A_result_1_PCA_dimVar5-Copy2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using package ml ml \n",
    "\n",
    "import torch\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import cosine\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import decomposition\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchmetrics.functional import pairwise_cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj/english-adjectives.txt') as f:\n",
    "    adjatives = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "model = SentenceTransformer('bert-base-nli-mean-tokens')\n",
    "\n",
    "#from scipy import cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import parrot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# par = parrot(model_tag=\"prithivida/parrot_paraphraser_on_T5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# {'Q22.2': 'I like the idea of safe automated vehicles if they make it easier for me to travel.',\n",
    "#  'Q22.3': 'I trust automated vehicles to operate safely.',\n",
    "#  'Q22.4': 'I would consider buying an automated vehicle.',\n",
    "#  'Q22.5': 'I would be comfortable riding in an automated vehicle.',\n",
    "#  'Q22.6': 'I would allow my children to be transported in an automated vehicle.',\n",
    "#  'Q22.7': 'I would pay a higher price for a partly or fully automated vehicle than a non-automated vehicle.',\n",
    "#  'Q22.8': 'I would enjoy being able to use my time in an automated vehicle for things like reading, or relaxing rather than driving.',\n",
    "#  'Q22.9': 'I enjoy actually driving a car, and would prefer that to riding in an automated vehicle.'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pca = 40\n",
    "scen = 0\n",
    "embed_dim = num_pca\n",
    "removed_quest = ['Q20.2', 'Q21.2', 'Q22.2']\n",
    "\n",
    "pca = decomposition.PCA(n_components=embed_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "compress_method = \"PCA\" \n",
    "# none\n",
    "# PCA\n",
    "# auto_encoder\n",
    "NN_PARAM = [100, 10] # last number \n",
    "lamb = 5\n",
    "# parrot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "USE_MANUAL = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=num_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_sentences(doc_name=\"sentences.txt\"):\n",
    "    file = open(doc_name, 'r')\n",
    "    lines = file.readlines()\n",
    "    \n",
    "    ret_dic = {}\n",
    "    for index, line in enumerate(lines):\n",
    "        if index%2 == 0:\n",
    "            key= line.strip()\n",
    "        else:\n",
    "            ret_dic[key] = line.strip()\n",
    "    \n",
    "    return ret_dic\n",
    "\n",
    "sentences =load_sentences()\n",
    "question_num = list(sentences.keys())[:-1]\n",
    "\n",
    "def load_sentences_excel(doc_name=\"manual_values.xlsx\", subset = 0):\n",
    "    if subset == 0:\n",
    "        sheet_name = \"raw_questions\"\n",
    "    else:\n",
    "        sheet_name = f\"subset_{subset}\"\n",
    "        \n",
    "    df = pd.read_excel(\"manual_values.xlsx\", sheet_name=sheet_name)\n",
    "    \n",
    "    return dict(list(zip(df[\"Question\"], df[\"Sentence\"]))), df\n",
    "# sentences =load_sentences()\n",
    "# question_num = list(sentences.keys())[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_text_to_tens(sentences):\n",
    "    return torch.FloatTensor(model.encode(sentences))\n",
    "\n",
    "adj_embed = list_text_to_tens(adjatives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proces_sent(sent_dic, NUM_PEOPLE, get_strange_quest=7):\n",
    "    #assume reponses are ordered 1 per person\n",
    "    \n",
    "    sentences = []\n",
    "    questions = []\n",
    "    for key, sentence in sent_dic.items():\n",
    "        questions.append(key)\n",
    "        sentences.append(sentence)\n",
    "    \n",
    "    sent_embed = model.encode(sentences)\n",
    "    \n",
    "    \n",
    "    # find wierdest questions to be removed\n",
    "    sent_dic_distances = []\n",
    "    for base_vec, question in zip(sent_embed, sent_dic.keys()):\n",
    "        distances = []\n",
    "        for compare_vec in sent_embed:\n",
    "            distances.append(cosine(compare_vec, base_vec))\n",
    "        sent_dic_distances.append((question, sum(distances)/len(distances)))\n",
    "        \n",
    "    #pca.fit(sent_embed)\n",
    "    #red_embed = pca.transform(sent_embed)\n",
    "    sorted_sent_dic_distances = sorted(sent_dic_distances, key=lambda x: x[1])\n",
    "    bad_questions = [quest for quest, _ in sorted_sent_dic_distances][-get_strange_quest:]\n",
    "    \n",
    "    ret_tens = torch.FloatTensor(sent_embed)\n",
    "    return torch.concat([ret_tens for _ in range(NUM_PEOPLE)]), bad_questions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# foo = dict([a for a in enumerate(adjatives)])\n",
    "\n",
    "# measurements, _ = proces_sent(foo, 1 ,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# measurements.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_responses(questions, filepath = \"responses.xlsx\"):\n",
    "    df = pd.read_excel(filepath, sheet_name=\"Mobility and Future Mobility CL\")\n",
    "    df = df[questions]\n",
    "    df = df.replace([\"Strongly disagree\", \"Disagree\", \"Somewhat disagree\", \"Somewhat agree\", \"Agree\", \"Strongly agree\"], \n",
    "                    [0, 1, 2, 3, 4, 5])\n",
    "    \n",
    "    #df = df.replace([0,1,2,3,4,5], [0,0,0,1,1,1])\n",
    "    # TODO investigate NANS\n",
    "    df = df.fillna(0)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def proces_responses(responses):\n",
    "    ret_tens = responses.to_numpy()\n",
    "    x, y = ret_tens.shape\n",
    "    ret_tens = ret_tens.reshape(x*y)\n",
    "    \n",
    "    #everyone at least strongly disagrees\n",
    "    disagree = 1.*(ret_tens >= .5).reshape(len(ret_tens), 1)\n",
    "    som_disagree = 1.*(ret_tens >=1.5).reshape(len(ret_tens), 1)\n",
    "    som_agree = 1.*(ret_tens >=2.5).reshape(len(ret_tens), 1)\n",
    "    agree = 1.*(ret_tens >=3.5).reshape(len(ret_tens), 1)\n",
    "    stong_agree = 1.*(ret_tens >= 4.5).reshape(len(ret_tens), 1)\n",
    "    \n",
    "    ret_likard = np.concatenate((disagree, som_disagree, som_agree, agree, stong_agree), axis=1)\n",
    "    \n",
    "    #TODO process into hot 1 embedding \n",
    "    return x, torch.FloatTensor(ret_likard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_sentence(in_string):\n",
    "    x = model.encode(in_string)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import floor\n",
    "\n",
    "def get_people_one_hot():\n",
    "    num_q = int(len(y_train)/NUM_PEOPLE)\n",
    "    to_tens = []\n",
    "\n",
    "    for index in range(len(x_train)):\n",
    "        one_hot = floor(index/num_q)\n",
    "        temp_list = [0. for i in range(NUM_PEOPLE)]\n",
    "        temp_list[one_hot] = 1.\n",
    "        to_tens.append(temp_list)\n",
    "    \n",
    "    return torch.tensor(to_tens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# list(questions.keys()).index(['Q22.2', 'Q22.3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_quest(rem_questions, data_set):\n",
    "    indexes_to_be_rem = [list(questions.keys()).index(key) for key in rem_questions]\n",
    "    \n",
    "    remain_quest = [index for index in range(len(data_set)) if index%len(questions) not in indexes_to_be_rem]\n",
    "    removed_quest = [index for index in range(len(data_set)) if index%len(questions) in indexes_to_be_rem]\n",
    "    \n",
    "    remain_data_set = data_set[remain_quest]\n",
    "    removed_data_set = data_set[removed_quest]\n",
    "    \n",
    "    return remain_data_set, removed_data_set\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_manual_embed(df):\n",
    "    return df.iloc[:, 2:].to_numpy()\n",
    "\n",
    "def get_manual_embed_for_training(df, x_train):\n",
    "    data = get_manual_embed(df)\n",
    "    \n",
    "    if data.shape[0] * NUM_PEOPLE == x_train.shape[0]:\n",
    "        to_be_concat = [torch.tensor(data.copy()).type(torch.float32) for _ in range(NUM_PEOPLE)]\n",
    "    \n",
    "    return torch.concat(to_be_concat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_person_and_responses(data, one_hot_people, person_ID):\n",
    "    # used for taking the average of the persons responses\n",
    "    persons_questions = one_hot_people[:, person_ID] >= 0.5\n",
    "    \n",
    "    return data[persons_questions].sum(axis=1)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load quesitions\n",
    "questions, df = load_sentences_excel(subset=scen)\n",
    "\n",
    "quest_subset = questions.keys()\n",
    "\n",
    "#load responses\n",
    "responses = load_responses(quest_subset)\n",
    "\n",
    "\n",
    "#process everything\n",
    "NUM_PEOPLE, y_train = proces_responses(responses)\n",
    "\n",
    "x_train, bad_questions = proces_sent(questions, NUM_PEOPLE, get_strange_quest=5)\n",
    "\n",
    "one_hot_people = get_people_one_hot()\n",
    "\n",
    "if USE_MANUAL:\n",
    "    manual_embed = get_manual_embed_for_training(df, x_train)\n",
    "    x_train = manual_embed\n",
    "\n",
    "x_all = x_train\n",
    "y_all = y_train\n",
    "one_hot_all = one_hot_people\n",
    "# remove given questions\n",
    "x_train, x_test = remove_quest(removed_quest, x_train)\n",
    "y_train, y_test = remove_quest(removed_quest, y_train)\n",
    "one_hot_people, one_hot_people_test = remove_quest(removed_quest, one_hot_people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "927"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NUM_PEOPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7416"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8*927"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([37080, 768])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([37080, 768])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2781, 768])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([37080, 5])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2781, 5])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Logit(torch.nn.Module):\n",
    "    \"\"\"\n",
    "    Logit model between two options \n",
    "    \"\"\"\n",
    "    def __init__(self, input_size, num_people=927, dim_var=1):\n",
    "        super(Logit, self).__init__()\n",
    "        self.num_people = num_people\n",
    "        \n",
    "        if compress_method == \"auto_encoder\" or compress_method == \"parrot\":\n",
    "            self.input_size = NN_PARAM[-1]\n",
    "        elif compress_method == \"PCA\":\n",
    "            self.input_size = num_pca\n",
    "        elif compress_method == \"cosine_words\":\n",
    "            self.input_size = len(adj_embed)\n",
    "        else:\n",
    "            self.input_size = input_size\n",
    "        \n",
    "        self.has_fit_pca = False\n",
    "        \n",
    "        self.embedd_weights = torch.nn.Parameter(torch.rand(self.input_size, 1))\n",
    "        self.bias = torch.nn.Parameter(torch.rand(1,1))\n",
    "    \n",
    "        self.person_het_vector = torch.nn.Parameter(torch.rand(num_people, dim_var))\n",
    "        self.het_dimension = torch.nn.Parameter(torch.rand(dim_var, self.input_size))\n",
    "        \n",
    "        #self.util_to_likert = torch.nn.Linear(1, 5)\n",
    "        self.likert = torch.nn.Parameter(torch.rand(1, 5))\n",
    "        \n",
    "        \n",
    "        self.sigmoid = torch.nn.Sigmoid()\n",
    "        self.relu = torch.nn.ReLU()\n",
    "        self.softmax = torch.nn.Softmax(dim=1)\n",
    "        \n",
    "        # create an encoding nn network\n",
    "        if compress_method == \"auto_encoder\" or compress_method == \"parrot\":\n",
    "            #initialise hidden layers \n",
    "            self.encoder = torch.nn.ModuleList()\n",
    "            prev_layer = input_size\n",
    "            for layer_size in NN_PARAM:\n",
    "                self.encoder.append(torch.nn.Linear(prev_layer, layer_size))\n",
    "                prev_layer = layer_size\n",
    "    \n",
    "    def forward(self, chosen_options, pers_respond, floating_likart=False):\n",
    "        \n",
    "        chosen_options = self.compress_data(chosen_options, pers_respond)\n",
    "        \n",
    "        num_entries = chosen_options.shape[0]\n",
    "        \n",
    "        individ_variability = pers_respond @ self.person_het_vector\n",
    "        variabilty_vecs = individ_variability @ self.het_dimension\n",
    "        \n",
    "        seed_values = (self.embedd_weights @ torch.ones(1, num_entries)).T\n",
    "        \n",
    "        variabilty_vecs = variabilty_vecs + seed_values \n",
    "        \n",
    "        utility =  torch.sum((chosen_options * variabilty_vecs),  dim=1) + self.bias\n",
    "        \n",
    "        likart = utility.T @ torch.FloatTensor([[1,1,1,1,1]]) + torch.ones_like(utility.T) @ self.likert \n",
    "        \n",
    "        likart = self.sigmoid(likart)\n",
    "        # instead ->\n",
    "        return likart\n",
    "    \n",
    "    def get_specific_activations(self, chosen_options, pers_respond, floating_likart=False):\n",
    "        chosen_options = self.compress_data(chosen_options, pers_respond)\n",
    "        \n",
    "        num_entries = chosen_options.shape[0]\n",
    "        \n",
    "        individ_variability = pers_respond @ self.person_het_vector\n",
    "        variabilty_vecs = individ_variability @ self.het_dimension\n",
    "        \n",
    "        seed_values = (self.embedd_weights @ torch.ones(1, num_entries)).T\n",
    "        \n",
    "        variabilty_vecs = variabilty_vecs + seed_values \n",
    "        \n",
    "        return chosen_options * variabilty_vecs\n",
    "    \n",
    "    \n",
    "    def compress_data(self, x_data, pers_respond):\n",
    "        \n",
    "        if compress_method == \"none\":\n",
    "            return x_data\n",
    "        elif compress_method == \"PCA\":\n",
    "            return self.PCA(x_data, pers_respond)\n",
    "        elif compress_method == \"auto_encoder\" or compress_method == \"parrot\":\n",
    "            return self.auto_encoder(x_data)\n",
    "        \n",
    "        elif compress_method == \"cosine_words\":\n",
    "            return self.cosine_words(x_data, pers_respond)\n",
    "# none\n",
    "# PCA\n",
    "# auto_encoder\n",
    "# parrot\n",
    "    def cosine_words(self, x_data, pers_respond, compress_output=False):\n",
    "        new_distances = pairwise_cosine_similarity(x_data, adj_embed)\n",
    "        \n",
    "        \n",
    "        if compress_output:\n",
    "            return PCA(new_distances, pers_respond)\n",
    "        else:\n",
    "            return new_distances\n",
    "\n",
    "    def PCA(self, x_data, pers_respond):\n",
    "        #if already has transform -> transform\n",
    "        pers_respond\n",
    "        if not self.has_fit_pca:\n",
    "            single_set_of_questions = pers_respond[:,0] > 0.5\n",
    "            \n",
    "            fitting_space = x_data[single_set_of_questions]\n",
    "            pca.fit(fitting_space)\n",
    "            \n",
    "            self.has_fit_pca = True\n",
    "            \n",
    "        return_val = pca.transform(x_data)\n",
    "        return_val = torch.FloatTensor(return_val)\n",
    "        return return_val\n",
    "        \n",
    "    \n",
    "    def auto_encoder(self, x_data):\n",
    "        #simple forward pass NN to pass\n",
    "        for layer in self.encoder:\n",
    "            x_data = self.relu(layer(x_data))\n",
    "                                    \n",
    "        return x_data\n",
    "        \n",
    "    def sentence_to_neurons(self, sentence):\n",
    "        sentence = torch.tensor(encode_sentence(sentence)).reshape((1,768))\n",
    "        neuron_activation = self.compress_data(sentence, None)\n",
    "        \n",
    "        return neuron_activation\n",
    "    \n",
    "    def poll_question(self, sentence):\n",
    "        #polls a question with every participant in the study \n",
    "        option = torch.tensor(encode_sentence(sentence))\n",
    "        sentence_option = torch.ones(self.num_people, 1) @ option.reshape((1, len(option)))\n",
    "        one_hot_person = torch.eye(self.num_people)\n",
    "        \n",
    "        return self.forward(sentence_option, one_hot_person)\n",
    "        \n",
    "    def get_persons_values(self, ID):\n",
    "        one_hot_person = torch.zeros(NUM_PEOPLE)\n",
    "        one_hot_person[ID] = 1.\n",
    "        \n",
    "        individ_variability = one_hot_person @ self.person_het_vector\n",
    "        variabilty_vecs = individ_variability @ self.het_dimension\n",
    "        \n",
    "        #print(\"1\", variabilty_vecs)\n",
    "        #print(\"2\", self.embedd_weights)\n",
    "        \n",
    "        return variabilty_vecs + self.embedd_weights.T\n",
    "        \n",
    "    def get_individ_utility(self, sentence, ID):\n",
    "        #BROKEN\n",
    "        \n",
    "        values = self.get_persons_values(ID)\n",
    "        option = torch.tensor(encode_sentence(sentence))\n",
    "        compress_data(get_individ_utility)\n",
    "        #print(values.shape, option.shape)\n",
    "        \n",
    "        return  self.sigmoid(torch.sum((option * values),  dim=1) + self.bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 2])\n",
      "torch.Size([2, 2])\n"
     ]
    }
   ],
   "source": [
    "x = torch.tensor([[2, 3], [3, 5], [5, 8]], dtype=torch.float32)\n",
    "y = torch.tensor([[1, 0], [2, 1]], dtype=torch.float32)\n",
    "\n",
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim_of_variability = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit_model = Logit(input_size = x_train.shape[1], dim_var=dim_of_variability)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4881"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(logit_model.parameters(), lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "BCE_crit = torch.nn.BCELoss()\n",
    "def sparse_loss(y_pred, y_test, model, lamb):\n",
    "    loss_criteria = BCE_crit(y_pred.squeeze(), y_test)\n",
    "    \n",
    "    for w in model.encoder:\n",
    "        loss_criteria = loss_criteria + lamb * torch.norm(w.weight,p='fro')**2\n",
    "    \n",
    "    return loss_criteria\n",
    "    \n",
    "#criterion = torch.nn.BCELoss()\n",
    "criterion = torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "lamb = 0.005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss before training 15.592974662780762\n"
     ]
    }
   ],
   "source": [
    "logit_model.eval()\n",
    "y_pred = logit_model(x_train, one_hot_people)\n",
    "before_train = criterion(y_pred.squeeze(), y_train)\n",
    "print('Test loss before training' , before_train.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "perform = []\n",
    "test_perform = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: train loss: 15.592974662780762\n",
      "Epoch 0: test_loss loss: 23.448707580566406\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-43-02b55d234890>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;31m# -> https://datascience.stackexchange.com/questions/27561/can-the-number-of-epochs-influence-overfitting\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m \u001b[0mtrain_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprint_prog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-43-02b55d234890>\u001b[0m in \u001b[0;36mtrain_model\u001b[1;34m(print_prog)\u001b[0m\n\u001b[0;32m     10\u001b[0m         \u001b[0moptimizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m         \u001b[1;31m# Forward pass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m         \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogit_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mone_hot_people\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m         \u001b[1;31m# Compute Loss\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m         \u001b[0mloss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[0;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[1;32m-> 1102\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1103\u001b[0m         \u001b[1;31m# Do not call functions when jit is used\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-34-5fc4cd2269c4>\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, chosen_options, pers_respond, floating_likart)\u001b[0m\n\u001b[0;32m     43\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchosen_options\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpers_respond\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfloating_likart\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     44\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 45\u001b[1;33m         \u001b[0mchosen_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompress_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchosen_options\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpers_respond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     47\u001b[0m         \u001b[0mnum_entries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mchosen_options\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-34-5fc4cd2269c4>\u001b[0m in \u001b[0;36mcompress_data\u001b[1;34m(self, x_data, pers_respond)\u001b[0m\n\u001b[0;32m     82\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mx_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcompress_method\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"PCA\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpers_respond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcompress_method\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"auto_encoder\"\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mcompress_method\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"parrot\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_encoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-34-5fc4cd2269c4>\u001b[0m in \u001b[0;36mPCA\u001b[1;34m(self, x_data, pers_respond)\u001b[0m\n\u001b[0;32m    112\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhas_fit_pca\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 114\u001b[1;33m         \u001b[0mreturn_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    115\u001b[0m         \u001b[0mreturn_val\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFloatTensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreturn_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mreturn_val\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\sklearn\\decomposition\\_base.py\u001b[0m in \u001b[0;36mtransform\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    128\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean_\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m             \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 130\u001b[1;33m         \u001b[0mX_transformed\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcomponents_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    131\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhiten\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m             \u001b[0mX_transformed\u001b[0m \u001b[1;33m/=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplained_variance_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\numpy\\core\\overrides.py\u001b[0m in \u001b[0;36mdot\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "def train_model(print_prog = False):\n",
    "    # logit model \n",
    "    logit_model.train()\n",
    "    epoch = 1000\n",
    "    prev_test_loss = 100\n",
    "    \n",
    "    num_bad_steps = 8000000\n",
    "    \n",
    "    for epoch in range(epoch):\n",
    "        optimizer.zero_grad()\n",
    "        # Forward pass\n",
    "        y_pred = logit_model(x_train, one_hot_people)\n",
    "        # Compute Loss\n",
    "        loss = criterion(y_pred.squeeze(), y_train)\n",
    "        # Backward pass\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        perform.append(loss.item())\n",
    "        \n",
    "        \n",
    "        y_pred = logit_model(x_test, one_hot_people_test)\n",
    "        # Compute Loss\n",
    "        loss_test = criterion(y_pred.squeeze(), y_test)\n",
    "        test_perform.append(loss_test.item())\n",
    "        \n",
    "        if prev_test_loss <= loss_test.item():\n",
    "            num_bad_steps = num_bad_steps - 1\n",
    "            if num_bad_steps <= 0:\n",
    "                break\n",
    "            \n",
    "        prev_test_loss = loss_test.item()\n",
    "        \n",
    "        \n",
    "        if print_prog and epoch%100 == 0:\n",
    "            print('Epoch {}: train loss: {}'.format(epoch, loss.item()))\n",
    "            print('Epoch {}: test_loss loss: {}'.format(epoch, loss_test.item()))\n",
    "            print(\"------------------------------------------------------\")\n",
    "            \n",
    "    if print_prog:\n",
    "        print(\"VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\")        \n",
    "        print(f\"done with training loss of:{loss.item()} \\nand test loss of: {loss_test.item()}\")\n",
    "    \n",
    "# early stop is leagal \n",
    "# -> https://datascience.stackexchange.com/questions/27561/can-the-number-of-epochs-influence-overfitting\n",
    "\n",
    "train_model(print_prog = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 3.0)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=np.arange(len(perform)), y=perform, label=\"training_performance\")\n",
    "plt.plot(np.arange(len(test_perform)), test_perform, label=\"test_performance\")\n",
    "plt.legend()\n",
    "plt.ylim(0, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Model Works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logit_model(x_test, one_hot_people_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.9492, 0.8691, 0.7315, 0.3705, 0.0926],\n",
       "        [0.9690, 0.9172, 0.8198, 0.4956, 0.1455],\n",
       "        [0.9252, 0.8146, 0.6432, 0.2803, 0.0632],\n",
       "        ...,\n",
       "        [0.9421, 0.8525, 0.7035, 0.3389, 0.0816],\n",
       "        [0.9195, 0.8021, 0.6246, 0.2644, 0.0586],\n",
       "        [0.6303, 0.3771, 0.1990, 0.0509, 0.0092]], grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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": 74,
   "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": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from matplotlib.pyplot import figure\n",
    "import scipy\n",
    "\n",
    "def get_performance(model, question, print_conf_mat = True):\n",
    "    y_pred = model(x_all, one_hot_all)\n",
    "    \n",
    "    #fig = make_subplots(rows=rows, cols=col)\n",
    "    \n",
    "    _, quest_pred = remove_quest([question], y_pred)\n",
    "    _, quest_response = remove_quest([question], y_all)\n",
    "    \n",
    "#     # build 1 way \n",
    "#     scen=['Agreed With Prompt', 'Disagreed With Prompt']\n",
    "\n",
    "#     response_y = quest_response > 0.5\n",
    "#     model_y = quest_pred > 0.5\n",
    "\n",
    "#     #given the model predicted y:\n",
    "#     response_where_model_pred_y = response_y[model_y]\n",
    "\n",
    "#     prob_y_given_mod_y = sum(response_where_model_pred_y)/(len(response_where_model_pred_y))\n",
    "#     prob_n_given_mod_y = sum(~response_where_model_pred_y)/(len(response_where_model_pred_y))\n",
    "#     print(\"given predicted Y, model response of YN: \", prob_y_given_mod_y, prob_n_given_mod_y)\n",
    "\n",
    "#     response_where_model_pred_n = response_y[~model_y]\n",
    "#     prob_y_given_mod_n = sum(response_where_model_pred_n)/(len(response_where_model_pred_n))\n",
    "#     prob_n_given_mod_n = sum(~response_where_model_pred_n)/(len(response_where_model_pred_n))\n",
    "#    print(\"given predicted N, model response of YN: \", prob_y_given_mod_n, prob_n_given_mod_n)\n",
    "    \n",
    "    predictions = tens_to_int(quest_pred)\n",
    "    responses = tens_to_int(quest_response)\n",
    "    \n",
    "    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(predictions, responses)\n",
    "    average_diff = sum((predictions - responses)**2)/len(predictions)\n",
    "    \n",
    "    \n",
    "    scen=[\"Strongly\\ndisagree\", \"Disagree\", \"Somewhat\\ndisagree\", \"Somewhat\\nagree\", \"Agree\", \"Strongly\\nagree\"]\n",
    "#     fig = go.Figure(data=[\n",
    "#         go.Bar(name='Model Predicts Agree', x=scen, y=[prob_y_given_mod_y, prob_n_given_mod_y]),\n",
    "#         go.Bar(name='Model Predicts Disagree', x=scen, y=[prob_y_given_mod_n, prob_n_given_mod_n])\n",
    "#     ])\n",
    "    \n",
    "#     # Change the bar mode\n",
    "#     fig.update_layout(barmode='group')\n",
    "#     fig.show()\n",
    "    if print_conf_mat:\n",
    "        print(question, \": \", questions[question])\n",
    "        print(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        confusion_matrix = metrics.confusion_matrix(responses, predictions)\n",
    "        cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = scen)\n",
    "\n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "        cm_display.plot(ax=ax)\n",
    "        plt.title(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        plt.show()\n",
    "\n",
    "    return slope, r_value, float(average_diff) \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def predictions_from_average(question, print_conf_mat = True):\n",
    "    _, quest_response = remove_quest([question], y_all)\n",
    "    responses_of_question = tens_to_int(quest_response)\n",
    "    \n",
    "    \n",
    "    train_averages = []\n",
    "    for i in range(NUM_PEOPLE):\n",
    "        train_respons = get_person_and_responses(y_train, one_hot_people, i)\n",
    "        train_averages.append(round(float(sum(train_respons)/len(train_respons))))\n",
    "    \n",
    "    train_averages = np.array(train_averages)\n",
    "    \n",
    "    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(train_averages, responses_of_question)\n",
    "    \n",
    "    average_diff = sum((train_averages - responses_of_question.numpy())**2)/len(train_averages)\n",
    "    if print_conf_mat:\n",
    "        print(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        scen=[\"Strongly\\ndisagree\", \"Disagree\", \"Somewhat\\ndisagree\", \"Somewhat\\nagree\", \"Agree\", \"Strongly\\nagree\"]\n",
    "        confusion_matrix = metrics.confusion_matrix(responses_of_question, train_averages)\n",
    "        cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix= confusion_matrix, display_labels = scen)\n",
    "        fig, ax = plt.subplots(figsize=(8,8))\n",
    "        cm_display.plot(ax=ax)\n",
    "        plt.title(f\"slope={slope:.2f}    r={r_value:.2f}    diff={float(average_diff):.2f}\")\n",
    "        plt.show()\n",
    "    return slope, r_value, average_diff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 43/43 [00:46<00:00,  1.08s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 22\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_average(list_dict, key):\n",
    "    rel_list = [x[key] for x in list_dict]\n",
    "    return sum(rel_list)/len(rel_list)\n",
    "\n",
    "def get_all_performance():\n",
    "    test_diff = []\n",
    "    train_diff = []\n",
    "    test_r2 = []\n",
    "    train_r2 = []\n",
    "    for quest in tqdm(questions.keys()):\n",
    "        _, av_r2, av_diff = predictions_from_average(quest, False)\n",
    "        _, md_r2, md_diff = get_performance(logit_model, quest, False)\n",
    "        if quest in removed_quest:\n",
    "            test_diff.append({\"Question\": quest, \"Model\": md_diff, \"Average\": av_diff})\n",
    "            test_r2.append({\"Question\": quest, \"Model\": md_r2, \"Average\": av_r2})\n",
    "            if quest == \"Q22.2\":\n",
    "                Q22_av_r2 = av_r2\n",
    "                Q22_av_diff = av_diff\n",
    "                Q22_md_r2 = md_r2\n",
    "                Q22_md_diff = md_diff\n",
    "        else:\n",
    "            train_diff.append({\"Question\": quest, \"Model\": md_diff, \"Average\": av_diff})\n",
    "            train_r2.append({\"Question\": quest, \"Model\": md_r2, \"Average\": av_r2})\n",
    "            \n",
    "    \n",
    "    print(\"Test Set\")\n",
    "    labels = ['r2', 'Difference\\nSquared']\n",
    "    model = [get_average(test_r2, \"Model\"), get_average(test_diff, \"Model\")]\n",
    "    baseline = [get_average(test_r2, \"Average\"), get_average(test_diff, \"Average\")]\n",
    "\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(9,6))\n",
    "    rects1 = ax.bar(x - width/2, model, width, label='Model Performance')\n",
    "    rects2 = ax.bar(x + width/2, baseline, width, label='Naive Estimation Performance')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_xticks(x, labels)\n",
    "    ax.legend()\n",
    "\n",
    "#     ax.bar_label(rects1, padding=3)\n",
    "#     ax.bar_label(rects2, padding=3)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"Training Set\")\n",
    "    labels = ['r2', 'Difference\\nSquared']\n",
    "    model = [get_average(train_r2, \"Model\"), get_average(train_diff, \"Model\")]\n",
    "    baseline = [get_average(train_r2, \"Average\"), get_average(train_diff, \"Average\")]\n",
    "\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(9,6))\n",
    "    rects1 = ax.bar(x - width/2, model, width, label='Model Performance')\n",
    "    rects2 = ax.bar(x + width/2, baseline, width, label='Naive Estimation Performance')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_xticks(x, labels)\n",
    "    ax.legend()\n",
    "    \n",
    "#     ax.bar_label(rects1, padding=5)\n",
    "#     ax.bar_label(rects2, padding=5)\n",
    "    plt.show()\n",
    "    \n",
    "    \n",
    "    print(\"Question 22\")\n",
    "    labels = ['r2', 'Difference\\nSquared']\n",
    "    model = [Q22_md_r2, Q22_md_diff]\n",
    "    baseline = [Q22_av_r2, Q22_av_diff]\n",
    "\n",
    "    x = np.arange(len(labels))  # the label locations\n",
    "    width = 0.35  # the width of the bars\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(9,6))\n",
    "    rects1 = ax.bar(x - width/2, model, width, label='Model Performance')\n",
    "    rects2 = ax.bar(x + width/2, baseline, width, label='Naive Estimation Performance')\n",
    "\n",
    "    # Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "    ax.set_ylabel('Scores')\n",
    "    ax.set_xticks(x, labels)\n",
    "    ax.legend()\n",
    "\n",
    "#     ax.bar_label(rects1, padding=3)\n",
    "#     ax.bar_label(rects2, padding=3)\n",
    "    plt.show()\n",
    "get_all_performance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q19.5 :  I would be willing to pay between $600 and $1200 a month for a luxury car subscription with all vehicle costs covered (excluding fuel).\n",
      "slope=0.86    r=0.51    diff=1.11\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": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = get_performance(logit_model, \"Q19.5\")\n",
    "_ = predictions_from_average(\"Q19.5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q22.2 :  I like the idea of safe automated vehicles if they make it easier for me to travel.\n",
      "slope=0.86    r=0.60    diff=1.86\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": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q21.2 :  I support electric vehicles because they are better for the environment.\n",
      "slope=0.76    r=0.55    diff=1.81\n"
     ]
    },
    {
     "data": {
      "image/png": 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E5gPt6njaS8ChktaR1AY4LC2rVUTMAeZL2iUtOnolId1I0l0wpCEMUhw9shPz5zZfoWzxwuV5XKvWlUQJXe4yZmTnb9QX4Jdnfch//rk5ERkEVSRj3u7M/Hkr1vXAH3zGkFs2ZllFOQBzZ7fMIrR6IL5alNSxWbOgWbMoqb/jXLOmNWfs6OTCqMULy5n0SSu6rleRcVTFs8UOi5g8oQVfftaSZRVlvPBwRwZ+Z+6qn9jABQ2um2A/4MOI+Ly6QFI3SeXp/Y1Jus0/XdWBGkrLQFvgqrQpfxkwFjiBpPnjSUmTc8YNABARIyXdDLyZFt0YEW9L6rOS8xwP3CCpCniRFUdf5nqEpHsg8y6ClfnJyWPZ96DJLFzQjHNPWO2kslEZsNc0Zk5rxfhP6soNS0evDReydd/Z/ORXH7N0aRn/vmJLPnm/Y9ZhFUVZWXDVfz+gZ58lPHprNz4a1SbrkIque+8lbLLNIj58u3Tr2mW9CqZPXt6NN2NKc7bc8RsXhVmeJN0FDCLpTvgcuCgi/k3ypbbmwME9gd9LqgCqgJMiotbBh7kaRDIQESOAXWvZdFV6q95vhfbDiLgcuLxG2QRgm5zHl+VsHhMR2wGkYxKGp/u8QNJFUW17koGDH652ZerRrddsyq3XbMqRx43n4KMmccf1m2QdUlG0bFXJkcd9ygUn91v1ziWgrDxo176CM38+kM23msu5l47i+EP3guL0VWaqqkqcfMBWtGm/jN8OHseGmy9m4sel2XQO0GqdSi4c/CnXX9ybRQvKsw7H1kAWMxBGxDF1lP+slrL7WT62Lm8NopugHn0vvaxwNMlgwz/W3CFNEu4HzlvZgSSdIGm4pOFLqxYXJ9o8Pf/4euy279RMYyim9XovonuvxVx992vc9Ngwuq67hCvueJ1OXUqzL33mtFa8+nx3QHz8fkcioH3HpVmHVVQL5zXjndfa0X9Q429Krkt5s+DCwZ/y3IOdeeWJTlmHU1Qzv2xOt57L/2a79qhgxpRvdv1Zw9GkkoGIuCci+kbENhHxvdouGYyIP0fEhhHx8iqONTgi+kdE/xZl9f9NpucGC7++P2DQdD6fULpNjhPHtuPY/fbmuIP25LiD9mTGtJacfuwAZs8szb70117oznb9ZwLJ69yseZTElRM1dehcQZv2ywBo0bKKHfeYz6RxpTrALDjzsolMGtuKB24ohVH1K/fRqHXotdFSuq+/hGbNqxh0yBxeH9oh67DWWiCqovC3hqBBdBNUk3QxsABoDwyLiGeyjahhOOdP77Jdv9m071jBrU8O4/brN2Gn3WfQa8OFRJWYNqUVV1/yrazDLJhzLn2XbfvNon3HCm554kXuuH4Thj7cO+uwiuKcP45K67qUWx57jjsGb8bTj/TmjN++xzV3v8SyijIuv3g7SrGLoPO6FZx1+QTKy0FlwbDHOvHmsx2zDqsott5pIfsdPotPP2jFtU99AMB//tKTt55r/B+QtamqFNec34tL7/yUsnIYendnJn5cGoleqS5UpGhAQ7Ork4Ea/fyFPodI6l1VqGN2aL5uDOx6RKEO1+Alv8Imorxp9etWTp2WdQj1Kqoazvtf0VVlfmFUvXkjnmVezCr4G9UG27SPs+7budCH5YxvPTtiTS4tLKTMU5x0gaKPJb0MbJGW3Szp8PT+nyW9n04jfFladrCkN9IZlp6R1D0t7ybp6XQa4hslTZTUNZ2i+CNJtwKjSWY7/F9Jb6XH/V1OPD+S9GY6tuBf1ZdomJlZ0xZAVZQV/NYQZBpFOjvS0STzChwI7FRjexeS+QO2Tq8CqB7w9zIwICJ2AO4GzknLLwKeS6chvg/YIOdwmwHXptu2SB/vnJ67n6Q9JX0LOArYLWfK4swnHTIzMyumrMcM7EEypfAiAEmP1Ng+F/gK+LeSJYarlxnuTTJlcQ+S1Q3Hp+W7kyQPRMSTkmbnHGtiRLye3t8/vb2dPm5LkhxsRzKT01tpU3hroGm1m5qZWR1EZQmO34Hsk4GViohlknYG9iVZxfAUYB+SuQcuj4hHJA0CLs7jcAtz7gv4U0T8K3cHSacCt0TESi8rNDOzpqe6m6AUZV2rYSRTCreW1A44OHejpLZAh4h4HPgNyWRAAB2AL9L7uWsYvEKylCOS9gfqupj3KeC49PhI6iVpXeBZ4PD0PpI6S9pwLetoZmbWoGXaMpBOKXwP8A5Jc/xbNXZpBzwsqRXJt/kz0/KLgSFpN8BzwEZp+e+AuyT9GHgN+JJkfYOaMxcOTccHvJZ2BywAfhQR70u6ABgqqQyoAE4mWSfBzMyaOHcTFElEXAJcspJdvnEdR0Q8TLLMcE1zge+k3QsDgZ3SFZwmkDNFcXqMK4Arajn2PcA9eVfAzMyskcs8GSiwDYB702/1S4FfZhyPmZmViAiV7JiBkkoGIuITYIes4zAzs9K0lksON1ilWSszMzPLW0m1DJiZmRVLAFUlOoDQLQNmZmZNnFsGzMzM8iKPGTAzM7PS5JYBMzOzPCTTEZfmmAEnA2ZmZnmqLNEG9dKslZmZmeXNLQNmZmZ5CFSy3QRuGTAzM2vi3DJgZmaWp6oS/Q7tZMDMzCwPEVDpbgIzMzMrRW4ZMDMzy5MHEJqZmVlJcsuAmZlZHpJLC0vzO7STgUKorCTmzss6ivrTunXWEdQbtWyRdQj1qqxL56xDqFeVU6dlHYI1MpVewtjMzMxKkVsGzMzM8lDKCxW5ZcDMzKyJc8uAmZlZXkp3AGFp1srMzMzy5pYBMzOzPFWV6NUETgbMzMzy4LUJzMzMrGS5ZcDMzCxPHkBoZmZmJcktA2ZmZnlI1iYozTEDTgbMzMzyVKpXE7ibwMzMrIlzy4CZmVkevDaBmZmZZULSTZKmSRqdU3axpC8kjUpvB+ZsO0/SWEkfSfpOPudwy4CZmVmeMrq08GbgauDWGuX/iIjLcgskbQUcDWwN9ASekbR5RFSu7AROBszMzPIR2VxNEBHDJPXJc/dDgLsjYgkwXtJYYGfgtZU9yd0EZmZmjdMpkt5NuxE6pWW9gEk5+3yelq2UkwEzM7M8BMmlhYW+AV0lDc+5nZBHONcBmwB9gSnA39embu4mMDMzy9aMiOi/Ok+IiKnV9yXdADyWPvwCWD9n195p2Uq5ZcDMzCxPVem4gULe1oSkHjkPDwOqrzR4BDhaUktJGwGbAW+u6nhuGTAzM2vAJN0FDCLpTvgcuAgYJKkvSe/FBOBEgIgYI+le4H1gGXDyqq4kACcDZmZmeclq0qGIOKaW4n+vZP9LgEtW5xxOBszMzPJUqjMQOhlohG4e9jaLFpZTVSkqK8Xph2yTdUgFdcYfPmTnvWYyZ1Zzfn3ozgC07VDBeZe9z7q9vmLaF63401lbsWBe84wjXXunXzSanfeYzpxZLTj5yN1W2HbYjybwizM/5ph9BjFvTouMIiysMy4aw857JvX99RG7AvDjX49lwF7TqQqYO6sFl1+0NbOmt8o40sLrP2geJ/1hMuVlwRN3debeq7tnHVJRNbX6NnaZDSCUVJlOoThG0juSzpJUlm7rL+nKrGJrDM794bc45aBtSy4RAHjmofW48MTtVig78hefMeqNjvzywF0Y9UZHjvjFZxlFV1jPPNqT357S7xvlXbt/xQ4DZzJtSml9KD7zaE8uPHnHFcruu6UPJx81kFOPHsibL3Xjhyd8mlF0xVNWFpx86RdccOxG/HLQFux9yBw22OyrrMMqmlKtb/USxg1hAGGhZXk1weKI6BsRWwPfBg4gGRRBRAyPiNOKdWJJbhFpwEaP6Mj8uSu+RAP2nsEzD60HJMnCwH1mZBFawY0Z2Zn5c7/ZwvHLsz7kP//cnIgMgiqi0SM7faO+ixcuf61bta4kGsibYyFtscMiJk9owZeftWRZRRkvPNyRgd+Zm3VYRdPU6lsKGsSlhRExDTiBZDYlSRok6TEASXvlLMTwtqR2ktpKelbSSEnvSTqk+liSLkwXZ3hZ0l2Szk7LX5D0T0nDgdMl9ZP0oqQRkp6qvkxD0iaSnkzLX5K0ZQa/kpWKEJfc8iFXPvweBxw9Letw6kXHLkuZPaMlALNntKBjl6UZR1Q8A/aaxsxprRj/SbusQ6k3Pzl5LLc8MYxBB0zhtus2yTqcguuyXgXTJy/v6pkxpTlde1RkGFFxlXJ9izTpUOYazDfkiPhUUjmwbo1NZ5NcGvGKpLZAdVvTYRExT1JX4HVJjwD9gR8A2wPNgZHAiJxjtYiI/pKaAy8Ch0TEdElHkYy8PA4YDJwUEZ9I2gW4FtinKJVeQ2cfuRUzp7agQ5cKLr31QyaNa8Xot9pnHVY9Ukl+ewRo2aqSI4/7lAtO/mbXQSm79ZpNufWaTTnyuPEcfNQk7ri+9BICKwFRugMIG0TLwCq8Alwu6TSgY0QsAwRcKuld4BmSeZe7A7sBD0fEVxExH3i0xrHuSX9uAWwDPC1pFHAB0DtNNnYFhqTl/wJ6UAtJJ1RPHbmUJYWrbR5mTk0y7rkzm/Pq0E5ssf3Cej1/FubMbEGnrsnvuVPXJcyd1fgHD9Zmvd6L6N5rMVff/Ro3PTaMrusu4Yo7XqdTl/r9G8vK84+vx277Tl31jo3MzC+b063n8tasrj0qmDGlNP+GoenVtxQ0mGRA0sZAJbBCu3dE/Bn4BdAaeCVttj8W6Ab0i4i+wFQgn5FW1Z+aAsakYxb6RsS2EbE/ye9jTk5534j4Vm0HiojBEdE/Ivq3oOXqV3gNtWxdSes2lV/f33H3uUz4uHW9nT8rrz/flf0O/RKA/Q79ktef75pxRMUxcWw7jt1vb447aE+OO2hPZkxryenHDmD2zPr7G6tvPTdYnswOGDSdzye0yTCa4vho1Dr02mgp3ddfQrPmVQw6ZA6vD+2QdVhFU6r1rZ5noBQHEDaIbgJJ3YDrgasjIiTlbtskIt4D3pO0E7Al0AGYFhEVkvYGNkx3fwX4l6Q/kdTtIJJm/5o+ArpJGhgRr6XdBpunMzeNl3RERAxREsh2EfFOkaq+2jp1reDC6z8BoLw8eOGRLowY1jHboArsnL+9z3Y7zaF9xwpuffZVbr9mI4bcuAHnXT6G/f/nS6ZNbsmfzto66zAL4pxL32XbfrNo37GCW554kTuu34ShD/fOOqyiOedP77Jdv9nJa/vkMG6/fhN22n0GvTZcSFSJaVNacfUltebfjVpVpbjm/F5ceuenlJXD0Ls7M/Hj0rpSJFdTq28pUGQ0XFlSJfAeSd/+MuA24PKIqJI0CDg7Ig6SdBWwN1AFjAF+BrQj6QJoCwwHBgAHRMQESRcDPyRpLZgGPBkRN0h6IT3m8PT8fYErSRKLZsA/0/02IlkNqkca290R8fuV1aVDWZcY0OrAAvxWGge1Lv2WiGpqWRrX9+crq/eDrFRObRoDcJuaN+JZ5sWsgn/lbr9F99jp+mMLfVie2+cfI1Z3oaJCy6xlICLKV7LtBeCF9P6pteyyBBhYx9Mvi4iLJa0DDCMdQBgRg2qcYxSwZy3nHg98d1Xxm5lZ01I9z0ApahDdBAU2WNJWJGMIbomIkVkHZGZm1pCVXDIQET/MOgYzMytNpXpZc4O5msDMzMyyUXItA2ZmZsXSUGYMLDS3DJiZmTVxbhkwMzPLQ5TwdMROBszMzPLkAYRmZmZWktwyYGZmlpfSnXTILQNmZmZNnFsGzMzM8lSqYwacDJiZmeWhegnjUuRuAjMzsybOLQNmZmb5iGSugVLklgEzM7Mmzi0DZmZmeSrVtQmcDJiZmeUhKN2rCdxNYGZm1sS5ZcDMzCwvnoHQzMzMSpRbBszMzPLkSwvNzMysJLllwMzMLE+lejWBkwEzM7M8RDgZsJWRoLw86yjqT4vmWUdQb6rW65J1CPUqykvzja5OU6dlHYFZg+BkwMzMLE++tNDMzMxKklsGzMzM8lSqlxY6GTAzM8tTqQ4gdDeBmZlZE+eWATMzszwEcsuAmZmZlSa3DJiZmeWpRMcPumXAzMwsL+kMhIW+rYqkmyRNkzQ6p+xvkj6U9K6kByV1TMv7SFosaVR6uz6fqjkZMDMza9huBr5bo+xpYJuI2A74GDgvZ9u4iOib3k7K5wROBszMzPIVRbit6pQRw4BZNcqGRsSy9OHrQO+1qZaTATMzs8btOOCJnMcbSXpb0ouS9sjnAB5AaGZmlqciXVrYVdLwnMeDI2JwPk+UdD6wDLgjLZoCbBARMyX1Ax6StHVEzFvZcZwMmJmZ5alI0xHPiIj+q/skST8DDgL2jUgii4glwJL0/ghJ44DNgeF1HQfcTWBmZtboSPoucA7w/YhYlFPeTVJ5en9jYDPg01Udzy0DZmZmeQiyWZtA0l3AIJLuhM+Bi0iuHmgJPC0J4PX0yoE9gd9LqgCqgJMiYlatB87hZMDMzKwBi4hjain+dx373g/cv7rncDJgZmaWjwC8NoGZmZmVIrcMmJmZ5alIVxNkzsmAmZlZvko0GShqN4Gk8yWNSRdSGCVpl2KebxWxLFjN/QdJ2rVY8ZiZmTUURWsZkDSQZDKEHSNiiaSuQItina8IBgELgFczjsPMzBqE/FYZbIyK2TLQg2RWpeqZkGZExGRJ+6ZzJr+XLsvYEkDSBEl/SlsQhkvaUdJTksZJ+nrVJUn/K+mttLXhdzllp6X3/yHpufT+PpLuyHnuJZLekfS6pO5p2cGS3khjekZSd0l9gJOA36Tx5DW3s5mZWWNUzGRgKLC+pI8lXStpL0mtSJZiPCoitiVpmfhVznM+i4i+wEvpfocDA4DqD/39SWZT2hnoC/STtGe6f/UHdn+graTmadmwtLwNyaQM26dlv0zLXwYGRMQOwN3AORExAbge+Ee6BORLhfqlmJlZI5bBqoX1oWjdBBGxIF0kYQ9gb+Ae4E/A+Ij4ON3tFuBk4J/p40fSn+8BbSNiPjBf0hJJHYH909vb6X5tSZKDW0kSg/YkczKPJEkK9gBOS/ddCjyW3h8BfDu93xu4R1IPkm6M8YWof7H02mgx513x0dePe6y/hNuuWJ+Hbu6ZYVSFdcZFY9h5z+nMmdWCXx+RDNv48a/HMmCv6VQFzJ3Vgssv2ppZ01tlHOna69p1IWef9TqdOn1FBDzx5KY8/PAWbLTRbE495S1atV7GtKlt+Otfd2XR4uZZh7tWmjev5LI/P03z5lWUlwcvvbI+t9+5HQd/7yMO+/5H9Oy5gCOP/R/mzWv8r2tt+g+ax0l/mEx5WfDEXZ259+ruWYdUVCVZ38hmBsL6UNSrCSKiEngBeEHSeyQf/CuzJP1ZlXO/+nEzQMCfIuJfNZ8oaTzwM5I+/ndJEpBNgQ/SXSqqF3IAKlle96uAyyPiEUmDgIvzqlxGvhjfmlO+3xeAsrLgtpeH8+rQztkGVWDPPNqTR+9Zn7P+MPrrsvtu6cNt124KwPeP+YwfnvApV1+yVVYhFkxlZRk33LgD48Z1pnXrCq688ineHrkeZ5z+JjfeuAPvjV6X/b89jh8c/gG33bZd1uGulYqKMv7f+fvy1VfNKS+v4u9/eZrhI3ry/gfdePOtXvz10mezDrFoysqCky/9gvOO3pgZU5pz1eOf8PpTHfjsk9JMfJpafUtB0boJJG0habOcor7AOKCPpE3Tsh8DL67GYZ8CjpPUNj1HL0nrptteAs4m6QJ4iaTP/+2cBKAuHYAv0vs/zSmfD7RbjdjqXd9d5zLls1ZMm1xa/8FGj+zE/LkrfgtevHB53tqqdWXJZOezZ7dm3LgkmVu8uDmTPmtPl66L6NVrPu+N7gbAyLfXY/fdJmUZZoGIr75KXtdmzapo1qyKCBj3aWemTmubcWzFtcUOi5g8oQVfftaSZRVlvPBwRwZ+Z27WYRVNSde3RLsJijlmoC1wi6T3Jb0LbAWcC/wcGJK2FFSR9M3nJSKGAncCr6XPv4/lH9gvkQxafC0ipgJfpWWrcnEazwhgRk75o8BhDXkA4V7fm8GLj3XNOox685OTx3LLE8MYdMAUbrtuk6zDKbh1113AJpvM5qMPuzJxYgcGDkxy1D32mETXrotW8ezGoaysimuueJy7b3uAkW+vx0cfN42/3y7rVTB98vKLqWZMaU7XHhUZRlRcTa2+paCYYwZGALVdp/8ssEMt+/fJuX8zyQDC2rZdAVxRy/OfBZrnPN68xva2OffvI0kkiIiHgYdrOd7HQINtl23WvIpd9pnFfy7bIOtQ6s2t12zKrddsypHHjefgoyZxx/WlkxC0alXBBee/zL8G78iixc35xz934VcnjeCYo0fz+hu9WLasNGYOr6oq4+TTD6RNm6X89v+GseEGc5j4WceswzJbDaXRKllTabzDZEDSCeklkMOXxlf1fv7+e85h3PttmDOzMU3dUBjPP74eu+07NeswCqa8vIoLzn+Z51/ow6uvrg/A55+35/wL9ua007/Liy9uyJQppdWMvnBhC955rzv9+03JOpR6MfPL5nTrufTrx117VDBjSuMeELoyJV1fdxNYrogYHBH9I6J/C9V/n/2gg6bzQhPqIui5wcKv7w8YNJ3PJ7TJMJpCCs444w0mTWrPgw9u+XVphw5JgikFRx89hscf37SuAzQaHdp/RZs2yQdEixbL2LHvl0z6vH3GUdWPj0atQ6+NltJ9/SU0a17FoEPm8PrQDlmHVTRNrb6loEGtTSDpYpJZ/9oDwyLimWwjaphatq5kh93mcuWFpdNMnuucP73Ldv1m075jBbc+OYzbr9+EnXafQa8NFxJVYtqUVlx9ybeyDrMgtt5qBvvtO4Hx4ztw9VVPAHDLLdvTs9d8DjroEwBefaU3Q5/eOMswC6Jz58WcdcbrlJcFKguGvbwBb77Vi0MO/ojD/+d9Onf6iuuufIK3RvTkn1dlNnN5UVRVimvO78Wld35KWTkMvbszEz8urYG/uUq6vg3km3yhadWD7etPdTIQEZcV8RwiqXdVoY7ZobxrDFjnoEIdrsFT21L5Vr5q0aPptL4ARHlp9ofWJUaMyToEK4I34lnmxayC/zG33Kh39Ljo1EIflok/P3dERPQv+IFXQ+bdBOliRh9LehnYIi27WdLh6f0/V1+RIOmytOwbUwin5d0kPZ0ujnSjpImSukrqI+kjSbcCo0lmRvzGtMbpMX4k6c30KoJ/SSqv91+KmZk1PAGECn9rADJNBtIZCo8mmYPgQGCnGtu7AIcBW0fEdsAf003fmEI4Lb8IeC4itia5WiB3qP1mwLXpti2oZVpjSd8CjgJ2S6dFrgSOLWCVzczMGpysxwzsATwYEYsAJD1SY/tckvkC/i3pMZZPJ1zXFMK7kyQPRMSTkmbnHGtiRLye3q9rWuPtgH7AW0lvAq2BaQWop5mZlYAG1LNeUHUmA5KuYiVDJSLitLq2FUpELJO0M7AvyaJFpwD7sGZTCC/MuV/rtMaSTgVuiYjz1j56MzMrOSWaDKysm2A4yYI+dd0KYRhwqKTWktoBB+duTKcd7hARjwO/AbZPN9U1hfArwJHpc/cHOtVx3rqmNX4WOLx6imNJnSVtuJZ1NDMza9DqbBmIiFtyH0tap7o5v1AiYqSke4B3SJrj36qxSzvg4XTpYwFnpuUXk0whPBt4DtgoLf8dcJekHwOvAV+SrDGwwowtETE0HR/wWtodsAD4UUS8L+kCYKikMqCCZHGliYWrtZmZNVoNZMBfoa1yzICkgcC/ST5QN5C0PXBiRPy6EAFExCXAJSvZZedanlPrFMIkYwy+k3YvDAR2ioglwARgmxrHqGta43tIlls2MzNrEvIZQPhP4DvAIwAR8Y6kPYsZ1FrYALg3/Va/FPhlxvGYmVkJUYmOGcjraoKImJQ2p1erLE44ayciPqGWRZDMzMzWWgNaS6DQ8kkGJknaFQhJzYHTgQ+KG5aZmZnVl3ySgZNI+tZ7AZNJRuKfXMygzMzMGp6GM2Ngoa0yGYiIGXgWPjMzs5K1yumIJW0s6VFJ0yVNk/SwpMa/hJqZmdnqiiLcGoB81ia4E7gX6AH0BIYAdxUzKDMzswapCScD60TEbRGxLL3dDpTIwtRmZma2srUJOqd3n5B0LsnqgEGyqt/j9RCbmZlZw9JAvskX2soGEI4gqXb10MkTc7YF4MV8zMzMSsDK1ibYqK5tZmZmTU7QdC8tBJC0DbAVOWMFIuLWYgVlZmZm9SefhYouAgaRJAOPAwcALwNOBszMrEkp1bUJ8rma4HBgX+DLiPg5sD3QoahRmZmZNURN+NLCxRFRBSyT1B6YBqxf3LDMzMysvuQzZmC4pI7ADSRXGCwAXitmUGZmZlZ/8lmb4Nfp3eslPQm0j4h3ixuWmZmZ1ZeVTTq048q2RcTI4oRkZmbWMJXqAMKVtQz8fSXbAtinwLE0XmVlqG2brKOoN2rRIusQ6s3n+3bMOoR69cvj/pt1CPXqv9t3yzqEehPLlmUdQmloavMMRMTe9RmImZmZZSOfqwnMzMysGJcV5tHtIOkmSdMkjc4p6yzpaUmfpD87peWSdKWksZLeXVmXfy4nA2ZmZg3bzcB3a5SdCzwbEZsBz6aPIZkYcLP0dgJwXT4ncDJgZmaWrwxaBiJiGDCrRvEhwC3p/VuAQ3PKb43E60BHST1WdY5VJgNpk8OPJP02fbyBpJ1XHb6ZmVlpURT+toa6R8SU9P6XQPf0fi9gUs5+n6dlK5VPy8C1wEDgmPTxfOCavEI1MzOzVekqaXjO7YTVeXJErPXExvnMQLhLROwo6e30pLMlNZ1ry8zMzKoVZ56BGRHRfzWfM1VSj4iYknYDTEvLv2DFJQN6p2UrlU/LQIWkctJfgaRuQNXqxWxmZmYF9Ajw0/T+T4GHc8p/knbxDwDm5nQn1CmfloErgQeBdSVdQrKK4QWrHbaZmVljl8EMhJLuAgaRdCd8DlwE/Bm4V9LxwETgyHT3x4EDgbHAIuDn+Zwjn7UJ7pA0gmQZYwGHRsQHq1cVMzMzWxMRcUwdm/atZd8ATl7dc6wyGZC0AUl28WhuWUR8tronMzMza6zWcvR/g5ZPN8F/SRpGBLQCNgI+ArYuYlxmZmYNT1Nbm6BaRGyb+zid2vDXdexuZmZmjUw+LQMriIiRknYpRjBmZmYNWlPtJpB0Zs7DMmBHYHLRIjIzM7N6lU/LQLuc+8tIxhDcX5xwzMzMGq4mOYAwnWyoXUScXU/xmJmZNVwlmgzUOQOhpGYRUQnsVo/xmJmZWT1bWcvAmyTjA0ZJegQYAiys3hgRDxQ5NjMzs4ajic8z0AqYCezD8vkGAnAyYGZmVgJWlgysm15JMJrlSUC1vHIjSecDPwQqSRY3OjEi3ljDWNeKpAUR0XY19h8ELI2IV4sWlJmZNS5NsGWgHGjLiklAtVX+OiQNBA4CdoyIJZK6Ao1p6eNBwALAyYCZmSWaYDIwJSJ+vxbH7kGyRvMSgIiYASBpX+Cy9NxvAb9Kk4UJwF3AASSXMJ4A/AnYFPhbRFyfPv9/SVZnagk8GBEXpWVLIuJKSf8Ato+IfSTtAxwfEcemz72EJEFZDBwSEVMlHUyyCmMLku6QY4HWwElApaQfAadGxEtr8bswMzNrsOq8moDaWwRWx1BgfUkfS7pW0l6SWgE3A0el0xw3A36V85zPIqIv8FK63+HAAOB3AJL2BzYDdgb6Av0k7Znuv0d6jP5AW0nN07JhaXkb4PWI2D4t+2Va/jIwICJ2AO4GzomICcD1wD8ioq8TATMzg+WLFRXy1hCsrGXgG0sjro6IWCCpH8kH8t7APSTf9MdHxMfpbreQLLX4z/TxI+nP94C2ETEfmC9piaSOwP7p7e10v7YkycGtJIlBe2AJMJIkKdgDOC3ddynwWHp/BPDt9H5v4B5JPUhaB8avTb2L4YyLxrDzntOZM6sFvz5iVwB+/OuxDNhrOlUBc2e14PKLtmbW9FYZR1oYp1/wDjvvNo05s1tw8g/3AuD//XEkvTdMLmZp07aChQuac+qP91jZYRqFPp1m89eDnv76ce8O87j21Z14c1IvLtxvGOs0r2DyvHac+/h+LFzamHrZlnvngnWY+mJzWnYO9np4HgAjzmrDwvHJd5GK+aJ5u2DPB+azdI4YcUYb5oxuRu9Dl7DtBYuzDL1gmres4rIhH9G8RVDeLHjp8U7cfnnPrMMqqv6D5nHSHyZTXhY8cVdn7r26e9Yh2UrUmQxExKy1PXg6T8ELwAuS3mPVaywvSX9W5dyvftyMpLXiTxHxr5pPlDQe+BlJH/+7JAnIpsAH6S4V6TrPkAxorK77VcDlEfFIOmjw4rwqV4+eebQnj96zPmf9YfTXZffd0ofbrt0UgO8f8xk/POFTrr5kq6xCLKhnHuvNY0P6cOZFo74u+8sFO359//jT3mfRwuYZRFZ4E2Z34sjbjgSgTFU8c+KtPPvJxvz9+0/x9xd3ZcTnPTl0mw/4Wf9RXPPqzhlHu2Z6H7qUPj9cwqjz2nxd1u/vX1+lzPt/bU2ztsl/zbIWwRanLmb+2HLmfVJe77EWS8US8f+O3pyvFpVT3iz4+/0fMvz59nz4dt5jmhuVsrLg5Eu/4LyjN2bGlOZc9fgnvP5UBz77pDS+sJSilXUTrBVJW0jaLKeoLzAO6CNp07Tsx8CLq3HYp4DjJLVNz9FL0rrptpeAs0m6AF4i6fN/OycBqEsH4Iv0/k9zyuez4lTMmRk9shPz56744bd44fI8rlXrSqKEltUcM6oL8+fV9WEf7LHfFF4cWnrfqnbZ4AsmzenAlPnt2LDTXEZ83gOA1yauz36bf5pxdGuuS/9lNO9Q+3/DCJj8VAt6fm8pAM3Wgc79KilrnI0gKyG+WpQkN82aBc2aRUn9n61pix0WMXlCC778rCXLKsp44eGODPzO3KzDspUoWjJA0oR/i6T3Jb0LbAWcC/wcGJK2FFSR9M3nJSKGAncCr6XPv4/lH9gvkQxafC0ipgJfpWWrcnEazwhgRk75o8BhkkZJapDt0T85eSy3PDGMQQdM4bbrNsk6nHqxdd9ZzJnVksmT2qx650bmu1uO5YkPkzx53MxO7L3pBAD233wc67VbkGFkxTNrRDNadqmi7YZVWYdSdGVlwTVPvM/db7/DyJfb89Go0vsbrtZlvQqmT16e0c2Y0pyuPSoyjKiAogi3BmC1lzDOV0SMAHatZdOzwA617N8n5/7NJAMIa9t2BXBFLc9/Fmie83jzGtvb5ty/jySRICIeBh6u5XgfA9vVEn+Dces1m3LrNZty5HHjOfioSdxxfeknBHvtP7kkWwWalVUyaJMJXPFSsjr4b5/am3P3fpkTBwznhXF9qKgsZt6encmPt6DngUuzDqNeVFWJkw/Yijbtl/HbwePYcPPFTPy4ddZh2epoQAP+Cq0032HqgaQTJA2XNHxpVbaDnJ5/fD1223dqpjHUh7LyKnbd+0uGPdMj61AKbveNPuODqV2ZtWgdACbM6sRJ9x/M0bcfwRMfbsakOR0yjrDwqpbBlGea0/O7TSMZqLZwXjPeea0d/QeVbrP5zC+b063n8te1a48KZkwpjXE+pcrJwBqKiMER0T8i+rcoq//svucGywdgDRg0nc8nlG6TY7UddprB5xPaMnNa6X2bOmDLsTzx4fIhNp1bLwJABCfsMoIh75bG4NBcM15rRtuNKmm9Xol+1crRoXMFbdovA6BFyyp23GM+k8aV7mC6j0atQ6+NltJ9/SU0a17FoEPm8PrQEklo3U1gWTnnT++yXb/ZtO9Ywa1PDuP26zdhp91n0GvDhUSVmDalFVdf8q2swyyYc/7wNtvuOJP2HZdyy6PPcsfgzRj66Abs+e3SHDjYulkFAzecxB+e3vPrsgO2HMtRfZOrR54duzEPjd4yq/DW2siz2zDzrWYsnSOe2acDm5+8mA1+sJTJT7SgVy1dBM9+uz3LFoiqCjH1uRbsMng+7TZt3GMKOq9bwVmXT6C8HFQWDHusE28+2zHrsIqmqlJcc34vLr3zU8rKYejdnZn4cekmP6VAqx5sb6vSofm6MbDrEVmHUW/UouSGetdp0pEbZB1Cvfrlcf/NOoR69d/tu2UdQr2JZcuyDqHevBHPMi9mFfxyjVY9148+vzyz0Iflo9+fOSIi+hf8wKuhSbYMSGoWEU3nf4aZma014QGEDZKkhySNkDRG0glp2fHpFMhvSrpB0tVp+c2Srpf0BvBXSZtIejJ9/kuStkz36ybpfklvpbfdMqyimZlZ0TX2loHjImKWpNbAW5L+C1wI7EgyadBzwDs5+/cGdo2ISknPAidFxCeSdgGuBfYhuWzxHxHxsqQNSCY6Kp0OeTMzW3Ml2jLQ2JOB0yQdlt5fn3RGw+qplCUNAXLnGxiSJgJtSeZAGCJ93a3UMv25H7BVTnl7SW0jojRnfTEzsyav0SYD6ToC+wEDI2KRpBeAD1n5t/jq6/HKgDnpCok1lZGsYvhVwYI1M7PGz5MONUgdgNlpIrAlyVLHbYC9JHWS1Az4QW1PjIh5wHhJRwAosX26eShwavW+kvoWsQ5mZtaYlOg8A405GXgSaCbpA+DPwOskCw5dCrwJvAJMAOqa5utY4HhJ7wBjgEPS8tOA/pLelfQ+yYJHZmZmJavRdhNExBLggJrlkoZHxOC0ZeBB4KF0/5/VeP544Lu1HHcGcFQRQjYzs8augXyTL7TG3DJQl4sljQJGA+NJkwEzMzOrXaNtGahLRJyddQxmZlaaPIDQzMzMSlLJtQyYmZkVTYm2DDgZMDMzy0cDuhSw0NxNYGZm1sS5ZcDMzCxPHkBoZmZmJcktA2ZmZvkq0ZYBJwNmZmZ5cjeBmZmZlSS3DJiZmeXLLQNmZmZWitwyYGZmlo+MJh2StAVwT07RxsBvgY7AL4Hpafn/RcTja3IOJwNmZmZ5UHqrbxHxEdAXQFI58AXwIPBz4B8RcdnansPdBGZmZo3HvsC4iJhYyIM6GTAzM8tXFOG2eo4G7sp5fIqkdyXdJKnTGtUJJwNmZmZZ6yppeM7thNp2ktQC+D4wJC26DtiEpAthCvD3NQ3AYwYKoaqKmL8g6yjqTVRVZR1Cven1VNusQ6hXp55V0JbHBu+JzXfJOoR6U/nhuKxDqD+VxTt0kSYdmhER/fPY7wBgZERMBaj+CSDpBuCxNQ3ALQNmZmaNwzHkdBFI6pGz7TBg9Joe2C0DZmZm+cpo0iFJbYBvAyfmFP9VUl+SqCbU2LZanAyYmZnlK6NkICIWAl1qlP24UMd3N4GZmVkT55YBMzOzfIRXLTQzM7MS5ZYBMzOzfJVoy4CTATMzszy5m8DMzMxKklsGzMzM8uWWATMzMytFbhkwMzPLU6mOGXAyYGZmlo81W3K4UXA3gZmZWRPnlgEzM7N8uWXAzMzMSpFbBszMzPIgSncAoVsGzMzMmji3DJiZmeWrRFsGnAyYmZnlSVGa2YC7CczMzJo4twyYmZnlw5MOmZmZWalyy4CZmVmeSvXSQicDjVCbdss440/j2HCzRUSIf5y3CR++3S7rsIrm5mFvs2hhOVWVorJSnH7INlmHVDBduy3i7P99g06dviICnnh8Ex5+aHPO/b9X6b3+fADatlnKgoUtOOVX38k42tU37Yvm/O30DZgzvTkoOPBHMznsFzOYN7ucS0/qw9TPW9C991LO/9cE2nWs5J1X23LxzzdivfWXArDbgXP40ZlTM67FmunabRFn/b8309dWPPnfjXn4wc049idj+M6BnzJ3TksAbrlpW4a/2SPjaAurW4+l/O8VE+jYdRkEPH5nVx7697pZh1UYTgYaFkmHAg8C34qIDzMOp16ddOEEhg/ryCWnbEGz5lW0bFWVdUhFd+4Pv8W82c2zDqPgKivFDYO3Z9zYzrRuXcGV1wzl7ZHd+fOlu369zy9OeJtFC1tkGOWaK28WnPDbyWy23WIWLSjjlO9uzo57zufpezqzw+7zOerUadxz1brcc/W6/OKCKQBss8sC/nDr+IwjX3uVleLG67dn3NhOyWt73TOMHNEdgIfu35wHhmyRcYTFU1kpBv++N2NHr0PrNpVc/cSHjBzWjs8+aZ11aFaHxjxm4Bjg5fRn3iSVFyec+rFO22Vss9M8nro3ybKXVZSxcH6jzemavNmzWjNubGcAFi9uzqTP2tOl6+KcPYI995rEC89vkE2Aa6lL92Vstl1Sn3XaVrH+pkuYMaU5rz3Vgf2OnAXAfkfO4rUnO2QZZlEkr20nIHltP/usPV1XeG1L16xpzRk7eh0AFi8sZ9Inrei6XkXGURWGovC3hqBRJgOS2gK7A8cDR6dlZZKulfShpKclPS7p8HTbBEl/kTQSOELS/pJekzRS0pD0eEjqJ+lFSSMkPSWpwbXdrbf+EubOasaZfxnH1Y+8w+mXjqNl68qswyqqCHHJLR9y5cPvccDR07IOp2jW7b6QTTadw0cfdvm6bJttpzN7dismT2783UBfTmrBuNGt2XLHRcye0Zwu3ZcB0HndZcyesbzV54MRbThpvy04/9iNmfBRq6zCLajktZ3Nhx8mid/Bh4zlmsFDOePst2jbdmnG0RVX995L2GSbRXz4dpusQ7GVaJTJAHAI8GREfAzMlNQP+B+gD7AV8GNgYI3nzIyIHYFngAuA/dLHw4EzJTUHrgIOj4h+wE3AJfVRmdVRXh5suvVC/ntnd075/vZ8taiMI0/8IuuwiursI7fi1O9vy4XHbclBP57KNjvNyzqkgmvVqoILfvsK/7puBxYtWv7BOGjQZ7zYSFsFci1eWMYfftGHk37/BW3arditJYHSr0ebbruI2958n+uf+YhDjpvO747bKItwC6pVq2Wcf9GrDL62L4sXNee/j2zC8T85kFNO/DazZrbiFye9k3WIRdNqnUouHPwp11/cm0ULGnWj7HJRhFsD0FiTgWOAu9P7d6ePdweGRERVRHwJPF/jOfekPweQJAyvSBoF/BTYENgC2AZ4Oi2/AOhdVwCSTpA0XNLwpfFVQSqVjxlftmDGly356J3km+LLT3Zh060X1tv5szBzatJfPndmc14d2oktti+t+paXV3HBb1/l+ec25NVXlv/JlZVVsevunzPsxcadDCyrgD/8og/7/M9sdj9wLgCdulYwc2rSvTVzajM6dklaCdq0q6J1myRZ2Hnf+VRWiLkzG++HSHl5Fedf/CovPLshr76cvLZz5rSiqkrJoMLHN2bzLWZlHGVxlDcLLhz8Kc892JlXnuiUdTiFUYQuAncTrCFJnYF9gBslTQD+FziSZEGplan+BBHwdET0TW9bRcTxafmYnPJtI2L/ug4WEYMjon9E9G+h+mvKnD2jBdOntKDXRknfY99d5/LZ2NIdlNOydSWt21R+fX/H3ecy4eNSqm9wxplvMumzdjx4/4oDynbYcSqfT2rPjBnrZBTb2ouAy8/agPU3W8IPTpz+dfmA/efxzL1Jk/kz93Zm4HeSJGHWtGZUz/b64dvrUFUF7Ts31m6w4IyzhzNpYnsevH/zr0s7dV4+bmDX3b9g4oTSGy8BwZmXTWTS2FY8cEP3rIOxPDTGkWeHA7dFxInVBZJeBGYBP5B0C9ANGATcWcvzXweukbRpRIyV1AboBXwEdJM0MCJeS7sNNo+IMUWuz2q77vcbcc7ln9C8eTBlUkv+8f82zTqkounUtYILr/8ESLpIXnikCyOGdcw2qALaeusZ7PftiYz/tANXX/cUkFxq9tZbPdlr0GeNduBgtTFvtuHZ+zqz0bcW86v9kmTn5+dN5qhTpnLJSX148u4urNsrubQQ4KXHOvLYrV0obwYtW1Vx3nUT0KrS/AZqq21msm/62l51/VAgeW0H7f0ZG286hwgx9ct1uOqf/TKOtPC23mkh+x0+i08/aMW1T30AwH/+0pO3niuBxKeBfJMvNEUjW3RB0vPAXyLiyZyy04BvkXy7HwRMSu//JSKeTlsQ+kfEjHT/fYC/AC3TQ1wQEY9I6gtcCXQgSZT+GRE3rCqmDuVdY0Dr7xWmgo1BVelfyvi1TftkHUG9emLo3aveqYQcuN+RWYdQbyo/HJd1CPXmjcqhzItZBU8j23RZP7Y58DeFPixv3n7WiIjoX/ADr4ZG1zIQEXvXUnYlJFcZRMQCSV2AN4H30u19auz/HLBTLccZBexZ+KjNzKyxEw2nj7/QGl0ysAqPSeoItAD+kA4kNDMzK4xG1pqer5JKBiJiUNYxmJmZNTYllQyYmZkVU6l2EzS6SwvNzMyssNwyYGZmlo8GNGNgobllwMzMrIlzy4CZmVmeVKLTrDgZMDMzy5e7CczMzKwUuWXAzMwsT7600MzMzEqSWwbMzMzyEXg6YjMzs6auVLsJnAyYmZk1cJImAPOBSmBZRPSX1Bm4B+gDTACOjIjZa3J8jxkwMzPLVxThlr+9I6JvRPRPH58LPBsRmwHPpo/XiJMBMzOzxukQ4Jb0/i3AoWt6ICcDZmZmeRDJmIFC3/IUwFBJIySdkJZ1j4gp6f0vge5rWjePGTAzM8tHRLGuJugqaXjO48ERMbjGPrtHxBeS1gWelvThiqFFSGs+vNHJgJmZWbZm5IwDqFVEfJH+nCbpQWBnYKqkHhExRVIPYNqaBuBuAjMzszxl0U0gqY2kdtX3gf2B0cAjwE/T3X4KPLym9XLLgJmZWcPWHXhQEiSf23dGxJOS3gLulXQ8MBE4ck1P4GTAzMwsXxlMOhQRnwLb11I+E9i3EOdwN4GZmVkT55YBMzOzPHk6YqtTVFVRtXhx1mFYEZR/PmXVO5WQzV/86ap3KiFdtm+ddQj1ptPUGVmHUG80p7w4Bw6gqjSzAXcTmJmZNXFuGTAzM8tXaTYMuGXAzMysqXPLgJmZWZ48gNDMzKypK87aBJlzN4GZmVkT55YBMzOzPJVqN4FbBszMzJo4twyYmZnlIyjZSwudDJiZmeVBgDyA0MzMzEqRWwbMzMzyVZV1AMXhlgEzM7Mmzi0DZmZmefKYATMzMytJbhkwMzPLhy8tNDMza+rCaxOYmZlZaXLLgJmZWZ68NoGZmZmVpAaTDEg6X9IYSe9KGiVpF0lnSFqnHs49SNJjxT6PmZk1chGFvzUADaKbQNJA4CBgx4hYIqkr0AK4B7gdWFTLc8ojorJ+IzUzsyYrQJ6BsKh6ADMiYglARMwADgd6As9Leh5A0gJJf5f0DjBQ0pmSRqe3M9J9+kj6QNINaUvDUEmt02075bQ8/E3S6NwgJJVJ+kRSt5zHY6sfm5mZlaKGkgwMBdaX9LGkayXtFRFXApOBvSNi73S/NsAbEbE9sBj4ObALMAD4paQd0v02A66JiK2BOcAP0vL/ACdGRF/gG60KEVFF0hJxbFq0H/BOREwvaG3NzKxxcjdB8UTEAkn9gD2AvYF7JJ1by66VwP3p/d2BByNiIYCkB9LnPwKMj4hR6X4jgD6SOgLtIuK1tPxOkq6Jmm4CHgb+CRxHkkA0GGf+/TN22W8ec2Y048R9t8w6nKJravU95Edf8J0jpiDBk0PW4+Hbemcd0lrrev1nrPP2fCrbN+OLv20BQKc7JrPOyHlQLiq6t2DGSRtQ1ab86+eUz1hK77M/Yvbh3Zl30LpZhb7WHjj/DhYtaUFllaisEsf98wds1nMG5xz+Ei2aVVJZJS67fw/en9R461jtjN9/wM57zmTOrBb8+n92BmD3/adx7K/Gs/7Gi/jNMf345P32GUdpdWkoLQNERGVEvBARFwGnsPzbfK6v8hwnsCTnfiWrkfRExCRgqqR9gJ2BJ/J9bn0Yem9nzj9246zDqDdNqb4bbrqQ7xwxhd8ctQMnH9aPnQfNoscGi7MOa60t2KszX5670QplX23bji/+ugVf/HULKnq0pMPDU1fY3uW2ySzu264+wyyak687iJ9efjjH/TN5Szv5oDf499B+/PTyw7nhyZ04+aDXM46wMJ55uAcX/mr7FcomftKGP/5mW0aP6JhNUMUQRbg1AA0iGZC0haTNcor6AhOB+UBd7wgvAYdKWkdSG+CwtKxWETEHmC9pl7To6JWEdCNJd8GQhjZIcfQbbZk/p3zVO5aIplTf9TdZxEfvtmPJV+VUVYrRb3Vgt/1mZB3WWvvqW22partiPr54u3ZQLgCWbNaGZrMqvt62zltzqVi3BUt7t6rXOOtLAG1aLQWgbeulzJjXJtuACmT0iI7Mn7vi6zxpfBu+mFD0C8LqlSIKfmsIGkQyALQFbpH0vqR3ga2Ai4HBwJPVAwhzRcRI4GbgTeAN4MaIeHsV5zkeuEHSKJLxB3Pr2O+RNKYG1UVgpW3iJ23Ypt882nWooGWrSvrvOYuuPZas+omNXLsXZrFo+6T5WF9V0uHRacz5QfeMoyqMCHHFCY/znzPu55AB7wPwz4d25ZSD3uChC2/n1INf47rHd844SrOGM2ZgBLBrLZuuSm/V+7Wt8bzLgctrlE0Atsl5fFnO5jERsR1AOiZheLrPC8ALOfttTzJw8MPVrozZGpr06ToMubE3f7zxPZYsLuPTD9tSVamswyqqDg9OJcpg4e4dAeh031TmHdCNaFUarUEnXX0I0+e1oVPbxVxx4mNMnNaRvbcbzxUPD+SF9zZm3+3H8X9Hvshp/6pt+JI1SA3km3yhNYhkoB59T9J5JPWeCPys5g5pkvArll9RUCtJJwAnALSitJrBLDtDH+jB0Ad6APDTM8Yz48uWGUdUPG1fnMU6b8/jy/M3ASVJT8uxi1jnjTl0unMyZYsqQSKalzH/O10zjnbNTE+7AGYvaM2L723EVhtM58D+H/OPh5LvPs++szHnHfliliGaAU0sGYiIe0gmMlrZPn8G/pzHsQaTdGPQXp1LM1W0eteh81LmzmpBtx5fset+MzjzmB1W/aRGqPWoeXR4dBpTfrsp0XJ5b+WUizf9+n7H+76kqlXjTQRataigTMGiJS1o1aKCXbb4nJuG7siMeeuwwyZTeHtcT/pv9gWTpnfIOlTLVwAlOulQk0oGSsG510xgu4EL6NB5GbcPH8Ntl63HU3d3yTqsomlq9T3/ivdp33EZyyrEtX/clIXzG/9/0W5XTqTVBwson7+M9U9+n9mHd6fjw9NQRbDepeMAWLJpG2b+ovFfRpmrc9vF/PnnTwFQXhYMHbkpr3+0AYuGNOc3h7xKeXkVSyua8ef79sw40sI45y9j2G6nObTvWMGtz7zK7df0Yf7c5vzq/z6hQ6elXHztu3z6YVsuPKlv1qFaLRQl2v9Rn9qrc+xStl/WYVgRlHdoWtdFj72uT9Yh1Ksuj7TOOoR602nox1mHUG9em/MAcyumF3zATYc2PWPAVicW+rAMHX7xiIjoX/ADr4bG/7VjDUhqFhHLso7DzMwamRL9At1QLi1cI5IekjQiXYPghLTs+HRa4zfT9QmuTstvlnS9pDeAv0raRNKT6fNfkrRlul83SfdLeiu97ZZhFc3MzIqusbcMHBcRs9KFiN6S9F/gQmBHkgmLngPeydm/N7BrRFRKehY4KSI+SSciuhbYB7gC+EdEvCxpA+Ap4Fv1WCczM2uoSrRloLEnA6dJOiy9vz7wY+DFiJgFIGkIsHnO/kPSRKAtybwGQ6Svu5Wqr+HaD9gqp7y9pLYRsaCI9TAzM8tMo00GJA0i+eAeGBGLJL0AfMjKv8UvTH+WAXPS1QtrKgMGRMRXBQvWzMwavxK+tLAxjxnoAMxOE4EtSZYxbgPsJamTpGbUvtgRETEPGC/pCAAlqlfYGAqcWr2vpL5FrIOZmTUiXpug4XkSaCbpA5JJgl4HvgAuJVmv4BVgAnWvP3AscLykd4AxwCFp+WlAf0nvSnofOKloNTAzM2sAGm03QUQsAQ6oWS5peEQMTlsGHgQeSvf/WY3njwe+W8txZwBHFSFkMzNr7BrIN/lCa8wtA3W5OF2VcDQwnjQZMDMza4wkrS/p+XRl3zGSTk/LL5b0haRR6e3ANT1Ho20ZqEtEnJ11DGZmVooiq5aBZcBZETFSUjtghKSn023/qLE67xopuWTAzMysKIJMkoGImAJMSe/PT8fK9SrkOUqxm8DMzKwkSeoD7AC8kRadkg54v0lSpzU9rpMBMzOzfFUV4QZdJQ3PuZ1Q26nTCfPuB85IL5G/DtgE6EvScvD3Na2WuwnMzMyyNWNVqxZKak6SCNwREQ8ARMTUnO03AI+taQBOBszMzPKUxSRBSubH/zfwQURcnlPeIx1PAHAYyVV0a8TJgJmZWcO2G8naO++ll84D/B9wTDpLbpBMsnfimp7AyYCZmVm+srma4GVAtWx6vFDncDJgZmaWjwCqPAOhmZmZlSC3DJiZmeUlsxkIi84tA2ZmZk2cWwbMzMzyVaItA04GzMzM8lWiyYC7CczMzJo4twyYmZnlw5cWmpmZWalyy0ABzGf2jGeqhkys59N2BWbU8zmzlE19Z9f7GatlU9+j6/2MkOHf8rgsTtq0/u9mVdcNi3PYgKgqzqEz5mSgACKiW32fU9LwVa1yVUpc39LVlOoKTau+JVlXDyA0MzOzUuSWATMzs3x4AKE1QIOzDqCeub6lqynVFZpWfZtSXRs1RYn2f5iZmRVShxbdY9fuhR9l++TnV47IemyFWwbMzMyaOCcD9UjS+ZLGSHpX0ihJu0g6Q9I69XDuQZIeK/Z50nNVpvUbI+kdSWdJKku39Zd0ZX3EUSy1vY4ZxrJgNfcfJGnX1di/ydR1TUg6VFJI2rKY56lPTeV9ao1FFP7WAHgAYT2RNBA4CNgxIpZI6gq0AO4BbgcW1fKc8oiorN9IC2JxRPQFkLQucCfQHrgoIoYDw4t1YknNImJZEY9f1+vYWAwCFgCvrmrHplTXtXAM8HL686J8n9RQ/283sfepNdBwPrwLzS0D9acHMCMilgBExAzgcKAn8Lyk5yH59iPp75LeAQZKOlPS6PR2RrpPH0kfSLohzeCHSmqdbtspJ6P/m6TRuUFIKpP0iaRuOY/HVj8utIiYBpwAnKLE15m/pL3SOEdJeltSO0ltJT0raaSk9yQdkhP7hZI+kvSypLsknZ2WvyDpn5KGA6dL6ifpRUkjJD0lqUe63yaSnkzLX1rDb3PfeB0jYrKkfdM6vCfpJkkt03NOkPSntI7DJe2YxjRO0kk5dftfSW+lr93vcspOS+//Q9Jz6f19JN2R89xLlLTAvC6pe1p2sKQ30piekdRdUh/gJOA3aTx7uK5rTlJbYHfgeNLpmtL/T9dK+lDS05Iel3R4zu/nL5JGAkdI2l/Sa+nf+pD0eNT191tPmuT7lDkZqE9DgfUlfZy+WewVEVcCk4G9I2LvdL82wBsRsT2wGPg5sAswAPilpB3S/TYDromIrYE5wA/S8v8AJ6bfzL+RrUdEFUmGf2xatB/wTkRML2htVzznp0A5sG6NTWcDJ6ex7kFS36+AwyJiR2Bv4O9K7ERSx+2BA4Cag21apANwrgSuAg6PiH7ATcAl6T6DgVPT8rOBa9egOt94HSW1Am4GjoqIbUla3H6V85zP0jq+lO53OMnrWf1BuD/J67kz0BfoJ2nPdP/qD7H+QFtJzdOyYWl5G+D19O9lGPDLtPxlYEBE7ADcDZwTEROA64F/RETfiHjJdV0rhwBPRsTHwExJ/YD/AfoAWwE/BgbWeM7M9G/7GeACYL/08XDgzLTOdf391ocm+z6VlwCqqgp/awCcDNSTiFgA9CP5ljwduEfSz2rZtRK4P72/O/BgRCxMn/8Ay98wx0fEqPT+CKCPpI5Au4h4LS2/s45wbgJ+kt4/juQ/ZhZeAS5PvxF2TJv3BVwq6V2SN8xeQHdgN+DhiPgqIuYDj9Y41j3pzy2AbYCnJY0iecPtnX7r2hUYkpb/i+Rb0Gqp7XUETiR5PT5Od7sF2DPnaY+kP98jeQOdn76pLUlfs/3T29vASGBLkjfRESQflu2BJcBrJB+Ue5B8eAIsBar7WEeQfBAB9AaekvQe8L/A1q5rwR1DknyQ/jyG5P/skIioiogvgedrPKf673QAScLwSvr3+FOSKXRr/fstYh1W4PeppstjBupR2q/2AvBC+sb101p2+yrP/rclOfcrgdarEcckSVMl7UPyDe3YVT1nbUjamCTGacC3cuL4s6T/AgeSvCl+h+RNshvQLyIqJE0AWuVxmoXVpwPGRMQK38jSD5k51WMZ1kYtr+PJq3hK9WtVxYqvWxXJ/0EBf4qIf9V8oqTxwM9I+r3fJWkt2RT4IN2lIpZfH1zJ8v/TVwGXR8QjkgYBF+dVuRqaUl1Xh6TOwD7AtpKCpOUrgAdX8dTcv9OnI+KYGsfdllr+futTU32fypvHDNjakLSFpM1yivoCE4H5QLs6nvYScKikdSS1AQ5j+bekb4iIOcB8LR/xvbILYm8kaYYbUszBP2kf3/XA1Tlv5NXbNomI9yLiL8BbJN8SOwDT0kRgb5YvOPIKcLCkVum3/IPqOOVHQDclA6GQ1FzS1hExDxgv6Yi0XJK2X4P61PY6jiP5xrNpWvZj4MXVOOxTwHE5fca9lAy8hOT1PpukWfwlkn7wt2v+LmvRAfgivZ/7Zr6yv7cVNKW6roHDgdsiYsOI6BMR6wPjgVnAD9I+7u4kgxhr8zqwW/XvUVIbSZtTx99vkerwDU31fWq1lOjVBE4G6k9b4BZJ76dN4FuRfIMZDDypdGBOrogYSdLv+ibwBnBjRLy9ivMcD9yQNjG2AebWsd8jaUzFaHprnQ4MGkPS1D+UtM+4hjOUDDh6F6gAngDuAPqn30h+AnwIEBFvpTG/m+73HrXULSKWkrxR/0XJ4KZRJN0DkHyzOD4tH0PS57u6ansdzyXpMx2Sxl1FkgDlJSKGkjSVvpY+/z6Wv/G+RNKd8VpETCUZU5FP//fFaTwjWHHVuEeBw5TfoLqmVNfVdQzfbAW4H1gP+Bx4n+RDbCS1/51OJ2kFuSv93b4GbLmKv9/60JTepyyHZyAsMZLapv12SDoX6BERp9eyX3+SwVUFH2VdLNV1U3K98zDghPSNyKzByPk77ULyAblbOn7AUo31fapD826xa8cfrHrH1fTkjH9lPgOhxwyUnu9JOo/ktZ1I8u1jBel/vl/RUPrg8jdY0lYkYwhucSJgDdRj6SC5FsAfnAjUqpTfpxoltwyYmZnloUOzbjGw42EFP+5TM29wy4CZmVmjUaJLGDsZyICki0mmSW0PDIuIZ7KNyArFr62ZNUZOBjIUEb8t1rEliaQbqGFMb9XE+LVdPSrymhKWnZJ7bUu0a92XFtYTJSuBfSzpZZJZxpB0s5bPW/7n6st5JF2Wln1jzvW0vJuSec/HSLpR0kRJXZXMBf6RpFuB0STTin5jHvj0GD+S9GZ62dW/JJXX+y+lRPi1XeF38ZCSOfXHSDohLTs+/f28qWSe+qtzfkfXS3oD+KvqWDsi/Z3cn9b1LUm71Vd9bDm/tqXNLQP1QMmc5UeTTODRjOTa4xE527uQTNSxZUREOhIZls+5HpJ+AZwDnEWyOtpzEfEnSd8luWa32mbATyPida04D7yAR5TMAz8dOIrkkqcKSdeSjNi9tSi/gBLm1/YbjouIWUoWpHlLyQyTFwI7kkxc8xzwTs7+vYFdI6JS0rPASRHxiZIJaa4lmeXvCpLLy16WtAHJxEXfwuqbX9uIBrOWQKE5Gagfe5DM3b0IQNIjNbbPJZlg5d9KVvSrnn+9N8nc4D1ILlMan5bvTvIBQ0Q8KWl2zrEmRsTr6f3ceeAhmbxjM2A7kvnH35IEyRSh0wpQz6bIr+2KTpNUPdx6fdIZCiNiFoCkIcDmOfsPST8scteOqN7WMv25H7BVTnl75VynbvXGr20JczLQAETEMkk7A/uSzD52CknWvCZzri/MuV/rPPCSTiW5Tv+8tY/eVqYpvbZpPfYDBkbEIkkvkMwgubJvetV1KqPutSPKSFpRvipYsLZa/Nrm8JgBWwvDSObubi2pHXBw7sY0c+4QEY8DvyFZphfqnnP9FeDI9Ln7A53qOG9d88A/Cxye3kdSZ0kb1nEMWzm/tst1AGanHxZbkiw61QbYS1InSc1YvoTtClaxdsRQ4NTqfSX1LWIdrHZ+bVNRVVXwW0PgZKAepDPl3UPSn/YEyaI8udqRzFr2Lklf8plp+cXUPuf674D9JY0GjgC+JOmzq3neWueBj4j3SZZGHZqe82nWYDlf82tbw5NAM0kfAH8mWYznC+BSkml5XwEmUPc89HWtHXEayXoV70p6n2QBI6tffm1LnGcgbIQktQQq0ybogcB1dTTBWSNTiq+tls/V34xkcZ+bImJVS/1aI9DUXtsO5V1iQKvvFfy4Qxfd5hkIbY1sANwrqQxYCvwy43iscErxtb1Y0n4ka0oMBR7KNhwrIL+2JcLJQCMUEZ8AO2QdhxVeKb62EXF21jFYcTS51zbwdMRmZmZNXulM/LkCDyA0MzNr4twyYGZmlocAokS7CdwyYJYRSZVK1g8YLWmIpHXW4li5ayHcKGmrlew7SNKua3COCZK65lteY5/VmlFO0sWSmlZ/tFmGnAyYZWdxRPSNiG1IrhxY4Rrr9HKt1RYRv0jnG6jLIJLpYc1sdUQkYwYKfWsAnAyYNQwvAZum39pfStc4eF9SuaS/afnqhCfC17O4Xa1kJcNngHWrDyTpBUn90/vflTRS0juSnpXUhyTp+E3aKrGH6lg5TlIXSUOVrqBIMgXySqmWle1ytv0jLX9WUre0rNbV7MwaqqiKgt/ykf5f/kjSWEnnFrpeTgbMMpa2ABwAvJcW7QicHhGbk6xaODcidgJ2An4paSOSxYy2ALYCfkIt3/TTD9wbgB9ExPbAERExAbieZKW4vhHxEstXjtuJZErZG9NDXAS8HBFbk0wos0Ee1TkuIvoB/UkWtumSlrcBhqfHejE9NsBg4NT0OWeTrGZnZjmULEN+Dcn7xFbAMSvrClwTHkBolp3Wkkal918C/k3yof5mRFSvYrg/sF31eACSOeI3A/YE7oqISmCypOdqOf4AYFj1sapXl6tFrSvHpef4n/S5/9WKKyjWpebKdpsBM4EqkmmbAW4HHtDKV7Mza5iyadbfGRgbEZ8CSLqbZErnlXUHrhYnA2bZWVxzquH0Q7Hm6oSnRsRTNfY7sIBx1LpyXM4HdF5U+8p2rerYPVj5anZmtlwvYFLO48+BXQp5AicDZg3bU8CvJD0XERWSNidZIGYYcKKkW0jGC+xNsnBRrteBayVtFBHjJXVOWwfmA+1z9qteOe5vkKwcFxGj0nP8EPijpAOoewXFarWtbFetjGQJ57vTY74cEfMkjZd0REQMUZJ9bBcR76zOL8isvsxn9lPPxH0rvXJmDbWSNDzn8eCIGFyE89TJyYBZw3Yj0AcYmX5YTgcOJenD34ekmfAz4LWaT4yI6ekgvgfStQ6mAd8GHgXuk3QISRJwGnCNklUOm5EkASeRrKB4l6QxwKvpeVbmSeCkdGW7j0iSkWoLgZ0lXZDGcVRafixwXVrenCRZcDJgDVJEfDejU39B0u1WrTfLl0AvCK9aaGZm1oClg4w/BvYlSQLeAn4YEWMKdQ63DJiZmTVg6ZLmp5B0G5aTLBVdsEQA3DJgZmbW5HmeATMzsybOyYCZmVkT52TAzMysiXMyYGZm1sQ5GTAzM2vinAyYmZk1cU4GzMzMmjgnA2ZmZk3c/weqytvYMYLxqgAAAABJRU5ErkJggg==\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": [
    "# Neuron Examination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj/english-adjectives.txt') as f:\n",
    "    adjatives = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list(logit_model.sentence_to_neurons(\"slow\").detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj.loc[\"abandoned\"] = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.164455890655518"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj.loc[\"abandoned\", 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#list(logit_model.sentence_to_neurons(\"word\").detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 43%|██████████████████████████████████▎                                            | 584/1347 [00:45<00:59, 12.73it/s]\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-65-b3ecbc18fb61>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_weight\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mneurons\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----> 7\u001b[1;33m         \u001b[0madj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mn_weight\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    664\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    665\u001b[0m             \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 666\u001b[1;33m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_setitem_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\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    667\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_setitem_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    668\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_setitem_indexer\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    607\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\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    608\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 609\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_setter\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[0;32m    610\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    611\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_tuple\u001b[1;34m(self, key, is_setter)\u001b[0m\n\u001b[0;32m    732\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    733\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Too many indexers\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 734\u001b[1;33m                 \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mis_setter\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mis_setter\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    735\u001b[0m                 \u001b[0mkeyidx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    736\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyidx\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\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[1;34m(self, key, axis, is_setter)\u001b[0m\n\u001b[0;32m   1160\u001b[0m             \u001b[1;31m# if we are a label return me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1161\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1162\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\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   1163\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mLookupError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1164\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCMultiIndex\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\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2893\u001b[0m             \u001b[0mcasted_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2894\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2895\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\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   2896\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._get_loc_duplicates\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._maybe_get_bool_indexer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._unpack_bool_indexer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\numpy\\core\\overrides.py\u001b[0m in \u001b[0;36mwhere\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj[2].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# person activations\n",
    "personal_activations = logit_model.get_specific_activations(x_all, one_hot_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fit neurons to questions\n",
    "interpretter = PCA(n_components=40)\n",
    "interpretter.fit(x_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_dict = dict(list(enumerate(adjatives)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_embeds, _ = proces_sent(adj_dict, int(NUM_PEOPLE/3), get_strange_quest=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2679030/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "activations = logit_model.get_specific_activations(adj_embed_short, one_hot_adj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#re orent activations into orthogonal dimensions\n",
    "activation_interpreter = PCA(n_components=35)\n",
    "activation_interpreter.fit(activations.detach().numpy())\n",
    "re_oriented_act = activation_interpreter.transform(activations.detach().numpy())\n",
    "\n",
    "# average each word:\n",
    "to_be_concat = []\n",
    "for index, word in adj_dict.items():\n",
    "    import_indexes = list(range(index, len(activations), len(adj_dict)))\n",
    "    \n",
    "    to_be_concat.append(np.sum(re_oriented_act[import_indexes], axis=0).reshape(1, -1))\n",
    "average_for_word = np.concatenate(to_be_concat)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# look at the words that maximise each activation\n",
    "to_be_df = {\"word\": adj_dict.values()}\n",
    "for i in range(35):\n",
    "    to_be_df[i] = average_for_word[:, i]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_act_2 = pd.DataFrame.from_dict(to_be_df).set_index(\"word\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index=2\n",
    "print(adj_act_2[index].sort_values().head(10))\n",
    "print(adj_act_2[index].sort_values().tail(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit_mod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(adj_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_for_word.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupe questions \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "50/2.5*3.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_array.shape[0] * NUM_PEOPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_embeds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = 75+72+65+50\n",
    "a/4\n",
    "3600*4/20\n",
    "8*4/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2679030 / 39861"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "interpretter.transform(x_all)\n",
    "\n",
    "questions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO\n",
    "# auto confusion matrix (for scenario)\n",
    "\n",
    "# ordered logit model\n",
    "\n",
    "# extract actual values (language model backwards)\n",
    "\n",
    "# kick out covariances with people socieal data\n",
    "#     - IE men care about speed\n",
    "#     - rich people care about status\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "fig = go.Figure(data=[go.Sankey(\n",
    "    node = dict(\n",
    "      pad = 15,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"black\", width = 0.5),\n",
    "      label = [\"Model Predicts Agree\", \"Model Predicts Disagree\", \"Survey Agrees\", \"Survey Disagrees\"],\n",
    "      color = \"blue\"\n",
    "    ),\n",
    "    link = dict(\n",
    "      source = [0, 0, 1, 1], # indices correspond to labels, eg A1, A2, A1, B1, ...\n",
    "      target = [2, 3, 2, 3],\n",
    "      value = [8, 4, 2, 8]\n",
    "  ))])\n",
    "\n",
    "fig.update_layout(title_text=\"Basic Sankey Diagram\", font_size=10)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "removed_quest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "_, working_sample = remove_quest(\"Q22.4\", y_all)\n",
    "_, broken_sample = remove_quest(removed_quest, y_all)\n",
    "\n",
    "working_sample = working_sample[:100]\n",
    "broken_sample = broken_sample[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "working_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_sample_people = 100\n",
    "sent_to_model = 'I would consider buying an automated vehicle.'\n",
    "sent_to_model = 'I trust automated vehicles to operate safely.'\n",
    "sent_to_model = 'I would consider buying a vehicle.'# (0.7697223874366013)\n",
    "sent_to_model = 'I trust buying an automated vehicle.' #(-0.705674437480165)\n",
    "sent_to_model = 'never gonna give you up'# -0.6907486661293478\n",
    "sent_to_model ='I trust automated vehicles to operate safely.'\n",
    "new_pred = [float(logit_model.get_individ_utility(sent_to_model, ID)[0,0]) for ID in range(num_sample_people)]\n",
    "\n",
    "new_pred = np.array(new_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "\n",
    "print(scipy.stats.pearsonr(working_sample, new_pred))\n",
    "print(scipy.stats.pearsonr(broken_sample, new_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$$ $$\n",
    "$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$$$ $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Out of bound Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 random guys on the internet is better then 1\n",
    "\n",
    "# is it overfitting\n",
    "def get_n_params(model):\n",
    "    pp=0\n",
    "    for p in list(model.parameters()):\n",
    "        nn=1\n",
    "        for s in list(p.size()):\n",
    "            nn = nn*s\n",
    "        pp += nn\n",
    "    return pp\n",
    "\n",
    "get_n_params(logit_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_parameters = filter(lambda p: p.requires_grad, logit_model.parameters())\n",
    "sum([np.prod(p.size()) for p in model_parameters])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(removed_quest)\n",
    "_, reomved_quest_results = remove_quest(removed_quest, y_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "questions.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = logit_model.poll_question(questions['Q16.2']).detach().numpy()\n",
    "x = reomved_quest_results\n",
    "\n",
    "import scipy\n",
    "\n",
    "print(scipy.stats.pearsonr(x, y))\n",
    "\n",
    "def jitter(values):\n",
    "    return values + np.random.normal(0,0.05,values.shape)\n",
    "\n",
    "sns.scatterplot(x=jitter(x), y=jitter(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dont redo PCA - fix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unkown_sent = questions[removed_quest]\n",
    "print(unkown_sent)\n",
    "logit_model.get_individ_utility(unkown_sent, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "#new_pred = [logit_model.get_individ_utility(unkown_sent, ID) for ID in tqdm(range(NUM_PEOPLE))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logit_model(x_test, one_hot_people_test)\n",
    "y_pred = y_pred.detach().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_test)\n",
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(sum(y_test))\n",
    "print(sum(y_pred >= 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jitter(values):\n",
    "    return values + np.random.normal(0,0.05,values.shape)\n",
    "sns.scatterplot(x=jitter(y_test), y=jitter(y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_test) - sum(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import random \n",
    "\n",
    "# rand_list = [0]*591 + [1]*336\n",
    "# random.shuffle(rand_list)\n",
    "# rand_list = np.array(rand_list)\n",
    "\n",
    "# pool_1 = y_pred[rand_list==1]\n",
    "# pool_2 = y_pred[rand_list==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool_1 = y_pred[y_test==1]\n",
    "pool_2 = y_pred[y_test==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "\n",
    "t_value,p_value=stats.ttest_ind(pool_1, pool_2)\n",
    "p_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "\n",
    "scipy.stats.pearsonr(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum(pool_1)/len(pool_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sum(pool_2)/len(pool_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = torch.nn.Linear(1,6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a(torch.FloatTensor([0.5]))"
   ]
  }
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