linear_bag_of_word.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn import linear_model\n",
    "\n",
    "from scipy.stats import spearmanr\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = r'D:\\Workspace\\video-mem\\training_set/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_data = pd.read_csv(data_path+'scores_v2.csv')\n",
    "human_caption_data = pd.read_csv(data_path+'text_descriptions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>video_id</th>\n",
       "      <th>short-term_memorability</th>\n",
       "      <th>long-term_memorability</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2 men on a stage hug and walk away</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.57</td>\n",
       "      <td>two young man are standing on a stage embracin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.43</td>\n",
       "      <td>deadpool drinking from a cup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.43</td>\n",
       "      <td>persons in costumes dance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.75</td>\n",
       "      <td>a soccer player shoots a ball into a tiny goal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.75</td>\n",
       "      <td>soccer ball rolling into a small goal on a soc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>46</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.57</td>\n",
       "      <td>multiple chinese persons dance onstage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>46</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.57</td>\n",
       "      <td>on a podium 5 person perform a dance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>64</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.56</td>\n",
       "      <td>an asian man in a clothing store embraces an o...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>64</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.56</td>\n",
       "      <td>an asian man in cloth shop opens his jacket an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   video_id  short-term_memorability  long-term_memorability  \\\n",
       "0         8                     0.75                    0.57   \n",
       "1         8                     0.75                    0.57   \n",
       "2        26                     0.87                    0.43   \n",
       "3        26                     0.87                    0.43   \n",
       "4        33                     0.69                    0.75   \n",
       "5        33                     0.69                    0.75   \n",
       "6        46                     0.87                    0.57   \n",
       "7        46                     0.87                    0.57   \n",
       "8        64                     0.84                    0.56   \n",
       "9        64                     0.84                    0.56   \n",
       "\n",
       "                                         description  \n",
       "0                 2 men on a stage hug and walk away  \n",
       "1  two young man are standing on a stage embracin...  \n",
       "2                       deadpool drinking from a cup  \n",
       "3                          persons in costumes dance  \n",
       "4  a soccer player shoots a ball into a tiny goal...  \n",
       "5  soccer ball rolling into a small goal on a soc...  \n",
       "6             multiple chinese persons dance onstage  \n",
       "7               on a podium 5 person perform a dance  \n",
       "8  an asian man in a clothing store embraces an o...  \n",
       "9  an asian man in cloth shop opens his jacket an...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_and_caption = pd.merge(left = score_data, right = human_caption_data, how = 'inner', on = ['video_id'])[['video_id', 'part_1_scores', 'part_2_scores', 'description']]\n",
    "score_and_caption.columns = ['video_id', 'short-term_memorability', 'long-term_memorability', 'description']\n",
    "score_and_caption.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_bow(X):\n",
    "    # bag of word\n",
    "    cv = CountVectorizer(ngram_range=(2,4), stop_words='english')\n",
    "    X_CV = cv.fit_transform(X)\n",
    "    x = X_CV.toarray()\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_different_linear(df):\n",
    "    X = df['description']\n",
    "    x_raw = get_bow(X)\n",
    "    Y_short = df['short-term_memorability']\n",
    "    Y_long = df['long-term_memorability']\n",
    "    \n",
    "    x_short_train, x_short_val, y_short_train, y_short_val = train_test_split(\n",
    "    x_raw,\n",
    "    Y_short,\n",
    "    test_size=0.125,\n",
    "    random_state=42)\n",
    "    \n",
    "    print(x_short_train.shape, x_short_val.shape)\n",
    "\n",
    "    # linear regression\n",
    "    linear_reg = linear_model.LinearRegression().fit(x_short_train, y_short_train)\n",
    "    y_linear_pred = linear_reg.predict(x_short_val)\n",
    "    print('linear regression: ', spearmanr(y_short_val, y_linear_pred))\n",
    "    \n",
    "    best_alpha = 0\n",
    "    best_spearmanr = 0\n",
    "    # ridge\n",
    "    for alpha_test in np.linspace(1, 30, num = 30):\n",
    "        ridge_reg = linear_model.Ridge(alpha= alpha_test)\n",
    "        ridge_reg.fit(x_short_train, y_short_train)\n",
    "        y_ridge_pred = ridge_reg.predict(x_short_val)\n",
    "        spearman_rank = spearmanr(y_short_val, y_ridge_pred)\n",
    "        print('ridge regression: ', spearman_rank, \"alpha = \", alpha_test)\n",
    "        if spearman_rank[0] > best_spearmanr:\n",
    "            best_alpha = alpha_test\n",
    "            best_spearmanr = spearman_rank[0]\n",
    "    print('the best alpha: ', best_alpha, \"the best spearman rank: \", best_spearmanr)\n",
    "    \n",
    "    # lasso\n",
    "    # for alpha_test in np.linspace(1, 30, num = 30):\n",
    "    lasso_reg = linear_model.Lasso(alpha = alpha_test)\n",
    "    lasso_reg.fit(x_short_train, y_short_train)\n",
    "    y_lasso_pred = lasso_reg.predict(x_short_val)\n",
    "    print('lasso: regression:', spearmanr(y_short_val, y_lasso_pred))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1917, 43982) (274, 43982)\n",
      "linear regression:  SpearmanrResult(correlation=0.3353936744168005, pvalue=1.2549277630819479e-08)\n",
      "ridge regression:  SpearmanrResult(correlation=0.34505722930476884, pvalue=4.4436053767948036e-09) alpha =  1.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3513635810160045, pvalue=2.213567151170895e-09) alpha =  2.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3542772825545336, pvalue=1.595842312825175e-09) alpha =  3.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.355382660957193, pvalue=1.4083189789855454e-09) alpha =  4.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3579059025421275, pvalue=1.056794745867181e-09) alpha =  5.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.35960208160755563, pvalue=8.70043768952626e-10) alpha =  6.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36116596571488485, pvalue=7.265024679705871e-10) alpha =  7.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3600395602990177, pvalue=8.273307483754844e-10) alpha =  8.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3604124977416058, pvalue=7.925311481233165e-10) alpha =  9.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.361074556615801, pvalue=7.342190418705971e-10) alpha =  10.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3607985770674487, pvalue=7.580021678230136e-10) alpha =  11.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3614054400319735, pvalue=7.066576894928753e-10) alpha =  12.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36044987909841963, pvalue=7.891223489327392e-10) alpha =  13.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3622473966858354, pvalue=6.409751662754464e-10) alpha =  14.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3628145419587454, pvalue=6.001121806478187e-10) alpha =  15.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36120714361575007, pvalue=7.230520692689443e-10) alpha =  16.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36194104478507194, pvalue=6.641578915006282e-10) alpha =  17.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3614489542676396, pvalue=7.031086525194581e-10) alpha =  18.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36229616767480344, pvalue=6.373576898134144e-10) alpha =  19.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36259463444561396, pvalue=6.156473157217044e-10) alpha =  20.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36389860130634694, pvalue=5.289231412303418e-10) alpha =  21.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36350463685055096, pvalue=5.537911347554388e-10) alpha =  22.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36466608728843114, pvalue=4.835519317059191e-10) alpha =  23.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3639467882116147, pvalue=5.259568860272861e-10) alpha =  24.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36404695856620184, pvalue=5.198422409563152e-10) alpha =  25.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36351982302675667, pvalue=5.528118647475449e-10) alpha =  26.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36345498973603263, pvalue=5.570043557575194e-10) alpha =  27.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3627403633288179, pvalue=6.053094515493602e-10) alpha =  28.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.36426978649783437, pvalue=5.064866721691512e-10) alpha =  29.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.3629284382802876, pvalue=5.922162993507994e-10) alpha =  30.0\n",
      "the best alpha:  23.0 the best spearman rank:  0.36466608728843114\n",
      "lasso: regression: SpearmanrResult(correlation=nan, pvalue=nan)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Enviroment\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    }
   ],
   "source": [
    "# no_automatic\n",
    "train_different_linear(score_and_caption)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>video_id</th>\n",
       "      <th>short-term_memorability</th>\n",
       "      <th>long-term_memorability</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2 men on a stage hug and walk away</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.57</td>\n",
       "      <td>a man is standing in a street with a dog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.57</td>\n",
       "      <td>a woman is standing in front of a tie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.57</td>\n",
       "      <td>a man wearing a hat and a tie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.57</td>\n",
       "      <td>two young man are standing on a stage embracin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.43</td>\n",
       "      <td>a woman is sitting on a street with an umbrella</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.43</td>\n",
       "      <td>a man wearing a shirt and a tie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.43</td>\n",
       "      <td>a woman sitting on a bench with a tie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.43</td>\n",
       "      <td>deadpool drinking from a cup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>26</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.43</td>\n",
       "      <td>persons in costumes dance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   video_id  short-term_memorability  long-term_memorability  \\\n",
       "0         8                     0.75                    0.57   \n",
       "1         8                     0.75                    0.57   \n",
       "2         8                     0.75                    0.57   \n",
       "3         8                     0.75                    0.57   \n",
       "4         8                     0.75                    0.57   \n",
       "5        26                     0.87                    0.43   \n",
       "6        26                     0.87                    0.43   \n",
       "7        26                     0.87                    0.43   \n",
       "8        26                     0.87                    0.43   \n",
       "9        26                     0.87                    0.43   \n",
       "\n",
       "                                         description  \n",
       "0                 2 men on a stage hug and walk away  \n",
       "1           a man is standing in a street with a dog  \n",
       "2              a woman is standing in front of a tie  \n",
       "3                      a man wearing a hat and a tie  \n",
       "4  two young man are standing on a stage embracin...  \n",
       "5    a woman is sitting on a street with an umbrella  \n",
       "6                    a man wearing a shirt and a tie  \n",
       "7              a woman sitting on a bench with a tie  \n",
       "8                       deadpool drinking from a cup  \n",
       "9                          persons in costumes dance  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# with automatic\n",
    "with open(data_path + 'combined_captions.pkl', \"rb\") as file:\n",
    "        automatic_caption = pickle.load(file)\n",
    "        \n",
    "score_and_caption2 = pd.merge(left = score_data, right = automatic_caption, how = 'inner', on = ['video_id'])[['video_id', 'part_1_scores', 'part_2_scores', 'description']]\n",
    "score_and_caption2.columns = ['video_id', 'short-term_memorability', 'long-term_memorability', 'description']\n",
    "score_and_caption2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3465, 47265) (496, 47265)\n",
      "linear regression:  SpearmanrResult(correlation=-0.061029378216204716, pvalue=0.1747733904955961)\n",
      "ridge regression:  SpearmanrResult(correlation=0.19521814351086786, pvalue=1.1925471324685823e-05) alpha =  1.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19193997285432013, pvalue=1.6773945387767728e-05) alpha =  2.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19156681812939302, pvalue=1.7431824392318428e-05) alpha =  3.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19295386898835118, pvalue=1.5103471306981995e-05) alpha =  4.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19417663727387136, pvalue=1.3298933331630173e-05) alpha =  5.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.1959958034697934, pvalue=1.0989130051203396e-05) alpha =  6.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19615457386581223, pvalue=1.0806765495202085e-05) alpha =  7.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19656769187702802, pvalue=1.0345658759161909e-05) alpha =  8.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19673945526267764, pvalue=1.0159509515347411e-05) alpha =  9.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19753753980757585, pvalue=9.335634683322772e-06) alpha =  10.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19672651148891668, pvalue=1.0173425526282155e-05) alpha =  11.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19664768743823735, pvalue=1.0258562821568182e-05) alpha =  12.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.1979444763600718, pvalue=8.940443169661271e-06) alpha =  13.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19814791502500473, pvalue=8.748902077253122e-06) alpha =  14.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19793495795632107, pvalue=8.94950203404044e-06) alpha =  15.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19832996452744545, pvalue=8.580819496004914e-06) alpha =  16.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19790852903423115, pvalue=8.97470086173581e-06) alpha =  17.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19789041759413972, pvalue=8.992008346596688e-06) alpha =  18.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19716081208429778, pvalue=9.716275283046651e-06) alpha =  19.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19776585222764137, pvalue=9.111908544240929e-06) alpha =  20.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.1971804492158706, pvalue=9.696074367174702e-06) alpha =  21.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19689197524323018, pvalue=9.996892496254888e-06) alpha =  22.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.1963296839306092, pvalue=1.0608974800315321e-05) alpha =  23.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19611544724931038, pvalue=1.0851437165470358e-05) alpha =  24.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19581153925321107, pvalue=1.1204449404024173e-05) alpha =  25.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.1956988841272076, pvalue=1.133806253875082e-05) alpha =  26.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19418274004172825, pvalue=1.3290463952588856e-05) alpha =  27.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19412732297231808, pvalue=1.336755995288055e-05) alpha =  28.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19461574126434875, pvalue=1.270246932010987e-05) alpha =  29.0\n",
      "ridge regression:  SpearmanrResult(correlation=0.19420045775486117, pvalue=1.326590448854633e-05) alpha =  30.0\n",
      "the best alpha:  16.0 the best spearman rank:  0.19832996452744545\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Enviroment\\Anaconda\\lib\\site-packages\\scipy\\stats\\stats.py:4196: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.\n",
      "  warnings.warn(SpearmanRConstantInputWarning())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lasso: regression: SpearmanrResult(correlation=nan, pvalue=nan)\n"
     ]
    }
   ],
   "source": [
    "train_different_linear(score_and_caption2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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