A_result_1_Auto_dimVar2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using package ml ml \n",
    "\n",
    "import torch\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.spatial.distance import cosine\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import decomposition\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchmetrics.functional import pairwise_cosine_similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj/english-adjectives.txt') as f:\n",
    "    adjatives = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "model = SentenceTransformer('bert-base-nli-mean-tokens')\n",
    "\n",
    "#from scipy import cosine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import parrot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# par = parrot(model_tag=\"prithivida/parrot_paraphraser_on_T5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# {'Q22.2': 'I like the idea of safe automated vehicles if they make it easier for me to travel.',\n",
    "#  'Q22.3': 'I trust automated vehicles to operate safely.',\n",
    "#  'Q22.4': 'I would consider buying an automated vehicle.',\n",
    "#  'Q22.5': 'I would be comfortable riding in an automated vehicle.',\n",
    "#  'Q22.6': 'I would allow my children to be transported in an automated vehicle.',\n",
    "#  'Q22.7': 'I would pay a higher price for a partly or fully automated vehicle than a non-automated vehicle.',\n",
    "#  'Q22.8': 'I would enjoy being able to use my time in an automated vehicle for things like reading, or relaxing rather than driving.',\n",
    "#  'Q22.9': 'I enjoy actually driving a car, and would prefer that to riding in an automated vehicle.'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_pca = 35\n",
    "scen = 0\n",
    "embed_dim = num_pca\n",
    "removed_quest = ['Q20.2', 'Q21.2', 'Q22.2']\n",
    "\n",
    "dim_of_variability = 2\n",
    "pca = decomposition.PCA(n_components=embed_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "compress_method = \"auto_encoder\" \n",
    "# none\n",
    "# PCA\n",
    "# auto_encoder\n",
    "NN_PARAM = [100, 10] # last number \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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "dim_of_variability = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "logit_model = Logit(input_size = x_train.shape[1], dim_var=dim_of_variability)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(logit_model.parameters(), lr = 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = torch.nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss before training 1.1245627403259277\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": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "perform = []\n",
    "test_perform = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: train loss: 0.5408116579055786\n",
      "Epoch 0: test_loss loss: 0.5285604000091553\n",
      "------------------------------------------------------\n",
      "Epoch 10: train loss: 0.5408115983009338\n",
      "Epoch 10: test_loss loss: 0.5285605788230896\n",
      "------------------------------------------------------\n",
      "Epoch 20: train loss: 0.5408115983009338\n",
      "Epoch 20: test_loss loss: 0.5285607576370239\n",
      "------------------------------------------------------\n",
      "Epoch 30: train loss: 0.5408116579055786\n",
      "Epoch 30: test_loss loss: 0.5285607576370239\n",
      "------------------------------------------------------\n",
      "Epoch 40: train loss: 0.5408116579055786\n",
      "Epoch 40: test_loss loss: 0.5285608172416687\n",
      "------------------------------------------------------\n",
      "Epoch 50: train loss: 0.5408116579055786\n",
      "Epoch 50: test_loss loss: 0.528560996055603\n",
      "------------------------------------------------------\n",
      "Epoch 60: train loss: 0.5408116579055786\n",
      "Epoch 60: test_loss loss: 0.5285610556602478\n",
      "------------------------------------------------------\n",
      "Epoch 70: train loss: 0.5408116579055786\n",
      "Epoch 70: test_loss loss: 0.5285611152648926\n",
      "------------------------------------------------------\n",
      "Epoch 80: train loss: 0.5408116579055786\n",
      "Epoch 80: test_loss loss: 0.5285612344741821\n",
      "------------------------------------------------------\n",
      "Epoch 90: train loss: 0.5408116579055786\n",
      "Epoch 90: test_loss loss: 0.5285612940788269\n",
      "------------------------------------------------------\n",
      "Epoch 100: train loss: 0.5408116579055786\n",
      "Epoch 100: test_loss loss: 0.5285613536834717\n",
      "------------------------------------------------------\n",
      "Epoch 110: train loss: 0.5408115983009338\n",
      "Epoch 110: test_loss loss: 0.5285614132881165\n",
      "------------------------------------------------------\n",
      "Epoch 120: train loss: 0.5408116579055786\n",
      "Epoch 120: test_loss loss: 0.5285613536834717\n",
      "------------------------------------------------------\n",
      "Epoch 130: train loss: 0.5408116579055786\n",
      "Epoch 130: test_loss loss: 0.5285614132881165\n",
      "------------------------------------------------------\n",
      "Epoch 140: train loss: 0.5408116579055786\n",
      "Epoch 140: test_loss loss: 0.5285614728927612\n",
      "------------------------------------------------------\n",
      "Epoch 150: train loss: 0.5408116579055786\n",
      "Epoch 150: test_loss loss: 0.5285614728927612\n",
      "------------------------------------------------------\n",
      "Epoch 160: train loss: 0.5408116579055786\n",
      "Epoch 160: test_loss loss: 0.528561532497406\n",
      "------------------------------------------------------\n",
      "Epoch 170: train loss: 0.5408116579055786\n",
      "Epoch 170: test_loss loss: 0.528561532497406\n",
      "------------------------------------------------------\n",
      "Epoch 180: train loss: 0.5408116579055786\n",
      "Epoch 180: test_loss loss: 0.528561532497406\n",
      "------------------------------------------------------\n",
      "Epoch 190: train loss: 0.5408116579055786\n",
      "Epoch 190: test_loss loss: 0.5285615921020508\n",
      "------------------------------------------------------\n",
      "Epoch 200: train loss: 0.5408115983009338\n",
      "Epoch 200: test_loss loss: 0.5285615921020508\n",
      "------------------------------------------------------\n",
      "Epoch 210: train loss: 0.5408115983009338\n",
      "Epoch 210: test_loss loss: 0.5285615921020508\n",
      "------------------------------------------------------\n",
      "Epoch 220: train loss: 0.5408115983009338\n",
      "Epoch 220: test_loss loss: 0.5285616517066956\n",
      "------------------------------------------------------\n",
      "Epoch 230: train loss: 0.5408115983009338\n",
      "Epoch 230: test_loss loss: 0.5285617113113403\n",
      "------------------------------------------------------\n",
      "Epoch 240: train loss: 0.5408115983009338\n",
      "Epoch 240: test_loss loss: 0.5285616517066956\n",
      "------------------------------------------------------\n",
      "Epoch 250: train loss: 0.5408116579055786\n",
      "Epoch 250: test_loss loss: 0.5285617113113403\n",
      "------------------------------------------------------\n",
      "Epoch 260: train loss: 0.5408116579055786\n",
      "Epoch 260: test_loss loss: 0.5285617113113403\n",
      "------------------------------------------------------\n",
      "Epoch 270: train loss: 0.5408116579055786\n",
      "Epoch 270: test_loss loss: 0.5285617113113403\n",
      "------------------------------------------------------\n",
      "Epoch 280: train loss: 0.5408116579055786\n",
      "Epoch 280: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 290: train loss: 0.5408116579055786\n",
      "Epoch 290: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 300: train loss: 0.5408115983009338\n",
      "Epoch 300: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 310: train loss: 0.5408116579055786\n",
      "Epoch 310: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 320: train loss: 0.5408116579055786\n",
      "Epoch 320: test_loss loss: 0.5285617113113403\n",
      "------------------------------------------------------\n",
      "Epoch 330: train loss: 0.5408115983009338\n",
      "Epoch 330: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 340: train loss: 0.5408116579055786\n",
      "Epoch 340: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 350: train loss: 0.5408116579055786\n",
      "Epoch 350: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 360: train loss: 0.5408116579055786\n",
      "Epoch 360: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 370: train loss: 0.5408115983009338\n",
      "Epoch 370: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 380: train loss: 0.5408115983009338\n",
      "Epoch 380: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 390: train loss: 0.5408116579055786\n",
      "Epoch 390: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 400: train loss: 0.5408115983009338\n",
      "Epoch 400: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 410: train loss: 0.5408115983009338\n",
      "Epoch 410: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 420: train loss: 0.5408115983009338\n",
      "Epoch 420: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 430: train loss: 0.5408115983009338\n",
      "Epoch 430: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 440: train loss: 0.5408115983009338\n",
      "Epoch 440: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 450: train loss: 0.5408116579055786\n",
      "Epoch 450: test_loss loss: 0.5285618305206299\n",
      "------------------------------------------------------\n",
      "Epoch 460: train loss: 0.5408116579055786\n",
      "Epoch 460: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 470: train loss: 0.5408116579055786\n",
      "Epoch 470: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 480: train loss: 0.5408116579055786\n",
      "Epoch 480: test_loss loss: 0.5285618305206299\n",
      "------------------------------------------------------\n",
      "Epoch 490: train loss: 0.5408116579055786\n",
      "Epoch 490: test_loss loss: 0.5285618305206299\n",
      "------------------------------------------------------\n",
      "Epoch 500: train loss: 0.5408116579055786\n",
      "Epoch 500: test_loss loss: 0.5285618305206299\n",
      "------------------------------------------------------\n",
      "Epoch 510: train loss: 0.5408116579055786\n",
      "Epoch 510: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 520: train loss: 0.5408116579055786\n",
      "Epoch 520: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 530: train loss: 0.5408116579055786\n",
      "Epoch 530: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 540: train loss: 0.5408116579055786\n",
      "Epoch 540: test_loss loss: 0.5285617709159851\n",
      "------------------------------------------------------\n",
      "Epoch 550: train loss: 0.5408116579055786\n",
      "Epoch 550: test_loss loss: 0.5285618305206299\n",
      "------------------------------------------------------\n",
      "VVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVV\n",
      "done with training loss of:0.5408116579055786 \n",
      "and test loss of: 0.5285618305206299\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def train_model(print_prog = False):\n",
    "    # logit model \n",
    "    logit_model.train()\n",
    "    epoch = 10000\n",
    "    prev_test_loss = 100\n",
    "    \n",
    "    num_bad_steps = 500\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%10 == 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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=np.arange(len(perform)), y=perform, label=\"training_performance\")\n",
    "plt.plot(np.arange(len(test_perform)), test_perform, label=\"test_performance\")\n",
    "plt.legend()\n",
    "plt.ylim(0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check Model Works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logit_model(x_test, one_hot_people_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.8159, 0.6757, 0.5248, 0.2721, 0.0973],\n",
       "        [0.8159, 0.6757, 0.5248, 0.2721, 0.0973],\n",
       "        [0.8159, 0.6757, 0.5248, 0.2721, 0.0973],\n",
       "        ...,\n",
       "        [0.8159, 0.6757, 0.5248, 0.2721, 0.0973],\n",
       "        [0.8159, 0.6757, 0.5248, 0.2721, 0.0973],\n",
       "        [0.8159, 0.6757, 0.5248, 0.2721, 0.0973]], grad_fn=<SigmoidBackward0>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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": 52,
   "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "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": 54,
   "metadata": {},
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|                                                                                           | 0/43 [00:00<?, ?it/s]C:\\Users\\lachl\\anaconda3\\envs\\ml\\lib\\site-packages\\scipy\\stats\\_stats_mstats_common.py:130: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  slope = r_num / ssxm\n",
      "C:\\Users\\lachl\\anaconda3\\envs\\ml\\lib\\site-packages\\scipy\\stats\\_stats_mstats_common.py:142: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  sterrest = np.sqrt((1 - r**2) * ssym / ssxm / df)\n",
      "100%|██████████████████████████████████████████████████████████████████████████████████| 43/43 [00:37<00:00,  1.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
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Xraplw+2an/+Gv5uGhgZmzJjB9OnTef7559l5550ZOnRo01ufKysriYjNnhtASolRo0bxne98Z6vqlqTtkZd72tjHPvYxvvSlL3HPPfc0tdXX17PPPvsAbPRtwl27dmXAgAFceumlnHzyyVRUVLDbbrux//7785Of/ARo/A/Z3LlzW13La6+9xj/8wz9wwQUXMHr0aObMmQM0/sdzzZo1W3uKGz2fXXfdlb///e8f6t+tWzf22GOPpvtN7r///qaRjI9i2LBhTZeggFaFubaupb6+nj322IOdd96Zl19+mRdeeGGz2xx33HHceeedAKxdu5b6+nqOO+44pk6dyhtvvAHAypUree2117a6LknaHhhSCnDFFVfw5ptvNi2PGzeOM844g379+m3yyZ+RI0fywAMPrHfJYdKkSdxzzz307duXPn368J//+Z8tbrvhPSnPPfccM2bMoG/fvhx22GFMmTKFSy+9FIAxY8ZQXV3ddOPsltrY+Zxyyin89Kc/bbpBtbn77ruPq666iurqaurq6vjWt761Vcdubvz48dTW1lJdXc0hhxzChAkTWrVdW9YyfPhwGhoaOPjgg7n66qsZNGjQZre59dZbefrpp6mqqqJfv34sWLCAQw45hBtuuIFhw4ZRXV3N8ccfz7Jly7a6LknaHkRKqdw1bJH+/fun2tra9dp8fb06Cv+u56X31Y+Xu4QOaclNnyt3CWpDETE7pdS/pXWOpEiSpCwZUiRJUpYMKZIkKUvbTUhpb/fWSFvKv+OSOprtIqTstNNOrFixwi9xbbdSSqxYsaLFOWEkaXu1XUzm1rNnT5YuXcry5cvLXYpUmJ122omePXuWuwxJ2ma2i5BSWVm52anZJUlS+7JdXO6RJEnbH0OKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKUmEhJSJ6RcTTEbEgIl6KiEtb6BMRMT4iFkfEvIg4vKh6JElS+9K5wH03AFeklOZExK7A7Ih4IqW0oFmfE4EDSz9HAHeW/leSJHVwhY2kpJSWpZTmlD7/HVgI7LNBt1OBH6dGLwC7R8TeRdUkSZLaj21yT0pE9AYOA36zwap9gNebLS/lw0FGkiR1QIWHlIjoCjwMXJZSemsr9zEmImojonb58uVtW6AkScpSoSElIippDCiTUkqPtNDlz0CvZss9S23rSSlNTCn1Tyn179GjRzHFSpKkrBT5dE8A9wALU0rf30i3acB5pad8BgH1KaVlRdUkSZLajyKf7jkKOBf4XUTUldquAfYFSClNAH4OnAQsBt4BvlJgPZIkqR0pLKSklH4NxGb6JOBrRdUgSZLaL2eclSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZ6lzuAiRJ2iLjupW7go5pXP02P6QjKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJUWEiJiB9FxBsRMX8j64dGRH1E1JV+vlVULZIkqf3pXOC+7wVuB368iT4zU0onF1iDJElqpwobSUkpPQOsLGr/kiRp+1bue1I+HRFzI+IXEdGnzLVIkqSMFHm5Z3PmAPullFZFxEnAo8CBLXWMiDHAGIB99913mxUoSZLKp2wjKSmlt1JKq0qffw5URkT3jfSdmFLqn1Lq36NHj21apyRJKo+yhZSI+HhEROnzwFItK8pVjyRJykthl3siYjIwFOgeEUuB64BKgJTSBOCLwD9HRAOwGjgzpZSKqkeSJLUvhYWUlNJZm1l/O42PKEuSJH1IuZ/ukSRJapEhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlqVUhJSI+ERE7lj4PjYhLImL3QiuTJEkdWmtHUh4G1kbEJ4GJQC/gwcKqkiRJHV5rQ8oHKaUG4HTgtpTSVcDexZUlSZI6utaGlDURcRYwCnis1FZZTEmSJEmtDylfAT4N/FtK6Y8RsT9wf3FlSZKkjq5zazqllBZExDeAfUvLfwS+W2RhkiSpY2vt0z2nAHXAf5WWayJiWoF1SZKkDq61l3vGAQOBvwGklOqAAwqpSJIkiS24cTalVL9B2wdtXYwkSdI6rbonBXgpIv4PUBERBwKXAM8VV5YkSeroWjuScjHQB3iPxknc6oHLCqpJkiRp8yMpEVEBPJ5SOga4tviSJEmSWjGSklJaC3wQEd22QT2SJElA6+9JWQX8LiKeAN5e15hSuqSQqiRJUofX2pDySOlHkiRpm2jtjLP3RcQOwEGlpt+nlNYUV5YkSeroWhVSImIocB+wBAigV0SMSik9U1hlkiSpQ2vt5Z5/B4allH4PEBEHAZOBfkUVJkmSOrbWzpNSuS6gAKSUFgGVxZQkSZLU+pGU2oj4D+CB0vLZQG0xJUmSJLU+pPwz8DUap8MHmAn8sJCKJEmSaH1I6QzcmlL6PjTNQrtjYVVJkqQOr7X3pDwJdGm23AWY3vblSJIkNWptSNkppbRq3ULp887FlCRJktT6kPJ2RBy+biEi+gOriylJkiSp9fekXAb8JCL+p7S8NzCykIokSZLYzEhKRAyIiI+nlGYB/wRMAdYA/wX8cRvUJ0mSOqjNXe65C3i/9PnTwDXAHcBfgYkF1iVJkjq4zV3uqUgprSx9HglMTCk9DDwcEXWFViZJkjq0zY2kVETEuiBzHPBUs3WtvZ9FkiRpi20uaEwGfhURb9L4NM9MgIj4JFBfcG2SJKkD22RISSn9W0Q8SePTPL9MKaXSqk7AxUUXJ0mSOq7NXrJJKb3QQtuiYsqRJElq1NrJ3CRJkrYpQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSslRYSImIH0XEGxExfyPrIyLGR8TiiJgXEYcXVYskSWp/ihxJuRcYvon1JwIHln7GAHcWWIskSWpnCgspKaVngJWb6HIq8OPU6AVg94jYu6h6JElS+1LOe1L2AV5vtry01CZJktQ+bpyNiDERURsRtcuXLy93OZIkaRsoZ0j5M9Cr2XLPUtuHpJQmppT6p5T69+jRY5sUJ0mSyqucIWUacF7pKZ9BQH1KaVkZ65EkSRnpXNSOI2IyMBToHhFLgeuASoCU0gTg58BJwGLgHeArRdUiSZLan8JCSkrprM2sT8DXijq+JElq39rFjbOSJKnjMaRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKy1LncBUiM61buCjqmcfXlrkCSNsmRFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBUaUiJieET8PiIWR8TVLaz/ckQsj4i60s/oIuuRJEntR2HzpEREBXAHcDywFJgVEdNSSgs26DolpXRRUXVIkqT2qciRlIHA4pTSH1JK7wMPAacWeDxJkrQdKTKk7AO83mx5aaltQ1+IiHkRMTUiehVYjyRJakfKfePsz4DeKaVq4AngvpY6RcSYiKiNiNrly5dv0wIlSVJ5FBlS/gw0HxnpWWprklJakVJ6r7T4H0C/lnaUUpqYUuqfUurfo0ePQoqVJEl5KTKkzAIOjIj9I2IH4ExgWvMOEbF3s8URwMIC65EkSe1IYU/3pJQaIuIi4L+BCuBHKaWXIuLbQG1KaRpwSUSMABqAlcCXi6pHkiS1L4WFFICU0s+Bn2/Q9q1mn78JfLPIGiRJUvtU7htnJUmSWmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKkiFFkiRlyZAiSZKyZEiRJElZMqRIkqQsGVIkSVKWDCmSJClLhhRJkpQlQ4okScqSIUWSJGXJkCJJkrJkSJEkSVkypEiSpCwZUiRJUpYMKZIkKUuGFEmSlCVDiiRJypIhRZIkZcmQIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUJUOKJEnKUqEhJSKGR8TvI2JxRFzdwvodI2JKaf1vIqJ3kfVIkqT2o7CQEhEVwB3AicAhwFkRccgG3f4v8NeU0ieBW4DvFlWPJElqX4ocSRkILE4p/SGl9D7wEHDqBn1OBe4rfZ4KHBcRUWBNkiSpnSgypOwDvN5seWmprcU+KaUGoB7Ys8CaJElSO9G53AW0RkSMAcaUFldFxO/LWY/aXHfgzXIX0eFc76Cl2qfwO6M8ivvO2G9jK4oMKX8GejVb7llqa6nP0ojoDHQDVmy4o5TSRGBiQXWqzCKiNqXUv9x1SGof/M7oOIq83DMLODAi9o+IHYAzgWkb9JkGjCp9/iLwVEopFViTJElqJwobSUkpNUTERcB/AxXAj1JKL0XEt4HalNI04B7g/ohYDKykMchIkiQRDlyo3CJiTOmSniRtlt8ZHYchRZIkZclp8SVJUpYMKSqriNg5Ih6PiJcj4qWIuKncNUnaehGxNiLqSv9/nhsRV0REp9K6/hExvvR5x4iYXuo7MiIGl7api4gu5T0L5aJdzJOi7VoA308pPVl6CuzJiDgxpfSLchcmaausTinVAETEXsCDwG7AdSmlWqC21O8wgGZ9JwDfSSk90JqDlGYnj5TSB21avbLiSIq2uYjoXXrx5I+BF4HFAKXXJ8yhcU4dSe1cSukNGifivCgaDY2Ix0rh5QFgQGnk5KvAl4D/FxGTACLiqoiYFRHzIuL6Ulvz7475QK9N9FsYEXeXRmd+uW50JiI+WRrBmRsRcyLiExs7nsrPkKJyORD4YUqpT0rpNYCI2B04BXiynIVJajsppT/QOA3FXs3a3gBGAzNTSjUppbtonDfrqpTS2RExjMbviIFADdAvIoaUNm/67gA+tZl+d5T6/Q34Qql9Uqm9L3AksGwzx1MZeblH5fJaSumFdQulGYcnA+NLX2qSOq5hpZ/flpa70hgi/sT63x2b6vfHlFJdqX020DsidgX2SSn9FCCl9C5AKaS0tJ9nijg5tZ4hReXy9gbLE4FXUko/KEMtkgoSEQcAa4E3gINbuxmN96fctcG+erP+d8em+r3XrGktsKmbcVvcj8rPyz0qu4i4gcb3Nl1W5lIktaGI6AFMAG7fwlee/DdwfkR0Le1nn9J9LFvbD4CU0t9pfFfcaaX+O0bEzlu6H207jqSorCKiJ3At8DIwp/GGfW5PKf1HWQuTtLW6REQdUAk0APcD39+SHaSUfhkRBwPPl74TVgHn0DgissX9NnAucFfpFS1rgDM2sZ83tqRutT1nnJUkSVnyco8kScqSIUWSJGXJkCJJkrJkSJEkFSoiri3N/DqvNMPsERnU9OWIuL3cdWjTfLpHklSYiPg0cDJweErpvYjoDuxQ4PEqUkqberpH7YgjKZKkIu0NvJlSeg8gpfRmSul/ImJ46e3ncyJifEQ8BhAR4yLiynUbR8T80uRsRMSjETG7NCozplmfVRHx7xExF/h0RJwTES+WRm3uioiKUr+vRMSiiHgROGrb/Qq0tQwpkqQi/ZLGFwEuiogfRsTREbETcDeN7+rqB3y8lfs6P6XUD+gPXBIRe5badwF+U3ofzwpgJHBU6Q3La4GzI2Jv4Hoaw8lngEPa5vRUJEOKJKkwKaVVNAaRMcByYAowlsZ367xSmon2gVbu7pLSaMkLQC8a368DjUHk4dLn40rHm1WaVO444ADgCGBGSml56Y3rUz7qual43pMiSSpU6R6RGcCMiPgdMGoT3RtY/x/QOwFExFDgs8CnU0rvRMSMdeuAd5vdhxLAfSmlbzbf6bqp8NW+OJIiSSpMRHwqIg5s1lQD/IXGtxJ/otR2VrP1S4DDS9seDuxfau8G/LUUUP4JGLSRQz4JfHHdu3ci4mMRsR/wG+DoiNgzIiqBMz7yyalwjqRIkorUFbgtInancZRkMY2XfqYCj0fEO8BMYNdS/4eB8yLiJRqDxaJS+38BYyNiIfB7Gi/5fEhKaUFE/Cvwy4joROP7eb6WUnohIsYBzwN/A+ra9jRVBN/dI0kqq9KlnCtTSieXuRRlxss9kiQpS46kSJKkLDmSIkmSsmRIkSRJWTKkSJKkLBlSJElSlgwpkiQpS4YUSZKUpf8PutvntWgKarMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question 22\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "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": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q19.5 :  I would be willing to pay between $600 and $1200 a month for a luxury car subscription with all vehicle costs covered (excluding fuel).\n",
      "slope=nan    r=0.00    diff=5.85\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": 57,
   "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=nan    r=0.00    diff=2.65\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": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q21.2 :  I support electric vehicles because they are better for the environment.\n",
      "slope=nan    r=0.00    diff=1.91\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope=0.81    r=0.52    diff=2.34\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = get_performance(logit_model, \"Q21.2\")\n",
    "_ = predictions_from_average(\"Q21.2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Het analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'logit_model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-9b30797df249>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mhet_vec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogit_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mperson_het_vector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhet_vec\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhet_vec\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"responses.xlsx\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Q1.1\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'logit_model' is not defined"
     ]
    }
   ],
   "source": [
    "het_vec = logit_model.person_het_vector.clone()\n",
    "x = het_vec[:,0].detach().numpy()\n",
    "y = het_vec[:,1].detach().numpy()\n",
    "data = pd.read_excel(\"responses.xlsx\")\n",
    "sns.scatterplot(x,y, hue=data[\"Q1.1\"])\n",
    "plt.show()\n",
    "\n",
    "sns.kdeplot(x[data[\"Q1.1\"] == \"Male\"], label=\"Male\")\n",
    "sns.kdeplot(x[data[\"Q1.1\"] == \"Female\"], label=\"Female\")\n",
    "plt.xlabel(\"Latent Parameter for Respondent\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# from https://www.geeksforgeeks.org/how-to-perform-an-f-test-in-python/\n",
    "import scipy.stats as stats\n",
    "  \n",
    "# Create data\n",
    "group1 = x[data[\"Q1.1\"] == \"Male\"]\n",
    "group2 = x[data[\"Q1.1\"] == \"Female\"]\n",
    "  \n",
    "# # converting the list to array\n",
    "# x = np.array(group1)\n",
    "# y = np.array(group2)\n",
    "  \n",
    "# calculate variance of each group\n",
    "# print(np.var(group1), np.var(group2))\n",
    "  \n",
    "def f_test(group1, group2):\n",
    "    f = np.var(group1, ddof=1)/np.var(group2, ddof=1)\n",
    "    nun = x.size-1\n",
    "    dun = y.size-1\n",
    "    p_value = 1-scipy.stats.f.cdf(f, nun, dun)\n",
    "    return f, p_value\n",
    "print(f_test(group1, group2))\n",
    "print(stats.ttest_ind(group1, group2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neuron Examination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('adj/english-adjectives.txt') as f:\n",
    "    adjatives = [line.strip() for line in f]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = list(logit_model.sentence_to_neurons(\"slow\").detach().numpy())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj.loc[\"abandoned\"] = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7934706211090088"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj.loc[\"abandoned\", 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1347/1347 [05:49<00:00,  3.85it/s]\n"
     ]
    }
   ],
   "source": [
    "a = list(logit_model.sentence_to_neurons(\"slow\").detach().numpy())[0]\n",
    "\n",
    "for word in tqdm(adjatives):\n",
    "    neurons = list(logit_model.sentence_to_neurons(word).detach().numpy())[0]\n",
    "    \n",
    "    for index, n_weight in enumerate(neurons):\n",
    "        adj.loc[word, index] = n_weight\n",
    "    \n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adj\n",
       "overjoyed      0.253524\n",
       "big-hearted    0.269258\n",
       "tall           0.288081\n",
       "giddy          0.292218\n",
       "jubilant       0.294603\n",
       "                 ...   \n",
       "grounded       0.926432\n",
       "unused         0.938474\n",
       "lost           0.939501\n",
       "neglected      0.948647\n",
       "abandoned      1.000000\n",
       "Name: 0, Length: 1347, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj[0].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adj\n",
       "dead            0.334573\n",
       "weekly          0.374188\n",
       "sweltering      0.375276\n",
       "silent          0.387088\n",
       "snoopy          0.396236\n",
       "                  ...   \n",
       "accomplished    0.935110\n",
       "wise            0.936585\n",
       "usable          0.937112\n",
       "aware           0.942989\n",
       "able            1.000001\n",
       "Name: 1, Length: 1347, dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj[1].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "adj\n",
       "snoopy        0.367102\n",
       "kosher        0.371090\n",
       "sleepy        0.400575\n",
       "criminal      0.406012\n",
       "sad           0.415528\n",
       "                ...   \n",
       "definitive    0.907479\n",
       "whole         0.918217\n",
       "utter         0.932635\n",
       "complete      0.938344\n",
       "absolute      1.000000\n",
       "Name: 2, Length: 1347, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adj[2].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# person activations\n",
    "personal_activations = logit_model.get_specific_activations(x_all, one_hot_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=40)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit neurons to questions\n",
    "interpretter = PCA(n_components=40)\n",
    "interpretter.fit(x_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_dict = dict(list(enumerate(adjatives)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "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-76-cb1350ddd559>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0madj_embeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mproces_sent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0madj_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNUM_PEOPLE\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mget_strange_quest\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[0m",
      "\u001b[1;32m<ipython-input-15-44b853cf73cf>\u001b[0m in \u001b[0;36mproces_sent\u001b[1;34m(sent_dic, NUM_PEOPLE, get_strange_quest)\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[0mdistances\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[0;32m     17\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mcompare_vec\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msent_embed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m             \u001b[0mdistances\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcosine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcompare_vec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbase_vec\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m         \u001b[0msent_dic_distances\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mquestion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdistances\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdistances\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[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\scipy\\spatial\\distance.py\u001b[0m in \u001b[0;36mcosine\u001b[1;34m(u, v, w)\u001b[0m\n\u001b[0;32m    759\u001b[0m     \u001b[1;31m# cosine distance is also referred to as 'uncentered correlation',\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    760\u001b[0m     \u001b[1;31m#   or 'reflective correlation'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 761\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcorrelation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mw\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcentered\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[0m\n\u001b[0m\u001b[0;32m    762\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    763\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\scipy\\spatial\\distance.py\u001b[0m in \u001b[0;36mcorrelation\u001b[1;34m(u, v, w, centered)\u001b[0m\n\u001b[0;32m    709\u001b[0m         \u001b[0mu\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mu\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mumu\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    710\u001b[0m         \u001b[0mv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mvmu\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 711\u001b[1;33m     \u001b[0muv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mw\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    712\u001b[0m     \u001b[0muu\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    713\u001b[0m     \u001b[0mvv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maverage\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msquare\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mw\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;36maverage\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36maverage\u001b[1;34m(a, axis, weights, returned)\u001b[0m\n\u001b[0;32m    493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    494\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mweights\u001b[0m \u001b[1;32mis\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[1;32m--> 495\u001b[1;33m         \u001b[0mavg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\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    496\u001b[0m         \u001b[0mscl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mavg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mavg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    497\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[1;32m~\\anaconda3\\envs\\ml\\lib\\site-packages\\numpy\\core\\_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[1;34m(a, axis, dtype, out, keepdims, where)\u001b[0m\n\u001b[0;32m    177\u001b[0m             \u001b[0mis_float16_result\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    178\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 179\u001b[1;33m     \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwhere\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    180\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mret\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\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    181\u001b[0m         ret = um.true_divide(\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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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