hans/main.ipynb

## main.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import operator\n",
    "import os\n",
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "import re\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data and preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Map from test suite tag to high-level circuit.\n",
    "circuits = {\n",
    "    \"Licensing\": [\"npi\", \"reflexive\"],\n",
    "    \"Long-Distance Dependencies\": [\"fgd\", \"position\", \"embed\", \"number\", \"comparison\"],\n",
    "    \"Garden-Path Effects\": [\"gardenpath\", \"npz\", \"mvrr\"],\n",
    "    \"Gross Syntactic State\": [\"subordination\"],\n",
    "    \"Center Embedding\": [\"center\"],\n",
    "    \"Transformations\": [\"passive\", \"cleft\"],\n",
    "}\n",
    "\n",
    "tag_to_circuit = {tag: circuit\n",
    "                  for circuit, tags in circuits.items()\n",
    "                  for tag in tags}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exclusions\n",
    "exclude_suite_re = re.compile(r\"^cleft_modifier|^fgd-embed[34]|^npz_(ambig)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"..\")\n",
    "ppl_data_path = Path(\"data/raw/perplexity.csv\")\n",
    "test_suite_results_path = Path(\"data/raw/test_suite_results\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:15: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dropping 7373 results / 5 suites due to exclusions.\n",
      "Tags missing circuit:  nn\n"
     ]
    }
   ],
   "source": [
    "perplexity_df = pd.read_csv(ppl_data_path, index_col=[\"model\", \"corpus\", \"seed\"])\n",
    "perplexity_df.index.set_names(\"model_name\", level=0, inplace=True)\n",
    "\n",
    "results_df = pd.concat([pd.read_csv(f) for f in test_suite_results_path.glob(\"*.csv\")])\n",
    "\n",
    "# Split model_id into constituent parts\n",
    "model_ids = results_df.model.str.split(\"_\", expand=True).rename(columns={0: \"model_name\", 1: \"corpus\", 2: \"seed\"})\n",
    "results_df = results_df.join(model_ids).drop(columns=[\"model\"])\n",
    "results_df[\"seed\"] = results_df.seed.astype(int)\n",
    "\n",
    "# Manual fixes\n",
    "results_df.loc[results_df.model_name.isin((\"bllip-lg\", \"bllip-md\", \"bllip-sm\", \"bllip-xs\")), \"model_name\"] = \"vanilla\"\n",
    "\n",
    "# Exclude test suites\n",
    "exclude_filter = results_df.suite.str.contains(exclude_suite_re)\n",
    "print(\"Dropping %i results / %i suites due to exclusions.\"\n",
    "      % (exclude_filter.sum(), len(results_df[exclude_filter].suite.unique())))\n",
    "results_df = results_df[~exclude_filter]\n",
    "\n",
    "# Add tags\n",
    "results_df[\"tag\"] = results_df.suite.transform(lambda s: re.split(r\"[-_0-9]\", s)[0])\n",
    "results_df[\"circuit\"] = results_df.tag.map(tag_to_circuit)\n",
    "tags_missing_circuit = set(results_df.tag.unique()) - set(tag_to_circuit.keys())\n",
    "if tags_missing_circuit:\n",
    "    print(\"Tags missing circuit: \", \", \".join(tags_missing_circuit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suite</th>\n",
       "      <th>item</th>\n",
       "      <th>correct</th>\n",
       "      <th>model_name</th>\n",
       "      <th>corpus</th>\n",
       "      <th>seed</th>\n",
       "      <th>tag</th>\n",
       "      <th>circuit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>reflexive_orc_fem</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>reflexive</td>\n",
       "      <td>Licensing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>reflexive_orc_fem</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-md</td>\n",
       "      <td>111</td>\n",
       "      <td>reflexive</td>\n",
       "      <td>Licensing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>reflexive_orc_fem</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-sm</td>\n",
       "      <td>120</td>\n",
       "      <td>reflexive</td>\n",
       "      <td>Licensing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>reflexive_orc_fem</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-xs</td>\n",
       "      <td>922</td>\n",
       "      <td>reflexive</td>\n",
       "      <td>Licensing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>reflexive_orc_fem</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>vanilla</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>reflexive</td>\n",
       "      <td>Licensing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               suite  item  correct       model_name    corpus  seed  \\\n",
       "0  reflexive_orc_fem     0    False  ordered-neurons  bllip-lg   111   \n",
       "0  reflexive_orc_fem     0    False  ordered-neurons  bllip-md   111   \n",
       "0  reflexive_orc_fem     0    False  ordered-neurons  bllip-sm   120   \n",
       "0  reflexive_orc_fem     0    False  ordered-neurons  bllip-xs   922   \n",
       "0  reflexive_orc_fem     0    False          vanilla  bllip-lg   111   \n",
       "\n",
       "         tag    circuit  \n",
       "0  reflexive  Licensing  \n",
       "0  reflexive  Licensing  \n",
       "0  reflexive  Licensing  \n",
       "0  reflexive  Licensing  \n",
       "0  reflexive  Licensing  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Each model--corpus--seed should have perplexity data.\n",
    "ids_from_results = results_df.set_index([\"model_name\", \"corpus\", \"seed\"]).sort_index().index\n",
    "ids_from_ppl = perplexity_df.sort_index().index\n",
    "diff = set(ids_from_results) - set(ids_from_ppl)\n",
    "if diff:\n",
    "    print(\"Missing perplexity results for:\")\n",
    "    pprint(diff)\n",
    "    #raise ValueError(\"Each model--corpus--seed must have perplexity data.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main analyses\n",
    "\n",
    "1. barplot ranking model accuracies\n",
    "2. scatter plot with ppl on x-axis and SG score on y-axis (for a given dataset size -- or maybe all of them together?)\n",
    "3. variance in ppl vs variance in SG score for a single model across seeds and/or sizes\n",
    "\n",
    "Test suite analyses\n",
    "\n",
    "1. within-tag/circuit ppl-SG correlations\n",
    "2. circuit-circuit coordination heatmap\n",
    "3. robustness to stability modification}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global settings\n",
    "\n",
    "e.g. to maintain consistent hues across model graphs, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_order = sorted(set(results_df.model_name))\n",
    "corpus_order = [\"bllip-lg\", \"bllip-md\", \"bllip-sm\", \"bllip-xs\"]\n",
    "circuit_order = sorted([c for c in results_df.circuit.dropna().unique()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "suites_df = results_df.groupby([\"model_name\", \"corpus\", \"seed\", \"suite\"]).correct.mean().reset_index()\n",
    "suites_df[\"tag\"] = suites_df.suite.transform(lambda s: re.split(r\"[-_0-9]\", s)[0])\n",
    "suites_df[\"circuit\"] = suites_df.tag.map(tag_to_circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>corpus</th>\n",
       "      <th>seed</th>\n",
       "      <th>correct</th>\n",
       "      <th>pid</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_ppl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>0.359028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.03</td>\n",
       "      <td>56.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-md</td>\n",
       "      <td>111</td>\n",
       "      <td>0.359028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.02</td>\n",
       "      <td>55.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-sm</td>\n",
       "      <td>111</td>\n",
       "      <td>0.381460</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.99</td>\n",
       "      <td>53.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-sm</td>\n",
       "      <td>120</td>\n",
       "      <td>0.359037</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.99</td>\n",
       "      <td>54.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-xs</td>\n",
       "      <td>111</td>\n",
       "      <td>0.371505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.30</td>\n",
       "      <td>73.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        model_name    corpus  seed   correct  pid  test_loss  test_ppl\n",
       "0  ordered-neurons  bllip-lg   111  0.359028  NaN       4.03     56.38\n",
       "1  ordered-neurons  bllip-md   111  0.359028  NaN       4.02     55.96\n",
       "2  ordered-neurons  bllip-sm   111  0.381460  NaN       3.99     53.85\n",
       "3  ordered-neurons  bllip-sm   120  0.359037  NaN       3.99     54.06\n",
       "4  ordered-neurons  bllip-xs   111  0.371505  NaN       4.30     73.46"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join PPL and accuracy data.\n",
    "joined_data = suites_df.groupby([\"model_name\", \"corpus\", \"seed\"]).correct.agg(\"mean\")\n",
    "joined_data = pd.DataFrame(joined_data).join(perplexity_df).reset_index()\n",
    "joined_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>corpus</th>\n",
       "      <th>seed</th>\n",
       "      <th>circuit</th>\n",
       "      <th>correct</th>\n",
       "      <th>pid</th>\n",
       "      <th>test_loss</th>\n",
       "      <th>test_ppl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>Center Embedding</td>\n",
       "      <td>0.630792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.03</td>\n",
       "      <td>56.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>Garden-Path Effects</td>\n",
       "      <td>0.460169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.03</td>\n",
       "      <td>56.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>Gross Syntactic State</td>\n",
       "      <td>0.362319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.03</td>\n",
       "      <td>56.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>Licensing</td>\n",
       "      <td>0.114165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.03</td>\n",
       "      <td>56.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>Long-Distance Dependencies</td>\n",
       "      <td>0.434109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.03</td>\n",
       "      <td>56.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        model_name    corpus  seed                     circuit   correct  pid  \\\n",
       "0  ordered-neurons  bllip-lg   111            Center Embedding  0.630792  NaN   \n",
       "1  ordered-neurons  bllip-lg   111         Garden-Path Effects  0.460169  NaN   \n",
       "2  ordered-neurons  bllip-lg   111       Gross Syntactic State  0.362319  NaN   \n",
       "3  ordered-neurons  bllip-lg   111                   Licensing  0.114165  NaN   \n",
       "4  ordered-neurons  bllip-lg   111  Long-Distance Dependencies  0.434109  NaN   \n",
       "\n",
       "   test_loss  test_ppl  \n",
       "0       4.03     56.38  \n",
       "1       4.03     56.38  \n",
       "2       4.03     56.38  \n",
       "3       4.03     56.38  \n",
       "4       4.03     56.38  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join PPL and accuracy data, splitting on circuit.\n",
    "joined_data_circuits = suites_df.groupby([\"model_name\", \"corpus\", \"seed\", \"circuit\"]).correct.agg(\"mean\")\n",
    "joined_data_circuits = pd.DataFrame(joined_data_circuits).reset_index().set_index([\"model_name\", \"corpus\", \"seed\"]).join(perplexity_df).reset_index()\n",
    "joined_data_circuits.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>suite</th>\n",
       "      <th>item</th>\n",
       "      <th>correct</th>\n",
       "      <th>model_name</th>\n",
       "      <th>corpus</th>\n",
       "      <th>seed</th>\n",
       "      <th>tag</th>\n",
       "      <th>circuit</th>\n",
       "      <th>has_modifier</th>\n",
       "      <th>test_suite_base</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>mvrr_mod</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>mvrr</td>\n",
       "      <td>Garden-Path Effects</td>\n",
       "      <td>True</td>\n",
       "      <td>v</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>mvrr_mod</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-md</td>\n",
       "      <td>111</td>\n",
       "      <td>mvrr</td>\n",
       "      <td>Garden-Path Effects</td>\n",
       "      <td>True</td>\n",
       "      <td>v</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>mvrr_mod</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-sm</td>\n",
       "      <td>120</td>\n",
       "      <td>mvrr</td>\n",
       "      <td>Garden-Path Effects</td>\n",
       "      <td>True</td>\n",
       "      <td>v</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>mvrr_mod</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>ordered-neurons</td>\n",
       "      <td>bllip-xs</td>\n",
       "      <td>922</td>\n",
       "      <td>mvrr</td>\n",
       "      <td>Garden-Path Effects</td>\n",
       "      <td>True</td>\n",
       "      <td>v</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>mvrr_mod</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>vanilla</td>\n",
       "      <td>bllip-lg</td>\n",
       "      <td>111</td>\n",
       "      <td>mvrr</td>\n",
       "      <td>Garden-Path Effects</td>\n",
       "      <td>True</td>\n",
       "      <td>v</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       suite  item  correct       model_name    corpus  seed   tag  \\\n",
       "21  mvrr_mod     0    False  ordered-neurons  bllip-lg   111  mvrr   \n",
       "21  mvrr_mod     0    False  ordered-neurons  bllip-md   111  mvrr   \n",
       "21  mvrr_mod     0    False  ordered-neurons  bllip-sm   120  mvrr   \n",
       "21  mvrr_mod     0    False  ordered-neurons  bllip-xs   922  mvrr   \n",
       "21  mvrr_mod     0    False          vanilla  bllip-lg   111  mvrr   \n",
       "\n",
       "                circuit has_modifier test_suite_base  \n",
       "21  Garden-Path Effects         True               v  \n",
       "21  Garden-Path Effects         True               v  \n",
       "21  Garden-Path Effects         True               v  \n",
       "21  Garden-Path Effects         True               v  \n",
       "21  Garden-Path Effects         True               v  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analyze stability to modification.\n",
    "def has_modifier(ts):\n",
    "    if ts.endswith((\"_modifier\", \"_mod\")):\n",
    "        return True\n",
    "    else:\n",
    "        return None\n",
    "results_df[\"has_modifier\"] = results_df.suite.transform(has_modifier)\n",
    "\n",
    "# Mark \"non-modifier\" test suites\n",
    "modifier_ts = results_df[results_df.has_modifier == True].suite.unique()\n",
    "no_modifier_ts = [re.sub(r\"_mod(ifier)?$\", \"\", ts) for ts in modifier_ts]\n",
    "results_df.loc[results_df.suite.isin(no_modifier_ts), \"has_modifier\"] = False\n",
    "# Store subset of test suites which have definite modifier/no-modifier marking\n",
    "results_df_mod = results_df[~(results_df.has_modifier.isna())]\n",
    "# Get base test suite (without modifier/no-modifier marking)\n",
    "results_df_mod[\"test_suite_base\"] = results_df_mod.suite.transform(lambda ts: ts.strip(\"_no-modifier\").strip(\"_modifier\"))\n",
    "results_df_mod.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy across models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Accuracy')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=results_df.reset_index(), x=\"model_name\", y=\"correct\")\n",
    "\n",
    "plt.xlabel(\"Model\")\n",
    "plt.ylabel(\"Accuracy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy vs perplexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_to_size = {\n",
    "    \"bllip-xs\": 2,\n",
    "    \"bllip-sm\": 8,\n",
    "    \"bllip-md\": 14,\n",
    "    \"bllip-lg\": 20,\n",
    "}\n",
    "\n",
    "model_colors = dict(zip(model_order,\n",
    "                        ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2b6164502c88>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(10, 10))\n",
    "# sns.regplot(data=graph_data, x=\"test_ppl\", y=\"correct\",\n",
    "#             scatter_kws={\"s\": graph_data[\"corpus\"].map(corpus_to_size),\n",
    "#                          \"facecolors\": graph_data.model.map(model_colors)})\n",
    "sns.scatterplot(data=joined_data, x=\"test_ppl\", y=\"correct\",\n",
    "                hue=\"model_name\", size=\"corpus\",\n",
    "                hue_order=model_order, size_order=corpus_order)\n",
    "plt.xlabel(\"Test corpus perplexity\")\n",
    "plt.ylabel(\"SyntaxGym score\")\n",
    "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b6165c964a8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 436.75x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(data=joined_data, x=\"test_ppl\", y=\"correct\",\n",
    "           hue=\"corpus\", truncate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b6165c86d68>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ljC9fov9TCN8Sab8eyrc1bWXvLY+ux3T08P0QXNf29Buwp3Osr78nkzlfE/6mAxJdI7zdS147i4FUj9vEl/V8fCD6gKCOtbVmhZ7PuU8BRwKnAD9IcNO0t8+Lru9V23FiXY4ZA7LMT+ByBHCIc64+CPb19v5trQyJvgubzQ/fcSz+8+VL+CD7Dk1jECb2JPBZMxsCYGZlSe7zpWD7cPAjeS0wNGgtEaXzj6d41fhxlbpxzl3unNsvwSNRhf0T8L/BhzRBWc4zs+H4O11fM2sfW2L/JF5Ts3WMBfgkcIaZDQ32LzOzXZLIIxXOivvb1vrqMfyXOEF52r4on8N3OcPMpuPvXLWln2Z+rLhC4NN9LMML+AtczOy4uHzF21nrzPXABdZ5drj4Wb6KgVXB8v9u5bXEn3d747sZA7wCHGodY2fmmVlPLYDayv+poLwXb2Wzti+wDcHd5PiZvXp8b1JsCVBhZh8HMD/W22Tn3BZgpZmdGqRHzY/V06lcQeupZc65P+Dv8k3pdoSBb6DWm67i68dRwAbn3BYzG++cW+Sc+zW+y9PE4HtlnXPuJuBv+EBkOzMrCPJosx++W0ubW/Gtzf+xlfK06fp+xNf3C+LSHwO+aGZtQfy2/1NP72ev/1cz+6R1jGFViL9r/WEPZfooaBHyOXxL3UTHfhT4knWMwbiHmeX3+Mqlv3bWutubl4EjzWxcUM6219Wna8igpUjIOXcf8EO61ONe6Hpr6wbSufc2sGvbNRL+M+7ZXvZJeH44566PO/7qrjsFN44uAW7qkl4AFDvnZgHfwH+nQPfXXYjvJREh+D7rxWPA5+M+58uC66PlZnZmkGZBIH2r4sueRB5nmh8nbTx+DOkl9P374W1gXJAHQPxN7r7+nuzpMyVewt90ia4RejnWzmQg1eNEivHXci1mdiwwamsbm5/XYHTQUOBKfNf0rteVbwITgt8PAOfR++dFb2XcFAQHJwMfA3/TIChTopvJzwFnB+//MOBQ/LmZUHB9V+Scewh/wzeZ+EvGKUCYgHNuMfBz4FkzWwD8XxK7fR3f5W8RvjnpZOdcM34Q9FfxTUp7uutxI35Az6f7We61+Oj0b8wPkPkWvmvtFnzLiAiw0Pxgpj9NIssbg+3vcM69CfwAeMx898nH8f3sk9E2yG/bI7uPLy1qvqXR1/GVC+Ay/KClC803Y/9ikP5j4Agzm4uP0H8I4Jybi296PB/fHbqv05X/GDguyHc6fpyE6j7mMWDtxHVmDT7w/EszW2pmL+GDbW3N1a/Bd+N4Htiwlaz+AhQEdePbwGtB/uvxgYa7gudeIQUXOM65SvxF7iJ8M/zZcU/PAP5qnScpScbhXerpGVvb2DnXhH+vfh38z+fju72Av8i/LHjNL+HH6VgItJgffPhy/Pv+hvlm+BPxgZ1BZQDXm66uIfi8xg/+3BZs/4b5gZ4XAPXAw/hWGfPNbB6+W/L/65KXAd8OvuPm4z+bL4h7/g78D8a76N3T+G5N883sLODa4DW9SEcgDnzX5Q/x34cLgHOD9ITvZ5L/1wOBOcF78jJ+fK3ZdK8nf8bf+HsF37247Q551+1uxl88zw2+429AvUTSZmetu4GjzQ/o3vZobwUefGddAtwfvK62Llt9vYYcBTwT1NEZQKLeJz3R9dZWDKRzD/9j+UL8ddYifMuev/aSR1/Oj7ZJqN4Bvodv+fZWl20KgYeCz+Jn6fidcTd+ksR5QaDsh/j36nF6fq/aOecewd/8nBPUg7Yuuf8DXBT87xbjW0v1texby2NJ8DoeBr7o/DAvffp+CPa5BPiv+VZV8Tfh+vRZsJXPlHg9/aZLdI0wIOzk9TgZtwHTzGwOcCbQ2zBCWfjeFAuBucCvnXOd6rXz3fUvwp9Li/At9m7qllPy/gvkBe//1fj3sM3f8Od4198m9+Lf4wXAE/hxD9fRs2J8PVoAPIUfs36H1zZgoohsRXBXpjW4E/Jx4C/b2EVARETSJAhsn+Kc+1ymyyIifafrLdkanR89M7MZ+MlEEo37LiKSFN1dFknOWOAeMwvhByL9Qi/bi4jIdmR+QoTp9Dz+lIjs+HS9JVuj80NEJI3UglBERERERERERGQQS9sYhGb2dzNbF4xPkOh5M7M/BOMXLTSzvgxgLCIiIiIiIiIiIimQzklKZgDHb+X56cCE4HEJfpB/ERERERERERER2Y7SFiB0zj0HbNrKJqcAtzrvFaDEzHqdFff44493gB56DJZHyqkO6THIHimnOqTHIHuknOqQHoPskXKqQ3oMskdaqB7pMYgeSUtnC8LejAJWxK2vDNK2asOGDWkrkMhgoDok0j+qQyL9ozok0j+qQyL9p3ok0l0mA4SWIC1hdNPMLjGzOWY2Z/369WkulsjAozok0j+qQyL9ozok0j+qQyL9p3oksnWZDBCuBMbErY8GVifa0Dl3o3NuqnNuakVFxXYpnMhAojok0j+qQyL9ozok0j+qQyL9p3oksnWZDBDOBM4PZjM+BKhyzn2UwfKIiIiIiIiIiIgMOlnpytjM7gKOAsrNbCXwIyAC4Jz7KzALOAFYCtQBF6arLCIiIiIiIiIifdUai+GArFAm21eJpF/aAoTOuXN6ed4BX0nX8UVEREREREREtkVTSytrqhr52wvLiDn4/KHjGFGSQ04knOmiiaRF2gKEIiIiIiIiIiI7o3XVjRz3+2dpaI4B8M/ZK3js8iPYtTw/wyUTSQ+1kRURERERERERiXPf6yvbg4MATa0xbnn5fXxnSJGBRy0IRURERERERETiRBN0Jc6JhDEzADbWNLKuupHm1hjFuRGKso3C2BayQgZ55RCO2z/WCrXrofJDyC2FvCGQV5a6wjbVQn2lz79kDOSUQLQgdfnLoKAAoYiIiIiIiIhInFP3G8lfnnmPqvpmAAqiWZx38C4AbKhp5KJbZrNgRRUAN392PJ9oeZHwC78FC8FR34WJJ0Juic9s8/tw89FQv9mv7/NZmP4rHyjsr5YmWPok3HuBD0RaCE67ASadDFk5/c9fBg0FCEVERERERERE4lQU5vDoN47gkTc+oiXmOHHKCCoKogC8/N7G9uDgbuX57Bf9iPDMKzp2/s+XYehTMOpAaKiGx3/YERwEWHQPHHZ5agKE9Zvgwct8cBDAxeC/V8C4I6BweP/zlx1D7UZoqvb/52gRFFSk/BAKEIqIiIiIiIiIxAmHjOHFOVxw6Lhuzy1dV9O+fMAuJZS9e3P3DBbe6wOErQ2w+YPuz1evhmGT+l/QWGvn4CNAYzXEmvuddWVdEzWNLcRikB8NMyQIkMp2VrsB3rgPikZC3Ub44GU47qdQMDSlh9EkJSIiIiIiIiIiSTpxyoj25Y+qGqgeMqX7RqP2839zSmHfczo/F8mFoZNTU5hIDuwyrcuxD4Cs3H5lu7GmkavuX8hhv36aI657ms/PmM366sZ+5SnbqHELbHgHHvoGzP4b7H0afPgqxGK979sHChCKiIiIiIiIiCRpZEkOMy78GJNHFrF+SyPsdRJu1NSODXY5FHb7pF8OZ/kA4SevhrLdfDDvosdT070YfD5n/N0fo3gM7H0GnHUH5Jf3K9s3VlXxyBtr29cXrKzi/rkricU0i/N21dIE8+6E2Tf7loRrFsI950PZuJS0Eo2nLsYiIiIiIiIiIl3UNrbQ2BKjJDdCKGTt6QXRCEftOZR9RhXTEnPEsozWs+8iq6kazPwYcfEBuvwhcOhlcMB5EIqkdgZjgMIRcOJvobEGsvNTMoPxwlVV3dLmr6ikORYjGuo+w7OkSUMVLHmoc1pLI1StgOF7p/RQChCKiIiIiIiIiARaY44Vm+v4zaNLWLm5nrM+NprjJ4+gND+703adx+QbGjx6EI5AwbC0lBfwgcHs/JRld8xew/jtY+90Sjtlv5FEsxQc3K4iOTBkAqx7q3N6afexMftLAUIRERERERERkcDGmkZOvf5FKut8F875KyppanGcd/BYwuHBMVLbyJJcfnvmvlz76Ns0NMe4+PBxHDwuRd2iJXnRQj8hycrXoHqNTzvo0rQEmxUgFBEREREREREJLNtQ2x4cbHPHqx9w4j4jKC8cHDP5FudGOGW/kRyxRznOQUleNtlZHcFR5xwbahqpqmsmL5pFQTSLotxIBks8gJXsApc8B/UbIbvABw1zS1N+GAUIRUREREREREQCpXndA13lBVEiWZZg64GluTVGdUMzudlZ5EbCVBTmJNxu5eZ6zvzry6zZ0gDAxYeP46uf2J2SvOyE20s/mEHhMP9Io8HRNlZEREREREREJAkVhVGO2atjPMGcSIgfnjSJ4tyBHfzaWNPI9U8v5XN/e42fPriYNVX1CberaWjmF7Peag8OAtz8/HI21jZtr6JKGqgFoYiIiIiIiIhIoCw/yrVnTGHV5nrWbGlgn1HFlOUP7OBgTWMzv5z1NvfOXQnA4tVbeP2DSu64+OBu3aobmmO8u66mWx6rK+sZX9H/GZQlM9SCUEREREREREQkTll+lH1Gl3DspOEML84le4DP3lvX1MoD81d1Sluytpq65pZu2xblRjhhn+Gd0rLDISYMVXBwZ6YWhCIiIiIiIiIig5gBQwqyWbulsT0tHDIiCWZtzs4K8b8f35Xq+hbun7eKEcU5/Py0vTX+4E5OAUIRERERERERkUGsLC+bn526D5feNoeY82lfOmo8hdHEYaMhBVG+ffxEvnTUeMIhY0jB4JjdeSBTgFBEREREREREZBALh0NMGz+E5779CRav3sL4inzKC6IU5HSf0blNbnaY3OyB3fV6MFGAUERERERERERkkMuPZpEfzWJ0aV6miyIZoElKREREREREREREBjEFCEVERERERERERAYxBQhFREREREREREQGsbQGCM3seDNbYmZLzeyqBM+PNbOnzWyemS00sxPSWR4REREREREREZGdUmMNtDSmJeu0BQjNLAxcD0wHJgHnmNmkLpv9ALjHObc/cDbw53SVR0REREREREREZKdTXwlLn4L7Pg+PXAWVKyAWS+kh0jmL8UHAUufcMgAzuxs4BXgzbhsHFAXLxcDqNJZHRERERERERERk5/LBi3D3uR3ri/8NX34FCoen7BDp7GI8ClgRt74ySIt3DXCema0EZgFfS5SRmV1iZnPMbM769evTUVaRAU11SKR/VIdE+kd1SKR/VIdE+k/1SHZadZvhpT92TqvfDB8tTOlh0hkgtARprsv6OcAM59xo4ATgNjPrVibn3I3OuanOuakVFRVpKKrIwKY6JNI/qkMi/aM6JNI/qkMi/ad6JDutUBiiRd3TcxKk9ecwKc2ts5XAmLj10XTvQnwRcA+Ac+5lIAcoT2OZREREREREREREdg45RXDM1ZAV7UgbsS+UjU/pYdI5BuFsYIKZjQNW4SchObfLNh8CRwMzzGwvfIBQbX1FREREREREREQAynaHr74OS5+EopEwcj8oSG1L2LQFCJ1zLWb2VeBRIAz83Tm32Mx+Asxxzs0EvgncZGaX47sfX+Cc69oNWUREREREREREZHCK5EDJGJh6QdoOkc4WhDjnZuEnH4lPuzpu+U3g0HSWQURERERERERERHqWzjEIRUREREREREREZAenAKGIiIiIiIiIiMggpgChiIiIiIiIiIjIIKYAoYiIiIiIiIiIyCCmAKGIiIiIiIiIiMggpgDhAFPf1EJNQ3OmiyEiIiIiIiIiIjuJrEwXQFKjqaWVFZvq+d0T71Dd0MIXj9yNvUcVU5gTyXTRRERERERERERkB6YA4QCxoaaJE//4PA3NMQCefWc991z6cQ4aV5bhkomIiIiIiIiIyI5MXYwHiGeWrG8PDrb52wvLqGtqyVCJRERERERERERkZ6AA4QAxpCC7W1pZfpSskGWgNJKQc9Cq8SFFREREREREZMeiAOEAccDYUnYfWtC+XpSTxZePGk92VjiDpZJ2Nevg5evhgS/Be09D3eZMl0hEREREREREBNAYhANGRWGUu75wCItXV1Hd0MLHxpVRkd+9VaFkQM16uP10WLPIry/6F5z4WzjgAgirCoqIiIiIiIhIZik6MYBUFEY5as+hmS6GdFW/uSM42OaF38PET0PhsMyUSUREREREREQkoC7GIun+Kxr0AAAgAElEQVQWStDNOxwB0/iQIiIiIiIiIpJ5akEokm45JXDY5ZAVhY3vwVsz4eirIa880yUTERERERERERmYAcKG5lY21jbxynsbGVWay4ShBQwpiGa6WDJoOSgYBktmwcgD4GtzIVoEITXgFREREREREZHMG5ABwnfXVvOZv7xMU2sMgEN2K+P6cw9QkFC2v8YaeOqn8PoMv778OfjwJTj7rowWS0RERERERESkzYALEFbWNfGLWW+3BwcBXlm2ibVbGjoFCDfUNNLYEsOA7HCI7KwQRbmRxJnGYlC3AVwMIrmQU5zaQrc0Qn2lX84p8seQgaGpBubf0TltxWvQVAv56mIsIiIiIiIiIpk34AKErTFHZX1Tt/Tqhpb25eUbarn0tjm8s7aG0aW5/L+z9mVYuJr8gjDhSBQK4mYCbm6AVXPggS9D1QrYYzqc9LvUzT5btwnm3Q7PXQeuFT7+VTj4Usgbkpr8JcMMsgv8TMbtSSE/SYmIiIiIiIiIyA5gwAUIS/OyuWDaOL5z38L2tCH52YwrzwdgY00jX7ljLu+srWl/bje3ktIHLvQTSAzZHc66HSom+llm6zfD7af7Vn4AS/4LeWUw/VrIzut/gTe8A4//sGP92V/DqKmwx3H9yra+qYWq+mY+3FTHiOJcSvIiFOYoKLXd5ZXB6TdDayPklsCqudBY7YOGIiIiIiIiIiI7gKQChGYWdc419pa2IwiFjE9NHkZB9ADufO0Dxpbl8ZVP7E5Foe9e3NzqePOjLe3b/+CockpnngWb3/cJG5fC3efC5x/1LQm3rOoIDrZZ+gQ0bklNgPDth7qnLb4Pdj9mmyexiMUcr3+wmQtnzKa51QFw9acncdbUMeRHB1xMeMfWGrRc/e83oWYNjD8aTv6j70ouIiIiIiIiIrIDSDYC9XKSaTuEkrxsTpwygr+cdyDXfHoyo0vzMDMAssLGHsM6Wm8Nz7eO4GCbTcs6goIFw3xLwnjDp0BWisYJHPvx7mm7HNavGW431jby7XsXtgcHAX41622qG5q7bRuLuW5pkkINm+Hus6H6I3DOB5ef/jk01WW6ZCIiIiIiIiIiQC8BQjMbbmYHArlmtr+ZHRA8jgJ6bT5nZseb2RIzW2pmV/WwzWfN7E0zW2xmd27Tq0igrrGF2oYWFqysZFVlfXtwrLwgyvXnHsDoUh/gq2oyKB7deefiMRDO9su5JTD9uo710nFwwrWQm6KJSsYcDJNP61jf/VjY8/h+ZekcrNnS0CmtqTVGU0tHMHB9dQN/e2E537l/Ia9/sImqBOM2SgpUroDWLoHZ95723YxFRERERERERHYAvfU3/RRwATAa+C3Q1pRuC/C9re1oZmHgeuBYYCUw28xmOufejNtmAvBd4FDn3GYzG5o4t75pbo3x4nsb+eLtr9Mac5jBrz8zhU/vO4LcSBa7Dy3g318+lPrmVnKzIPbZ2wndfY5v5VU4As66A/IrfGbRQtjvXJh4IrQ0+LHjClJSTC+/HE78HRz7U8D5/PPK+pVlTiTMcZOG88jiNe1p48rzyYuGAdhQ3ci5N73Ku+v8OIz/mrOSP56zPydNGdHe0lJSpGikb4Hq4lpqjtxfM1WLiIiIiIiIyA5jqwFC59wtwC1m9hnn3H19zPsgYKlzbhmAmd0NnAK8GbfNF4DrnXObg+Ot6+MxEtpc18RV9y2kNeg+6xxcM3MxR+5RQW4kCzNrH5MQgIIpcOmzfsbirBwftIvv4pud7x/pklfqHylSlBvhp6fuTXlBNs+8s569Rxbzg5P2orzAv+Z11Q3twcE2f3zqXT4+fkj7NpIiOSVw0v+Dh7/tA8zle8Dxv9IYhCIiIiIiIiKyw0h2xooDzexJ51wlgJmVAt90zv1gK/uMAlbEra8EDu6yzR5Bfi8CYeAa59wjXTMys0uASwDGjh3ba2FjMdhY27nLbF1TK82tscQ7hMJ+rMEBpKIwyvdPnMRlRzeTmx3uNINxolaC4ZChtoNpkFMI+5wJE47zMxlH8lLbAjVJfa1DItKZ6pBI/6gOifSP6pBI/6keiWxdsjNhTG8LDgIELf5O6GWfRPGmrjNiZAETgKOAc4Cbzayk207O3eicm+qcm1pRUdFrYXMiIaaNH9IpbfLIInIj4V73HUhys8MMLcrpFBwEHzycPLJzC7Yrjt2TIWo9mB7ZeVA0Akp3zUhwEPpeh0SkM9Uhkf5RHRLpH9Uhkf5TPRLZumRbEIbNLOqcawQws1ygt2jSSmBM3PpoYHWCbV5xzjUDy81sCT5gODvJciVUkpfN78/aj+seW8LL721k/7ElfG/6XgqABcoLotxy4UE8+856lqyp5tT9RzGmTGPiiYiIiIiIiIgMRskGCG8HnjSzf+BbAX4euKWXfWYDE8xsHLAKOBs4t8s2D+BbDs4ws3J8l+NlSZZpq4YW5fDjT0+mpqmFvOwwBdFI7zsNIuWFUT5z4OjeNxQZ5OqbWohkhcgKJdvgWkRERERERGTnklSA0Dl3rZktBI7Bdx3+qXPu0V72aTGzrwKP4scX/LtzbrGZ/QSY45ybGTx3nJm9CbQCVzrnNvbj9XSSF80iL5psDFREBrvKuiaq6pupbWxhSEE28z+s4t65K9lrRCHnHbwLQ4tyMl1EERERERERkZTrS/TsLaDFOfeEmeWZWaFzrnprOzjnZgGzuqRdHbfsgCuCh4hIxmyubeLns97i3tdXcvK+I5kwrIDfPvYOAI+/uZb/LlzDPy89RDN9i4iIiIiIyICTVJ85M/sCcC9wQ5A0Ct89WERkQFizpYF7X18JwLGThnHXqx92ev699TVs7jI7uoiIiIiIiMhAkOygWl8BDgW2ADjn3gUyMx2riEgarKqsb19ubGklN7t7A+tIWOMQZkwsBjVroWoV1K7PdGlEREREREQGlGR/7TY659qbzphZFn6yEhGRAWHyiCKygwDg/XNX8ZVPjMes4/lP7FlBUa4mO8qI1hb4aB7cfAz8bhLceipsWp7pUomIiIiIiAwYyY5B+KyZfQ/INbNjgS8DD6avWCIi21dpfjb/vPQQfjRzMcvW11KYE+Gxy4/g2SXr2XNYIZNGFlGWn53pYg5OdRvgjjOgbpNfX/sG3Pd5OPdfkF+e2bKJiIiIiIgMAMkGCK8CLgIWAZfiJx65OV2FEhHZ3nIiYfYfW8qMCz9GS6ujJC+b7KwQE4YWZrpo0lzXERxss2ouxFoyUx4REREREZEBptcAoZmFgVucc+cBN6W/SCIimVOWr1mKdziRPMgphoaqjrRhe0MonLkyiYiIiIiIDCC9jkHonGsFKsxMfetERGT7yy2Ds+6AvCF+vWQXOOMfkF+R2XKJiIiIiIgMEMl2MX4feNHMZgK1bYnOuf9LR6FERETaZWXD2EPgSy9BSyNEchUcFBERERERSaFkA4Srg0cI0IBcIiKyfYUjUDg806UQEREREREZkJIdg7DAOXfldiiPiIiIiIiIiIiIbEe9Bgidc61mdsD2KIyI7DiqG5pZU9XAc+9uYPLIIiYMLWBIgSbwkAxorIHGLX6SktxSPxZhOJLpUomIiIiIiAwYyXYxnh+MP/gvOo9BeH9aSiUiGdXaGuOZJev52l3z2tNO3nckPzllMiV5mq9ItqPGWlh0L8y6AmKtEC2C/30QRu6X6ZKJiIiIiIgMGL3OYhwoAzYCnwQ+HTxOSlehRCSzNtU184tZb3VKm7lgNbWNLT3v1FwP1WuhYUuaSyeDSmMVPPwtHxwE35LwgS9B7frMlisJzjk21jSyqbYx00URERERERHZqqRaEDrnLkx3QURkx+Fw1DZ1Dwa2xBwATS2tbK5r5sONdQwtijIiq5rI89diS5+A4fvA8b+EkrHbu9gyEDXXQWtz57SN70IslpnyJKmqvokXl27k+qeXkhUyrvzURPYbU0JBTrIN90VERERERLafpFoQmtloM/u3ma0zs7Vmdp+ZjU534UQkM4pzIlwwbVyntMkji8iP+uDG22uqOeq6Zzjzhpd5av57ZD3yHWzO36DyA3j7Ibj1FKhZ17Fz7Xp46yF4/EewYjbUbU5tgavXwGs3wTO/gk3LfWvG7aCpJcaW+mZiQeBU0iC7EAqGdU6bcDxEcjNTniS9u7aGL98xl8Wrt7BgZRWf+/urfFS1fc5LERERERGRvkq2KcM/gDuBM4P184K0Y9NRKBHJrJaY46ypoxlRnMPjb65lyqhizj1kLOUFUTbWNPLd+xdR3+y7fB4xLp/QCzM7Z7BpGTQFw5XWbYIHL4e3H/TrL/4epl8HB14AWSkYz7B6Ldz0Sdiyyq8//xu49HkYulf/896KdVsa+NuLy1m0soqTpozg+L1HUJav8RlTLr/cjzk482uw7i2YcBx86ueQU5TpkvWoJRbjztc+7JTmnO+m/83j9sxQqURERERERHqWbICwwjn3j7j1GWb2jXQUSEQyb9n6Gk65/kU+tmsZ+40pYe2WBggaybXGHCs217Vvu7GumfHFo6EyLiASjkBWMONxU01HcLDNM7+Ayad0bxm2LT58uSM4CL476rPXwqnXQySv//knsKGmkfP//hpvr6kG4KX3NvL+xjquOHYPciLhtBxz0AqFoWJPOOcu/7/NzodoYaZLtVVhM8ZX5HdLH19RkIHSiIiIJKmxxl+3AeSWdlzLiYjIoJDsJCUbzOw8MwsHj/Pwk5aIyABT29jCH59aSszBq8s3ccNzy7hr9gpe/8B3Cy6IZnHSPiPbt//9y5uoOu73PijY5pgf+9lmwTed6irW2h5w7LdYgolTWpsTHzdFahtb2oODbe545QO2NDT3sIf0W94QKBy+wwcHAcyMMw8cwy5DOgLUk0cWcdju5Rks1fbX2hpj7ZYGXlm2kXfWVrOptinTRRIRkTibahv5YGMtH26spWnLOtwj34XfTYY/fQzm3gr1lZkuooiIbEfJtiD8PPAn4Hf4n/UvAZq4RGSAMus5LS+axRXH7UFudohHF68lKxSmsmxfIl+aR7TmQ8LFoyG3BKJBa6loAYw7ApY/15HZtMv8Nqmw62E+eFQX3LOwEBzxLd/SLE0i4e73VopyIxgJ3jgZlIYW5XDvF6excnMdkXCI4cU5lBcMrpYYH2yq49TrX2RLgw/in7D3cH522j7qii8isgPYUN3IJbfPYe4HlXxiz3Ku32M+2fNu9U82boFZ34JdpnW+XmtthYZKPw5wdhp6aTTX+yFqokXbPAxNQ3MrVfXNVNY1UZKXTXFupNfeHc2tMbJChiW6ABYRGUSSDRD+FPhf59xmADMrA36DDxyKyACSH83i60dP4Im31tEaTL4xvCiHA3Ypbd+mvCDKlZ+ayKVHjMcMQmZEcssIl4/pnmHeEDjj736Skg9fgr3PhNEHpq7bSv5QP+bgnL9D3QY46FIo3SU1efd0yOwszpo6hn/OWdGedvWnJynwIZ1UFEapKBxcQcE2NQ3N/PLht9qDgwCz3ljD146eoHoiIrIdra9uYHVlA9lZIYYWRhkS3Kx6aOFq5n7gWwhOHRElb/mj3Xde/jwMm+yXazfAgrvhjXuhfA/45A+gZGzqCrrlI3juOlg1B/Y4Hg76AuRX9CmL5tZWXlm2kUtve53GlhjRrBA3fO5ADt29POHN3cq6JhasrOS+11exz+hiTt1v1KD93t5umup88DeUbBhCRLanZGvmlLbgIIBzbpOZ7Z+mMolIhu1ans/jlx/BPXNWUlGYzUlTRjK0MKfTNjmRcPLj7eVXwNQL4YDz/ZhyqRQKQfEof6HqnF9Ps+K8CN+ZPpGzDxrD22uqOWS3MioKooRDuvMsAtDYEmN1ZUO39HVbGthrxI47wYyIyECyurKes258mRWb6gHYf2wJN50/leLcCHM/7Og+/OaGZmqHH0z+0ic7ZzB6qv/b0ggv/xle+G2Q8Tx4/wW45OnUjCddux7uPBPWLPLrHy2ATe/Dib+FnOSHFtlc28zl/5xPY0sM8N9FV9yzgEe+fjhDizpfx7a0xnhg3iquefBNwE8k9sC8Vdz6+YPag6iSQnWb/Dmz4E4Yvq//XVA4PNOlEpEukv0lHTKz9uZDQQtChf1FBqi87Cx2qyjgqukTueiw3RjW5aJqm6U6OBjPbLsEB9uU5Wez/9hSzjloLOPKCyjIifS+k0hP6qv8D6MnfwJv/Btq1me6RP1SkpfNGQeO7pSWlx1mooKDIiLbxea6Rma89H57cBBg3oeVvP7BZiLhEKfuP6o9/bG31rNu98/ixh3pE0Jh+PjXoGycX6+vhPm3dT7AllVQm6Ih6ZtqO4KDbRbfC821fcqmOebYXNd5POhNtU00x7qPS725rpm/Prus8yFXb6GqXuNJp1xrM8y7He75HCx5GJ79Fdx6ClSt6n1fEdmukg3y/RZ4yczuxY9B+Fng52krlYiISLxYKzRUQVZOesY9yqRYDJY/6y+c24ydBmfdBvk758Qm4ZBx6n6jaI057p69gmFFUa4+aRJl+Z0D6bWNLdQ1tZCfnUVeVPcdRURSZdXmBlZuruuW/t56P0vx/mNL+O70idzw3DKyQsbLa0NkH/NnhuXFyApnQXYB5AQ3dSzkWwrWrOucWarGew4HXU7jJ57LK4c+ju2ckxVi8sgiFq/e0p42eWQROVmJbiA7Ilnd81dvkDSo2wSv/Llz2vq3oWGzn+SwYGhmyiUi3STV3MY5dyvwGWAtsB443Tl329b3AjM73syWmNlSM7tqK9udYWbOzKYmW3ARERkkajfCazfBnZ+Fh78NVSszXaLUqtsAT/2kc9qHL0H95o71WAxq1kLlCv8DLdWzdLc0QvUafze/blNKsizNz+aCabty9yWH8OdzD2TP4UVEwh2tiNdU1fODf7/B6X95iWseXMy6Ld27JIuIyLbZXNvE9L1HdEozg09N8t06S/OyufDQcTz6jcP5z1cO5fi9h5NfOpSs0rFQNLIjOAhQUAEnXOeDOW32P99PJpIK0UI4/MrOBT3hN34c6z4YUhDlxs9N5cg9yimIZnHkHuXceP7UhF2Gy/KjXHncxE5ph08op1A9QlLPrIebuwaN1du9OCLSs6Rv1zvn3gTeTHZ7MwsD1wPHAiuB2WY2M8gnfrtC4DLg1WTzFhGRQaKlEV79Mzz3G7++crZvbXfRE1CYgnGPdgjOd7/pKtYa/I3BusVw97lQ+SGUjoOz74SheyWecryvGrbAWw/CI9/xF+rjjoDTb07J+5sVDiWcvXljTSNfuPV1Fq2qAmDFppWs3FzPn//nAEryNImJiEh/7Tm8kCffXsePT57MHa9+QE4kzJWf2pPhxR3DxmRnhagoTHIYmRH7wWXz/fiApbtA4QjIK+19v2REC+GQL8Lep8OGJTB8ig8OhvvesnxUaS5/OGd/GptjRCNhinMTB/zCIeOoPSuYddlhPLJ4DZNHFnPgLqWaSCsdcsvgqO/CfRd3pE041t+QLNstc+USkW7S2Z/nIGCpc24ZgJndDZxC9yDjT4FrgW+lsSwiIrIzqq+Eubd2Tqv8EOo3DZwAYW45HHYFPHhZR9qwyR0tJ2rXw11nd7Sc3LzcBwsveiw13XLqN8N/vtyxvvw5P5PkcT+FSG7/80+gobm1PTjY5qX3NtLQ3JqW44mIDDYVhVE+sedQXly6nh+cuBdjy/IZVhQlN3sbf/5FcqF4tH+kQ26Jf1Ts0e+sinOzIe7ra1NtI4ZR2iX4V5QbYVJuMZNGFvf7mLIV4Sx/U/OCh/zM2EN272hRmKbrDBHZNukMEI4CVsStrwQOjt8gmAl5jHPuITPrMUBoZpcAlwCMHTs2DUUVGdhUh2SnZSE/C3bXcY8i23ccwrTWoXAY9jrZDwY/7zY/u9+UM32XLoDWxu7dqjcv9+mpsH5J97T3n/etCdN04Z4VDpGXHaauqSMgWJoXwVLRIlJ2SPoeEumfvtYhM2N4cQ6nHzAa5yA0CMfW29LQzOzlm/jDk+8SDhnfPG5PpowuVjfiTCgcBavmw6ZlsOI1f50z8kDIK9uuxdB3kcjWpXPKz0TfQu2DJplZCPgd8M3eMnLO3eicm+qcm1pRUZHCIooMDqpDstMqqIDp1/rBy9vse27qxj1KUtrrUF6p79p7yl9g2lf9YPBtwlEoGtV5+9JxPj0VErXW2OUw3+UrTYpzI/z45MntPaRDBr88fZ9uk5jIwKHvIZH+2dY6ZGaDMjgIsHx9LRfdMocFK6uY+2El5/3tVVZXarzbjMgrhX3PhuN+DqfdAFPOhqIRve+XYvouEtm6dLYgXAmMiVsfDayOWy8E9gaeCVoMDAdmmtnJzrk5aSyXiIjsTEYe4Mc9WjXHj1VTPDp14x5lUE1jC/VNLTS3OnIjISLhMAU5Cb6W88vhnLt8t+KqlVAyFs6+w7esTIXcMjj5T/DIVdBUA7scCkdemdZuPzmRMMfvPZxp44ewYnM9Y8vyKM6NdJrERFKotdl3VV+zEAqG+zq0k86QLSKSjFjMceerH3RKcw7+PW8VV02f2MNeklbhrIEzPIzIAJXOAOFsYIKZjQNWAWcD57Y96ZyrAtqvTs3sGeBbCg6KiEgn2Xn+UTKm9213EhtqGvnlrLd4cMFHDC/O4bsnTGR1ZT0n7zuy+4DxoTAM2wcufsp3K86KQl5FaiYoAT9T5T5nwoRj/MQokdw+zxy5LQpzIhTmRBhVun27iw9KG5fCzUdDU61fn3AcnPoXBQlFZMAKhYzdKgq6pe9aru8cEZGepK2LsXOuBfgq8CjwFnCPc26xmf3EzE5O13FFRER2ZI0trdz0/DLum7uKptYYH26q47K75nHA2FKue3QJtY0t3XcKhfxd95KxvvtxKMVf35EcPyNl8ejtEhyU7aihCh77QUdwEODdx6B6TebKJCKyHZy2/yh2GdIREJwwtICjJ6Zgci8RkQEqnS0Icc7NAmZ1Sbu6h22PSmdZREREtqemllY21TXzzppqhhVFGVqYQ2l+NlvqW3jizbWdtm1udby/sY7Fq7ZQ29hCfjStX88ymLQ2JQ4G1qzFj/QiIjIwDS3K4d4vTmPFpjpCIWN0SS7lhSkav1dEZADSLxAREZE0WLq+ls/8+SXqm/1MvafuN5IffXoyudkh9hpRxHvrazttP6okl4rCKNlZ6Zw/TAad3DI48AKY9a2OtJxiyC2FmvUds2WLiAxAFYVRKhQUFBFJin6FiIiIpFhlXRM/nrm4PTgI8MD81Wyqa6IgGuGq6RMZW+a7PYUMLj1iN95cXcWPTp5ESV52pootA1EoDBNPhON+5if8mXginHM3PPEjaNFsniIiIiLiqQWhiIhIijW3xli7pXvwpbKuGYDRpXnc96Vp1DS2EAkbzkE0EqI8X60cJA0sBKsXwD5nQN0muO9iaKmHcCTTJRMRERGRHYRaEIqIiKRYSV42Z04d0yUtwpjS3Pb1isIo48rzGV2ax5iyPIYW5hAKpWhmYpF4eRVw6NehoRLWLPQTl5x+syakEREREZF2akEoIiKSYpFwiHMOGkN2Voj7565kdGku3zthEkMK1EJQMqB+Eyx/BlbNhd2PgZP/ADmlakEoIiIiIu0UIBQREUmDsvwoF07bldP3H0V2VojCHAVjJAPqNsPMr8I7j/j1pU/4QOGJv4FITmbLJiIiIiI7DHUxFhERSZOscIghBVEFByVzmus6goNt3rgXmuoyUx4RERER2SEpQCgiIiIyUJl170qclePTRUREREQCChCKiIiIDFTRIpj29c5pR34HcoozUx4RERER2SFpDEIRERGRgSpaAB//Ckw8EVa8CrtMg+KxEMntfV8RERERGTQUIBQREREZyPLK/GPUAZkuiYiIiIjsoNTFWEREREREREREZBBTC0IRERFJibqmFrbUt9DUGiM3EqKiMCfTRRIRERERkSQoQCgiIiL9VtPYwqxFH/HDB96gsSXGrkPyuO2igxlTlpfpoomIiIiISC/UxVhERET6rbqhmavuW0hjSwyA9zfWcfV/3mBLfXOGSyYiIiIiIr1RgFBERET6bX11IzHXOW3RqioamlszUyAREREREUmaAoQiIiLSb8OKcohmdb6sOHxCBflRjWYiIiIiIrKjU4BQRERE+q04N8KMCz/GqJJczODIPSu4avpEBQhFRERERHYCumoXERGRfsuJhDl43BAe+Mo0Yg5yssIU50UyXSwREREREUmCAoQiIiKSEqGQUVGYk+liiIiIiIhIH6mLsYiIiIiIiIiIyCCmAKGIiIiIiIiIiMggltYAoZkdb2ZLzGypmV2V4PkrzOxNM1toZk+a2S7pLI+IiIiIiIiIiIh0lrYAoZmFgeuB6cAk4Bwzm9Rls3nAVOfcFOBe4Np0lUdERERERERERES6S2cLwoOApc65Zc65JuBu4JT4DZxzTzvn6oLVV4DRaSyPiIiIiIiIiIiIdJHOAOEoYEXc+sogrScXAQ8nesLMLjGzOWY2Z/369SksosjgoDok0j+qQyL9ozok0j+qQyL9p3oksnXpDBBagjSXcEOz84CpwHWJnnfO3eicm+qcm1pRUZHCIooMDqpDIv2jOiTSP6pDIv2jOiTSf6pHIluXlca8VwJj4tZHA6u7bmRmxwDfB450zjWmsTwiIiIiIiIiIiLSRTpbEM4GJpjZODPLBs4GZsZvYGb7AzcAJzvn1qWxLCIiIiIiIiIiIpJA2gKEzrkW4KvAo8BbwD3OucVm9hMzOznY7DqgAPiXmc03s5k9ZCciIiIig0xVfTOVdU2ZLoaIiIjIgJfOLsY452YBs7qkXR23fEw6jy8iIiIiO5+6xhbeWVfNdY/+//buPEjOus7j+Ps7M5npzB2SEHIQ7hgVJQRElLtgEXAV3RXlcJWCkhKkFFjLRbeU0q0tl0JrS9ddjnXZlFAgLoqwsBJYimNxkfsK13KEI5CQgyRMMmSu/PaP7sAMTGZ66J7uZ6bfr6qu7n76+jz9zIFxfF4AAA6FSURBVGdq6je/53meoW8gcfYRe7Lv/E7aclOqHU21qLcbtmyA9S9CxzzIdUKuvdqpJEkqq3EdIJQkSZLG6vWuHv7y4nsY2Jo/v91Xlt/H7886iEXzO6ucLDu6tvSxdlMvD7+8ng/NaWen9hydzY3VjjX5bB2Al++Bq78EA30QAcdeBItOhsaWaqeTJKlsHCCUJElSptz46GtvDw5u86s/vcjecz9KQ/14HkJ7Yugb2Mp/P/U6517z6NvLzj5iT75+2O60OsuyvLrXwg1n5wcHAVKCW/4WFn7aAUJJ0qTiX1iSJEnKlDmdU9+zbG7nVOoiqpAme9Z39/Kj/3xyyLJL7nyerp7+KiWaxNJW6Fo5dFl/Dwx4bExJ0uTiDEJJkiRlyqELZvDhOe0cstcMDl0wk56+rXxkXgd1dSUMEPZsgt7N0NAEU8dhV+Xu9flBo6Y2aGwu29tu7umnvi7ITal/Z2GCri1DBwP7tyb6BxIqsynNsOBYeGbQYdVnLIA6Z2pKkiYXZxBKkiQpU2a25bj81I+xobuPUy+/n+/89jHuX/4GmwYNir35Vh+rNm7htQ1vsfrNLbzZ3QOb10FP13vfsGsV3HQeXHIQXHta/mQT5TIwAGuehmtOhksPgVt/AJvWvK+32tDdyzOrurjxsdd4dX03f3phHWdf9TDf/d3jvLh2M30DAwA0N9Zz/KK5Q167786dNDfWD/e2KkWuAz79U9j3y9A5Hz74GThhCax+CjavrXY6SZLKxhmEkiRJypT+ga1c9/Cr/Pr+VwBY09XDWVc9xJ3fPpzWXAPrNvXwg+uf4L+WraSlsZ6bvrY3nStvg0eWQNtsOOqHMG03qG+AtzbADd+EZ5fm3/z52+BXn4PTl0LrrNLDdq+By4+Bt9bn79//r5AG4Oi/H9NMwq4tfVxy5/NccucLLJjVyrl/toAzr3zo7ceXPrGK2/76MGZ3TKU1N4XvHbeQPXZs4banVrP/LtM4/eDdmN7aVPr6aKiBfnj0mvzM0MO/C2+8AFd8Ho7/BfRtqXY6SZLKxgFCSZIkZUpXTz+3PLFqyLKU4OFXNjCncyq/eWAFNz2ePy7cx3adxrSXltJ463nvPHn5XfCN+6F9NvRvgeduGfoB65dDb3d5wm5e+87g4DZPXg+H/c2YBgg39fRz2V0vAHDkwln89sEVQx7v7h3g7mfXcsL+OwMwvbWJMw7ZnZMPmE9zYwONDe4YNC76NsPyO+CFO4YuX7UMZi+qRiJJksaFf0lIkiQpU5qn1LPPvPceJ3DBrDa6ewe48/9Wv73sswum0v7EFUOf2NMFry/L346AjvlDH29ogoZcecLmOvKfMdgOu0H92I5R1zewlW0nbt7c20/71Pe+vrO5ccj9hvo6OpsbHRwcT42tsOBT712+60FQ74xNSdLk4V8TkiRJypSmKfV8/fA9+NDsdgDqAs44dHdmd+RobqznwN2nv/3cNd2JrS3D7CrcMjN/3TwTPnfxOwOCUQfHXgS59vKEzbXDYecPCt8On/kZNE/f/muG0dzYwMKd2gC46bGVnPLxXWgZdEzBPWa2smjncTi5ikZWVw8f+SIsOgXqGvIDwsdeBJ27wNSOaqeTJKls3MVYkiRJmTOrPccVpx/A5t4BptQHrU0NtOXys+pO+fguPPDiG9z93Dp+/eh6TjnpAlpeuht6N+VfvOdR0DEvf7uuDubuB996BN5cCa075gd5GlvKEzTXAQeeCYtOhu43oG0naJ4x5reZ0drEktMO4F9uf47HVmzklTe6ufW8w7j3hXW0T53CR+d1MrPNGWtV0TIDjrkQjvw+UJc/C3aD20KSNLk4QChJkqRMmt7axHDz8Ga2NfFPJy2muzd/Vt8t9VvJfeN+6l5/nGjdETp2zg/qbDMlB1Nm509gMh5yHflL5/zRnzuCndpzfO+4D/JW7wBtuQYa6uv4/OJ5ZQqpkuTa8hdJkiYpBwglSZI04UxraWTakEmALdAxp1pxyiY3pZ7clPrRnyhJklRGHoNQkiRJkiRJqmEOEEqSJEmSJEk1zAFCSZIkSZIkqYY5QChJkiRJkiTVsEgpVTvDmETEGuClaucomAGsrXaIQbKWB8xUrO1lWptSOqacH2SHRmWm4kyUTHao8sxUnImSyQ5VnpmKM1Ey2aHKM1NxJkqmsncIMtWjibIdqs1MxSmpQxNugDBLIuKBlNL+1c6xTdbygJmKlcVMlZDF9TZTccyUDVlcZzMVx0zZkMV1NlNxzJQNWVxnMxXHTNmQxXU2U3EmYyZ3MZYkSZIkSZJqmAOEkiRJkiRJUg1zgLA0l1U7wLtkLQ+YqVhZzFQJWVxvMxXHTNmQxXU2U3HMlA1ZXGczFcdM2ZDFdTZTccyUDVlcZzMVZ9Jl8hiEkiRJkiRJUg1zBqEkSZIkSZJUwxwglCRJkiRJkmqYA4RFiojOiLg2Ip6OiKci4hMRsUNE3BoRzxaup1Uwzwci4pFBlzcj4pxqZirkOjcinoiIZRFxdUTkImK3iLi3kOmaiGiscKZvFfI8ERHnFJZV9HuKiMsjYnVELBu0bNgMkffziHguIh6LiMXjma1S7FDRuezQ8BnskB0qNpcdGj6DHbJDxeayQ8NnsEN2qNhcdmj4DHbIDhWbyw5tP8e49sgBwuL9DLg5pbQQ2Ad4CjgfuC2ltBdwW+F+RaSUnkkpLUopLQL2A7qB66qZKSLmAt8E9k8p7Q3UAycCFwL/WMi0Hji9gpn2Br4GHEB+u/15ROxF5b+nJcAx71q2vQzHAnsVLmcAF49ztkqxQ6OwQyNagh2yQ6OwQyNagh2yQ6OwQyNagh2yQ6OwQyNagh2yQ6OwQ6Nawnj2KKXkZZQL0A4sp3BSl0HLnwFmF27PBp6pUr6jgT9WOxMwF3gF2AFoAG4EPgWsBRoKz/kEsLSCmU4Afjno/veB71TjewJ2BZaN9vMDXAqcNNzzJurFDhWdww6NnMUO2aHRctihkbPYITs0Wg47NHIWO2SHRsthh0bOYofs0Gg57NDoecatR84gLM7uwBrg3yPi4Yj4ZUS0ALNSSisBCtc7VinficDVhdtVy5RSehX4CfAysBLYCDwIbEgp9ReetoJ86StlGXBoREyPiGbgOGBnsrHttpdh2y/FbSr9nY0HO1QEOzRmdigb2wHs0EjsUDbYoSLYoTGzQ9nYDmCHRmKHssEOFcEOvS9l65EDhMVpABYDF6eU9gU2U8FptiMp7Hv/WeA/MpBlGnA8sBswB2ghP6313VKlMqWUniI/HflW4GbgUaB/xBdVXwyzrGLf2TixQ8VlsUPlYYcqyA6NzA5lhh0qLosdKg87VEF2aGR2KDPsUHFZ7FD5jLlHDhAWZwWwIqV0b+H+teTL/XpEzAYoXK+uQrZjgYdSSq8X7lcz01HA8pTSmpRSH/A74JNAZ0Q0FJ4zD3itgplIKf1bSmlxSulQ4A3gWbKx7baXYQX5/0hsU/HvbBzYoeLYobGxQ9nYDnZoFHYoE+xQcezQ2NihbGwHOzQKO5QJdqg4dmjsytYjBwiLkFJaBbwSER8oLDoSeBK4AfhqYdlXgeurEO8k3pkKDNXN9DJwYEQ0R0Twzvd0O/CFKmUiInYsXM8H/oL895WFbbe9DDcAXymcdehAYOO2KcMTlR0qmh0aGzuUje1gh0Zhh6rPDhXNDo2NHcrGdrBDo7BD1WeHimaHxq58PUpVOADmRLwAi4AHgMeA3wPTgOnkzxLzbOF6hwpnagbWAR2DllU70w+Bp8nvp38F0ET+eAv3Ac+Rn7bcVOFM/0P+l8qjwJHV+J7I/wJZCfSRH8k/fXsZyE8F/mfgeeBx8mdwqujP+zh9B3aouEx2aPgMdsgOFZvJDg2fwQ7ZoWIz2aHhM9ghO1RsJjs0fAY7ZIeKzWSHtp9jXHsUhRdKkiRJkiRJqkHuYixJkiRJkiTVMAcIJUmSJEmSpBrmAKEkSZIkSZJUwxwglCRJkiRJkmqYA4SSJEmSJElSDXOAUJIkSZIkSaphDhBOYhHRGRFnvc/XnhMRzWXOc2pE/KKc7ymNJzsklcYOSaWxQ1Jp7JBUGjtUWxwgnNw6gfdVZuAcoKxlliYgOySVxg5JpbFDUmnskFQaO1RDGqodQOPqH4A9IuIR4FZgNfBFoAm4LqV0QUS0AL8B5gH1wN8Bs4A5wO0RsTaldMRwbx4Rm4BLgSOA9cCJKaU1EXEH8AhwANAOnJZSum/8VlMaN3ZIKo0dkkpjh6TS2CGpNHaohjiDcHI7H3g+pbSIfJn3Il+wRcB+EXEocAzwWkppn5TS3sDNKaWfA68BR2yvyAUtwEMppcXAncAFgx9LKX2S/H8bLi/3ikkVYoek0tghqTR2SCqNHZJKY4dqiAOEtePowuVh4CFgIflyPw4cFREXRsQhKaWNY3jPrcA1hdtXAgcPeuxqgJTSXUB7RHSWmF+qNjsklcYOSaWxQ1Jp7JBUGjs0ybmLce0I4McppUvf80DEfsBxwI8j4paU0o/e52ek7dwe7r400dghqTR2SCqNHZJKY4ek0tihSc4ZhJNbF9BWuL0UOC0iWgEiYm5E7BgRc4DulNKVwE+AxcO8dnvqgC8Ubp8M3D3osS8VPudgYOMY/4sgZYUdkkpjh6TS2CGpNHZIKo0dqiHOIJzEUkrrIuKPEbEM+ANwFXBPRABsAr4M7AlcFBFbgT7gzMLLLwP+EBErRzhmwGbgwxHxILCRQoEL1kfE/1I4oGiZV02qCDsklcYOSaWxQ1Jp7JBUGjtUWyIlZ2nq/YmITSml1mGW3wF8O6X0QOVTSROHHZJKY4ek0tghqTR2SCqNHcoWdzGWJEmSJEmSapgzCDWqiLgXaHrX4r9KKT1ejTzSRGOHpNLYIak0dkgqjR2SSmOHJgYHCCVJkiRJkqQa5i7GkiRJkiRJUg1zgFCSJEmSJEmqYQ4QSpIkSZIkSTXMAUJJkiRJkiSphv0/kBNmp9JL3s0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data=joined_data_circuits, col=\"circuit\")\n",
    "g.map(sns.scatterplot, \"test_ppl\", \"correct\", \"model_name\",\n",
    "      hue_order=model_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model_name       corpus    circuit                   \n",
       "ordered-neurons  bllip-lg  Center Embedding              0.630792\n",
       "                           Garden-Path Effects           0.460169\n",
       "                           Gross Syntactic State         0.362319\n",
       "                           Licensing                     0.114165\n",
       "                           Long-Distance Dependencies    0.434109\n",
       "                           Transformations               0.932143\n",
       "                 bllip-md  Center Embedding              0.630792\n",
       "                           Garden-Path Effects           0.460169\n",
       "                           Gross Syntactic State         0.362319\n",
       "                           Licensing                     0.114165\n",
       "                           Long-Distance Dependencies    0.434109\n",
       "                           Transformations               0.932143\n",
       "                 bllip-sm  Center Embedding              0.623237\n",
       "                           Garden-Path Effects           0.464840\n",
       "                           Gross Syntactic State         0.404622\n",
       "                           Licensing                     0.135399\n",
       "                           Long-Distance Dependencies    0.419527\n",
       "                           Transformations               0.921905\n",
       "                 bllip-xs  Center Embedding              0.642430\n",
       "                           Garden-Path Effects           0.521296\n",
       "                           Gross Syntactic State         0.365386\n",
       "                           Licensing                     0.125586\n",
       "                           Long-Distance Dependencies    0.410803\n",
       "                           Transformations               0.883909\n",
       "vanilla          bllip-lg  Center Embedding              0.624386\n",
       "                           Garden-Path Effects           0.463641\n",
       "                           Gross Syntactic State         0.410628\n",
       "                           Licensing                     0.112460\n",
       "                           Long-Distance Dependencies    0.439241\n",
       "                           Transformations               0.929630\n",
       "                 bllip-md  Center Embedding              0.630357\n",
       "                           Garden-Path Effects           0.455322\n",
       "                           Gross Syntactic State         0.394928\n",
       "                           Licensing                     0.119484\n",
       "                           Long-Distance Dependencies    0.424937\n",
       "                           Transformations               0.932143\n",
       "                 bllip-sm  Center Embedding              0.630357\n",
       "                           Garden-Path Effects           0.455322\n",
       "                           Gross Syntactic State         0.394928\n",
       "                           Licensing                     0.119484\n",
       "                           Long-Distance Dependencies    0.424937\n",
       "                           Transformations               0.932143\n",
       "                 bllip-xs  Center Embedding              0.648206\n",
       "                           Garden-Path Effects           0.471096\n",
       "                           Gross Syntactic State         0.343460\n",
       "                           Licensing                     0.131103\n",
       "                           Long-Distance Dependencies    0.412647\n",
       "                           Transformations               0.874564\n",
       "Name: correct, dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_data_circuits.groupby([\"model_name\", \"corpus\", \"circuit\"]).correct.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variance in accuracy vs variance in perplexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 479.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "catplot_ticks = [\"correct\", \"test_ppl\"]\n",
    "catplot_data = joined_data.copy()\n",
    "catplot_data[\"correct\"] *= 100\n",
    "catplot_data = catplot_data.melt(id_vars=set(catplot_data.columns) - set(catplot_ticks))\n",
    "catplot_data[\"corpus_size\"] = catplot_data.corpus.map(corpus_to_size)\n",
    "\n",
    "g = sns.catplot(data=catplot_data,\n",
    "                x=\"variable\", y=\"value\", hue=\"model_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Circuit–circuit correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:10: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:11: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Circuit--circuit correlations')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axs = plt.subplots(len(circuit_order), len(circuit_order), figsize=(20, 20))\n",
    "plt.subplots_adjust(hspace=0.5, wspace=0.5)\n",
    "\n",
    "for c1, row in zip(circuit_order, axs):\n",
    "    for c2, ax in zip(circuit_order, row):\n",
    "        if c1 <= c2:\n",
    "            ax.axis(\"off\")\n",
    "            continue\n",
    "            \n",
    "        xs = results_df[results_df.circuit == c1].groupby([\"model_name\", \"corpus\", \"seed\"]).correct.agg({c1: \"mean\"})\n",
    "        ys = results_df[results_df.circuit == c2].groupby([\"model_name\", \"corpus\", \"seed\"]).correct.agg({c2: \"mean\"})\n",
    "        df = pd.concat([xs, ys], axis=1)\n",
    "        ax.set_title(\"%s /\\n %s\" % (c1, c2))\n",
    "        sns.regplot(data=df, x=c1, y=c2, ax=ax)\n",
    "        \n",
    "plt.suptitle(\"Circuit--circuit correlations\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stability to modification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Stability to modification')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15, 10))\n",
    "sns.barplot(data=results_df_mod, x=\"model_name\", y=\"correct\", hue=\"has_modifier\")\n",
    "plt.title(\"Stability to modification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Stability to modification')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15, 10))\n",
    "sns.barplot(data=results_df_mod, x=\"corpus\", y=\"correct\", hue=\"has_modifier\")\n",
    "plt.title(\"Stability to modification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Change in accuracy due to modification')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_mod_results = results_df_mod.groupby([\"model_name\", \"test_suite_base\", \"has_modifier\"]).correct.agg({\"correct\": \"mean\"}).sort_index()\n",
    "avg_mod_diffs = avg_mod_results.xs(True, level=\"has_modifier\") - avg_mod_results.xs(False, level=\"has_modifier\")\n",
    "\n",
    "plt.subplots(figsize=(15, 10))\n",
    "sns.boxplot(data=avg_mod_diffs.reset_index(), x=\"model_name\", y=\"correct\")\n",
    "plt.title(\"Change in accuracy due to modification\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}