post-eval_experiments_test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f304736-244d-43de-b0ef-61de1da0782f",
   "metadata": {},
   "source": [
    "## Post-evaluation experiments on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a5596877-ef32-4185-bd41-e7096e61c634",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils_test import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c10bb4a-cda1-4b32-a906-e96533dbc869",
   "metadata": {},
   "source": [
    "### Post-evaluation for the SG step:  model 'roberta-base' during SG step is changed into 'roberta-large': \n",
    "This code corresponds to the post-evaluation results with robertalarge for the SG step, presented in section 5.5 of my thesis, applied to the best model before post-evaluation. model 'roberta-base' during SG step is changed into 'roberta-large'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dca60294-723a-47c8-87cb-2c860e316a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "data, substitutes_df = get_data_and_create_empty_df()\n",
    "\n",
    "model = 'roberta-large'\n",
    "model_name_str = get_str_for_file_name(model)\n",
    "\n",
    "nlp, lm_tokenizer, lm_model, fill_mask = instantiate_spacy_tokenizer_model_pipeline(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b43975b-001d-498b-bc03-c2e273dfab11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Option 3f: Bertscore with robertalarge:\n",
    "score_model = 'roberta-large'\n",
    "letter = 'f'\n",
    "substitutes_df, score_model_name_str = substitute_selection_phase_2_option_3(data, substitutes_df, lm_tokenizer, fill_mask, model_name_str, nlp, score_model, letter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec21223e-19e6-4f02-842f-a620bdfcbe74",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/SS_phase2_option3f_BSrobertalarge_robertalarge.tsv --output_file ./output/test/SS_phase2_option3f_BSrobertalarge_robertalarge.tsv"
   ]
  },
  {
   "cell_type": "raw",
   "id": "62f9f067-8314-4dd5-b0ef-9bf04eed4b81",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/SS_phase2_option3f_BSrobertalarge_robertalarge.tsv\n",
    "OUTPUT file = ./output/test/SS_phase2_option3f_BSrobertalarge_robertalarge.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.6021\n",
    "\n",
    "MAP@3 = 0.4259\n",
    "MAP@5 = 0.3124\n",
    "MAP@10 = 0.1881\n",
    "\n",
    "Potential@3 = 0.8333\n",
    "Potential@5 = 0.8951\n",
    "Potential@10 = 0.9408\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.2661\n",
    "Accuracy@2@top_gold_1 = 0.4005\n",
    "Accuracy@3@top_gold_1 = 0.4758"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e03bcdf0-c77f-4297-93c9-7a6aabdfb403",
   "metadata": {},
   "source": [
    "##### Results after post-evaluation with robertalarge during SG step:\n",
    "\n",
    "This post-evaluated model scores 0.6021 on ACC@1, which is reported in section 5.5 of the thesis. Based on this metric, the model is a bit inferior to the model that used roberta-base for the SG step (SS_phase2_option3f_BSrobertalarge_robertabase (ACC@1 score: 0.6263; specified in thesis in tables 5.1 and 5.2 in sections 5.1 and 5.2; model name in thesis: RB_BSrl)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22097079-bddc-4066-bcd6-981db0cde8bc",
   "metadata": {},
   "source": [
    "### Post-evaluation for the SR step: application of a variety of hypernym-hyponym relations:\n",
    "\n",
    "The code below corresponds to the results presented in sections 5.5 of the thesis.\n",
    "It concerns 8 models newly evaluated on the test set as as post-hoc evaluation, and 1 model that had already been evaluated in the regular evaluation process. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f522d7b-034a-45d7-9ce0-dca0cb4fa421",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = './predictions/test/SS_phase2_option3f_BSrobertalarge_robertabase.tsv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4b47300-b66d-404d-bd18-3e2da6ec18a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "posteval_Hyper1-Hypo exported to path './predictions/test/posteval_Hyper1-Hypo.tsv'\n",
      "\n",
      "posteval_Hyper2-Hypo exported to path './predictions/test/posteval_Hyper2-Hypo.tsv'\n",
      "\n",
      "posteval_Hyper3-Hypo exported to path './predictions/test/posteval_Hyper3-Hypo.tsv'\n",
      "\n",
      "posteval_Hyper4-Hypo exported to path './predictions/test/posteval_Hyper4-Hypo.tsv'\n",
      "\n",
      "posteval_Hyper5-Hypo exported to path './predictions/test/posteval_Hyper5-Hypo.tsv'\n",
      "\n",
      "posteval_Hyper1_2-Hypo exported to path './predictions/test/posteval_Hyper1_2-Hypo.tsv'\n",
      "\n",
      "posteval_Hyper1_2_3-Hypo exported to path './predictions/test/posteval_Hyper1_2_3-Hypo.tsv'\n",
      "\n",
      "posteval_Hyper1_2_3_4-Hypo exported to path './predictions/test/posteval_Hyper1_2_3_4-Hypo.tsv'\n",
      "\n",
      "posteval_Hyper1_2_3_4_5-Hypo exported to path './predictions/test/posteval_Hyper1_2_3_4_5-Hypo.tsv'\n",
      "\n"
     ]
    }
   ],
   "source": [
    "posteval_substitute_ranking_hyper(prediction, levels = [1])\n",
    "posteval_substitute_ranking_hyper(prediction, levels = [2])  #  equal to 'SS_no3_SR_option1b_Hyper2Hypo_robertabase' (in thesis: RB_BSrl_Hyper2-Hypo).\n",
    "posteval_substitute_ranking_hyper(prediction, levels = [3])\n",
    "posteval_substitute_ranking_hyper(prediction, levels = [4])\n",
    "posteval_substitute_ranking_hyper(prediction, levels = [5])\n",
    "\n",
    "posteval_substitute_ranking_hyper(prediction, levels=[1,2])\n",
    "posteval_substitute_ranking_hyper(prediction, levels=[1,2,3])\n",
    "posteval_substitute_ranking_hyper(prediction, levels=[1,2,3,4])\n",
    "posteval_substitute_ranking_hyper(prediction, levels=[1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6d4f404-1122-495d-9b81-04646ce07aab",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/posteval_Hyper1-Hypo.tsv --output_file ./output/test/posteval_Hyper1-Hypo.tsv"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f8692dcd-a5c3-4ece-a6d3-186757ee84a1",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/posteval_Hyper1-Hypo.tsv\n",
    "OUTPUT file = ./output/test/posteval_Hyper1-Hypo.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.672\n",
    "\n",
    "MAP@3 = 0.4551\n",
    "MAP@5 = 0.3453\n",
    "MAP@10 = 0.2112\n",
    "\n",
    "Potential@3 = 0.8629\n",
    "Potential@5 = 0.9274\n",
    "Potential@10 = 0.9677\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.2741\n",
    "Accuracy@2@top_gold_1 = 0.4166\n",
    "Accuracy@3@top_gold_1 = 0.4892"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0139ac74-7790-45e5-961a-4167b8482d6d",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/posteval_Hyper2-Hypo.tsv --output_file ./output/test/posteval_Hyper2-Hypo.tsv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f19fb61e-216a-44db-8e56-eebad3e2ca7d",
   "metadata": {},
   "source": [
    "NB This is the model with 2 level up hypernyms that had already been evaluated on the test set before postevaluation.\n",
    "(equivalent model name: SS_no3_SR_option1b_Hyper2Hypo_robertabase, in thesis: RB_BSrl_Hyper2-Hypo). See notebook: evaluations_SR_Hyper-Hypo_test.ipynb."
   ]
  },
  {
   "cell_type": "raw",
   "id": "ffe42650-041f-4678-9121-2bfd5e0f474a",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/posteval_Hyper2-Hypo.tsv\n",
    "OUTPUT file = ./output/test/posteval_Hyper2-Hypo.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.6075\n",
    "\n",
    "MAP@3 = 0.4205\n",
    "MAP@5 = 0.3244\n",
    "MAP@10 = 0.2023\n",
    "\n",
    "Potential@3 = 0.8521\n",
    "Potential@5 = 0.9274\n",
    "Potential@10 = 0.9677\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.25\n",
    "Accuracy@2@top_gold_1 = 0.3736\n",
    "Accuracy@3@top_gold_1 = 0.4596\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f083db83-e7f3-43ae-bce2-f3983770faae",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/posteval_Hyper3-Hypo.tsv --output_file ./output/test/posteval_Hyper3-Hypo.tsv"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3f98eab9-612d-485d-8b8a-87f095004d0e",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/posteval_Hyper3-Hypo.tsv\n",
    "OUTPUT file = ./output/test/posteval_Hyper3-Hypo.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.6102\n",
    "\n",
    "MAP@3 = 0.4215\n",
    "MAP@5 = 0.3239\n",
    "MAP@10 = 0.2018\n",
    "\n",
    "Potential@3 = 0.8413\n",
    "Potential@5 = 0.9247\n",
    "Potential@10 = 0.9677\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.2715\n",
    "Accuracy@2@top_gold_1 = 0.3844\n",
    "Accuracy@3@top_gold_1 = 0.4704"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e615542-aaba-4ad1-bf3d-31ba51cf1962",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/posteval_Hyper4-Hypo.tsv --output_file ./output/test/posteval_Hyper4-Hypo.tsv"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b93f2a2c-528e-402a-8516-bb3916652f19",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/posteval_Hyper4-Hypo.tsv\n",
    "OUTPUT file = ./output/test/posteval_Hyper4-Hypo.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.5967\n",
    "\n",
    "MAP@3 = 0.4184\n",
    "MAP@5 = 0.321\n",
    "MAP@10 = 0.201\n",
    "\n",
    "Potential@3 = 0.8387\n",
    "Potential@5 = 0.922\n",
    "Potential@10 = 0.9677\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.258\n",
    "Accuracy@2@top_gold_1 = 0.4059\n",
    "Accuracy@3@top_gold_1 = 0.4731\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3e2ec7e-718f-4b03-b505-291f74116059",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/posteval_Hyper5-Hypo.tsv --output_file ./output/test/posteval_Hyper5-Hypo.tsv"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d4c8b0e6-ff76-4e8f-b83d-89164c8b6742",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/posteval_Hyper5-Hypo.tsv\n",
    "OUTPUT file = ./output/test/posteval_Hyper5-Hypo.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.6048\n",
    "\n",
    "MAP@3 = 0.4233\n",
    "MAP@5 = 0.3238\n",
    "MAP@10 = 0.2022\n",
    "\n",
    "Potential@3 = 0.8413\n",
    "Potential@5 = 0.922\n",
    "Potential@10 = 0.9677\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.2688\n",
    "Accuracy@2@top_gold_1 = 0.4086\n",
    "Accuracy@3@top_gold_1 = 0.4704\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54afb18-1f35-4eec-963c-de5998690a1f",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/posteval_Hyper1_2-Hypo.tsv --output_file ./output/test/posteval_Hyper1_2-Hypo.tsv"
   ]
  },
  {
   "cell_type": "raw",
   "id": "080547c0-6d30-4012-b655-f002347e589e",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/posteval_Hyper1_2-Hypo.tsv\n",
    "OUTPUT file = ./output/test/posteval_Hyper1_2-Hypo.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.6666\n",
    "\n",
    "MAP@3 = 0.4514\n",
    "MAP@5 = 0.3463\n",
    "MAP@10 = 0.211\n",
    "\n",
    "Potential@3 = 0.8575\n",
    "Potential@5 = 0.9354\n",
    "Potential@10 = 0.9677\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.2661\n",
    "Accuracy@2@top_gold_1 = 0.4139\n",
    "Accuracy@3@top_gold_1 = 0.4865\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3171c83-0188-4c7c-a0ac-7d6bd3dcbc1d",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/posteval_Hyper1_2_3-Hypo.tsv --output_file ./output/test/posteval_Hyper1_2_3-Hypo.tsv"
   ]
  },
  {
   "cell_type": "raw",
   "id": "03e5df82-65bd-42b2-845a-79c5792e308d",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/posteval_Hyper1_2_3-Hypo.tsv\n",
    "OUTPUT file = ./output/test/posteval_Hyper1_2_3-Hypo.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.6586\n",
    "\n",
    "MAP@3 = 0.4495\n",
    "MAP@5 = 0.3439\n",
    "MAP@10 = 0.2103\n",
    "\n",
    "Potential@3 = 0.8575\n",
    "Potential@5 = 0.9354\n",
    "Potential@10 = 0.9677\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.2661\n",
    "Accuracy@2@top_gold_1 = 0.4059\n",
    "Accuracy@3@top_gold_1 = 0.4892\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b6dc14-6b1e-4386-92b8-58ccda6b5871",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/posteval_Hyper1_2_3_4-Hypo.tsv --output_file ./output/test/posteval_Hyper1_2_3_4-Hypo.tsv"
   ]
  },
  {
   "cell_type": "raw",
   "id": "98e27f88-26ac-43be-8161-5a6daee62f23",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/posteval_Hyper1_2_3_4-Hypo.tsv\n",
    "OUTPUT file = ./output/test/posteval_Hyper1_2_3_4-Hypo.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.6505\n",
    "\n",
    "MAP@3 = 0.4432\n",
    "MAP@5 = 0.3411\n",
    "MAP@10 = 0.2091\n",
    "\n",
    "Potential@3 = 0.8575\n",
    "Potential@5 = 0.9327\n",
    "Potential@10 = 0.9677\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.2607\n",
    "Accuracy@2@top_gold_1 = 0.3951\n",
    "Accuracy@3@top_gold_1 = 0.4865"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "398edac2-d5a1-41f0-aea5-44692c8a8add",
   "metadata": {},
   "source": [
    "python tsar_eval.py --gold_file ./data/test/tsar2022_en_test_gold_no_noise.tsv --predictions_file ./predictions/test/posteval_Hyper1_2_3_4_5-Hypo.tsv --output_file ./output/test/posteval_Hyper1_2_3_4_5-Hypo.tsv"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1976df55-d851-4cf7-8e4e-ca5d54f4516b",
   "metadata": {},
   "source": [
    "=========   EVALUATION config.=========\n",
    "GOLD file = ./data/test/tsar2022_en_test_gold_no_noise.tsv\n",
    "PREDICTION LABELS file = ./predictions/test/posteval_Hyper1_2_3_4_5-Hypo.tsv\n",
    "OUTPUT file = ./output/test/posteval_Hyper1_2_3_4_5-Hypo.tsv\n",
    "===============   RESULTS  =============\n",
    "\n",
    "MAP@1/Potential@1/Precision@1 = 0.6451\n",
    "\n",
    "MAP@3 = 0.4413\n",
    "MAP@5 = 0.3403\n",
    "MAP@10 = 0.2086\n",
    "\n",
    "Potential@3 = 0.8575\n",
    "Potential@5 = 0.9301\n",
    "Potential@10 = 0.9677\n",
    "\n",
    "Accuracy@1@top_gold_1 = 0.2607\n",
    "Accuracy@2@top_gold_1 = 0.3951\n",
    "Accuracy@3@top_gold_1 = 0.4892\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a75636f2-4169-45ba-b6bf-7387fbdeda96",
   "metadata": {},
   "source": [
    "##### Results after post-evaluation with various hypernym-hyponym relations:\n",
    "The model with 1-level up hypernyms performed the best on the test set. Model name: posteval_Hyper1-Hypo (ACC@1 score: 0.672). Model name in thesis: RB_BSrl_Hyper1 - Hypo. See table 5.5 in section 5.5 in thesis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71541a94-de15-467f-a91d-108d7e2f99d1",
   "metadata": {},
   "source": [
    "### Post-evaluation analysis on test set: Substitute Ranking (SR) step with CEFR levels:\n",
    "\n",
    "The code below corresponds to the post-evaluation analysis for the CEFR-all dataset, presented in section 6.4 of the thesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "04e8ef67-ebf3-4643-a9c2-0a9dadfb741f",
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_dataset = './cefr_all/cefr_all_combined.tsv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d26eb05e-c836-4145-bfdc-1f6fe40bd22a",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = './predictions/test/SS_phase2_option3f_BSrobertalarge_robertabase.tsv'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0496978-7f43-45fe-9b84-6e929f93e635",
   "metadata": {},
   "source": [
    "#### Complex word and substitutes: \n",
    "Code includes averages not found in the combined CEFR database, and statistical measures of variation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dbd7eea3-6791-4739-8a06-b1363dc6de27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On average, there were 6.088709677419355 substitutes of the provided 10 substitutes that had not been found in the combined CEFR dataset.\n",
      "Count and Percentage of rows with unavailable CEFR level for the complex word: 253 and 68.01075268817205%\n",
      "Average CEFR level for substitutes (except level 7): 3.6608618961086274\n",
      "Median CEFR level for substitutes (except level 7): 3.728168684924261\n",
      "Standard Deviation of CEFR level for substitutes (except level 7): 0.8639482364847901\n",
      "Kurtosis of CEFR level for substitutes (except level 7): 0.20093206190829704\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CEFR level for complex words: 4.3234102185155665\n",
      "Median CEFR level for complex words: 4.0\n",
      "Standard Deviation of CEFR level for complex words: 0.7150626125397226\n",
      "Kurtosis of CEFR level for complex words: 0.4238483388516179\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "posteval_analysis_cefr_weighted_stats_complexword_substitutes(weighted_dataset, prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255813fc-5388-4b33-9997-f3a935556b88",
   "metadata": {},
   "source": [
    "#### Gold labels: \n",
    "\n",
    "Code includes averages of gold labels not found in the combined CEFR database, and statistical measures of variation for gold labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dd986f76-1d20-4191-ac3f-ac22c5207938",
   "metadata": {},
   "outputs": [],
   "source": [
    "gold = './data/test/tsar2022_en_test_gold_no_noise.tsv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ba71ddb0-bdaa-452c-8ad9-b4030ea37bea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CEFR level for gold substitutes (except level 7): 3.5747650935785895\n",
      "Median CEFR level for gold substitutes (except level 7): 3.633617439763973\n",
      "Standard Deviation of CEFR level for gold substitutes (except level 7): 0.9118945563913673\n",
      "Kurtosis of CEFR level for gold substitutes (except level 7): -0.12481104762541229\n"
     ]
    },
    {
     "data": {
      "image/png": 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Vq1Y1MmfOHO86KgnFdOrUKaNDhw6Gr6+v4e7ubhQvXtyYOHFirOuoTJw4McHP9Kh79+4ZU6ZMMapVq2Zkz57dcHNzM3LmzGlUr17dGD9+vHH16tVEffbYZuAcPnzYqF+/vpEpUyYjZ86cRvfu3Y0VK1YkOOvn3r17Ru/evY1SpUoZ2bJlM7y8vIzixYsbo0aNMm7fvh1VLjIy0pgwYYLx5JNPGpkyZTIqVKhgbNy4Md51VN544w3Dz8/P8PT0NKpXr27s27cvqty///5rdO3a1XjmmWeMLFmyGFmzZjVKlSplfPHFF9H+nJ08edJo0KCB4e3tHe86Kg8NHz7cyJcvX9TfhYefPTw83Bg2bJhRoEABw8vLy6hZs6YRHBwc67OcNGmSUaRIEcPV1TXGe2zZssVo0qSJkTNnTsPd3d144oknjCZNmkTNhLP1eYokxGIYNq4DLpLGzJ07l4CAAIYNG8b48eOTXN/vv/9O9erVKVOmDGvWrMHLyysZohQRkfhojIqkW126dGHcuHFMmDCBDz74IMn1lSxZkhUrVrBr1y5atmxJREREMkQpIiLxUYuKiIiIOC21qIiIiIjTUqIiIiIiTkuJioiIiDgtJSoiIiLitNL0gm+RkZGcP38eb29vm5f0FhEREccyDIObN2+SL18+XFzibzNJ04nK+fPnbdoUTkRERJzPmTNnyJ8/f7xl0nSi4u3tDZgf9NEluEVERMR5hYWFUaBAgajv8fik6UTlYXdPtmzZlKiIiIikMbYM29BgWhEREXFaSlRERETEaSlREREREaelREVERESclhIVERERcVpKVERERMRpKVERERERp6VERURERJyWEhURERFxWkpURERExGkpURERERGnpURFREREnJYSFREREYnd+vVw965DQ1CiIiIiItFFRMCQIdCggfmrA7k59N1FRETEuYSEQNu2sHeveZwpE0RGgotj2jaUqIiIiIgpKAi6dYPQUMiRAwIDoVkzh4akrh8REZGM7t49GDAAWrUyk5QXX4SDBx2epIASFRERkYzt77+hShWYOtU8HjYMtm6FQoUcG9f/U9ePiIhIRrV4MfToATdvgq8vzJ0LjRs7Oqpo1KIiIiKS0dy9C717Q7t2ZpJSrRoEBztdkgJKVERERDKWo0fNMSgzZ4LFAu++C5s2Qf78jo4sVur6ERERySjmzTNbUm7fBj8/87hBA0dHFS+1qIiIiKR3d+5A9+7QubOZpNSuDb/95vRJCihRERERSd8OH4aKFeHbb82unlGjzKXx/f0dHZlN1PUjIiKSHhmGuWBbv37m4Nm8eWH+fKhTx9GR2UWJioiISHpz6xb07Qvff28e169v/pwnj2PjSgR1/YiIiKQnhw6ZXT3ff2/uz/PRR7B2bZpMUkAtKiIiIumDYcDXX8PAgeaS+PnywcKFUKOGoyNLEiUqIiIiaV1YGPTqBYsWmceNGsF335lTkNM4df2IiIikZQcPQvnyZpLi6grjx8OqVekiSQG1qIiIiKRNhgHTpsGQIRARAQUKmMlKlSqOjixZKVERERFJa27cMDcT/PFH87hZM5gzB3LmdGhYKUFdPyIiImnJ3r1QrpyZpLi7w+efw/Ll6TJJAbWoiIiIpA2GAV9+CcOGwf37ULgwLF4MlSo5OrIUpURFRETE2V27Bt26wYoV5nHLlvDNN5A9u0PDSg3q+hEREXFmu3ZB2bJmkuLhAVOmmN0+GSBJASUqIiIizikyEiZOhOrV4fRpKFoUdu6E/v3NzQUzCHX9iIiIOJsrV6BrV1i92jxu2xZmzYJs2RwaliOoRUVERMSZbNsGZcqYSYqnJ8yYYS6FnwGTFFCiIiIi4hwiI+GTT6B2bTh3DooVg927zaXxM1BXz+PU9SMiIumS1QqbN5uvyEhz7OmNG+a1uH7OmRNy54arV8HXN/5f/fwgb17zvkuXzPti+9nf31wsdscOuHDBPK5e3by+bZt5rmCmS1SZ3hnL+nUAhFTrxNl3p1Pl+ay4PvJ5HpZ/WIfrw4uPfW5bytny/JKjniQzHOzs2bNGx44djZw5cxpeXl5G6dKljX379tl0b2hoqAEYoaGhKRyliIikJUuXGoavr2GYi484/uXqGv3Y1/e/+GqyyTiHv2GAcRsv4zW+MSDSAMPIn9/8LEuXmj8/WsfDa49/blvK2fL8kqOeuNjz/W0xDMNwQH4EwPXr1ylbtiy1a9emT58+5M6dmxMnTlC4cGGKFi2a4P1hYWH4+PgQGhpKtgzadyciItEFBUGrVo6OImEuWHmfjxjJB7gSyZ+UoA1LOMxzUWUsFjNNiM3D3qAffzSXVQkKgtatY5Z/vFxCkque+Njz/e3QROWdd95h+/btbNu2LVH3K1EREZFHWa1QqJA5xMOZ5eEi8+lIXTYC8C2vMYAp3CGLXfVYLJA/P/z9tzl7+ezZ+MuFhMTffWO1mgveJrWehNjz/e3QwbQrV66kQoUKvPrqq+TOnZuyZcvy9ddfx1k+PDycsLCwaC8REZGHtm1z/iSlLhv4jdLUZSO3yEJn5tKdb+1OUsBs9ThzxtxEOa7k4tFyCbULbNuWPPUkJ4cmKv/88w/Tp0/n6aef5ueff6Z379688cYbzJ07N9byY8eOxcfHJ+pVoECBVI5YRESc2YULjo4gbq484EPeZx0NyMMlDlGSCuxjHp2TXPeJE7aVS+j52Pr8UvM5OzRRiYyMpFy5cnzyySeULVuWXr160aNHD6ZPnx5r+eHDhxMaGhr1OnPmTCpHLCIizszf39ERxC4f5/iFurzPx7hgMJOevMBujvJMstRvw7BOIOHnY+vzS83n7NBExd/fnxIlSkQ79+yzz3L69OlYy3t6epItW7ZoLxERkYeqV4cnnnB0FNG9xBqCKUNNtnKTrLRjIb2ZyT28kly3xQIFCkDfvubYkbiWW3lY7uG06LhUr5489SQnhyYqVatW5ejRo9HOHTt2jEKFCjkoIhERSctcXWHyZEdHYXLjPuN4mzU0xo8rHKQM5TjAYtrZXMejCcPjycPD40mTzL0Kv/wy4XIJDYB1dU2eepKTQxOVwYMHs2vXLj755BP+/vtvFixYwKxZs+jXr58jwxIRkTSsZUtYutRcmM1RCnCazdTibSYA8JWlH5XZyd88DZixPR5fbOfy5zc/y9KlMVuK8uePPlW4ZUvzOKFyCUmuepKLQ6cnA6xatYrhw4dz/PhxihQpwpAhQ+jRo4dN92p6soiIxMVRK9O6rfkfVWYF4H7zOhFe2Tj+9jc8Pbx1vCvTxnfuYetFelqZNs2so5JUSlRERMRpRETA8OHw+efmcYUKsHgxPPmkY+NyQvZ8f2uvHxERkaQKCYF27WDPHvN40CAYP94cPCJJokRFREQkKYKCoFs3CA01+5ECA6F5c0dHlW44dDCtiIhImhUeDgMGmBsLhYbCiy9CcLCSlGSmREVERMRef/8NVarA1Knm8dChsHWrudGQJCt1/YiIiNhjyRJ4/XW4edOcAvTdd9CkiaOjSrfUoiIiImKLu3ehTx9o29ZMUqpVM7t6lKSkKCUqIiIiCTl61ByDMmOGeTx8OGzaZK6CJilKXT8iIiLxmT8fevWC27fNVd6+/x4aNnR0VBmGWlRERERic+eOORalUyczSalVy+zqUZKSqpSoiIiIPO7wYahUCb75xtyNb9Qo2LAB8uVzdGQZjrp+REREHhUYCP36mS0qefOaXT916jg6qgxLLSoiIiIAt25BQAC89pqZpNSrZ3b1KElxKCUqIiIiv/8OFSvC3Lng4gIffQRr10KePI6OLMNT14+IiGRchgGzZ8Mbb8C9e+YYlIULoUYNR0cm/0+JioiIZEw3b5rTjhcuNI9feslsUfHzc2xcEo26fkREJOM5eBDKlTOTFFdXGDcOVq9WkuKE1KIiIiLpitUKmzebLzCXP6lVy8xHMAyYPh2GDDF3Py5QABYtMjcYFKekREVERNKNoCDo2ROuXv3v3EcfmXsHfvtFKM1Wvg4//mheaNoU5swxL4rTUqIiIiLpQlAQtGoV+7XCV/fxfJc2QAi4ucGECTBokLmYmzg1JSoiIpLmWa3mxJ2YDN5gMhMZigf3Oe1amCe2Lsa1cqXUDlESSYNpRUQkzdu2Dc6di34uO9cJoiVfMggP7hPEK5SyHmRbuJKUtESJioiIpHkXLkQ/foFdHKQsr7CccDzozxRasZRQsscoK85NiYqIiKR5/v7mrxYieZNP2UZ1CnOKvylKFXbwFf0BS7SykjYoURERkTSvenV4Lu9VVtKMTxmKOw9YTBvKs58DlI8qlz+/WVbSDg2mFRGRNM9156/sud+ezJzlHp4MYhIz6cXDVpSHvvzy/9dTkTRDLSoiIpJ2RUbC2LFQqxaZr57lZr5iNPTZzUx682iS4usLS5dCy5aOC1USRy0qIiKSNl26BF26wM8/m8cdO+I9fTobM3vHvTKtpDlKVEREJO3ZsgXatzen+3h5wdSp8NprYLHgCtSta74k7VPXj4iIpB1WK3zwAdSpYyYpzz4Le/ZAt25aZTadUouKiIikDRcvQseOsHGjedy1q9mSkiWLQ8OSlKVERUREnN+GDdCpE/z7L2TObO6A3KWLo6OSVKCuHxERcV4PHsCIEdCggZmkPP887N+vJCUDUYuKiIg4p3PnoEMH2LrVPO7Rw1wIxcvLsXFJqlKiIiIizmftWujcGa5cgaxZYdYsc5aPZDhKVERExHncvw8jR8K4ceZxmTKweDEUKxbvbVaruW7Kxo1w+jQULGhODNL6KWmfEhUREXEOZ85Au3awY4d53LcvfPYZZMoU721BQdCzJ1y9Gv38J5+YK9LOmqUVadMyDaYVERHHW7XKbD3ZsQOyZYMffoCvvrIpSWnVKmaS8tDVq+b1oKDkD1lShxIVERFxnIgIePNNaNoUrl2DChXg4EFo3TrBW61WeOMN295m4ECzvKQ9SlRERMQxTp6EGjXg88/N44ED4ddf4cknbbp92zZzYpAtzp41y0vaozEqIiKS+pYtM5e9v3EDsmeHOXOgRQu7qrhwwb63tLe8OAe1qIiISOoJDzf7a1q2NJOUF16A4GC7kxQAf/+ULS/OQYmKiIikjhMnoGpVmDLFPH7rLbM/plChRFVXvTo88YRtZfPnN8tL2qNERUREUt4PP0C5cuby9zlzmrN8Jk4Ed/dEV+nqCpMn21b2yy+1nkpapURFRERSzr175noobdpAWJjZohIcDE2aJEv1LVvC0qXmeimx8fU1r2sdlbRLg2lFRCRlHDtmJii//WYeDx8OH3wAbsn71dOyJTRvrpVp0yuHJiqjR49mzJgx0c7lyZOHixcvOigiEZH0KSICJk0yJ9ecPGkeP2SxmK/ISHBxif7FbrGYx66u5jokVqv5s5ubWTYy8r/rnp7mWFmrFV69v4BPb/Uiq3GLKy5+DPb9nq3zG2JZAJkzm2UfxuDp+d+9j/+aKdN/exDeu2f+7OdnxvUoFxdzqIuSk/TH4S0qzz33HBs2bIg6dtWfLhGRZDVsmDkcxBaRkfDgQeLfy4s7fMlAejAbgE3UomPkfC5czpf4Su2gZfPTH4cnKm5ubuTNm9fRYYiIpEv2JClJ9QxHWEIbSvIHkVj4kBF8wEgiSd3/gD5cNl9jU9IHhw+mPX78OPny5aNIkSK0a9eOf/75x9EhiYikCxERqZekdOE79lGBkvzBRfJQn/WMZkyqJymP0rL56YNDE5UXXniBuXPn8vPPP/P1119z8eJFqlSpwtU4dpcKDw8nLCws2ktERGI3bVrKv0dmbhNIAN/RlSzcYQN1KUMwG6mb8m+eAC2bnz44NFFp1KgRrVq1omTJktSrV4/Vq1cD8N1338VafuzYsfj4+ES9ChQokJrhioikKWvXpmz9z/M7+6hAAHOx4sL7fEhDfuZfnKc7X8vmp30O7/p5VJYsWShZsiTHjx+P9frw4cMJDQ2Nep05cyaVIxQRSRusVli/PqVqN+jObPZQiWf5i3Pkow4b+Zj3HdrVExstm5/2OXww7aPCw8M5cuQI1eNY59jT0xNPT89UjkpEJO3ZuNGcwZPcsnKTGfSmIwsAWMNLdGEuV/BL/jdLIi2bnz44tEXlrbfeYsuWLYSEhLB7925at25NWFgYAQEBjgxLRCTN+/775K+zNMHspzwdWcADXHmbcTRhtVMmKaBl89MLh7aonD17lvbt23PlyhX8/Px48cUX2bVrF4USuUGViIiYkneugUFvZvAFg8lEOGfITzsWsYOqyfkmyUbrqKQvDk1UFi1a5Mi3FxFJt/LZsb6aq2vcK9P6EMr0Bz1oaf0BgNUuL9MnUyBh7r7kiGdlWldXc1XZx1eQtVi0Mq3Yx6nGqIiISPKw9Yt67lzo3DmOi/v2Qdu28M8/5pr548fTZPBgTj+eJYikICUqIiLpjNUKtjZYx7rKg2HAlCnw1ltw/77ZVLF4MbzwQrLGKWILJSoiIunMtm1w5UrC5fz8YpkVc/06dO8Oy5aZxy1awLffQo4cyR2miE2cah0VERFJOlsXOevY8bEuot27oWxZM0nx8IDJkyEoSEmKOJQSFRGRdMbWRc6aN///HwwDPvsMqlWDU6fgySdhxw4YMCDmqFWRVKZERUQknale3VzsLD4FCvx/t8/Vq9CsmTke5cEDePVVOHAAypdPlVhFEqJERUQknXF1hfbt4y/Trh247toOZcrAqlXmfODp081Bsz4+qRKniC2UqIiIpDNWqzn+NS4WIsn61TiMmjXNLYaffhp27YLevdXVI05Hs35ERNKZzZvNHp3Y5OIyc+lCozv/v7Vyhw4wYwZ4e6dafCL2UIuKiEg6M21a7OdrsIVgytCItdwlE0GNZ8O8eUpSxKkpURERSUesVlixIvo5F6y8z4dspA5PcJ7DPEtF9rIsZ3d19YjTU6IiIpKObNxoJisP5eEiP9OQDxmJK5EEEkBF9vInz1OwoOPiFLGVxqiIiKQj33333891+IX5dCQv/3KbzPRlGnMJ+O96HQcEKGIntaiIiKQjJ0+aXT2jGcV66pOXf/md56nAvmhJipubucuwiLNTi4qISDoRFAT/bD/PL3SgFlsA+JrXGciX3CVztLKVK9u+w7KIIylRERFJB4KCYGarn/mNTvhxhZtkpRczWUiHWMu//34qByiSSEpURETSOGv4A052GMHPjAMgmNK0YQnHKRZreS8vqFs3NSMUSTyNURERcUK3bsHLL5tjSSyWuF8FLGfYmakWQ8LNJOUr+vIiu+JMUsBciFbdPpJWqEVFRMTJlCsHBw8mXK4xq5lLF3y5RijZeJ3Z/MirCd7n4ZEMQYqkEiUqIiJOxNMTIiLiL+PGfcYynLf4DIB9lKcti/mHoja9R8WKSY1SJPUoURERcRK2JCmFOMki2vEiuwH4kjcYxgQi8LT5fT77LClRiqQuJSoiIk6gUKGEk5TmLGcOr5GDG1wnO68xhxW0sOt9mjc3B9OKpBV2D6Y9c+YMZ8+ejTres2cPgwYNYtasWckamIhIRtG0KZw+Hfd1D8L5gkEs5xVycINdvEBZDtqdpDRtCsuXJylUkVRnd6LSoUMHNm3aBMDFixepX78+e/bs4d133+WDDz5I9gBFRNKzxYth1aq4rxfhH7ZTlUF8CcCnvEkNtnKKwna9T5EisHJlEgIVcRC7E5U//viDSpUqAbBkyRKef/55duzYwYIFCwgMDEzu+ERE0i2rFbp2jft6K37kIGWpwH6ukpOX+R9D+ZT72D9t59ChxMcp4kh2Jyr379/H09MctLVhwwaaNWsGwDPPPMOFCxeSNzoRkXRs82a4dy/meU/u8RV9+ZFX8SGMX6lKGYJZzcuJep+KFSFr1qTFKuIodicqzz33HDNmzGDbtm2sX7+el156CYDz58/j6+ub7AGKiKRXDRvGPPcUx9lJZfoyHYBPGE5tNnGWAol6j4oVYc+epEQp4lh2Jyrjx49n5syZ1KpVi/bt21O6dGkAVq5cGdUlJCIi8StUyOz6eVQ7FnKAcpQlmMvkoiFreY9PeIC7zfW6uIC3NzRpAjdvKkmRtM/u6cm1atXiypUrhIWFkSNHjqjzPXv2JHPmzPHcKSIiEHOWTybuMpk36MFsADZTkw4s4AL5oso0aRL/oFuR9CpRe/0YhsH+/fuZOXMmN2/eBMDDw0OJiohIAh6f5fMMR9hDJXowm0gsfMAI6rEhWpKSJYuSFMm47G5ROXXqFC+99BKnT58mPDyc+vXr4+3tzYQJE7h37x4zZsxIiThFRNI8qxXatfvvuDNzmU4fsnCHi+ShE/P4hXox7gsKSsUgRZyM3S0qAwcOpEKFCly/fh2vR5Y3fOWVV/jll1+SNTgRkfSkeHHz18zc5lteYy4BZOEOG6hLGYJjTVIyZYK6dVM5UBEnYneLyq+//sr27dvxeGz7zUKFCnHu3LlkC0xEJD1p3hxOnIDn+IMltKEER7DiwmhG8wnvEolrrPd9/z24xn5JJEOwO1GJjIzE+vhQdeDs2bN4e3snS1AiIunJ3buwcqVBN75lCgPIzF3O4097FrKVmnHeN2QItG6dioGKOCG7u37q16/PpEmToo4tFgu3bt1i1KhRNG7cODljExFJF94feJPv6cw3vE5m7rKWhpQhON4kpXFj7XIsAmAxDMOw54bz589Tu3ZtXF1dOX78OBUqVOD48ePkypWLrVu3kjt37pSKNYawsDB8fHwIDQ0lW7Zsqfa+IiI2++03TlRoQ9EHx3iAK+/zERMYhhHP/xP9/ODSpVSMUSSV2fP9bXeiAnD37l0WLlzIgQMHiIyMpFy5cnTs2DHa4NrUoERFRJyWYcDMmRiDBmEJD+cM+WnHInZQ1aZbRdKzFE9UnIUSFRFxSqGh0LMnLFkCwP94ma4Eco2Etxm5eVP78kj6Z8/3t92DaefOnRvv9S5duthbpYhI+rF/P7Rta07xcXNjtNc4xtwcAlgSvNXXV0mKyOPsblF5dNl8MHdTvnPnTtTKtNeuXUvWAOOjFhURcRqGAVOnwltvQUQEFCrEve8W4VXrRZur+PlnaNAgBWMUcRIp2qJy/fr1GOeOHz9Onz59GDp0qL3ViYikfdevQ/fusGyZedyiBXz7LQWK5Yj3tke5uGhhN5HYJGqvn8c9/fTTjBs3joEDByZHdSIiaceePVCunJmkuLvDl19CUBC33HNw5Yrt1ZQvr4XdRGKTLIkKgKurK+fPn0+u6kREnJthwOefQ9WqcPIkPPkk7NgBb7wBFgv2rtTw6B5AIvIfu7t+Vq5cGe3YMAwuXLjA1KlTqVo14Wl3IiJp3rVr0LUr/O9/5nHr1jB7Nvj4ANC3r7karT3690/eEEXSC7sTlRYtWkQ7tlgs+Pn5UadOHT7TMooikt7t2GE2f5w5A56e8MUX0Ls3WMxZPRUqmBN/7NG6NTy2fZqI/D+toyIiGdatW9CmDaxZk3BZC5EMZSIf8x5uWDnG07RhCb9RJkkxuLiYk4Q0PkUyEnu+v5NtjEpSjR07FovFwqBBgxwdiohkAGXLgre3bUlKLi6zmiaM5x3csDKfDpRnf5KTFIARI5SkiMTHpq6fIUOG2Fzh559/bncQe/fuZdasWZQqVcrue0VEbHX3LvToAfPn235PdbaykPY8wXnukokBTOEbumPLAm4JcXMzExURiZtNicrBgwdtqsxisf8v7q1bt+jYsSNff/01H330kd33i4jYomlTWLXK9vIuWBnOWMYwClciOcIztGEJf1Ay2WKaO1etKSIJsSlR2bRpU4oF0K9fP5o0aUK9evUSTFTCw8MJDw+POg4LC0uxuEQk/ciTx77diHPzL/PoRH02ABBIAP34ijtkSbaYihWD9u2TrTqRdMvuWT/JadGiRRw4cIC9e/faVH7s2LGMGTMmhaMSkfTE29scNGur2mxkAR3Iy7/cJjN9mcZcApI1JosFDh9O1ipF0q1EJSp79+7lhx9+4PTp00RERES7FhQUZFMdZ86cYeDAgaxbt45MmTLZdM/w4cOjjZcJCwujQIECtgcuIhlK+fK2JykuWBnJB4zgQ1ww+IPnaMMSjlAi2eP68Ud1+YjYyu5ZP4sWLaJq1aocPnyYZcuWcf/+fQ4fPszGjRvx+f/Fjmyxf/9+Ll26RPny5XFzc8PNzY0tW7YwefJk3NzcsFqtMe7x9PQkW7Zs0V4iIrG5eBEOHLCtrD/n2UA9RvEBLhh8zetUYk+KJCmLF0PLlslerUi6ZXeLyieffMIXX3xBv3798Pb25ssvv6RIkSL06tULf39/m+upW7cuv//+e7Rzr732Gs888wxvv/02rvrvhogkkj2LrtVnHfPoRG4uc5Os9GImC+mQInG9+aa5bouI2M7uROXEiRM0adIEMFs4bt++jcViYfDgwdSpU8fmMSTe3t48//zz0c5lyZIFX1/fGOdFRGxhtUK2bHDnTsJlXXnAB4zkXcYCEExp2rCE4xRLkdiGDoUJE1KkapF0ze6un5w5c3Lz5k0AnnjiCf744w8Abty4wR1b/nUQEUkBQUHmuiS2/DP0BGfZRO2oJGUafXiRXcmepGTPDh99BOHhSlJEEsvuFpXq1auzfv16SpYsSZs2bRg4cCAbN25k/fr11K1bN0nBbN68OUn3i0jGNGcOdOtmW9nGrOY7AsjFVcLw5nVm8wNtcHGBO7fAyytlYxUR+9icqAQHB1OmTBmmTp3KvXv3AHMWjru7O7/++istW7ZkhJZYFJFUdPEi2Do0zo37fMK7DOVTAPZRnrYs5h+KcukS+PmlYKAikmg2b0ro4uJC2bJlef311+nQoYNdM3xSijYlFMmYQkMhRw6wdUvVgpxiEe2ozC4AJjOAoUwkAk+b6xCR5JMimxJu376dcuXK8c477+Dv70+nTp1SdMVaEZHYPPmkOfbD1gSjGSsIpgyV2cV1svMKQQxkMhF4cuNGSkYqIsnB5kSlcuXKfP3111y8eJHp06dz9uxZ6tWrR9GiRfn44485e/ZsSsYpIoKnJ4SE2FbWnQi+YBAraEEObrCbSpTlIMt5BYCiRcEJGoZFJAF2z/rx8vIiICCAzZs3c+zYMdq3b8/MmTMpUqQIjRs3TokYRUTInBkeWwg7TkX4h+1UZRBfAvApb1KdbZyiMGAmKX//nUKBikiysnmMSlxu3brF/Pnzeffdd7lx40asK8qmFI1REckYsmSxbdoxQCt+5Bu640MYV8lJVwJZRdOo6zduqCVFxNFSZIzK47Zs2UJAQAB58+Zl2LBhtGzZku3btye2OhGRWFkstiUpntxjKv34kVfxIYztVKEMwVFJStmy5rgWJSkiaYtd66icOXOGwMBAAgMDCQkJoUqVKkyZMoU2bdqQJUvybX8uIgJmkmKLpzjOYtpSjoMAjOUdRvIBD3AH4OZNyJo1paIUkZRkc6JSv359Nm3ahJ+fH126dKFbt24UL148JWMTkQwqIsIcOGuLtizia3rgzS0uk4vOfM/PvASYOxQ/eJCCgYpIirM5UfHy8mLp0qW8/PLL2jBQRFLMm2/C558nXC4Td5nEIHoxC4At1KADCzjPEwBcuAB586ZkpCKSGmxOVFauXJmScYiIULEi7NuXcLni/MUS2lCK34nEwse8xxhGYcVNCYpIOmP3Xj8iIimhQgXYvz/hcp2Zy3T6kIU7XCQPnZjHL9TDwwMehKd8nCKSuhI960dEJLkMHJhwkpKZ23zLa8wlgCzc4RfqUIZgfqEerq7mDsUikv4oURERhxoyBCZPjr9MCf5kD5V4jUCsuDCSMTRgHf+SFxcXDZgVSc+UqIiIwwweDF98EV8Jg9f4lr1U5DkOcx5/6vILHzKSSFwpVQpScY1JEXEAm8ao2DOQtlmzZokORkQyjsaNYc2auK9n4RYz6E0n5gPwMw3ozPdcJnfU/atXp0akIuJINiUqLVq0iHZssVh4dOV9yyOrMqXmEvoikrZERMAnn8CYMfGXK8VvLKENxTnGA1x5n4+YwDCM/28ErlhRSYpIRmFT109kZGTUa926dZQpU4Y1a9Zw48YNQkND+emnnyhXrhxr165N6XhFJI0aNsxcxC3+JMWgJzPZzQsU5xhnyE8tNjOed6KSlAULYM+eVAlZRJyA3dOTBw0axIwZM6hWrVrUuYYNG5I5c2Z69uzJkSNHkjVAEUn7hg2DiRPjL+NNGLPoSTsWA7CKJnQlkKvkiirz4IG52qyIZBx2D6Y9ceIEPrHs6uXj48PJkyeTIyYRSUciIhJOUspygAOUox2LuY8bbzGRZqxUkiIi9icqFStWZNCgQVy4cCHq3MWLF3nzzTepVKlSsgYnImlfx47xXTXox1R2UpmnOMFJClGdbXzGW1FdPQBLlypJEcmo7O76+fbbb3nllVcoVKgQBQsWBOD06dMUK1aM5cuXJ3d8IpKGeXqaLSqx8eEG39CdVgQBsJzmvMYcbpAjWrmlS6Fly5SOVEScld2JylNPPcWhQ4dYv349f/31F4ZhUKJECerVqxdt9o+IZGwuLvDI5MBoKrKHxbSlCCeJwJ2hTGQybwDR/w1Rd4+IJGqvH4vFQoMGDahRowaenp5KUEQkmuzZ40pSDAYxifG8jQf3OcGTtGUx+6kQs2QcSY6IZCx2j1GJjIzkww8/5IknniBr1qyEhIQAMGLECL755ptkD1BE0pamTSE0NOb5HFxjBc35giF4cJ8faE05DihJEZF42Z2ofPTRRwQGBjJhwgQ8PDyizpcsWZLZs2cna3AikrbcvQurVsU8X5kdBFOGZvyPe3jSh2m0YQlhRJ9BWKaMkhQRic7uRGXu3LnMmjWLjh074vpI53GpUqX466+/kjU4EUk7IiIgc+bo5yxEMpQJbKUGBTnDMZ7mRXYxgz48Oh4lSxa4eRMOHkzdmEXE+dmdqJw7d46nnnoqxvnIyEju37+fLEGJSNphtULz5uYMn0fl4jKreJkJvI0bVhbQnvLs5zfKRCtnscCtW5A1a+rFLCJph92JynPPPce2bdtinP/hhx8oW7ZssgQlImlDUBC4ucHj+5ZWYxvBlKExa7hLJnowi47M5xbeMeqIjEylYEUkTbJ71s+oUaPo3Lkz586dIzIykqCgII4ePcrcuXNZFVvntIikS0FB0KpV9HMWIhnOWD5gJK5EcoRnaMMS/qBkjPs9PCA8PJWCFZE0y+4WlaZNm7J48WJ++uknLBYLI0eO5MiRI/zvf/+jfv36KRGjiDgZqzVmkpKbf1nLS3zM+7gSyXd0oSJ7Y01SSpRQkiIitrGrReXBgwd8/PHHdOvWjS1btqRUTCLi5B6Z8AdAbTYyn474c5HbZKYfX/EdXWO918UF/vwz5WMUkfTBrhYVNzc3Jk6ciNVqTal4RMTJ5cjx37gSF6yMYjQbqIc/F/mD56jI3jiTFDAHzoqI2Mrurp969eqxefPmFAhFRJydiwvcuGH+nJcLbKAeoxmDCwaz6U4l9nCEEnHe37w5eHmlTqwikj7YPZi2UaNGDB8+nD/++IPy5cuTJUuWaNebNWuWbMGJiPNwc/tvMbb6rGMencjNZW6RhV7MZAHxbpNM8+agfUtFxF4Ww7BvHUgXl7gbYSwWS6p2C4WFheHj40NoaCjZsmVLtfcVyWiyZzeXxXflAWMYxXDG4oJBMKVpwxKOUyze++/cUUuKiPzHnu9vu1tUIrXogUiGcesWeP//0idPcJYFdKAG5jpK0+nNED7nHnFnINmzw/XrqRCoiKRbdo9RedS9e/eSKw4RcTJly/6XpDTiJ4IpQw22EYY3bVhMX6bHm6RcuKAkRUSSzu5ExWq1Rts9+Z9//gG0e7JIehEaai5rHxwMbtxnPMP4iSbk4ir7KUc5DvADbeKtY8AAyJs3deIVkfTN7kTl448/1u7JIumQ1WomF9mzm8cFOM0WajKMiQBMZgBV2MEJYu719ahs2WDy5BQOVkQyDO2eLCJ8/705q+fff83jpqwkmDJUYSc38KElSxnIZCLwjLceNzezRUZEJLnYPZhWuyeLpC958sClS+bP7kQwnrcZzCQAdlOJdiziJEUSrCd/fjhzJgUDFZEMSbsni2RQERHmAm4Pk5TChPAr1aKSlM8YQnW22ZSk5MypJEVEUoZ2TxbJgPr3h6+++u+4JUv5hu5kJ5Rr5CCA71hFU5vqcneHq1dTKFARyfC0e7JIOnXrFjRpYs7gefz1MEnx5B5T6M9SWpOdULZThTIE25ykFCxotsyIiKSURK2j0rBhQ7Zs2cKtW7e4c+cOv/76Kw0aNLC7nunTp1OqVCmyZctGtmzZqFy5MmvWrElMSCLyiIoVzTVQfvop7jJF+ZsdVKE/ZtYyjrepxWbOUNCm95g3D06dSo5oRUTiZnfXT3LKnz8/48aNixqc+91339G8eXMOHjzIc88958jQRNKsp56CEyfiL9OWRcyiJ9m4yWVy0YW5rKWRTfVXrAg7d8Ijk/5ERFKMTXv95MiRA4vFYlOF165dS1JAOXPmZOLEiXTv3j3BstrrRyS6r7+Gnj3jvp6Ju0xiEL2YBcBWqtOehZznCZvq1549IpIckn2vn0mTJkX9fPXqVT766CMaNmxI5cqVAdi5cyc///wzI0aMSHTQVquVH374gdu3b0fV+7jw8HDCw8OjjsPCwhL9fiLpSUSEuVhbfEvWF+MoS2hDaQ4RiYWPeY8xjMJqwz8DPXrArFnJGLCIiI3s3j25VatW1K5dm/79+0c7P3XqVDZs2MByO/dx//3336lcuTL37t0ja9asLFiwgMaNG8dadvTo0YwZMybGebWoSEY2eDA88n+JWHVkHjPoTVZu8y+56cQ8NpDw4HeLBe7dg0cWoRYRSTJ7WlTsTlSyZs1KcHBwjEXfjh8/TtmyZbl165ZdwUZERHD69Glu3LjB0qVLmT17Nlu2bKFEiRIxysbWolKgQAElKpJhFS0K/7/dVqy8uMNU+tONOQBspDYdmc9F/BOsu1Gj+Afjiogklj2Jit2zfnx9fVm2bFmM88uXL8fX19fe6vDw8OCpp56iQoUKjB07ltKlS/Pll1/GWtbT0zNqhtDDl0hG5ecXf5JSgj/ZS0W6MQcrLoxkDPVZb1OSsmiRkhQRcQ52z/oZM2YM3bt3Z/PmzVFjSXbt2sXatWuTZVNCwzCitZqISEze3uY6KbEz6EogX9GPzNzlPP50YAFbqJVgvblywcWLmtEjIs7D7kSla9euPPvss0yePJmgoCAMw6BEiRJs376dF154wa663n33XRo1akSBAgW4efMmixYtYvPmzaxdu9besEQyjKxZ4fbt2K9l4RbT6UNn5gHwMw3ozPdcJne8dbq4wLVr4OOT3NGKiCSNXYnK/fv36dmzJyNGjGD+/PlJfvN///2Xzp07c+HCBXx8fChVqhRr167VCrcicfDwgLj2/izJIZbQhmc4ygNcGcGHjOdtjDh6eC0WKF4ctm839+oREXFGdg+mzZ49OwcOHODJJ59MqZhspnVUJKOwWsEtzv9WGPTgaybzBpkI5yxP0I5FbKdazJJ2/W0XEUkZKTqY9pVXXrF7CrKI2M9qhRUrIHfuuJMUb8JYQAdm0YtMhLOaxpQhOEaSkjmzkhQRSZvsHqPy1FNP8eGHH7Jjxw7Kly9PlixZol1/4403ki04kYwqKAjatDGTlbiU4SBLaMPT/M193BjOWD5nSIyunsBACAhI2XhFRFKK3V0/RYoUibsyi4V/4psvmczU9SPpUVAQtGoVXwmDvkzjc4bgSQSnKEg7FrGL6Cs6e3iYS95rBo+IOJtkX0L/USEhIYkOTETiZ7XGn6T4cIPZvE5rlgKwgma8xhyuE300rK8vXLmSkpGKiKQOu8eoPHTlyhWuXr2anLGIZHhZs8Z9rQJ7OUA5WrOUCNwZyCRasDxGklK4sJIUEUk/7EpUbty4Qb9+/ciVKxd58uQhd+7c5MqVi/79+3Pjxo0UClEkYyhb1txXJyaDgUxiO1V5khD+oQhV2c5kBgKWGHWo0VNE0hObu36uXbtG5cqVOXfuHB07duTZZ5/FMAyOHDlCYGAgv/zyCzt27CBHjhwpGa9IujRkCAQHxzyfg2vM4TWasxKAH2nF68wmlOwxyjZtCitXpmycIiKpzebBtIMGDeKXX35hw4YN5MmTJ9q1ixcv0qBBA+rWrcsXX3yRIoHGRoNpJT2IiABPz5jnX2Qni2hHIU4TjgeD+YLp9OHxVhQw9+Zp2zblYxURSQ4pso7K8uXL+fTTT2MkKQB58+ZlwoQJsW5WKCLxy5s3+rGFSN5iIlupQSFOc5yneJFdTKcvsSUpDx4oSRGR9MvmROXChQs899xzcV5//vnnuXjxYrIEJZJRzJ0L16//d+zLFf5HUyYyDHcesJB2lGc/wZSNcW/p0uYibpp+LCLpmc2JSq5cuTh58mSc10NCQvD19U2OmEQyhIEDoy/EVo1tBFOGJvzEXTLRg1l0YAE3id4s2qgR3LwZ+5gWEZH0xubBtC+99BLvvfce69evx8PDI9q18PBwRowYwUsvvZTsAYqkR0WLwsO1ES1E8g7j+ICRuGHlL4rThiX8TqkY9925A15eqRysiIgD2ZyojBkzhgoVKvD000/Tr18/nnnmGQAOHz7MtGnTCA8P5/vvv0+xQEXSurt3oV8/mDPnv3N+XGIenWjAegDm0pm+TOM2MRdUefllJSkikvHYtYR+SEgIffv2Zd26dTy8zWKxUL9+faZOncpTTz2VYoHGRrN+JK1o0cLcYPBRtdjEAjrgz0Xu4EU/viKQrsQ2YDZ79uhjWURE0rIUW0K/SJEirFmzhuvXr3P8+HHA3KQwZ86cCdwpknE9nqS4YOV9PmIkH+BKJH/wHG1YwhFKxFmHVpoVkYzK7r1+AHLkyEGlSpWSOxaRdOfu3ehJSl4uMI9O1GUjAN/QjQFM4S6Z46yjSxfN7BGRjCtRiYqI2ObZZ//7uR7rmUcn8nCJW2ShNzOYT6cE6/j66xQMUETEySV6U0IRid/ChXDqFLjygA95n59pSB4u8RulKM9+m5KUoUPhsUl2IiIZilpURFJAUBB06ABPcJYFdKAG2wCYQS8G8wX3SHj6ztChMGFCSkcqIuLclKiIJDOr1VzS/iXW8D2dycVVwvCmB1+zhITXus+Xz9wBWS0pIiLq+hFJdqWevc9HD95mDY3JxVX2U45yHLApSSlfHs6dU5IiIvKQEhWRZNSt3mm+Pl6TtzH7bKbQnyrs4AQJrzE0eDDs25fSEYqIpC3q+hFJJrObreTTX7qSk+vcwIfufEMQrRK875NP4M031YoiIhIbJSoiSRURwZYq7/D6/i8A2ENF2rKYkxRJ8NZ586Bjx5QOUEQk7VLXj0hShIRw9bnq1Pz/JOVzBlONX21KUrJlU5IiIpIQtaiIJNL9JUFYA7rhey+Ua+SgK4H8j2Y233/xYgoGJyKSTqhFRcRe4eFsLzcA97atyHQvlB1UpgzBdiUpTZtqJ2QREVsoURGxx99/c6ZgFaoenArAeIZRky2coaDNVRQtCitXplSAIiLpixIVEVstXsydZ8tR4NIBruBLY1bzDuN5gLvNVZQpA3//nXIhioikN0pURBJy9y707g3t2pH5wU22Up0yBLOGxnZVU6QIHDyYQjGKiKRTSlRE4nP0KLz4IsycSSQWPuI96rCRc+S3q5rCheGff1ImRBGR9EyzfkTiMm+e2ZJy+zbXPXLTJmIeG6hvdzVZsph794iIiP3UoiLyuDt3oHt36NwZbt/m0nO1KRERnKgkBeDy5WSOT0QkA1GiIvKow4ehUiX49luwWPi91Wj8/1zPRfwTVV3z5pqGLCKSFEpURB4KDIQKFeDPPzHy5qVv8V8otXQUkbgmqrrmzWH58mSNUEQkw1GiInLrFgQEwGuvwd27/Fu6Pv7//sb0v2rbXZXFAj16mL1HSlJERJJOg2klY/v9d2jTBv76C1xc+L3Nh5Re9A5GInL4J56As2dTIEYRkQxMLSqSMRkGfP21OR7lr78w8j1B18KbKbXo3UQlKaDpxyIiKUEtKpLxhIVBr16waBEA+3I34qXzc7lKrkRXOWgQeHgkU3wiIhJFLSqSsRw8COXLm0mKqyvjck6g0qVVSUpSnnwSvvgiGWMUEZEoSlQkYzAMmDbNXGX277+hYEEmtdrG8GtDE93VA+DnBydOJGOcIiISjbp+JP0LDYXXX4cffzSPmzUjYuYcBvvnTFK1uXLBpUvJEJ+IiMRJLSqSvu3dC2XLmkmKu7vZR7N8OfmeT1qS8vLLWnFWRCQ1qEVF0ifDgMmTYehQuH/f3Lp48WKs5SpS5UW4ejXxVd+8CVmzJl+oIiISNyUqkv5cuwbdusGKFQBEvtKKr8rOZmqn7Bw7lrSqly5VkiIikpoc2vUzduxYKlasiLe3N7lz56ZFixYcPXrUkSFJGmW1wrp1MKL+Ls7nLQsrVhCOB4PcpuK67AfeGJm0JMXd3UxSWrZMvphFRCRhDk1UtmzZQr9+/di1axfr16/nwYMHNGjQgNu3bzsyLElDIiLM1e893SNZ1/BTRm6oTr77p/mbolRmJ18+6AdYEl2/xQLvvw937ypJERFxBIthGIajg3jo8uXL5M6dmy1btlCjRo0Ey4eFheHj40NoaCjZsmVLhQjFmQwdCp9+Cr5cIZCuvMxqABbRlp7M4iZJ+zPh6wvnz2shNxGR5GbP97dTjVEJDQ0FIGfOpM3IkPSvWTP43/+gKr+ykPYU4Cz38OQNJvM1PUhKK8pDs2YpSRERcTSnSVQMw2DIkCFUq1aN559/PtYy4eHhhIeHRx2HhYWlVnjiJKxWqFYNdu+K5B3G8yEjcMPKXxSnDUv4nVJJfg8XF/jhB3X1iIg4A6dJVPr378+hQ4f49ddf4ywzduxYxowZk4pRiaNYrbB5M2zYYC6FcucOXLwIISHgxyXW0JmGrANgLp3pyzRukzzTcW7dAi+vZKlKRESSyCnGqAwYMIDly5ezdetWihQpEme52FpUChQooDEqadzjScnZs+ay9A8exCxbk80soAP5uMAdvOjHVwTSleTo6gFz3MuECclSlYiIxCHNjFExDIMBAwawbNkyNm/eHG+SAuDp6Ymnp2cqRSepISgIevZMeAE2F6y8x8eMYgyuRPInJWjDEg7zXLLFoiRFRMT5ODRR6devHwsWLGDFihV4e3tz8eJFAHx8fPBS23u68rDVZONGOHnSPD58GH7/PeF783CR+XSkLhsB+IZuDGAKd8mcLLF16gTffKOBsyIizsihXT8WS+zN9XPmzKFr164J3q/pyWmDra0msanLBubTkTxc4hZZ6MN05tE5SfFkyWJuojx0KNSrB66uSapORETslKa6fiR9slph2zZYvhy+/NL++115wCjG8B4f44LBIUrShiUc5Rmb7s+SBTJnNuNwd4cnnzRn8bzxhlpORETSEqeZ9SPpx48/Qt++id9dOB/nWEAHarIVgBn0YjBfcA/bugMXLYK2bRP33iIi4lwcuoS+pD/DhsGrryY+SWnIWoIpQ022EoY37VhIH2bYlKR4epr78ShJERFJP9SiIon2cIDs5s0QGQmnTsH8+Ymry437fMgI3mE8AAcoS1sW8zdP23R/69ZmS4rGm4iIpC9KVCROjyciOXNC7txma8n27fDTT3DvXtLfJz9nWEQ7qrIDgCn0ZygTCSdTgvd6e5szdl59NelxiIiI81GiIrFKykwde7zM/wikK75c4wY+dOcbgmgVo5zFAoUKgb+/OVC2YkWoWxdq1VIriohIeqZERaIkdaaOPdyJYCzDeZPPAdhDRdqxiBCeBOD556FUKShcGOrUUUIiIpJRKVHJoB4mJefOmV05J0/CggWJHwRrj8KEsIh2vMAeAL5gEG8znvt44Otr7lqsDQFFRASUqGQoD5OTFSvMQa+pkZQ8rgXLmMNrZCeUUNccfPJ0IAfyN+MtdeWIiEgslKhkEEFBMHCgueGfI3gQzkSG8gZTALha7EV81y1ifKFCjglIRETSBK2jks5ZrfDBB9CqleOSlCc5wQ5L1agkJfKtYfj+sdUcHSsiIhIPtaikY0FB5pLx5845LoZXWcJsXiebcRN8fWHuXFwaN3ZcQCIikqYoUUmngoLMRdActZ2SJ/f4gsH0YYZ5olo1WLgQ8ud3TEAiIpImKVFJh6xWczxKaiUpmTJBkybwwgtw7Ro8OHyMIbva4H/pNwyLBcu778Lo0eCmP24iImIffXOkQ9u2pcx4lIAAc2bO5cvmQnAuLuYsnWgzdebPhym94PZt8PPDMn8+1K+f/MGIiEiGoEQlHbpwIXnrK1AAJk1KYG2TO3fMATHffGMe165tJi3+/skbjIiIZChKVNKhpOQGfn7Qvj0UKWL+/MQTUL16AmubHD4MbdrAn3+aa92PHAkjRmhBFBERSTIlKulQ9ermmNVz5+Ifp5IrF3ToYGdS8rjAQOjXz2xRyZvXbEWpUyepH0FERARQopIuubqae/W0bm02cMSWrIwZA++9l4RGj1u3zARl7lzzuH59+P57yJMn0XGLiIg8Tgu+pVMtW8KPP5qtJI8qUACWLjV7ZxKdpPz+u7l98dy55ojajz6CtWuVpIiISLJTi0o61rIlNG9uzgK6cMEcu2J3186jDMMcLDtgANy7B/nymWuj1KiRrHGLiIg8pEQlnXN1NacPJ9nNm9Crl5mYADRqBN99Zw5uERERSSHq+pGEBQdD+fJmkuLqChMmwKpVSlJERCTFqUVF4mYYMGMGDB4M4eHmAJfFi6FyZUdHJiIiGYQSFYldaCj06AE//GAeN2sGc+ZAzpyOjUtERDIUdf1ITPv2QblyZpLi7g6ffw7LlytJERGRVKcWFfmPYcDkyTB0KNy/D4ULm109lSo5OjIREcmglKiI6fp16NbNbDkBc27zN99A9uyOjEpERDI4df0I7N4NZcuaSYqHB0yZYq4WpyRFREQcTIlKRhYZCZ99BtWqwalTULQo7NwJ/fuba++LiIg4mLp+MqqrVyEgAFavNo/btoVZsyBbNsfGJSIi8gi1qGRE27dDmTJmkuLpaa6VsnChkhQREXE6SlQykshIGDcOataEs2ehWDFzfEqvXurqERERp6SuHydmtca+oeDj56tUgR074j6uXh1cr16CLl3g55/Nyjt1gunTIWtWx35IERGReChRcVJBQTBwoNnw8VD+/NC+vdlL8+j5h8lLXMet/bYw90F7vK5fAC8vmDoVXntNrSgiIuL0lKg4oaAgaN3aXH/tUWfPwsSJMcs/mpQ8euyClXf5hNGXR+NKJGH5S5Bt7RJ47rmUCVxERCSZaYyKk7FazZaUx5MUe+XhIj/TkA8ZiSuRzOE1Khp7sD6jJEVERNIOJSpOZtu26N06iVGHXwimDPX4hVtkoTNz6ca3HDuXhW3bkidOERGR1KBExclcuJD4e115wBhGsp765OVfDlGSCuxjHp2TpX4REZHUpjEqTsbfP5H3cZ6FtKcmWwGYSU8GMYl7eCVL/SIiIo6gFhUnU726ObvHngk5DVnLb5SmJlu5SVbasZDezIyWpFgsUKCAWb+IiEhaoUTFybi6wpdfmj8nlKy48oBPGM5aGuHHFQ5ShnIcYDHtopV7WM+kSWb9IiIiaYUSFSfUsqW5efETT0Q/X6AADB1qtrjk5wybqcVwxgEwlX5UZid/83SMZCR/frO+li1T6QOIiIgkE4thJHUirOOEhYXh4+NDaGgo2dLhPjVxrky7YhWRXQJwD7vGgyzZMGZ/w/a8reNfmVYtKSIi4iTs+f7WYFon5uoKtWo9ciIiAt5+F9fPPsMVoEIF3BYvhiefpNZj99Z6/ISIiEgapEQlrTh5Etq1MzcRBBg0CMaPBw8PR0YlIiKSopSoxCKuLpeUqMOmcsuXm3vz3LgB2bNDYCA0b56ozyYiIpKWOHQw7datW2natCn58uXDYrGwfPlyR4YDmPvsFC4MtWtDhw7mr4ULm+eTu44Ey4WHm+vpv/KKmaS8+CIEBytJERGRDMOhicrt27cpXbo0U6dOdWQYUR5uBvj4EvbnzpnnbUlWbK0joXI/TzsBVavC5MnmhaFDYetWKFQocR9OREQkDXKaWT8Wi4Vly5bRokULm+9Jzlk/VqvZmhHXPjsWiznNNyQk7m4gW+v4+28oWjTucq/yA7Mtr5PNCANfX/juO2jSxN6PJCIi4pTs+f5OU+uohIeHExYWFu2VXBLaDNAw4MwZ4t3Uz9Y6pk2LvZwn9/iKviyhDdmMMG6UrGZ29ShJERGRDCpNJSpjx47Fx8cn6lWgQIFkq9vWzfriK2drHSdOxDz3NMfYxYv0ZTqRWPiYd1k7bJPZBCMiIpJBpalEZfjw4YSGhka9zpw5k2x127pZX3zlbK2jaNHox+1ZwH7KU4bfuIQfL7GW9/mYvPk1KUtERDK2NJWoeHp6ki1btmiv5JLQZoC2bOpnax19+5rlMnOHWfRgAR3x5habqEUZgtlgaaANBEVEREhjiUpKim8zQFs39bO1Dg8P+HboEXbxAj2YTSQWRjOKemzgoiWfTe8lIiKSETg0Ubl16xbBwcEEBwcDEBISQnBwMKdPn3ZIPHFtBmjPpn421fHdd9QfXoGS/MG/LnmpxwbGMJpIXLWBoIiIyCMcOj158+bN1K5dO8b5gIAAAgMDE7w/pTYlTLGVae/dhn79zOnGAPXqYf1uHtuO5dEGgiIikmHY8/3tNOuoJEaa2j35jz/g1Vfhr7/AxQU++ADeeUdZiYiIZDjaPdmZGAZ88w0MGAD37kG+fLBwIdSo4ejIREREnJ4SlZR08yb07g0LFpjHL70Ec+eCn59j4xIREUkjNOsnpQQHQ4UKZpLi6grjx8Pq1UpSRERE7KAWleRmGDBjBgwebO5+XKAALFoEVao4OjIREZE0R4lKcgoNhZ49YckS87hpU5gzx9xYUEREROymrp/ksn8/lCtnJilubvD557BihZIUERGRJFCLSlIZBkydCm+9BRERULgwLF4MlSo5OjIREZE0T4lKUly/Dt27w7Jl5vErr8C330L27A4NS0REJL1Q109i7d5tdvUsW2Zu3jNlCixdqiRFREQkGSlRsZdhwGefQbVqcPIkFC0KO3ZA//5xb5ssIiIiiaKuH3tcvQpdu8KqVeZxmzYwaxb4+Dg0LBERkfRKLSq22r4dypY1kxRPT3OtlEWLlKSIiIikICUqCYmMhHHjoGZNOHMGihUzx6f06qWuHhERkRSmrp/4XL4MXbrA2rXmcceOMH06eHs7Ni4REZEMQolKXLZuhfbt4fx58PIy10p57TW1ooiIiKQiJSqxmTED+vUzu32efdZcbfb55x0dlYiISIajMSqxqVjR3PG4a1fYu1dJioiIiIOoRSU25cvD779D8eKOjkRERCRDU4tKXJSkiIiIOJwSFREREXFaSlRERETEaSlREREREaelREVERESclhIVERERcVpKVERERMRpKVERERERp6VERURERJyWEhURERFxWkpURERExGkpURERERGnpURFREREnJYSFREREXFabo4OICkMwwAgLCzMwZGIiIiIrR5+bz/8Ho9Pmk5Ubt68CUCBAgUcHImIiIjY6+bNm/j4+MRbxmLYks44qcjISM6fP4+3tzcWi8XR4cQpLCyMAgUKcObMGbJly+bocJyCnklMeibR6XnEpGcSnZ5HTGnlmRiGwc2bN8mXLx8uLvGPQknTLSouLi7kz5/f0WHYLFu2bE79B8cR9Exi0jOJTs8jJj2T6PQ8YkoLzyShlpSHNJhWREREnJYSFREREXFaSlRSgaenJ6NGjcLT09PRoTgNPZOY9Eyi0/OISc8kOj2PmNLjM0nTg2lFREQkfVOLioiIiDgtJSoiIiLitJSoiIiIiNNSoiIiIiJOS4mKAzRr1oyCBQuSKVMm/P396dy5M+fPn3d0WA5x8uRJunfvTpEiRfDy8qJo0aKMGjWKiIgIR4fmUB9//DFVqlQhc+bMZM+e3dHhOMS0adMoUqQImTJlonz58mzbts3RITnM1q1badq0Kfny5cNisbB8+XJHh+RQY8eOpWLFinh7e5M7d25atGjB0aNHHR2WQ02fPp1SpUpFLfRWuXJl1qxZ4+iwkoUSFQeoXbs2S5Ys4ejRoyxdupQTJ07QunVrR4flEH/99ReRkZHMnDmTP//8ky+++IIZM2bw7rvvOjo0h4qIiODVV1+lT58+jg7FIRYvXsygQYN47733OHjwINWrV6dRo0acPn3a0aE5xO3btyldujRTp051dChOYcuWLfTr149du3axfv16Hjx4QIMGDbh9+7ajQ3OY/PnzM27cOPbt28e+ffuoU6cOzZs3588//3R0aEmm6clOYOXKlbRo0YLw8HDc3d0dHY7DTZw4kenTp/PPP/84OhSHCwwMZNCgQdy4ccPRoaSqF154gXLlyjF9+vSoc88++ywtWrRg7NixDozM8SwWC8uWLaNFixaODsVpXL58mdy5c7NlyxZq1Kjh6HCcRs6cOZk4cSLdu3d3dChJohYVB7t27Rrz58+nSpUqSlL+X2hoKDlz5nR0GOIgERER7N+/nwYNGkQ736BBA3bs2OGgqMSZhYaGAujfjf9ntVpZtGgRt2/fpnLlyo4OJ8mUqDjI22+/TZYsWfD19eX06dOsWLHC0SE5hRMnTjBlyhR69+7t6FDEQa5cuYLVaiVPnjzRzufJk4eLFy86KCpxVoZhMGTIEKpVq8bzzz/v6HAc6vfffydr1qx4enrSu3dvli1bRokSJRwdVpIpUUkmo0ePxmKxxPvat29fVPmhQ4dy8OBB1q1bh6urK126dCE99cLZ+zwAzp8/z0svvcSrr77K66+/7qDIU05inklGZrFYoh0bhhHjnEj//v05dOgQCxcudHQoDle8eHGCg4PZtWsXffr0ISAggMOHDzs6rCRzc3QA6UX//v1p165dvGUKFy4c9XOuXLnIlSsXxYoV49lnn6VAgQLs2rUrXTTTgf3P4/z589SuXZvKlSsza9asFI7OMex9JhlVrly5cHV1jdF6cunSpRitLJKxDRgwgJUrV7J161by58/v6HAczsPDg6eeegqAChUqsHfvXr788ktmzpzp4MiSRolKMnmYeCTGw5aU8PDw5AzJoex5HufOnaN27dqUL1+eOXPm4OKSPhv6kvJnJCPx8PCgfPnyrF+/nldeeSXq/Pr162nevLkDIxNnYRgGAwYMYNmyZWzevJkiRYo4OiSnZBhGuvheUaKSyvbs2cOePXuoVq0aOXLk4J9//mHkyJEULVo03bSm2OP8+fPUqlWLggUL8umnn3L58uWoa3nz5nVgZI51+vRprl27xunTp7FarQQHBwPw1FNPkTVrVscGlwqGDBlC586dqVChQlQr2+nTpzPs2KVbt27x999/Rx2HhIQQHBxMzpw5KViwoAMjc4x+/fqxYMECVqxYgbe3d1Trm4+PD15eXg6OzjHeffddGjVqRIECBbh58yaLFi1i8+bNrF271tGhJZ0hqerQoUNG7dq1jZw5cxqenp5G4cKFjd69extnz551dGgOMWfOHAOI9ZWRBQQExPpMNm3a5OjQUs1XX31lFCpUyPDw8DDKlStnbNmyxdEhOcymTZti/fMQEBDg6NAcIq5/M+bMmePo0BymW7duUX9f/Pz8jLp16xrr1q1zdFjJQuuoiIiIiNNKn4MBREREJF1QoiIiIiJOS4mKiIiIOC0lKiIiIuK0lKiIiIiI01KiIiIiIk5LiYqIiIg4LSUqIk7s5MmTWCyWqJVp04rChQszadKkZKuvVq1aDBo0KNnqcwSLxcLy5cuBtPv7KuIISlREHCShnZS7du3q6BATFBgYSPbs2WOc37t3Lz179kz9gJzA6NGjKVOmTIzzFy5coFGjRqkfkEgap71+RBzkwoULUT8vXryYkSNHcvTo0ahzXl5eXL9+3RGhYbVasVgsid4g0s/PL5kjSvsy8t5VIkmhFhURB8mbN2/Uy8fHB4vFEuPcQ//88w+1a9cmc+bMlC5dmp07d0ara8eOHdSoUQMvLy8KFCjAG2+8we3bt6OuX79+nS5dupAjRw4yZ85Mo0aNOH78eNT1hy0jq1atokSJEnh6enLq1CkiIiIYNmwYTzzxBFmyZOGFF15g8+bNAGzevJnXXnuN0NDQqFag0aNHAzG7fm7cuEHPnj3JkycPmTJl4vnnn2fVqlUAXL16lfbt25M/f34yZ85MyZIlWbhwod3Pc9y4ceTJkwdvb2+6d+/OO++8E61lI7buoxYtWkRruZo3bx4VKlTA29ubvHnz0qFDBy5duhR1ffPmzVgsFn755RcqVKhA5syZqVKlSlSCGRgYyJgxY/jtt9+inklgYCAQvesnNocPH6Zx48ZkzZqVPHny0LlzZ65cuRJ1/ccff6RkyZJ4eXnh6+tLvXr1ov0ei6RXSlRE0oD33nuPt956i+DgYIoVK0b79u158OABAL///jsNGzakZcuWHDp0iMWLF/Prr7/Sv3//qPu7du3Kvn37WLlyJTt37sQwDBo3bsz9+/ejyty5c4exY8cye/Zs/vzzT3Lnzs1rr73G9u3bWbRoEYcOHeLVV1/lpZde4vjx41SpUoVJkyaRLVs2Lly4wIULF3jrrbdixB4ZGUmjRo3YsWMH8+bN4/Dhw4wbNw5XV1cA7t27R/ny5Vm1ahV//PEHPXv2pHPnzuzevdvm57NkyRJGjRrFxx9/zL59+/D392fatGl2P+eIiAg+/PBDfvvtN5YvX05ISEisXXDvvfcen332Gfv27cPNzY1u3boB0LZtW958802ee+65qGfStm3bBN/3woUL1KxZkzJlyrBv3z7Wrl3Lv//+S5s2baKut2/fnm7dunHkyBE2b95My5Yt0VZtkiE4dk9EETEMcxdpHx+fGOdDQkIMwJg9e3bUuT///NMAjCNHjhiGYRidO3c2evbsGe2+bdu2GS4uLsbdu3eNY8eOGYCxffv2qOtXrlwxvLy8jCVLlkS9P2AEBwdHlfn7778Ni8VinDt3LlrddevWNYYPHx5v3IUKFTK++OILwzAM4+effzZcXFyMo0eP2vw8GjdubLz55ptRxzVr1jQGDhwYZ/nKlSsbvXv3jnbuhRdeMEqXLh1vHc2bN493B+I9e/YYgHHz5k3DMP7bxXjDhg1RZVavXm0Axt27dw3DMIxRo0ZFe9+HAGPZsmWGYfz3+3rw4EHDMAxjxIgRRoMGDaKVP3PmjAEYR48eNfbv328AxsmTJ+OMVSS9UouKSBpQqlSpqJ/9/f0Borok9u/fT2BgIFmzZo16NWzYkMjISEJCQjhy5Ahubm688MILUXX4+vpSvHhxjhw5EnXOw8Mj2vscOHAAwzAoVqxYtLq3bNnCiRMnbI49ODiY/PnzU6xYsVivW61WPv74Y0qVKoWvry9Zs2Zl3bp1nD592ub3OHLkCJUrV4527vFjWxw8eJDmzZtTqFAhvL29qVWrFkCMWOL7/UiM/fv3s2nTpmjP+ZlnngHgxIkTlC5dmrp161KyZEleffVVvv76a4eNXxJJbRpMK5IGuLu7R/1ssVgAs0vl4a+9evXijTfeiHFfwYIFOXbsWKx1GoYRVReYg3cfPY6MjMTV1ZX9+/dHddM8lDVrVptj9/Lyivf6Z599xhdffMGkSZMoWbIkWbJkYdCgQURERNj8HrZwcXGJ0VXyaNfX7du3adCgAQ0aNGDevHn4+flx+vRpGjZsGCOW+H4/EiMyMpKmTZsyfvz4GNf8/f1xdXVl/fr17Nixg3Xr1jFlyhTee+89du/eTZEiRRL9viJpgRIVkTSuXLly/Pnnnzz11FOxXi9RogQPHjxg9+7dVKlSBTAHsB47doxnn302znrLli2L1Wrl0qVLVK9ePdYyHh4eWK3WeOMrVaoUZ8+e5dixY7G2qmzbto3mzZvTqVMnwPzSPn78eLyxPe7ZZ59l165ddOnSJercrl27opXx8/OLNtPKarXyxx9/ULt2bQD++usvrly5wrhx4yhQoAAA+/btszmGh2x5Jo8rV64cS5cupXDhwri5xf7PssVioWrVqlStWpWRI0dSqFAhli1bxpAhQ+yOUSQtUdePSBr39ttvs3PnTvr160dwcDDHjx9n5cqVDBgwAICnn36a5s2b06NHD3799Vd+++03OnXqxBNPPEHz5s3jrLdYsWJ07NiRLl26EBQUREhICHv37mX8+PH89NNPgDm759atW/zyyy9cuXKFO3fuxKinZs2a1KhRg1atWrF+/XpCQkJYs2YNa9euBeCpp56Kai04cuQIvXr14uLFi3Y9g4EDB/Ltt9/y7bffcuzYMUaNGsWff/4ZrUydOnVYvXo1q1ev5q+//qJv377cuHEj6nrBggXx8PBgypQp/PPPP6xcuZIPP/zQrjgePpOQkBCCg4O5cuUK4eHhCd7Tr18/rl27Rvv27dmzZw///PMP69ato1u3blitVnbv3s0nn3zCvn37OH36NEFBQVy+fNmuZE4krVKiIpLGlSpVii1btnD8+HGqV69O2bJlGTFiRNTYCYA5c+ZQvnx5Xn75ZSpXroxhGPz000/RujBiM2fOHLp06cKbb75J8eLFadasGbt3745qcahSpQq9e/embdu2+Pn5MWHChFjrWbp0KRUrVqR9+/aUKFGCYcOGRbU6jBgxgnLlytGwYUNq1apF3rx5adGihV3PoG3btowcOZK3336b8uXLc+rUKfr06ROtTLdu3QgICKBLly7UrFmTIkWKRLWmgNniEhgYyA8//ECJEiUYN24cn376qV1xALRq1YqXXnqJ2rVr4+fnZ9NU63z58rF9+3asVisNGzbk+eefZ+DAgfj4+ODi4kK2bNnYunUrjRs3plixYrz//vt89tlnWkBOMgSL8XinrYhIOjB69GiWL1+uZepF0ji1qIiIiIjTUqIiIiIiTktdPyIiIuK01KIiIiIiTkuJioiIiDgtJSoiIiLitJSoiIiIiNNSoiIiIiJOS4mKiIiIOC0lKiIiIuK0lKiIiIiI01KiIiIiIk7r/wCbyEvh5ZiviwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows with 0 CEFR level: 120\n",
      "Rows with 1 CEFR level: 30\n",
      "Rows with more than 1 CEFR level: 223\n",
      "Rows where the first CEFR level is lower than the last: 110 (49.327354260089685%)\n",
      "Rows where the first CEFR level is higher than the last: 107 (47.98206278026906%)\n",
      "Rows where the first CEFR level is equal to the last: 6 (2.690582959641256%)\n",
      "Rows where the first CEFR level is the lowest of all: 43 (19.282511210762333%)\n",
      "Rows where CEFR levels are in ascending order: 11 (2.949061662198391%)\n"
     ]
    }
   ],
   "source": [
    "posteval_analysis_cefr_weighted_stats_goldlabels(weighted_dataset,gold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a132f10b-efdc-47cc-996d-0d66fe26ba7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow_env",
   "language": "python",
   "name": "tensorflow_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}