A_result_1_PCA_dimVar5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using package ml ml \n",
    "\n",
    "import torch\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import cosine\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import decomposition\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchmetrics.functional import pairwise_cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj/english-adjectives.txt') as f:\n",
    "    adjatives = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "model = SentenceTransformer('bert-base-nli-mean-tokens')\n",
    "\n",
    "#from scipy import cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import parrot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# par = parrot(model_tag=\"prithivida/parrot_paraphraser_on_T5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# {'Q22.2': 'I like the idea of safe automated vehicles if they make it easier for me to travel.',\n",
    "#  'Q22.3': 'I trust automated vehicles to operate safely.',\n",
    "#  'Q22.4': 'I would consider buying an automated vehicle.',\n",
    "#  'Q22.5': 'I would be comfortable riding in an automated vehicle.',\n",
    "#  'Q22.6': 'I would allow my children to be transported in an automated vehicle.',\n",
    "#  'Q22.7': 'I would pay a higher price for a partly or fully automated vehicle than a non-automated vehicle.',\n",
    "#  'Q22.8': 'I would enjoy being able to use my time in an automated vehicle for things like reading, or relaxing rather than driving.',\n",
    "#  'Q22.9': 'I enjoy actually driving a car, and would prefer that to riding in an automated vehicle.'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pca = 40\n",
    "scen = 0\n",
    "embed_dim = num_pca\n",
    "removed_quest = ['Q20.2', 'Q21.2', 'Q22.2']\n",
    "\n",
    "pca = decomposition.PCA(n_components=embed_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "compress_method = \"PCA\" \n",
    "# none\n",
    "# PCA\n",
    "# auto_encoder\n",
    "NN_PARAM = [100, 10] # last number \n",
    "lamb = 5\n",
    "# parrot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_MANUAL = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=num_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_sentences(doc_name=\"sentences.txt\"):\n",
    "    file = open(doc_name, 'r')\n",
    "    lines = file.readlines()\n",
    "    \n",
    "    ret_dic = {}\n",
    "    for index, line in enumerate(lines):\n",
    "        if index%2 == 0:\n",
    "            key= line.strip()\n",
    "        else:\n",
    "            ret_dic[key] = line.strip()\n",
    "    \n",
    "    return ret_dic\n",
    "\n",
    "sentences =load_sentences()\n",
    "question_num = list(sentences.keys())[:-1]\n",
    "\n",
    "def load_sentences_excel(doc_name=\"manual_values.xlsx\", subset = 0):\n",
    "    if subset == 0:\n",
    "        sheet_name = \"raw_questions\"\n",
    "    else:\n",
    "        sheet_name = f\"subset_{subset}\"\n",
    "        \n",
    "    df = pd.read_excel(\"manual_values.xlsx\", sheet_name=sheet_name)\n",
    "    \n",
    "    return dict(list(zip(df[\"Question\"], df[\"Sentence\"]))), df\n",
    "# sentences =load_sentences()\n",
    "# question_num = list(sentences.keys())[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_text_to_tens(sentences):\n",
    "    return torch.FloatTensor(model.encode(sentences))\n",
    "\n",
    "adj_embed = list_text_to_tens(adjatives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proces_sent(sent_dic, NUM_PEOPLE, get_strange_quest=7):\n",
    "    #assume reponses are ordered 1 per person\n",
    "    \n",
    "    sentences = []\n",
    "    questions = []\n",
    "    for key, sentence in sent_dic.items():\n",
    "        questions.append(key)\n",
    "        sentences.append(sentence)\n",
    "    \n",
    "    sent_embed = model.encode(sentences)\n",
    "    \n",
    "    \n",
    "    # find wierdest questions to be removed\n",
    "    sent_dic_distances = []\n",
    "    for base_vec, question in zip(sent_embed, sent_dic.keys()):\n",
    "        distances = []\n",
    "        for compare_vec in sent_embed:\n",
    "            distances.append(cosine(compare_vec, base_vec))\n",
    "        sent_dic_distances.append((question, sum(distances)/len(distances)))\n",
    "        \n",
    "    #pca.fit(sent_embed)\n",
    "    #red_embed = pca.transform(sent_embed)\n",
    "    sorted_sent_dic_distances = sorted(sent_dic_distances, key=lambda x: x[1])\n",
    "    bad_questions = [quest for quest, _ in sorted_sent_dic_distances][-get_strange_quest:]\n",
    "    \n",
    "    ret_tens = torch.FloatTensor(sent_embed)\n",
    "    return torch.concat([ret_tens for _ in range(NUM_PEOPLE)]), bad_questions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# foo = dict([a for a in enumerate(adjatives)])\n",
    "\n",
    "# measurements, _ = proces_sent(foo, 1 ,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# measurements.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_responses(questions, filepath = \"responses.xlsx\"):\n",
    "    df = pd.read_excel(filepath, sheet_name=\"Mobility and Future Mobility CL\")\n",
    "    df = df[questions]\n",
    "    df = df.replace([\"Strongly disagree\", \"Disagree\", \"Somewhat disagree\", \"Somewhat agree\", \"Agree\", \"Strongly agree\"], \n",
    "                    [0, 1, 2, 3, 4, 5])\n",
    "    \n",
    "    #df = df.replace([0,1,2,3,4,5], [0,0,0,1,1,1])\n",
    "    # TODO investigate NANS\n",
    "    df = df.fillna(0)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proces_responses(responses):\n",
    "    ret_tens = responses.to_numpy()\n",
    "    x, y = ret_tens.shape\n",
    "    ret_tens = ret_tens.reshape(x*y)\n",
    "    \n",
    "    #everyone at least strongly disagrees\n",
    "    disagree = 1.*(ret_tens >= .5).reshape(len(ret_tens), 1)\n",
    "    som_disagree = 1.*(ret_tens >=1.5).reshape(len(ret_tens), 1)\n",
    "    som_agree = 1.*(ret_tens >=2.5).reshape(len(ret_tens), 1)\n",
    "    agree = 1.*(ret_tens >=3.5).reshape(len(ret_tens), 1)\n",
    "    stong_agree = 1.*(ret_tens >= 4.5).reshape(len(ret_tens), 1)\n",
    "    \n",
    "    ret_likard = np.concatenate((disagree, som_disagree, som_agree, agree, stong_agree), axis=1)\n",
    "    \n",
    "    #TODO process into hot 1 embedding \n",
    "    return x, torch.FloatTensor(ret_likard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_sentence(in_string):\n",
    "    x = model.encode(in_string)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import floor\n",
    "\n",
    "def get_people_one_hot():\n",
    "    num_q = int(len(y_train)/NUM_PEOPLE)\n",
    "    to_tens = []\n",
    "\n",
    "    for index in range(len(x_train)):\n",
    "        one_hot = floor(index/num_q)\n",
    "        temp_list = [0. for i in range(NUM_PEOPLE)]\n",
    "        temp_list[one_hot] = 1.\n",
    "        to_tens.append(temp_list)\n",
    "    \n",
    "    return torch.tensor(to_tens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list(questions.keys()).index(['Q22.2', 'Q22.3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_quest(rem_questions, data_set):\n",
    "    indexes_to_be_rem = [list(questions.keys()).index(key) for key in rem_questions]\n",
    "    \n",
    "    remain_quest = [index for index in range(len(data_set)) if index%len(questions) not in indexes_to_be_rem]\n",
    "    removed_quest = [index for index in range(len(data_set)) if index%len(questions) in indexes_to_be_rem]\n",
    "    \n",
    "    remain_data_set = data_set[remain_quest]\n",
    "    removed_data_set = data_set[removed_quest]\n",
    "    \n",
    "    return remain_data_set, removed_data_set\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_manual_embed(df):\n",
    "    return df.iloc[:, 2:].to_numpy()\n",
    "\n",
    "def get_manual_embed_for_training(df, x_train):\n",
    "    data = get_manual_embed(df)\n",
    "    \n",
    "    if data.shape[0] * NUM_PEOPLE == x_train.shape[0]:\n",
    "        to_be_concat = [torch.tensor(data.copy()).type(torch.float32) for _ in range(NUM_PEOPLE)]\n",
    "    \n",
    "    return torch.concat(to_be_concat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_person_and_responses(data, one_hot_people, person_ID):\n",
    "    # used for taking the average of the persons responses\n",
    "    persons_questions = one_hot_people[:, person_ID] >= 0.5\n",
    "    \n",
    "    return data[persons_questions].sum(axis=1)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load quesitions\n",
    "questions, df = load_sentences_excel(subset=scen)\n",
    "\n",
    "quest_subset = questions.keys()\n",
    "\n",
    "#load responses\n",
    "responses = load_responses(quest_subset)\n",
    "\n",
    "\n",
    "#process everything\n",
    "NUM_PEOPLE, y_train = proces_responses(responses)\n",
    "\n",
    "x_train, bad_questions = proces_sent(questions, NUM_PEOPLE, get_strange_quest=5)\n",
    "\n",
    "one_hot_people = get_people_one_hot()\n",
    "\n",
    "if USE_MANUAL:\n",
    "    manual_embed = get_manual_embed_for_training(df, x_train)\n",
    "    x_train = manual_embed\n",
    "\n",
    "x_all = x_train\n",
    "y_all = y_train\n",
    "one_hot_all = one_hot_people\n",
    "# remove given questions\n",
    "x_train, x_test = remove_quest(removed_quest, x_train)\n",
    "y_train, y_test = remove_quest(removed_quest, y_train)\n",
    "one_hot_people, one_hot_people_test = remove_quest(removed_quest, one_hot_people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "927"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NUM_PEOPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7416"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8*927"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([37080, 768])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([37080, 768])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2781, 768])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([37080, 5])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2781, 5])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Logit(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    Logit model between two options \n",
    "    \"\"\"\n",
    "    def __init__(self, input_size, num_people=927, dim_var=1):\n",
    "        super(Logit, self).__init__()\n",
    "        self.num_people = num_people\n",
    "        \n",
    "        if compress_method == \"auto_encoder\" or compress_method == \"parrot\":\n",
    "            self.input_size = NN_PARAM[-1]\n",
    "        elif compress_method == \"PCA\":\n",
    "            self.input_size = num_pca\n",
    "        elif compress_method == \"cosine_words\":\n",
    "            self.input_size = len(adj_embed)\n",
    "        else:\n",
    "            self.input_size = input_size\n",
    "        \n",
    "        self.has_fit_pca = False\n",
    "        \n",
    "        self.embedd_weights = torch.nn.Parameter(torch.rand(self.input_size, 1))\n",
    "        self.bias = torch.nn.Parameter(torch.rand(1,1))\n",
    "    \n",
    "        self.person_het_vector = torch.nn.Parameter(torch.rand(num_people, dim_var))\n",
    "        self.het_dimension = torch.nn.Parameter(torch.rand(dim_var, self.input_size))\n",
    "        \n",
    "        #self.util_to_likert = torch.nn.Linear(1, 5)\n",
    "        self.likert = torch.nn.Parameter(torch.rand(1, 5))\n",
    "        \n",
    "        \n",
    "        self.sigmoid = torch.nn.Sigmoid()\n",
    "        self.relu = torch.nn.ReLU()\n",
    "        self.softmax = torch.nn.Softmax(dim=1)\n",
    "        \n",
    "        # create an encoding nn network\n",
    "        if compress_method == \"auto_encoder\" or compress_method == \"parrot\":\n",
    "            #initialise hidden layers \n",
    "            self.encoder = torch.nn.ModuleList()\n",
    "            prev_layer = input_size\n",
    "            for layer_size in NN_PARAM:\n",
    "                self.encoder.append(torch.nn.Linear(prev_layer, layer_size))\n",
    "                prev_layer = layer_size\n",
    "    \n",
    "    def forward(self, chosen_options, pers_respond, floating_likart=False):\n",
    "        \n",
    "        chosen_options = self.compress_data(chosen_options, pers_respond)\n",
    "        \n",
    "        num_entries = chosen_options.shape[0]\n",
    "        \n",
    "        individ_variability = pers_respond @ self.person_het_vector\n",
    "        variabilty_vecs = individ_variability @ self.het_dimension\n",
    "        \n",
    "        seed_values = (self.embedd_weights @ torch.ones(1, num_entries)).T\n",
    "        \n",
    "        variabilty_vecs = variabilty_vecs + seed_values \n",
    "        \n",
    "        utility =  torch.sum((chosen_options * variabilty_vecs),  dim=1) + self.bias\n",
    "        \n",
    "        likart = utility.T @ torch.FloatTensor([[1,1,1,1,1]]) + torch.ones_like(utility.T) @ self.likert \n",
    "        \n",
    "        likart = self.sigmoid(likart)\n",
    "        # instead ->\n",
    "        return likart\n",
    "    \n",
    "    def get_specific_activations(self, chosen_options, pers_respond, floating_likart=False):\n",
    "        chosen_options = self.compress_data(chosen_options, pers_respond)\n",
    "        \n",
    "        num_entries = chosen_options.shape[0]\n",
    "        \n",
    "        individ_variability = pers_respond @ self.person_het_vector\n",
    "        variabilty_vecs = individ_variability @ self.het_dimension\n",
    "        \n",
    "        seed_values = (self.embedd_weights @ torch.ones(1, num_entries)).T\n",
    "        \n",
    "        variabilty_vecs = variabilty_vecs + seed_values \n",
    "        \n",
    "        return chosen_options * variabilty_vecs\n",
    "    \n",
    "    \n",
    "    def compress_data(self, x_data, pers_respond):\n",
    "        \n",
    "        if compress_method == \"none\":\n",
    "            return x_data\n",
    "        elif compress_method == \"PCA\":\n",
    "            return self.PCA(x_data, pers_respond)\n",
    "        elif compress_method == \"auto_encoder\" or compress_method == \"parrot\":\n",
    "            return self.auto_encoder(x_data)\n",
    "        \n",
    "        elif compress_method == \"cosine_words\":\n",
    "            return self.cosine_words(x_data, pers_respond)\n",
    "# none\n",
    "# PCA\n",
    "# auto_encoder\n",
    "# parrot\n",
    "    def cosine_words(self, x_data, pers_respond, compress_output=False):\n",
    "        new_distances = pairwise_cosine_similarity(x_data, adj_embed)\n",
    "        \n",
    "        \n",
    "        if compress_output:\n",
    "            return PCA(new_distances, pers_respond)\n",
    "        else:\n",
    "            return new_distances\n",
    "\n",
    "    def PCA(self, x_data, pers_respond):\n",
    "        #if already has transform -> transform\n",
    "        pers_respond\n",
    "        if not self.has_fit_pca:\n",
    "            single_set_of_questions = pers_respond[:,0] > 0.5\n",
    "            \n",
    "            fitting_space = x_data[single_set_of_questions]\n",
    "            pca.fit(fitting_space)\n",
    "            \n",
    "            self.has_fit_pca = True\n",
    "            \n",
    "        return_val = pca.transform(x_data)\n",
    "        return_val = torch.FloatTensor(return_val)\n",
    "        return return_val\n",
    "        \n",
    "    \n",
    "    def auto_encoder(self, x_data):\n",
    "        #simple forward pass NN to pass\n",
    "        for layer in self.encoder:\n",
    "            x_data = self.relu(layer(x_data))\n",
    "                                    \n",
    "        return x_data\n",
    "        \n",
    "    def sentence_to_neurons(self, sentence):\n",
    "        sentence = torch.tensor(encode_sentence(sentence)).reshape((1,768))\n",
    "        neuron_activation = self.compress_data(sentence, None)\n",
    "        \n",
    "        return neuron_activation\n",
    "    \n",
    "    def poll_question(self, sentence):\n",
    "        #polls a question with every participant in the study \n",
    "        option = torch.tensor(encode_sentence(sentence))\n",
    "        sentence_option = torch.ones(self.num_people, 1) @ option.reshape((1, len(option)))\n",
    "        one_hot_person = torch.eye(self.num_people)\n",
    "        \n",
    "        return self.forward(sentence_option, one_hot_person)\n",
    "        \n",
    "    def get_persons_values(self, ID):\n",
    "        one_hot_person = torch.zeros(NUM_PEOPLE)\n",
    "        one_hot_person[ID] = 1.\n",
    "        \n",
    "        individ_variability = one_hot_person @ self.person_het_vector\n",
    "        variabilty_vecs = individ_variability @ self.het_dimension\n",
    "        \n",
    "        #print(\"1\", variabilty_vecs)\n",
    "        #print(\"2\", self.embedd_weights)\n",
    "        \n",
    "        return variabilty_vecs + self.embedd_weights.T\n",
    "        \n",
    "    def get_individ_utility(self, sentence, ID):\n",
    "        #BROKEN\n",
    "        \n",
    "        values = self.get_persons_values(ID)\n",
    "        option = torch.tensor(encode_sentence(sentence))\n",
    "        compress_data(get_individ_utility)\n",
    "        #print(values.shape, option.shape)\n",
    "        \n",
    "        return  self.sigmoid(torch.sum((option * values),  dim=1) + self.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 2])\n",
      "torch.Size([2, 2])\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)\n",
    "y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)\n",
    "\n",
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim_of_variability = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit_model = Logit(input_size = x_train.shape[1], dim_var=dim_of_variability)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(logit_model.parameters(), lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "BCE_crit = torch.nn.BCELoss()\n",
    "def sparse_loss(y_pred, y_test, model, lamb):\n",
    "    loss_criteria = BCE_crit(y_pred.squeeze(), y_test)\n",
    "    \n",
    "    for w in model.encoder:\n",
    "        loss_criteria = loss_criteria + lamb * torch.norm(w.weight,p='fro')**2\n",
    "    \n",
    "    return loss_criteria\n",
    "    \n",
    "#criterion = torch.nn.BCELoss()\n",
    "criterion = torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "lamb = 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss before training 14.395318031311035\n"
     ]
    }
   ],
   "source": [
    "logit_model.eval()\n",
    "y_pred = logit_model(x_train, one_hot_people)\n",
    "before_train = criterion(y_pred.squeeze(), y_train)\n",
    "print('Test loss before training' , before_train.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "perform = []\n",
    "test_perform = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: train loss: 0.41088607907295227\n",
      "Epoch 0: test_loss loss: 0.5647361278533936\n",
      "------------------------------------------------------\n",
      "Epoch 100: train loss: 0.4062577486038208\n",
      "Epoch 100: test_loss loss: 0.571047306060791\n",
      "------------------------------------------------------\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-75-02b55d234890>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;31m# -> https://datascience.stackexchange.com/questions/27561/can-the-number-of-epochs-influence-overfitting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m \u001b[0mtrain_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprint_prog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-75-02b55d234890>\u001b[0m in \u001b[0;36mtrain_model\u001b[1;34m(print_prog)\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[1;31m# Forward pass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m         \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogit_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mone_hot_people\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m         \u001b[1;31m# Compute Loss\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1103\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-34-5fc4cd2269c4>\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, chosen_options, pers_respond, floating_likart)\u001b[0m\n\u001b[0;32m     43\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchosen_options\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpers_respond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloating_likart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 45\u001b[1;33m         \u001b[0mchosen_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchosen_options\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpers_respond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m         \u001b[0mnum_entries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mchosen_options\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-34-5fc4cd2269c4>\u001b[0m in \u001b[0;36mcompress_data\u001b[1;34m(self, x_data, pers_respond)\u001b[0m\n\u001b[0;32m     82\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mx_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcompress_method\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"PCA\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpers_respond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcompress_method\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"auto_encoder\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mcompress_method\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"parrot\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_encoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-34-5fc4cd2269c4>\u001b[0m in \u001b[0;36mPCA\u001b[1;34m(self, x_data, pers_respond)\u001b[0m\n\u001b[0;32m    112\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_fit_pca\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 114\u001b[1;33m         \u001b[0mreturn_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    115\u001b[0m         \u001b[0mreturn_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFloatTensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreturn_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mreturn_val\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\sklearn\\decomposition\\_base.py\u001b[0m in \u001b[0;36mtransform\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    128\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean_\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 130\u001b[1;33m         \u001b[0mX_transformed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomponents_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    131\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhiten\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m             \u001b[0mX_transformed\u001b[0m \u001b[1;33m/=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplained_variance_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\numpy\\core\\overrides.py\u001b[0m in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "def train_model(print_prog = False):\n",
    "    # logit model \n",
    "    logit_model.train()\n",
    "    epoch = 1000\n",
    "    prev_test_loss = 100\n",
    "    \n",
    "    num_bad_steps = 8000000\n",
    "    \n",
    "    for epoch in range(epoch):\n",
    "        optimizer.zero_grad()\n",
    "        # Forward pass\n",
    "        y_pred = logit_model(x_train, one_hot_people)\n",
    "        # Compute Loss\n",
    "        loss = criterion(y_pred.squeeze(), y_train)\n",
    "        # Backward pass\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        perform.append(loss.item())\n",
    "        \n",
    "        \n",
    "        y_pred = logit_model(x_test, one_hot_people_test)\n",
    "        # Compute Loss\n",
    "        loss_test = criterion(y_pred.squeeze(), y_test)\n",
    "        test_perform.append(loss_test.item())\n",
    "        \n",
    "        if prev_test_loss <= loss_test.item():\n",
    "            num_bad_steps = num_bad_steps - 1\n",
    "            if num_bad_steps <= 0:\n",
    "                break\n",
    "            \n",
    "        prev_test_loss = loss_test.item()\n",
    "        \n",
    "        \n",
    "        if print_prog and epoch%100 == 0:\n",
    "            print('Epoch {}: train loss: {}'.format(epoch, loss.item()))\n",
    "            print('Epoch {}: test_loss loss: {}'.format(epoch, loss_test.item()))\n",
    "            print(\"------------------------------------------------------\")\n",
    "            \n",
    "    if print_prog:\n",
    "        print(\"VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\")        \n",
    "        print(f\"done with training loss of:{loss.item()} \\nand test loss of: {loss_test.item()}\")\n",
    "    \n",
    "# early stop is leagal \n",
    "# -> https://datascience.stackexchange.com/questions/27561/can-the-number-of-epochs-influence-overfitting\n",
    "\n",
    "train_model(print_prog = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 3.0)"
      ]
     },
     "execution_count": 76,
     "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.lineplot(x=np.arange(len(perform)), y=perform, label=\"training_performance\")\n",
    "plt.plot(np.arange(len(test_perform)), test_perform, label=\"test_performance\")\n",
    "plt.legend()\n",
    "plt.ylim(0, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Model Works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logit_model(x_test, one_hot_people_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 0.],\n",
       "        ...,\n",
       "        [1., 0., 0., 0., 0.],\n",
       "        [1., 1., 1., 0., 0.],\n",
       "        [1., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.9520, 0.8843, 0.7632, 0.4149, 0.1344],\n",
       "        [0.9771, 0.9427, 0.8740, 0.6042, 0.2505],\n",
       "        [0.9230, 0.8220, 0.6607, 0.2999, 0.0858],\n",
       "        ...,\n",
       "        [0.9557, 0.8926, 0.7780, 0.4354, 0.1445],\n",
       "        [0.9172, 0.8102, 0.6429, 0.2837, 0.0798],\n",
       "        [0.5458, 0.3166, 0.1635, 0.0412, 0.0093]], grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tens_to_int(y_vals):\n",
    "    ret_val = y_vals >= 0.5\n",
    "    return ret_val.sum(axis=1)\n",
    "    \n",
    "\n",
    "def int_to_lik(y_vals):\n",
    "    ret_vals = y_vals.detach().numpy()\n",
    "    \n",
    "    for index, likard in enumerate([\"Strongly disagree\", \"Disagree\", \"Somewhat disagree\", \"Somewhat agree\", \"Agree\", \"Strongly agree\"]):\n",
    "        #np.where(ret_vals == index, )\n",
    "        1\n",
    "    return 1\n",
    "    \n",
    "#int_to_lik(tens_to_int(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Q16.2': 'It is easy for me to travel to and from public transport (e.g. stops/stations are close by or have parking or good access via footpaths/bike paths).',\n",
       " 'Q16.3': 'I would cycle more if I had an e-bike (electric bike) which reduces the physical effort required.',\n",
       " 'Q16.4': 'I would cycle more if I could hire a normal or e-bike near my key origin and destinations.',\n",
       " 'Q16.5': 'I am happy to change modes a few times during my journey – e.g. from a bus to a train, or car to bus.',\n",
       " 'Q16.6': 'I am happy to walk a short distance (up to 10 minutes) to reach my transport.',\n",
       " 'Q16.7': 'I would use public transport more if it picked me up and dropped me off right near the start and end of my trip (less than 2 minute walk).',\n",
       " 'Q17.2': 'I would use a shared taxi or shuttle bus if it was cheaper than taking a private taxi.',\n",
       " 'Q17.3': 'I would consider not owning a car if I could access a vehicle for a comparable or lower cost in other ways when I really need it i.e. Car hire/sharing',\n",
       " 'Q17.4': 'If fast, convenient, affordable public transport and shared taxis were available to me, I would use private cars/my car less.',\n",
       " 'Q17.5': 'I would rather carpool or share a taxi/uber with other travellers for an affordable cost than take public transport.',\n",
       " 'Q18.2': 'I use transport planning websites and apps (e.g. Google Maps or TransLink Journey Planner) regularly.',\n",
       " 'Q18.3': 'I find it easy to plan my journeys.',\n",
       " 'Q18.4': 'Information about public transport is easy to find.',\n",
       " 'Q18.5': 'I would be happy to use my phone to tap on and off and pay for transport services.',\n",
       " 'Q18.6': 'I would be happy to use my debit card to tap on and off and pay for transport services.',\n",
       " 'Q19.2': 'I like the idea of having flexible access to my choice of car with included insurance, maintenance and roadside assistance for a weekly or monthly fee.',\n",
       " 'Q19.3': 'It would be useful to have access to a car on a month by month basis.',\n",
       " 'Q19.4': 'I would be willing to pay between $400 and $600 a month for a basic car subscription with all vehicle costs covered (excluding fuel).',\n",
       " 'Q19.5': 'I would be willing to pay between $600 and $1200 a month for a luxury car subscription with all vehicle costs covered (excluding fuel).',\n",
       " 'Q19.6': 'It would be good to be able to reduce my expenses by only paying for a vehicle when I need one.',\n",
       " 'Q19.7': 'I would consider using a car subscription service instead of buying or leasing a new car myself.',\n",
       " 'Q20.2': 'I like the idea of having all my transport costs bundled into a convenient monthly subscription package or bill – like a mobile phone or internet plan.',\n",
       " 'Q20.3': 'I would want my ‘transport credits’ in a mobility package to rollover to the next month if they were not used.',\n",
       " 'Q20.4': 'I would prefer that money was only deducted from my travel account when I complete a trip, rather than having a pre-paid fixed monthly cost for all my regular trips.',\n",
       " 'Q20.5': 'If my transport package included other modes I haven’t used before, I would try them.',\n",
       " 'Q20.6': 'I would give up car ownership or choose not to buy a car if affordable, fast, and convenient public transport, active travel, or taxi style options were available to me.',\n",
       " 'Q20.7': 'I would be likely to use Mobility as a Service for regular trips, but I would still want to own a car.',\n",
       " 'Q21.2': 'I support electric vehicles because they are better for the environment.',\n",
       " 'Q21.3': 'Electric vehicles have enough battery range (kilometres) to get me to and from the places I need to go.',\n",
       " 'Q21.4': 'I would prefer a hybrid vehicle over a petrol or diesel vehicle.',\n",
       " 'Q21.5': 'I would happily charge an electric vehicle at home or work using a normal powerpoint or electric vehicle charger.',\n",
       " 'Q21.6': 'For longer trips I would happily charge my electric vehicle for 20-30 minutes every 2-4 hours at fast chargers.',\n",
       " 'Q21.7': 'Electric vehicles are too expensive to buy even if the running and maintenance costs are lower.',\n",
       " 'Q21.8': 'I would consider buying an electric vehicle for my next car.',\n",
       " 'Q21.9': 'I would buy an electric vehicle if there were cost discounts, subsidies, or rebates.',\n",
       " 'Q22.2': 'I like the idea of safe automated vehicles if they make it easier for me to travel.',\n",
       " 'Q22.3': 'I trust automated vehicles to operate safely.',\n",
       " 'Q22.4': 'I would consider buying an automated vehicle.',\n",
       " 'Q22.5': 'I would be comfortable riding in an automated vehicle.',\n",
       " 'Q22.6': 'I would allow my children to be transported in an automated vehicle.',\n",
       " 'Q22.7': 'I would pay a higher price for a partly or fully automated vehicle than a non-automated vehicle.',\n",
       " 'Q22.8': 'I would enjoy being able to use my time in an automated vehicle for things like reading, or relaxing rather than driving.',\n",
       " 'Q22.9': 'I enjoy actually driving a car, and would prefer that to riding in an automated vehicle.'}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from matplotlib.pyplot import figure\n",
    "import scipy\n",
    "\n",
    "def get_performance(model, question, print_conf_mat = True):\n",
    "    y_pred = model(x_all, one_hot_all)\n",
    "    \n",
    "    #fig = make_subplots(rows=rows, cols=col)\n",
    "    \n",
    "    _, quest_pred = remove_quest([question], y_pred)\n",
    "    _, quest_response = remove_quest([question], y_all)\n",
    "    \n",
    "#     # build 1 way \n",
    "#     scen=['Agreed With Prompt', 'Disagreed With Prompt']\n",
    "\n",
    "#     response_y = quest_response > 0.5\n",
    "#     model_y = quest_pred > 0.5\n",
    "\n",
    "#     #given the model predicted y:\n",
    "#     response_where_model_pred_y = response_y[model_y]\n",
    "\n",
    "#     prob_y_given_mod_y = sum(response_where_model_pred_y)/(len(response_where_model_pred_y))\n",
    "#     prob_n_given_mod_y = sum(~response_where_model_pred_y)/(len(response_where_model_pred_y))\n",
    "#     print(\"given predicted Y, model response of YN: \", prob_y_given_mod_y, prob_n_given_mod_y)\n",
    "\n",
    "#     response_where_model_pred_n = response_y[~model_y]\n",
    "#     prob_y_given_mod_n = sum(response_where_model_pred_n)/(len(response_where_model_pred_n))\n",
    "#     prob_n_given_mod_n = sum(~response_where_model_pred_n)/(len(response_where_model_pred_n))\n",
    "#    print(\"given predicted N, model response of YN: \", prob_y_given_mod_n, prob_n_given_mod_n)\n",
    "    \n",
    "    predictions = tens_to_int(quest_pred)\n",
    "    responses = tens_to_int(quest_response)\n",
    "    \n",
    "    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(predictions, responses)\n",
    "    average_diff = sum((predictions - responses)**2)/len(predictions)\n",
    "    \n",
    "    \n",
    "    scen=[\"Strongly\\ndisagree\", \"Disagree\", \"Somewhat\\ndisagree\", \"Somewhat\\nagree\", \"Agree\", \"Strongly\\nagree\"]\n",
    "#     fig = go.Figure(data=[\n",
    "#         go.Bar(name='Model Predicts Agree', x=scen, y=[prob_y_given_mod_y, prob_n_given_mod_y]),\n",
    "#         go.Bar(name='Model Predicts Disagree', x=scen, y=[prob_y_given_mod_n, prob_n_given_mod_n])\n",
    "#     ])\n",
    "    \n",
    "#     # Change the bar mode\n",
    "#     fig.update_layout(barmode='group')\n",
    "#     fig.show()\n",
    "    if print_conf_mat:\n",
    "        print(question, \": \", questions[question])\n",
    "        print(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        confusion_matrix = metrics.confusion_matrix(responses, predictions)\n",
    "        cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = scen)\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "        cm_display.plot(ax=ax)\n",
    "        plt.title(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        plt.show()\n",
    "\n",
    "    return slope, r_value, float(average_diff) \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def predictions_from_average(question, print_conf_mat = True):\n",
    "    _, quest_response = remove_quest([question], y_all)\n",
    "    responses_of_question = tens_to_int(quest_response)\n",
    "    \n",
    "    \n",
    "    train_averages = []\n",
    "    for i in range(NUM_PEOPLE):\n",
    "        train_respons = get_person_and_responses(y_train, one_hot_people, i)\n",
    "        train_averages.append(round(float(sum(train_respons)/len(train_respons))))\n",
    "    \n",
    "    train_averages = np.array(train_averages)\n",
    "    \n",
    "    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(train_averages, responses_of_question)\n",
    "    \n",
    "    average_diff = sum((train_averages - responses_of_question.numpy())**2)/len(train_averages)\n",
    "    if print_conf_mat:\n",
    "        print(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        scen=[\"Strongly\\ndisagree\", \"Disagree\", \"Somewhat\\ndisagree\", \"Somewhat\\nagree\", \"Agree\", \"Strongly\\nagree\"]\n",
    "        confusion_matrix = metrics.confusion_matrix(responses_of_question, train_averages)\n",
    "        cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix= confusion_matrix, display_labels = scen)\n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "        cm_display.plot(ax=ax)\n",
    "        plt.title(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        plt.show()\n",
    "    return slope, r_value, average_diff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 43/43 [00:57<00:00,  1.34s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 22\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_average(list_dict, key):\n",
    "    rel_list = [x[key] for x in list_dict]\n",
    "    return sum(rel_list)/len(rel_list)\n",
    "\n",
    "def get_all_performance():\n",
    "    test_diff = []\n",
    "    train_diff = []\n",
    "    test_r2 = []\n",
    "    train_r2 = []\n",
    "    for quest in tqdm(questions.keys()):\n",
    "        _, av_r2, av_diff = predictions_from_average(quest, False)\n",
    "        _, md_r2, md_diff = get_performance(logit_model, quest, False)\n",
    "        if quest in removed_quest:\n",
    "            test_diff.append({\"Question\": quest, \"Model\": md_diff, \"Average\": av_diff})\n",
    "            test_r2.append({\"Question\": quest, \"Model\": md_r2, \"Average\": av_r2})\n",
    "            if quest == \"Q22.2\":\n",
    "                Q22_av_r2 = av_r2\n",
    "                Q22_av_diff = av_diff\n",
    "                Q22_md_r2 = md_r2\n",
    "                Q22_md_diff = md_diff\n",
    "        else:\n",
    "            train_diff.append({\"Question\": quest, \"Model\": md_diff, \"Average\": av_diff})\n",
    "            train_r2.append({\"Question\": quest, \"Model\": md_r2, \"Average\": av_r2})\n",
    "            \n",
    "    \n",
    "    print(\"Test Set\")\n",
    "    labels = ['r2', 'Difference\\nSquared']\n",
    "    model = [get_average(test_r2, \"Model\"), get_average(test_diff, \"Model\")]\n",
    "    baseline = [get_average(test_r2, \"Average\"), get_average(test_diff, \"Average\")]\n",
    "\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(9,6))\n",
    "    rects1 = ax.bar(x - width/2, model, width, label='Model Performance')\n",
    "    rects2 = ax.bar(x + width/2, baseline, width, label='Naive Estimation Performance')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_xticks(x, labels)\n",
    "    ax.legend()\n",
    "\n",
    "#     ax.bar_label(rects1, padding=3)\n",
    "#     ax.bar_label(rects2, padding=3)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"Training Set\")\n",
    "    labels = ['r2', 'Difference\\nSquared']\n",
    "    model = [get_average(train_r2, \"Model\"), get_average(train_diff, \"Model\")]\n",
    "    baseline = [get_average(train_r2, \"Average\"), get_average(train_diff, \"Average\")]\n",
    "\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(9,6))\n",
    "    rects1 = ax.bar(x - width/2, model, width, label='Model Performance')\n",
    "    rects2 = ax.bar(x + width/2, baseline, width, label='Naive Estimation Performance')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_xticks(x, labels)\n",
    "    ax.legend()\n",
    "    \n",
    "#     ax.bar_label(rects1, padding=5)\n",
    "#     ax.bar_label(rects2, padding=5)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    print(\"Question 22\")\n",
    "    labels = ['r2', 'Difference\\nSquared']\n",
    "    model = [Q22_md_r2, Q22_md_diff]\n",
    "    baseline = [Q22_av_r2, Q22_av_diff]\n",
    "\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(9,6))\n",
    "    rects1 = ax.bar(x - width/2, model, width, label='Model Performance')\n",
    "    rects2 = ax.bar(x + width/2, baseline, width, label='Naive Estimation Performance')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_xticks(x, labels)\n",
    "    ax.legend()\n",
    "\n",
    "#     ax.bar_label(rects1, padding=3)\n",
    "#     ax.bar_label(rects2, padding=3)\n",
    "    plt.show()\n",
    "get_all_performance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q19.5 :  I would be willing to pay between $600 and $1200 a month for a luxury car subscription with all vehicle costs covered (excluding fuel).\n",
      "slope=1.23    r=0.54    diff=1.18\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope=0.63    r=0.47    diff=3.39\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = get_performance(logit_model, \"Q19.5\")\n",
    "_ = predictions_from_average(\"Q19.5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q22.2 :  I like the idea of safe automated vehicles if they make it easier for me to travel.\n",
      "slope=0.86    r=0.57    diff=1.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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nqrsWcPM5u7F8US4PvzudMcMKmTu9QdShSSWiz0kAZraXmXVLWtWTYICEtUCTKg4bBZxmZo3MrDFweriuUu6+ClhrZn3CVedUE9LTBE0Eg2pDh8SsjaU0mr6WNYe1ClbkZJFolMOGHoWQHdRON+1WQM6q4gijTBdjY3Fw5ZuTnSAnOwEepOQK8oPyFeQXs2xt4yiDTKs2Bes4Yrc5/Gvi3pvXlVcCwMnPKcVjeBUCkJ3t5OUnyMpOkN+wjBXL8qMOKSMmjW3C2lXxy3RUZa9eG1g4O4/Fc/MpLcli5OBm9Dt+ddRh7TRHTQOZVAA8HKbvSwlGRLocOBcYamYLk/oJAODuX5jZs8Bn4aqn3f1LM+tSzetcCjxlZgngP0BVf5lDCJoEakWzQO7yYsqa5NL2uVnkz99IUadGLD27E56/5YOl6SfLWNu7RYRRpk+WJXjxijfo2GI1r322L5MWtOX2If158Lx3KSrNYX1RHhc/fXrUYabNjUd/wv3/6UfjvK0rcn864UMO320uM1c0528fHRpRdJmzYlkD3nyhC8+++zHFRVl88WlLvhzTKuqwJA1a7lLCsoV5m58vX5RL9wN/cPOX1BLRV0UAdx/v7oe6ew9339/df+ruy939YXffq7wS4O4FFY67z933DR8PhOtmu/u+Sfvc6+63hU8nh+fvSTAl47hwn5Huntxf4ACCToLfZqrM26XMyZ+7ntVHtWHuLfuQyM+ixdAtQ0e3eHchZBtr+7SMMMj0SXgWP3/8LE687wL2bb+U3dus5Lx+X/Obl07ipPsuYMiXe3Hd8f+NOsy0OHK32azc0JBvlvzwxpT/G3o0x/79QmauaM7x3b+LILrMKmhSQt/+S7nk5CO44PijaNCwjAEnLYw6LJFqlZGV9kfUoo+gZv04vHVwEkHHwjsq7hB2UnwDuLm6E5nZ5WY2zszGla1dn5loQ6XN8yhtnsemrkE9aN2BLcifG9Sum/53OY2/XsWiS3cDi1f6eN2mfMbN3pVD95jLnm1XMGlB0Nno/cm7s3/HJRFHlx492y+m/x6zeffyF/nLKe9zcKcF3PXjDzZvT3gWQ7/dg2P3nBlhlJnRs88KlixoxJpVeZSVZvHfD9uy9/6rog5L0mDF4lxa77olw9WqXQnLF8Wvs2tc1JamgRrh7gMJBimqbp+7CSZz2Na5ngSeBMjv0mGH54FORVlhLiXN88hdvJGSXRrS6Ns1FLdrSKNJq2k+fBHzr++O58Wj/bFZo42UJrJYtymf/JxS+uw2n+c+6UVBg2I6tVzF3BXN6LPbfGYtaxZ1qGnx0Ki+PDSqLwC9Oy7gooO/4n//fQwdm61m3qpCwOm/x2xmrWwWaZyZsGxxA/babxX5Dcoo2pTFAYesYMaU+NwJUp9NndCI9l2LaduxiBWLc+l/6iruvqpz1GHtNMdIxHCI4VpVETCz24B1QFPgY3f/oPoj6o9l53Sm3TMzsTKnpFVwq2CnP0/BShO0f2AqEHQYXHpel2gD3Umtmmzgj6d9SHaWY+Z8MHl3Rk3rzB1DjuKenw0n4caaTXn8afCAbZ+sjjLg9pM+pCCvGMOZuqwVd75/ZNRhpd3USc34ZERbHnzpU8rKjJlTm/Lemx2iDisjbnpkFvv3W0thi1Je/HwiL/ytHcNejW9/iESZ8ejv23PXyzPJyobhr7ZgzrR43DFQG1L56WbBREW1Q3lFwN3vzeBrGEG5E+k6Z36XDr7L769J1+lqvRbja1X9MaNKG8ev9l+dDq/Ery9CdUqXLo86hJqTiPwGqBoz1kewxlem/Z+3075N/frXD0n3afnt3iPGu3vvtJ84RZFXbcLJhqaZ2Whgr3Dds2Z2Zrh8t5lNCYcGvjdcd4qZjTWzL83sAzNrG65vbWbvh0MLP21mc8ysVTjs8FQzex6YRDCK4e/M7PPwvH9Miud8M/ss7EvwhJnFI+cuIiI7xQn67aT7EbVIIwhHEzyHYNyAk4CDK2xvSTA+wD7uvj9bOveNBvq6ey/gVeDGcP2twIfh0MKvA52STtcNeCzctlf4/JDwtQ8ysyPNbG/gbOCwpGGIa8WAQiIiIpkQdY73CIJhgjcAmNmQCttXA5uAZ8JpgsunCu5AMAxxO4JZCmeF6w8nqDjg7kPN7Pukc81x9zHh8nHh48vweQFBxWB/4CDg86AFgYbA0jSUU0RE6jyjLIaDe0VdEaiWu5ea2SHAMQSzEf4aOBp4GLjP3YeYWX/gthROl3yPnwF/dvcnkncws6uB59y92lsHRUSk/ilvGoibqEv0McEwwQ3NrAlwSvJGMysACt39XeBagoF+AAqBBeFy8pwEnwA/C489DmhexesOAy4Jz4+ZtTezNsAI4MxwGTNrYWZ1/54XERGRKkSaEQiHCR4IfEWQgv+8wi5NgMFm1oDgKv66cP1twKAw9f8h0DVc/0fgFTO7APgUWEwwX0HFEQmHh/0BPg2bANYB57v7FDO7BRhuZllACXAVwbwHIiJSz6lpIAPc/U7gzmp2+cG9Gu4+mGCq4IpWA8eHTQr9gIPdvQiYDeybvKO7Pwg8WMm5tznokIiISFxEXhFIs07Aa+HVfDFwWcTxiIhITLhbLPsIxKoi4O7TgV5RxyEiIvFUG6YNTrf4lUhERERSFquMgIiISKY4kIhhZ0FlBEREROoxZQRERERSYuojICIiIvGijICIiEgKgiGG49dHQBUBERGRFJXFMJEevxKJiIhIypQREBERSYFjsWwaUEZARESkHlNGQEREJEWJGF4/qyIgIiKSAncoU9OAiIiIxIkyAiIiIilSZ0ERERGJFWUEREREUhDcPhi/62dVBNKgwZIy9r7v+6jDqDFzftom6hBqTJO5iahDqFn5eVFHULMSZVFHIHVMmaYhFhERkThRRkBERCQFcZ10SBkBERGRekwZARERkZTEs7Ng/EokIiIiKVNGQEREJEWJGN41oIqAiIhICjTXgIiIiMSOMgIiIiIpUmdBERERiRVlBERERFIQzDUQvz4CqgiIiIikKI53DahpQEREpB5TRkBERCQFmmtAREREYkcZARERkRTF8fZBVQRERERS4fG8ayB+VRsRERFJmTICIiIiKXB0+6CIiIjEjDICIiIiKVIfAREREYkVZQRERERSENcBhVQREBERSZEqAhKJ3944jkP6LmbVqnx+dcmPALjgF5Ppe9hCEm6s/j6f+/7Sm5UrGkYcafpkWYLXzn6DJesac9U7J/H8Gf+icW4JAC0abmTi0jZc8+8TI45y5+XllPL3Xw4hL6eM7Cznw4ldeXr4wfzvWSPZu8MyzGDuskJuHziAjcW5UYe7037z+6845NAlrPo+n6vOPwqAgqbF3HT7F7Rpt4Glixpx9y0Hsm5tXsSRpl/v/mu48vaFZGc5773SgtceaRt1SBlV38pbl0VWETCzMmAikAuUAs8D97t7wsx6Axe6+zVRxVebfDC0M2//a3euv3nc5nWvD9yTF/65DwA/+ekMfn7hNzxy/4FRhZh2FxwwkZkrm9E4L/jyv/CN0zdve+DEoXw4q2tUoaVVcWk2v37iFDYW55KdVcaTVw3h02878cCQQ9lQFHwZ/uaU/3LmYZN44aNeEUe78z74dwfeGdSF6/5vwuZ1Z10wg6/GtWLQC3tw1gUzOOuC7/jnY3tHF2QGZGU5V921gJvP2Y3li3J5+N3pjBlWyNzpDaIOLSPiWt64TkMcZWfBje7e0933AX4EnAjcCuDu4zJZCTCzOpUJmfR1a9au2foKaeOGLVeHDRqU4l7TUWVO28brOLLLHN6Y8sMvg8a5xRzSYQEjvotHRQBs85V+TnaCnKwEOJsrAeDk55ZBTD58Jk9oydo1W2c2+h6xhA/e7QDAB+92oO+Ri6MILaP26rWBhbPzWDw3n9KSLEYObka/41dHHVbG1Lfy1nW14gvR3Zea2eXA52Z2G3AUcIO7n2xmRwEPlu8KHBn+HAw0J8go3OLugwHM7A/A+cAyYB4w3t3vNbORwATgcOCV8Pl9QAGwHLjY3ReZ2e7Ao0BrYANwmbt/m9nfwI658NJJHHPcXNavz+Wma4+MOpy0uenIT/jbJ/1onFf8g23H7D6LsfM7sL4kPqnjLEvw7G/fpEPL1bzx332YPC9Iod7ys484tPs8Zi1pzoNv9404ysxp1qKI71cEV4rfr8inWYuiiCNKv5a7lLBs4Za/2eWLcul+4IYII8qsOJc3qgGFzOxa4P8RfP9NBH4BtANeBVoC44EL3P2HH5zbUGtuH3T3mUA20KbCphuAq9y9J3AEsBHYBJzu7gcCA4C/WeBg4AzgAIIMQ+8K58pz997AQ8DDwJnufhDwD+DOcJ8ngavD9TcAj6W1oGn0/DP7ctHZJzHyg46ccvp3UYeTFkd1mc3KDQ2Zsqx1pdtP2nM6707bo4ajyqyEZ3Hh/WfykzvOp0fHZezWdiUAd7w2gJNvP5/ZS5tx7AHxeH+3zWKT/ZAY8qCzYLof22Jm7YFrgN7uvi/Bd+U5wF8ImtT3AL4HLt2RYtWaikA1PgHuM7NrgGbuXgoYcJeZfQ18ALQH2gKHAYPdfZO7rwXernCugeHPvYB9gffNbAJwC9DBzAqAQ4FB4fonCGpcP2Bml5vZODMbV1wabU33ow86cdiRCyKNIV16tVtM/91mM/yiF7n3+Pfp02EBd//oAwCaNdjIfm2W8p/ZnSOOMjPWbcpn/He70rf7vM3rEp7F+xN2Z8B+syKMLLNWrcynectNADRvuYlV38cn21NuxeJcWu+65UKtVbsSli+q+50/q1LfyltDcoCGYdN2I2ARcDTwerj9OeC0HTlxrakImNluQBmwNHm9u99NkA5pCHxiZt2B8whS9weFmYIlQCq9UNaXvxwwOeyj0NPd93P34wh+H6uS1vd090p7Lbn7k+7e29175+U02v4C76Rd26/dvNz3sIXMn9ukxmPIhAc+7csx/7yQ4547nxuG/Yix89tz0/vHAnDcHjP5z+zOFJfVihattGjWeCMFDYJUeH5OKYd0m8/cpYV0aFnenuocsc8c5ixrFlmMmTZ2dFuOPWk+AMeeNJ8xo+LXu3zqhEa071pM245F5OQm6H/qKsYML4w6rIyJa3nLxxGo6YyAuy8A7gXmElQAVhM0BawKL44B5hNcFG+3WvGJamatgceBR9zdzSx52+7uPhGYGKb+uwOFwFJ3LzGzAUD5JeInwBNm9meCsp1MkOqvaCrQ2sz6ufunZpYL7Onuk81slpmd5e6DLAhkf3f/KkNFT8mNt4xl/57LaVpYxPOvvcuLz+7NwX0W077jOjwBS5c0itUdA1U5sdsMnhlf93vOJ2vVdAN/OPsjsrMcM2fEV7vzybedeeJXg2mUX4KZM2NhS/7y5hFRh5oWN/7xC/Y7cAVNmxXz3OAPeOnpPRn0/B7cdOd4fnTKXJYtbsSfb4nf33KizHj09+256+WZZGXD8FdbMGda3e5BX536Vt40aGVm45KeP+num7+7zKw5cCrQFVgFDAJOSNeLm0fU3byS2wdfAO4Lbx/sz5bOgg8T9ANIAJOBi4EmBGn/AmAc0Bc40d1nh50Nf06QJVgKDHX3p8LOgTe4+7jw9XsS9BUoJKg0PBDu1xX4O0GTQC7wqrv/qbqyFDZs5/26XLzzv5Q6Ys5PK3bjiK8mcxNRh1CjWo6ORxNTqkrnzNv2TlLnjPURrPGVae9s0nSvtn7w4+el+7R8ePT948P+a5Uys7OAE9z90vD5hUA/4CxgF3cvNbN+wG3ufvz2vn5kGQF3z65m20hgZLh8dSW7FBH8Eipzr7vfZmaNgI8J0ie4e/8KrzGB4A6Eiq89izTWtEREJB4iHEdgLtA3/F7bCBxDcBH8EXAmwZ0DFxHcTbfdak0fgTR6Muzo9wXwhrt/EXE8IiIiO8zdxxJ0CvyCIJOeRdDs/T/AdWY2g+AWwmd25Py1oo9AOrn7z6OOQURE4skjur3V3W8lHHQvyUzgkJ09dxwzAiIiIpKi2GUEREREMiWqkQUzSRkBERGRekwZARERkRR4OMRw3KgiICIikqKoOgtmkpoGRERE6jFlBERERFIS2YBCGaWMgIiISD2mjICIiEiK4thHQBUBERGRFJRPQxw3ahoQERGpx5QREBERSYUHYwnEjTICIiIi9ZgyAiIiIimK41wDqgiIiIikwInnXQNqGhAREanHlBEQERFJiUYWFBERkZhRRkBERCRFun1QREREYkUZARERkRTF8a4BVQRERERS4K6KgAgAWWVRR1BzlveKOoKaVZbXIeoQalSLf86LOgSRyKkiICIikiLdPigiIiKxooyAiIhIiuJ4+6AqAiIiIimKY2dBNQ2IiIjUY8oIiIiIpMAxZQREREQkXpQREBERSVEM+wqqIiAiIpKSmI4sqKYBERGRekwZARERkVTFsG1AGQEREZF6TBkBERGRFMWxj4AqAiIiIimK4xDDahoQERGpx5QREBERSYETz6YBZQRERETqMWUEREREUuGAMgIiIiISJ8oIiIiIpCiOdw2oIiAiIpKqGFYEMto0YGa/N7PJZva1mU0wsz6ZfL1txLJuO/fvb2aHZioeERGR2iBjGQEz6wecDBzo7kVm1grIy9TrZUB/YB3w34jjEBGRWsF0++B2agcsd/ciAHdf7u4LzewYM/vSzCaa2T/MLB/AzGab2Z/DzME4MzvQzIaZ2XdmdmX5Sc3sd2b2eZhl+GPSumvC5fvN7MNw+Wgzeynp2DvN7CszG2NmbcN1p5jZ2DCmD8ysrZl1Aa4Erg3jOSKDvycREZHIZLIiMBzoaGbTzOwxMzvKzBoAzwJnu/t+BBmJXyYdM9fdewKjwv3OBPoC5V/4xwHdgEOAnsBBZnZkuH/5l3VvoMDMcsN1H4frGwNj3P2AcN1l4frRQF937wW8Ctzo7rOBx4H73b2nu49K1y9FRETqMM/AI2IZaxpw93VmdhDBl/EAYCDwZ2CWu08Ld3sOuAp4IHw+JPw5EShw97XAWjMrMrNmwHHh48twvwKCisHzBJWCpkAR8AVBheAI4Jpw32LgnXB5PPCjcLkDMNDM2hE0XcxKR/nT6bc3juOQvotZtSqfX10ShH3BLybT97CFJNxY/X0+9/2lNytXNIw40vTJsgSvnvsGS9c15tdDTuKO4z7koPYLWVcctC7dMvxopi5rFXGU6THyJy+xvjSPMjfKEsbpw84A4II9J3F+t8kk3PhoYSf+OqFvxJHunLycUp68bDC52QlyshKMmLwbT444GHB++aPPOGbfmSQSxhuf7cPAT/eLOty0691/DVfevpDsLOe9V1rw2iNtow4po2JZXo/nyIIZvWvA3cuAkcBIM5tI8KVfnaLwZyJpufx5DmDAn939iYoHmtks4GKCNv2vCSofewDfhLuUuG++8aOMLWV/GLjP3YeYWX/gtpQKV4M+GNqZt/+1O9ffPG7zutcH7skL/9wHgJ/8dAY/v/AbHrn/wKhCTLvze05k1spmNM4r2bzuvlH9eH/G7hFGlTnnjziZ74u2VOT6tlnAsR1mc8p7Z1KcyKZF/sYIo0uP4tJsfvnMT9hYnEt2VhlPXz6Y/07rRNfW39O2cD1nPXAO7kbzxnW/rBVlZTlX3bWAm8/ZjeWLcnn43emMGVbI3OkNog4tI+pbeeu6jDUNmNleZtYtaVVP4Dugi5ntEa67APjPdpx2GHCJmRWEr9HezNqE20YBNxCk/UcRtPF/mfTlX5VCYEG4fFHS+rVAk+2ILWMmfd2atWu27me5cUPu5uUGDUpjdW9r24J1HNF1Dm9M2jvqUCLz825TeGJyT4oT2QCsLIpDtsfYWBz83eZkJ8jJTuAOZ/SZzNMfHrT5Suv79XEo69b26rWBhbPzWDw3n9KSLEYObka/41dHHVbGxLq8ahrYLgXAw2FKvxSYAVwOvAIMMrMc4HOCtviUuPtwM9sb+NTMIOjVfz6wlODL//fAp+6+3sw2heu25bYwnu+BD4Gu4fq3gdfN7FTg6trYT+DCSydxzHFzWb8+l5uuPTLqcNLmxqM+4f7R/WiUV7zV+qsPHcuVfcYxdl4H7v+kLyVl2RFFmF6O8eyAd3Hglel7M/C7HnRpupqD2yziugM+p7gsmz9/2ZeJK9ts81y1XZYleOGqN+jQYjWDxu7L5Pltad9iDT/afwb9e8xm1foG3PvOYcxb0SzqUNOq5S4lLFu4pTK/fFEu3Q/cEGFEmVXfylvXZbKPwHigsvvwRwC9Ktm/S9LyswSdBSvb9iDwYCXHjwByk57vWWF7QdLy68Dr4fJgYHAl55sG7F9J/LXG88/sy/PP7MvPfv4tp5z+HS892yPqkHbakV1ns3JDQ6YsbU3vDgs2r39gdB+Wb2hEbnaCW48ZyaW9v+Txsb2jCzSNznn/VJZsbEyL/I08d/Q7zFzTjBxLUJhXxJnDT2P/lst46PAPGDDkXILWsbor4Vmc98hZFDQo4p7zhrF7m5XkZZdRXJLDRY+dwYAeM/nDT0dy+VOnRR2qSBXq9v9gZTTXwA4ys8vD2xzHFZdGW9P96INOHHbkgm3vWAf02nUxA3abzdBLXuSeE9/nkI4L+PPxH7B8Q2PAKCnL5q0p3dm37dKoQ02bJRsbA0H6//35Xdm/5TIWb2jM8HldAePrFW1wN1rkb4o20DRatymf8TN3pd+ec1m6poCPpgSJuI+mdKXbLisjji79VizOpfWuWzJcrdqVsHxRbjVH1G2xLm8MmwZUEdhB7v6ku/d29955OY1q/PV3bb9283LfwxYyf26t6M6w0x78pC/HPnMhJ/zjfH733o/4bF57bh52LK0arQ/3cI7efRYzVrSINM50aZhdQuOc4s3Lh+8yn+mrm/P+/K70absQgC5NVpGbVcbKorrd0apZo40UNAj6AOfnlHLIHvOZvaw5/5nShYN2C8p6YNeFzF1eGGWYGTF1QiPady2mbccicnIT9D91FWOGx6+c5epbeeu6WjXXgJndRtDu3xT42N0/iDai2uHGW8ayf8/lNC0s4vnX3uXFZ/fm4D6Lad9xHZ6ApUsaxeqOgcrcfeIHtGi4CXCmLmvFnz48KuqQ0qJVg408duQwAHLMGTJnDz5e1IncrDLu7jOSd096jZJENr8bM4C6npJs1WQDt535IVlZTpY5H0zcndFTOzNhzi7c/rMR/PzQr9lQnMsd/4rHe5ssUWY8+vv23PXyTLKyYfirLZgzrW5X7KoT6/LWgiv4dLNtd6qvOeUVAXe/N4OvYQTlTqTrnIUN23m/Lhen63S13rxT636ntVRtbJO2P5M6ofnkul3Z2F4t/vlp1CFIBoz1EazxlWn/Y87v2sHb3Xp1uk/LnF/cNN7dI+v0FHnTQDgx0TQzGw3sFa571szODJfvNrMp4ZDC94brfjAscLi+tZm9H0509LSZzTGzVmbWxcymmtnzwCSCEQ9/MFRxeI7zzeyzcGjhJ8wsHl3TRURk5zjglv5HxCKtCIQjD55DMMbAScDBFba3BE4H9nH3/YE7wk0/GBY4XH8r8KG770NwV0CnpNN1Ax4Lt+1FJUMVh7cmng0cFg51XAacl8Yii4iI1CpR9xE4AviXu28AMLMhFbavBjYBz5jZO2wZIriqYYEPJ6g44O5Dw7EBys1x9zHhclVDFe8PHAR8Ho5T0JBgjAIREZFYDd5WrsqKgJk9TDXdItz9mqq2pYu7l5rZIcAxBBMQ/Ro4mh0bFnh90nKlQxWb2dXAc+5+885HLyIisRPDikB1TQPjCCbnqeqRDh8Dp5lZQzNrApySvDEcSrjQ3d8FrgUOCDdVNSzwJ8DPwmOPA5pX8bpVDVU8AjizfNhiM2thZp13sowiIiK1VpUZAXd/Lvm5mTUqT+Gni7t/YWYDga8IUvCfV9ilCTA4nL7YgOvC9bdR+bDAfwReMbMLgE+BxQRzBhQkn7SqoYrdfYqZ3QIMN7MsoIRgoqQ56Su1iIjUWbWgc1+6bbOPgJn1A54h+DLtZGYHAFe4+6/SEYC73wncWc0uh1RyTKXDAhP0KTg+bFLoBxzs7kXAbGDfCueoaqjigQRTJouIiMReKp0FHwCOB4YAuPtXZlZbZ7jpBLwWXs0XA5dFHI+IiMSIxbCPQEp3Dbj7vDCFXq4sM+HsHHefTiUTGomIiOy0WjI3QLqlUhGYZ2aHAm5mucBvgG8yG5aIiIjUhFQGFLqSoMNce2AhwQA8V2UwJhERkVooA6MKptj50MyamdnrZvatmX1jZv3CO9veN7Pp4c+q7pSr1jYrAu6+3N3Pc/e27t7a3c939xU78mIiIiKyQx4Ehrp7d4Jb6b8BbgJGuHs3gtvfb9qRE2+zImBmu5nZ22a2zMyWmtlgM9ttR15MRESkTvMMPLbBzAqBIwnu4MPdi919FXAqUH6r/3PAaTtSpFSaBl4GXgPaAbsCg4BXduTFRERE6rQIKgIEY+UsA/4ZTrb3tJk1Btq6+6Jwn8VA2x0pUioVgUbu/oK7l4aPF4GYTCwtIiISuVZmNi7pcXmF7TnAgcDfw8n21lOhGcDdd/iehurmGmgRLr5nZjcRzPLnBLPzvbsjLyYiIlKnZeb2weXu3rua7fOB+e4+Nnz+OkFFYImZtXP3ReEkfDs0SV51tw+OJyhyeZfGK5K2OaCJeURERDLM3Reb2Twz28vdpxJMxDclfFwE3B3+rGzE3W2qbq6BrlVtExERqXecKOcauBp4yczygJnALwia918zs0sJ5sT52Y6cOKWRBc1sX6AHSX0D3P35HXlBERER2T7uPgGorPngmJ09dyqTDt0K9CeoCLwLnAiMBlQREBGReiWOcw2kctfAmQQ1jsXu/guCgQwKMxqViIhIbRTN7YMZlUpFYKO7J4BSM2tK0CuxY2bDEhERkZqQSh+BcWbWDHiK4E6CdcCnmQxKREREasY2KwLu/qtw8XEzGwo0dfevMxuWiIiI1ITqBhQ6sLpt7v5FZkISERGpneLYWbC6jMDfqtnmwNFpjqXuSjhWXBJ1FDWmydxE1CHUmF9cODzqEGrUdT+fGXUINeqEFw+JOoQa4yXFUYcQD9GNI5Ax1Q0oNKAmAxEREZGal9KAQiIiIvVeLbndL91SuX1QREREYkoZARERkVTVx4yABc43s/8Ln3cys/rTw0ZERCRknv5H1FJpGngM6AecGz5fCzyasYhERESkxqTSNNDH3Q80sy8B3P37cBpEERGR+qUWXMGnWyoZgRIzyyYsvpm1BurPjeQiIiIxlkpF4CHgX0AbM7uTYAriuzIalYiISG0Uw9kHU5lr4CUzG08wFbEBp7n7NxmPTERERDJumxUBM+sEbADeTl7n7nMzGZiIiEhtUlt6+adbKp0F/02QvDCgAdAVmArsk8G4REREap/6NNdAOXffL/l5OCvhr6rYXUREROqQ7R5Z0N2/MLM+mQhGRESkVquPTQNmdl3S0yzgQGBhxiISERGRGpNKRqBJ0nIpQZ+BNzITjoiISO1V7zoLhgMJNXH3G2ooHhERkdorhhWBKgcUMrMcdy8DDqvBeERERKQGVZcR+IygP8AEMxsCDALWl2909zczHJuIiEjtUY/HEWgArACOZst4Ag6oIiAiIlLHVVcRaBPeMTCJLRWAcinViczs98DPgTKCiYqucPexOxjrTjGzde5esB379weK3f2/GQtKRETqlnqWEcgGCti6AlBum78KM+sHnAwc6O5FZtYKqEvTF/cH1gGqCIiISKCeVQQWufufduLc7YDl7l4E4O7LAczsGODe8LU/B34ZVhRmA68AJxLcpng58GdgD+Aed388PP53wM+AfOBf7n5ruK7I3R8ys/uBA9z9aDM7GrjU3c8Lj72ToHKyETjV3ZeY2SnALQSVlBXAeUBD4EqgzMzOB65291E78bsQERGplaqbhnhnB1QeDnQ0s2lm9piZHWVmDYBngbPDoYtzgF8mHTPX3XsCo8L9zgT6An8EMLPjgG7AIUBP4CAzOzLc/4jwHL2BAjPLDdd9HK5vDIxx9wPCdZeF60cDfd29F/AqcKO7zwYeB+53956qBIiICGyZeCidj6hVlxE4ZmdO7O7rzOwggi/jAcBAgiv8We4+LdztOeAq4IHw+ZDw50SgwN3XAmvNrMjMmgHHhY8vw/0KCCoGzxNUCpoCRcAXBBWCI4Brwn2LgXfC5fHAj8LlDsBAM2tHkBWYtTPlzoTf3Pwlhxy2hFXf53PVBQMAOHzAQn5+6VQ6dl7LtZcdyYxvm0UbZJrk5ZTy2K+HkJtTRnaW89FXXXlm2MHcet4IundcRllZFlPmtuEvg46gLJEddbhpMeeFPOa/kYs7dDizhC4XFG/eNvvZPKbe24ABo9aS17wWfGLsgL9d25GxHzSlWatSnvxoKgAfv13IC3/bhXnTG/DQu9PY84CNWx2zdH4ul/XvzvnXL+asXy6LIuy0atWuiN/dP4tmrUrA4d2XWzP4n7tEHVZG9e6/hitvX0h2lvPeKy147ZG2UYckVagyI+DuK3f25O5e5u4j3f1W4NfAads4pCj8mUhaLn+eQ5Cl+HN4ld7T3fdw92fcvYTgC/xigjb9UQSVjz2Ab8JzlLh7+SdpGVsqQQ8Dj4QZiisI7pKoVT54txP/d13frdbNmdmEO//3YCZNaBlRVJlRXJrN1Y+dwkX3nsVF955B3+7z2afzEoZ/0Y1z7z6b8+85i/zcUn7S99uoQ02LtdOzmP9GLn1fWc+hb6xn2X9yWD83SMZtXGQs/28ODdolIo5y5xx39krufGnmVuu6dN/E/z09m/36rq/0mCf+2J6Dj15bE+HViESZ8dQdHbni2P347Wk9OOXCpXTqtnHbB9ZRWVnOVXct4JbzunJZ/70YcOoqOnXbFHVYUoXqmgZ2ipntZWbdklb1BL4DupjZHuG6C4D/bMdphwGXmFlB+BrtzaxNuG0UcANB2n8UQRv/l0lf/lUpBBaEyxclrV/L1sMrR2byVy1Zu2brfpbz5jRhwdyUb4KoQ4yNxbkA5GQnyMlO4A6fftOJoB5oTJnbhjaFlX+B1DXrZ2ZRuF8Z2Q0hKwda9C5l6QdB+af+tQF7Xrdp5xvpIrZf3/U0aV621bpO3YrouEdRpfv/971CdulYTOc94/PFsXJpHjMmNQZg4/ps5s1oSMu2xds4qu7aq9cGFs7OY/HcfEpLshg5uBn9jl8ddVhShYxVBAjS9s+Z2RQz+xroAdwE/AIYZGYTCa70H0/1hO4+HHgZ+DQ8/nW2fFmPIuig+Km7LwE2heu25bYwnvHA8qT1bwOnm9kEMzui0iMlI7IswbPXv86///Q8n09rz5S5W1KK2VllnNB7OmO+7RhhhOlTsEeC77/IpniVUbYRlo3KYdPiLJZ+mEN+mwRNu9ftbMD22rg+i9cea8P51y+OOpSMaduhiN332cDUCXGsyAda7lLCsoVbLl6WL8qlVbuSCCNKI8/AI2LbPQ1xqtx9PHBoJZtGAL0q2b9L0vKzBJ0FK9v2IPBgJcePAHKTnu9ZYXtB0vLrBJUI3H0wMLiS800D9q8kfsmwhGdx8d/OpKBBEX++ZDi77bKSmYtbAPC7M0czYeYufDWrXcRRpkfB7gm6XlLM+Msbkd3QabpXgkQxzHwqn4OejEfWY3u8cO8unH7ZMho2jmcFqEGjMm55fAZP/KkjG9bFo49LvVJLOvelW8YqAnFnZpcT3OJIg5xa0YIQO+s25fPFjF3p030eMxe34JLjxtGs8Sb+Mui4qENLqw5nlNDhjOBqadoD+eS3dJZ+mMN/zwjqrkVLjE/PakzfV9eT3yqGn0JJvv2yEaP/3Yxn7tiVdWuysSwnL9859ZLl2z64lsvOSfCHx2fw0Vst+WRoi6jDyagVi3NpveuWpo9W7UpYvii3miMkSqoI7CB3fxJ4EqAwf5d4fzrXoGaNN1JalsW6Tfnk5ZZy8J7zefHDnpzS5xv67DWfqx8/Gfc63mheQdEKI7+ls3GRsXREDn1eWk/npDsH/nNcAf0Grq+zdw1sj/vemrF5+YV7d6FB47JYVALAufavs5k7oyFvPh3vuwUApk5oRPuuxbTtWMSKxbn0P3UVd1/VOeqw0iOG/4aqCNQBN942nv16Ladps2Ke+9dwXnpmL9auyePKaydS2KyY2+4Zw8zphfzfdf2iDnWntWy6gT+c+xFZWU6WOSO+2p3/TunMx/c8yZLvm/DkNW8B8J+JXfnn8IOiDTZNJlzbkJJVhuXA3r/fRG7TqCNKrz//sjNff1rA6pU5nHdQDy64fjFNmpfx2C3tWb0ihz9csBu777ORu16Zue2T1VH79F7HsWesYNY3DXn03UkAPHtPBz7/qFm0gWVIosx49PftuevlmWRlw/BXWzBnWq27IUtCtu1O9bIthfm7+KEdzo86jBrzfZ94tM+n4sxbhkcdQo26rkV8v4wrc0LnQ6IOocZ4SXzvUqhorI9gja9Me+qwwa4dvctl16X7tEz903Xj3b132k+conqZETCzHHcvjToOERGpO4x4dhbM5O2DGWdmb5nZeDObHHbew8wuDYc1/szMnjKzR8L1z5rZ42Y2Fvirme1uZkPD40eZWfdwv9Zm9oaZfR4+DouwiCIiIhlV1zMCl7j7SjNrCHxuZv8G/gAcSDAg0IfAV0n7dwAOdfcyMxsBXOnu082sD/AYcDTBrYn3u/toM+tEMIjR3jVYJhERqa1imBGo6xWBa8zs9HC5I+FIheXDI5vZICB5PIFBYSWggGCMg0Fmm5uR8sOfxwI9ktY3NbMCd1+XwXKIiIhEos5WBMysP8GXdj9332BmI4Fvqf7qvXyElixgVTjTYUVZBLMRxmd8UxER2XkxHVCoLvcRKAS+DysB3QmmK24MHGVmzc0sBzijsgPdfQ0wy8zOArDAAeHm4cDV5fuaWc8MlkFEROqSGA4xXJcrAkOBHDP7BrgbGEMwedBdwGfAJ8BsoKqZLs4DLjWzr4DJwKnh+muA3mb2tZlNIZi8SEREJJbqbNOAuxcBJ1Zcb2bj3P3JMCPwL+CtcP+LKxw/CzihkvMuB87OQMgiIlLX1YIr+HSryxmBqtxmZhOAScAswoqAiIiI/FCdzQhUxd1viDoGERGJJ3UWFBERkViJXUZAREQkY2KYEVBFQEREJBW15Ha/dFPTgIiISD2mjICIiEiK1FlQREREYkUZARERkVTFMCOgioCIiEiK1DQgIiIisaKMgIiISKqUERAREZE4UUZAREQkFTEdUEgVARERkRRY+IgbNQ2IiIjUY8oIiIiIpCqGTQPKCIiIiNRjygikQ0kJiUVLoo6ixjQfXRp1CDXm8bePjzqEGnX1hY9EHUKNymrcMOoQakzZquKoQ4gFDSgkIiIiNc7Mss3sSzN7J3ze1czGmtkMMxtoZnk7em5VBERERFLlGXik5jfAN0nP/wLc7+57AN8Dl+5okVQREBERSVUEFQEz6wD8GHg6fG7A0cDr4S7PAaftaJFUERAREandHgBuBBLh85bAKncv77A1H2i/oydXRUBERCQVHnQWTPcDaGVm45Iel5e/pJmdDCx19/GZKpbuGhAREYnWcnfvXcW2w4CfmNlJQAOgKfAg0MzMcsKsQAdgwY6+uDICIiIiqarhPgLufrO7d3D3LsA5wIfufh7wEXBmuNtFwOAdLZIqAiIiIinKUNPAjvgf4Dozm0HQZ+CZHT2RmgZERETqAHcfCYwMl2cCh6TjvKoIiIiIpEojC4qIiEicKCMgIiKSojjONaCKgIiISCq2b0jgOkNNAyIiIvWYMgIiIiKpUkZARERE4kQZARERkRQY8ewsqIyAiIhIPaaMgIiISKpimBFQRUBERCRF5vGrCahpQEREpB5TRkBERCQVGlBIRERE4kYZARERkRTF8fZBVQTqmNy8BPcMnEJunpOd7Ywe2oIXH+gQdVgZ85OzZ3H8afMwg2FvdWTwq12jDintPjzjRdaX5JFwozSRxRn/PoPf9PyMYzrOxjFWbGrITaMHsHRj46hD3SEPXNeZzz8opLBVKY99OAWA0W834+X7dmXe9Abc9+9v6XbABgCmftmIR27sDIA7/Pz6RRx64qqoQk+rf74/lo3rsylLGIlS4zc/OzDqkDKqd/81XHn7QrKznPdeacFrj7SNOqT0UEWg9jCz04B/AXu7+7cRh1NjSoqNm87bm00bssnOSXDva1MYN7KQbyc0iTq0tOu821qOP20e1118GCWlxu0Pfs5no9uwaH7d/EKszoXDTuH7ooabnz89uScPTjgEgAu6T+SqA8Zz65gjowpvpxz7sxWc/Iul3PebLZW4zt038b9PfccjN3Xeat/O3TfywHvfkJ0DK5fkcPWPetDnR6vIrrOfVFu76eIDWLMqN+owMi4ry7nqrgXcfM5uLF+Uy8PvTmfMsELmTm8QdWhSibrcR+BcYHT4M2Vmlp2ZcGqKsWlDUIScHCcnx3G3iGPKjI5d1zFtcjOKirJJlGUx8YsWHDpgcdRh1Yj1JXmblxvllNTpi5B9+66jSbOyrdZ17LaJDnsU/WDfBg1985d+cVEWFs8/7djbq9cGFs7OY/HcfEpLshg5uBn9jl8ddVhpYZ7+R9TqZD3bzAqAw4EBwNvArWaWBTwCHA3MA0qAf7j762Y2GxgI/Aj4q5mtBP4I5APfAb9w93VmdhBwH1AALAcudvdFNVq4FGRlOQ8NmcSunTfxzottmfpVQdQhZcSc75pw4S+n0qSwmOJN2fQ+bBkzvimMOqy0czf+8aN/48DAqT0YOL0HANf2Gstpu09jbXEeFwz7SbRB1qCpXzTiweu7sHR+Htc9NDs22QB3uOPpibjDe6+1Y+igdlGHlDEtdylh2cItldnli3LpfuCGCCOS6tTVf7FTgaHuPs3MVoRf4F2BLkAPoA3wDfCPpGNWuPuBZtYKeBM41t3Xm9n/ANeZ2Z+Bh4FT3X2ZmZ0N3AlcUnPFSk0iYfz65P1o3KSUPzw+jc57bmDOtEZRh5V282YX8Przu3PHQ5+xaVM2M6c1pawsfpeIPx96Kks2FNCiwUae/dE7fLemGeOW7Mr9X/bh/i/7cMW+X3BB90k89NXBUYdaI/Y6cAOPfTQl6D/w2y70HrCavAa14LJpJ/3u/J6sWJpPYYti7nx6IvNnNmTS+GZRhyXbq+7/Kf5AXW0aOBd4NVx+NXx+ODDI3RPuvhj4qMIxA8OffQkqC5+Y2QTgIqAzsBewL/B+uP4WoMpeeGZ2uZmNM7NxxfwwxVkT1q/N4esxTel9ZDxSbpUZPqQjv7nocP7nin6sW5PLwrnx6x+wZEOQ0Vm5qSHvz+3C/q2WbrV9yKxuHNd5ZhShRapjt000bFTGnKkNt71zHbBiaT4Aq1fm8emIluy5/9qII8qcFYtzab1r8ebnrdqVsHxRDPpGZKBZoDY0DdS5ioCZtSBI/z8dpvx/B/yMYGKo6qwvPwXwvrv3DB893P3ScP3kpPX7uftxVZ3M3Z90997u3juP/J0uV6oKW5TQuEkpAHn5CXodvoZ5M+PbAaeweVDJat12I4cOWMzIYbtGHFF6NcwpoXFO8eblw3adz/TvW9C5yarN+xzbcTYzVzePKMKatXhuHmXBnzdL5+cx/7sGtOkYTUU7nfIbltGwUenm5V6HrmLO9PhVastNndCI9l2LaduxiJzcBP1PXcWY4fFr1ouLutg0cCbwgrtfUb7CzP4DrATOMLPngNZAf+DlSo4fAzxqZnu4+wwzawy0B6YCrc2sn7t/ama5wJ7uPjnD5dkuzduUcMM935GV7ZjBqHdb8NmH8f2S+N+/fEHTpiWUlhl/v2cf1q+LwVVFklYNNvLogGEAZGcleHvmHoxa2ImH+w+ja9NVJNxYuL4Jt445IuJId9xff9WViZ82Yc3KHC46aD/Ou2EhBc3KeOKWjqxemcMfL9yDrvts4PaXZzDlswJef3QXsnOcrCz45V1zKWxRtu0XqeWatyzmloeCWyezc5yR/27D+NEtIo4qcxJlxqO/b89dL88kKxuGv9qCOdNicsFSC67g0828jk2gYGYfAX9x96FJ664B9ia4qu9P0FnQwv3eDzMHvd19ebj/0cBfYPOl/C3uPsTMegIPAYUElaQH3P2pbcVUmNXS+zY4KT0FrAOyWrWMOoQaM/2qTlGHUKMmX/hI1CHUqFP3OTrqEGpM2ar4NiFWNNZHsMZXpr1DUeOWHX3fk65N92n57MXrx7t777SfOEV1LiPg7gMqWfcQBHcThL3/WwKfARPD7V0q7P8h8IOeV+4+AaibN2uLiEhGGbWjTT/d6lxFYBveMbNmQB5we9hpUEREJD3qWBY9FbGqCLh7/6hjEBERqUtiVREQERHJpDg2DdS52wdFREQkfZQREBERSYUTy9sHlREQERGpx5QREBERSZEloo4g/VQREBERSZWaBkRERCROlBEQERFJkW4fFBERkVhRRkBERCQVjoYYFhERqc/UNCAiIiKxooyAiIhIqpQREBERkThRRkBERCQFRjz7CKgiICIikgr3WN41oKYBERGRekwZARERkRTFsWlAGQEREZF6TBkBERGRVCkjICIiInGijICIiEiK4thHQBWBNHDAyxJRh1FjEstXRB1Cjdn9pSZRh1Cjuje4KuoQalT3gnlRh1BzVq2OOoK6z4FE/GoCahoQERGpx5QREBERSVX8EgLKCIiIiNRnygiIiIikSJ0FRURE6jPNNSAiIiJxooyAiIhIiuLYNKCMgIiISD2mjICIiEgqnFjePqiKgIiISAoMMHUWFBERkThRRkBERCRVMZxWRhkBERGRekwZARERkRSpj4CIiIjEijICIiIiqdDtgyIiIvWZa64BERERiRdVBERERFJknv7HNl/TrKOZfWRmU8xsspn9JlzfwszeN7Pp4c/mO1ImVQRERERqt1LgenfvAfQFrjKzHsBNwAh37waMCJ9vt1pTETCz34c1na/NbIKZ9TGz35pZoxp47f5m9k6mX0dEROo49/Q/tvmSvsjdvwiX1wLfAO2BU4Hnwt2eA07bkSLVis6CZtYPOBk40N2LzKwVkAcMBF4ENlRyTLa7l9VspCIiUm85WMQjC5pZF6AXMBZo6+6Lwk2LgbY7cs7akhFoByx39yIAd18OnAnsCnxkZh8BmNk6M/ubmX0F9DOz68xsUvj4bbhPFzP7xsyeCjMMw82sYbjt4KSMwz1mNik5CDPLCttaWic9n1H+XEREJANamdm4pMflle1kZgXAG8Bv3X1N8jZ33+GbG2tLRWA40NHMppnZY2Z2lLs/BCwEBrj7gHC/xsBYdz8A2Aj8AuhD0GZymZn1CvfrBjzq7vsAq4AzwvX/BK5w957AD7IJ7p4gyECcF646FvjK3ZeltbQiIlI3ZaZpYLm79056PFnxZc0sl6AS8JK7vxmuXmJm7cLt7YClO1KkWtE04O7rzOwg4AhgADDQzCrr9FBG8IsAOBz4l7uvBzCzN8PjhwCz3H1CuN94oIuZNQOauPun4fqXCZojKvoHMBh4ALiEoPJQa7RqV8Tv7p9Fs1Yl4PDuy60Z/M9dog4rY3LzEtwzcAq5eU52tjN6aAtefKBD1GGlzbXXfcYhfReyalU+v7z8RADOu2ASJ5w4k9Wr8wF47h/78fnnu0YZZtp0/tMXJBpkgxmeZcy/fj/aPjeNvKWbAMjaWEqiYQ7zfrd/xJGm30/OnsXxp83DDIa91ZHBr3aNOqSM6t1/DVfevpDsLOe9V1rw2iM7lLUWwMwMeAb4xt3vS9o0BLgIuDv8OXhHzl8rKgIAYXv/SGCkmU0kKFRFm1LsF1CUtFwGNNyOOOaZ2RIzOxo4hC3ZgVohUWY8dUdHZkxqTMPGZTz8zmS+HF3I3OkpF7FOKSk2bjpvbzZtyCY7J8G9r01h3MhCvp3QJOrQ0uL997swZMge3HDj2K3Wv/XmnrzxeveIosqsBb/qQaIgd/PzJRftuXm55eA5QUUhZjrvtpbjT5vHdRcfRkmpcfuDn/PZ6DYsmt846tAyIivLuequBdx8zm4sX5TLw+9OZ8ywQuZObxB1aDsvmvGEDgMuACaa2YRw3f8SVABeM7NLgTnAz3bk5LWiacDM9jKzbkmrehIUai1Q1Sf+KOA0M2tkZo2B08N1lXL3VcBaM+sTrjqnmpCeJmgiGFTbOiSuXJrHjEnBh8fG9dnMm9GQlm2LI44qk4xNG4IvhpwcJyfHcbeIY0qfSRPbsHZtftRh1A7uFExYwboDW0YdSdp17LqOaZObUVSUTaIsi4lftODQAYujDitj9uq1gYWz81g8N5/SkixGDm5Gv+NXRx1WWph72h/b4u6j3d3cfX937xk+3nX3Fe5+jLt3c/dj3X3ljpSpVlQEgALguXCwhK+BHsBtwJPA0PLOgsnCWymeBT4j6D35tLt/uY3XuRR4KqxRNQaq+sscEsZUq5oFKmrboYjd99nA1AkFUYeSUVlZziPvTOSVz7/gy08KmfpVvMsLcMpPpvPY40O59rrPKCiIUUXPjF0f/4YOf5tI0/8u2WpTg5lrKSvIpaR1/LJbc75rwj49V9KksJj8/DJ6H7aM1m03RR1WxrTcpYRlC/M2P1++KJdW7UoijEiqUyuaBtx9PHBoJZseDh/l+231DRC2ldxXYd1sYN+k5/cmbZ7s7vsDhH0QxoX7jCRolih3AEEnwW+3uzA1pEGjMm55fAZP/KkjG9bFL5WaLJEwfn3yfjRuUsofHp9G5z03MGdaxoeXiMy/396DV17qgbtx4UUTuezyCdx/3yFRh5UW86/eh7JmeWSvLWHXx7+huG1DNu3eFIAmXyyPZTYAYN7sAl5/fnfueOgzNm3KZua0ppSVxSezVa9oroE678fhrYOTCDoW3lFxh7CC8AZwc3UnMrPLy2/1KPGardln5yT4w+Mz+OitlnwytEWNvnaU1q/N4esxTel9ZDxSjFVZtaoBiUQW7sZ77+3Ont1XRB1S2pQ1C64Sy5rksn6/5jSYuy7c4DT++nvW9opnRQBg+JCO/Oaiw/mfK/qxbk0uC+fGs38AwIrFubTedUsmq1W7EpYvyq3mCIlSvaoIuPvAsG1lX3f/cWW3Bbr73e7e2d1Hb+NcT5bf6pFrNdkBxrn2r7OZO6Mhbz4d37sFyhW2KKFxk1IA8vIT9Dp8DfNmxqDDUTWat9i4efnQw+YzZ3ZhhNGkjxWVYZvKNi83nLqa4l2CzE6jaaspaduAsmbx7S9R2Dzow9y67UYOHbCYkcPicSdIZaZOaET7rsW07VhETm6C/qeuYszwGPwdO5DIwCNitaJpQFK3T+91HHvGCmZ905BH3w3GQ3r2ng58/lGzaAPLkOZtSrjhnu/IynbMYNS7Lfjswx2aV6NW+p+bP2X//ZfStLCIF14awgsv7Mv++y9lt91XgcOSJY156MHeUYeZFtlrS2j3z2nBkzJn3UGt2LB3MwAKvlzO2l6toguuBvzvX76gadMSSsuMv9+zD+vXxfcKOVFmPPr79tz18kyysmH4qy2YMy3eFfi6zDyG7R01rWlWS++be0LUYdQYy65HiaQ9ukQdQY2admmzqEOoUd3/Ni/qEGpM6fwFUYdQY8b6CNb4yrR3wihsvKv37XFFuk/L8HG3jXf3yGr89TIjYGY57l4adRwiIlLHxPDiuU5f2pnZW2Y2PpxT4PJw3aXhUMWfhfMNPBKuf9bMHjezscBfzWx3MxsaHj/KzLqH+7U2szfM7PPwcViERRQREcmoup4RuMTdV4aTCn1uZv8G/gAcSDAY0YfAV0n7dwAOdfcyMxsBXOnu08NBhh4DjgYeBO5399Fm1gkYBuxdg2USEZHaKoYZgbpeEbjGzE4PlzsSDMH4n/LRlcxsELBn0v6DwkpAAcG4BYOCIZwBKO+ufCzQI2l9UzMrcPd1GSyHiIhIJOpsRcDM+hN8afdz9w1mNhL4luqv3teHP7OAVeEshBVlAX3da3hwABERqd3Kbx+MmbrcR6AQ+D6sBHQnmIq4MXCUmTU3sxy2TD+8lXAe51lmdhYEMzuZ2QHh5uHA1eX7mlnPDJZBRETqkCjmGsi0ulwRGArkmNk3BDMwjQEWAHcRzD/wCTCbqucTOA+41My+AiYDp4brrwF6m9nXZjYFuDJjJRAREYlYnW0acPci4MSK681snLs/GWYE/gW8Fe5/cYXjZwE/uPnf3ZcDZ2cgZBERqetqwRV8utXljEBVbgtnF5wEzCKsCIiIiMgP1dmMQFXc/YaoYxARkTjyWGYEYlcREBERyQgnlhWBODYNiIiISIqUERAREUmVxhEQERGROFFGQEREJEW1YQCgdFNGQEREpB5TRkBERCRVMcwIqCIgIiKSCgcS8asIqGlARESkHlNGQEREJCXxHFlQGQEREZF6TBkBERGRVMUwI6CKgIiISKpiWBFQ04CIiEg9poyAiIhIKnT7oIiIiMSNMgJpsNZXLn+/+OU5NfyyrYDlNfyaUYqmvBNr/BXLRVPe39b4K0KEf8uzo3jR+vW/G1VZO2fmtA4ev+kHVRFIA3dvXdOvaWbj3L13Tb9uVFTe+KpPZYX6Vd5YllWdBUVERCROlBEQERFJhToLSi3zZNQB1DCVN77qU1mhfpW3PpW1zjKPYXuHiIhIuhXmtfVD256T9vMOnf/Q+Cj7UigjICIiUo+pIlCDzOz3ZjbZzL42swlm1sfMfmtmjWrgtfub2TuZfp3wtcrC8k02s6/M7Hozywq39Tazh2oijkyp7H2MMJZ127l/fzM7dDv2rzdl3RFmdpqZuZl1z+Tr1KT68jm1w9zT/4iYOgvWEDPrB5wMHOjuRWbWCsgDBgIvAhsqOSbb3ctqNtK02OjuPQHMrA3wMtAUuNXdxwHjMvXCZpbj7qUZPH9V72Nd0R9YB/x3WzvWp7LuhHOB0eHPW1M9qLb+b9ezz6kdUDu+uNNNGYGa0w5Y7u5FAO6+HDgT2BX4yMw+guCqx8z+ZmZfAf3M7DozmxQ+fhvu08XMvjGzp8Ka+3AzaxhuOzipJn+PmU1KDsLMssxsupm1Tno+o/x5urn7UuBy4NcW2FzjN7OjwjgnmNmXZtbEzArMbISZfWFmE83s1KTY/2BmU81stJm9YmY3hOtHmtkDZjYO+I2ZHWRm/zGz8WY2zMzahfvtbmZDw/WjdvAq7gfvo7svNLNjwjJMNLN/mFl++JqzzezPYRnHmdmBYUzfmdmVSWX7nZl9Hr53f0xad024fL+ZfRguH21mLyUde6cFmZcxZtY2XHeKmY0NY/rAzNqaWRfgSuDaMJ4jVNYdZ2YFwOHApcA54bosM3vMzL41s/fN7F0zOzPp9/MXM/sCOMvMjjOzT8O/9UHh+ajq77eG1MvPqfpOFYGaMxzoaGbTwg+Ko9z9IWAhMMDdB4T7NQbGuvsBwEbgF0AfoC9wmZn1CvfrBjzq7vsAq4AzwvX/BK4Ir8h/UEt39wRBzf68cNWxwFfuviytpd36NWcC2UCbCptuAK4KYz2CoLybgNPd/UBgAPA3CxxMUMYDgBOBih1r8sLONg8BDwNnuvtBwD+AO8N9ngSuDtffADy2A8X5wftoZg2AZ4Gz3X0/gkzbL5OOmRuWcVS435kE72f5l+BxBO/nIUBP4CAzOzLcv/wLrDdQYGa54bqPw/WNgTHh38vHwGXh+tFAX3fvBbwK3Ojus4HHgfvdvae7j1JZd8qpwFB3nwasMLODgJ8CXYAewAVAvwrHrAj/tj8AbgGODZ+PA64Ly1zV329NqLefUylxIJFI/yNiqgjUEHdfBxxEcHW8DBhoZhdXsmsZ8Ea4fDjwL3dfHx7/Jls+LGe5+4RweTzQxcyaAU3c/dNw/ctVhPMP4MJw+RKCf8oofALcF14JNgtT+gbcZWZfE3xYtgfaAocBg919k7uvBd6ucK6B4c+9gH2B981sAsGHbYfwautQYFC4/gmCq5/tUtn7CFxB8H5MC3d7Djgy6bAh4c+JBB+ea8MPtKLwPTsufHwJfAF0J/gAHU/wRdkUKAI+JfiSPILgixOgGChvUx1P8CUE0AEYZmYTgd8B+6isaXcuQcWD8Oe5BP+zg9w94e6LgY8qHFP+d9qXoLLwSfj3eBHBsLiV/v1msAxb0edU/aQ+AjUobEcbCYwMP7QuqmS3TSm2txUlLZcBDbcjjnlmtsTMjia4MjtvW8fsDDPbjSDGpcDeSXHcbWb/Bk4i+EA8nuADsjVwkLuXmNlsoEEKL7O+/OWAye6+1ZVY+AWzqrzvws6o5H28ahuHlL9XCbZ+3xIE/4MG/Nndn6h4oJnNAi4maOf+miBLsgfwTbhLiW+5B7iMLf/TDwP3ufsQM+sP3JZS4SqoT2XdHmbWAjga2M/MnCDj5cC/tnFo8t/p++5+boXz7kclf781qb5+TqVMfQRkR5nZXmbWLWlVT2AOsBZoUsVho4DTzKyRmTUGTmfL1dEPuPsqYK1t6dld3Q2vTxOk3gZlsqNP2Kb3OPBI0od4+bbd3X2iu/8F+Jzg6rAQWBpWAgawZfKQT4BTzKxBeHV/chUvORVobUGnJ8ws18z2cfc1wCwzOytcb2Z2wA6Up7L38TuCK509wnUXAP/ZjtMOAy5JaiNub0EnSwje7xsIUuGjCNq9v6z4u6xEIbAgXE7+IK/u720r9amsO+BM4AV37+zuXdy9IzALWAmcEbZptyXosFiZMcBh5b9HM2tsZntSxd9vhsrwA/X1c2q7xPCuAVUEak4B8JyZTQnT3j0IrlyeBIZa2Aknmbt/QdDO+hkwFnja3b/cxutcCjwVphUbA6ur2G9IGFMm0m0Nw05AkwnS+8MJ24gr+K0FnYu+BkqA94CXgN7hlciFwLcA7v55GPPX4X4TqaRs7l5M8CH9Fws6Mk0gaBKA4Iri0nD9ZII23u1V2ft4E0Eb6aAw7gRB5Scl7j6cID36aXj862z50B1F0ITxqbsvIehDkUp7921hPOPZeva3t4HTLbUOdPWprNvrXH549f8GsAswH5hC8AX2BZX/nS4jyH68Ev5uPwW6b+PvtybUp88pCWlkwZgxs4KwnQ4zuwlo5+6/qWS/3gQdqdLemzpTystmwf3MHwOXhx9CIrVG0t9pS4Ivx8PC/gISqqufU4W5rf3QZmdse8ftNHT5E5GOLKg+AvHzYzO7meC9nUNw1bGV8B/vl9SWNrfUPWlmPQj6DDynSoDUUu+EHeLygNtVCahUnD+n6hxlBERERFJQmNPa+zU7Pe3nHbbiKWUERERE6oQYTkOsikAEzOw2gqFPmwIfu/sH0UYk6aL3VkTqGlUEIuTu/5epc5uZETT9RD9sVT2k93b7WIbniJDoxO69jWFzum4frCEWzOg1zcxGE4wehpk9a1vGIb+7/JYdM7s3XPeDMdTD9a0tGMd8spk9bWZzzKyVBWN7TzWz54FJBEOF/mBc9/Ac55vZZ+GtVU+YWXaN/1JiQu/tVr+LtywYI3+ymV0errs0/P18ZsG4848k/Y4eN7OxwF+tirkgwt/JG2FZPzezw2qqPLKF3tv4UkagBlgwBvk5BINz5BDcWzw+aXtLgkE4uru7hz2OYcsY6m5m/w+4EbieYJazD939z2Z2AsE9ueW6ARe5+xjbelx3A4ZYMK77MuBsgtuaSszsMYKeuc9n5BcQY3pvf+ASd19pweQyn1swcuQfgAMJBqX5EPgqaf8OwKHuXmZmI4Ar3X26BYPNPEYwet+DBLeQjTazTgSDEu2N1DS9t+61Ym6AdFNFoGYcQTAW9wYAMxtSYftqgsFTnrFgZr7y8dQ7EIz13Y7gVqRZ4frDCb5ccPehZvZ90rnmuPuYcDl5XHcIBuboBuxPMJ7452YGwbCfS9NQzvpI7+3WrjGz8m7VHQlHHnT3lQBmNgjYM2n/QeEXRfJcEOXb8sOfxwI9ktY3taT70KXG6L2NKVUEagF3LzWzQ4BjCEYV+zVBbXlHxlBfn7Rc6bjuZnY1wX34N+989FKd+vTehuU4Fujn7hvMbCTByJDVXeGVlymLqueCyCLInmxKW7CyXfTeJlEfAdlBHxOMxd3QzJoApyRvDGvMhe7+LnAtwVS7UPUY6p8APwuPPQ5oXsXrVjWu+wjgzHAZM2thZp2rOIdUT+/tFoXA9+EXRXeCCaQaA0eZWXMzy2HLNLRb2cZcEMOBq8v3NbOeGSyDVE7vbcgTibQ/oqaKQA0IR8AbSNB+9h7BBDvJmhCMRvY1QdvxdeH626h8DPU/AseZ2STgLGAxQRtdxdetdFx3d59CML3p8PA132cHpuQVvbcVDAVyzOwb4G6CiXUWAHcRDLX7CTCbqseVr2ouiGsI5p/42symEExGJDVL722MaWTBOsjM8oGyMO3cD/h7FWk3qWPi+N7alrH3cwgm6vmHu29rul6pA+rbe1uY3dL7Nvhx2s87fMMLGllQtlsn4DUzywKKgcsijkfSJ47v7W1mdizBHBHDgbeiDUfSSO9tDKgiUAe5+3SgV9RxSPrF8b119xuijkEyo969t46GGBYREanX4jOg52bqLCgiIlKPKSMgIiKSAgc8hk0DygiIRMTMyiyYD2CSmQ0ys0Y7ca7kuQ2eNrMe1ezb38wO3YHXmG1mrVJdX2Gf7RopzsxuM7P61f4sEhFVBESis9Hde7r7vgR3CGx1D3V4S9Z2c/f/F44nUJX+BEO+isj2cA/6CKT7ETFVBERqh1HAHuHV+qhwzoIpZpZtZvfYllkGr4DNo7M9YsGMhB8AbcpPZGYjzax3uHyCmX1hZl+Z2Qgz60JQ4bg2zEYcYVXMAGdmLc1suIUzIRIMa1wtq2SGuqRt94frR5hZ63BdpbPSidRWnvC0P1IR/i9PNbMZZnZTOsukioBIxMIr/xOBieGqA4HfuPueBLMPrnb3g4GDgcvMrCvBxER7AT2AC6nkCj/8sn0KOMPdDwDOcvfZwOMEM771dPdRbJkB7mCCYWKfDk9xKzDa3fchGCymUwrFucTdDwJ6E0xS0zJc3xgYF57rP+G5AZ4Erg6PuYFgVjoRSWLBVOKPEnxO9ADOra75b3ups6BIdBqa2YRweRTwDMEX+mfuXj4b4XHA/uXt/wRjvncDjgRecfcyYKGZfVjJ+fsCH5efq3yWuEpUOgNc+Bo/DY/9t209E2JVKs5Q1w1YASQIhmIGeBF406qflU6kdoomlX8IMMPdZwKY2asEwzRX1wSYMlUERKKzseLwweEXYsVZBq9292EV9jspjXFUOgNc0pdzSqzyGeoaVLG7U/2sdCKyRXtgXtLz+UCfdJ1cFQGR2m0Y8Esz+9DdS8xsT4LJXj4GrjCz5wj6BwwgmIQo2RjgMTPr6u6zzKxFmBVYCzRN2q98Brh7IJgBzt0nhK/xc+AOMzuRqmdCLFfZDHXlsgimYX41POdod19jZrPM7Cx3H2RBzWN/d/9qe35BIjVlLd8P+8Bfr/YOmR3UwMzGJT1/0t2fzMDrVEoVAZHa7WmgC/BF+EW5DDiNoM3+aILU4Fzg04oHuvuysMPem+HcBUuBHwFvA6+b2akEFYBrgEctmK0wh6ACcCXBTIivmNlk4L/h61RnKHBlOEPdVIKKSLn1wCFmdksYx9nh+vOAv4frcwkqCqoISK3k7idE9NILCJraynVgyzTmO02zD4qIiNRiYYfiacAxBBWAz4Gfu/vkdJxfGQEREZFaLJyW/NcETYXZBNM9p6USAMoIiIiI1GsaR0BERKQeU0VARESkHlNFQEREpB5TRUBERKQeU0VARESkHlNFQEREpB5TRUBERKQeU0VARESkHvv/BzZs8XvOggAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope=1.10    r=0.61    diff=1.55\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = get_performance(logit_model, \"Q22.2\")\n",
    "_ = predictions_from_average(\"Q22.2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q21.2 :  I support electric vehicles because they are better for the environment.\n",
      "slope=0.86    r=0.51    diff=1.57\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope=0.81    r=0.52    diff=2.34\n"
     ]
    },
    {
     "data": {
      "image/png": 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hm+2WcdjRM/nZt4ZQWSku/uubvPpsN2ZNy/s4y0ZRXVXGjTfvwcSPu9KmTSVXX/Eob4xLptV+YMQO/Ps/OxU4wtx68qHePHR3X35+8boeq298fxLjXu3Kvf/oz7Hfn8Sx35/MP64aUMAo8+eo789m6kdtaNu+tIf/lJUFp106g/OP+xzzZlVw9SMf8srjnZj6YetCh5YXza28paAo2ickDZSU+Wk3CJgCLAU61LPb88BXJbWV1A74WppWp4hYBCyVtHeadFwDIf2dpLvg3mIbpNi3/womvNWR1avKqa4q450xndn3kNLpxViwsA0TP+4KwMqVFUyb3olu3T4zfrRkvPN6F5YuXv+qf+jwuTz5UG8gqSwMO3BOIULLu+5brmavAxfx+N2lPyRn4O4rmDm5JbOntmJtZRmjRnRm2GGLCx1W3pRqeYPS7SYojiigPXCrpHclvQXsBFwE3AA8VjOAMFNEvE6yrOOrwGjg7xHxxgbOcxJwo6RxJOMP6vvtfDCNqei6CKZMbMcueyyiQ6dKWrWuYsj+8+nec/WGd2yCem6xjG0/t4AJE7oD8JUvfcBfr/ovPzvjZdq3K80yA3TutoaF81oBsHBeSzp3W1PgiPLjlF9O4abLt6a6ujSbXTN127KSuTNbfvp63qwKuveqLGBE+dXcylsKiqKbICLGAvvUsenq9FGTr32t/a4ArqiVNhnYJeP1HzM2j4+IXQHSMQlj0jyjSLooauxGMnDw/Y0uTJ5Nm9SOe/+xDb+9fhyrV5bx8YQOVFcXOqrca926kgvPe57r/z6YFSsrePjRAdxx9y5EiO+e8CY/Oul1/nzVsEKH2QhExIZzNTV7HbSQRfMrmPhOOz6/95JCh2OWtULMQNgYiqIy0Ii+JOl8knJPAb5XO0NaSfgxGxgrIOlk4GSA1mXtG8qacyMf6M3IB5Jm5BPP+Ih5n7Rq1PPnW3l5Nb8873meebYfL768NQCLFq0b9vHYyO349S9HFSi6/Fs0vyVduq9m4bxWdOm+msULWm54pyZmp8FLGXrwQvYcvoiKVkHb9lX84oqJ/OHs7QodWl7Mn11Bj97rWni696pk3qzSHBQKza+8paA0qzj1iIi7I2JQROwSEV+q65bBiLg8IraJiBc2cKwbImJIRAxpWda4g2I6dU3+yHpsuYp9Dp7LqEdKYZRujeBnp7/C1OkduX/Ejp+mdu2y8tPn+wydxuQpnQsQW+N45dkeHHLkTAAOOXImr4wqvT71W/6wNd/Zdw++d8DuXH7Gdrz5cseSrQgATBjXlj7919Cz72paVFQz/KhFvDKyU6HDyptSLW8gqiP3j2JQVC0Dki4ClgEdgeci4snCRlScLrjibTp2qmTt2jKuu3R7lpfQbWc77ziXQw6axKTJnbn2L48AyW2Eww+YzOf6LwTEJ5+046rr9m74QE3EuZe9xa6DF9KxcyW3PfYct/9tW+79Rz/O/93bHPrVGcyZldxaaE1bdZW49oI+XHrHx5SVw8i7ujLlg9IdWV/K5S3VbgJFEXVI1lQGavXz5/ocIil3znraO1X0iGFdjt5wxhJR3a9XoUNoNGXTSnMkf31icfPqv69etarQIVgejI6nWBILcn7JvfUuHePn9+2V68Ny1o5PjY2IITk/8EYoeBUnXaDoA0kvAAPTtFskHZM+v7zmLgNJf0zTjpQ0WtIbkp6U1DNN7yHpiXQa4r9LmiKpezpF8QRJtwHvkMx2+AtJr6XH/XVGPN+W9Go6ZfH1ksob/YdiZmZFJ4DqKMv5oxgUNIp01sHjSOYV+CKwZ63t3UjmD9g5vQvgt+mmF4ChEbE7cBdwbpr+K+DpdBri+4CtMw43ALgu3TYwfb1Xeu7Bkg6QtCPwTWDfjCmLi2rSITMzs1wr9JiB/UmmFF4BIOnBWtsXA6uAm9IlhmuWGd6KZMriXiSrG05K0/cjqTwQEY9JWphxrCkR8Ur6/ND0UTMvQXuSysGuwGDgtaQ3gTZA82onNjOzeogqT0fc+CJiraS9gINJVjH8KXAQydwDV0TEg5KGk0xQtCHLM54LuCwirs/MIOl04NaIOH/zozczs1JS001QigpdqudIphRuI6kDcGTmRkntgU4R8QjwM5LJgAA6ATPS55lrGLwIfCPd91CgSz3nfRz4QXp8JPWRtAXwFHBM+hxJXSVts5llNDMzK2oFbRmIiNcl3Q28SdIc/1qtLB2AEZJak1zNn52mXwTcm3YDPA30T9N/Ddwp6TvAy8BskvUNas9cODIdH/By2h2wDPh2RLwr6UJgpKQyoBI4jWSCIjMza+bcTZAnEXEJcEkDWT5zH0dEjCBZZri2xcBhaffCMGDPiFgNTCZjiuL0GFcCV9Zx7LuBu7MugJmZWRNX8MpAjm0N3JNe1a8BflTgeMzMrEREqGTHDJRUZSAiPgR2L3QcZmZWmoplyeFcK81SmZmZWdZKqmXAzMwsXwKoLtEBhG4ZMDMza+bcMmBmZpYVecyAmZmZlSa3DJiZmWUhmY64NMcMuDJgZmaWpaoSbVAvzVKZmZlZ1twyYGZmloVAJdtN4JYBMzOzZs4tA2ZmZlmqLtFraFcGzMzMshABVe4mMDMzs1LklgEzM7MseQChmZmZlSS3DJiZmWUhubWwNK+hXRnIgVhbRdX8BYUOo9G0aN260CE0muotuxU6hMa1YmWhI2hcq1YVOgJrYqq8hLGZmZmVIrcMmJmZZaGUFypyy4CZmVkz55YBMzOzrJTuAMLSLJWZmZllzS0DZmZmWar23QRmZmbNV83aBLl+bIikmyXNkfRORtofJL0v6S1JD0jqnLHtfEkTJU2QdFg2ZXNlwMzMrLjdAhxeK+0JYJeI2BX4ADgfQNJOwHHAzuk+10kq39AJXBkwMzPLUnWU5fyxIRHxHLCgVtrIiFibvnwF2Cp9fhRwV0SsjohJwERgrw2dw5UBMzOzpu0HwKPp8z7AtIxt09O0BnkAoZmZWRaStQnyMoCwu6QxGa9viIgbstlR0gXAWuBfmxOAKwNmZmZZytPdBPMiYsjG7iTpe8CXgYMjItLkGUDfjGxbpWkNcjeBmZlZEyPpcOBc4CsRsSJj04PAcZJaSeoPDABe3dDx3DJgZmaWhUKtTSDpTmA4SXfCdOBXJHcPtAKekATwSkScGhHjJd0DvEvSfXBaRFRt6ByuDJiZmRWxiDi+juSbGsh/CXDJxpzDlQEzM7MsleraBK4MmJmZZSPydjdBwZVmFcfMzMyy5pYBMzOzLAReqMjMzMxKlFsGzMzMsuQxA2ZmZlaS3DJgZmaWhUJNOtQYXBkwMzPLkisDVhTO/tNU9j5kCYvmteCUg3codDh5ceaFb7HXfnNYtLAlpx1/AADfPuUDhh7wCRGwaEEr/vybXVkwr3WBI9183bsv55xzRtOlyyoi4NFHt2XEiIGcdNI49t57BmvXljFrVnuuuGJvli9vWehwc65dh7Wc9dsP2Wb7FUTAn//fAN4f17HQYeXFkOFLOPXimZSXBY/e2ZV7rulZ6JDyqrmVt6kr2JgBSVWSxkkaL+lNST+XVJZuGyLpqkLFVsxG3tOVC074XKHDyKsn/7sV/3fmnuul/fv2/vz0hP05/dv78+oLW3D8Dz8sUHS5VVVVxo03DuKUU77Iz372P3z5yxPZeuvFvPFGT0499Qh+8pMjmDGjA9/85ruFDjUvTr3gY8Y834WTjxjMaUftzrSP2hY6pLwoKwtOu3QGF57Qnx8NH8iBRy1i6wGrCh1W3pRqeWuWMM71oxgUcgDhyogYFBE7A/8DHEGy+AIRMSYizsjXiSU12RaRd0a3Z+mi8kKHkVfj3+jK0iUV66WtXL7udes2a4ki+QPaXAsXtuGjj7oCsHJlBdOmdaRbt5W8/novqquTP8/33+9O9+4rCxlmXrRtv5Zd9lzM4/clV4xrK8tYvrTJ/mk2aODuK5g5uSWzp7ZibWUZo0Z0ZthhiwsdVt40t/KWgqK4myAi5gAnAz9VYrikhwEkfSFtQRgn6Q1JHSS1l/SUpNclvS3pqJpjSfqlpAmSXpB0p6Rz0vRRkv4iaQxwpqTBkp6VNFbS45J6pfm2lfRYmv68pNJsi2+CvvvjCdzy0NMMP3wmt18/oNDh5NwWWyxj220XMmFCt/XSDz30Y157rVeBosqfLbdaxeIFFZx92Ydc88AbnPnbD2nVZoOLqzVJ3basZO7Mdd0882ZV0L1XZQEjyq9SLm81yvmjGBRFZQAgIj4GyoEtam06h2QJxkHA/sBKYBXwtYjYAzgQ+FNaidgTOBrYjaSlYUitY7WMiCHAVcDVwDERMRi4mXUrPN0AnJ6mnwNcl9OC2ia77a8D+d6RBzHqsd4ceeyUQoeTU61bV3LhhS9y/fW7s2LFulaQ444bT1WVeOaZbQoYXX6Utwi222kZ/72zFz/92u6sWlnGN06eXuiwzOoXuJuggF4ErpB0BtA5ItYCAi6V9BbwJNAH6AnsC4yIiFURsRR4qNax7k7/HwjsQrIO9DjgQmArSe2BfYB70/TrgTovySSdLGmMpDGVrM5daW2DRj3Wh30Oml3oMHKmvLyaCy98kWee2YaXXur7afohh3zMXnvN5Pe/HwZFcvWQS/Nmt2Le7FZMeKsDAC881p3tdlpW4KjyY/7sCnr0XvPp6+69Kpk3q6KBPZq25lbeUlA0lQFJnwOqgDmZ6RFxOfBDoA3wYtpsfwLQAxicthh8AmQztHx5zemA8emYhUER8fmIOJTk57EoI31QROxY14Ei4oaIGBIRQypotfEFto3Su+/yT58P/cInTJ/cvoDR5FJw1lmvMm1aRx54YF2P1ODBszj22Pf59a/3Z/Xq0uxHXzivJXNnt6JP/xUADBq2iKklOoBwwri29Om/hp59V9OioprhRy3ilZGdCh1W3pRqeWvmGSjFloGi+JSR1AP4G3BNRISkzG3bRsTbwNtpN8AOQCdgTkRUSjoQqGlDfRG4XtJlJGX7Mkmzf20TgB6ShkXEy5IqgO0jYrykSZKOjYh7lQSya0S8maeib7Tzrp3MrsOW0anrWm4fM55//nFLHr+r24Z3bELOvfgNPj94AR07r+HWh57mXzcOYMg+c+izzXKiWsyZ3YZrL9+l0GHmxM47z+OQQyYzaVInrrnmMQBuvXVXTj31dSoqqrjkklEAvP9+N665Zs8GjtQ0/fXiz3HuHz+goqKaWdNa8+fzty90SHlRXSWuvaAPl97xMWXlMPKurkz5oOnfGluf5lbeUqCIKMyJpSrgbaACWAv8E7giIqolDQfOiYgvS7qaZFxANTAe+B7QgaQLoD0wBhgKHBERkyVdBHyLpLVgDvBYRNwoaVR6zDHp+QeRjB3oRFJx+Euarz/wV5LugQrgroj4TUNl6aiusXfZITn4qTQNLfr0LnQIjaa6W2ne816vj5tXn3310qWFDsHyYHQ8xZJYkPNL7o4De8aefzsh14fl6YP+PDYdz1YwBWsZiIh674+LiFHAqPT56XVkWQ0Mq2f3P0bERZLaAs8BY9PjDK91jnHAAXWcexJw+IbiNzOz5qVmnoFSVBTdBDl2g6SdSMYQ3BoRrxc6IDMzs2JWcpWBiPhWoWMwM7PSVCoTntVWNHcTmJmZWWGUXMuAmZlZvhTLjIG55pYBMzOzZs4tA2ZmZlmIdDriUuTKgJmZWZY8gNDMzMxKklsGzMzMslK6kw65ZcDMzKyZc8uAmZlZlkp1zIArA2ZmZlmoWcK4FLmbwMzMrJlzy4CZmVk2IplroBS5ZcDMzKyZc8uAmZlZlkp1bQJXBszMzLIQlO7dBO4mMDMza+bcMmBmZpYVz0BoZmZmJcotA2ZmZlnyrYVmZmZWktwyYGZmlqVSvZvAlQEzM7MsRLgyYPap6u6dCh1Co1kyoEOhQ2hUned3LHQIjap66dJCh2BWFFwZMDMzy5JvLTQzM7NGJ+lmSXMkvZOR1lXSE5I+TP/vkqZL0lWSJkp6S9Ie2ZzDlQEzM7MsReT+kYVbgMNrpZ0HPBURA4Cn0tcARwAD0sfJwF+zOYErA2ZmZlmKUM4fGz5nPAcsqJV8FHBr+vxW4KsZ6bdF4hWgs6ReGzqHKwNmZmZNT8+ImJU+nw30TJ/3AaZl5JuepjXIAwjNzMyyEGR3Jb8Juksak/H6hoi4IdudIyIkbdbciK4MmJmZFda8iBiykft8IqlXRMxKuwHmpOkzgL4Z+bZK0xrkbgIzM7MsRR4em+hB4MT0+YnAiIz076Z3FQwFFmd0J9TLLQNmZmbZKNAMhJLuBIaTdCdMB34FXA7cI+kkYArwjTT7I8AXgYnACuD72ZzDlQEzM7MiFhHH17Pp4DryBnDaxp7DlQEzM7NseQljMzMzK0VuGTAzM8uSVy00MzNr5rKcPrjJcTeBmZlZM+eWATMzsywEpdtN4JYBMzOzZs4tA2ZmZtkIwC0DZmZmVorcMmBmZpalUr2bwJUBMzOzbJVoZSCv3QSSLpA0XtJbksZJ2juf59tALMs2Mv9wSfvkKx4zM7NikbeWAUnDgC8De0TEakndgZb5Ol8eDAeWAS8VOA4zMysK8q2Fm6AXMC8iVgNExLyImCnpYElvSHpb0s2SWgFImizpsrQFYYykPSQ9LukjSafWHFTSLyS9lrY2/Doj7Yz0+Z8lPZ0+P0jSvzL2vUTSm5JekdQzTTtS0ug0picl9ZTUDzgV+Fkaz/55/DmZmZkVVD4rAyOBvpI+kHSdpC9Iag3cAnwzIj5P0jLx44x9pkbEIOD5NN8xwFCg5kv/UGAAsBcwCBgs6YA0f80X9hCgvaSKNO25NL0d8EpE7Jam/ShNfwEYGhG7A3cB50bEZOBvwJ8jYlBEPJ+rH4qZmTVhkYdHEchbN0FELJM0mOQL+UDgbuAyYFJEfJBmu5Vk3eW/pK8fTP9/G2gfEUuBpZJWS+oMHJo+3kjztSepHNxGUjHoCKwGXiepFOwPnJHmXQM8nD4fC/xP+nwr4G5JvUi6MSblovz5cvafprL3IUtYNK8Fpxy8Q6HDybnu3Zdzzs9foUuXVUTAo49tx4gRA/nOd95i2NDpVFeLxYtb86cr9mbBgraFDjdnylTNTec8wNzF7Tj3hsM57/hn2aHvXCSYNqcTl/xrOCvXVBQ6zM125oVvsdd+c1i0sCWnHX8AAN8+5QOGHvAJEbBoQSv+/JtdWTCvdYEjzb0hw5dw6sUzKS8LHr2zK/dc07PQIeVVSZY3SncGwrzeTRARVcAoYJSkt0m++BuyOv2/OuN5zesWgIDLIuL62jtKmgR8j6SP/y2SCsh2wHtplsqIT28KqWJd2a8GroiIByUNBy7KqnAFMvKerjz4j+784sqphQ4lL6qqyrjx77vz0UddadOmkquuepw3Xt+Sf9+3I//8564AfOUrE/jWt8ZzzTV7Fjja3Dn2C+8w+ZPOtGtdCcBV9w9jxepkiM3pX32Zow8Yz+1PDipghLnx5H+34uF7t+Hsi978NO3ft/fn9uu3B+DIb0zm+B9+yLWXf75QIeZFWVlw2qUzOP+4zzFvVgVXP/Ihrzzeiakfll6lB5pfeUtB3roJJA2UNCAjaRDwEdBP0nZp2neAZzfisI8DP5DUPj1HH0lbpNueB84h6QJ4nqTP/42MCkB9OgEz0ucnZqQvBTpsRGyN4p3R7Vm6qLzQYeTNwoVt+OijrgCsXFnBtKkd6dZ9BStWrrsqbt16bdE0reVCj07L2GfnqTz08rqWnpqKAAStKtaWzL3N49/oytIl67dwrFye8d62WVuSV14Dd1/BzMktmT21FWsryxg1ojPDDltc6LDypqTL626CjdYeuDpt3l8LTAROBu4E7pXUAniNpG8+KxExUtKOwMuSIBnt/21gDkkF4ALg5YhYLmlVmrYhF6XxLASeBvqn6Q8B90k6Cjjd4wYa3xZbLGPbbRcy4f3uAJz43Tc5+ODJLF9ewXnnHVTg6HLnzK+/zHUj9qZt2ipQ4/99axTDdprG5Nmdufo/wwoUXeP47o8ncNAXZ7B8WQvO/3HB7kDOm25bVjJ35rqbqebNqmCHPVYUMKL8am7lLQX5HDMwFqjrPv2ngN3ryN8v4/ktJAMI69p2JXBlHfs/BVRkvN6+1vb2Gc/vA+5Ln48ARtRxvA+AXeuI3xpB69aVXHjBC1x/wx6ftgrcettu3HrbbnzjG+M58sgPuf1fTb8peZ+dp7BwWRsmTO/B7tvNXG/bpXcMp0zV/OyYlzh4j494ZPTAAkWZf7f9dSC3/XUgx544kSOPncK/btx+wzuZFUTptVyB1ybYZJJOTm+BHFO53vAG21zl5dVceMELPDOqHy+91Pcz2595ph/77jutAJHl3q79P2G/XaZw3//dwa9PfIrBA2bwf995+tPt1VHGk69vy/Ddinpca86MeqwP+xw0u9Bh5Nz82RX06L3m09fde1Uyb1bTHxBan5Iub4l2E7gysIki4oaIGBIRQypoVehwSkhw1lmjmTatIw88sK4PvXfvpZ8+HzZ0BtOndyxEcDn3t4f34mu/OoFjfvMtfnXrwYz9sA+/+eeB9Ole078a7LfLFKZ80rmQYeZV777LP30+9AufMH1y+wZyN00TxrWlT/819Oy7mhYV1Qw/ahGvjOxU6LDyprmVtxQU1doEki4iGQfQEXguIp4sbETF57xrJ7PrsGV06rqW28eM559/3JLH7+pW6LByZued5nHIwZOZNKkT11z9KAC33robhx72EVv1WUoEzJnTjqtL6E6C2iS48IRRtGu9BgkmzujGH+7Zr9Bh5cS5F7/B5wcvoGPnNdz60NP868YBDNlnDn22WU5Uizmz23Dt5bsUOsycq64S117Qh0vv+Jiychh5V1emfFC6I+tLurxFciWfa9rwYPvGU1MZiIg/5vEcIil3da6O2VFdY++yQ3J1uKJXttuOhQ6h0SwZUHQ3lORV55enFzqERrV2+owNZ7ImZ3Q8xZJYkPPO/Vb9t4pevzo914dlyvfPGxsRQ3J+4I1Q8G6CdDGjDyS9AAxM026RdEz6/HJJ76bTD/8xTfvMFMJpeg9JT6SLI/1d0hRJ3SX1kzRB0m3AOyQzI35mWuP0GN+W9Go6DfH1kkr3Pj4zM8teAKHcP4pAQSsD6QyFx5HMQfBFYM9a27sBXwN2johdgd+mmz4zhXCa/ivg6YjYmeRuga0zDjcAuC7dNpA6pjVOb1v8JrBvOi1yFXBCDotsZmZWdAo9ZmB/4IGIWAEg6cFa2xcDq4CbJD3MuumE65tCeD+SygMR8Vg6d0CNKRHxSvq8vmmNdwUGA6+l8xi0IZnDwMzMrGQmAKut3sqApKtpYKhERJxR37ZciYi1kvYCDiZZtOinwEFs2hTCyzOe1zmtsaTTgVsj4vzNj97MzEpOiVYGGuomGEOyoE99j1x4DviqpDaSOgBHZm5Mpx3uFBGPAD8Ddks31TeF8IvAN9J9DwW61HPe+qY1fgo4pmaKY0ldJW2zmWU0MzMravW2DETErZmvJbWtac7PlYh4XdLdwJskzfGv1crSARiRLn0s4Ow0/SLqnkL418Cdkr4DvAzMJlljYL0bl+ub1jgi3pV0ITBSUhlQSbK40pTcldrMzJqsIhnwl2sbHDMgaRhwE8kX6taSdgNOiYif5CKAiLgEuKSBLHvVsU+dUwiTjDE4LO1eGAbsGRGrgcnAejcvNzCt8d0kyy2bmZk1C9kMIPwLcBjwIEBEvCnpgHwGtRm2Bu5Jr+rXAD8qcDxmZlZCVKJjBrK6myAipqXN6TWq8hPO5omID6ljESQzM7PNVkRrCeRaNpWBaZL2AUJSBXAm8F5+wzIzM7PGkk1l4FSSvvU+wEySkfin5TMoMzOz4lM8Mwbm2gYrAxExD8/CZ2ZmVrI2OB2xpM9JekjSXElzJI2Q9LnGCM7MzKyoRB4eRSCbtQnuAO4BegG9gXuBO/MZlJmZWVFqxpWBthHxz4hYmz5uB0pkYWozMzNraG2CrunTRyWdR7I6YJCs6vdII8RmZmZWXIrkSj7XGhpAOJak2DVDJ0/J2BaAF/MxMzMrAQ2tTdC/vm1mZmbNTtB8by0EkLQLsBMZYwUi4rZ8BWVmZmaNJ5uFin4FDCepDDwCHAG8ALgyYGZmzUqprk2Qzd0ExwAHA7Mj4vvAbkCnvEZlZmZWjJrxrYUrI6IaWCupIzAH6JvfsMzMzKyxZFMZGCOpM3AjyR0GrwMv5zMoMzMzW0fSzySNl/SOpDsltZbUX9JoSRMl3S2p5aYef4OVgYj4SUQsioi/Af8DnJh2F5iZmVmeSeoDnAEMiYhdgHLgOOB3wJ8jYjtgIXDSpp6joUmH9mhoW0S8vqknNTMza4oKOICwBdBGUiXQFpgFHAR8K91+K3AR8NdNPXh9/tTAtkiDsBpRJKNAGsGqLdsVOoRG8+KV1xc6hEa13Z2nFjqERrX9ZasKHUKjqZo3v9AhlIb8zDPQXdKYjNc3RMQNn54yYoakPwJTgZXASJJu+0URsTbNNh3os6kBNDTp0IGbelAzMzPL2ryIGFLfRkldgKOA/sAikgUDD89lAFlNOmRmZtbsFe5WwEOASRExF0DS/cC+QGdJLdLWga2AGZt6gmzuJjAzM7PCmQoMldRWkkjm/nkXeIZkLiCAE4ERm3oCVwbMzMyyVYBJhyJiNHAfya39b5N8d98A/C9wtqSJQDfgpk0tVjbTEQs4AfhcRPxG0tbAlhHx6qae1MzMrCkq1N0EEfEr4Fe1kj8G9srF8bNpGbgOGAYcn75eClybi5ObmZlZ4WUzgHDviNhD0hsAEbFwc2Y5MjMza7JK9C7ybFoGKiWVk/4IJPUAqvMalZmZmTWabCoDVwEPAFtIuoRk+eJL8xqVmZlZMSrRVQs32E0QEf+SNJbkVgYBX42I9/IemZmZmTWKbO4m2BpYATyUmRYRU/MZmJmZWTFRFHRtgrzKZgDhf0kaMgS0JpkOcQKwcx7jMjMzKz75WZug4LLpJvh85ut0NcOf5C0iMzMza1QbvTZBRLwuae98BGNmZlbUmms3gaSzM16WAXsAM/MWkZmZmTWqbFoGOmQ8X0syhuDf+QnHzMyseDXLAYTpZEMdIuKcRorHzMyseJVoZaDeSYfSNZKrSNZMNjMzsxLVUMvAqyTjA8ZJehC4F1heszEi7s9zbGZmZsWjmc8z0BqYDxzEuvkGAnBlwMzMrAQ0VBnYIr2T4B3WVQJqZFU3knQB8C2gimRxo1MiYvQmxrpZJC2LiPYbkX84sCYiXspbUGZm1rQ0w5aBcqA961cCamzwxyFpGPBlYI+IWC2pO9CUlj4eDiwDXBkwM7NEM6wMzIqI32zGsXsB8yJiNUBEzAOQdDDwx/TcrwE/TisLk4E7gSNIbmE8GbgM2A74Q0T8Ld3/F8A3gFbAAxHxqzRtdURcJenPwG4RcZCkg4CTIuKEdN9LSCooK4GjIuITSUcCF5JUVOYDJwBtgFOBKknfBk6PiOc342dhZmZWtBpawnhzJ2AeCfSV9IGk6yR9QVJr4Bbgm+k0xy2AH2fsMzUiBgHPp/mOAYYCvwaQdCgwANgLGAQMlnRAmn//9BhDgPaSKtK059L0dsArEbFbmvajNP0FYGhE7A7cBZwbEZOBvwF/johBrgiYmRmsW6wol49i0FDLwMGbc+CIWCZpMMkX8oHA3SRX+pMi4oM0263AacBf0tcPpv+/DbSPiKXAUkmrJXUGDk0fb6T52pNUDm4jqRh0BFYDr5NUCvYHzkjzrgEeTp+PBf4nfb4VcLekXiStA5M2p9yNYcjwJZx68UzKy4JH7+zKPdf0LHRIOVPRYi1XnvdfWlZUUV5WzbNj+nPLiMH87w+eZbeBs1i+MulpuvymL/DRtG4FjnbT/OlnfRn9ZEc6d1/LDc9M+DR9xE3defCW7pSVB3sfvIQf/nIWAHddvQWP3dmN8rLgx7+dwZDhSwsV+mbp32ERV+775Kevt26/hL+8PYRXPunDxXs+R9sWa5mxvD1nv3Qwy9Y2pR7F7Bx1wjQOO3omAh67vzcjbu9b6JDyqpQ/p0pRvZWBiFiwuQdP5ykYBYyS9DbJF39DVqf/V2c8r3ndgqS14rKIuL72jpImAd8j6eN/i6QCsh3wXpqlMiJq6mBVrCv71cAVEfFgOmjwoqwKVyBlZcFpl87g/OM+x7xZFVz9yIe88ngnpn7YutCh5UTl2nLO/sMXWbW6gvLyaq4+/yFGv518aP7tnr15bmz/Ake4+Q795gK+8v15/OHMrT9NG/die156vBN/fXICLVsFi+Ylv55TPmjFqBFduOGZ91nwSQXnfXNbbnrhPcrLCxX9ppu0tDNfeewYAMpUzYtH3c7Iaf25Zr8nuPyNobw6tzfHfO59frjjm/zl7T0LHG1ubbPdMg47eiY/+9YQKivFxX99k1ef7casaW0LHVpelPrnVClqqJtgs0gaKGlARtIg4COgn6Tt0rTvAM9uxGEfB34gqX16jj6Stki3PQ+cQ9IF8DxJn/8bGRWA+nQCZqTPT8xIX8r6UzEXhYG7r2Dm5JbMntqKtZVljBrRmWGHLS50WDkkVq2uAKBFeTXl5dUFjif3Pj90OR26VK2X9vBt3fjmTz+hZavk17Vz97UAvPx4J4YftZCWrYItt15D736rmfBG0/8C2afnDKYu68jMFR3o32Exr87tBcCLs7fi8L4fFzi63OvbfwUT3urI6lXlVFeV8c6Yzux7yNxCh5U3pf85VXryVhkgacK/VdK7kt4CdgLOA74P3Ju2FFST9M1nJSJGAncAL6f738e6L+znSQYtvhwRnwCr0rQNuSiNZywwLyP9IeBrksZJ2r/OPQug25aVzJ25rgl13qwKuveqLGBEuVemam686H4e+MvtjB3fh/c+Tup7Jx09hr//+t/85LhXqGhRtYGjNC0zPmrNO6Pbc8aXBnDO17djwrg2QPL+9ui97v3t3quS+bMrChVmznxpm494eEpyTfDh4i4c0mcyAEf0/Zgt2y5vYM+macrEduyyxyI6dKqkVesqhuw/n+49V294xyaqpD+nIg+PIrDRSxhnKyLGAvvUsekpYPc68vfLeH4LyQDCurZdCVxZx/5PARUZr7evtb19xvP7SCoSRMQIYEQdx/sA2LWO+C3PqqOMH130ddq1Wc3FP32Sfn0WcOO/92TB4jZUtKjm5ye+wPFHvMltD+1R6FBzpqoKli4q58qHP2TCuLZccko/bn3lvQ3v2ARVlFVxcJ8p/PHNvQA4b/QX+L/BL/HTXV7nqRnbUFmdz2uUwpg2qR33/mMbfnv9OFavLOPjCR2oLr1Gr9JXRAP+ci1vlYFSJ+lkktsfaU3jNdvOn11Bj95rPn3dvVcl82Y1/SvFuixf2Ypx7/dir12mc8/jSb2scm05j74wgG8e/naBo8ut7r0q2feLi5Fgh91XUFYGixeU071XJXNnrnt/582qoNuWTfsK6wu9pvHugu7MX5X83Xy8tAvfG/UlAPp1WMTw3lMLGV7ejHygNyMf6A3AiWd8xLxPWhU4ovxpTp9TpaL0quCNJCJuiIghETGkgsb7o54wri19+q+hZ9/VtKioZvhRi3hlZKdGO3++deqwknZtkubTlhVrGbzzDKbO7kzXTivSHMF+e0xh0owuhQsyD/Y5fDFvvpg0Xk3/qBWVa0SnrlUMPXQJo0Z0Yc1qMXtqS2ZMasXA3Vds4GjF7cvbTOShKdt++rprq5UAiOC0nV/nzok7FSq0vOrUNfly7LHlKvY5eC6jHind0fUl/TnlbgIrBtVV4toL+nDpHR9TVg4j7+rKlA9KZ4Rut04rOO+k5ygrq6ZMMOq1/rzy5tb86Rf/pXOHVQiYOK0rV9y2X6FD3WSX/Xgb3nq5PYsXtOCEwTvxnZ/P5rDjFnDF2X05+cCBVFQEv7hyKhL0G7iKA45cxMnDd6C8PPjppdOb5J0ENdqUV7LvltO58LV1w3CO3GYi3x4wHoCR0/tz38cDCxVeXl1wxdt07FTJ2rVlXHfp9ixfWrpXyqX+OVWKtOHB9rYhHdU19tZmTcvQpKw5vLRu+2rIMzffWOgQGtV2d55a6BAa1faXfVjoEBpN1bz5hQ6h0YyOp1gSCzZ34rzPaN27b/T70dm5PiwTfnP22IgYkvMDb4Rm2TIgqUVErC10HGZm1nSI0h1A2KTHDEj6j6SxksanA/qQdFI6BfKrkm6UdE2afoukv0kaDfxe0raSHkv3f17SDmm+HpL+Lem19LFvAYtoZmaWd029ZeAHEbFAUhvgNUn/BX4J7EEyadDTwJsZ+bcC9omIKklPAadGxIeS9gauAw4iuW3xzxHxgqStSSY62rERy2RmZsWqRFsGmnpl4AxJX0uf9yWd0bBmKmVJ9wKZ8w3cm1YE2pPMgXCv9Gm3Us0tAYcAO2Wkd5TUPiKW5bEcZmZmBdNkKwPpOgKHAMMiYoWkUcD7NHwVXzO1WRmwKF0hsbYyklUMV+UsWDMza/pKeNKhpjxmoBOwMK0I7ECy1HE74AuSukhqARxd144RsQSYJOlYACV2SzePBE6vyStpUB7LYGZmTUmJzjPQlCsDjwEtJL0HXA68QrLg0KXAq8CLwGSgvtUxTgBOkvQmMB44Kk0/Axgi6S1J75IseGRmZlaymmw3QUSsBo6onS5pTETckLYMPAD8J83/vVr7TwIOr+O484Bv5iFkMzNr6orkSj7XmnLLQH0ukjQOeAeYRFoZMDMzs7o12ZaB+kTEOYWOwczMSpMHEJqZmVlJKrmWATMzs7wp0ZYBVwbMzMyyUUS3AuaauwnMzMyaObcMmJmZZckDCM3MzKwkuWXAzMwsW24ZMDMza94UuX9kdV6ps6T7JL0v6T1JwyR1lfSEpA/T/7tsarlcGTAzMyt+VwKPRcQOwG7Ae8B5wFMRMQB4Kn29SVwZMDMzy1YBVi2U1Ak4ALgJICLWRMQikgX2bk2z3Qp8dVOL5cqAmZlZcesPzAX+IekNSX+X1A7oGRGz0jyzgZ6begJXBszMzLKRj1aBpGWgu6QxGY+Ta525BbAH8NeI2B1YTq0ugYjYrCmRfDeBmZlZFpQ+8mBeRAxpYPt0YHpEjE5f30dSGfhEUq+ImCWpFzBnUwNwy4CZmVkRi4jZwDRJA9Okg4F3gQeBE9O0E4ERm3oOtwyYmZllq3DzDJwO/EtSS+Bj4PskF/T3SDoJmAJ8Y1MP7sqAmZlZkYuIcUBdXQkH5+L4rgzkivLUk1SE2r43u9AhNJqBz3+30CE0qiu+cluhQ2hU1197aKFDaDzz5hc6gpLgtQnMzMysJLllwMzMLFsl2jLgyoCZmVm2SrQy4G4CMzOzZs4tA2ZmZtnYiFUGmxq3DJiZmTVzbhkwMzPLVom2DLgyYGZmliV3E5iZmVlJcsuAmZlZttwyYGZmZqXILQNmZmZZKtUxA64MmJmZZSNwN4GZmZmVJrcMmJmZZcstA2ZmZlaK3DJgZmaWBVG6AwjdMmBmZtbMuWXAzMwsWyXaMuDKgJmZWZYUpVkbcDeBmZlZM+eWATMzs2x40iEzMzMrVW4ZMDMzy1Kp3lroykATc/afprL3IUtYNK8Fpxy8Q6HDyYszL3iTvfb5hEULW3Hat7+w3ravHf8RPzzjPY4//FCWLG5ZoAhzq8Mj8+jw1HwAlh3UlSVf6kHLySvpduMMVFlNlIv5J/VhzXZtCxzppnnqvJ5MeaYdbbpVcfwjU9bb9sZNXXjp8h78YPRE2nStZuFHFTx13pbMHd+KoWfPZ/cfLixQ1JvvzPPfYK99Zie/x9896NP0I4/+mC99fRLV1eK1l3ryj7/uXMAo82fI8CWcevFMysuCR+/syj3X9Cx0SLlRopWBJttNIOmrkkJSaX4j1mPkPV254ITPFTqMvHryv1vxfz/b+zPp3bdYye57zWPOrDYFiCo/KqauosNT85l16QBm/n572ry+lBazV9PlX7NYdMwWzPz99iz6Rk+6/mtWoUPdZDt+fQlH3jzjM+lLZ7Vg2gttad+78tO0Vp2r2f+Xc5p0JaDGk4/05f9+Pmy9tF13n8vQ/Wfx0+8N5yffOYj779yuQNHlV1lZcNqlM7jwhP78aPhADjxqEVsPWFXosKwBTbYyABwPvJD+nzVJ5fkJp3G8M7o9Sxc16SJs0Phx3Vi6pOIz6T86czz/uHbHkqqYV8xYxeoBbYlWZVAuVu3UjrajFwOgldUAlK2oYm2Xz/48moree62kVaeqz6S/eEkP9jl3LtK6tLbdqui562rKSqDNcvyb3Vm6ZP3Wqy9+bTL33j6AtZXJ3/DiRa0KEVreDdx9BTMnt2T21FasrSxj1IjODDtscaHDyglF7h/FoElWBiS1B/YDTgKOS9PKJF0n6X1JT0h6RNIx6bbJkn4n6XXgWEmHSnpZ0uuS7k2Ph6TBkp6VNFbS45J6FaqMtr6h+89m/tzWTJrYsdCh5FRl39a0fn85ZUvXotXVtHljKS3mV7LgxN50vX0WW/3kPbr8cxYLj9+y0KHm1MdPtqNdz7V033FNoUNpVH36LmPnXRdwxQ3PcvnVLzBgh6bfAlKXbltWMnfmuorQvFkVdO9V2cAeVmhNsjIAHAU8FhEfAPMlDQa+DvQDdgK+Awyrtc/8iNgDeBK4EDgkfT0GOFtSBXA1cExEDAZuBi5pjMJYw1q1quIbJ07k9hsHFjqUnKvcqjWLv7IFPS+ZRM9LJ7GmX2soEx2emM+CE3sz/bodWXBib7r/bXqhQ82ZypVi7F+7stdZ8wsdSqMrKw86dFzD2ScfwM3X7cx5vxlDyXZCl6rIw6MINNXGuOOBK9Pnd6WvWwD3RkQ1MFvSM7X2uTv9fyhJheFFJe2TLYGXgYHALsATaXo5UG9HraSTgZMBWtM0B3Y1FVtutZyevVZwzT+fA6B7j1VcectznH3Sfixc0LrA0W2+ZQd1ZdlBXQHofOcsqrpW0OXO2Sz4Xm8AVgztRPfrS6cysGRqBUunV3D3kdsAsGx2C+756jYc8++ptOvx2e6EUjJ/bhteerYXID54rwsR0LHzGpaUWHfB/NkV9Oi9rtWne69K5s1qul1dnyqiZv1ca3KVAUldgYOAz0sKki/tAB7YwK7Law4BPBER6401kPR5YHxE1G5RqFNE3ADcANBRXUv016M4TPmoIyd86dBPX998/1Oc9f39S+ZugrLFa6nu1ILyeWto9+oSZv12Ozo8Np/W7y5n1c7taf3OMiq3LJ0vi24D1/CD0R9/+vq24f059v4ptOlaXcCoGsfLz23JrnvM4603etC77zJatKhmyaLS+D3ONGFcW/r0X0PPvquZP7uC4Uct4vLTtil0WNaAJlcZAI4B/hkRp9QkSHoWWAAcLelWoAcwHLijjv1fAa6VtF1ETJTUDugDTAB6SBoWES+n3QbbR8T4PJdno5x37WR2HbaMTl3XcvuY8fzzj1vy+F3dCh1WTp3769f5/B7z6dh5DbeOeJJ//X17Rj60daHDypstrphM2dIqKBfzf9Cb6nblzD9lK7reMhNVBdFSzD+5T6HD3GQjz9qSGa+2ZdXCcm7Zrz97nTmfnY5dUmfe5XPLufdrW7NmWRkqgzdv6cy3Hp1Cyw5Nr6Jw7kVj+Pygecnv8f2P86+bduCJ/27DWee/wbW3Pc3ayjKuuGQPkuuT0lJdJa69oA+X3vExZeUw8q6uTPmg6bfiAUXTrJ9riia26ELa/P+7iHgsI+0MYEeSv6rhwLT0+e8i4glJk4EhETEvzX8Q8Dug5nLrwoh4UNIg4CqgE0lF6S8RceOGYuqorrF32SG5KWAT0GLrrQodQqOZ+PsuhQ6hUf1hj/sKHUKjuv6wQzecqUSs/XhyoUNoNKPjKZbEgpzXstp16xu7fPFnuT4sr97+87ERMSTnB94ITa5lICIOrCPtKkjuMoiIZZK6Aa8Cb6fb+9XK/zSwZx3HGQcckPuozcysqRMeM9BUPCypM8mgwIsjYnaB4zEzs1LSxFrTs1VSlYGIGF7oGMzMzJqakqoMmJmZ5VOpdhM01UmHzMzMLEfcMmBmZpaNIpoxMNfcMmBmZtbMuWXAzMwsS2p6819lxZUBMzOzbLmbwMzMzEqRWwbMzMyy5FsLzczMrCAklUt6Q9LD6ev+kkZLmijpbkmbtfylKwNmZmbZCJLpiHP9yM6ZwHsZr38H/DkitgMWAidtTtFcGTAzM8uSIvePDZ5T2gr4EvD39LWAg4CaZUZvBb66OeVyZcDMzKy4/QU4F6i5sbEbsCgi1qavpwN9NucErgyYmZllK/LwgO6SxmQ8Tq45naQvA3MiYmw+i+W7CczMzAprXkQMqWfbvsBXJH0RaA10BK4EOktqkbYObAXM2JwA3DJgZmaWBdH4YwYi4vyI2Coi+gHHAU9HxAnAM8AxabYTgRGbUzZXBszMzLKRjzsJsr+boLb/Bc6WNJFkDMFNm1M0dxOYmZk1ARExChiVPv8Y2CtXx3ZlwMzMLEuegdDMzMxKklsGzMzMsuWWATMzMytFbhkwMzPLUqmOGXBlIFc2/faQJmft1OmFDqHRtBzTt9AhNKqfVX6z0CE0qt6DNmuhtyal/ew5hQ6h0Whlnhq9A6guzc96dxOYmZk1c24ZMDMzy1ZpNgy4ZcDMzKy5c8uAmZlZljyA0MzMrLkr0cHi7iYwMzNr5twyYGZmlqVS7SZwy4CZmVkz55YBMzOzbAQle2uhKwNmZmZZECAPIDQzM7NS5JYBMzOzbFUXOoD8cMuAmZlZM+eWATMzsyx5zICZmZmVJLcMmJmZZcO3FpqZmTV34bUJzMzMrDS5ZcDMzCxLXpvAzMzMSlLRVAYkXSBpvKS3JI2TtLeksyS1bYRzD5f0cL7PY2ZmTVxE7h9FoCi6CSQNA74M7BERqyV1B1oCdwO3Ayvq2Kc8IqoaN1IzM2u2AuQZCPOqFzAvIlYDRMQ84BigN/CMpGcAJC2T9CdJbwLDJJ0t6Z30cVaap5+k9yTdmLY0jJTUJt22Z0bLwx8kvZMZhKQySR9K6pHxemLNazMzs1JULJWBkUBfSR9Iuk7SFyLiKmAmcGBEHJjmaweMjojdgJXA94G9gaHAjyTtnuYbAFwbETsDi4Cj0/R/AKdExCDgM60KEVFN0hJxQpp0CPBmRMzNaWnNzKxpcjdB/kTEMkmDgf2BA4G7JZ1XR9Yq4N/p8/2AByJiOYCk+9P9HwQmRcS4NN9YoJ+kzkCHiHg5Tb+DpGuitpuBEcBfgB+QVCCKypDhSzj14pmUlwWP3tmVe67pWeiQ8ubsP01l70OWsGheC045eIdCh5MXHVqt5qL/GcWAbguIgP974kD27zeVA7edRHWIBSvbcOHjBzF3ebtCh7rZOj02h46j5kPAkgO7sfjwLT7d1vmRT+h+x0w+/uvnqe5QFB9NOVGmam489wHmLW7H//7tcL5+wDsce+A7bNVjCV/+3++yeHnrQoeYF7eMep0Vy8uorhJVVeLMr+1a6JCsAUXzF5f2/48CRkl6GzixjmyrshwnsDrjeRXQZiPimCbpE0kHAXuxrpWgKJSVBaddOoPzj/sc82ZVcPUjH/LK452Y+mFpfqCMvKcrD/6jO7+4cmqhQ8mb/x3+Ai9O7svPHz6MFmVVtKlYy8T5Xbnm5b0A+Nagtzh16BgufuoLBY5087SctpKOo+Yz/dcDiRai9+8nsmJQJyq3bEWL+Wto+/ZSKrtVFDrMnDv2wHeY8kln2rWuBODtj7fkpXe24aozHypwZPl33rd3ZsnCEntPi+NCPueKoptA0kBJAzKSBgFTgKVAh3p2ex74qqS2ktoBX0vT6hQRi4ClkvZOk45rIKS/k3QX3FtsgxQH7r6CmZNbMntqK9ZWljFqRGeGHba40GHlzTuj27N0UXmhw8ib9i1XM7jPLO5/Z0cA1laXs3R1K5avaflpnjYVa4ulJXGzVMxcxept2xKtyqBcrNyhA+3GLAKg++0zmHdcb5AKG2SO9ei8jGE7T+Xhl9a1an04vTuzF9T3sWbFThE5fxSDYmkZaA9cnTblrwUmAicDxwOPSZqZMW4AgIh4XdItwKtp0t8j4g1J/Ro4z0nAjZKqgWeB+r5FHyTpHii6LoJuW1Yyd+a6L4p5syrYYY/P3GxhTUSfTktZuLINvz30GbbvMZ93P+nO70btx8q1FZy+z2i+stMElq5uyUn3HVXoUDfbmq3a0O3eWZQtXUu0LKPdm4tZ1b8t7cYuYm2XCtZsk/e7iBvdGUe/zHX/2Zu2aatAcxIBl9zyHhHw6J09efTu0u3OLAVFURmIiLHAPnVsujp91ORrX2u/K4AraqVNBnbJeP3HjM3jI2JXgHRMwpg0zyiSLooau5EMHHx/owtjthHKy6rZcYu5XPbMfrw9uyf/O/wFTtrzDa55eS+ufmlvrn5pb07a83WOH/Q216XdBk1VZZ/WLPxyT3r/biLRqozV27RFa4MuD37CzP/drtDh5dw+u0xh4dI2fDCtB4MGzCx0OI3unON2Zv4nrejUtZJLb32XaR+34Z3XOhY6rM1XJFfyuVYU3QSN6EvpbYXvkAw2/G3tDGkl4d/A+Q0dSNLJksZIGlO53hCF/Jo/u4Ievdd8+rp7r0rmzSqxPrlm5JOl7flkaXvenp1cNT3x4efYcYv1b1757/sDOGS7jwsRXs4tHd6N6b/dgRm/3J6qtuWs6dOaFnPX0Pf/vc82Z42nxYI19L3wfcoXNf0r6c9/7hP2/fwU7vn1HVz0/afYY/sZ/PK7Txc6rEYz/5NWACxeUMFLT3Rl4K7LChyRNaQoWgYaS0TcTTKRUUN5Lgcuz+JYNwA3AHRU10arKk4Y15Y+/dfQs+9q5s+uYPhRi7j8tG0a6/SWY/NXtGX2snb067KQyQu7sHffGXy0oAtbd17E1EWdATho28lMWtilsIHmSPniSqo6VdBi3hraj1nE9Iu2X++Ogm3OGs+0iweWxN0E1z+4F9c/mLTmDBowk+MPfouLbzuowFE1jlZtqigrg5XLy2nVpoo99lvEHddsVeiwNl8AJTrpUNP/i2tmqqvEtRf04dI7PqasHEbe1ZUpH5TmnQQA5107mV2HLaNT17XcPmY8//zjljx+V7dCh5VTlz2zP5cf8RQVZVVMX9yRX448iIv+5xn6dVlEhJi5tAMXP3lAocPMiS2vnET5siqiBcw9sS/V7ZrfR9DRX3iHbx3yJl07ruCW/3cfr4zvy+/uaNp3itTWpXslv7xuAgDlLYJRD3Zn7HOlUaEtVYoS7f9oTB3VNfbWwYUOo/GU2Ijvhsw8Z1ihQ2hUK3ZbWegQGlXv+1tuOFOJaP/Y24UOodG8svK/LK6al/MPqk7tesfQnU7J9WEZOeaisRExJOcH3gjNr1oOSGoREWsLHYeZmTUxJXoB3aQHEEr6j6Sx6RoEJ6dpJ6XTGr+ark9wTZp+i6S/SRoN/F7StpIeS/d/XtIOab4ekv4t6bX0sW8Bi2hmZpZ3Tb1l4AcRsSBdiOg1Sf8FfgnsQTJh0dPAmxn5twL2iYgqSU8Bp0bEh+lERNcBBwFXAn+OiBckbQ08DuzYiGUyM7NiVaItA029MnCGpK+lz/sC3wGejYgFAJLuBbbPyH9vWhFoTzKvwb1a1//dKv3/EGCnjPSOktpHhO+LMTOzktRkKwOShpN8cQ+LiBWSRgHv0/BV/PL0/zJgUbp6YW1lwNCIWJWzYM3MrOkr4VsLm/KYgU7AwrQisAPJMsbtgC9I6iKpBeuWLl5PRCwBJkk6FkCJ3dLNI4HTa/JKGpTHMpiZWRNSqmsTNOXKwGNAC0nvkUwS9AowA7iUZL2CF4HJ1L/+wAnASZLeBMYDNZO/nwEMkfSWpHeBU/NWAjMzsyLQZLsJImI1cETtdEljIuKGtGXgAeA/af7v1dp/EnB4HcedB3wzDyGbmVlTV4AreUl9gduAniSdFTdExJWSupLMqtuP5OL3GxGxcFPO0ZRbBupzkaRxwDvAJNLKgJmZWRO1Fvh5ROxE0iV+mqSdgPOApyJiAPBU+nqTNNmWgfpExDmFjsHMzEpRFKRlICJmAbPS50vT7vE+JN3bw9Nst5Ksvvu/m3KOkqsMmJmZ5UWQr8pAd0ljMl7fkC6G9xmS+gG7A6OBnmlFAWA2STfCJnFlwMzMrLDmZbM2QTpHzr+BsyJiScZ8OERESNrkmoorA2ZmZtkq0DwDkipIKgL/ioj70+RPJPWKiFmSegFzNvX4pTiA0MzMrGQoaQK4CXgvIq7I2PQgcGL6/ERgxKaewy0DZmZmWSrQJEH7kky3/3Z6txzA/yOZY+ceSScBU4BvbOoJXBkwMzMrYhHxAqB6Nh+ci3O4MmBmZpatIpk+ONdcGTAzM8tGANWlWRnwAEIzM7Nmzi0DZmZmWSnMDISNwS0DZmZmzZxbBszMzLJVoi0DrgyYmZllq0QrA+4mMDMza+bcMmBmZpYN31poZmZmpcotAzmwlIXznoz7pjTyabsD8xr5nInCVIwLU97f39vop0wV7v1tfAUr6+RCnNTvbWPYJj+HDYgCLVuYZ64M5EBE9Gjsc0oak83616XC5S1dzams0LzKW5Jl9QBCMzMzK0VuGTAzM8uGBxBaEbqh0AE0Mpe3dDWnskLzKm9zKmuTpijR/g8zM7Nc6tSyZ+zT87icH/ex6VeNLfTYCrcMmJmZNXOuDDQiSRdIGi/pLUnjJO0t6SxJbRvh3MMlPZzv86TnqkrLN17Sm5J+Lqks3TZE0lWNEUe+1PU+FjCWZRuZf7ikfTYif7Mp66aQ9FVJIWmHfJ6nMTWXz6lNFpH7RxHwAMJGImkY8GVgj4hYLak70BK4G7gdWFHHPuURUdW4kebEyogYBCBpC+AOoCPwq4gYA4zJ14kltYiItXk8fn3vY1MxHFgGvLShjM2prJvheOCF9P9fZbtTsf5tN7PPqU1QPF/eueaWgcbTC5gXEasBImIecAzQG3hG0jOQXP1I+pOkN4Fhks6W9E76OCvN00/Se5JuTGvwIyW1SbftmVGj/4OkdzKDkFQm6UNJPTJeT6x5nWsRMQc4GfipEp/W/CV9IY1znKQ3JHWQ1F7SU5Jel/S2pKMyYv+lpAmSXpB0p6Rz0vRRkv4iaQxwpqTBkp6VNFbS45J6pfm2lfRYmv78Jl7NfeZ9jIiZkg5Oy/C2pJsltUrPOVnSZWkZx0jaI43pI0mnZpTtF5JeS9+7X2eknZE+/7Okp9PnB0n6V8a+lyhpgXlFUs807UhJo9OYnpTUU1I/4FTgZ2k8+7usm05Se2A/4CTguDStTNJ1kt6X9ISkRyQdk/Hz+Z2k14FjJR0q6eX0d/3e9HjU9/vbSJrl55S5MtCYRgJ9JX2Qflh8ISKuAmYCB0bEgWm+dsDoiNgNWAl8H9gbGAr8SNLuab4BwLURsTOwCDg6Tf8HcEp6Zf6Z2npEVJPU8E9Ikw4B3oyIuTkt7frn/BgoB7aotekc4LQ01v1JyrsK+FpE7AEcCPxJiT1JyrgbcARQe7BNy3QAzlXA1cAxETEYuBm4JM1zA3B6mn4OcN0mFOcz76Ok1sAtwDcj4vMkLW4/zthnalrG59N8x5C8nzVfhIeSvJ97AYOAwZIOSPPXfIkNAdpLqkjTnkvT2wGvpL8vzwE/StNfAIZGxO7AXcC5ETEZ+Bvw54gYFBHPu6yb5SjgsYj4AJgvaTDwdaAfsBPwHWBYrX3mp7/bTwIXAoekr8cAZ6dlru/3tzE028+prARQXZ37RxFwZaCRRMQyYDDJVfJc4G5J36sjaxXw7/T5fsADEbE83f9+1n1gToqIcenzsUA/SZ2BDhHxcpp+Rz3h3Ax8N33+A5I/zEJ4EbgivSLsnDbvC7hU0lskH5h9gJ7AvsCIiFgVEUuBh2od6+70/4HALsATksaRfOBulV517QPcm6ZfT3IVtFHqeh+BU0jejw/SbLcCB2Ts9mD6/9skH6BL0w+11el7dmj6eAN4HdiB5EN0LMmXZUdgNfAyyRfl/iRfngBrgJo+1rEkX0QAWwGPS3ob+AWws8uac8eTVD5I/z+e5G/23oiojojZwDO19qn5PR1KUmF4Mf19PJFkCt06f3/zWIb1+HOq+fKYgUaU9quNAkalH1wn1pFtVZb9b6sznlcBbTYijmmSPpF0EMkV2gkb2mdzSPocSYxzgB0z4rhc0n+BL5J8KB5G8iHZAxgcEZWSJgOtszjN8prTAeMjYr0rsvRLZlHNWIbNUcf7eNoGdql5r6pZ/32rJvkbFHBZRFxfe0dJk4DvkfR7v0XSWrId8F6apTLW3R9cxbq/6auBKyLiQUnDgYuyKlwtzamsG0NSV+Ag4POSgqTlK4AHNrBr5u/pExFxfK3jfp46fn8bU3P9nMqaxwzY5pA0UNKAjKRBwBRgKdChnt2eB74qqa2kdsDXWHeV9BkRsQhYqnUjvhu6IfbvJM1w9+Zz8E/ax/c34JqMD/KabdtGxNsR8TvgNZKrxE7AnLQicCDrFhx5EThSUuv0Kv/L9ZxyAtBDyUAoJFVI2jkilgCTJB2bpkvSbptQnrrex49Irni2S9O+Azy7EYd9HPhBRp9xHyUDLyF5v88haRZ/nqQf/I3aP8s6dAJmpM8zP8wb+n1bT3Mq6yY4BvhnRGwTEf0ioi8wCVgAHJ32cfckGcRYl1eAfWt+jpLaSdqeen5/81SGz2iun1MbpUTvJnBloPG0B26V9G7aBL4TyRXMDcBjSgfmZIqI10n6XV8FRgN/j4g3NnCek4Ab0ybGdsDievI9mMaUj6a3NunAoPEkTf0jSfuMazlLyYCjt4BK4FHgX8CQ9Irku8D7ABHxWhrzW2m+t6mjbBGxhuSD+ndKBjeNI+kegOTK4qQ0fTxJn+/Gqut9PI+kz/TeNO5qkgpQViJiJElT6cvp/vex7oP3eZLujJcj4hOSMRXZ9H9flMYzlvVXjXsI+JqyG1TXnMq6sY7ns60A/wa2BKYD75J8ib1O3b+nc0laQe5Mf7YvAzts4Pe3MTSnzynL4BkIS4yk9mm/HZLOA3pFxJl15BtCMrgq56Os86WmbErud34OODn9IDIrGhm/p91IviD3TccPWKqpfk51qugR+3Q+esMZN9Jj864v+AyEHjNQer4k6XyS93YKydXHetI/vh9TLH1w2btB0k4kYwhudUXAitTD6SC5lsDFrgjUqZQ/p5oktwyYmZlloVOLHjGs89dyftzH59/olgEzM7Mmo0SXMHZloAAkXUQyTWpH4LmIeLKwEVmu+L01s6bIlYECioj/y9exJYmkG6g4prdqZvzebhzleU0JK5ySe29LtGvdtxY2EiUrgX0g6QWSWcaQdIvWzVt+ec3tPJL+mKZ9Zs71NL2HknnPx0v6u6QpkrormQt8gqTbgHdIphX9zDzw6TG+LenV9Lar6yWVN/oPpUT4vV3vZ/EfJXPqj5d0cpp2UvrzeVXJPPXXZPyM/iZpNPB71bN2RPoz+Xda1tck7dtY5bF1/N6WNrcMNAIlc5YfRzKBRwuSe4/HZmzvRjJRxw4REelIZFg353pI+iFwLvBzktXRno6IyyQdTnLPbo0BwIkR8YrWnwdewINK5oGfC3yT5JanSknXkYzYvS0vP4AS5vf2M34QEQuULEjzmpIZJn8J7EEycc3TwJsZ+bcC9omIKklPAadGxIdKJqS5jmSWvytJbi97QdLWJBMX7Yg1Nr+3EUWzlkCuuTLQOPYnmbt7BYCkB2ttX0wywcpNSlb0q5l/fSuSucF7kdymNClN34/kC4aIeEzSwoxjTYmIV9LnmfPAQzJ5xwBgV5L5x1+TBMkUoXNyUM7myO/t+s6QVDPcui/pDIURsQBA0r3A9hn5702/LDLXjqjZ1ir9/xBgp4z0jsq4T90ajd/bEubKQBGIiLWS9gIOJpl97KckteZNmXN9ecbzOueBl3Q6yX36529+9NaQ5vTepuU4BBgWESskjSKZQbKhK72aMpVR/9oRZSStKKtyFqxtFL+3GTxmwDbDcyRzd7eR1AE4MnNjWnPuFBGPAD8jWaYX6p9z/UXgG+m+hwJd6jlvffPAPwUckz5HUldJ29RzDGuY39t1OgEL0y+LHUgWnWoHfEFSF0ktWLeE7Xo2sHbESOD0mrySBuWxDFY3v7epqK7O+aMYuDLQCNKZ8u4m6U97lGRRnkwdSGYte4ukL/nsNP0i6p5z/dfAoZLeAY4FZpP02dU+b53zwEfEuyRLo45Mz/kEm7Ccr/m9reUxoIWk94DLSRbjmQFcSjIt74vAZOqfh76+tSPOIFmv4i1J75IsYGSNy+9tifMMhE2QpFZAVdoEPQz4az1NcNbElOJ7q3Vz9bcgWdzn5ojY0FK/1gQ0t/e2U3m3GNr6Szk/7sgV//QMhLZJtgbukVQGrAF+VOB4LHdK8b29SNIhJGtKjAT+U9hwLIf83pYIVwaaoIj4ENi90HFY7pXiexsR5xQ6BsuPZvfeBp6O2MzMrNkrnYk/1+MBhGZmZs2cWwbMzMyyEECUaDeBWwbMCkRSlZL1A96RdK+ktptxrMy1EP4uaacG8g6XtM8mnGOypO7ZptfKs1Ezykm6SFLz6o82KyBXBswKZ2VEDIqIXUjuHFjvHuv0dq2NFhE/TOcbqM9wkulhzWxjRCRjBnL9KAKuDJgVh+eB7dKr9ufTNQ7elVQu6Q9atzrhKfDpLG7XKFnJ8Elgi5oDSRolaUj6/HBJr0t6U9JTkvqRVDp+lrZK7K96Vo6T1E3SSKUrKJJMgdwg1bGyXca2P6fpT0nqkabVuZqdWbGK6sj5Ixvp3/IESRMlnZfrcrkyYFZgaQvAEcDbadIewJkRsT3JqoWLI2JPYE/gR5L6kyxmNBDYCfgudVzpp1+4NwJHR8RuwLERMRn4G8lKcYMi4nnWrRy3J8mUsn9PD/Er4IWI2JlkQpmtsyjODyJiMDCEZGGbbml6O2BMeqxn02MD3ACcnu5zDslqdmaWQcky5NeSfE7sBBzfUFfgpvAAQrPCaSNpXPr8eeAmki/1VyOiZhXDQ4Fda8YDkMwRPwA4ALgzIqqAmZKeruP4Q4Hnao5Vs7pcHepcOS49x9fTff+r9VdQrE/tle0GAPOBapJpmwFuB+5Xw6vZmRWnwjTr7wVMjIiPASTdRTKlc0PdgRvFlQGzwllZe6rh9Eux9uqEp0fE47XyfTGHcdS5clzGF3RWVPfKdq3ryR40vJqdma3TB5iW8Xo6sHcuT+DKgFlxexz4saSnI6JS0vYkC8Q8B5wi6VaS8QIHkixclOkV4DpJ/SNikqSuaevAUqBjRr6aleP+AMnKcRExLj3Ht4DfSjqC+ldQrFHXynY1ykiWcL4rPeYLEbFE0iRJx0bEvUpqH7tGxJsb8wMyayxLWfj4k3Ffg3fObKLWksZkvL4hIm7Iw3nq5cqAWXH7O9APeD39spwLfJWkD/8gkmbCqcDLtXeMiLnpIL7707UO5gD/AzwE3CfpKJJKwBnAtUpWOWxBUgk4lWQFxTsljQdeSs/TkMeAU9OV7SaQVEZqLAf2knRhGsc30/QTgL+m6RUklQVXBqwoRcThBTr1DJJutxpbsW4J9JzwqoVmZmZFLB1k/AFwMEkl4DXgWxExPlfncMuAmZlZEUuXNP8pSbdhOclS0TmrCIBbBszMzJo9zzNgZmbWzLkyYGZm1sy5MmBmZtbMuTJgZmbWzLkyYGZm1sy5MmBmZtbMuTJgZmbWzLkyYGZm1sz9f3wlJvieyeW3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = get_performance(logit_model, \"Q21.2\")\n",
    "_ = predictions_from_average(\"Q21.2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# het"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'logit_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-69b6c5dd640b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhet_vec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogit_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mperson_het_vector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhet_vec\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhet_vec\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"responses.xlsx\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Q1.1\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'logit_model' is not defined"
     ]
    }
   ],
   "source": [
    "het_vec = logit_model.person_het_vector.clone()\n",
    "x = het_vec[:,1].detach().numpy()\n",
    "y = het_vec[:,1].detach().numpy()\n",
    "data = pd.read_excel(\"responses.xlsx\")\n",
    "sns.scatterplot(x,y, hue=data[\"Q1.1\"])\n",
    "plt.show()\n",
    "\n",
    "group1 = x[data[\"Q1.1\"] == \"Male\"]\n",
    "group2 = x[data[\"Q1.1\"] == \"Female\"]\n",
    "\n",
    "sns.kdeplot(group1, label=\"Male\")\n",
    "sns.kdeplot(group2, label=\"Female\")\n",
    "plt.xlabel(\"Latent Parameter for Respondent\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# from https://www.geeksforgeeks.org/how-to-perform-an-f-test-in-python/\n",
    "import scipy.stats as stats\n",
    "  \n",
    "# Create data\n",
    "\n",
    "  \n",
    "# # converting the list to array\n",
    "# x = np.array(group1)\n",
    "# y = np.array(group2)\n",
    "  \n",
    "# calculate variance of each group\n",
    "# print(np.var(group1), np.var(group2))\n",
    "  \n",
    "def f_test(group1, group2):\n",
    "    f = np.var(group1, ddof=1)/np.var(group2, ddof=1)\n",
    "    nun = x.size-1\n",
    "    dun = y.size-1\n",
    "    p_value = 1-scipy.stats.f.cdf(f, nun, dun)\n",
    "    return f, p_value\n",
    "print(f_test(group1, group2))\n",
    "print(stats.ttest_ind(group1, group2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neuron Examination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj/english-adjectives.txt') as f:\n",
    "    adjatives = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list(logit_model.sentence_to_neurons(\"slow\").detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj = pd.DataFrame.from_dict({\"adj\": adjatives})\n",
    "for nur_num, nur_act in enumerate(a):\n",
    "    adj[nur_num] = np.NAN\n",
    "    \n",
    "adj = adj.set_index(\"adj\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj.loc[\"abandoned\"] = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.164455890655518"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj.loc[\"abandoned\", 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list(logit_model.sentence_to_neurons(\"word\").detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1347/1347 [01:39<00:00, 13.50it/s]\n"
     ]
    }
   ],
   "source": [
    "a = list(logit_model.sentence_to_neurons(\"slow\").detach().numpy())[0]\n",
    "\n",
    "for word in tqdm(adjatives):\n",
    "    neurons = list(logit_model.sentence_to_neurons(word).detach().numpy())[0]\n",
    "    \n",
    "    for index, n_weight in enumerate(neurons):\n",
    "        adj.loc[word, index] = n_weight\n",
    "    \n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adj\n",
       "elementary   -3.766869\n",
       "arctic       -3.261044\n",
       "revolving    -3.247416\n",
       "electric     -3.204469\n",
       "general      -3.152063\n",
       "                ...   \n",
       "expensive     5.032906\n",
       "fat           5.189440\n",
       "shoddy        5.236141\n",
       "wobbly        5.317357\n",
       "lanky         5.441111\n",
       "Name: 2, Length: 1347, dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj[2].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# person activations\n",
    "personal_activations = logit_model.get_specific_activations(x_all, one_hot_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=40)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit neurons to questions\n",
    "interpretter = PCA(n_components=40)\n",
    "interpretter.fit(x_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_dict = dict(list(enumerate(adjatives)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_embeds, _ = proces_sent(adj_dict, int(NUM_PEOPLE/3), get_strange_quest=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_embeds.shape\n",
    "NUM_ROWS = int(2679030/3)\n",
    "adj_embed_short = adj_embeds[:NUM_ROWS]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "893010.0"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2679030/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "#usual function for this broke, make a new one that doesnt run out of memory\n",
    "to_be_concat = []\n",
    "np_height = len(adj_dict)\n",
    "for i in range(int(NUM_PEOPLE/3)):\n",
    "    temp_array = torch.zeros((np_height, NUM_PEOPLE))\n",
    "    temp_array[:, i] = 1\n",
    "    to_be_concat.append(temp_array)\n",
    "    \n",
    "one_hot_adj = torch.concat(to_be_concat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "activations = logit_model.get_specific_activations(adj_embed_short, one_hot_adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "#re orent activations into orthogonal dimensions\n",
    "activation_interpreter = PCA(n_components=35)\n",
    "activation_interpreter.fit(activations.detach().numpy())\n",
    "re_oriented_act = activation_interpreter.transform(activations.detach().numpy())\n",
    "\n",
    "# average each word:\n",
    "to_be_concat = []\n",
    "for index, word in adj_dict.items():\n",
    "    import_indexes = list(range(index, len(activations), len(adj_dict)))\n",
    "    \n",
    "    to_be_concat.append(np.sum(re_oriented_act[import_indexes], axis=0).reshape(1, -1))\n",
    "average_for_word = np.concatenate(to_be_concat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# look at the words that maximise each activation\n",
    "to_be_df = {\"word\": adj_dict.values()}\n",
    "for i in range(35):\n",
    "    to_be_df[i] = average_for_word[:, i]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_act_2 = pd.DataFrame.from_dict(to_be_df).set_index(\"word\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "word\n",
       "urban           -215.681290\n",
       "biodegradable   -173.883362\n",
       "suburban        -148.434174\n",
       "sociable        -148.187729\n",
       "affectionate    -145.814148\n",
       "                    ...    \n",
       "untrue           113.414398\n",
       "delayed          116.688362\n",
       "lopsided         118.120865\n",
       "hasty            126.584503\n",
       "impish           128.545319\n",
       "Name: 0, Length: 1347, dtype: float32"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index=0\n",
    "adj_act_2[index].sort_values()\n",
    "# print(adj_act_2[index].sort_values().head(10))\n",
    "# print(adj_act_2[index].sort_values().tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit_mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(adj_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_for_word.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupe questions \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "50/2.5*3.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_array.shape[0] * NUM_PEOPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_embeds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 75+72+65+50\n",
    "a/4\n",
    "3600*4/20\n",
    "8*4/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2679030 / 39861"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpretter.transform(x_all)\n",
    "\n",
    "questions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO\n",
    "# auto confusion matrix (for scenario)\n",
    "\n",
    "# ordered logit model\n",
    "\n",
    "# extract actual values (language model backwards)\n",
    "\n",
    "# kick out covariances with people socieal data\n",
    "#     - IE men care about speed\n",
    "#     - rich people care about status\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    node = dict(\n",
    "      pad = 15,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"black\", width = 0.5),\n",
    "      label = [\"Model Predicts Agree\", \"Model Predicts Disagree\", \"Survey Agrees\", \"Survey Disagrees\"],\n",
    "      color = \"blue\"\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = [0, 0, 1, 1], # indices correspond to labels, eg A1, A2, A1, B1, ...\n",
    "      target = [2, 3, 2, 3],\n",
    "      value = [8, 4, 2, 8]\n",
    "  ))])\n",
    "\n",
    "fig.update_layout(title_text=\"Basic Sankey Diagram\", font_size=10)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_quest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "_, working_sample = remove_quest(\"Q22.4\", y_all)\n",
    "_, broken_sample = remove_quest(removed_quest, y_all)\n",
    "\n",
    "working_sample = working_sample[:100]\n",
    "broken_sample = broken_sample[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_sample_people = 100\n",
    "sent_to_model = 'I would consider buying an automated vehicle.'\n",
    "sent_to_model = 'I trust automated vehicles to operate safely.'\n",
    "sent_to_model = 'I would consider buying a vehicle.'# (0.7697223874366013)\n",
    "sent_to_model = 'I trust buying an automated vehicle.' #(-0.705674437480165)\n",
    "sent_to_model = 'never gonna give you up'# -0.6907486661293478\n",
    "sent_to_model ='I trust automated vehicles to operate safely.'\n",
    "new_pred = [float(logit_model.get_individ_utility(sent_to_model, ID)[0,0]) for ID in range(num_sample_people)]\n",
    "\n",
    "new_pred = np.array(new_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "\n",
    "print(scipy.stats.pearsonr(working_sample, new_pred))\n",
    "print(scipy.stats.pearsonr(broken_sample, new_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$$ $$\n",
    "$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Out of bound Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 random guys on the internet is better then 1\n",
    "\n",
    "# is it overfitting\n",
    "def get_n_params(model):\n",
    "    pp=0\n",
    "    for p in list(model.parameters()):\n",
    "        nn=1\n",
    "        for s in list(p.size()):\n",
    "            nn = nn*s\n",
    "        pp += nn\n",
    "    return pp\n",
    "\n",
    "get_n_params(logit_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_parameters = filter(lambda p: p.requires_grad, logit_model.parameters())\n",
    "sum([np.prod(p.size()) for p in model_parameters])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(removed_quest)\n",
    "_, reomved_quest_results = remove_quest(removed_quest, y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = logit_model.poll_question(questions['Q16.2']).detach().numpy()\n",
    "x = reomved_quest_results\n",
    "\n",
    "import scipy\n",
    "\n",
    "print(scipy.stats.pearsonr(x, y))\n",
    "\n",
    "def jitter(values):\n",
    "    return values + np.random.normal(0,0.05,values.shape)\n",
    "\n",
    "sns.scatterplot(x=jitter(x), y=jitter(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dont redo PCA - fix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unkown_sent = questions[removed_quest]\n",
    "print(unkown_sent)\n",
    "logit_model.get_individ_utility(unkown_sent, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "#new_pred = [logit_model.get_individ_utility(unkown_sent, ID) for ID in tqdm(range(NUM_PEOPLE))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logit_model(x_test, one_hot_people_test)\n",
    "y_pred = y_pred.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_test)\n",
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(sum(y_test))\n",
    "print(sum(y_pred >= 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jitter(values):\n",
    "    return values + np.random.normal(0,0.05,values.shape)\n",
    "sns.scatterplot(x=jitter(y_test), y=jitter(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_test) - sum(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import random \n",
    "\n",
    "# rand_list = [0]*591 + [1]*336\n",
    "# random.shuffle(rand_list)\n",
    "# rand_list = np.array(rand_list)\n",
    "\n",
    "# pool_1 = y_pred[rand_list==1]\n",
    "# pool_2 = y_pred[rand_list==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool_1 = y_pred[y_test==1]\n",
    "pool_2 = y_pred[y_test==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "t_value,p_value=stats.ttest_ind(pool_1, pool_2)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "\n",
    "scipy.stats.pearsonr(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum(pool_1)/len(pool_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum(pool_2)/len(pool_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.nn.Linear(1,6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a(torch.FloatTensor([0.5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1f0f5fed730>"
      ]
     },
     "execution_count": 32,
     "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": [
    "\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x_data = [0.4, 0.6, 0.9, 1.1, 1.4, 1.7, 1.9, 4.6]\n",
    "y_data = [0.4, 0.5, 0.54, 0.45, 0.6, 0.62, 0.68, 1.04]\n",
    "\n",
    "sns.scatterplot(x=x_data, y=y_data)\n",
    "x=np.linspace(0, 5)\n",
    "y=0.19*x+0.3\n",
    "plt.plot(x, y, label=\"Linear Model\")\n",
    "\n",
    "x=np.linspace(0, 5)\n",
    "y=0.19*x+0.3-0.001*x*x*x*x\n",
    "plt.plot(x, y, label=\"Nonlinear Model \")\n",
    "\n",
    "plt.legend()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}