A_result_2_sparse_auto.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 = 35\n",
    "scen = 0\n",
    "embed_dim = num_pca\n",
    "removed_quest = ['Q20.2', 'Q21.2', 'Q22.2']\n",
    "\n",
    "dim_of_variability = 1\n",
    "pca = decomposition.PCA(n_components=embed_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "compress_method = \"auto_encoder\" \n",
    "# none\n",
    "# PCA\n",
    "# auto_encoder\n",
    "NN_PARAM = [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": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim_of_variability = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit_model = Logit(input_size = x_train.shape[1], dim_var=dim_of_variability)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(logit_model.parameters(), lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(2.4495)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.norm(1-torch.FloatTensor([[1, 0, 0],\n",
    "                              [0, 1, 0],\n",
    "                              [0, 0, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "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 # lamb * torch.norm(1-w.weight,p='fro')**2\n",
    "    \n",
    "    return loss_criteria\n",
    "    \n",
    "#criterion = torch.nn.BCELoss()\n",
    "criterion = sparse_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lamb = 0.1\n",
    "# lamb = 0.08\n",
    "lamb = 0.07#*10/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss before training 1.5453916788101196\n"
     ]
    }
   ],
   "source": [
    "logit_model.eval()\n",
    "y_pred = logit_model(x_train, one_hot_people)\n",
    "before_train = criterion(y_pred.squeeze(), y_train, logit_model, lamb)\n",
    "print('Test loss before training' , before_train.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "perform = []\n",
    "test_perform = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: train loss: 0.3921092748641968\n",
      "Epoch 0: test_loss loss: 0.4985876679420471\n",
      "------------------------------------------------------\n",
      "Epoch 100: train loss: 0.3973306715488434\n",
      "Epoch 100: test_loss loss: 0.515815019607544\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-218-d91cedd5ebe6>\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-218-d91cedd5ebe6>\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[0mlogit_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlamb\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     84\u001b[0m             \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[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[1;32m---> 86\u001b[1;33m             \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[0m\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcompress_method\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"cosine_words\"\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;36mauto_encoder\u001b[1;34m(self, x_data)\u001b[0m\n\u001b[0;32m    120\u001b[0m         \u001b[1;31m#simple forward pass NN to pass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoder\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m             \u001b[0mx_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayer\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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    123\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    124\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[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~\\anaconda3\\envs\\ml\\lib\\site-packages\\torch\\nn\\modules\\linear.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    102\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[0minput\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 103\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbias\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    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mstr\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\\functional.py\u001b[0m in \u001b[0;36mlinear\u001b[1;34m(input, weight, bias)\u001b[0m\n\u001b[0;32m   1846\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mhas_torch_function_variadic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\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   1847\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mhandle_torch_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlinear\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbias\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1848\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbias\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   1849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1850\u001b[0m \u001b[1;33m\u001b[0m\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 = 250\n",
    "    prev_test_loss = 100\n",
    "    \n",
    "    num_bad_steps = 300\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, logit_model, lamb)\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, logit_model, lamb)\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": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.7291644, 0.       , 0.       , 0.       , 0.       , 0.6614427,\n",
       "       0.       , 0.       , 0.       , 0.       ], dtype=float32)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(logit_model.sentence_to_neurons(sentences['Q17.2']).detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for lamb in np.random.rand(100)/10:\n",
    "#     logit_model = Logit(input_size = x_train.shape[1], dim_var=dim_of_variability)\n",
    "#     train_model(print_prog = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 221,
     "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, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Model Works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logit_model(x_test, one_hot_people_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.9730, 0.9239, 0.8271, 0.5076, 0.1933],\n",
       "        [0.9476, 0.8593, 0.7062, 0.3412, 0.1075],\n",
       "        [0.9092, 0.7717, 0.5709, 0.2228, 0.0625],\n",
       "        ...,\n",
       "        [0.9446, 0.8520, 0.6939, 0.3282, 0.1020],\n",
       "        [0.8877, 0.7275, 0.5125, 0.1847, 0.0500],\n",
       "        [0.7858, 0.5533, 0.3278, 0.0951, 0.0238]], grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "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": 227,
   "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": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from matplotlib.pyplot import figure\n",
    "import scipy\n",
    "\n",
    "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": 229,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 43/43 [00:34<00:00,  1.24it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAikAAAFxCAYAAAC2pjgZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAh+0lEQVR4nO3df5SWdZ3/8eebcRQURQPcXFHR0lZwhlF+SBqImoCmqLWGflUov0isKWrqZtpJ7OuaddxM1ERcO5oickJzSWs3UUlSSQYaCMEQC5OWTYQaA1EZ/Hz/mJv7DMiPAefm/gzzfJwzx/v6XJ/rut7XgLcvP5/rR6SUkCRJyk27chcgSZK0OYYUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZ2q3cBWyvLl26pO7du5e7DEmS1ALmzJnzVkqp6+bWtbqQ0r17d2pra8tdhiRJagER8fqW1jndI0mSsmRIkSRJWTKkSJKkLLW6a1I2Z926dSxbtox333233KVIJdO+fXu6detGZWVluUuRpJ1ilwgpy5YtY++996Z79+5ERLnLkVpcSomVK1eybNkyDj300HKXI0k7xS4x3fPuu+/SuXNnA4p2WRFB586dHS2U1KbsEiEFMKBol+ffcUltzS4TUsotIrjggguKyw0NDXTt2pXTTz99u/bTvXt33nrrrR3q0717d6qqqqiurmbw4MH87//+73Yde/z48Rx55JGcf/7527WdJEmlsEtck7Kp7tc+2aL7W3rL57bZZ6+99mLBggWsXbuWDh068NRTT3HggQe2aB3N8eyzz9KlSxeuu+46br75ZsaPH7/NbRoaGthtt9344Q9/yPTp0+nWrVuzjrVhO0mSSsGRlBZ02mmn8eSTjQFp8uTJnHfeecV1q1at4qyzzqK6upr+/fszf/58AFauXMngwYPp2bMno0aNIqVU3Oahhx6iX79+1NTU8JWvfIX169c3u5aBAweyZMkS1q9fzzXXXEPfvn2prq7mnnvuAWDGjBkMGDCAYcOG0aNHD8aMGcMf/vAHTj31VG677bYt1jtu3DguvPBCjj/+eC688ELGjRvHyJEjGTBgAIcccgiPPfYY//qv/0pVVRVDhw5l3bp1AHz729+mb9++HHXUUYwePbp4noMGDeLrX/86/fr144gjjmDmzJkArF+/nquvvpqjjjqK6upq7rjjDgDmzJnDCSecQO/evRkyZAjLly/foT8rSVL+DCkt6Nxzz+WRRx7h3XffZf78+Rx77LHFdTfccANHH3008+fP5+abb2bEiBEA3HjjjXzmM5/h5Zdf5uyzz+ZPf/oTAIsWLWLKlCk8//zz1NXVUVFRwaRJk5pdyxNPPEFVVRX33XcfnTp1Yvbs2cyePZt7772XP/7xjwDMnTuX22+/ncWLFzNhwgT+8R//kWeffZYrr7xyi/UCLFy4kOnTpzN58mQAXnvtNZ555hmmTZvGBRdcwIknnsjvfvc7OnToUAxtl156KbNnzy6ONj3xxBPF/TU0NPDSSy/xgx/8gBtvvBGAiRMnsnTpUurq6pg/fz7nn38+69at47LLLmPq1KnMmTOHiy66iOuvv35H/qgkSa2AY/UtqLq6mqVLlzJ58mROO+20jdb9+te/5tFHHwXgpJNOYuXKlbz99ts899xzPPbYYwB87nOfY7/99gPg6aefZs6cOfTt2xeAtWvXsv/++2+zhhNPPJGKigqqq6u56aabGDVqFPPnz2fq1KkA1NfX8+qrr7L77rvTr1+/Ld7OuqV6AYYNG0aHDh2KfU899VQqKyupqqpi/fr1DB06FICqqiqWLl0KNE5Dfe973+Odd95h1apV9OzZkzPOOAOAz3/+8wD07t272H/69OmMGTOmOJ30sY99jAULFrBgwQJOOeUUoHG05YADDtjm70SS1DoZUlrYsGHDuPrqq5kxYwYrV67c4f2klBg5ciTf+c53tmu7DdekNN3PHXfcwZAhQzbqN2PGDPbaa68dqm3T7fbYYw8A2rVrR2VlZfEulHbt2tHQ0MC7777LJZdcQm1tLQcddBDjxo3b6FbaDdtXVFTQ0NCwxeOmlOjZsycvvvjiDtUtSWpdnO5pYRdddBE33HADVVVVG7UPGDCgOF0zY8YMunTpwj777MPAgQN5+OGHAfjFL37BX//6VwBOPvlkpk6dyptvvgk0XtPy+utbfFHkFg0ZMoS77767eG3I4sWLWbNmzTa321K9O2JDIOnSpQurV68ujupszSmnnMI999xTDC2rVq3iU5/6FCtWrCiGlHXr1vHyyy/vUE2SpPw5ktLCunXrxtixYz/UPm7cOC666CKqq6vZc889eeCBB4DGa1XOO+88evbsyXHHHcfBBx8MQI8ePbjpppsYPHgwH3zwAZWVldx1110ccsgh21XPqFGjWLp0KccccwwpJbp27crjjz++ze22VO+O2Hfffbn44os56qij+PjHP16cwtpW3YsXL6a6uprKykouvvhiLr30UqZOncrYsWOpr6+noaGBK664gp49e+5wbdJH0dJ3Eqp5mnPHpXYN0fRuktagT58+qba2dqO2RYsWceSRR5apImnn8e96Xgwp5WFI2bVExJyUUp/NrXO6R5IkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4aUFhIRXHXVVcXlW2+9lXHjxm11m2nTpnHLLbd85GPff//9dO3alZqamuLPwoULt9j/5ptv3mj5uOOO+8g1QOND31544YXi8oQJE/jxj3/8kfe7dOlSOnToQE1NTfFliB988MF27eO8886jurqa22677SPXI0naOXbNh7mN69TC+6vfZpc99tiDxx57jG984xsbPZZ+a4YNG8awYcM+anUADB8+nDvvvLNZfW+++Wauu+664nLTYPFRzJgxg44dOxZDz5gxY1pkvwCf+MQnqKuro6GhgZNOOonHH3+8+M6frWloaOCtt95i9uzZLFmypNnHa2hoKL43SJJUHo6ktJDddtuN0aNHb/b/1H/2s59x7LHHcvTRR/PZz36Wv/zlL0DjCMill15KfX09hxxySHF0YM2aNRx00EGsW7eO1157jaFDh9K7d28GDBjAK6+80uyali9fzsCBA6mpqeGoo45i5syZXHvttaxdu5aamhrOP/98ADp27Ag0howTTjiBM888k8MOO4xrr72WSZMm0a9fP6qqqnjttde2eD5Lly5lwoQJ3HbbbdTU1DBz5kzGjRvHrbfeCkBdXR39+/enurqas88+u/j4/0GDBvH1r3+dfv36ccQRRzBz5sxt/p6PO+44lixZwooVK/jCF75A37596du3L88//zzQ+LTcCy+8kOOPP54LL7yQwYMH8+c//7lY19ZqueKKK+jTpw+33347gwYN4sorr6RPnz4ceeSRzJ49m89//vMcfvjhfPOb3yzWdNZZZ9G7d2969uzJxIkTi+0dO3bk+uuvp1evXvTv37/45/6Xv/yFs88+m169etGrV69iSHzooYfo168fNTU1fOUrX2H9+vXN/rOWpF2RIaUFffWrX2XSpEnU12888vKZz3yGWbNm8dvf/pZzzz2X733vexut79SpEzU1NfzqV78C4IknnmDIkCFUVlYyevRo7rjjDubMmcOtt97KJZdcstljT5kyZaPpnrVr1/Lwww8zZMgQ6urqmDdvHjU1Ndxyyy106NCBurq64rt5mpo3bx4TJkxg0aJFPPjggyxevJiXXnqJUaNGcccdd2zxfLp3786YMWO48sorqaurY8CAARvtd8SIEXz3u99l/vz5VFVVceONNxbXNTQ08NJLL/GDH/xgo/bNeeedd3j66aepqqri8ssv58orr2T27Nk8+uijjBo1qthv4cKFTJ8+ncmTJzNt2rTiSMyAAQO2Wsv7779PbW1tcepu9913p7a2ljFjxnDmmWdy1113sWDBAu6///7iCyR/9KMfMWfOHGpraxk/fnyxfc2aNfTv35958+YxcOBA7r33XgDGjh3LCSecwLx585g7dy49e/Zk0aJFTJkyheeff566ujoqKio2++cjSW2J49ktaJ999mHEiBGMHz+eDh06FNuXLVvG8OHDWb58Oe+//z6HHnroh7YdPnw4U6ZM4cQTT+SRRx7hkksuYfXq1bzwwgucc845xX7vvffeZo+9uemevn37ctFFF7Fu3TrOOussampqtnkOffv25YADDgAap1gGDx4MQFVVFc8++2yzz6ep+vp6/va3v3HCCScAMHLkyI3OacO0Te/evVm6dOlm9/Haa69RU1NDRHDmmWdy6qmnMnLkyI2uvXn77bdZvXo10DiV1vTPoLm1DB8+fKP+G6bjqqqq6NmzZ/F3c9hhh/HGG2/QuXNnxo8fz09/+lMA3njjDV599VU6d+7M7rvvzumnn148t6eeegqAZ555pnitTkVFBZ06deLBBx9kzpw5xfcarV27lv3333+rv1dJ2tUZUlrYFVdcwTHHHMOXv/zlYttll13G1772NYYNG8aMGTM2e0HtsGHDuO6661i1ahVz5szhpJNOYs2aNey7777U1dXtUC0DBw7kueee48knn+RLX/oSX/va1xgxYsRWt9ljjz2Kn9u1a1dcbteuXfGNxM05n+2x4RgVFRXFY2xqw0hIUx988AGzZs2iffv2H+q/11577VAtm27X9Pw3/d00NDQwY8YMpk+fzosvvsiee+7JoEGDim99rqysJCK2eW4AKSVGjhzJd77znR2qW5J2RU73tLCPfexjfPGLX+S+++4rttXX13PggQcCbPFtwh07dqRv375cfvnlnH766VRUVLDPPvtw6KGH8pOf/ARo/A/ZvHnzml3L66+/zj/8wz9w8cUXM2rUKObOnQs0/sdz3bp1O3qKWzyfvffem7///e8f6t+pUyf222+/4vUmDz74YHEk46MYPHhwcQoKaFaYa+la6uvr2W+//dhzzz155ZVXmDVr1ja3Ofnkk7n77rsBWL9+PfX19Zx88slMnTqVN998E4BVq1bx+uuv73BdkrQrMKSUwFVXXcVbb71VXB43bhznnHMOvXv33uqdP8OHD+ehhx7aaMph0qRJ3HffffTq1YuePXvyn//5n5vddtNrUl544QVmzJhBr169OProo5kyZQqXX345AKNHj6a6urp44ez22tL5nHHGGfz0pz8tXqDa1AMPPMA111xDdXU1dXV1fOtb39qhYzc1fvx4amtrqa6upkePHkyYMKFZ27VkLUOHDqWhoYEjjzySa6+9lv79+29zm9tvv51nn32WqqoqevfuzcKFC+nRowc33XQTgwcPprq6mlNOOYXly5fvcF2StCuIlFK5a9guffr0SbW1tRu1+fp6tRX+Xc9L92ufLHcJbdLSWz5X7hLUgiJiTkqpz+bWOZIiSZKyZEiRJElZMqRIkqQs7TIhpbVdWyNtL/+OS2prdomQ0r59e1auXOmXuHZZKSVWrly52WfCSNKuapd4mFu3bt1YtmwZK1asKHcpUsm0b9+ebt26lbsMSdppdomQUllZuc1Hs0uSpNZll5jukSRJux5DiiRJylLJQkpEHBQRz0bEwoh4OSIu30yfiIjxEbEkIuZHxDGlqkeSJLUupbwmpQG4KqU0NyL2BuZExFMppYVN+pwKHF74ORa4u/BPSZLUxpVsJCWltDylNLfw+e/AIuDATbqdCfw4NZoF7BsRB5SqJkmS1HrslGtSIqI7cDTwm01WHQi80WR5GR8OMpIkqQ0qeUiJiI7Ao8AVKaW3d3AfoyOiNiJqfRaKJEltQ0lDSkRU0hhQJqWUHttMlz8DBzVZ7lZo20hKaWJKqU9KqU/Xrl1LU6wkScpKKe/uCeA+YFFK6ftb6DYNGFG4y6c/UJ9SWl6qmiRJUutRyrt7jgcuBH4XEXWFtuuAgwFSShOAnwOnAUuAd4Avl7AeSZLUipQspKSUfg3ENvok4KulqkGSJLVePnFWkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpZ2K3cBkiRtl3Gdyl1B2zSufqcf0pEUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlkoWUiLiRxHxZkQs2ML6QRFRHxF1hZ9vlaoWSZLU+uxWwn3fD9wJ/HgrfWamlE4vYQ2SJKmVKtlISkrpOWBVqfYvSZJ2beW+JuXTETEvIn4RET3LXIskScpIKad7tmUucEhKaXVEnAY8Dhy+uY4RMRoYDXDwwQfvtAIlSVL5lG0kJaX0dkppdeHzz4HKiOiyhb4TU0p9Ukp9unbtulPrlCRJ5VG2kBIRH4+IKHzuV6hlZbnqkSRJeSnZdE9ETAYGAV0iYhlwA1AJkFKaAPwz8C8R0QCsBc5NKaVS1SNJklqXkoWUlNJ521h/J423KEuSJH1Iue/ukSRJ2ixDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSstSskBIRn4iIPQqfB0XE2IjYt6SVSZKkNq25IymPAusj4pPAROAg4OGSVSVJktq85oaUD1JKDcDZwB0ppWuAA0pXliRJauuaG1LWRcR5wEjgiUJbZWlKkiRJan5I+TLwaeDfUkp/jIhDgQdLV5YkSWrrdmtOp5TSwoj4OnBwYfmPwHdLWZgkSWrbmnt3zxlAHfBfheWaiJhWwrokSVIb19zpnnFAP+BvACmlOuCwklQkSZLEdlw4m1Kq36Ttg5YuRpIkaYNmXZMCvBwR/weoiIjDgbHAC6UrS5IktXXNHUm5DOgJvEfjQ9zqgStKVJMkSdK2R1IiogJ4MqV0InB96UuSJElqxkhKSmk98EFEdNoJ9UiSJAHNvyZlNfC7iHgKWLOhMaU0tiRVSZKkNq+5IeWxwo8kSdJO0dwnzj4QEbsDRxSafp9SWle6siRJUlvXrJASEYOAB4ClQAAHRcTIlNJzJatMkiS1ac2d7vl3YHBK6fcAEXEEMBnoXarCJElS29bc56RUbggoACmlxUBlaUqSJElq/khKbUT8B/BQYfl8oLY0JUmSJDU/pPwL8FUaH4cPMBP4YUkqkiRJovkhZTfg9pTS96H4FNo9SlaVJElq85p7TcrTQIcmyx2A6S1fjiRJUqPmhpT2KaXVGxYKn/csTUmSJEnNDylrIuKYDQsR0QdYW5qSJEmSmn9NyhXATyLifwrLBwDDS1KRJEkS2xhJiYi+EfHxlNJs4J+AKcA64L+AP+6E+iRJUhu1remee4D3C58/DVwH3AX8FZhYwrokSVIbt63pnoqU0qrC5+HAxJTSo8CjEVFX0sokSVKbtq2RlIqI2BBkTgaeabKuudezSJIkbbdtBY3JwK8i4i0a7+aZCRARnwTqS1ybJElqw7YaUlJK/xYRT9N4N88vU0qpsKodcFmpi5MkSW3XNqdsUkqzNtO2uDTlSJIkNWruw9y2W0T8KCLejIgFW1gfETE+IpZExPymD4uTJEkqWUgB7geGbmX9qcDhhZ/RwN0lrEWSJLUyJQspKaXngFVb6XIm8OPUaBawb0QcUKp6JElS61LKkZRtORB4o8nyskKbJElSWUNKs0XE6IiojYjaFStWlLscSZK0E5QzpPwZOKjJcrdC24eklCamlPqklPp07dp1pxQnSZLKq5whZRowonCXT3+gPqW0vIz1SJKkjJTs0fYRMRkYBHSJiGXADUAlQEppAvBz4DRgCfAO8OVS1SJJklqfkoWUlNJ521ifgK+W6viSJKl1axUXzkqSpLbHkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpZ2K3cBEuM6lbuCtmlcfbkrkKStciRFkiRlyZAiSZKy5HRPE92vfbLcJbRJS9uXuwJJUo4cSZEkSVkypEiSpCwZUiRJUpYMKZIkKUslDSkRMTQifh8RSyLi2s2s/1JErIiIusLPqFLWI0mSWo+S3d0TERXAXcApwDJgdkRMSykt3KTrlJTSpaWqQ5IktU6lHEnpByxJKf0hpfQ+8AhwZgmPJ0mSdiGlDCkHAm80WV5WaNvUFyJifkRMjYiDSliPJElqRcp94ezPgO4ppWrgKeCBzXWKiNERURsRtStWrNipBUqSpPIoZUj5M9B0ZKRboa0opbQypfReYfE/gN6b21FKaWJKqU9KqU/Xrl1LUqwkScpLKUPKbODwiDg0InYHzgWmNe0QEQc0WRwGLCphPZIkqRUp2d09KaWGiLgU+G+gAvhRSunliPg2UJtSmgaMjYhhQAOwCvhSqeqRJEmtS0lfMJhS+jnw803avtXk8zeAb5SyBkmS1DqV+8JZSZKkzTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpSlkoaUiBgaEb+PiCURce1m1u8REVMK638TEd1LWY8kSWo9ShZSIqICuAs4FegBnBcRPTbp9n+Bv6aUPgncBny3VPVIkqTWpZQjKf2AJSmlP6SU3gceAc7cpM+ZwAOFz1OBkyMiSliTJElqJUoZUg4E3miyvKzQttk+KaUGoB7oXMKaJElSK7FbuQtojogYDYwuLK6OiN+Xsx61rIAuwFvlrqPNudFBS7VOfmeUSem+Mw7Z0opShpQ/Awc1We5WaNtcn2URsRvQCVi56Y5SShOBiSWqU2UWEbUppT7lrkNS6+B3RttRyume2cDhEXFoROwOnAtM26TPNGBk4fM/A8+klFIJa5IkSa1EyUZSUkoNEXEp8N9ABfCjlNLLEfFtoDalNA24D3gwIpYAq2gMMpIkSYQDFyq3iBhdmNKTpG3yO6PtMKRIkqQs+Vh8SZKUJUOKyioi9oyIJyPilYh4OSJuKXdNknZcRKyPiLrCv8/zIuKqiGhXWNcnIsYXPu8REdMLfYdHxIDCNnUR0aG8Z6FctIrnpGiXFsD3U0pPF+4CezoiTk0p/aLchUnaIWtTSjUAEbE/8DCwD3BDSqkWqC30OxqgSd8JwHdSSg815yCFp5NHSumDFq1eWXEkRTtdRHQvvHjyx8BLwBKAwusT5tL4TB1JrVxK6U0aH8R5aTQaFBFPFMLLQ0DfwsjJV4AvAv8vIiYBRMQ1ETE7IuZHxI2FtqbfHQuAg7bSb1FE3FsYnfnlhtGZiPhkYQRnXkTMjYhPbOl4Kj9DisrlcOCHKaWeKaXXASJiX+AM4OlyFiap5aSU/kDjYyj2b9L2JjAKmJlSqkkp3UPjc7OuSSmdHxGDafyO6AfUAL0jYmBh8+J3B/CpbfS7q9Dvb8AXCu2TCu29gOOA5ds4nsrI6R6Vy+sppVkbFgpPHJ4MjC98qUlquwYXfn5bWO5IY4j4Ext/d2yt3x9TSnWF9jlA94jYGzgwpfRTgJTSuwCFkLK5/TxXipNT8xlSVC5rNlmeCLyaUvpBGWqRVCIRcRiwHngTOLK5m9F4fco9m+yrOxt/d2yt33tNmtYDW7sYd7P7Ufk53aOyi4ibaHxv0xVlLkVSC4qIrsAE4M7tfOXJfwMXRUTHwn4OLFzHsqP9AEgp/Z3Gd8WdVei/R0Tsub370c7jSIrKKiK6AdcDrwBzGy/Y586U0n+UtTBJO6pDRNQBlUAD8CDw/e3ZQUrplxFxJPBi4TthNXABjSMi291vExcC9xRe0bIOOGcr+3lze+pWy/OJs5IkKUtO90iSpCwZUiRJUpYMKZIkKUuGFElSSUXE9YUnv84vPGH22Axq+lJE3FnuOrR13t0jSSqZiPg0cDpwTErpvYjoAuxewuNVpJS2dnePWhFHUiRJpXQA8FZK6T2AlNJbKaX/iYihhbefz42I8RHxBEBEjIuIqzdsHBELCg9nIyIej4g5hVGZ0U36rI6If4+IecCnI+KCiHipMGpzT0RUFPp9OSIWR8RLwPE771egHWVIkSSV0i9pfBHg4oj4YUScEBHtgXtpfFdXb+DjzdzXRSml3kAfYGxEdC607wX8pvA+npXAcOD4whuW1wPnR8QBwI00hpPPAD1a5vRUSoYUSVLJpJRW0xhERgMrgCnAGBrfrfNq4Um0DzVzd2MLoyWzgINofL8ONAaRRwufTy4cb3bhoXInA4cBxwIzUkorCm9cn/JRz02l5zUpkqSSKlwjMgOYERG/A0ZupXsDG/8PdHuAiBgEfBb4dErpnYiYsWEd8G6T61ACeCCl9I2mO93wKHy1Lo6kSJJKJiI+FRGHN2mqAf5C41uJP1FoO6/J+qXAMYVtjwEOLbR3Av5aCCj/BPTfwiGfBv55w7t3IuJjEXEI8BvghIjoHBGVwDkf+eRUco6kSJJKqSNwR0TsS+MoyRIap36mAk9GxDvATGDvQv9HgRER8TKNwWJxof2/gDERsQj4PY1TPh+SUloYEd8EfhkR7Wh8P89XU0qzImIc8CLwN6CuZU9TpeC7eyRJZVWYyrk6pXR6mUtRZpzukSRJWXIkRZIkZcmRFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLP1/MNSBBx77xI8AAAAASUVORK5CYII=\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": [
    "from tqdm import tqdm\n",
    "\n",
    "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 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",
    "\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": 230,
   "metadata": {},
   "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=0.61    r=0.66    diff=1.12\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=0.63    r=0.47    diff=3.39\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAHHCAYAAAAiSltoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABYpklEQVR4nO3dd5xU1f3/8dd7YelNiogogoooGkQEFCuWaGJM0NiSmMREY0+MMeWnX/UbUjTJNybGGqOmaIw9scaCYsMuYAMpFkCkSJNeXHY/vz/uXRhwFwaY2ZmdeT8fj3kwc+bcez9n7zBz7jnnnqOIwMzMzMpXRaEDMDMzs8JyZcDMzKzMuTJgZmZW5lwZMDMzK3OuDJiZmZU5VwbMzMzKXNNCB2BmZtYYHHlI65i/oDrn+x3z1qrHI+ILOd/xJnBlwMzMLAvzF1Tz6uM9cr7fJt3e7ZzznW4iVwbMzMyyEEANNYUOIy9cGTAzM8tKUB2lWRnwAEIzM7My55YBMzOzLCTdBKW5no9bBszMzMqcWwbMzMyy5AGEZmZmZSwIqsPdBGZmZlaC3DJgZmaWJQ8gNDMzs5LklgEzM7MsBFDtlgEzMzMrRW4ZMDMzy1KpjhlwZcDMzCwLAb610MzMzEqTWwbMzMyyVJrzD7plwMzMrOy5ZcDMzCwLQZTsrYWuDJiZmWUjoLo06wLuJjAzMyt3bhkwMzPLQuABhGYFIWm4pNsKHYcVP0nPSPpe+vxkSSMy3ttf0ruSlko6RlJXSc9JWiLpD4WL2qw4uDJgtgkkdZR0n6RlkqZJ+sZG8g9If3SWSvpY0g8z3nta0lxJiyW9KWlY/kuwcZtaxnSbZpImSPooI+3AtNyZj5B0XH5LABHxr4g4IiPpl8C1EdEmIu4HzgDmAe0i4sfZ7lfS1yRNkrRI0hxJt0hqV0/ezpJekDRf0kJJL0naP+P95pKulDRT0ieSrpdUuXkltoYhqvPwKAauDJhtmuuAT4GuwMnAnyXtXldGSZ2Bx4C/AJ2AnYERGVl+CHSLiHYkP063SeqW64AlbWp3YNZlzPBTYG5mQkSMSn9820REG+BoYCnJ36Sh7QCMX+/1OxGbPJ3cC8D+EdEe2JGkq/XX9eRdCpwKdAG2An4HPJRxPi4EBgJ7ALsAA4BLNjEea0AB1ETuH8XAlQErCpL+n6QZabPtJEmH1ZPvK5LGp1daz0jaLeO9qZIukvROeqX1d0ktMt4/WtIb6bYvSuq3iTG2Bo4DLo2IpRHxPPAg8K16NrkAeDy9Sl0VEUsiYkLtmxHxVkSsrn0JVALbb0pMG4h1avo3fQtYlm2FYDPKiKRewDeB32xk96cA90bEsqwKsfFYPy9pYnqVfi2svcSS9B1Jz6fP3yf54X4obZ24I43lZ+nrw7M9ZkRMj4h5GUnVJJW8uvKujIhJEVGTxlZNUinomGb5MnB1RCyIiLnA1SSVB7MG58qAFZykPsD3gUER0RY4EphaR75dgDuA80muth4h+YJvlpHt5HT7nUiuti5Jt90L+BtwJslV+l+AByU1T99/OK0k1PV4ON33LsDqiJiccbw3gfqumvcFFqQVjzmSHpLUY70yPSxpJfAK8AwweiN/rk3xdeBLQIeIWJ2nMgJcA/wPsKK+DGkl43jgli0q0dr9dQb+Q3J+OwPvA/vXlTcidgI+BL6ctlJ8HfgX8H/p6yclfWMDf5uFmedN0gGSFgFLSCpOf9pIrG8BK0kqVTdHxJzMt9d7vp2k9pv0x7AG5W4Cs/ypBpoDfSVVRsTUiHi/jnwnAf+NiCciogq4AmgJ7JeR59r06m0BcBnJDyIkzfB/iYhXIqI6Im4BVpH8YBMRR0dEh3oeR6f7aAMsXi+mRUDbesq1HckV6A+BHsAUksrMGum+2wJHASPSq8hcuTr9W6yoPVauyyjpWKBJRNy3kVi+StJH/+zmF2cdRwHjI+Le9LPwJ2D25u4sIm7fwN+mQ0R8mJH3+bSbYDvg99RRcV1v3/2AdsA3gOcz3noM+KGkLpK2Ac5L01ttbjnMNpcrA1ZwEfEeydX+cGCOpDslbVtH1m2BaRnb1QDTge4ZeaZnPJ+WbgNJH/GPM6/2SJrk6zpOfZaSfKlnakdyhViXFcB9EfFaRKwEfgHst/6VX0RURcSjwBGSvrIJ8WzM9I1n+Yysy5he7f8fa3/ENuQU4NbN6KOvz7ZklC/d7+aUd7NFxAySH/Q7s8i7MiLuAC6UtGeafBnwOvAG8CJwP1AFfJyPeG3LBW4ZMMur9MrsAJIf7SAZbLW+men7AEgSyQ/6jIw8mX3uPdJtIPmhuGy9q71W6Rc0kh7VZ0e+1z4eTfcxGWgqqXfGMfZk3YFpmd5Ky7KmmBv4E0AyGG2njeTZFOscLw9l7A30BEZJmk3SbN9N0mxJPTOOuz0wFLg1VwUDZpFxrjM+C5tFya2I9f1tlq7fvZNhU89ZJcn4BSJiRUR8PyK6R8SOwHxgTI5bhyzHakI5fxQDVwas4CT1kXRo2n+/kuSKuq4vxLuBL0k6TMktWD8maep/MSPPuZK2k9QRuBi4K02/CThL0j5KtJb0JUltASLii5kj39d7fDHNs4zkB++X6fb7A8OAf9ZTtL8Dx0rqn8Z7KfB8RCyStKukL0pqKalS0jeBg8hdM/pn5KGM40h+gPunj++RXNX2Z92r9G8BL9bT9bO5/gvsLumrSgZHngdss7k7Swd51ve3aVPbTZBWGnqkz3cgubofWdc+Je2bji9olp7n/0dyh8Yr6fvdJW2bfh73Jfl8/Hxzy2C2JVwZsGLQHPgtSZ/ybGBr4KL1M0XEJJJR69ekeb9MMijs04xst5PcvvcByaCyX6fbjgZOB64FPgHeA76zGbGeQzJOYQ5J///ZETEe1t5XnxHvUyQD6/6b5t+ZpN8YksFiw9P0uSTjCk6KiLGbEVOuZVXGiFgdEbNrH8ACoCZ9XZ2xv2+To4GDtdIR/SeQfG7mk7RSvJDLY9SjL/CipGXp8SaRfK6ANa0v/5O+bE5ym+Z8ktaro4AvRURta9VOJBXZZSR/nwsjIvPWUysypdxNoNx14ZkVlqSpwPci4slCx2Jmpadvv2Zx28Ob3QBVr713mD4mIgbmfMebwGsTmJmZZSEQ1SXaoF6apTIzM7OsuWXASkZE9Cx0DGZW2opl9H+uuTJgZmaWhdoBhKXI3QRmZmZlzi0DOdCsokW0rKhvRtrSs2rrFhvPVCL26DJ345lKyLh5XQodQoNqPmdVoUNoMLF69cYzlYiVLOPTWJWHS3hRHaV5De3KQA60rGjLkPbHFjqMBjP1e7ttPFOJePWc6wsdQoPa9aZzCh1Cg9rx+vcKHUKDqZ47v9AhNJhXqj1dw6ZyZcDMzCwLAdSUaO+6KwNmZmZZ8gBCMzMzK0luGTAzM8tCROkOICzNUpmZmVnW3DJgZmaWpZoSHTPgyoCZmVkWkhkIS7NBvTRLZWZmZllzy4CZmVlWPIDQzMzMSpRbBszMzLJQyjMQlmapzMzMLGtuGTAzM8tSdfjWQjMzs7IVyLcWmpmZWWlyy4CZmVmWanxroZmZmZUitwyYmZlloZSnI3ZlwMzMLAuBSvZugqKp4ki6WNJ4SW9JekPSPpLOl9SqAY49VNLD+T6OmZlZMSqKlgFJQ4CjgQERsUpSZ6AZcBdwG7C8jm2aRER1w0ZqZmblzDMQ5lc3YF5ErAKIiHnA8cC2wNOSngaQtFTSHyS9CQyRdIGkcenj/DRPT0kTJN2UtjSMkNQyfW9QRsvD7yWNywxCUoWkdyV1yXj9Xu1rMzOzUlQslYERwPaSJku6XtLBEXE1MBM4JCIOSfO1Bl6JiD2BFcB3gX2AfYHTJe2V5usNXBcRuwMLgePS9L8DZ0ZEf+AzrQoRUUPSEnFymnQ48GZEzM1pac3MrNGJgOqoyPmjGBRFN0FELJW0N3AgcAhwl6QL68haDfw7fX4AcF9ELAOQ9J90+weBKRHxRppvDNBTUgegbUS8lKbfTtI1sb6/AQ8AfwJOJalAFNT5v5rI4IPns3BBJeccMxiAU3/8PvsMncfqqgpmTW/JlZf0YdmSygJHuvl+fcjTHLzDVBasaMmwu74GQPvmK/nDEU/Qve0SZixpywUjjmDxquZrttlj6znc/tX/8JMRn2fEBzsVKvRNNmdGJb//YQ8Wzq0EBUd9cz7Hfm/emvfvvaELN/2yO3e//TbtO1Xz5ottGP7dXmyz/acA7H/UQr55wceFCn+TXXbQ0wztMZX5K1rylX+vPbd/PHTtuf3RyCNY/Glz2jVbxWUHP02PtotYVd2Ui58byrufdCpwCTbP+T8fz+CD5rJwQTPOOWG/Nelf/tqHHH3idGpqxGujOvO3q3YpYJT50aXbp/z0qql06LwaAh65vTP3/3XrQoeVA6IGDyDMq4iojohnIuLnwPdZezWfaWWW4wRWZTyvZhMqPRExHfhY0qHAYODRbLfNlyfv34ZLz+y3TtrrL23F2ccM4tyvDmLGtJacePqHBYouN+6b2IczHl63bva9Aa/z8kfd+eLt3+Dlj7rzvb3GrnmvQjVcsO9LvDh9+4YOdYs1aRqc8b8zuenZiVz18Ls89I/OTJucVHLmzKhk7LNt2br7p+tss8c+S/nzk5P485OTGlVFAOC+yX04/dF1z+3pe77OyzO784W7v8HLM7tzev/k3J7ZfwwT53di2H9O4v89cyj/M+SFQoScE08+tC2XnjtgnbR+Axew79C5nHvSEM4+fj/+fWvPwgSXZ9XV4sZfbscZh/blh1/pw5dPmUuP3isKHZZtQFFUBiT1kdQ7I6k/MA1YArStZ7NRwDGSWklqDRybptUpIhYCSyTtkyZ9bQMh3UzSXXBPMQxSHDemA0sWrVufef3FjtRUJ6dv4pvt6Nx1VV2bNhpjZm3LooyrfoBDe07h/kl9ALh/Uh8O6zVlzXsnf+5tnvhgJ+avaNmgceZCp66r6d0v+WJs1aaG7XdexbxZSavOX4Z357RLZqISuvgYPfuz5/awHaZw/+T03E7uw+E7JOd2p60+4eWZ3QGYsmgrurddQqeWnxk/3CiMG7sVSxat21r3pRM+4p6/92R1VfJ/d9EnzQoRWt4tmFPJe+OSG8FWLGvC9Hdb0HmbqgJHteWC0u0mKI4ooA1wi6R3JL0F9AWGAzcCj9UOIMwUEWOBfwCvAq8AN0fE6xs5zmnATZLeIBl/sKiefA+mMRW8iyAbR3x1NqNHdSx0GDnXqdUK5i1vDcC85a3o1Cr5Ad269VIO7zWFO8ftXsjwcmL29Ga8P64luw5YzouPtaPzNlXstPvKz+SbMKY1Zx3eh4tP3pGpk1oUINLc6tRyBXNXJOd27opWdGqZnNtJ8zvx+Z5JxeBzXT5m2zZL2Kb1soLFmWvb7rCM3fdayJW3vsLvbn6N3n3r+woqHV23W8VOeyxn4uutCx2KbUCxjBkYA+xXx1vXpI/afG3W2+6PwB/XS5sK7JHx+oqMt8dHRD+AdEzC6DTPM8AzGfn2JBk4OHGTC9PATjpjGtWrxdMPdy10KHkmIpJnF+3/An94eV+ikffdrVhWwa++15OzfjmDJk2CO6/pym/ueP8z+Xb+3HL++eo7tGxdw6sj2/KLU3vx9xcmFCDifBHpqeXGNwdw8ZDnue+rdzN5QScmzO9MdU3jPs+ZmjQJ2rav4kffHswuuy/mov97i1OPPgAa+We5Pi1aVXPpjR9ww/DtWL60SaHDyQnPQFgaviTpIpJyTwO+s36GtJJwNmvvKKiTpDOAMwBaVLTZUNa8OfyYWQw+eD7/c9qelOKXyfzlLencahnzlremc6tlLEi7BHbfei5/+PyTAGzVcgUH9ZhGdVQwckqvQoa7SVZXwa++15NDv/oJBxy1iCkTWjD7w2acffiuAMydVcm5R/bh6kcm03Hr1Wu2G3zYEq69SCya34T2nQreg7XZ5q9oSZeWy5i7ojVdWq49t8uqmvE/zx2a5gpGfu1fTF/SrnCB5ti8j1vw4sitATF5fHuiRrTbqorFJdhd0KRpcOmNH/DUfR154dGtCh2ObURZVQYi4i6SiYw2lOe3wG+z2NeNJN0YtG/aJTaSPef2PmA+x586nZ+d0p9VK0ujxr2+p6f25Jg+k7j59QEc02cST01NfuyPuO2ba/JcduhTPDt1h0ZVEYiAP/64B9v3XsVxZyZ3rfbabSV3vz1+TZ5vD+7LNY9Oon2nahbMacpWXVYjwcTXW1FTA+06Nt6KAMBT03pyzC6TuOnNARyzyyRGTkvOX9tmq1i5uilVNU04oc8EXpvdjWVVpfND+fIzXeg3aAFvje5I9x7LaFpZw+JPGu9dQPULLrhiGtPfa8F/biqdVstA1JTodMRFVRmQNBxYCrQDnouIJwsbUXH42e/fod+ghbTrUMWtI1/ktut6ceLp06isDC67+U0AJr3Zjmt/2afAkW6+33/+CQZvO5MOLVby1Ldv5drXBnHT2AFceeQIjtttIjOXtOGCEUcUOsycGP9qa0be25Feu63g7MOTc/bdi2Yy+LAldeYf9XAHHr61E02aQvMWNVz056mNaoDhHw55gkHbzmSrFit55uu3cs3YQdz05gCuPGwEx/WZyMylbfjRyOTc7tThE3479Cki4N1POnLJc4dsZO/F62e/eYt+e3+S/L997Dluu2EnRtzfnfOHj+f6e15kdVUFf/zfPSjFVr3dBy3j8OMX8MGEFlz/eNKl9fffbctrT7UvcGRbrlS7CRTR4Be19aqtDKzXz5/rY4ik3DW52mf7pl1iSPtjc7W7ojf13N0KHUKDeeec6wsdQoPa9aZzCh1Cg9rx+vcKHUKDqZ47v9AhNJhXqkewOBbkvJbVY4928eN7B+d6t5y/28gxETEw5zveBAWv4qQLFE2W9DzQJ037h6Tj0+e/rb3LQNIVadqXJb0i6XVJT0rqmqZ3kfREOg3xzZKmSeqcTlE8SdKtwDiS2Q5/Kum1dL+/yIjnm5JeTacs/ouk0myDNzOzTRJATVTk/FEMChpFOuvg10jmFTgKGLTe+51I5g/YPb0L4NfpW88D+0bEXsCdwM/S9J8DT6XTEN8L9MjYXW/g+vS9Punrwemx95Z0kKTdgJOA/TOmLN7gQEIzM7PGrtBjBg4kmVJ4OYCkB9d7fxGwEvhrusRw7TLD25FMWdyNZHXD2tloDiCpPBARj0n6JGNf0yLi5fT5Eemjdl6CNiSVg37A3sBrSW8CLYE5OSinmZk1eqK6BMd4QBF0E2xIRKwmuXq/l2QdgcfSt64Bro2IzwFnAtnMwpI5c4mA30RE//Sxc0T8NU2/JSO9T0QMz1V5zMys8SpUN4Gk7SU9nXaZj5f0wzS9Y9o1/m7671ZpuiRdna66+5akARs+QuErA8+RTCncUlJb4MuZb0pqA7SPiEeAH5FMBgTQHpiRPj8lY5MXgBPTbY8A6ru59XHg1HT/SOouaWtgJHB8+rz2D73DFpbRzMxsS6wGfhwRfUlW6T1XUl/gQmBkRPQm+f2qXeDviySt3b1J5sP588YOUNBugogYK+ku4E2S5vjX1svSFnhAUguSq/YL0vThwD1pN8BTQO1N5r8A7pD0LeAlYDbJ+gbrz1w4Ih0f8FLaHbAU+GZEvCPpEmCEpAqgCjiXZIIiMzMrc4XoJoiIWcCs9PkSSROA7sAwYGia7RaSmXT/X5p+ayS3C74sqYOkbul+6lToMQNExGXAZRvI8pn7OCLiAZJlhte3CDgyIlZLGgIMiohVwFQypihO93EVcFUd+97oxERmZmaFIKknsBfJmjxdM37gZwO1Mzx1B6ZnbPZRmla8lYEc6wHcnV7VfwqcXuB4zMysREQoX7cCdpY0OuP1jekst+tIu7b/DZwfEYuVMftYRISkzZ44qKQqAxHxLkmNyczMLOfytOTwvI1NOiSpkqQi8K+I+E+a/HFt8396d13t3W8zgO0zNt+OtePs6lToAYRmZma2AenMuX8FJqSr9dZ6kLWD6E9hbff5g8C307sK9gUWbWi8AJRYy4CZmVm+BFBTmHkG9ge+Bbwt6Y007X9IFtW7W9JpJAPdT0zfe4RkIr/3gOXAdzd2AFcGzMzMilhEPE/9K1odVkf+ILkTLmuuDJiZmWVF+RozUHClWSozMzPLmlsGzMzMspBMR1yaaxO4MmBmZpal6hJtUC/NUpmZmVnW3DJgZmaWhUAl203glgEzM7My55YBMzOzLNWU6DW0KwNmZmZZiIBqdxOYmZlZKXLLgJmZWZY8gNDMzMxKklsGzMzMspDcWlia19CuDORAVFdT/cknhQ6jwfT4/ZhCh9Bgdq8+p9AhNKhmqwodQQNbVUYFjppCR1ASqguzhHHelWYVx8zMzLLmlgEzM7MslPJCRW4ZMDMzK3NuGTAzM8tK6Q4gLM1SmZmZWdbcMmBmZpalmhK9m8CVATMzsyx4bQIzMzMrWW4ZMDMzy5IHEJqZmVlJcsuAmZlZFpK1CUpzzIArA2ZmZlkq1bsJ3E1gZmZW5twyYGZmlgWvTWBmZmYlyy0DZmZmWSrVWwtdGTAzM8tGlO7dBKVZxTEzM7OsuWXAzMwsC4FvLTQzM7MS5ZYBMzOzLHnMgJmZmZUktwyYmZlloZQnHXJlwMzMLEuuDFjRGDh0MWf9aiZNKoJH7+jI3dd2LXRIeXXLqDdYvrQJNTWiejWcN2yPQoe0RX512NMc3HMqC1a05JjbvwZA++YrueILT9C93RJmLG7Ljx87gsWrmgPBRQe9wEE7TGPF6qZc/OShTJjbpbAF2AInD3iL4/q9A8B/3tqN28buybn7v8ohO0+hJsSC5S259NFDmbusdYEj3XLn/3oSgw9ewMIFlZwzbCAABxw5l5PPncb2Oy7nRyftxbvj2xY4yvy44A8fss/hi1k4rylnHrZrocOxLBRszICkaklvSBov6U1JP5ZUkb43UNLVhYqtmFVUBOdePoNLTu7F6UP7cMiwhfTovbLQYeXd//vGrpz7pT0afUUA4P4JfTjzwaPXSfve3q/zykfdOeqf3+CVj7rzvb3HAnDgDh+yQ4eFfPGf32D4Uwfzv0OfK0TIObFz5/kc1+8dvnHbcZxwy4kctNM0tu+wiH+81p/jbzmJE289kefe34Ezh4wudKg58eR9Xbn0jHU/r9Pebc2vz+vLuNHtCxRVwxhxd0cuPnnHQoeRc7VLGOf6UQwKOYBwRUT0j4jdgc8DXwR+DhARoyPivHwdWFKjbRHps9dyZk5txuwPm7O6qoJnHujAkCMXFTos2wRjZm7LopXN10k7ZMcp3D+hD5BUFg7dcQoAh+44lQcn9AHEWx9vQ9vmq+jcallDh5wTvTou5K1ZXVm5upLqqGD09G05vPcHLPu02Zo8LStXFzDC3Bo3pgNLFlWukzb9g1bMmNqqQBE1nHGvtGHJwiaFDsM2QVHcTRARc4AzgO8rMVTSwwCSDk5bEN6Q9LqktpLaSBopaayktyUNq92XpEslTZL0vKQ7JP0kTX9G0p8kjQZ+KGlvSc9KGiPpcUnd0nw7SXosTR8lqajauDptU8XcmWu/POfNqqRzt6oCRpR/EXD5rZO45sFxfPHrcwodTl50arWCecuTpvF5y1vRqdUKALZuvYzZS9usyffx0jZ0bdM4KwPvzevIgO6zaN9iJS2aVnHgjh/Ste1SAH5wwCuMOONWvtR3Mte9MLjAkZrVrwbl/FEMiuYKOSI+kNQE2Hq9t34CnBsRL0hqA9S2iR8bEYsldQZelvQgMBA4DtgTqATGAmMy9tUsIgZKqgSeBYZFxFxJJwGXAacCNwJnRcS7kvYBrgcOzUuhLSs/PqEv8z9uRvtOVfzmnxOZ/n4Lxr3artBh5ZGIKHQMuTdlwVb8/dW9+MvxD7GiqpJJczqtaSK95vl9uOb5fTht8Fi+vtfbXP+iKwRWhMIDCAvpBeCPkv4F/CciPkp/zC+XdBBQA3QHugL7Aw9ExEpgpaSH1tvXXem/fYA9gCckATQBZqWVjf2Ae9J0gObUQdIZJK0ZtKDhmv3mz66ky7afrnnduVsV82ZVbmCLxm/+x0lLyKL5lbz4+Fb02XNZyVUG5i9vSedWy5i3vDWdWy1jwYqWAMxZ1ppt2ixdk69rm6V8vLTxDq67b9xu3DduNwDOO+BlPs5o9QD474TeXH/cf10ZMGtgRdFNACBpR6AaWKcdOCJ+C3wPaAm8kDbbnwx0AfaOiP7Ax0CLLA5T274qYHw6ZqF/RHwuIo4g+XsszEjvHxG71bWjiLgxIgZGxMDKuusLeTHpjVZ07/UpXbdfRdPKGoYOW8jLI0p3MFLzltW0bF295vmAAxczdVLLAkeVe09P6ckxu00C4JjdJvH0B73WpH9lt0lA0K/rbJZ+2nxNd0Jj1LHVcgC2abuEw3pP4ZEJvenRYeGa9w/ZeSpTFmxVoOjMNqx2noFSHEBYFC0DkroANwDXRkRkXJUjaaeIeBt4W9IgYFegPTAnIqokHQLskGZ/AfiLpN+QlO1okmb/9U0CukgaEhEvpS0Nu0TEeElTJJ0QEfcoCaRfRLyZp6Jvsppqcd3F3bn89g+oaAIj7uzItMnZ1IMap606V/G/f3kXgCZN4OkHOzHmuQ6FDWoL/f7IJxjUfSYdWqxk5Hdv5bpXBnHzmAH88Qsj+Grficxc0oYfP3oEAM9N7cFBO0zj0W/fzsqqplwy8pACR79l/viVx2nfchWrqyu4fOSBLFnVnF8c+TQ9Oy6kJsSsxW351RMHFTrMnPjZ7yfQb/Ai2nWo4tanXua2a3dgyaJKzr74Pdp3rGL4n8fxwcQ2XHrG5wodas5deN1U+g1ZSvuOq7lt9Hj+ecU2PH5np0KHZRugKFDnpKRq4G2Svv3VwD+BP0ZEjaShwE8i4mhJ1wCHkHQHjAe+A7QFHgLaAKOBfYEvRsRUScOBb5C0FswBHouImyQ9k+5zdHr8/sDVJBWLpsCf0ny9gD8D3dLY7oyIX26oLO3UMfbRYTn4qzQOat5wLSGFNv2CvQsdQoNqsqrQETSs7f42vtAhNJjqRYsLHUKDeaXmSRbHgpxfcrfr0zUG3XByrnfLU4deOSYiBuZ8x5ugYC0DEVHvfScR8QzwTPr8B3VkWQUMqWfzKyJiuKRWwHOkAwgjYuh6x3gD+MwlSERMAb6wsfjNzKy81M4zUIqKopsgx26U1JdkDMEtETG20AGZmZkVs5KrDETENwodg5mZlaYo0ZaBormbwMzMzAqj5FoGzMzM8qVYZgzMNbcMmJmZlTm3DJiZmWUhPB2xmZmZeQChmZmZlSS3DJiZmWWldCcdcsuAmZlZmXPLgJmZWZZKdcyAKwNmZmZZqF3CuBS5m8DMzKzMuWXAzMwsG5HMNVCK3DJgZmZW5twyYGZmlqVSXZvAlQEzM7MsBKV7N4G7CczMzMqcWwbMzMyy4hkIzczMrES5ZcDMzCxLvrXQzMzMSpJbBszMzLJUqncTuDJgZmaWhQhXBszWiE8/LXQIDWb7JxYXOoQGNW/PtoUOwfKlVDu7LSdcGTAzM8uSby00MzOzkuSWATMzsyyVam+LKwNmZmZZKtUBhO4mMDMzK3NuGTAzM8tCILcMmJmZWWlyZcDMzCxLkYfHxkj6m6Q5ksZlpA2XNEPSG+njqIz3LpL0nqRJko7MplzuJjAzM8tG4WYg/AdwLXDreulXRsQVmQmS+gJfA3YHtgWelLRLRFRv6ABuGTAzMytiEfEcsCDL7MOAOyNiVURMAd4DBm9sI1cGzMzMslWIfoL6fV/SW2k3wlZpWndgekaej9K0DXJlwMzMrLA6Sxqd8Tgji23+DOwE9AdmAX/YkgA8ZsDMzCxLeRozMC8iBm5aHPFx7XNJNwEPpy9nANtnZN0uTdsgtwyYmZllKVnGOLePzSGpW8bLY4HaOw0eBL4mqbmkXkBv4NWN7c8tA2ZmZkVM0h3AUJLuhI+AnwNDJfUnGXUwFTgTICLGS7obeAdYDZy7sTsJwJUBMzOzrASFubUwIr5eR/JfN5D/MuCyTTmGuwnMzMzKnFsGzMzMshGA1yYwMzOzUuSWATMzsyxt7uj/YufKgJmZWbZKtDKQ124CSRdLGp9Ol/iGpH3yebyNxLJ0E/MPlbRfvuIxMzMrFnlrGZA0BDgaGBARqyR1Bprl63h5MBRYCrxY4DjMzKwoqFCrFuZdPlsGupFMsbgKICLmRcRMSYdJel3S2+niCs0BJE2V9Ju0BWG0pAGSHpf0vqSzancq6aeSXktbG36RkXZe+vxKSU+lzw+V9K+MbS+T9KaklyV1TdO+LOmVNKYnJXWV1BM4C/hRGs+Befw7mZmZFVQ+KwMjgO0lTZZ0vaSDJbUgWZf5pIj4HEnLxNkZ23wYEf2BUWm+44F9gdof/SNIplYcTLI4w96SDkrz1/5gDwTaSKpM055L01sDL0fEnmna6Wn688C+EbEXcCfws4iYCtxAslZ0/4gYlas/ipmZNWLFtWphzuStmyAilkram+QH+RDgLuA3wJSImJxmuwU4F/hT+vrB9N+3gTYRsQRYImmVpA7AEenj9TRfG5LKwa0kFYN2wCpgLEml4EDgvDTvp6xdyGEM8Pn0+XbAXek8z82AKbkofz4NHLqYs341kyYVwaN3dOTua7sWOqS8ueAPH7LP4YtZOK8pZx62a6HDybnKymqu+M0TVFZW06RJMOqFHtx2Rz/695vN9747FglWrmzKFVcNYdastoUOd4vs0Hkhl5/4xJrX3bdazF+eGsToKdty0ZdH0ap5FTM/acul9x7GslWNqUexbuf/ehKDD17AwgWVnDMsWYPmgCPncvK509h+x+X86KS9eHd84z6nG1KS31NRmBkIG0JeBxBGRHVEPBMRPwe+DxyzkU1Wpf/WZDyvfd0UEPCb9Gq9f0TsHBF/jYgqkh/x75D08Y8iqYDsDExI91EVseamkGrWVoSuAa5NWyrOBFpsVmEbSEVFcO7lM7jk5F6cPrQPhwxbSI/eKwsdVt6MuLsjF5+8Y6HDyJuqqgr+3yWHcc4Pv8Q5PzyKgQNmsmufeXz/7Ff53R/259zzj+LpZ3vyjRPHbXxnRW7avA6cfP0JnHz9CXzrz8exsqopT7/Ti0uGPcu1T+zD1649kWcm9OJbB7xR6FBz4sn7unLpGXuskzbt3db8+ry+jBvdvkBRNYxy+54qBXmrDEjqI6l3RlJ/4H2gp6Sd07RvAc9uwm4fB06V1CY9RndJW6fvjQJ+QtIFMIqkz//1jApAfdqzdnnHUzLSlwBFV23vs9dyZk5txuwPm7O6qoJnHujAkCMXFTqsvBn3ShuWLGxS6DDySKxcWQlA0yY1NG1ak9zHHNCqVRUArVt/yvwFLQsYY+4N2nEGMxa0Y/aituzQeRFjpyYLsL3y3nYc2rfoG+eyMm5MB5YsqlwnbfoHrZgxtVWBImo4Jf095W6CTdYGuCZt3l8NvAecAdwB3COpKfAaSd98ViJihKTdgJckQTLa/5vAHJIKwMXASxGxTNLKNG1jhqfxfAI8BfRK0x8C7pU0DPhBsYwb6LRNFXNnrm1CnTerkl0HLC9gRLalKipquOaPj7FttyU89MguTJrcmSuv3Zdf/e/TrPq0CcuXV/Kjn36h0GHm1JGfe4/H306uFd6fsxUH7zaVZyf04vA93qdr+026C9iKkL+nGp98jhkYA9R1n/5IYK868vfMeP4PkgGEdb13FXBVHduPBCozXu+y3vttMp7fC9ybPn8AeKCO/U0G+tURv1lO1dRUcO75R9G69af870XPsUOPhXx12EQu/eUhTJrcmeOPfYczThvDn67dt9Ch5kTTJtUctOs0rn0imXbkl/cN5adfeoHvHTyG5yb1pKras6RbMSvNMQOegXAzSTqDpKWDFjRcs9/82ZV02fbTNa87d6ti3qzKDWxhjcWyZc148+2uDNp7Jr16fsKkyZ0BeHbUDvx6+FMFji539u/9IRNndWbBsuT/zbR5W/H9W44GoEenhRywy7RChmc5UNLfU0XSrJ9rroJvpoi4MSIGRsTASpo32HEnvdGK7r0+pev2q2haWcPQYQt5eURpD0YqZe3braR16+RLs1mz1QzoP4sPp7ejdesqum+7GIABe81i+kelc46P7Pcej7+185rXW7VeAYAUnDZ0LP9+bfdChWY54u+pxqeoWgYkDScZB9AOeC4inixsRMWnplpcd3F3Lr/9AyqawIg7OzJtclHfALFFLrxuKv2GLKV9x9XcNno8/7xiGx6/s1Ohw8qZjh1X8OPzX6JJRSAFzz2/A6+O3o6rrt2HSy58jgixdGkz/nh1aXQRtKisYvBOH3HZAwetSTvyc+9ywj7jAXj6nV48OLZPocLLqZ/9fgL9Bi+iXYcqbn3qZW67dgeWLKrk7Ivfo33HKob/eRwfTGzDpWd8rtCh5lxJf0+VaMuANj7YvuHUVgYi4oo8HkMk5a7J1T7bqWPso8Nytbvip9LsM6uL9i6vq9R5exbdDTR5tfW/3yl0CA2memGJjObPwisxksWxIOdfVM17bRfdfv6DXO+Wad+9cExEDMz5jjdBwbsJ0sWMJkt6HuiTpv1D0vHp899KeiedfviKNO0zUwin6V0kPZEujnSzpGmSOkvqKWmSpFuBcSQzI35mWuN0H9+U9Go6DfFfJJXyfW1mZpatAEK5fxSBglYG0hkKv0YyB8FRwKD13u8EHAvsHhH9gF+nb31mCuE0/efAUxGxO8ndAj0ydtcbuD59rw91TGuc3rZ4ErB/Oi1yNXByDotsZmZWdAo9ZuBA4L6IWA4g6cH13l8ErAT+Kulh1k4nXN8UwgeQVB6IiMfSuQNqTYuIl9Pn9U1r3A/YG3gtncegJckcBmZmZhRRz3pO1VsZkHQNGxgqERHn1fderkTEakmDgcNIFi36PnAoyRTCf4yIByUNJZk4aGOWZTyvndb4L5kZJP0AuCUiLtry6M3MrOSUaGVgQ90Eo0kW9KnvkQvPAcdIaimpLfDlzDfTaYfbR8QjwI+APdO36ptC+AXgxHTbI4Ct6jlufdMajwSOr53iWFJHSTtsYRnNzMyKWr0tAxFxS+ZrSa1qm/NzJSLGSroLeJOkOf619bK0BR5Ilz4WcEGaPpy6pxD+BXCHpG8BLwGzSdYYaJO50/qmNY6IdyRdAoyQVAFUkayq6FlQzMysaAb85dpGxwxIGgL8leQHtYekPYEzI+KcXAQQEZcBl20gy+A6tqlzCmGSMQZHpt0LQ4BBEbEKmAqss3zYBqY1votkuWUzM7OykM0Awj8BRwIPAkTEm5IO2uAWhdMDuDu9qv8UOL3A8ZiZWQlRiY4ZyOpugoiYrnUnmqnOTzhbJiLepY5FkMzMzLZYES05nGvZVAamS9oPCEmVwA+BCfkNy8zMzBpKNpWBs0j61rsDM0lG4p+bz6DMzMyKT/HMGJhrG60MRMQ8PAufmZlZydrodMSSdpT0kKS5kuZIekDSjg0RnJmZWVGJPDyKQDZrE9wO3A10A7YF7gHuyGdQZmZmRamMKwOtIuKfEbE6fdwGlMjC1GZmZrahtQk6pk8flXQhyeqAQbKq3yMNEJuZmVlxKZIr+Vzb0ADCMSTFrh06eWbGewF4MR8zM7MSsKG1CXrV956ZmVnZCcr31kIASXsAfckYKxARt+YrKDMzM2s42SxU9HNgKEll4BHgi8DzgCsDZmZWVkp1bYJs7iY4HjgMmB0R3wX2BNrnNSozM7NiVMa3Fq6IiBpgtaR2wBxg+/yGZWZmZg0lmzEDoyV1AG4iucNgKfBSPoMyMzOzhpPN2gTnpE9vkPQY0C4i3spvWGZmZtZQNjTp0IANvRcRY/MTkpmZWXEq1QGEG2oZ+MMG3gvg0BzHYlZ0Kt6bXugQGlSnpjsUOoSGpWyGTZllKLd5BiLikIYMxMzMzAojq0mHzMzMyl4R3QqYa24jMzMzK3NuGTAzM8tWubYMKPFNSf+bvu4haXD+QzMzMysuitw/ikE23QTXA0OAr6evlwDX5S0iMzMza1DZdBPsExEDJL0OEBGfSGqW57jMzMyKT5FcyedaNi0DVZKakP4JJHUBavIalZmZmTWYbCoDVwP3AVtLuoxk+eLL8xqVmZlZMSrRVQuzWZvgX5LGkCxjLOCYiJiQ98jMzMysQWy0MiCpB7AceCgzLSI+zGdgZmZmxaSYRv/nWjYDCP9L0pAhoAXQC5gE7J7HuMzMzIpPua1NUCsiPpf5Ol3N8Jx6spuZmVkjs8kzEEbEWEn75CMYMzOzolau3QSSLsh4WQEMAGbmLSIzMzNrUNm0DLTNeL6aZAzBv/MTjpmZWfEqywGE6WRDbSPiJw0Uj5mZWfEq0cpAvZMOSWoaEdXA/g0Yj5mZmTWwDbUMvEoyPuANSQ8C9wDLat+MiP/kOTYzM7PiUebzDLQA5gOHsna+gQBcGTAzMysBG6oMbJ3eSTCOtZWAWlnVjSRdDHwDqCZZ3OjMiHhlM2PdIpKWRkSbTcg/FPg0Il7MW1BmZta4lGHLQBOgDetWAmpt9M8haQhwNDAgIlZJ6gw0pqWPhwJLAVcGzMwsUYaVgVkR8cst2Hc3YF5ErAKIiHkAkg4DrkiP/RpwdlpZmArcAXyR5BbGM4DfADsDv4+IG9LtfwqcCDQH7ouIn6dpqyLiaklXAntGxKGSDgVOi4iT020vI6mgrACGRcTHkr4MXEJSUZkPnAy0BM4CqiV9E/hBRIzagr+FmZlZ0drQEsZbOgHzCGB7SZMlXS/pYEktgH8AJ6XTHDcFzs7Y5sOI6A+MSvMdD+wL/AJA0hFAb2Aw0B/YW9JBaf4D030MBNpIqkzTnkvTWwMvR8SeadrpafrzwL4RsRdwJ/CziJgK3ABcGRH9XREwMzNYu1hRLh/FYEMtA4dtyY4jYqmkvUl+kA8B7iK50p8SEZPTbLcA5wJ/Sl8/mP77NtAmIpYASyStktQBOCJ9vJ7ma0NSObiVpGLQDlgFjCWpFBwInJfm/RR4OH0+Bvh8+nw74C5J3UhaB6ZsSbkbwsChiznrVzNpUhE8ekdH7r62a6FDypsL/vAh+xy+mIXzmnLmYbsWOpycO//Xkxh88AIWLqjknGEDATjgyLmcfO40tt9xOT86aS/eHd92I3tpXCoqarjm/x5h/oJW/O/lh/KVL07k2KMnsm23JZxwygksXtKi0CHmxPm/msjgg+cn5/aYwQCc+uP32WfoPFZXVTBrekuuvKQPy5ZUFjjS/Cin76lSUG/LQEQs2NKdR0R1RDwTET8Hvg8cs5FNVqX/1mQ8r33dlKS14jfp1Xr/iNg5Iv4aEVUkP+LfIenjH0VSAdkZmJDuoyoiautg1aytCF0DXJu2VJxJcvdE0aqoCM69fAaXnNyL04f24ZBhC+nRe2Whw8qbEXd35OKTdyx0GHnz5H1dufSMPdZJm/Zua359Xl/GjW5foKjy65gvTWT6R2vLNn7i1lw4/HBmz2ldwKhy78n7t+HSM/utk/b6S1tx9jGDOPerg5gxrSUnnl6aK8GX2/dUKdhQN8EWkdRHUu+MpP7A+0BPSTunad8Cnt2E3T4OnCqpTXqM7pK2Tt8bBfyEpAtgFEmf/+sZFYD6tAdmpM9PyUhfwrpTMReFPnstZ+bUZsz+sDmrqyp45oEODDlyUaHDyptxr7RhycImhQ4jb8aN6cCSReteGU7/oBUzprYqUET51bnTMgbvPYNHn9x5Tdr7Uzry8dysb/RpNJJzu27j6+svdqSmOvnanfhmOzp3XVXXpo1euX1PlYK8VQZImvBvkfSOpLeAvsCFwHeBeyS9TXLFf0O2O4yIEcDtwEvp9vey9gd7FMmgxZci4mNgZZq2McPTeMYA8zLSHwKOlfSGpAPr3LIAOm1TxdyZa2/KmDerks7dqgoYkVn2zjp1NDffOoAo0TXhN8URX53N6FEdCx1GXpT091Tk4VEENnkJ42xFxBhgvzreGgnsVUf+nhnP/0EygLCu964Crqpj+5FAZcbrXdZ7v03G83tJKhJExAPAA3XsbzLQb/10M9s8++z9EQsXteC9DzrRb/fZhQ6noE46YxrVq8XTD7sfvVEpogF/uZa3ykCpk3QGye2PtKDhmnTnz66ky7afrnnduVsV82aV5gAkKy19d53DvoM+YtCAGTSrrKZVqyp+9sPn+b+rDih0aA3q8GNmMfjg+fzPaXuy5TdtFSd/TzU+rgxspoi4EbgRoJ06NlhdcdIbreje61O6br+K+bMrGTpsIb89d4eGOrzZZvv7vwbw938NAKDf7rM5ftg7ZVcR2PuA+Rx/6nR+dkp/Vq0s3bEwJf095ZYBKwY11eK6i7tz+e0fUNEERtzZkWmTi/oGiC1y4XVT6TdkKe07rua20eP55xXb8PidnQodVs787PcT6Dd4Ee06VHHrUy9z27U7sGRRJWdf/B7tO1Yx/M/j+GBiGy4943OFDjVvhh01gROOfYeOHVZww5UP8+rY7vzp+iGFDmuL/ez379Bv0MLk3I58kduu68WJp0+jsjK47OY3AZj0Zjuu/WWfAkeae+X2PVUKtPHB9rYx7dQx9tEWTcvQuKg0mzbr0qR9u0KH0KCqdy2Rq7csNZk0vdAhNJjqTz4pdAgN5pUYyeJYkPMvqhbbbh89T78g17tl0i8vGBMRA3O+401Qli0DkppGxOpCx2FmZo2HKN0BhPm8tTDvJN0vaYyk8emAPiSdlk6B/KqkmyRdm6b/Q9INkl4B/k/STpIeS7cfJWnXNF8XSf+W9Fr62L+ARTQzM8u7xt4ycGpELJDUEnhN0n+BS4EBJJMGPQW8mZF/O2C/iKiWNBI4KyLelbQPcD1wKMlti1dGxPOSepBMdLRbA5bJzMyKVYm2DDT2ysB5ko5Nn29POqNh7VTKku4BMucbuCetCLQhmQPhHq3t/26e/ns40DcjvZ2kNhGxNI/lMDMzq5Okv5GsuDsnIvZI0zqSrPnTE5gKnBgRnyj58boKOApYDnwnIsZu7BiNtptA0lCSH+4h6UqErwMTN7LZsvTfCmBhxhoH/SNit4z39s1I7+6KgJmZ1U46VIBVC/8BfGG9tAuBkRHRm2QyvwvT9C+SLODXm2QunD9nc4BGWxkgWVPgk4hYnvb370uyTPHBkraS1BQ4rq4NI2IxMEXSCQBK7Jm+PQL4QW1eSf3zWAYzM2tMCjAdcUQ8B6y/eOAwkpV/Sf89JiP91ki8DHRIV+XdoMZcGXgMaCppAvBb4GWSBYcuB14FXiBpOqlvdYyTgdMkvQmMJ/kDQrLk8UBJb0l6h2TBIzMzs3zpLGl0xuOMLLbpGhGz0uezgdq5rbsDmffMfpSmbVCjHTMQEatImkPWIWl0RNyYtgzcB9yf5v/OettP4bPNLkTEPOCkPIRsZmaNXX4GEM7bknkGIiKkLbvpsTG3DNRnuKQ3gHHAFNLKgJmZWQn5uLb5P/13Tpo+g2RAfa3t0rQNarQtA/WJiJ8UOgYzMytNRTTp0IPAKSTd5KewdvXdB4HvS7oT2AdYlNGdUK+SqwyYmZmVEkl3AENJxhZ8BPycpBJwt6TTgGnAiWn2R0huK3yP5NbC72ZzDFcGzMzMslWAloGI+Ho9b31mUZxIFhw6d1OP4cqAmZlZNrK8FbAxKsUBhGZmZrYJ3DJgZmaWpSIaQJhTbhkwMzMrc24ZMDMzy1aJtgy4MmBmZpYldxOYmZlZSXLLgJmZWbbcMmBmZmalyC0DZmZm2SjhSYdcGTAzM8uC0kcpcjeBmZlZmXPLgJmZWbZKtJvALQNmZmZlzi0DtumiRKvGdahevLTQITSoJu9+VOgQGlSsXl3oEBpORZNCR9BwqvO3a086ZGZmZiXJLQNmZmbZKtGWAVcGzMzMslWilQF3E5iZmZU5twyYmZllIzyA0MzMzEqUWwbMzMyyVaItA64MmJmZZcndBGZmZlaS3DJgZmaWLbcMmJmZWSlyy4CZmVmWSnXMgCsDZmZm2QjcTWBmZmalyS0DZmZm2XLLgJmZmZUitwyYmZllQZTuAEK3DJiZmZU5twyYmZllq0RbBlwZMDMzy5KiNGsD7iYwMzMrc24ZMDMzy4YnHTIzM7NS5ZYBMzOzLJXqrYWuDDRCA4cu5qxfzaRJRfDoHR25+9quhQ4pb8qprF26fcpPr5pKh86rIeCR2ztz/1+3LnRYOXX+Lycw+KD5LFzQjHO+OhiAb33/A/Y9ZB41NWLRgkr+eMluLJjbvMCR5t6wb8/gCyd8jASP3dOV+2/pXuiQ8qakP8slWhlotN0Eko6RFJJ2LXQsDamiIjj38hlccnIvTh/ah0OGLaRH75WFDisvyqmsANXV4sZfbscZh/blh1/pw5dPmUuP3isKHVZOPflANy49e8910u79ew/OPW4wPzhhEK8+25lvnDW1MMHl0Q69l/GFEz7m/BP25JxhezF46AK69Sitc5upHD7LpabRVgaArwPPp/9mTVKT/ITTMPrstZyZU5sx+8PmrK6q4JkHOjDkyEWFDisvyqmsAAvmVPLeuFYArFjWhOnvtqDzNlUFjiq3xo3pwJJF6zZIrli29nWLltWU4p1b2++0gklvtWXVyibUVIu3X2vP/kfML3RYeVPKn2VF7h/FoFFWBiS1AQ4ATgO+lqZVSLpe0kRJT0h6RNLx6XtTJf1O0ljgBElHSHpJ0lhJ96T7Q9Lekp6VNEbS45K6FaqM9em0TRVzZzZb83rerEo6dyuN/2TrK6eyrq/rdqvYaY/lTHy9daFDaRDf/sEH3PLEiwz90sf887pehQ4n56ZNbsXuey+ibYcqmreoZtBBn9Blm1WFDqtBlNtnubFqlJUBYBjwWERMBuZL2hv4KtAT6At8Cxiy3jbzI2IA8CRwCXB4+no0cIGkSuAa4PiI2Bv4G3BZQxTGLFOLVtVceuMH3DB8O5YvbdQNWVm79ZodOeXz+/HMf7vy5a/PKHQ4OTf9g1bcc/N2XPbXcfzq5vF8MLE1NTUqdFh5V5Kf5cjDowg01gGEXweuSp/fmb5uCtwTETXAbElPr7fNXem/+5JUGF6QBNAMeAnoA+wBPJGmNwFm1ReApDOAMwBa0GrLS5Sl+bMr6bLtp2ted+5WxbxZlQ12/IZUTmWt1aRpcOmNH/DUfR154dGtCh1Og3v6v135xfVv8a/rS691YMS92zDi3m0AOOVHU5n3cekNksxUkp/lImrWz7VG1zIgqSNwKHCzpKnAT4ETSRaU2pBltbsAnoiI/umjb0SclqaPz0j/XEQcUd/OIuLGiBgYEQMrabj/1JPeaEX3Xp/SdftVNK2sYeiwhbw8on2DHb8hlVNZE8EFV0xj+nst+M9NpXvXxPq27bF8zfN9D53HR1MarnLdkNp3TCq2XbqtZP8j5vPMQ10KHFE+lednuTFrjC0DxwP/jIgzaxMkPQssAI6TdAvQBRgK3F7H9i8D10naOSLek9Qa6A5MArpIGhIRL6XdBrtExPg8l2eT1FSL6y7uzuW3f0BFExhxZ0emTW5R6LDyopzKCrD7oGUcfvwCPpjQgusfnwDA33+3La89VToVoJ/9bjz9Bi2kXYcqbn3yRW67rieDDlxA957LiYA5M1tw7a/6FDrMvLjkmom061DF6tXi+l/sxLIljfHrNzsl/Vku0ZYBRSMbups2//8uIh7LSDsP2I3k6n4oMD19/ruIeCJtQRgYEfPS/IcCv4M1l/SXRMSDkvoDVwPtSSpKf4qImzYWUzt1jH10WG4KaMWlokT6ObPUZKsS+LLeBPFpeQxIBahZtnzjmUrEK9UjWBwLcj4oo3Wn7WOPo36U693y6m0/HhMRA3O+403Q6KqmEXFIHWlXQ3KXQUQsldQJeBV4O32/53r5nwIG1bGfN4CDch+1mZk1dqJ0xww0usrARjwsqQPJoMBfRcTsAsdjZmalpJG1pmerpCoDETG00DGYmZk1NiVVGTAzM8unUu0maHS3FpqZmVluuWXAzMwsG0U0Y2CuuWXAzMyszLllwMzMLEuqKXQE+eHKgJmZWbbcTWBmZmalyC0DZmZmWfKthWZmZlaS3DJgZmaWjcDTEZuZmZU7dxOYmZlZSXLLgJmZWbbcMmBmZmalyC0DZmZmWRClO2bAlQEzM7NsRJTs3QTuJjAzMytzbhkwMzPLUql2E7hlwMzMrMy5ZcDMzCxbbhkwMzOzUuSWATMzsyyV6pgBVwbMNqSmutARNKjqTxYVOoQGVdGieaFDaDhl9lnOiwBqSrM24G4CMzOzMueWATMzs2yVZsOAWwbMzMzKnVsGzMzMsuQBhGZmZuWuRNcmcGXAzMysyEmaCiwBqoHVETFQUkfgLqAnMBU4MSI+2Zz9e8yAmZlZlhS5f2yCQyKif0QMTF9fCIyMiN7AyPT1ZnFlwMzMrHEaBtySPr8FOGZzd+TKgJmZWTYiT4/sjz5C0hhJZ6RpXSNiVvp8NtB1c4vmMQNmZmZZEKD8DCDsLGl0xusbI+LG9fIcEBEzJG0NPCFpYuabERHS5t/r4MqAmZlZYc3LGAdQp4iYkf47R9J9wGDgY0ndImKWpG7AnM0NwN0EZmZm2arJw2MjJLWW1Lb2OXAEMA54EDglzXYK8MDmFsstA2ZmZsWtK3CfJEh+t2+PiMckvQbcLek0YBpw4uYewJUBMzOzLOVpzMAGRcQHwJ51pM8HDsvFMdxNYGZmVubcMmBmZpaNTbsVsFFxZcDMzCwrUbJrE7ibwMzMrMy5ZcDMzCxLpbqEsVsGzMzMylzRVAYkXSxpvKS3JL0haR9J50tq1QDHHirp4Xwfx8zMGrmI3D+KQFF0E0gaAhwNDIiIVZI6A81I1mm+DVhexzZNIqK6YSM1M7OyFaAsZgxsjIqlZaAbydzMqwAiYh5wPLAt8LSkpwEkLZX0B0lvAkMkXSBpXPo4P83TU9IESTelLQ0jJLVM3xuU0fLwe0njMoOQVCHpXUldMl6/V/vazMysFBVLZWAEsL2kyZKul3RwRFwNzAQOiYhD0nytgVciYk9gBfBdYB9gX+B0SXul+XoD10XE7sBC4Lg0/e/AmRHRH/hMq0JE1JC0RJycJh0OvBkRc3NaWjMza5zcTZA/EbFU0t7AgcAhwF2SLqwjazXw7/T5AcB9EbEMQNJ/0u0fBKZExBtpvjFAT0kdgLYR8VKafjtJ18T6/kay2MOfgFNJKhBFZeDQxZz1q5k0qQgevaMjd1+72UtYF71yKiuUV3m7dPuUn141lQ6dV0PAI7d35v6/bl3osPKie68VXHTV5DWvu/VYxT//tD33/6NbAaPKr3L6LJeCoqgMAKT9/88Az0h6m7UrMWVameU4gVUZz6uBlpsQx3RJH0s6lGSJyJM3tk1DqqgIzr18Bhd9bUfmzarkmkfe5eXH2/Phuy0KHVrOlVNZofzKW10tbvzldrw3rhUtW1dz7aMTGftcWz58N+v/ro3GjCkt+f5XkqnlKyqCf74whhdHdCxwVPlT0p/l4riQz7mi6CaQ1EdS74yk/iQrMC0B2taz2SjgGEmt0iUdj03T6hQRC4ElkvZJk762gZBuJukuuKfYBin22Ws5M6c2Y/aHzVldVcEzD3RgyJGLCh1WXpRTWaH8yrtgTiXvjUtuFlqxrAnT321B522qChxV/vXfbxGzPmzBnJnNCx1K3pTyZ1kROX8Ug6KoDABtgFskvSPpLaAvMBy4EXisdgBhpogYC/wDeBV4Bbg5Il7fyHFOA26S9AbJ+IP6Pp0PpjEVXRdBp22qmDuz2ZrX82ZV0rlbaX6BllNZofzKm6nrdqvYaY/lTHy9daFDybuDvzSPZx/uVOgw8qqcP8uNVVF0E0TEGGC/Ot66Jn3U5muz3nZ/BP64XtpUYI+M11dkvD0+IvoBpGMSRqd5niHpoqi1J8nAwYmbXBgz2yQtWlVz6Y0fcMPw7Vi+tEmhw8mrppU17HPYJ/z9ih6FDsU2V5FcyedaUVQGGtCXJF1EUu5pwHfWz5BWEs5mI2MFJJ0BnAHQgrzPi7TG/NmVdNn20zWvO3erYt6sygY7fkMqp7JC+ZUXoEnT4NIbP+Cp+zrywqNbFTqcvBt48ELef6c1C+c323jmRqwcP8uNXbF0EzSIiLgrIvpHxB4R8aW6bhmMiN9GxA4R8fxG9nVjRAyMiIGVNFzf36Q3WtG916d03X4VTStrGDpsIS+PaN9gx29I5VRWKL/yQnDBFdOY/l4L/nNTeYw0H3r0PJ55qHOhw8i7kv0sB1CTh0cRKLeWgUavplpcd3F3Lr/9AyqawIg7OzJtcgmM0K1DOZUVyq+8uw9axuHHL+CDCS24/vEJAPz9d9vy2lMl8KNRh+Ytq9lr/0VcfcmOhQ4l78rts1wKFCXa/9GQ2qlj7KPDCh2G2ZarKO0++/VVtCjdEf3rq1n+mVndS9YrMZLFsUC53m/71tvGvn3PzPVuGTF6+JiIGJjzHW+CsmwZkNQ0IlYXOg4zM2tkSvQCulGPGZB0v6Qx6RoEZ6Rpp6XTGr+ark9wbZr+D0k3SHoF+D9JO0l6LN1+lKRd03xdJP1b0mvpY/8CFtHMzCzvGnvLwKkRsSBdiOg1Sf8FLgUGkExY9BTwZkb+7YD9IqJa0kjgrIh4N52I6HrgUOAq4MqIeF5SD+BxYLcGLJOZmRWrEm0ZaOyVgfMkHZs+3x74FvBsRCwAkHQPsEtG/nvSikAbknkN7pHWdCvVdh4eDvTNSG8nqU1ELM1jOczMzAqm0VYGJA0l+eEeEhHLJT0DTGTDV/HL0n8rgIXp6oXrqwD2jYiVOQvWzMwav9pbC0tQYx4z0B74JK0I7EqyjHFr4GBJW0lqytqli9cREYuBKZJOAFBiz/TtEcAPavNK6p/HMpiZWSPitQmKz2NAU0kTgN8CLwMzgMtJ1it4AZhK/esPnAycJulNYDwwLE0/Dxgo6S1J7wBn5a0EZmZmRaDRdhNExCrgi+unSxodETemLQP3Afen+b+z3vZTgC/Usd95wEl5CNnMzBq7IrmSz7XG3DJQn+HpqoTjgCmklQEzMzOrW6NtGahPRPyk0DGYmVkpipJtGSi5yoCZmVleBCVbGSjFbgIzMzPbBG4ZMDMzy5bnGTAzM7NS5JYBMzOzLBXLJEG55pYBMzOzMueWATMzs2yVaMuAKwNmZmbZCKCmNCsD7iYwMzMrc24ZMDMzy0rpzkDolgEzM7My55YBMzOzbJVoy4ArA2ZmZtkq0cqAuwnMzMzKnFsGzMzMsuFbC83MzKxUuWUgB5bwybwn495pDXzYzsC8Bj5mIbm8DaG6wY8IhTy3ywpy1HL6LBeqrDvkZ7cBUZrLFroykAMR0aWhjylpdEQMbOjjForLW7rKqaxQXuUtybJ6AKGZmZmVIrcMmJmZZcMDCK0I3VjoABqYy1u6yqmsUF7lLaeyNmqKEu3/MDMzy6X2zbrGfl2/lvP9PvbR1WMKPbbCLQNmZmZlzpWBBiTpYknjJb0l6Q1J+0g6X1KrBjj2UEkP5/s46bGq0/KNl/SmpB9LqkjfGyjp6oaII1/qOo8FjGXpJuYfKmm/TchfNmXdHJKOkRSSds3ncRpSuXxPbbaI3D+KgAcQNhBJQ4CjgQERsUpSZ6AZcBdwG7C8jm2aRERh7vzeMisioj+ApK2B24F2wM8jYjQwOl8HltQ0Ilbncf/1ncfGYiiwFHhxYxnLqaxb4OvA8+m/P892o2L9v11m31OboXh+vHPNLQMNpxswLyJWAUTEPOB4YFvgaUlPQ3L1I+kPkt4Ehki6QNK49HF+mqenpAmSbkpr8CMktUzfG5RRo/+9pHGZQUiqkPSupC4Zr9+rfZ1rETEHOAP4vhJrav6SDk7jfEPS65LaSmojaaSksZLeljQsI/ZLJU2S9LykOyT9JE1/RtKfJI0Gfihpb0nPShoj6XFJ3dJ8O0l6LE0ftZlXc585jxExU9JhaRnelvQ3Sc3TY06V9Ju0jKMlDUhjel/SWRll+6mk19Jz94uMtPPS51dKeip9fqikf2Vse5mSFpiXJXVN074s6ZU0picldZXUEzgL+FEaz4Eu6+aT1AY4ADgN+FqaViHpekkTJT0h6RFJx2f8fX4naSxwgqQjJL2UftbvSfdHfZ/fBlKW31PmykBDGgFsL2ly+mVxcERcDcwEDomIQ9J8rYFXImJPYAXwXWAfYF/gdEl7pfl6A9dFxO7AQuC4NP3vwJnplflnausRUUNSwz85TToceDMi5ua0tOse8wOgCbD1em/9BDg3jfVAkvKuBI6NiAHAIcAflBhEUsY9gS8C6w+2aZYOwLkauAY4PiL2Bv4GXJbmuRH4QZr+E+D6zSjOZ86jpBbAP4CTIuJzJC1uZ2ds82FaxlFpvuNJzmftD+ERJOdzMNAf2FvSQWn+2h+xgUAbSZVp2nNpemvg5fTz8hxwepr+PLBvROwF3An8LCKmAjcAV0ZE/4gY5bJukWHAYxExGZgvaW/gq0BPoC/wLWDIetvMTz/bTwKXAIenr0cDF6Rlru/z2xDK9nsqKwHU1OT+UQRcGWggEbEU2JvkKnkucJek79SRtRr4d/r8AOC+iFiWbv8f1n5hTomIN9LnY4CekjoAbSPipTT99nrC+Rvw7fT5qST/MQvhBeCP6RVhh7R5X8Dlkt4i+cLsDnQF9gceiIiVEbEEeGi9fd2V/tsH2AN4QtIbJF+426VXXfsB96TpfyG5CtokdZ1H4EyS8zE5zXYLcFDGZg+m/75N8gW6JP1SW5WesyPSx+vAWGBXki/RMSQ/lu2AVcBLJD+UB5L8eAJ8CtT2sY4h+SEC2A54XNLbwE+B3V3WnPs6SeWD9N+vk/yfvSciaiJiNvD0etvUfk73JakwvJB+Hk8hmUK3zs9vHsuwDn9PlS+PGWhAab/aM8Az6RfXKXVkW5ll/9uqjOfVQMtNiGO6pI8lHUpyhXbyxrbZEpJ2JIlxDrBbRhy/lfRf4CiSL8UjSb4kuwB7R0SVpKlAiywOUzvLvIDxEbHOFVn6I7OwdizDlqjjPJ67kU1qz1UN6563GpL/gwJ+ExF/WX9DSVOA75D0e79F0lqyMzAhzVIVa+8Prmbt/+lrgD9GxIOShgLDsyrcesqprJtCUkfgUOBzkoKk5SuA+zayaebn9ImI+Pp6+/0cdXx+G1K5fk9lzWMGbEtI6iOpd0ZSf2AasARoW89mo4BjJLWS1Bo4lrVXSZ8REQuBJVo74ntDN8TeTNIMd08+B/+kfXw3ANdmfJHXvrdTRLwdEb8DXiO5SmwPzEkrAoewdsGRF4AvS2qRXuUfXc8hJwFdlAyEQlKlpN0jYjEwRdIJabok7bkZ5anrPL5PcsWzc5r2LeDZTdjt48CpGX3G3ZUMvITkfP+EpFl8FEk/+Ovr/y3r0B6YkT7P/DLf0OdtHeVU1s1wPPDPiNghInpGxPbAFGABcFzax92VZBBjXV4G9q/9O0pqLWkX6vn85qkMn1Gu31ObpETvJnBloOG0AW6R9E7aBN6X5ArmRuAxpQNzMkXEWJJ+11eBV4CbI+L1jRznNOCmtImxNbConnwPpjHlo+mtZTowaDxJU/8I0j7j9ZyvZMDRW0AV8CjwL2BgekXybWAiQES8lsb8VprvbeooW0R8SvJF/Tslg5veIOkegOTK4rQ0fTxJn++mqus8XkjSZ3pPGncNSQUoKxExgqSp9KV0+3tZ+8U7iqQ746WI+JhkTEU2/d/D03jGsO6qcQ8Bxyq7QXXlVNZN9XU+2wrwb2Ab4CPgHZIfsbHU/TmdS9IKckf6t30J2HUjn9+GUE7fU5bBMxCWGElt0n47JF0IdIuIH9aRbyDJ4Kqcj7LOl9qyKbnf+TngjPSLyKxoZHxOO5H8QO6fjh+wVGP9nmpf2SX263DcxjNuosfm/aXgMxB6zEDp+ZKki0jO7TSSq491pP/5zqZY+uCyd6OkviRjCG5xRcCK1MPpILlmwK9cEahTKX9PNUpuGTAzM8tC+6ZdYkiHY3O+38fn3+SWATMzs0ajRJcwdmWgACQNJ5kmtR3wXEQ8WdiILFd8bs2sMXJloIAi4n/ztW9JIukGKo7prcqMz+2mUZ7XlLDCKblzW6Jd6761sIEoWQlssqTnSWYZQ9I/tHbe8t/W3s4j6Yo07TNzrqfpXZTMez5e0s2SpknqrGQu8EmSbgXGkUwr+pl54NN9fFPSq+ltV3+R1KTB/yglwud2nb/F/Urm1B8v6Yw07bT07/Oqknnqr834G90g6RXg/1TP2hHp3+TfaVlfk7R/Q5XH1vK5LW1uGWgASuYs/xrJBB5NSe49HpPxfieSiTp2jYhIRyLD2jnXQ9L3gJ8BPyZZHe2piPiNpC+Q3LNbqzdwSkS8rHXngRfwoJJ54OcCJ5Hc8lQl6XqSEbu35uUPUMJ8bj/j1IhYoGRBmteUzDB5KTCAZOKap4A3M/JvB+wXEdWSRgJnRcS7SiakuZ5klr+rSG4ve15SD5KJi3bDGprPbUTRrCWQa64MNIwDSebuXg4g6cH13l9EMsHKX5Ws6Fc7//p2JHODdyO5TWlKmn4AyQ8MEfGYpE8y9jUtIl5On2fOAw/J5B29gX4k84+/JgmSKULn5KCc5cjndl3nSaodbr096QyFEbEAQNI9wC4Z+e9Jfywy146ofa95+u/hQN+M9HbKuE/dGozPbQlzZaAIRMRqSYOBw0hmH/s+Sa15c+ZcX5bxvM554CX9gOQ+/Yu2PHrbkHI6t2k5DgeGRMRySc+QzCC5oSu92jJVUP/aERUkrSgrcxasbRKf2wweM2Bb4DmSubtbSmoLfDnzzbTm3D4iHgF+RLJML9Q/5/oLwInptkcAW9Vz3PrmgR8JHJ8+R1JHSTvUsw/bMJ/btdoDn6Q/FruSLDrVGjhY0laSmrJ2Cdt1bGTtiBHAD2rzSuqfxzJY3XxuU1FTk/NHMXBloAGkM+XdRdKf9ijJojyZ2pLMWvYWSV/yBWn6cOqec/0XwBGSxgEnALNJ+uzWP26d88BHxDskS6OOSI/5BJuxnK/53K7nMaCppAnAb0kW45kBXE4yLe8LwFTqn4e+vrUjziNZr+ItSe+QLGBkDcvntsR5BsJGSFJzoDptgh4C/LmeJjhrZErx3GrtXP1NSRb3+VtEbGypX2sEyu3ctm/SKfZt8aWc73fE8n96BkLbLD2AuyVVAJ8Cpxc4HsudUjy3wyUdTrKmxAjg/sKGYznkc1siXBlohCLiXWCvQsdhuVeK5zYiflLoGCw/yu7cBp6O2MzMrOyVzsSf6/AAQjMzszLnlgEzM7MsBBAl2k3glgGzApFUrWT9gHGS7pHUagv2lbkWws2S+m4g71BJ+23GMaZK6pxt+np5NmlGOUnDJZVXf7RZAbkyYFY4KyKif0TsQXLnwDr3WKe3a22yiPheOt9AfYaSTA9rZpsiIhkzkOtHEXBlwKw4jAJ2Tq/aR6VrHLwjqYmk32vt6oRnwppZ3K5VspLhk8DWtTuS9IykgenzL0gaK+lNSSMl9SSpdPwobZU4UPWsHCepk6QRSldQJJkCeYNUx8p2Ge9dmaaPlNQlTatzNTuzYhU1kfNHNtL/y5MkvSfpwlyXy5UBswJLWwC+CLydJg0AfhgRu5CsWrgoIgYBg4DTJfUiWcyoD9AX+DZ1XOmnP7g3AcdFxJ7ACRExFbiBZKW4/hExirUrxw0imVL25nQXPweej4jdSSaU6ZFFcU6NiL2BgSQL23RK01sDo9N9PZvuG+BG4AfpNj8hWc3OzDIoWYb8OpLvib7A1zfUFbg5PIDQrHBaSnojfT4K+CvJj/qrEVG7iuERQL/a8QAkc8T3Bg4C7oiIamCmpKfq2P++wHO1+6pdXa4Oda4clx7jq+m2/9W6KyjWZ/2V7XoD84EakmmbAW4D/qMNr2ZnVpwK06w/GHgvIj4AkHQnyZTOG+oO3CSuDJgVzor1pxpOfxTXX53wBxHx+Hr5jsphHHWuHJfxA50V1b2yXYt6sgcbXs3OzNbqDkzPeP0RsE8uD+DKgFlxexw4W9JTEVElaReSBWKeA86UdAvJeIFDSBYuyvQycL2kXhExRVLHtHVgCdAuI1/tynG/h2TluIh4Iz3GN4BfS/oi9a+gWKuule1qVZAs4Xxnus/nI2KxpCmSToiIe5TUPvpFxJub8gcyayhL+OTxJ+PeDd45s5laSBqd8frGiLgxD8eplysDZsXtZqAnMDb9sZwLHEPSh38oSTPhh8BL628YEXPTQXz/Sdc6mAN8HngIuFfSMJJKwHnAdUpWOWxKUgk4i2QFxTskjQdeTI+zIY8BZ6Ur200iqYzUWgYMlnRJGsdJafrJwJ/T9EqSyoIrA1aUIuILBTr0DJJut1rbsXYJ9JzwqoVmZmZFLB1kPBk4jKQS8BrwjYgYn6tjuGXAzMysiKVLmn+fpNuwCclS0TmrCIBbBszMzMqe5xkwMzMrc64MmJmZlTlXBszMzMqcKwNmZmZlzpUBMzOzMufKgJmZWZlzZcDMzKzMuTJgZmZW5v4/gUWwfeFEMm4AAAAASUVORK5CYII=\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": 231,
   "metadata": {
    "scrolled": true
   },
   "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=1.00    r=0.65    diff=1.54\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=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": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q21.2 :  I support electric vehicles because they are better for the environment.\n",
      "slope=0.91    r=0.60    diff=1.45\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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\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\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lachl\\anaconda3\\envs\\ml\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1.1968678, 0.003150366435837215)\n",
      "Ttest_indResult(statistic=2.401726544352193, pvalue=0.016514268500800983)\n"
     ]
    }
   ],
   "source": [
    "het_vec = logit_model.person_het_vector.clone()\n",
    "x = het_vec[:,0].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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.0216366 , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.37498197, 0.        , 0.        , 0.        , 0.        ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(logit_model.sentence_to_neurons('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).').detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj/english-adjectives.txt') as f:\n",
    "    adjatives = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list(logit_model.sentence_to_neurons(\"slow\").detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "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": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj.loc[\"abandoned\"] = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3779393434524536"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj.loc[\"abandoned\", 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.89545083, 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(logit_model.sentence_to_neurons(word).detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1347/1347 [01:27<00:00, 15.48it/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": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adj\n",
       "zigzag       0.000000\n",
       "limping      0.000000\n",
       "loathsome    0.000000\n",
       "lonely       0.000000\n",
       "lost         0.000000\n",
       "               ...   \n",
       "recent       1.116578\n",
       "capital      1.118830\n",
       "early        1.119150\n",
       "front        1.177062\n",
       "annual       1.239342\n",
       "Name: 5, Length: 1347, dtype: float64"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj[5].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "# person activations\n",
    "personal_activations = logit_model.get_specific_activations(x_all, one_hot_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=40)"
      ]
     },
     "execution_count": 257,
     "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": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_dict = dict(list(enumerate(adjatives)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_embeds, _ = proces_sent(adj_dict, int(NUM_PEOPLE/3), get_strange_quest=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "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": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "893010.0"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2679030/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "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": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "activations = logit_model.get_specific_activations(adj_embed_short, one_hot_adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "#re orent activations into orthogonal dimensions\n",
    "activation_interpreter = PCA(n_components=7)\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": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "# look at the words that maximise each activation\n",
    "to_be_df = {\"word\": adj_dict.values()}\n",
    "for i in range(7):\n",
    "    to_be_df[i] = average_for_word[:, i]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_act_2 = pd.DataFrame.from_dict(to_be_df).set_index(\"word\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "word\n",
       "medium       -168.066177\n",
       "gregarious   -163.536331\n",
       "thick        -153.898056\n",
       "grandiose    -148.490005\n",
       "early        -148.208832\n",
       "                 ...    \n",
       "dental        206.647888\n",
       "disfigured    214.302948\n",
       "motherly      228.124924\n",
       "medical       230.919373\n",
       "zigzag        237.392609\n",
       "Name: 1, Length: 1347, dtype: float32"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index=1\n",
    "# print(adj_act_2[index].sort_values().head(10))\n",
    "# print(adj_act_2[index].sort_values().tail(10))\n",
    "adj_act_2[index].sort_values()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}