569589,\n",
       "   0.9813637035941429,\n",
       "   0.981370133070478,\n",
       "   0.9823270968060424],\n",
       "  'Recall': [0.9709841022139861,\n",
       "   0.9704964400663221,\n",
       "   0.9698137130595923,\n",
       "   0.9703989076367893,\n",
       "   0.9706427387106212,\n",
       "   0.9704476738515556,\n",
       "   0.9704964400663221,\n",
       "   0.9706915049253877,\n",
       "   0.9710328684287526,\n",
       "   0.9703989076367893],\n",
       "  'F1': [0.9762208276132576,\n",
       "   0.9757066163312332,\n",
       "   0.9757138651751546,\n",
       "   0.9759435003310528,\n",
       "   0.9757341046129713,\n",
       "   0.9759446801206445,\n",
       "   0.976137339055794,\n",
       "   0.9759984309495208,\n",
       "   0.9761741347190902,\n",
       "   0.9763265706645733],\n",
       "  'FPR': [0.002889683444810899,\n",
       "   0.002974447492525352,\n",
       "   0.002858860154732916,\n",
       "   0.0028819776222914033,\n",
       "   0.0029898591375643437,\n",
       "   0.002889683444810899,\n",
       "   0.002835742687174429,\n",
       "   0.0029128009123693863,\n",
       "   0.0029128009123693863,\n",
       "   0.0027586844619794716]},\n",
       " 'LightGBM': {'Accuracy': [0.9938846670836716,\n",
       "   0.9937449260703496,\n",
       "   0.993944556089381,\n",
       "   0.9937449260703496,\n",
       "   0.9937050000665434,\n",
       "   0.9937050000665434,\n",
       "   0.9937316174024142,\n",
       "   0.993645111060834,\n",
       "   0.9937648890722528,\n",
       "   0.9936584197287693],\n",
       "  'Weighted Accuracy': [0.9938846670836716,\n",
       "   0.9937449260703496,\n",
       "   0.993944556089381,\n",
       "   0.9937449260703496,\n",
       "   0.9937050000665434,\n",
       "   0.9937050000665434,\n",
       "   0.9937316174024142,\n",
       "   0.993645111060834,\n",
       "   0.9937648890722528,\n",
       "   0.9936584197287693],\n",
       "  'Precision': [0.9831286073701346,\n",
       "   0.9830157006023501,\n",
       "   0.9833744449925998,\n",
       "   0.9828726554787759,\n",
       "   0.9837273716490256,\n",
       "   0.9826769321883329,\n",
       "   0.983300395256917,\n",
       "   0.9820028598195355,\n",
       "   0.9830659096519377,\n",
       "   0.9829093603358854],\n",
       "  'Recall': [0.9718618940797815,\n",
       "   0.9709353359992198,\n",
       "   0.9720569589388471,\n",
       "   0.9710816346435189,\n",
       "   0.9699112454891251,\n",
       "   0.9709841022139861,\n",
       "   0.9705452062810884,\n",
       "   0.9712279332878182,\n",
       "   0.9710328684287526,\n",
       "   0.9703989076367893],\n",
       "  'F1': [0.9774627853937268,\n",
       "   0.9769381746810599,\n",
       "   0.9776829507553463,\n",
       "   0.9769415689545209,\n",
       "   0.9767704547686867,\n",
       "   0.9767955259026687,\n",
       "   0.9768811662494478,\n",
       "   0.9765856768087872,\n",
       "   0.977012340227178,\n",
       "   0.976614070820348],\n",
       "  'FPR': [0.00263539130166754,\n",
       "   0.0026508029467065316,\n",
       "   0.0025968621890700614,\n",
       "   0.0026739204142650188,\n",
       "   0.0025352156089140953,\n",
       "   0.0027047437043430014,\n",
       "   0.0026045680115895573,\n",
       "   0.0028126252196159418,\n",
       "   0.0026430971241870357,\n",
       "   0.002666214591745523]}}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b0ab5f6d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "771230a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "febd1afa",
   "metadata": {},
   "source": [
    "## Raw data display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6571d35d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# FIXED results measured before\n",
    "results_FIXED = {\n",
    "'Logistic Regression':\n",
    "{\n",
    "    'Accuracy': [0.9638669665553175,\n",
    "   0.9639202012270592,\n",
    "   0.9638270405515112,\n",
    "   0.9638336948854789,\n",
    "   0.9638536578873821,\n",
    "   0.9638403492194466,\n",
    "   0.9638137318835758,\n",
    "   0.9637804602137372,\n",
    "   0.9638736208892852,\n",
    "   0.963900238225156],\n",
    "  'Precision': [0.9061860114236447,\n",
    "   0.9066645098673568,\n",
    "   0.9061994609164421,\n",
    "   0.906599039551071,\n",
    "   0.9063072776280323,\n",
    "   0.9062971748975631,\n",
    "   0.9064084583018664,\n",
    "   0.9063393579714054,\n",
    "   0.9064100490592485,\n",
    "   0.9067810325295356],\n",
    "  'Recall': [0.8201014337267142,\n",
    "   0.8200039012971813,\n",
    "   0.8197600702233493,\n",
    "   0.8193699405052179,\n",
    "   0.8198576026528821,\n",
    "   0.8197600702233493,\n",
    "   0.8194187067199844,\n",
    "   0.8192236418609188,\n",
    "   0.8199063688676484,\n",
    "   0.8197113040085828],\n",
    "  'F1': [0.8609973377022323,\n",
    "   0.8611594796681348,\n",
    "   0.8608152396558789,\n",
    "   0.86078024539563,\n",
    "   0.8609176566980746,\n",
    "   0.8608593229886823,\n",
    "   0.8607212375781169,\n",
    "   0.8605824645885096,\n",
    "   0.8609909102547689,\n",
    "   0.8610506364777296],\n",
    "  'FPR': [0.013415837006442067,\n",
    "   0.01333877878124711,\n",
    "   0.013408131183922572,\n",
    "   0.01333877878124711,\n",
    "   0.013392719538883581,\n",
    "   0.013392719538883581,\n",
    "   0.013369602071325093,\n",
    "   0.013377307893844588,\n",
    "   0.013377307893844588,\n",
    "   0.013315661313688623]\n",
    "},\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "'SVM': {\n",
    "    \"Accuracy\": [\n",
    "        0.9883482612225343, # base_seed 42 n2\n",
    "        0.9883549155565019,\n",
    "        0.9882950265507925,\n",
    "        0.9882750635488894,\n",
    "        0.9882883722168249,\n",
    "        0.9882484462130185,\n",
    "        0.9882883722168249, # base_seed 142, pchranicky\n",
    "        0.9882817178828571,\n",
    "        0.9882351375450831,\n",
    "        0.9882551005469863  # base_seed 242, pcfitcrack\n",
    "\n",
    "    ],\n",
    "    \"Precision\": [\n",
    "        0.970026565084457,\n",
    "        0.9704524932276513,\n",
    "        0.9697318967677274,\n",
    "        0.9699157641395909,\n",
    "        0.9697303798737096,\n",
    "        0.9692038057085628,\n",
    "        0.9694480617048983,\n",
    "        0.9695876546921188,\n",
    "        0.9693887775551102,\n",
    "        0.9695344991732224\n",
    "\n",
    "    ],\n",
    "    \"Recall\": [\n",
    "        0.9437725543743295,\n",
    "        0.9433824246561981,\n",
    "        0.9436750219447967,\n",
    "        0.9433336584414318,\n",
    "        0.9436262557300302,\n",
    "        0.9438700868038623,\n",
    "        0.9439188530186287,\n",
    "        0.943723788159563,\n",
    "        0.9435774895152639,\n",
    "        0.9435774895152639\n",
    "\n",
    "    ],\n",
    "    \"F1\": [\n",
    "        0.9567194799416665,\n",
    "        0.9567260138476755,\n",
    "        0.9565260374187491,\n",
    "        0.9564400494437578,\n",
    "        0.9565002471576867,\n",
    "        0.9563692064433243,\n",
    "        0.9565131448902945,\n",
    "        0.9564809094279006,\n",
    "        0.9563090001482726,\n",
    "        0.9568226193124244\n",
    "\n",
    "    ],\n",
    "    \"FPR\": [\n",
    "        0.004608081866658447,\n",
    "        0.004538729463982986,\n",
    "        0.004654316801775422,\n",
    "        0.004623493511697439,\n",
    "        0.004654316801775422,\n",
    "        0.004739080849489875,\n",
    "        0.004700551736892396,\n",
    "        0.004677434269333909,\n",
    "        0.004708257559411891,\n",
    "        0.004685140091853404\n",
    "    ]\n",
    "},\n",
    "\n",
    "\n",
    "'DecisionTree': {\n",
    "  'Accuracy': [0.9824991016649144,\n",
    "   0.9824991016649144,\n",
    "   0.9825057559988821,\n",
    "   0.9824991016649144,\n",
    "   0.9825589906706238,\n",
    "   0.9824991016649144,\n",
    "   0.9825057559988821,\n",
    "   0.9825257190007852,\n",
    "   0.9824857929969789,\n",
    "   0.9825057559988821],\n",
    "  'Precision': [0.9650364203954215,\n",
    "   0.9651332223147377,\n",
    "   0.965183450429352,\n",
    "   0.9650848163180352,\n",
    "   0.9657794676806084,\n",
    "   0.9649880345437519,\n",
    "   0.9654256703983337,\n",
    "   0.96538261322228,\n",
    "   0.9651780137414117,\n",
    "   0.9650866330194079],\n",
    "  'Recall': [0.9045157514873695,\n",
    "   0.9044182190578367,\n",
    "   0.9044182190578367,\n",
    "   0.9044669852726032,\n",
    "   0.9042231541987711,\n",
    "   0.904564517702136,\n",
    "   0.9041743879840047,\n",
    "   0.9043694528430704,\n",
    "   0.9042719204135375,\n",
    "   0.9045157514873695],\n",
    "  'F1': [0.933796506066556,\n",
    "   0.9337898393837168,\n",
    "   0.9338133481030186,\n",
    "   0.9337931728929615,\n",
    "   0.9339881626999119,\n",
    "   0.933799838904551,\n",
    "   0.9337966810203722,\n",
    "   0.9338805519186223,\n",
    "   0.9337328163553049,\n",
    "   0.9338200125865324],\n",
    "  'FPR': [0.005178312733101131,\n",
    "   0.00516290108806214,\n",
    "   0.005155195265542644,\n",
    "   0.005170606910581636,\n",
    "   0.005062725395308695,\n",
    "   0.005186018555620627,\n",
    "   0.005116666152945165,\n",
    "   0.005124371975464661,\n",
    "   0.005155195265542644,\n",
    "   0.005170606910581636]\n",
    "},\n",
    "\n",
    "'RandomForest': {\n",
    "    \"Accuracy\": [\n",
    "        0.9846950318742597,\n",
    "        0.9846284885345826,\n",
    "        0.9844687845193575,\n",
    "        0.9846684145383888,\n",
    "        0.9847016862082274,\n",
    "        0.9845619451949055,\n",
    "        0.9844621301853897,\n",
    "        0.9846484515364857,\n",
    "        0.9846284885345826,\n",
    "        0.9845819081968086\n",
    "    ],\n",
    "    \"Precision\": [\n",
    "        0.9772965604026845,\n",
    "        0.9779867605337816,\n",
    "        0.9775068320369981,\n",
    "        0.977742782152231,\n",
    "        0.9783008458992276,\n",
    "        0.9773729525409491,\n",
    "        0.977355120054642,\n",
    "        0.9779902295529758,\n",
    "        0.9774349286314021,\n",
    "        0.977677399023058\n",
    "    ],\n",
    "    \"Recall\": [\n",
    "        0.9089534770311128,\n",
    "        0.907783087876719,\n",
    "        0.9070515946552229,\n",
    "        0.9083195162391495,\n",
    "        0.9080269189505511,\n",
    "        0.9078806203062518,\n",
    "        0.9071491270847557,\n",
    "        0.9079293865210183,\n",
    "        0.9083195162391495,\n",
    "        0.9077343216619526\n",
    "    ],\n",
    "    \"F1\": [\n",
    "        0.9418869068674516,\n",
    "        0.9415781487101669,\n",
    "        0.940962209743512,\n",
    "        0.941753463444231,\n",
    "        0.9418548774627583,\n",
    "        0.9413460079890783,\n",
    "        0.9409443840258985,\n",
    "        0.9416584477657235,\n",
    "        0.9416106364693392,\n",
    "        0.9414085219370337\n",
    "    ],\n",
    "    \"FPR\": [\n",
    "        0.0033366211509416517,\n",
    "        0.0032287396356687112,\n",
    "        0.0032980920383441728,\n",
    "        0.0032672687482661897,\n",
    "        0.003182504700551737,\n",
    "        0.00332120950590266,\n",
    "        0.00332120950590266,\n",
    "        0.0032287396356687112,\n",
    "        0.0033135036833831645,\n",
    "        0.0032749745707856856\n",
    "    ]\n",
    "},\n",
    "\n",
    "'AdaBoost':\n",
    "   {'Accuracy': [0.9902181290674616,\n",
    "   0.9902247834014294,\n",
    "   0.9902447464033325,\n",
    "   0.9902447464033325,\n",
    "   0.990231437735397,\n",
    "   0.9902447464033325,\n",
    "   0.9902447464033325,\n",
    "   0.9902181290674616,\n",
    "   0.9902380920693648,\n",
    "   0.990231437735397],\n",
    "  'Precision': [0.9705824186690398,\n",
    "   0.9705838730409848,\n",
    "   0.9707278481012658,\n",
    "   0.9707278481012658,\n",
    "   0.9707249530214618,\n",
    "   0.9707278481012658,\n",
    "   0.9707278481012658,\n",
    "   0.9705358908443741,\n",
    "   0.9706798516687268,\n",
    "   0.9707249530214618],\n",
    "  'Recall': [0.9573295620793913,\n",
    "   0.9573783282941578,\n",
    "   0.9573783282941578,\n",
    "   0.9573783282941578,\n",
    "   0.957280795864625,\n",
    "   0.9573783282941578,\n",
    "   0.9573783282941578,\n",
    "   0.9573783282941578,\n",
    "   0.9573783282941578,\n",
    "   0.957280795864625],\n",
    "  'F1': [0.9639104389669055,\n",
    "   0.963935875088994,\n",
    "   0.9640068745396513,\n",
    "   0.9640068745396513,\n",
    "   0.9639560007857003,\n",
    "   0.9640068745396513,\n",
    "   0.9640068745396513,\n",
    "   0.9639122109294447,\n",
    "   0.963983206894012,\n",
    "   0.9639560007857003],\n",
    "  'FPR': [0.00458496439909996,\n",
    "   0.00458496439909996,\n",
    "   0.004561846931541473,\n",
    "   0.004561846931541473,\n",
    "   0.004561846931541473,\n",
    "   0.004561846931541473,\n",
    "   0.004561846931541473,\n",
    "   0.0045926702216194555,\n",
    "   0.004569552754060968,\n",
    "   0.004561846931541473]},\n",
    "\n",
    "}\n",
    "\n",
    "    \n",
    "# UPDATE\n",
    "results = {**results_FIXED , **results}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "86fb9813",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_dict_by_key_priority(input_dict, key_priority):\n",
    "    sorted_dict = {key: input_dict[key] for key in key_priority if key in input_dict}\n",
    "    sorted_dict.update({key: input_dict[key] for key in input_dict if key not in key_priority})\n",
    "    return sorted_dict\n",
    "\n",
    "keys = [\"Logistic Regression\", \"SVM\", \"DecisionTree\", \"RandomForest\", \"AdaBoost\", \"XGBoost\", \"LightGBM\"]\n",
    "results = sort_dict_by_key_priority(results, keys)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "bef09d24",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': {'Accuracy': [0.9638669665553175,\n",
       "   0.9639202012270592,\n",
       "   0.9638270405515112,\n",
       "   0.9638336948854789,\n",
       "   0.9638536578873821,\n",
       "   0.9638403492194466,\n",
       "   0.9638137318835758,\n",
       "   0.9637804602137372,\n",
       "   0.9638736208892852,\n",
       "   0.963900238225156],\n",
       "  'Precision': [0.9061860114236447,\n",
       "   0.9066645098673568,\n",
       "   0.9061994609164421,\n",
       "   0.906599039551071,\n",
       "   0.9063072776280323,\n",
       "   0.9062971748975631,\n",
       "   0.9064084583018664,\n",
       "   0.9063393579714054,\n",
       "   0.9064100490592485,\n",
       "   0.9067810325295356],\n",
       "  'Recall': [0.8201014337267142,\n",
       "   0.8200039012971813,\n",
       "   0.8197600702233493,\n",
       "   0.8193699405052179,\n",
       "   0.8198576026528821,\n",
       "   0.8197600702233493,\n",
       "   0.8194187067199844,\n",
       "   0.8192236418609188,\n",
       "   0.8199063688676484,\n",
       "   0.8197113040085828],\n",
       "  'F1': [0.8609973377022323,\n",
       "   0.8611594796681348,\n",
       "   0.8608152396558789,\n",
       "   0.86078024539563,\n",
       "   0.8609176566980746,\n",
       "   0.8608593229886823,\n",
       "   0.8607212375781169,\n",
       "   0.8605824645885096,\n",
       "   0.8609909102547689,\n",
       "   0.8610506364777296],\n",
       "  'FPR': [0.013415837006442067,\n",
       "   0.01333877878124711,\n",
       "   0.013408131183922572,\n",
       "   0.01333877878124711,\n",
       "   0.013392719538883581,\n",
       "   0.013392719538883581,\n",
       "   0.013369602071325093,\n",
       "   0.013377307893844588,\n",
       "   0.013377307893844588,\n",
       "   0.013315661313688623]},\n",
       " 'SVM': {'Accuracy': [0.9883482612225343,\n",
       "   0.9883549155565019,\n",
       "   0.9882950265507925,\n",
       "   0.9882750635488894,\n",
       "   0.9882883722168249,\n",
       "   0.9882484462130185,\n",
       "   0.9882883722168249,\n",
       "   0.9882817178828571,\n",
       "   0.9882351375450831,\n",
       "   0.9882551005469863],\n",
       "  'Precision': [0.970026565084457,\n",
       "   0.9704524932276513,\n",
       "   0.9697318967677274,\n",
       "   0.9699157641395909,\n",
       "   0.9697303798737096,\n",
       "   0.9692038057085628,\n",
       "   0.9694480617048983,\n",
       "   0.9695876546921188,\n",
       "   0.9693887775551102,\n",
       "   0.9695344991732224],\n",
       "  'Recall': [0.9437725543743295,\n",
       "   0.9433824246561981,\n",
       "   0.9436750219447967,\n",
       "   0.9433336584414318,\n",
       "   0.9436262557300302,\n",
       "   0.9438700868038623,\n",
       "   0.9439188530186287,\n",
       "   0.943723788159563,\n",
       "   0.9435774895152639,\n",
       "   0.9435774895152639],\n",
       "  'F1': [0.9567194799416665,\n",
       "   0.9567260138476755,\n",
       "   0.9565260374187491,\n",
       "   0.9564400494437578,\n",
       "   0.9565002471576867,\n",
       "   0.9563692064433243,\n",
       "   0.9565131448902945,\n",
       "   0.9564809094279006,\n",
       "   0.9563090001482726,\n",
       "   0.9568226193124244],\n",
       "  'FPR': [0.004608081866658447,\n",
       "   0.004538729463982986,\n",
       "   0.004654316801775422,\n",
       "   0.004623493511697439,\n",
       "   0.004654316801775422,\n",
       "   0.004739080849489875,\n",
       "   0.004700551736892396,\n",
       "   0.004677434269333909,\n",
       "   0.004708257559411891,\n",
       "   0.004685140091853404]},\n",
       " 'DecisionTree': {'Accuracy': [0.9824991016649144,\n",
       "   0.9824991016649144,\n",
       "   0.9825057559988821,\n",
       "   0.9824991016649144,\n",
       "   0.9825589906706238,\n",
       "   0.9824991016649144,\n",
       "   0.9825057559988821,\n",
       "   0.9825257190007852,\n",
       "   0.9824857929969789,\n",
       "   0.9825057559988821],\n",
       "  'Precision': [0.9650364203954215,\n",
       "   0.9651332223147377,\n",
       "   0.965183450429352,\n",
       "   0.9650848163180352,\n",
       "   0.9657794676806084,\n",
       "   0.9649880345437519,\n",
       "   0.9654256703983337,\n",
       "   0.96538261322228,\n",
       "   0.9651780137414117,\n",
       "   0.9650866330194079],\n",
       "  'Recall': [0.9045157514873695,\n",
       "   0.9044182190578367,\n",
       "   0.9044182190578367,\n",
       "   0.9044669852726032,\n",
       "   0.9042231541987711,\n",
       "   0.904564517702136,\n",
       "   0.9041743879840047,\n",
       "   0.9043694528430704,\n",
       "   0.9042719204135375,\n",
       "   0.9045157514873695],\n",
       "  'F1': [0.933796506066556,\n",
       "   0.9337898393837168,\n",
       "   0.9338133481030186,\n",
       "   0.9337931728929615,\n",
       "   0.9339881626999119,\n",
       "   0.933799838904551,\n",
       "   0.9337966810203722,\n",
       "   0.9338805519186223,\n",
       "   0.9337328163553049,\n",
       "   0.9338200125865324],\n",
       "  'FPR': [0.005178312733101131,\n",
       "   0.00516290108806214,\n",
       "   0.005155195265542644,\n",
       "   0.005170606910581636,\n",
       "   0.005062725395308695,\n",
       "   0.005186018555620627,\n",
       "   0.005116666152945165,\n",
       "   0.005124371975464661,\n",
       "   0.005155195265542644,\n",
       "   0.005170606910581636]},\n",
       " 'RandomForest': {'Accuracy': [0.9846950318742597,\n",
       "   0.9846284885345826,\n",
       "   0.9844687845193575,\n",
       "   0.9846684145383888,\n",
       "   0.9847016862082274,\n",
       "   0.9845619451949055,\n",
       "   0.9844621301853897,\n",
       "   0.9846484515364857,\n",
       "   0.9846284885345826,\n",
       "   0.9845819081968086],\n",
       "  'Precision': [0.9772965604026845,\n",
       "   0.9779867605337816,\n",
       "   0.9775068320369981,\n",
       "   0.977742782152231,\n",
       "   0.9783008458992276,\n",
       "   0.9773729525409491,\n",
       "   0.977355120054642,\n",
       "   0.9779902295529758,\n",
       "   0.9774349286314021,\n",
       "   0.977677399023058],\n",
       "  'Recall': [0.9089534770311128,\n",
       "   0.907783087876719,\n",
       "   0.9070515946552229,\n",
       "   0.9083195162391495,\n",
       "   0.9080269189505511,\n",
       "   0.9078806203062518,\n",
       "   0.9071491270847557,\n",
       "   0.9079293865210183,\n",
       "   0.9083195162391495,\n",
       "   0.9077343216619526],\n",
       "  'F1': [0.9418869068674516,\n",
       "   0.9415781487101669,\n",
       "   0.940962209743512,\n",
       "   0.941753463444231,\n",
       "   0.9418548774627583,\n",
       "   0.9413460079890783,\n",
       "   0.9409443840258985,\n",
       "   0.9416584477657235,\n",
       "   0.9416106364693392,\n",
       "   0.9414085219370337],\n",
       "  'FPR': [0.0033366211509416517,\n",
       "   0.0032287396356687112,\n",
       "   0.0032980920383441728,\n",
       "   0.0032672687482661897,\n",
       "   0.003182504700551737,\n",
       "   0.00332120950590266,\n",
       "   0.00332120950590266,\n",
       "   0.0032287396356687112,\n",
       "   0.0033135036833831645,\n",
       "   0.0032749745707856856]},\n",
       " 'AdaBoost': {'Accuracy': [0.9902181290674616,\n",
       "   0.9902247834014294,\n",
       "   0.9902447464033325,\n",
       "   0.9902447464033325,\n",
       "   0.990231437735397,\n",
       "   0.9902447464033325,\n",
       "   0.9902447464033325,\n",
       "   0.9902181290674616,\n",
       "   0.9902380920693648,\n",
       "   0.990231437735397],\n",
       "  'Precision': [0.9705824186690398,\n",
       "   0.9705838730409848,\n",
       "   0.9707278481012658,\n",
       "   0.9707278481012658,\n",
       "   0.9707249530214618,\n",
       "   0.9707278481012658,\n",
       "   0.9707278481012658,\n",
       "   0.9705358908443741,\n",
       "   0.9706798516687268,\n",
       "   0.9707249530214618],\n",
       "  'Recall': [0.9573295620793913,\n",
       "   0.9573783282941578,\n",
       "   0.9573783282941578,\n",
       "   0.9573783282941578,\n",
       "   0.957280795864625,\n",
       "   0.9573783282941578,\n",
       "   0.9573783282941578,\n",
       "   0.9573783282941578,\n",
       "   0.9573783282941578,\n",
       "   0.957280795864625],\n",
       "  'F1': [0.9639104389669055,\n",
       "   0.963935875088994,\n",
       "   0.9640068745396513,\n",
       "   0.9640068745396513,\n",
       "   0.9639560007857003,\n",
       "   0.9640068745396513,\n",
       "   0.9640068745396513,\n",
       "   0.9639122109294447,\n",
       "   0.963983206894012,\n",
       "   0.9639560007857003],\n",
       "  'FPR': [0.00458496439909996,\n",
       "   0.00458496439909996,\n",
       "   0.004561846931541473,\n",
       "   0.004561846931541473,\n",
       "   0.004561846931541473,\n",
       "   0.004561846931541473,\n",
       "   0.004561846931541473,\n",
       "   0.0045926702216194555,\n",
       "   0.004569552754060968,\n",
       "   0.004561846931541473]},\n",
       " 'XGBoost': {'Accuracy': [0.9935452960513182,\n",
       "   0.9934055550379962,\n",
       "   0.993412209371964,\n",
       "   0.9934720983776734,\n",
       "   0.993412209371964,\n",
       "   0.9934720983776734,\n",
       "   0.993525333049415,\n",
       "   0.9934854070456088,\n",
       "   0.9935319873833828,\n",
       "   0.9935785677211568],\n",
       "  'Weighted Accuracy': [0.9935452960513182,\n",
       "   0.9934055550379962,\n",
       "   0.993412209371964,\n",
       "   0.9934720983776734,\n",
       "   0.993412209371964,\n",
       "   0.9934720983776734,\n",
       "   0.993525333049415,\n",
       "   0.9934854070456088,\n",
       "   0.9935319873833828,\n",
       "   0.9935785677211568],\n",
       "  'Precision': [0.9815143448683822,\n",
       "   0.9809730369201952,\n",
       "   0.9816862474084312,\n",
       "   0.9815518176885513,\n",
       "   0.9808791642026414,\n",
       "   0.9815043156596794,\n",
       "   0.981844195569589,\n",
       "   0.9813637035941429,\n",
       "   0.981370133070478,\n",
       "   0.9823270968060424],\n",
       "  'Recall': [0.9709841022139861,\n",
       "   0.9704964400663221,\n",
       "   0.9698137130595923,\n",
       "   0.9703989076367893,\n",
       "   0.9706427387106212,\n",
       "   0.9704476738515556,\n",
       "   0.9704964400663221,\n",
       "   0.9706915049253877,\n",
       "   0.9710328684287526,\n",
       "   0.9703989076367893],\n",
       "  'F1': [0.9762208276132576,\n",
       "   0.9757066163312332,\n",
       "   0.9757138651751546,\n",
       "   0.9759435003310528,\n",
       "   0.9757341046129713,\n",
       "   0.9759446801206445,\n",
       "   0.976137339055794,\n",
       "   0.9759984309495208,\n",
       "   0.9761741347190902,\n",
       "   0.9763265706645733],\n",
       "  'FPR': [0.002889683444810899,\n",
       "   0.002974447492525352,\n",
       "   0.002858860154732916,\n",
       "   0.0028819776222914033,\n",
       "   0.0029898591375643437,\n",
       "   0.002889683444810899,\n",
       "   0.002835742687174429,\n",
       "   0.0029128009123693863,\n",
       "   0.0029128009123693863,\n",
       "   0.0027586844619794716]},\n",
       " 'LightGBM': {'Accuracy': [0.9938846670836716,\n",
       "   0.9937449260703496,\n",
       "   0.993944556089381,\n",
       "   0.9937449260703496,\n",
       "   0.9937050000665434,\n",
       "   0.9937050000665434,\n",
       "   0.9937316174024142,\n",
       "   0.993645111060834,\n",
       "   0.9937648890722528,\n",
       "   0.9936584197287693],\n",
       "  'Weighted Accuracy': [0.9938846670836716,\n",
       "   0.9937449260703496,\n",
       "   0.993944556089381,\n",
       "   0.9937449260703496,\n",
       "   0.9937050000665434,\n",
       "   0.9937050000665434,\n",
       "   0.9937316174024142,\n",
       "   0.993645111060834,\n",
       "   0.9937648890722528,\n",
       "   0.9936584197287693],\n",
       "  'Precision': [0.9831286073701346,\n",
       "   0.9830157006023501,\n",
       "   0.9833744449925998,\n",
       "   0.9828726554787759,\n",
       "   0.9837273716490256,\n",
       "   0.9826769321883329,\n",
       "   0.983300395256917,\n",
       "   0.9820028598195355,\n",
       "   0.9830659096519377,\n",
       "   0.9829093603358854],\n",
       "  'Recall': [0.9718618940797815,\n",
       "   0.9709353359992198,\n",
       "   0.9720569589388471,\n",
       "   0.9710816346435189,\n",
       "   0.9699112454891251,\n",
       "   0.9709841022139861,\n",
       "   0.9705452062810884,\n",
       "   0.9712279332878182,\n",
       "   0.9710328684287526,\n",
       "   0.9703989076367893],\n",
       "  'F1': [0.9774627853937268,\n",
       "   0.9769381746810599,\n",
       "   0.9776829507553463,\n",
       "   0.9769415689545209,\n",
       "   0.9767704547686867,\n",
       "   0.9767955259026687,\n",
       "   0.9768811662494478,\n",
       "   0.9765856768087872,\n",
       "   0.977012340227178,\n",
       "   0.976614070820348],\n",
       "  'FPR': [0.00263539130166754,\n",
       "   0.0026508029467065316,\n",
       "   0.0025968621890700614,\n",
       "   0.0026739204142650188,\n",
       "   0.0025352156089140953,\n",
       "   0.0027047437043430014,\n",
       "   0.0026045680115895573,\n",
       "   0.0028126252196159418,\n",
       "   0.0026430971241870357,\n",
       "   0.002666214591745523]}}"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8c4fa67",
   "metadata": {},
   "source": [
    "## Draw table with averages and variances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "323380db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'table_with_variances.png'"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def calculate_averages_and_variances(results):\n",
    "    stats = {}\n",
    "    for classifier, metrics in results.items():\n",
    "        stats[classifier] = {}\n",
    "        for metric, values in metrics.items():\n",
    "            avg = np.mean(values)\n",
    "            var = np.var(values, ddof=1)  # Unbiased estimator, using sample variance\n",
    "            stats[classifier][metric] = {'average': avg, 'variance': var}\n",
    "    return stats\n",
    "\n",
    "def draw_table_with_variances(stats):\n",
    "    # Define the metrics\n",
    "    metrics = ['Precision', 'Recall', 'F1', 'FPR']\n",
    "    # Create headers for the table: 'Metric (Avg.)' followed by 'Metric (Variance)'\n",
    "    headers = ['Classifier']\n",
    "    for metric in metrics:\n",
    "        headers.extend([f'{metric} (Avg.)', f'{metric} (Variance)'])\n",
    "    \n",
    "    # Create the table data structure\n",
    "    cell_text = [headers]\n",
    "    \n",
    "    # Fill in the data rows\n",
    "    for classifier, metrics_data in stats.items():\n",
    "        row = [classifier]\n",
    "        for metric in metrics:\n",
    "            avg = metrics_data[metric]['average']\n",
    "            var = metrics_data[metric]['variance']\n",
    "            row.extend([f\"{avg:.6f}\", f\"{var:.2e}\"])\n",
    "        cell_text.append(row)\n",
    "\n",
    "    # Determine the figure width dynamically: base_width + (num_columns * width_per_column)\n",
    "    num_columns = len(headers)\n",
    "    width_per_column = 1.0  # starting point for width per column, may need adjustment\n",
    "    base_width = 4  # base width to account for the first column which typically needs more space\n",
    "    fig_width = base_width + (num_columns * width_per_column)\n",
    "\n",
    "    # Create the figure and axis\n",
    "    fig, ax = plt.subplots(figsize=(fig_width, 2 + 0.625 * len(cell_text)))  # Adjust the size as needed\n",
    "    ax.axis('off')  # Hide the axes\n",
    "\n",
    "    # Create the table and adjust the layout\n",
    "    table = ax.table(cellText=cell_text, loc='center', cellLoc='center')\n",
    "    table.auto_set_font_size(False)\n",
    "    table.set_fontsize(10)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    return fig\n",
    "\n",
    "# Calculate averages and variances\n",
    "stats = calculate_averages_and_variances(results)\n",
    "\n",
    "# Draw the table with variances\n",
    "fig = draw_table_with_variances(stats)\n",
    "\n",
    "# Save the figure to a file and show it\n",
    "output_file = \"table_with_variances.png\"\n",
    "fig.savefig(output_file)\n",
    "plt.show()\n",
    "\n",
    "# Return the path to the saved figure\n",
    "output_file\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4f8138b",
   "metadata": {},
   "source": [
    "## Shorthands for classifier names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f01bf503",
   "metadata": {},
   "outputs": [],
   "source": [
    "shorthands = {\n",
    "    'Logistic Regression': 'LR',\n",
    "    'DecisionTree': 'DT',\n",
    "    'RandomForest': 'RF',\n",
    "    'SVM': 'SVM',\n",
    "    'AdaBoost': 'ADAB',\n",
    "    'XGBoost': 'XGB',\n",
    "    'LightGBM': 'LGBM',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9529ab6a",
   "metadata": {},
   "source": [
    "## Generate LaTeX tabular code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "a41696e2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{|l|c|c|c|c|c|c|c|c|}\n",
      "\\hline\n",
      " & \\multicolumn{2}{c|}{\\textbf{Precision}} & \\multicolumn{2}{c|}{\\textbf{Recall}} & \\multicolumn{2}{c|}{\\textbf{F1}} & \\multicolumn{2}{c|}{\\textbf{FPR}} \\\\\n",
      "\\hline\n",
      "\\textbf{Classifier} & Avg. & Variance & Avg. & Variance & Avg. & Variance & Avg. & Variance \\\\\n",
      "\\hline\n",
      "Logistic Regression (LR) & 0.906419 & 4.00e-08 & 0.819711 & 8.24e-08 & 0.860887 & 2.92e-08 & 0.013373 & 1.06e-09 \\\\\n",
      "\\hline\n",
      "SVM & 0.969702 & 1.30e-07 & 0.943646 & 3.60e-08 & 0.956541 & 2.72e-08 & 0.004659 & 3.33e-09 \\\\\n",
      "\\hline\n",
      "DecisionTree (DT) & 0.965228 & 5.73e-08 & 0.904394 & 1.76e-08 & 0.933821 & 4.75e-09 & 0.005148 & 1.39e-09 \\\\\n",
      "\\hline\n",
      "RandomForest (RF) & 0.977666 & 1.13e-07 & 0.907915 & 3.11e-07 & 0.941500 & 1.13e-07 & 0.003277 & 2.55e-09 \\\\\n",
      "\\hline\n",
      "AdaBoost (ADAB) & 0.970674 & 5.82e-09 & 0.957354 & 1.72e-09 & 0.963968 & 1.56e-09 & 0.004570 & 1.51e-10 \\\\\n",
      "\\hline\n",
      "XGBoost (XGB) & 0.981501 & 1.71e-07 & 0.970540 & 1.17e-07 & 0.975990 & 4.98e-08 & 0.002890 & 4.37e-09 \\\\\n",
      "\\hline\n",
      "LightGBM (LGBM) & \\textbf{0.983007} & 2.11e-07 & \\textbf{0.971004} & 4.09e-07 & \\textbf{0.976968} & 1.23e-07 & \\textbf{0.002652} & 5.39e-09 \\\\\n",
      "\\hline\n",
      "\\end{tabular}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'table_with_variances.tex'"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_latex_tabular(stats):\n",
    "    # Define the metrics\n",
    "    metrics = ['Precision', 'Recall', 'F1', 'FPR']\n",
    "    \n",
    "    # Find the highest average for each metric except for FPR, where we find the lowest\n",
    "    highest_avg = {metric: max(metrics_data[metric]['average'] for metrics_data in stats.values()) for metric in metrics if metric != 'FPR'}\n",
    "    lowest_fpr = min(stats[classifier]['FPR']['average'] for classifier in stats)\n",
    "    \n",
    "    # Initialize the LaTeX table string\n",
    "    latex_table = \"\\\\begin{tabular}{|l|\" + \"c|c|\" * len(metrics) + \"}\\n\\\\hline\\n\"\n",
    "    \n",
    "    # Create the header row with multicolumns for average and variance\n",
    "    latex_table += \" & \" + \" & \".join([f\"\\\\multicolumn{{2}}{{c|}}{{\\\\textbf{{{metric}}}}}\" for metric in metrics]) + \" \\\\\\\\\\n\"\n",
    "    latex_table += \"\\\\hline\\n\"\n",
    "    latex_table += \"\\\\textbf{Classifier} & \" + \" & \".join([\"Avg. & Variance\" for _ in metrics]) + \" \\\\\\\\\\n\\\\hline\\n\"\n",
    "    \n",
    "    # Fill in the data rows\n",
    "    for classifier, metrics_data in stats.items():\n",
    "        cln = classifier\n",
    "        shorthand = shorthands[classifier]\n",
    "        if shorthand != classifier:\n",
    "            cln = f\"{classifier} ({shorthand})\"\n",
    "        row = [cln.replace('_', '\\\\_')]  # Escape underscores for LaTeX\n",
    "        for metric in metrics:\n",
    "            avg = metrics_data[metric]['average']\n",
    "            # Bold the highest average or the lowest FPR\n",
    "            if metric == 'FPR':\n",
    "                avg_str = f\"\\\\textbf{{{avg:.6f}}}\" if avg == lowest_fpr else f\"{avg:.6f}\"\n",
    "            else:\n",
    "                avg_str = f\"\\\\textbf{{{avg:.6f}}}\" if avg == highest_avg.get(metric, None) else f\"{avg:.6f}\"\n",
    "            var = metrics_data[metric]['variance']\n",
    "            # Display variance in scientific notation\n",
    "            var_str = \"{:.2e}\".format(var) if var > 0 else \"0.00e+00\"\n",
    "            row.extend([avg_str, var_str])\n",
    "        latex_table += \" & \".join(row) + \" \\\\\\\\\\n\\\\hline\\n\"\n",
    "    \n",
    "    # Close the LaTeX table string\n",
    "    latex_table += \"\\\\end{tabular}\"\n",
    "    \n",
    "    return latex_table\n",
    "\n",
    "# Calculate averages and variances\n",
    "stats = calculate_averages_and_variances(results)\n",
    "\n",
    "# Generate LaTeX tabular code for the table\n",
    "latex_tabular_code = generate_latex_tabular(stats)\n",
    "print(latex_tabular_code)\n",
    "\n",
    "# Save the LaTeX code to a file\n",
    "output_file = \"table_with_variances.tex\"\n",
    "with open(output_file, \"w\") as f:\n",
    "    f.write(latex_tabular_code)\n",
    "\n",
    "# Return the path to the saved LaTeX file\n",
    "output_file\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad9e6b5d",
   "metadata": {},
   "source": [
    "## Boxplots for boosted trees only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "36a09ce6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import copy\n",
    "\n",
    "#boosted_trees = [\"AdaBoost\", \"XGBoost\", \"LightGBM\"]\n",
    "boosted_trees = [\"XGBoost\", \"LightGBM\"]\n",
    "results_filtered = {shorthands[classifier]: results[classifier] for classifier in boosted_trees if classifier in results}\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Function to adjust the y-axis limits\n",
    "def get_ylim(data_list):\n",
    "    data_min = np.min(data_list)\n",
    "    data_max = np.max(data_list)\n",
    "    \n",
    "    # Inflate the range slightly to make sure all data points are comfortably within the plot\n",
    "    padding = (data_max - data_min) * 0.05\n",
    "    lower = data_min - padding\n",
    "    upper = data_max + padding\n",
    "    \n",
    "    # Ensure that the lower limit is not less than 0 since these metrics cannot be negative\n",
    "    lower = max(lower, 0)\n",
    "    return (lower, upper)\n",
    "\n",
    "\n",
    "# Adjust the plotting function to display results for all classifiers\n",
    "def plot_metric(metric, results, ax):\n",
    "    data = [results[classifier][metric] for classifier in results]\n",
    "    ax.boxplot(data, positions=range(1, len(results)+1), widths=0.5)\n",
    "    ax.set_title(metric)\n",
    "    # Set x-axis labels to classifier names\n",
    "    ax.set_xticks(range(1, len(results)+1))\n",
    "    ax.set_xticklabels(results.keys(), rotation=45)\n",
    "    \n",
    "    # Set y-axis limits\n",
    "    all_data = [item for sublist in data for item in sublist]  # Flatten the list\n",
    "    ax.set_ylim(*get_ylim(all_data))\n",
    "\n",
    "    \n",
    "# Display boxplots for each metric for all classifiers\n",
    "fig, axs = plt.subplots(2, 2, figsize=(7, 7))\n",
    "metrics = ['Precision', 'Recall', 'F1', 'FPR']\n",
    "for i, metric in enumerate(metrics):\n",
    "    plot_metric(metric, results_filtered, axs[i//2, i%2])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"classifier_comparison_boosted.png\")\n",
    "plt.savefig(\"classifier_comparison_boosted.pdf\", format=\"pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "847cb8b5",
   "metadata": {},
   "source": [
    "## Boxplots for all classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "c03348fe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "results_display = {shorthands[classifier]: results[classifier] for classifier in results}\n",
    "\n",
    "# Function to adjust the y-axis limits\n",
    "def get_ylim(data_list):\n",
    "    data_min = np.min(data_list)\n",
    "    data_max = np.max(data_list)\n",
    "    \n",
    "    # Inflate the range slightly to make sure all data points are comfortably within the plot\n",
    "    padding = (data_max - data_min) * 0.05\n",
    "    lower = data_min - padding\n",
    "    upper = data_max + padding\n",
    "    \n",
    "    # Ensure that the lower limit is not less than 0 since these metrics cannot be negative\n",
    "    lower = max(lower, 0)\n",
    "    return (lower, upper)\n",
    "\n",
    "\n",
    "# Adjust the plotting function to display results for all classifiers\n",
    "def plot_metric(metric, results_display, ax):\n",
    "    data = [results_display[classifier][metric] for classifier in results_display]\n",
    "    ax.boxplot(data, positions=range(1, len(results_display)+1), widths=0.5)\n",
    "    ax.set_title(metric)\n",
    "    # Set x-axis labels to classifier names\n",
    "    ax.set_xticks(range(1, len(results_display)+1))\n",
    "    ax.set_xticklabels(results_display.keys(), rotation=45)\n",
    "    \n",
    "    # Set y-axis limits\n",
    "    all_data = [item for sublist in data for item in sublist]  # Flatten the list\n",
    "    ax.set_ylim(*get_ylim(all_data))\n",
    "    \n",
    "    \n",
    "# Display boxplots for each metric for all classifiers\n",
    "fig, axs = plt.subplots(2, 2, figsize=(7, 7))\n",
    "metrics = ['Precision', 'Recall', 'F1', 'FPR']\n",
    "for i, metric in enumerate(metrics):\n",
    "    plot_metric(metric, results_display, axs[i//2, i%2])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"classifier_comparison_all.png\")\n",
    "plt.savefig(\"classifier_comparison_all.pdf\", format=\"pdf\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af1a720b",
   "metadata": {},
   "source": [
    "## Saved results\n",
    "\n",
    "### 20% of the dataset, 2 runs each\n",
    "{'Logistic Regression': {'Accuracy': [0.9643332446100612, 0.964399787064147],\n",
    "  'Precision': [0.9070018924033523, 0.9077131258457375],\n",
    "  'Recall': [0.8216997305902523, 0.8214548126377663],\n",
    "  'F1': [0.8622462092007196, 0.8624325019285163],\n",
    "  'FPR': [0.013244523158664766, 0.01312901859623455]},\n",
    " 'DecisionTree': {'Accuracy': [0.9767766835240884, 0.9768099547511312],\n",
    "  'Precision': [0.9338631120225583, 0.9349948612538541],\n",
    "  'Recall': [0.8922361009061964, 0.8912564290962528],\n",
    "  'F1': [0.9125751503006012, 0.9126018808777431],\n",
    "  'FPR': [0.009933392368998576, 0.009740884764948216]},\n",
    " 'RandomForest': {'Accuracy': [0.9792387543252595, 0.979138940644131],\n",
    "  'Precision': [0.931189229618549, 0.9315684315684316],\n",
    "  'Recall': [0.9147685525349009, 0.9135439627724712],\n",
    "  'F1': [0.9229058561897703, 0.9224681587733399],\n",
    "  'FPR': [0.010626419743579871, 0.010549416701959727]},\n",
    " 'SVM': {'Accuracy': [0.9833643864785734, 0.9831980303433591],\n",
    "  'Precision': [0.9538890296427667, 0.9536004056795132],\n",
    "  'Recall': [0.9221160911094783, 0.9211364192995346],\n",
    "  'F1': [0.937733499377335, 0.9370873302603713],\n",
    "  'FPR': [0.007007276787433104, 0.007045778308243176]},\n",
    " 'AdaBoost': {'Accuracy': [0.9860593558690445, 0.985926270960873],\n",
    "  'Precision': [0.9557213930348258, 0.9545454545454546],\n",
    "  'Recall': [0.940974773450894, 0.9412196914033799],\n",
    "  'F1': [0.9482907565099346, 0.9478357380688125],\n",
    "  'FPR': [0.006853270704192816, 0.007045778308243176]},\n",
    " 'XGBoost': {'Accuracy': [0.989353207346287, 0.9896526483896726],\n",
    "  'Precision': [0.9820650781450166, 0.9818599897802759],\n",
    "  'Recall': [0.9387705118785207, 0.9412196914033799],\n",
    "  'F1': [0.9599298772852491, 0.9611104164061524],\n",
    "  'FPR': [0.0026951064567050397, 0.0027336079775151117]},\n",
    " 'LightGBM': {'Accuracy': [0.9904178866116582, 0.9906507852009582],\n",
    "  'Precision': [0.9822109275730623, 0.9834689725330621],\n",
    "  'Recall': [0.94660788635807, 0.9470977222630419],\n",
    "  'F1': [0.9640808181591419, 0.9649407361197755],\n",
    "  'FPR': [0.0026951064567050397, 0.00250259885265468]}}\n",
    "\n",
    "### 20% of the dataset, 5 runs each\n",
    "{'Logistic Regression': {'Accuracy': [0.9642928439292511,\n",
    "   0.9642928439292511,\n",
    "   0.9643460786009929,\n",
    "   0.964226300589574,\n",
    "   0.9642396092575094],\n",
    "  'Precision': [0.9002855028021571,\n",
    "   0.9005396254364617,\n",
    "   0.9008360673087099,\n",
    "   0.9002327549724926,\n",
    "   0.9001586462189318],\n",
    "  'Recall': [0.8303101228788765,\n",
    "   0.8300175541252194,\n",
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    " 'XGBoost': {'Accuracy': [0.9919482558990671,\n",
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    "  'Recall': [0.9606007411741759,\n",
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9553d4d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9576d09c",
   "metadata": {},
   "outputs": [],
   "source": []
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