classify_2.ipynb

{
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   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/easeto/amira-learning/blob/main/classify_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "OgDj0aiRfHH8"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import string\n",
    "import re\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "6d0xJz3VzLOo"
   },
   "outputs": [],
   "source": [
    "# get authentication from google drive\n",
    "from google.colab import auth\n",
    "auth.authenticate_user()\n",
    "\n",
    "import gspread\n",
    "from oauth2client.client import GoogleCredentials\n",
    "\n",
    "gc = gspread.authorize(GoogleCredentials.get_application_default())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "J4QxBareshEV"
   },
   "outputs": [],
   "source": [
    "# open the google sheet\n",
    "sheet = gc.open('classify-error').sheet1\n",
    "\n",
    "# get_all_values gives a list of rows.\n",
    "rows = sheet.get_all_values()\n",
    "df = pd.DataFrame.from_records(rows)\n",
    "df.columns = df.iloc[0]\n",
    "df = df.drop(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "id": "0zdFwxK3hd52"
   },
   "outputs": [],
   "source": [
    "# drop rows that haven't been classified\n",
    "from numpy import nan\n",
    "nan_value = float(\"NaN\")\n",
    "df.replace(\"\", nan_value, inplace=True)\n",
    "df.dropna(subset = ['error_label_1'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "2561fb7nmN3D"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Compute the Damerau-Levenshtein distance between two given\n",
    "strings (s1 and s2)\n",
    "\"\"\"\n",
    "def damerau_levenshtein_distance(s1, s2):\n",
    "    d = {}\n",
    "    lenstr1 = len(s1)\n",
    "    lenstr2 = len(s2)\n",
    "    for i in range(-1,lenstr1+1):\n",
    "        d[(i,-1)] = i+1\n",
    "    for j in range(-1,lenstr2+1):\n",
    "        d[(-1,j)] = j+1\n",
    "\n",
    "    for i in range(lenstr1):\n",
    "        for j in range(lenstr2):\n",
    "            if s1[i] == s2[j]:\n",
    "                cost = 0\n",
    "            else:\n",
    "                cost = 1\n",
    "            d[(i,j)] = min(\n",
    "                           d[(i-1,j)] + 1, # deletion\n",
    "                           d[(i,j-1)] + 1, # insertion\n",
    "                           d[(i-1,j-1)] + cost, # substitution\n",
    "                          )\n",
    "            if i and j and s1[i]==s2[j-1] and s1[i-1] == s2[j]:\n",
    "                d[(i,j)] = min (d[(i,j)], d[i-2,j-2] + cost) # transposition\n",
    "\n",
    "    return d[lenstr1-1,lenstr2-1]/((lenstr1+lenstr2)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "1oojOshXpaOT"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Check if the two words are similar at the 0.4 threshold\n",
    "\"\"\"\n",
    "def is_similar(s1, s2):\n",
    "  if damerau_levenshtein_distance(s1,s2) > 0.4 and len(s1) >= 5 and len(s2) >= 5:\n",
    "    return True\n",
    "  return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "n370Fx6ssTSP"
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Remove punctuation \n",
    "\"\"\"\n",
    "import re\n",
    "def remove_punc(strlist):\n",
    "  result = []\n",
    "  for word in strlist:\n",
    "    res = ''.join(e for e in word if e.isalnum())\n",
    "    result.append(res)\n",
    "  return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8ycGhcAG6eGC"
   },
   "source": [
    "Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "dGGOApNt2HWg"
   },
   "outputs": [],
   "source": [
    "# transform string data type to list\n",
    "df['story_text'] = df.apply(lambda x: list(x.story_text.split(\",\")), axis = 1)\n",
    "df['transcript'] = df.apply(lambda x: list(x.transcript.split(\",\")), axis = 1)\n",
    "df['aligned_text'] = df.apply(lambda x: list(x.aligned_text.split(\",\")), axis = 1)\n",
    "df['aligned_transcript'] = df.apply(lambda x: list(x.aligned_transcript.split(\",\")), axis = 1)\n",
    "df['error_index'] = df.apply(lambda x: list(x.error_index.split(\",\")), axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "QWjz2Tgxat4k"
   },
   "outputs": [],
   "source": [
    "# remove punctuations in list\n",
    "df['story_text'] = df.apply(lambda x: remove_punc(x.story_text), axis = 1)\n",
    "df['transcript'] = df.apply(lambda x: remove_punc(x.transcript), axis = 1)\n",
    "df['aligned_text'] = df.apply(lambda x: remove_punc(x.aligned_text), axis = 1)\n",
    "df['aligned_transcript'] = df.apply(lambda x: remove_punc(x.aligned_transcript), axis = 1)\n",
    "df['error_index'] = df.apply(lambda x: remove_punc(x.error_index), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "WjrHfbxVtRnx"
   },
   "outputs": [],
   "source": [
    "def get_error_word(aligned_text, aligned_transcript, error_index):\n",
    "  \"\"\"\n",
    "  function to get the words that are incorrect in the transcript based on the index. \n",
    "  :param aligned_text: the text that was showed to students aligned to the transcript of what the students said\n",
    "  :param aligned_transcript: the transcript of what the student said aligned to the text that was showed\n",
    "  :param error_inex: the index of the error in aligned text/transcript (aligned text and transcript have the same length)\n",
    "  :return: index, the word that is correct, the word that is incorrectly said\n",
    "  \"\"\"\n",
    "  result = []\n",
    "  for index in error_index:\n",
    "    comp = []\n",
    "    comp.append(index)\n",
    "    comp.append(aligned_text[index])\n",
    "    comp.append(aligned_transcript[index])\n",
    "    result.append(comp)\n",
    "  return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "2Ao0Leb-oXxr"
   },
   "outputs": [],
   "source": [
    "def to_int(index):\n",
    "  result = []\n",
    "  for i in index:\n",
    "    i = int(i)\n",
    "    result.append(i)\n",
    "  return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "FovB6Ej6n8vg"
   },
   "outputs": [],
   "source": [
    "df['error_index'] =  df.apply(lambda x: to_int(x.error_index), axis = 1)\n",
    "df['error_words'] = df.apply(lambda x: get_error_word(x.aligned_text, x.aligned_transcript, x.error_index), axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hIzKabur6p3c"
   },
   "source": [
    "Classification Algorithm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "FCl3DNu6CuGd"
   },
   "outputs": [],
   "source": [
    "def classify(error_words, story_text, transcript):\n",
    "    \"\"\"\n",
    "    A function for classifying the error types\n",
    "    :param error_word (a list of error index, correct word, incorrect word)\n",
    "    :param story_text (the sentence that is showed to students)\n",
    "    :param transcript (the sentence that the students said)\n",
    "    :return result: index of the error in the story_text and the error type\n",
    "    \"\"\"\n",
    "    result = []\n",
    "    for error in error_words:\n",
    "      text = error[1] \n",
    "      trans = error[2]\n",
    "      res = []\n",
    "\n",
    "      # if the text is empty\n",
    "      if text == '':\n",
    "        if trans in story_text and trans in transcript: # student already said the correct word in transcript\n",
    "          e = 'correct - repetition'\n",
    "          id = story_text.index(trans)\n",
    "        elif trans in story_text and trans not in transcript: # student made a close attempt before this word and got this word correctly\n",
    "          e = 'correct - self-correction'\n",
    "          id = story_text.index(trans)\n",
    "        else: # student said something unrelated and cannot be found in the text\n",
    "          e = 'miscue - unrelated chatter'\n",
    "          id = error[0]\n",
    "      \n",
    "      # if the transcript is empty -> student did not make an attempt at saying the word -> skip\n",
    "      elif trans == '':\n",
    "        e = 'miscue - skip'\n",
    "        id = story_text.index(text)\n",
    "      \n",
    "      # if the text and transcript words are aligned\n",
    "      else:\n",
    "        if trans in story_text: # if the word is already said -> self-repetition\n",
    "          e = 'correct - repetition'\n",
    "          id = story_text.index(trans)\n",
    "        else:\n",
    "          if is_similar(text, trans): # if the text and transcript sound/look similar enough\n",
    "            if text in transcript: # and if the correct word is also said in the transcript -> self-correction\n",
    "              e = 'correct - self-correction'\n",
    "              id = story_text.index(text)\n",
    "            else: # if the correct word was not said -> student made an attempt at saying something similar -> substition\n",
    "              e = 'miscue - substitution'\n",
    "              id = error[0]\n",
    "          else: # if the aligned text and  aligned transcript do not look/sound similar -> no attempt was made -> unrelated chatter\n",
    "            e = 'miscue - unrelated chatter'\n",
    "            id = error[0]\n",
    "\n",
    "      \n",
    "      res.append(id)\n",
    "      res.append(trans)\n",
    "      res.append(e)\n",
    "      result.append(res)\n",
    "      \n",
    "    return result\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "id": "_N9jtwkPF49l"
   },
   "outputs": [],
   "source": [
    "df['classify'] = df.apply(lambda x: classify(x.error_words, x.story_text, x.transcript), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
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    "id": "DVnZpRH_DQVm",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>story_text</th>\n",
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       "      <th>classify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[no, it, wasnt, there]</td>\n",
       "      <td>[no, it, was, there]</td>\n",
       "      <td>[[2, miscue - substitution]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[baby, bunny, is, a, boy]</td>\n",
       "      <td>[baby, bunny, a, boy]</td>\n",
       "      <td>[[2, miscue - skip]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[i, love, bugs]</td>\n",
       "      <td>[i, i]</td>\n",
       "      <td>[[1, miscue - skip], [0, correct - repetition]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[the, mouse, ran, down]</td>\n",
       "      <td>[the, mouse, down]</td>\n",
       "      <td>[[2, miscue - skip]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[i, need, dad]</td>\n",
       "      <td>[dad, dad]</td>\n",
       "      <td>[[0, miscue - skip], [2, correct - repetition]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>[have, this, wish, i, wish, tonight]</td>\n",
       "      <td>[have, that, have, this, wish, i, wish, tonight]</td>\n",
       "      <td>[[0, correct - repetition], [1, miscue - unrel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>[boot]</td>\n",
       "      <td>[boot, boot]</td>\n",
       "      <td>[[0, correct - repetition]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>[do, you, know, the, muffin, man]</td>\n",
       "      <td>[do, you, know, the, muffin, the, muffin]</td>\n",
       "      <td>[[3, correct - repetition], [4, correct - repe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>[bird]</td>\n",
       "      <td>[bird, bird]</td>\n",
       "      <td>[[0, correct - repetition]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>[do, you, have, bugs]</td>\n",
       "      <td>[do, you, have]</td>\n",
       "      <td>[[3, miscue - skip]]</td>\n",
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       "<p>520 rows × 3 columns</p>\n",
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      "text/plain": [
       "0                              story_text  ...                                           classify\n",
       "1                  [no, it, wasnt, there]  ...                       [[2, miscue - substitution]]\n",
       "2               [baby, bunny, is, a, boy]  ...                               [[2, miscue - skip]]\n",
       "3                         [i, love, bugs]  ...    [[1, miscue - skip], [0, correct - repetition]]\n",
       "4                 [the, mouse, ran, down]  ...                               [[2, miscue - skip]]\n",
       "5                          [i, need, dad]  ...    [[0, miscue - skip], [2, correct - repetition]]\n",
       "..                                    ...  ...                                                ...\n",
       "563  [have, this, wish, i, wish, tonight]  ...  [[0, correct - repetition], [1, miscue - unrel...\n",
       "564                                [boot]  ...                        [[0, correct - repetition]]\n",
       "565     [do, you, know, the, muffin, man]  ...  [[3, correct - repetition], [4, correct - repe...\n",
       "566                                [bird]  ...                        [[0, correct - repetition]]\n",
       "568                 [do, you, have, bugs]  ...                               [[3, miscue - skip]]\n",
       "\n",
       "[520 rows x 3 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['story_text', 'transcript', 'classify']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4Sh3p0mWGzaX"
   },
   "source": [
    "Test Model Accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "id": "Ra_4DlYGn6pR"
   },
   "outputs": [],
   "source": [
    "label = df.columns[df.columns.str.startswith('error_label_')]\n",
    "#df.drop(unwanted, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "id": "jI_CPf9o7Fxb"
   },
   "outputs": [],
   "source": [
    "split_df = pd.DataFrame(df['classify'].tolist(), columns=['e1', 'e2', 'e3', 'e4', 'e5', 'e6', 'e7'])"
   ]
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       "                             e1                               e2  ...    e6    e7\n",
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       "..                          ...                              ...  ...   ...   ...\n",
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       "518   [0, correct - repetition]                             None  ...  None  None\n",
       "519          [3, miscue - skip]                             None  ...  None  None\n",
       "\n",
       "[520 rows x 7 columns]"
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     "execution_count": 83,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "id": "r3a84lan9HTy"
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   "source": [
    "df_label = df[label]"
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       "\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "0            error_label_1          error_label_2  ... error_label_6 error_label_7\n",
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       "5            miscue - skip  miscue - substitution  ...           NaN           NaN\n",
       "..                     ...                    ...  ...           ...           ...\n",
       "563  miscue - substitution                    NaN  ...           NaN           NaN\n",
       "564   correct - repetition                    NaN  ...           NaN           NaN\n",
       "565  miscue - substitution   correct - repetition  ...           NaN           NaN\n",
       "566   correct - repetition                    NaN  ...           NaN           NaN\n",
       "568          miscue - skip                    NaN  ...           NaN           NaN\n",
       "\n",
       "[520 rows x 7 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OpwSAsuudqK5"
   },
   "source": [
    "Back-end Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "zyLZNExWdt4x"
   },
   "outputs": [],
   "source": [
    "# we can use alignment for checking correct sentences. If classification = correct but not aligned, that means repetition or self-correct?\n",
    "def match_score(alpha, beta, gap_penalty):\n",
    "    \"\"\"\n",
    "    A function for determining the score between any two bases in alignment; helper function for needleman_wunsch\n",
    "    :param alpha\n",
    "    :param beta\n",
    "    :param gap_penalty\n",
    "    :return: mismatch_penalty (int)\n",
    "    \"\"\"\n",
    "    match_award = 1\n",
    "    mismatch_penalty = -1  # - scipy.spatial.distance.cosine(embeddings_map[alpha], embeddings_map[beta])\n",
    "    if alpha == beta:\n",
    "        return match_award\n",
    "    elif alpha == '-' or beta == '-':\n",
    "        return gap_penalty\n",
    "    else:\n",
    "        return mismatch_penalty\n",
    "\n",
    "def needleman_wunsch(seq1, seq2):\n",
    "    \"\"\"\n",
    "    :param seq1: First word sequence in list form (str[])\n",
    "    :param seq2: Second word sequence in list form (str[])\n",
    "    :return: Two lists corresponding to seq1 and seq2, respectively, that have placed the optimally aligned words in\n",
    "    matching indices and padded using '-'.\n",
    "    Example:\n",
    "    Input\n",
    "    seq1 = ['every', 'day', 'during', 'recess', 'mary', 'jason', 'and', 'their', 'classmates', 'played', 'together']\n",
    "    seq2 = ['every', 'day', 'during', 'recess', 'mary', 'jason', 'and', 'the', 'their', 'classmates', 'played',\n",
    "    'together', 'adam', 'day', 'played']\n",
    "    Output\n",
    "    align1 = ['every', 'day', 'during', 'recess', 'mary', 'jason', 'and', '-', 'their', 'classmates', 'played',\n",
    "    'together', '-', '-', '-']\n",
    "    align2 = ['every', 'day', 'during', 'recess', 'mary', 'jason', 'and', 'the', 'their', 'classmates', 'played',\n",
    "    'together', 'adam', 'day', 'played']\n",
    "    \"\"\"\n",
    "    # standard values\n",
    "    gap_penalty = -1\n",
    "\n",
    "    # store length of two sequences\n",
    "    n = len(seq1)\n",
    "    m = len(seq2)\n",
    "\n",
    "    # generate matrix of zeros to store scores\n",
    "    score = np.zeros((m + 1, n + 1))\n",
    "\n",
    "    # calculate score table\n",
    "\n",
    "    for i in range(0, m + 1):  # fill out first column\n",
    "        score[i][0] = gap_penalty * i\n",
    "\n",
    "    for j in range(0, n + 1):  # fill out first row\n",
    "        score[0][j] = gap_penalty * j\n",
    "\n",
    "    for i in range(1, m + 1):  # fill out all other values in the score matrix\n",
    "        for j in range(1, n + 1):\n",
    "            # calculate the score by checking the top, left, and diagonal cells\n",
    "            match = score[i - 1][j - 1] + match_score(seq1[j - 1], seq2[i - 1], gap_penalty)\n",
    "            delete = score[i - 1][j] + gap_penalty\n",
    "            insert = score[i][j - 1] + gap_penalty\n",
    "\n",
    "            # Record the maximum score from the three possible scores calculated above\n",
    "            score[i][j] = max(match, delete, insert)\n",
    "\n",
    "    # traceback and compute the alignment\n",
    "\n",
    "    # create variables to store alignment\n",
    "    align1 = []\n",
    "    align2 = []\n",
    "\n",
    "    # start from the bottom right cell in matrix\n",
    "    i = m\n",
    "    j = n\n",
    "\n",
    "    # use i and j to keep track of where we are in the matrix, just like above\n",
    "    while i > 0 and j > 0:  # end touching the top or the left edge\n",
    "        score_current = score[i][j]\n",
    "        score_diagonal = score[i - 1][j - 1]\n",
    "        score_up = score[i][j - 1]\n",
    "        score_left = score[i - 1][j]\n",
    "\n",
    "        # figure out which cell the current score was calculated from, update i and j to correspond to that cell.\n",
    "        if score_current == score_diagonal + match_score(seq1[j - 1], seq2[i - 1], gap_penalty):\n",
    "            align1.append(seq1[j - 1])\n",
    "            align2.append(seq2[i - 1])\n",
    "            i -= 1\n",
    "            j -= 1\n",
    "        elif score_current == score_up + gap_penalty:\n",
    "            align1.append(seq1[j - 1])\n",
    "            align2.append('-')\n",
    "            j -= 1\n",
    "        elif score_current == score_left + gap_penalty:\n",
    "            align1.append('-')\n",
    "            align2.append(seq2[i - 1])\n",
    "            i -= 1\n",
    "\n",
    "    # finish tracing up to the top left cell\n",
    "    while j > 0:\n",
    "        align1.append(seq1[j - 1])\n",
    "        align2.append('-')\n",
    "        j -= 1\n",
    "    while i > 0:\n",
    "        align1.append('-')\n",
    "        align2.append(seq2[i - 1])\n",
    "        i -= 1\n",
    "\n",
    "    # since we traversed the score matrix from the bottom right, our two sequences will be reversed.\n",
    "    # these two lines reverse the order of the characters in each sequence.\n",
    "    align1 = align1[::-1]\n",
    "    align2 = align2[::-1]\n",
    "\n",
    "    return align1, align2\n",
    "\n",
    "def error(text, trans):\n",
    "    error = []\n",
    "    for i in range(len(text)):\n",
    "        if text[i] != trans[i]:\n",
    "            error.append(i)\n",
    "    if not error:\n",
    "        return \"correct\"\n",
    "    return error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_story_idx(story, idx):\n",
    "    i = 0\n",
    "    count = 0\n",
    "    while i < idx:\n",
    "        if story[i] == '-':\n",
    "            count += 1\n",
    "        i += 1\n",
    "    return idx - count\n",
    "    \n",
    "\n",
    "def classify_new(story, trans):\n",
    "    errors = []\n",
    "    cur_s = story\n",
    "    cur_t = trans\n",
    "    start = 0\n",
    "    end = len(cur_s) - 1\n",
    "    \n",
    "    # ignore unrelated chatter in the beginning\n",
    "    while cur_s[start] == '-': start += 1\n",
    "        \n",
    "    # ignore unrelated chatter in the end\n",
    "    while cur_s[end] == '-': end -= 1\n",
    "    \n",
    "    prev_correct = end\n",
    "    \n",
    "    while end >= start:\n",
    "        if cur_s[end] == cur_t[end]:\n",
    "            prev_checked = end\n",
    "            prev_correct = end\n",
    "            error = None\n",
    "        elif cur_s[end] == '-':\n",
    "            if cur_t[end] in cur_s:\n",
    "                error = 'correct-repetition'\n",
    "                word = cur_t[end]\n",
    "                idx = cur_s.index(cur_t[end])\n",
    "            elif is_similar(cur_t[end], cur_t[prev_correct]) or cur_t[end] in cur_t[prev_correct]:\n",
    "                error = 'correct - self-correction'\n",
    "                word = cur_s[prev_correct]\n",
    "                idx = prev_correct\n",
    "            else:\n",
    "                # this is unrelated chatter but we will ignore it for now\n",
    "                error = None\n",
    "                word = cur_s[prev_checked]\n",
    "                idx = prev_checked\n",
    "        elif cur_t[end] == '-':\n",
    "            error = 'miscue - skip'\n",
    "            word = cur_s[end]\n",
    "            idx = end\n",
    "        else:\n",
    "            prev_checked = end\n",
    "            error = 'miscue - substitution'\n",
    "            word = cur_s[end]\n",
    "            idx = end\n",
    "        if error is not None:\n",
    "            errors.append([get_story_idx(story, idx), word, error])\n",
    "        \n",
    "        end -= 1\n",
    "    return errors[::-1]\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "id": "iDKqKeivfxMt"
   },
   "outputs": [],
   "source": [
    "def classify_sent(text, trans):\n",
    "    # convert string to list\n",
    "    ltext = text.split(\" \")\n",
    "    ltrans = trans.split(\" \")\n",
    "\n",
    "    # align text and transcript\n",
    "    aligned_text, aligned_trans = needleman_wunsch(ltext, ltrans)\n",
    "    \n",
    "    # classify errors\n",
    "    return classify_new(aligned_text, aligned_trans)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "chLSzyQGsqCB"
   },
   "source": [
    "Classify Front-End Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "id": "qLbF3iC-65OH"
   },
   "outputs": [],
   "source": [
    "#front end data\n",
    "with open('story_text.txt') as f:\n",
    "    lines = f.readlines()\n",
    "\n",
    "with open('transcript.txt') as f:\n",
    "    transcript = f.readlines()\n",
    "\n",
    "story_text = lines[0].split(\". \")\n",
    "\n",
    "for i in range(len(story_text)):\n",
    "  story_text[i] = story_text[i].lower().translate(str.maketrans('', '', string.punctuation))\n",
    "\n",
    "for i in range(len(transcript)):\n",
    "  transcript[i] = transcript[i].lower().translate(str.maketrans('', '', string.punctuation)).replace(\"\\n\",\"\").replace(\"  \", \"\")\n",
    "\n",
    "data = {'story_text':story_text, 'transcript':transcript}\n",
    "fdf = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 677
    },
    "id": "3APeoJcUswEG",
    "outputId": "45ab5419-60bc-4d5c-bef1-bfee93889bf9"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>story_text</th>\n",
       "      <th>transcript</th>\n",
       "      <th>classify</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>for spring break adam and his family were goin...</td>\n",
       "      <td>for spring break amanda and his family were go...</td>\n",
       "      <td>[[3, adam, miscue - substitution], [12, grandp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>just two days before the vacation he fell off ...</td>\n",
       "      <td>just two days before the vacation he feel off ...</td>\n",
       "      <td>[[7, fell, miscue - substitution]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>crack adam had never felt such a pain</td>\n",
       "      <td>bike crack amanda had never felt such a pain</td>\n",
       "      <td>[[1, adam, miscue - substitution]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adams mother took him to the hospital</td>\n",
       "      <td>felt such a pain edams mother took him to the ...</td>\n",
       "      <td>[[0, adams, miscue - substitution]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>he had to have x rays and get a shot</td>\n",
       "      <td>the hospital he had to have x rays and get a shot</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>after the x rays the doctor told adam his leg ...</td>\n",
       "      <td>after the x rays the doctor told edam his leg ...</td>\n",
       "      <td>[[7, adam, miscue - substitution]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>adam would have to spend a day in the hospital</td>\n",
       "      <td>edam would have to spend a day in the hospital</td>\n",
       "      <td>[[0, adam, miscue - substitution]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>the doctor put a cast on adams leg and said ad...</td>\n",
       "      <td>the doctor put a cast on edams leg and said ed...</td>\n",
       "      <td>[[6, adams, miscue - substitution], [10, adam,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>adam would also have to learn to walk with cru...</td>\n",
       "      <td>edam would also have to learn to walk without ...</td>\n",
       "      <td>[[0, adam, miscue - substitution], [8, with, m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>the crutches were easy to use and adams friend...</td>\n",
       "      <td>crutch the crutch were easy to use and edams f...</td>\n",
       "      <td>[[1, crutches, miscue - substitution], [7, ada...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>the shot helped the pain go away but adam stil...</td>\n",
       "      <td>visit him the help shot help helped the pain g...</td>\n",
       "      <td>[[2, helped, correct - self-correction], [8, a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>he thought spring break was ruined</td>\n",
       "      <td>felt bad he thought spring break was ruined</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>the doctor wanted adam to stay home and rest f...</td>\n",
       "      <td>the doctor wanted edam to stay home and rest f...</td>\n",
       "      <td>[[3, adam, miscue - substitution]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>that meant he would not get to see his grandpa...</td>\n",
       "      <td>days that meant he would not get to see his gr...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>adams parents told him they would go see his g...</td>\n",
       "      <td>edams parents told him they would go see his g...</td>\n",
       "      <td>[[0, adams, miscue - substitution]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>this made adam feel better</td>\n",
       "      <td>this made edam filber the next day</td>\n",
       "      <td>[[2, adam, miscue - substitution], [3, feel, m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>the next day adam heard a knock at the door</td>\n",
       "      <td>the next day edam heard a knock at the door hi...</td>\n",
       "      <td>[[3, adam, miscue - substitution]]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>his mom went to open it</td>\n",
       "      <td>at the door his mom went to open it adam looked</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>adam looked up and saw his grandparents smilin...</td>\n",
       "      <td>open it edam looked up and saw his grandparent...</td>\n",
       "      <td>[[0, adam, miscue - substitution], [7, smiling...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>it would be a great spring break after all</td>\n",
       "      <td>at him it would be a great spring break after all</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           story_text  \\\n",
       "0   for spring break adam and his family were goin...   \n",
       "1   just two days before the vacation he fell off ...   \n",
       "2               crack adam had never felt such a pain   \n",
       "3               adams mother took him to the hospital   \n",
       "4                he had to have x rays and get a shot   \n",
       "5   after the x rays the doctor told adam his leg ...   \n",
       "6      adam would have to spend a day in the hospital   \n",
       "7   the doctor put a cast on adams leg and said ad...   \n",
       "8   adam would also have to learn to walk with cru...   \n",
       "9   the crutches were easy to use and adams friend...   \n",
       "10  the shot helped the pain go away but adam stil...   \n",
       "11                 he thought spring break was ruined   \n",
       "12  the doctor wanted adam to stay home and rest f...   \n",
       "13  that meant he would not get to see his grandpa...   \n",
       "14  adams parents told him they would go see his g...   \n",
       "15                         this made adam feel better   \n",
       "16        the next day adam heard a knock at the door   \n",
       "17                            his mom went to open it   \n",
       "18  adam looked up and saw his grandparents smilin...   \n",
       "19         it would be a great spring break after all   \n",
       "\n",
       "                                           transcript  \\\n",
       "0   for spring break amanda and his family were go...   \n",
       "1   just two days before the vacation he feel off ...   \n",
       "2        bike crack amanda had never felt such a pain   \n",
       "3   felt such a pain edams mother took him to the ...   \n",
       "4   the hospital he had to have x rays and get a shot   \n",
       "5   after the x rays the doctor told edam his leg ...   \n",
       "6      edam would have to spend a day in the hospital   \n",
       "7   the doctor put a cast on edams leg and said ed...   \n",
       "8   edam would also have to learn to walk without ...   \n",
       "9   crutch the crutch were easy to use and edams f...   \n",
       "10  visit him the help shot help helped the pain g...   \n",
       "11        felt bad he thought spring break was ruined   \n",
       "12  the doctor wanted edam to stay home and rest f...   \n",
       "13  days that meant he would not get to see his gr...   \n",
       "14  edams parents told him they would go see his g...   \n",
       "15                 this made edam filber the next day   \n",
       "16  the next day edam heard a knock at the door hi...   \n",
       "17    at the door his mom went to open it adam looked   \n",
       "18  open it edam looked up and saw his grandparent...   \n",
       "19  at him it would be a great spring break after all   \n",
       "\n",
       "                                             classify  \n",
       "0   [[3, adam, miscue - substitution], [12, grandp...  \n",
       "1                  [[7, fell, miscue - substitution]]  \n",
       "2                  [[1, adam, miscue - substitution]]  \n",
       "3                 [[0, adams, miscue - substitution]]  \n",
       "4                                                  []  \n",
       "5                  [[7, adam, miscue - substitution]]  \n",
       "6                  [[0, adam, miscue - substitution]]  \n",
       "7   [[6, adams, miscue - substitution], [10, adam,...  \n",
       "8   [[0, adam, miscue - substitution], [8, with, m...  \n",
       "9   [[1, crutches, miscue - substitution], [7, ada...  \n",
       "10  [[2, helped, correct - self-correction], [8, a...  \n",
       "11                                                 []  \n",
       "12                 [[3, adam, miscue - substitution]]  \n",
       "13                                                 []  \n",
       "14                [[0, adams, miscue - substitution]]  \n",
       "15  [[2, adam, miscue - substitution], [3, feel, m...  \n",
       "16                 [[3, adam, miscue - substitution]]  \n",
       "17                                                 []  \n",
       "18  [[0, adam, miscue - substitution], [7, smiling...  \n",
       "19                                                 []  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdf['classify'] = fdf.apply(lambda x: classify_sent(x.story_text, x.transcript), axis = 1)\n",
    "fdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "73h2DX3l7wZn",
    "outputId": "738cc81f-3dc4-473e-f345-2c8fc16e3d8e"
   },
   "outputs": [
    {
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       "      <th>sent_index</th>\n",
       "      <th>error_index</th>\n",
       "      <th>error_word</th>\n",
       "      <th>error_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>grandparents</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>fell</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>adams</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>adams</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>with</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>crutches</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>crutches</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>adams</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>helped</td>\n",
       "      <td>correct - self-correction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>adams</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>feel</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>better</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>adam</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>smiling</td>\n",
       "      <td>miscue - substitution</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sent_index  error_index    error_word                 error_type\n",
       "0            0            3          adam      miscue - substitution\n",
       "1            0           12  grandparents      miscue - substitution\n",
       "2            1            7          fell      miscue - substitution\n",
       "3            2            1          adam      miscue - substitution\n",
       "4            3            0         adams      miscue - substitution\n",
       "5            5            7          adam      miscue - substitution\n",
       "6            6            0          adam      miscue - substitution\n",
       "7            7            6         adams      miscue - substitution\n",
       "8            7           10          adam      miscue - substitution\n",
       "9            8            0          adam      miscue - substitution\n",
       "10           8            8          with      miscue - substitution\n",
       "11           8            9      crutches      miscue - substitution\n",
       "12           9            1      crutches      miscue - substitution\n",
       "13           9            7         adams      miscue - substitution\n",
       "14          10            2        helped  correct - self-correction\n",
       "15          10            8          adam      miscue - substitution\n",
       "16          12            3          adam      miscue - substitution\n",
       "17          14            0         adams      miscue - substitution\n",
       "18          15            2          adam      miscue - substitution\n",
       "19          15            3          feel      miscue - substitution\n",
       "20          15            4        better      miscue - substitution\n",
       "21          16            3          adam      miscue - substitution\n",
       "22          18            0          adam      miscue - substitution\n",
       "23          18            7       smiling      miscue - substitution"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a table about error\n",
    "error = fdf['classify'].tolist()\n",
    "sent_index = []\n",
    "error_index = []\n",
    "error_word = []\n",
    "error_type = []\n",
    "for s_index in range(len(error)):\n",
    "  for es in error[s_index]:\n",
    "    sent_index.append(s_index)\n",
    "    error_index.append(es[0])\n",
    "    error_word.append(es[1])\n",
    "    error_type.append(es[2])\n",
    "\n",
    "error_data = {'sent_index':sent_index, 'error_index':error_index, 'error_word':error_word,'error_type':error_type}\n",
    "error_df = pd.DataFrame(error_data)\n",
    "error_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VGc4FSZZ_27P",
    "outputId": "5f5cd1b3-6bc4-400c-df28-80b50433a0fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "miscue - substitution        23\n",
       "correct - self-correction     1\n",
       "Name: error_type, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count error types\n",
    "error_df['error_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F7aSrzyHAQUz",
    "outputId": "3f71f470-95c3-4fe9-f817-8585f59b9547"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       amanda\n",
       "1      grandpa\n",
       "3        crack\n",
       "4       amanda\n",
       "5         bike\n",
       "6       amanda\n",
       "7         felt\n",
       "8         such\n",
       "9            a\n",
       "10        pain\n",
       "12          he\n",
       "13         had\n",
       "14         the\n",
       "15    hospital\n",
       "17        edam\n",
       "22     without\n",
       "23      crutch\n",
       "24         the\n",
       "25      crutch\n",
       "26        were\n",
       "27      crutch\n",
       "31        help\n",
       "32       visit\n",
       "33         him\n",
       "34        help\n",
       "35        help\n",
       "37          he\n",
       "38     thought\n",
       "39      spring\n",
       "40        felt\n",
       "41         bad\n",
       "43        that\n",
       "44       meant\n",
       "45        days\n",
       "46        edam\n",
       "48        this\n",
       "49        edam\n",
       "50      filber\n",
       "51         the\n",
       "52        next\n",
       "53         day\n",
       "55         his\n",
       "56         mam\n",
       "57        went\n",
       "58          at\n",
       "59         the\n",
       "60        door\n",
       "61        adam\n",
       "62      looked\n",
       "63        open\n",
       "64          it\n",
       "66       smile\n",
       "67          it\n",
       "68       would\n",
       "69          be\n",
       "70          at\n",
       "71         him\n",
       "Name: error_word, dtype: object"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of substitution errors\n",
    "error_df[error_df['error_type'] == 'miscue - substitution']['error_word']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dFsfPkemA372",
    "outputId": "c53edfa8-7c96-41f1-9c92-69820dce72e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2       feel\n",
       "11     edams\n",
       "16      edam\n",
       "18      edam\n",
       "19     edams\n",
       "20      edam\n",
       "21      edam\n",
       "28    crutch\n",
       "29     edams\n",
       "36      edam\n",
       "42      edam\n",
       "47     edams\n",
       "54      edam\n",
       "65      edam\n",
       "Name: error_word, dtype: object"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of unrelated chatter errors\n",
    "error_df[error_df['error_type'] == 'miscue - unrelated chatter']['error_word']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Lh24BxniA9je",
    "outputId": "3454884e-1d9f-46ea-9792-c1f022b5ccca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30    the\n",
       "Name: error_word, dtype: object"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of repetition errors\n",
    "error_df[error_df['error_type'] == 'correct - repetition']['error_word']"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNLZvnxnDT3qZkT2zFh6H3I",
   "include_colab_link": true,
   "name": "classify-2.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "mimetype": "text/x-python",
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