CLIP-14-Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7bd18d3",
   "metadata": {},
   "source": [
    "# Multi-modal Embedding using CLIP Large 14"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7e3f228",
   "metadata": {},
   "source": [
    "## Imports and Utilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "78762744",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer, util, models, InputExample, losses\n",
    "from PIL import Image\n",
    "import glob\n",
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "import pickle\n",
    "import zipfile\n",
    "from IPython.display import display\n",
    "from IPython.display import Image as IPImage\n",
    "import pandas as pd\n",
    "import os\n",
    "from tqdm.autonotebook import tqdm, trange\n",
    "import torch\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import image as mpimg\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix, f1_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9056b848",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_folder = 'images/'\n",
    "def show_image(img_id, img_folder=image_folder):\n",
    "    try:\n",
    "        image_link = img_folder + img_id + '.jpg'\n",
    "        plt.title(\"Image\")\n",
    "        plt.xlabel(\"X pixel scaling\")\n",
    "        plt.ylabel(\"Y pixels scaling\")\n",
    "\n",
    "        image = mpimg.imread(image_link)\n",
    "    except:\n",
    "        image_link = img_folder + img_id + '.png'\n",
    "        plt.title(\"Image\")\n",
    "        plt.xlabel(\"X pixel scaling\")\n",
    "        plt.ylabel(\"Y pixels scaling\")\n",
    "\n",
    "        image = mpimg.imread(image_link)\n",
    "    plt.imshow(image)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0415eab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "def set_seed_everywhere(seed, cuda):\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    random.seed(seed)\n",
    "    if cuda:\n",
    "        torch.cuda.manual_seed_all(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "366ad8a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>index</th>\n",
       "      <th>id</th>\n",
       "      <th>skepticism</th>\n",
       "      <th>mainstream</th>\n",
       "      <th>label</th>\n",
       "      <th>text_NL</th>\n",
       "      <th>text_EN</th>\n",
       "      <th>split</th>\n",
       "      <th>available</th>\n",
       "      <th>target</th>\n",
       "      <th>unseen_images</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>956</td>\n",
       "      <td>327027443732983808</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>RT @USER : 3D waterkaart , hoe stroomt het wat...</td>\n",
       "      <td>RT @USER : 3D water map , how the water flows ...</td>\n",
       "      <td>test</td>\n",
       "      <td>True</td>\n",
       "      <td>3_327026285966667776</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>1211</td>\n",
       "      <td>375896131127218176</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>Kom naar de Wilhelminakade R'dam en klim boven...</td>\n",
       "      <td>Come to the Wilhelminakade R'dam and climb abo...</td>\n",
       "      <td>val</td>\n",
       "      <td>True</td>\n",
       "      <td>3_375896131013971968</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1212</th>\n",
       "      <td>1212</td>\n",
       "      <td>375899134915772416</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>RT @USER : Kom naar de Wilhelminakade R'dam en...</td>\n",
       "      <td>RT @USER : Come to the Wilhelminakade R'dam an...</td>\n",
       "      <td>val</td>\n",
       "      <td>True</td>\n",
       "      <td>3_375896131013971968</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1214</th>\n",
       "      <td>1214</td>\n",
       "      <td>375946829881278464</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>Scholieren klimmen met Jeugdwaterschap naar ni...</td>\n",
       "      <td>Students climb to the water level of their hou...</td>\n",
       "      <td>val</td>\n",
       "      <td>True</td>\n",
       "      <td>3_375946829747089408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>1215</td>\n",
       "      <td>375946961951539200</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>RT @USER : Scholieren klimmen met Jeugdwatersc...</td>\n",
       "      <td>RT @USER : Students climb to the water level o...</td>\n",
       "      <td>train</td>\n",
       "      <td>True</td>\n",
       "      <td>3_375946829747089408</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index                  id  skepticism  mainstream  label  \\\n",
       "956     956  327027443732983808       False        True      1   \n",
       "1211   1211  375896131127218176       False        True      1   \n",
       "1212   1212  375899134915772416       False        True      1   \n",
       "1214   1214  375946829881278464       False        True      1   \n",
       "1215   1215  375946961951539200       False        True      1   \n",
       "\n",
       "                                                text_NL  \\\n",
       "956   RT @USER : 3D waterkaart , hoe stroomt het wat...   \n",
       "1211  Kom naar de Wilhelminakade R'dam en klim boven...   \n",
       "1212  RT @USER : Kom naar de Wilhelminakade R'dam en...   \n",
       "1214  Scholieren klimmen met Jeugdwaterschap naar ni...   \n",
       "1215  RT @USER : Scholieren klimmen met Jeugdwatersc...   \n",
       "\n",
       "                                                text_EN  split  available  \\\n",
       "956   RT @USER : 3D water map , how the water flows ...   test       True   \n",
       "1211  Come to the Wilhelminakade R'dam and climb abo...    val       True   \n",
       "1212  RT @USER : Come to the Wilhelminakade R'dam an...    val       True   \n",
       "1214  Students climb to the water level of their hou...    val       True   \n",
       "1215  RT @USER : Students climb to the water level o...  train       True   \n",
       "\n",
       "                    target  unseen_images  \n",
       "956   3_327026285966667776              1  \n",
       "1211  3_375896131013971968              1  \n",
       "1212  3_375896131013971968              1  \n",
       "1214  3_375946829747089408              0  \n",
       "1215  3_375946829747089408              0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/tweets_subset.csv', index_col='Unnamed: 0').reset_index()\n",
    "df[df.available].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "44baebe8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"id2label\"]` will be overriden.\n",
      "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"bos_token_id\"]` will be overriden.\n",
      "`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config[\"eos_token_id\"]` will be overriden.\n"
     ]
    }
   ],
   "source": [
    "# We use the original CLIP model for computing image embeddings and English text embeddings\n",
    "en_model = SentenceTransformer('clip-ViT-L-14')\n",
    "tokenizer = en_model._first_module().processor.tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c10f520a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def embed_documents(documents, embedding_model=en_model, convert_to_tensor = True, verbose = False):\n",
    "    \"\"\" Embed a list of n documents/words into an n-dimensional\n",
    "    matrix of embeddings\n",
    "\n",
    "    Arguments:\n",
    "        documents: A list of documents or words to be embedded\n",
    "        verbose: Controls the verbosity of the process\n",
    "\n",
    "    Returns:\n",
    "        Document/words embeddings with shape (n, m) with `n` documents/words\n",
    "        that each have an embeddings size of `m`\n",
    "    \"\"\"\n",
    "    truncated_docs = [truncate_document(doc) for doc in documents]\n",
    "    embeddings = embedding_model.encode(truncated_docs, show_progress_bar=verbose, convert_to_tensor=convert_to_tensor)\n",
    "    return embeddings\n",
    "\n",
    "def truncate_document(document, tokenizer=tokenizer):\n",
    "    tokens = tokenizer.encode(document)\n",
    "\n",
    "    if len(tokens) > 77:\n",
    "        # Skip the starting token, only include 75 tokens\n",
    "        truncated_tokens = tokens[1:76]\n",
    "        document = tokenizer.decode(truncated_tokens)\n",
    "\n",
    "        # Recursive call here, because the encode(decode()) can have different result\n",
    "        return truncate_document(document)\n",
    "\n",
    "    else:\n",
    "        return document"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a5a051b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Token indices sequence length is longer than the specified maximum sequence length for this model (80 > 77). Running this sequence through the model will result in indexing errors\n"
     ]
    }
   ],
   "source": [
    "df['text_EN_trun'] = df['text_EN'].apply(truncate_document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d195eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def embed_images(images, verbose=True, batch_size=32, embedding_model=en_model, convert_to_tensor=True):\n",
    "    if batch_size:\n",
    "        nr_iterations = int(np.ceil(len(images) / batch_size))\n",
    "\n",
    "        # Embed images per batch\n",
    "        embeddings = []\n",
    "        for i in tqdm(range(nr_iterations), disable=not verbose):\n",
    "            start_index = i * batch_size\n",
    "            end_index = (i * batch_size) + batch_size\n",
    "\n",
    "            images_to_embed = [Image.open(img_name_dict[image]) if (isinstance(image, str) and not str(image)=='nan') \n",
    "                               else (Image.new('RGB', (800, 600), color = (255,255,255)) if str(image)=='nan'\n",
    "                               else image) for image in images[start_index:end_index]]\n",
    "            img_emb = embedding_model.encode(images_to_embed, show_progress_bar=False, convert_to_tensor=convert_to_tensor)\n",
    "            embeddings.extend(img_emb.tolist())\n",
    "\n",
    "            # Close images\n",
    "            if isinstance(images[0], str):\n",
    "                for image in images_to_embed:\n",
    "                    image.close()\n",
    "        if not convert_to_tensor:\n",
    "            embeddings = np.array(embeddings)\n",
    "        else:\n",
    "            embeddings = torch.tensor(embeddings).to(device='cuda')\n",
    "    else:\n",
    "        images_to_embed = [Image.open(filepath) for filepath in images]\n",
    "        embeddings = embedding_model.encode(images_to_embed, show_progress_bar=False, convert_to_tensor=convert_to_tensor)\n",
    "    return embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "54bd8cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_report(labels, preds, ids=[]):\n",
    "    if len(ids)==0:\n",
    "        print(metrics.classification_report(labels.cpu(), preds.cpu(), digits=4))\n",
    "        return metrics.classification_report(labels.cpu(), preds.cpu(), digits=4, output_dict=True)\n",
    "    else:\n",
    "        print(metrics.classification_report(labels[torch.tensor(list(ids))].cpu(), preds[torch.tensor(list(ids))].cpu(), digits=4))\n",
    "        return metrics.classification_report(labels[torch.tensor(list(ids))].cpu(), preds[torch.tensor(list(ids))].cpu(), digits=4, output_dict=True)\n",
    "\n",
    "def evaluate_dataset_F1(df, preds_all, device = 'cuda', val_set = 'val'):\n",
    "    preds_all = preds_all.float()\n",
    "    label_all = torch.tensor(np.array(df['skepticism'])).to(device).float()\n",
    "    train = df[df['split'] == 'train'].index\n",
    "    val = df[df['split'] == 'val'].index\n",
    "    test = df[df['split'] == 'test'].index\n",
    "    image = df[df['available']].index\n",
    "    unseen = df[df['unseen_images']==1].index\n",
    "    \n",
    "    if val_set=='val':\n",
    "        temp = val.copy()\n",
    "    elif val_set=='test':\n",
    "        temp = test.copy()\n",
    "    \n",
    "    a = temp.intersection(image)\n",
    "    b = temp.difference(image)\n",
    "    c = temp.intersection(image).intersection(unseen)    \n",
    "    \n",
    "    print('Overall Dataset:')\n",
    "    all_report = return_report(label_all, preds_all)\n",
    "    \n",
    "    print('Training Set:')\n",
    "    train_report = return_report(label_all, preds_all, train)\n",
    "    \n",
    "    print('Validation Set:')\n",
    "    val_report = return_report(label_all, preds_all, temp)\n",
    "    \n",
    "    print('Validation Set with both Images and Texts:')\n",
    "    val_img_report = return_report(label_all, preds_all, a)\n",
    "    \n",
    "    print('Validation Set with only Texts:')\n",
    "    val_txt_report = return_report(label_all, preds_all, b)\n",
    "    \n",
    "    print('Validation Set with unseen Images:')\n",
    "    val_unseen_report = return_report(label_all, preds_all, c)\n",
    "    \n",
    "    all_acc = ((label_all==preds_all).sum()/len(preds_all)).item()*100\n",
    "    train_acc = ((label_all[torch.tensor(list(train))]==preds_all[torch.tensor(list(train))]).sum()/len(train)).item()*100\n",
    "    val_acc = ((label_all[torch.tensor(list(temp))]==preds_all[torch.tensor(list(temp))]).sum()/len(temp)).item()*100\n",
    "    val_img_acc = ((label_all[torch.tensor(list((a)))]==preds_all[torch.tensor(list(a))]).sum()/len(a)).item()*100\n",
    "    val_txt_acc = ((label_all[torch.tensor(list((b)))]==preds_all[torch.tensor(list((b)))]).sum()/len(b)).item()*100\n",
    "    val_unseen = ((label_all[torch.tensor(list((c)))]==preds_all[torch.tensor(list((c)))]).sum()/len(c)).item()*100\n",
    "    \n",
    "    print('''\n",
    "    Overall Accuracy: {:.2f}\n",
    "    Training Set Accuracy: {:.2f}\n",
    "    Validation Set Accuracy: {:.2f}\n",
    "    Validation Set with both Images and Texts: {:.2f}\n",
    "    Validation Set with only Texts: {:.2f}\n",
    "    Validation Set with unseen Images: {:.2f}\n",
    "    '''.format(all_acc, train_acc, val_acc, val_img_acc, val_txt_acc, val_unseen))\n",
    "    \n",
    "    return all_report, train_report, val_report, val_img_report, val_txt_report, val_unseen_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10dd7c2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_dataset(df, preds_all, device = 'cuda', val_set = 'val'):\n",
    "    preds_all = preds_all.float()\n",
    "    label_all = torch.tensor(np.array(df['skepticism'])).to(device).float()\n",
    "    #train = torch.tensor(np.array(df_all['split'] == 'train')).to(device).float()\n",
    "    train = df[df['split'] == 'train'].index\n",
    "    #val = torch.tensor(np.array(df_all['split'] == 'val')).to(device).float()\n",
    "    val = df[df['split'] == 'val'].index\n",
    "    #test = torch.tensor(np.array(df_all['split'] == 'test')).to(device).float()\n",
    "    test = df[df['split'] == 'test'].index\n",
    "    #image = torch.tensor(np.array(df_all['available'])).to(device).float()\n",
    "    image = df[df['available']].index\n",
    "    #unseen = torch.tensor(np.array(df_all['unseen_images'])).to(device).float()\n",
    "    unseen = df[df['unseen_images']==1].index\n",
    "    \n",
    "    if val_set=='val':\n",
    "        a = val.intersection(image)\n",
    "        b = val.difference(image)\n",
    "        c = val.intersection(image).intersection(unseen)\n",
    "        val_acc = ((label_all[torch.tensor(list(val))]==preds_all[torch.tensor(list(val))]).sum()/len(val)).item()*100\n",
    "            \n",
    "    elif val_set=='test':\n",
    "        a = test.intersection(image)\n",
    "        b = test.difference(image)\n",
    "        c = test.intersection(image).intersection(unseen)\n",
    "        val_acc = ((label_all[torch.tensor(list(test))]==preds_all[torch.tensor(list(test))]).sum()/len(test)).item()*100\n",
    "    \n",
    "    all_acc = ((label_all==preds_all).sum()/len(preds_all)).item()*100\n",
    "    train_acc = ((label_all[torch.tensor(list(train))]==preds_all[torch.tensor(list(train))]).sum()/len(train)).item()*100\n",
    "    \n",
    "    val_img_acc = ((label_all[torch.tensor(list((a)))]==preds_all[torch.tensor(list(a))]).sum()/len(a)).item()*100\n",
    "    val_txt_acc = ((label_all[torch.tensor(list((b)))]==preds_all[torch.tensor(list((b)))]).sum()/len(b)).item()*100\n",
    "    val_unseen = ((label_all[torch.tensor(list((c)))]==preds_all[torch.tensor(list((c)))]).sum()/len(c)).item()*100\n",
    "    \n",
    "    print('''\n",
    "    Overall Accuracy: {:.2f}\n",
    "    Training Set Accuracy: {:.2f}\n",
    "    Validation Set Accuracy: {:.2f}\n",
    "    Validation Set with both Images and Texts: {:.2f}\n",
    "    Validation Set with only Texts: {:.2f}\n",
    "    Validation Set with unseen Images: {:.2f}\n",
    "    '''.format(all_acc, train_acc, val_acc, val_img_acc, val_txt_acc, val_unseen))\n",
    "    \n",
    "    return all_acc, train_acc, val_acc, val_img_acc, val_txt_acc, val_unseen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f428c71",
   "metadata": {},
   "source": [
    "## Process Raw Data into Embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecee2fc",
   "metadata": {},
   "source": [
    "Two sentences to compute embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fbd4296e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a229c6016310455fbfcc205c31f061ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['Climate change is a very serious issue to pay attention to! We should care about climate change!', \n",
    "          'Climate change is not even a real problem to concern about! I do not believe in it!']\n",
    "lab_emb = en_model.encode(labels, convert_to_tensor=True)\n",
    "empty_img_emb = embed_images(['nan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3dea33a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 768])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_img_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7feb65d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt_emb = embed_documents(list(df['text_EN']), convert_to_tensor=True, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca41aab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_emb = embed_images(list(df['target']), convert_to_tensor=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1f1816f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('data/CLIP14/embeddings.pkl', \"wb\") as fOut:\n",
    "    pickle.dump({'sentences': list(df['text_EN_trun']), 'txt_emb': txt_emb, 'img_emb': img_emb, \n",
    "                 'lab_emb': lab_emb, 'empty_img_emb': empty_img_emb}, fOut, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "96befd9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/CLIP14/embeddings.pkl', \"rb\") as fIn:\n",
    "    stored_data = pickle.load(fIn)\n",
    "    #stored_sentences = stored_data['sentences']\n",
    "    txt_emb = stored_data['txt_emb']\n",
    "    img_emb = stored_data['img_emb']\n",
    "    lab_emb = stored_data['lab_emb']\n",
    "    empty_img_emb = stored_data['empty_img_emb']\n",
    "\n",
    "labs = {}\n",
    "labs['txt_emb'] = lab_emb\n",
    "labs['img_emb'] = torch.vstack([empty_img_emb] * lab_emb.shape[0])\n",
    "mul_emb = (img_emb + txt_emb)/2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d53de3d",
   "metadata": {},
   "source": [
    "## Zero-shot Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da062fc9",
   "metadata": {},
   "source": [
    "Using Cosine Similarity to make classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "990b44e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def zero_shot(df_all, model_name='CLIP14', merge = 'average', from_stored_embedding=False, pre_computed = True,\n",
    "             stored_data='', embedding_model = '', val_set='val'):\n",
    "    \n",
    "    if from_stored_embedding:\n",
    "        folder = 'model_storage/{}/embeddings.pkl'.format(model_name)\n",
    "        with open(folder, \"rb\") as fIn:\n",
    "            stored_data = pickle.load(fIn)\n",
    "            #stored_sentences = stored_data['sentences']\n",
    "            txt_emb = stored_data['txt_emb']\n",
    "            img_emb = stored_data['img_emb']\n",
    "            lab_emb = stored_data['lab_emb']\n",
    "    elif pre_computed:\n",
    "        stored_data = stored_data\n",
    "        #stored_sentences = stored_data['sentences']\n",
    "        txt_emb = stored_data['txt_emb']\n",
    "        img_emb = stored_data['img_emb']\n",
    "        lab_emb = stored_data['lab_emb']\n",
    "    else:\n",
    "        txt_emb = embed_documents(list(df_all['text_EN_trun']), embedding_model=embedding_model, convert_to_tensor=True, verbose=True)\n",
    "        img_emb = embed_images(list(df_all['target']), embedding_model=embedding_model, convert_to_tensor=True, verbose=True)\n",
    "        labels = ['Climate change is a very serious issue to pay attention to! We should care about climate change!', \n",
    "                  'Climate change is not even a real problem to concern about! I do not believe in it!']\n",
    "        lab_emb = embedding_model.encode(labels, convert_to_tensor=True)\n",
    "        with open('model_storage/CLIP/embeddings_re.pkl', \"wb\") as fOut:\n",
    "            pickle.dump({'sentences': list(df['text_EN_trun']), 'txt_emb': txt_emb, 'img_emb': img_emb, 'lab_emb': lab_emb}, fOut, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "    if merge == 'average':\n",
    "        mul_emb = (img_emb + txt_emb)/2\n",
    "    elif merge == 'max':\n",
    "        mul_emb = torch.max(img_emb, txt_emb)\n",
    "    else:\n",
    "        print(\"'Merger' input must be one of 'average' or 'max'\")\n",
    "    \n",
    "    TXT_LAB = util.cos_sim(lab_emb, txt_emb)\n",
    "    IMG_LAB = util.cos_sim(lab_emb, img_emb)\n",
    "    MUL_LAB = util.cos_sim(lab_emb, mul_emb)\n",
    "    \n",
    "    pred_TXT = torch.argmax(TXT_LAB[:2,:], dim=0).float()\n",
    "    pred_IMG = torch.argmax(IMG_LAB[:2,:], dim=0).float()\n",
    "    pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "    \n",
    "    lab = torch.tensor(np.array(df_all['skepticism'])).to('cuda').float()\n",
    "    \n",
    "    set_seed_everywhere(42, cuda=True)\n",
    "    lab_rand = (torch.rand(len(lab))>0.5).float().to('cuda')\n",
    "    \n",
    "    print('Using textual embedding only:')\n",
    "    evaluate_dataset(df_all, pred_TXT, val_set=val_set)\n",
    "    \n",
    "    print('Using image embedding only:')\n",
    "    evaluate_dataset(df_all, pred_IMG, val_set=val_set)\n",
    "    \n",
    "    print('Using multi-modal embedding:')\n",
    "    evaluate_dataset(df_all, pred_MUL, val_set=val_set)\n",
    "    \n",
    "    print('Comparing to a random prediction:')\n",
    "    evaluate_dataset(df_all, lab_rand, val_set=val_set)\n",
    "    \n",
    "    print('Test set performance multi-modal:')\n",
    "    evaluate_dataset(df_all, pred_MUL, val_set='test')\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cb85005f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using textual embedding only:\n",
      "\n",
      "    Overall Accuracy: 63.39\n",
      "    Training Set Accuracy: 63.48\n",
      "    Validation Set Accuracy: 63.96\n",
      "    Validation Set with both Images and Texts: 74.78\n",
      "    Validation Set with only Texts: 61.97\n",
      "    Validation Set with unseen Images: 68.74\n",
      "    \n",
      "Using image embedding only:\n",
      "\n",
      "    Overall Accuracy: 42.60\n",
      "    Training Set Accuracy: 42.71\n",
      "    Validation Set Accuracy: 42.87\n",
      "    Validation Set with both Images and Texts: 45.04\n",
      "    Validation Set with only Texts: 42.47\n",
      "    Validation Set with unseen Images: 46.38\n",
      "    \n",
      "Using multi-modal embedding:\n",
      "\n",
      "    Overall Accuracy: 58.10\n",
      "    Training Set Accuracy: 58.01\n",
      "    Validation Set Accuracy: 58.60\n",
      "    Validation Set with both Images and Texts: 63.83\n",
      "    Validation Set with only Texts: 57.64\n",
      "    Validation Set with unseen Images: 59.21\n",
      "    \n",
      "Comparing to a random prediction:\n",
      "\n",
      "    Overall Accuracy: 49.64\n",
      "    Training Set Accuracy: 49.67\n",
      "    Validation Set Accuracy: 49.99\n",
      "    Validation Set with both Images and Texts: 48.09\n",
      "    Validation Set with only Texts: 50.34\n",
      "    Validation Set with unseen Images: 47.20\n",
      "    \n",
      "Test set performance multi-modal:\n",
      "\n",
      "    Overall Accuracy: 58.10\n",
      "    Training Set Accuracy: 58.01\n",
      "    Validation Set Accuracy: 58.03\n",
      "    Validation Set with both Images and Texts: 63.53\n",
      "    Validation Set with only Texts: 57.10\n",
      "    Validation Set with unseen Images: 62.67\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "zero_shot(df, stored_data = stored_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92f68085",
   "metadata": {},
   "source": [
    "## Training an additional MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "496fe10f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from argparse import Namespace\n",
    "args = Namespace(\n",
    "    # Data and Path information\n",
    "    model_state_file='MLP_classifier.pth',\n",
    "    save_dir='model_storage/CLIP14/',\n",
    "    result_dir='results/CLIP14/',\n",
    "    embedding_size=768,\n",
    "    early_stopping_criteria=20,\n",
    "    num_epochs=20,\n",
    "    seed=42,\n",
    "    \n",
    "    # Training hyper parameters\n",
    "    batch_size=32,\n",
    "    embedding_type='text',\n",
    "    fusion_type='average',\n",
    "    hidden_dim=128, \n",
    "    learning_rate=5e-5,\n",
    "    l2=1e-5,\n",
    "    dropout_p=0.1,\n",
    "    \n",
    "    # Runtime options\n",
    "    catch_keyboard_interrupt=True,\n",
    "    device= 'cuda',\n",
    "    cuda=True,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8720053",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "class MLPClassifier(nn.Module):\n",
    "    \n",
    "    def __init__(self, embedding_size, embedding_type, fusion_type, hidden_dim, num_classes, dropout_p, \n",
    "                 pretrained_embeddings=None):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            embedding_size (int): size of the embedding vectors\n",
    "            embedding_type (str): type of embedding to be used, can be one of {'text', 'image', 'multi'}\n",
    "            fusion_type (str): type of fusing multimodal embeddings if embedding_type=='multi', can be one of \n",
    "                                {'average', 'concat', 'complex'}\n",
    "            hidden_dim (int): the size of the hidden dimension\n",
    "            num_classes (int): the number of classes in classification\n",
    "            dropout_p (float): a dropout parameter \n",
    "            pretrained_embeddings (numpy.array): previously trained embeddings\n",
    "            \n",
    "        \"\"\"\n",
    "        super(MLPClassifier, self).__init__()\n",
    "\n",
    "        self.embedding_type = embedding_type\n",
    "        self.fusion_type = fusion_type\n",
    "        self._dropout_p = dropout_p\n",
    "        self.dropout = nn.Dropout(dropout_p)\n",
    "        #self.prelu = nn.PReLU()\n",
    "        \n",
    "        self.fc1 = nn.Linear(embedding_size, hidden_dim)\n",
    "        self.fc2 = nn.Linear(hidden_dim, num_classes)\n",
    "        \n",
    "        self.fc_concat = nn.Linear(2*embedding_size, hidden_dim)\n",
    "        self.fc_complex = nn.Linear(5*embedding_size, 2*embedding_size)\n",
    "\n",
    "    def forward(self, x_in, apply_softmax=False):\n",
    "        \"\"\"The forward pass of the classifier\n",
    "        \n",
    "        Args:\n",
    "            x_in (dictionary): a dictionary of input data tensors with embeddings. \n",
    "                x_in.shape should be (batch, dataset._max_seq_length)\n",
    "            apply_softmax (bool): a flag for the softmax activation\n",
    "                should be false if used with the Cross Entropy losses\n",
    "        Returns:\n",
    "            the resulting tensor. tensor.shape should be (batch, num_classes)\n",
    "        \"\"\"\n",
    "        \n",
    "        # embed and permute so features are channels\n",
    "        x_embedded = x_in\n",
    "                \n",
    "        # logistic regression\n",
    "        if (not self.embedding_type=='multi') or self.fusion_type=='average':\n",
    "            intermediate_vector = F.relu(self.dropout(self.fc1(x_embedded)))\n",
    "            prediction_vector = self.fc2(intermediate_vector)\n",
    "        elif self.embedding_type=='multi' and self.fusion_type=='concat':\n",
    "            intermediate_vector = F.relu(self.dropout(self.fc_concat(x_embedded)))\n",
    "            prediction_vector = self.fc2(intermediate_vector)\n",
    "        elif self.embedding_type=='multi' and self.fusion_type=='complex':\n",
    "            first_vector = F.relu(self.dropout(self.fc_complex(x_embedded)))\n",
    "            intermediate_vector = F.relu(self.dropout(self.fc_concat(first_vector)))\n",
    "            prediction_vector = self.fc2(intermediate_vector)\n",
    "        \n",
    "        if apply_softmax:\n",
    "            prediction_vector = F.softmax(prediction_vector, dim=1)\n",
    "\n",
    "        return prediction_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4928e3f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, df_all, embeddings):\n",
    "        self.df = df_all\n",
    "        self.labels = torch.tensor(np.array(self.df['skepticism'])).to(args.device).float()\n",
    "        measure_len = lambda context: len(context.split(\" \"))\n",
    "        self._max_seq_length = max(map(measure_len, self.df.text_EN_trun)) + 0\n",
    "        self.embeddings = embeddings\n",
    "        \n",
    "        train = torch.tensor(df[df['split'] == 'train'].index)\n",
    "        self.train_df = self.df[self.df.split=='train']\n",
    "        self.train_size = len(self.train_df)\n",
    "        self.train_emb = self.embeddings[train]\n",
    "        \n",
    "        val = torch.tensor(df[df['split'] == 'val'].index)\n",
    "        self.val_df = self.df[self.df.split=='val']\n",
    "        self.validation_size = len(self.val_df)\n",
    "        self.val_emb = self.embeddings[val]\n",
    "                \n",
    "        test = torch.tensor(df[df['split'] == 'test'].index)\n",
    "        self.test_df = self.df[self.df.split=='test']\n",
    "        self.test_size = len(self.test_df)\n",
    "        self.test_emb = self.embeddings[test]\n",
    "\n",
    "        self._lookup_dict = {'train': (self.train_df, self.train_size, self.train_emb),\n",
    "                             'val': (self.val_df, self.validation_size, self.val_emb),\n",
    "                             'test': (self.test_df, self.test_size, self.test_emb)}\n",
    "\n",
    "        self.set_split('train')\n",
    "            \n",
    "    def set_split(self, split=\"train\"):\n",
    "        \"\"\" selects the splits in the dataset using a column in the dataframe \n",
    "        \n",
    "        Args:\n",
    "            split (str): one of \"train\", \"val\", or \"test\"\n",
    "        \"\"\"\n",
    "        self._target_split = split\n",
    "        self._target_df, self._target_size, self._target_emb = self._lookup_dict[split]\n",
    "        \n",
    "    def get_num_batches(self, batch_size):\n",
    "        \"\"\"Given a batch size, return the number of batches in the dataset\n",
    "        \n",
    "        Args:\n",
    "            batch_size (int)\n",
    "        Returns:\n",
    "            number of batches in the dataset\n",
    "        \"\"\"\n",
    "        if len(self) % batch_size==0:\n",
    "            return len(self) // batch_size\n",
    "        else:\n",
    "            return len(self) // batch_size + 1\n",
    "\n",
    "    def __len__(self):\n",
    "        return self._target_size\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        emb = self._target_emb[idx].to(args.device)\n",
    "        row = self._target_df.iloc[idx]\n",
    "        label = torch.tensor(row[['mainstream', 'skepticism']].astype(int)).to(args.device)\n",
    "        \n",
    "        return {'emb': emb, \n",
    "                'label': label}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e98c3e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_train_state(args):\n",
    "    return {'stop_early': False,\n",
    "            'early_stopping_step': 0,\n",
    "            'early_stopping_best_1_acc_val': 0,\n",
    "            'learning_rate': args.learning_rate,\n",
    "            'epoch_index': 0,\n",
    "            'golden_loss': [],\n",
    "            'train_loss': [],\n",
    "            'train_1_acc': [],\n",
    "            'train_acc': [],\n",
    "            'val_loss': [],\n",
    "            'val_1_acc': [],\n",
    "            'val_acc': [],\n",
    "            'val_img_txt': [],\n",
    "            'val_txt_only': [],\n",
    "            'val_unseen_img': [],\n",
    "            'test_loss': -1,\n",
    "            'test_1_acc': -1,\n",
    "            'test_acc': -1,\n",
    "            'test_img_txt': -1,\n",
    "            'test_txt_only': -1,\n",
    "            'test_unseen_img': -1,\n",
    "            'model_filename': args.model_state_file}\n",
    "\n",
    "def update_train_state(args, model, train_state):\n",
    "    \"\"\"Handle the training state updates.\n",
    "\n",
    "    Components:\n",
    "     - Early Stopping: Prevent overfitting.\n",
    "     - Model Checkpoint: Model is saved if the model is better\n",
    "\n",
    "    :param args: main arguments\n",
    "    :param model: model to train\n",
    "    :param train_state: a dictionary representing the training state values\n",
    "    :returns:\n",
    "        a new train_state\n",
    "    \"\"\"\n",
    "\n",
    "    # Save one model at least\n",
    "    if train_state['epoch_index'] == 0:\n",
    "        torch.save(model.state_dict(), args.save_dir + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file)\n",
    "        train_state['stop_early'] = False\n",
    "\n",
    "    # Save model if performance improved\n",
    "    elif train_state['epoch_index'] >= 1:\n",
    "        acc_tm1, acc_t = train_state['val_1_acc'][-2:]\n",
    "\n",
    "        # If accuracy worsened\n",
    "        if acc_t <= train_state['early_stopping_best_1_acc_val']:\n",
    "            # Update step\n",
    "            train_state['early_stopping_step'] += 1\n",
    "        # Loss decreased\n",
    "        else:\n",
    "            # Save the best model from sklearn\n",
    "            if acc_t > train_state['early_stopping_best_1_acc_val']:\n",
    "                train_state['early_stopping_best_1_acc_val'] = acc_t\n",
    "                torch.save(model.state_dict(), args.save_dir + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file)\n",
    "                \n",
    "            # Reset early stopping step\n",
    "            train_state['early_stopping_step'] = 0\n",
    "\n",
    "        # Stop early ?\n",
    "        train_state['stop_early'] = \\\n",
    "            train_state['early_stopping_step'] >= args.early_stopping_criteria\n",
    "\n",
    "    return train_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "63db575f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_cross_entropy(y_pred, y_target):\n",
    "    y_target = y_target.cpu().float()\n",
    "    y_pred = y_pred.cpu().float()\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    return criterion(y_target, y_pred)\n",
    "\n",
    "def compute_1_accuracy(y_pred, y_target):\n",
    "    y_target_indices = y_target.max(dim=1)[1]\n",
    "    y_pred_indices = y_pred.max(dim=1)[1]\n",
    "    n_correct = torch.eq(y_pred_indices, y_target_indices).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100\n",
    "\n",
    "def compute_k_accuracy(y_pred, y_target, k=3):\n",
    "    y_pred_indices = y_pred.topk(k, dim=1)[1]\n",
    "    y_target_indices = y_target.max(dim=1)[1]\n",
    "    n_correct = torch.tensor([y_pred_indices[i] in y_target_indices[i] for i in range(len(y_pred))]).sum().item()\n",
    "    return n_correct / len(y_pred_indices) * 100\n",
    "\n",
    "def compute_k_jaccard_index(y_pred, y_target, k=3):\n",
    "    y_target_indices = y_target.topk(k, dim=1)[1]\n",
    "    y_pred_indices = y_pred.max(dim=1)[1]\n",
    "    jaccard = torch.tensor([len(np.intersect1d(y_target_indices[i], y_pred_indices[i]))/\n",
    "                            len(np.union1d(y_target_indices[i], y_pred_indices[i]))\n",
    "                            for i in range(len(y_pred))]).sum().item()\n",
    "    return jaccard / len(y_pred_indices)\n",
    "\n",
    "def compute_jaccard_index(y_pred, y_target, k=3, multilabel=False):\n",
    "    \n",
    "    threshold = 1.0/(k+1)\n",
    "    threshold_2 = 0.5\n",
    "    \n",
    "    if multilabel:\n",
    "        y_pred_indices = y_pred.gt(threshold_2)\n",
    "    else:\n",
    "        y_pred_indices = y_pred.gt(threshold)\n",
    "    \n",
    "    y_target_indices = y_target.gt(threshold)\n",
    "        \n",
    "    jaccard = ((y_target_indices*y_pred_indices).sum(axis=1)/((y_target_indices+y_pred_indices).sum(axis=1)+1e-8)).sum().item()\n",
    "    return jaccard / len(y_pred_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6414e4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax_sensitive(T):\n",
    "    T = np.exp(T) - np.exp(0) + 1e-9\n",
    "    if len(T.shape)==1:\n",
    "        return T/T.sum()\n",
    "    return  T/(T.sum(axis=1).unsqueeze(1))\n",
    "\n",
    "def cross_entropy(pred, soft_targets):\n",
    "    logsoftmax = nn.LogSoftmax(dim=1)\n",
    "    return torch.mean(torch.sum(- soft_targets * logsoftmax(pred), 1))\n",
    "\n",
    "# convert a df to tensor to be used in pytorch\n",
    "def df_to_tensor(df):\n",
    "    device = args.device\n",
    "    return torch.from_numpy(df.values).float().to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9b7c48a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_embedding(embeddings, embedding_type, fusion_type):\n",
    "    '''\n",
    "    Args:\n",
    "        embeddings (dictionary): a dictionary of input data tensors with embeddings. \n",
    "        embedding_type (str): type of embedding to be used, can be one of {'text', 'image', 'multi'}\n",
    "        fusion_type (str): type of fusing multimodal embeddings if embedding_type=='multi', can be one of \n",
    "                        {'average', 'concat', 'complex'}'''\n",
    "    if embedding_type=='text':\n",
    "        x_embedded = embeddings['txt_emb']\n",
    "    elif embedding_type=='image':\n",
    "        x_embedded = embeddings['img_emb']\n",
    "    elif embedding_type=='multi':\n",
    "        if fusion_type=='average':\n",
    "            x_embedded = (embeddings['txt_emb']+embeddings['img_emb'])/2\n",
    "        elif fusion_type=='concat':\n",
    "            x_embedded = torch.hstack([embeddings['txt_emb'], embeddings['img_emb']])\n",
    "        elif fusion_type=='complex':\n",
    "            txt_emb = embeddings['txt_emb']\n",
    "            img_emb = embeddings['img_emb']\n",
    "            x_embedded = torch.hstack([txt_emb, img_emb, txt_emb+img_emb, txt_emb-img_emb, txt_emb*img_emb])\n",
    "        else:\n",
    "            print('fusion_type is not supported')\n",
    "    else:\n",
    "        print('embedding_type is not supported')\n",
    "        \n",
    "    return x_embedded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "72be7dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialization(df, stored_data):\n",
    "    set_seed_everywhere(args.seed, args.cuda)\n",
    "    embeddings = prepare_embedding(stored_data, args.embedding_type, args.fusion_type)\n",
    "    dataset = CustomDataset(df, embeddings)\n",
    "    \n",
    "    classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "    \n",
    "    return embeddings, dataset, classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "789f15fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def write_train_state_evaluation(classifier, embeddings, train_state, df):\n",
    "    preds_all = classifier(embeddings).argmax(dim=1)\n",
    "    _, train_acc, val_acc, val_img_acc, val_txt_acc, val_unseen = evaluate_dataset(df, preds_all)\n",
    "    \n",
    "    train_state['train_acc'].append(train_acc)\n",
    "    train_state['val_acc'].append(val_acc)\n",
    "    train_state['val_img_txt'].append(val_img_acc)\n",
    "    train_state['val_txt_only'].append(val_txt_acc)\n",
    "    train_state['val_unseen_img'].append(val_unseen)\n",
    "    \n",
    "    return train_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fff070d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_loop(df, stored_data, labs=None, verbose=True):\n",
    "    \n",
    "    embeddings, dataset, classifier = initialization(df, stored_data)\n",
    "    if labs:\n",
    "        lab_embeddings = prepare_embedding(labs, args.embedding_type, args.fusion_type)\n",
    "    classifier = classifier.to(args.device)\n",
    "\n",
    "    loss_func = cross_entropy\n",
    "    optimizer = optim.Adam(classifier.parameters(), lr=args.learning_rate, weight_decay=args.l2)\n",
    "    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer,\n",
    "                                               mode='min', factor=0.5,\n",
    "                                               patience=1)\n",
    "    train_state = make_train_state(args)\n",
    "    \n",
    "    print('Using {} embedding with {} fusion method'.format(args.embedding_type, args.fusion_type))\n",
    "    \n",
    "    set_seed_everywhere(args.seed, cuda=True)\n",
    "    train_state = write_train_state_evaluation(classifier, embeddings, train_state, df)\n",
    "    \n",
    "    set_seed_everywhere(args.seed, cuda=True)\n",
    "    try:\n",
    "        for epoch_index in trange(args.num_epochs, desc=\"Epoch\", disable=not args.verbose):\n",
    "            train_state['epoch_index'] = epoch_index\n",
    "\n",
    "            # Iterate over training dataset\n",
    "\n",
    "            # setup: batch generator, set loss and acc to 0, set train mode on\n",
    "            dataset.set_split('train')\n",
    "            train_dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True)\n",
    "            \n",
    "            running_loss = 0.0\n",
    "            running_1_acc = 0.0\n",
    "            classifier.train()\n",
    "\n",
    "            for batch_index, batch_dict in enumerate(train_dataloader):\n",
    "\n",
    "                # step 1. zero the gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # step 2. get the data compute fuzzy labels\n",
    "                X = batch_dict['emb']\n",
    "                y = batch_dict['label']\n",
    "\n",
    "                size = y.shape[0]\n",
    "\n",
    "                # step 3. compute the output\n",
    "                y_pred = classifier(X)\n",
    "\n",
    "                # step 4. compute the loss\n",
    "                loss = loss_func(y_pred, y)\n",
    "                loss_t = loss.item()\n",
    "\n",
    "                running_loss = (running_loss * batch_index * args.batch_size + loss_t * size)/(batch_index * args.batch_size + size)\n",
    "                \n",
    "                # step 5. use loss to produce gradients\n",
    "                loss.backward()\n",
    "\n",
    "                # step 6. use optimizer to take gradient step\n",
    "                optimizer.step()\n",
    "\n",
    "                # -----------------------------------------\n",
    "                # compute the accuracies\n",
    "                acc_1_t = compute_1_accuracy(y_pred, y)\n",
    "                running_1_acc = (running_1_acc * batch_index * args.batch_size + acc_1_t * size)/(batch_index * args.batch_size + size)\n",
    "            \n",
    "            if labs:\n",
    "                optimizer.zero_grad()\n",
    "                y_pred = classifier(lab_embeddings)\n",
    "                y = torch.tensor([[1,0], [0,1]]).to(args.device)\n",
    "                loss = loss_func(y_pred, y)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "                train_state['golden_loss'].append(loss.item())\n",
    "            else:\n",
    "                train_state['golden_loss'].append('not implemented')\n",
    "            \n",
    "            train_state['train_loss'].append(running_loss)\n",
    "            train_state['train_1_acc'].append(running_1_acc)\n",
    "\n",
    "            # Iterate over val dataset\n",
    "\n",
    "            # setup: batch generator, set loss and acc to 0; set eval mode on\n",
    "\n",
    "            dataset.set_split('val')\n",
    "            val_dataloader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True)\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_1_acc = 0.0\n",
    "            classifier.eval()\n",
    "\n",
    "            for batch_index, batch_dict in enumerate(val_dataloader):\n",
    "\n",
    "                # step 2. get the data compute fuzzy labels\n",
    "                X = batch_dict['emb']\n",
    "                y = batch_dict['label']\n",
    "                size = y.shape[0]\n",
    "\n",
    "                # step 3. compute the output\n",
    "                y_pred = classifier(X)\n",
    "\n",
    "                # step 4. compute the loss\n",
    "                loss = loss_func(y_pred, y)\n",
    "                loss_t = loss.item()\n",
    "                running_loss = (running_loss * batch_index * args.batch_size + loss_t * size)/(batch_index * args.batch_size + size)\n",
    "\n",
    "                # -----------------------------------------\n",
    "                # compute the accuracies\n",
    "                acc_1_t = compute_1_accuracy(y_pred, y)\n",
    "                running_1_acc = (running_1_acc * batch_index * args.batch_size + acc_1_t * size)/(batch_index * args.batch_size + size)\n",
    "\n",
    "            train_state['val_loss'].append(running_loss)\n",
    "            train_state['val_1_acc'].append(running_1_acc)\n",
    "\n",
    "            train_state = write_train_state_evaluation(classifier, embeddings, train_state, df)\n",
    "            \n",
    "            print('Current Running Accuracy Training Set: {:.2f}, Validation Set: {:.2f}, Golden Set: {:.2f}'.format(train_state['train_1_acc'][-1], train_state['val_1_acc'][-1], train_state['golden_loss'][-1]))\n",
    "\n",
    "            train_state = update_train_state(args=args, model=classifier, train_state=train_state)\n",
    "\n",
    "            scheduler.step(train_state['val_loss'][-1])\n",
    "\n",
    "            if train_state['stop_early']:\n",
    "                break\n",
    "\n",
    "            with open(args.save_dir + args.embedding_type + \"_\" + args.fusion_type + \"_\" + 'train_state.p', 'wb') as fp:\n",
    "                pickle.dump(train_state, fp, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "    \n",
    "    except KeyboardInterrupt:\n",
    "        print(\"Exiting loop\")\n",
    "        pass\n",
    "    \n",
    "    return train_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1a3c2e58",
   "metadata": {},
   "outputs": [],
   "source": [
    "def experiment(df, stored_data, labs = None, hyperdict={}):\n",
    "    emb_types = ['text', 'image', 'multi']\n",
    "    fusion_types = ['average', 'concat', 'complex']\n",
    "    combi = {(a,b) if a=='multi' else (a, fusion_types[0]) for a in emb_types for b in fusion_types}\n",
    "    \n",
    "    for key in combi:\n",
    "        print(key)\n",
    "        args.embedding_type = key[0]\n",
    "        args.fusion_type = key[1]\n",
    "        if not key in hyperdict:\n",
    "            train_state = training_loop(df, stored_data, labs=labs)\n",
    "            hyperdict[key] = train_state\n",
    "            \n",
    "        with open(args.save_dir+'hyperdict.p', 'wb') as fp:\n",
    "            pickle.dump(hyperdict,fp, protocol=pickle.HIGHEST_PROTOCOL)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0465b2d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def demonstration(hyperdict):\n",
    "    emb_types = ['text', 'image', 'multi']\n",
    "    fusion_types = ['average', 'concat', 'complex']\n",
    "    metrics = ['train_acc', 'val_acc', 'val_img_txt', 'val_txt_only', 'val_unseen_img']\n",
    "    combi = {(a,b) if a=='multi' else (a, fusion_types[0]) for a in emb_types for b in fusion_types}\n",
    "    \n",
    "    for m in metrics:\n",
    "        print(m)\n",
    "        for key in combi:\n",
    "            train_state = hyperdict[key]\n",
    "            pd.Series(train_state[m]).plot(label=str(key))\n",
    "            plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "76eee907",
   "metadata": {},
   "outputs": [],
   "source": [
    "def statistics(hyperdict, stored_data, df, labs=None, subfolder = ''):\n",
    "    emb_types = ['text', 'image', 'multi']\n",
    "    fusion_types = ['average', 'concat', 'complex']\n",
    "    combi = {(a,b) if a=='multi' else (a, fusion_types[0]) for a in emb_types for b in fusion_types}\n",
    "    train = df[df['split'] == 'train'].index\n",
    "    val = df[df['split'] == 'val'].index\n",
    "    results = {}\n",
    "    \n",
    "    for key in combi:\n",
    "        print(key)\n",
    "        print()\n",
    "        results[key] = {}\n",
    "        args.embedding_type = key[0]\n",
    "        args.fusion_type = key[1]\n",
    "        classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "        classifier.load_state_dict(torch.load(args.save_dir + subfolder + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file, map_location=torch.device('cpu')))\n",
    "        classifier.eval()\n",
    "        y = F.softmax(classifier(prepare_embedding(stored_data, args.embedding_type, args.fusion_type)), dim=1)\n",
    "        pd.DataFrame(y.cpu().detach().numpy()).to_csv(args.save_dir + subfolder + args.embedding_type + \"_\" + args.fusion_type + \"_\" +'prediction.csv')\n",
    "        \n",
    "        conf, pred = y.max(dim=1)\n",
    "        pd.Series(conf.cpu().detach().numpy()).hist()\n",
    "        \n",
    "        results[key]['performance']={}\n",
    "        \n",
    "        print('Validation Set:')\n",
    "        results[key]['performance']['val'] = evaluate_dataset(df, pred)\n",
    "        \n",
    "        print('Test Set:')\n",
    "        results[key]['performance']['test'] = evaluate_dataset(df, pred, val_set='test')\n",
    "        \n",
    "        if labs:\n",
    "            print('Prediction for Golden Sentences:')\n",
    "            print(F.softmax(classifier(prepare_embedding(labs, args.embedding_type, args.fusion_type)), dim=1).cpu().detach().numpy())\n",
    "            results[key]['golden'] = F.softmax(classifier(prepare_embedding(labs, args.embedding_type, args.fusion_type)), dim=1).cpu().detach().numpy()\n",
    "            print()\n",
    "        \n",
    "        print('Full Dataset:')\n",
    "        print(pd.Series(conf.cpu().detach().numpy()).describe())\n",
    "        print('Training Set:')\n",
    "        print(pd.Series(conf[train].cpu().detach().numpy()).describe())\n",
    "        print('Validation Set:')\n",
    "        print(pd.Series(conf[val].cpu().detach().numpy()).describe())\n",
    "        plt.show()\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "731aeca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def statistics_F1(hyperdict, stored_data, df, labs=None, subfolder = ''):\n",
    "    emb_types = ['text', 'image', 'multi']\n",
    "    fusion_types = ['average', 'concat', 'complex']\n",
    "    combi = {(a,b) if a=='multi' else (a, fusion_types[0]) for a in emb_types for b in fusion_types}\n",
    "    train = df[df['split'] == 'train'].index\n",
    "    val = df[df['split'] == 'val'].index\n",
    "    results = {}\n",
    "    \n",
    "    for key in combi:\n",
    "        print(key)\n",
    "        print()\n",
    "        results[key] = {}\n",
    "        args.embedding_type = key[0]\n",
    "        args.fusion_type = key[1]\n",
    "        classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "        classifier.load_state_dict(torch.load(args.save_dir + subfolder + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file, map_location=torch.device('cpu')))\n",
    "        classifier.eval()\n",
    "        y = F.softmax(classifier(prepare_embedding(stored_data, args.embedding_type, args.fusion_type)), dim=1)\n",
    "        #pd.DataFrame(y.cpu().detach().numpy()).to_csv(args.save_dir + subfolder + args.embedding_type + \"_\" + args.fusion_type + \"_\" +'prediction.csv')\n",
    "        \n",
    "        conf, pred = y.max(dim=1)\n",
    "        pd.Series(conf.cpu().detach().numpy()).hist()\n",
    "        \n",
    "        results[key]['performance']={}\n",
    "        \n",
    "        print('Validation Set:')\n",
    "        results[key]['performance']['val'] = evaluate_dataset_F1(df, pred)\n",
    "        \n",
    "        print('Test Set:')\n",
    "        results[key]['performance']['test'] = evaluate_dataset_F1(df, pred, val_set='test')\n",
    "        \n",
    "        if labs:\n",
    "            print('Prediction for Golden Sentences:')\n",
    "            print(F.softmax(classifier(prepare_embedding(labs, args.embedding_type, args.fusion_type)), dim=1).cpu().detach().numpy())\n",
    "            results[key]['golden'] = F.softmax(classifier(prepare_embedding(labs, args.embedding_type, args.fusion_type)), dim=1).cpu().detach().numpy()\n",
    "            print()\n",
    "        \n",
    "        print('Full Dataset:')\n",
    "        print(pd.Series(conf.cpu().detach().numpy()).describe())\n",
    "        print('Training Set:')\n",
    "        print(pd.Series(conf[train].cpu().detach().numpy()).describe())\n",
    "        print('Validation Set:')\n",
    "        print(pd.Series(conf[val].cpu().detach().numpy()).describe())\n",
    "        plt.show()\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3bb41d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperdict = {}\n",
    "experiment(df, stored_data, hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c8dc402",
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperdict = {}\n",
    "experiment(df, stored_data, labs, hyperdict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efffe68d",
   "metadata": {},
   "source": [
    "## Performance on Trained Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f6295688",
   "metadata": {},
   "outputs": [],
   "source": [
    "perform_results = {}\n",
    "for o in ['O1', 'O2']:\n",
    "    perform_results[o] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "52cde508",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(args.save_dir+'001/hyperdict.p', 'rb') as fp:\n",
    "    hyperdict = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "52c83fcf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_acc\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_acc\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_img_txt\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_txt_only\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_unseen_img\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demonstration(hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3e468adb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7844    0.7869    0.7857     29306\n",
      "         1.0     0.6865    0.6832    0.6848     20010\n",
      "\n",
      "    accuracy                         0.7448     49316\n",
      "   macro avg     0.7354    0.7351    0.7352     49316\n",
      "weighted avg     0.7447    0.7448    0.7448     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7909    0.7933    0.7921     20463\n",
      "         1.0     0.6977    0.6946    0.6962     14058\n",
      "\n",
      "    accuracy                         0.7531     34521\n",
      "   macro avg     0.7443    0.7440    0.7441     34521\n",
      "weighted avg     0.7529    0.7531    0.7530     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7692    0.7743    0.7718      4400\n",
      "         1.0     0.6654    0.6590    0.6622      2997\n",
      "\n",
      "    accuracy                         0.7276      7397\n",
      "   macro avg     0.7173    0.7167    0.7170      7397\n",
      "weighted avg     0.7272    0.7276    0.7274      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8897    0.9107    0.9001       806\n",
      "         1.0     0.7785    0.7355    0.7564       344\n",
      "\n",
      "    accuracy                         0.8583      1150\n",
      "   macro avg     0.8341    0.8231    0.8282      1150\n",
      "weighted avg     0.8564    0.8583    0.8571      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7417    0.7437    0.7427      3594\n",
      "         1.0     0.6515    0.6491    0.6503      2653\n",
      "\n",
      "    accuracy                         0.7035      6247\n",
      "   macro avg     0.6966    0.6964    0.6965      6247\n",
      "weighted avg     0.7034    0.7035    0.7035      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8248    0.8426    0.8336       324\n",
      "         1.0     0.6645    0.6352    0.6495       159\n",
      "\n",
      "    accuracy                         0.7743       483\n",
      "   macro avg     0.7446    0.7389    0.7416       483\n",
      "weighted avg     0.7720    0.7743    0.7730       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.48\n",
      "    Training Set Accuracy: 75.31\n",
      "    Validation Set Accuracy: 72.76\n",
      "    Validation Set with both Images and Texts: 85.83\n",
      "    Validation Set with only Texts: 70.35\n",
      "    Validation Set with unseen Images: 77.43\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7844    0.7869    0.7857     29306\n",
      "         1.0     0.6865    0.6832    0.6848     20010\n",
      "\n",
      "    accuracy                         0.7448     49316\n",
      "   macro avg     0.7354    0.7351    0.7352     49316\n",
      "weighted avg     0.7447    0.7448    0.7448     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7909    0.7933    0.7921     20463\n",
      "         1.0     0.6977    0.6946    0.6962     14058\n",
      "\n",
      "    accuracy                         0.7531     34521\n",
      "   macro avg     0.7443    0.7440    0.7441     34521\n",
      "weighted avg     0.7529    0.7531    0.7530     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7697    0.7702    0.7699      4443\n",
      "         1.0     0.6541    0.6535    0.6538      2955\n",
      "\n",
      "    accuracy                         0.7236      7398\n",
      "   macro avg     0.7119    0.7118    0.7119      7398\n",
      "weighted avg     0.7235    0.7236    0.7236      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8843    0.9176    0.9006       716\n",
      "         1.0     0.8162    0.7529    0.7833       348\n",
      "\n",
      "    accuracy                         0.8637      1064\n",
      "   macro avg     0.8502    0.8352    0.8419      1064\n",
      "weighted avg     0.8620    0.8637    0.8622      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7467    0.7419    0.7443      3727\n",
      "         1.0     0.6344    0.6402    0.6373      2607\n",
      "\n",
      "    accuracy                         0.7000      6334\n",
      "   macro avg     0.6905    0.6910    0.6908      6334\n",
      "weighted avg     0.7005    0.7000    0.7002      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8000    0.8602    0.8290       279\n",
      "         1.0     0.7090    0.6129    0.6574       155\n",
      "\n",
      "    accuracy                         0.7719       434\n",
      "   macro avg     0.7545    0.7366    0.7432       434\n",
      "weighted avg     0.7675    0.7719    0.7677       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.48\n",
      "    Training Set Accuracy: 75.31\n",
      "    Validation Set Accuracy: 72.36\n",
      "    Validation Set with both Images and Texts: 86.37\n",
      "    Validation Set with only Texts: 70.00\n",
      "    Validation Set with unseen Images: 77.19\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.712108   0.287892  ]\n",
      " [0.30684382 0.6931562 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.729720\n",
      "std          0.162603\n",
      "min          0.500003\n",
      "25%          0.582848\n",
      "50%          0.696047\n",
      "75%          0.883694\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.730175\n",
      "std          0.162042\n",
      "min          0.500013\n",
      "25%          0.584122\n",
      "50%          0.697191\n",
      "75%          0.883102\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.728800\n",
      "std         0.163870\n",
      "min         0.500003\n",
      "25%         0.579779\n",
      "50%         0.692603\n",
      "75%         0.884803\n",
      "max         0.999988\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'concat')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8107    0.7893    0.7999     29306\n",
      "         1.0     0.7029    0.7301    0.7163     20010\n",
      "\n",
      "    accuracy                         0.7653     49316\n",
      "   macro avg     0.7568    0.7597    0.7581     49316\n",
      "weighted avg     0.7670    0.7653    0.7659     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8229    0.8009    0.8117     20463\n",
      "         1.0     0.7210    0.7490    0.7348     14058\n",
      "\n",
      "    accuracy                         0.7798     34521\n",
      "   macro avg     0.7720    0.7750    0.7733     34521\n",
      "weighted avg     0.7814    0.7798    0.7804     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7831    0.7664    0.7746      4400\n",
      "         1.0     0.6674    0.6884    0.6777      2997\n",
      "\n",
      "    accuracy                         0.7348      7397\n",
      "   macro avg     0.7253    0.7274    0.7262      7397\n",
      "weighted avg     0.7362    0.7348    0.7354      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9128    0.9094    0.9111       806\n",
      "         1.0     0.7896    0.7965    0.7931       344\n",
      "\n",
      "    accuracy                         0.8757      1150\n",
      "   macro avg     0.8512    0.8530    0.8521      1150\n",
      "weighted avg     0.8760    0.8757    0.8758      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7534    0.7343    0.7437      3594\n",
      "         1.0     0.6520    0.6743    0.6630      2653\n",
      "\n",
      "    accuracy                         0.7088      6247\n",
      "   macro avg     0.7027    0.7043    0.7033      6247\n",
      "weighted avg     0.7103    0.7088    0.7094      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8339    0.8210    0.8274       324\n",
      "         1.0     0.6463    0.6667    0.6563       159\n",
      "\n",
      "    accuracy                         0.7702       483\n",
      "   macro avg     0.7401    0.7438    0.7419       483\n",
      "weighted avg     0.7721    0.7702    0.7711       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 76.53\n",
      "    Training Set Accuracy: 77.98\n",
      "    Validation Set Accuracy: 73.48\n",
      "    Validation Set with both Images and Texts: 87.57\n",
      "    Validation Set with only Texts: 70.88\n",
      "    Validation Set with unseen Images: 77.02\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8107    0.7893    0.7999     29306\n",
      "         1.0     0.7029    0.7301    0.7163     20010\n",
      "\n",
      "    accuracy                         0.7653     49316\n",
      "   macro avg     0.7568    0.7597    0.7581     49316\n",
      "weighted avg     0.7670    0.7653    0.7659     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8229    0.8009    0.8117     20463\n",
      "         1.0     0.7210    0.7490    0.7348     14058\n",
      "\n",
      "    accuracy                         0.7798     34521\n",
      "   macro avg     0.7720    0.7750    0.7733     34521\n",
      "weighted avg     0.7814    0.7798    0.7804     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7821    0.7587    0.7703      4443\n",
      "         1.0     0.6528    0.6822    0.6672      2955\n",
      "\n",
      "    accuracy                         0.7282      7398\n",
      "   macro avg     0.7175    0.7205    0.7187      7398\n",
      "weighted avg     0.7305    0.7282    0.7291      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8856    0.9190    0.9020       716\n",
      "         1.0     0.8193    0.7557    0.7862       348\n",
      "\n",
      "    accuracy                         0.8656      1064\n",
      "   macro avg     0.8525    0.8374    0.8441      1064\n",
      "weighted avg     0.8639    0.8656    0.8641      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7606    0.7279    0.7439      3727\n",
      "         1.0     0.6335    0.6724    0.6524      2607\n",
      "\n",
      "    accuracy                         0.7051      6334\n",
      "   macro avg     0.6971    0.7002    0.6981      6334\n",
      "weighted avg     0.7083    0.7051    0.7062      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7893    0.8459    0.8166       279\n",
      "         1.0     0.6815    0.5935    0.6345       155\n",
      "\n",
      "    accuracy                         0.7558       434\n",
      "   macro avg     0.7354    0.7197    0.7255       434\n",
      "weighted avg     0.7508    0.7558    0.7516       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 76.53\n",
      "    Training Set Accuracy: 77.98\n",
      "    Validation Set Accuracy: 72.82\n",
      "    Validation Set with both Images and Texts: 86.56\n",
      "    Validation Set with only Texts: 70.51\n",
      "    Validation Set with unseen Images: 75.58\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.71630204 0.283698  ]\n",
      " [0.32216275 0.6778373 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.745384\n",
      "std          0.165753\n",
      "min          0.500005\n",
      "25%          0.594629\n",
      "50%          0.719695\n",
      "75%          0.911673\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.746503\n",
      "std          0.165320\n",
      "min          0.500005\n",
      "25%          0.595601\n",
      "50%          0.722543\n",
      "75%          0.911969\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.743813\n",
      "std         0.166786\n",
      "min         0.500051\n",
      "25%         0.590966\n",
      "50%         0.714304\n",
      "75%         0.909577\n",
      "max         0.999991\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('text', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8101    0.7623    0.7855     29306\n",
      "         1.0     0.6796    0.7383    0.7077     20010\n",
      "\n",
      "    accuracy                         0.7526     49316\n",
      "   macro avg     0.7448    0.7503    0.7466     49316\n",
      "weighted avg     0.7571    0.7526    0.7539     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8186    0.7710    0.7941     20463\n",
      "         1.0     0.6927    0.7512    0.7208     14058\n",
      "\n",
      "    accuracy                         0.7630     34521\n",
      "   macro avg     0.7556    0.7611    0.7574     34521\n",
      "weighted avg     0.7673    0.7630    0.7642     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7902    0.7448    0.7668      4400\n",
      "         1.0     0.6545    0.7097    0.6810      2997\n",
      "\n",
      "    accuracy                         0.7306      7397\n",
      "   macro avg     0.7223    0.7272    0.7239      7397\n",
      "weighted avg     0.7352    0.7306    0.7320      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8878    0.9032    0.8954       806\n",
      "         1.0     0.7636    0.7326    0.7478       344\n",
      "\n",
      "    accuracy                         0.8522      1150\n",
      "   macro avg     0.8257    0.8179    0.8216      1150\n",
      "weighted avg     0.8507    0.8522    0.8513      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7662    0.7092    0.7366      3594\n",
      "         1.0     0.6421    0.7067    0.6729      2653\n",
      "\n",
      "    accuracy                         0.7082      6247\n",
      "   macro avg     0.7041    0.7080    0.7047      6247\n",
      "weighted avg     0.7135    0.7082    0.7095      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8384    0.8488    0.8436       324\n",
      "         1.0     0.6839    0.6667    0.6752       159\n",
      "\n",
      "    accuracy                         0.7888       483\n",
      "   macro avg     0.7611    0.7577    0.7594       483\n",
      "weighted avg     0.7875    0.7888    0.7881       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.26\n",
      "    Training Set Accuracy: 76.30\n",
      "    Validation Set Accuracy: 73.06\n",
      "    Validation Set with both Images and Texts: 85.22\n",
      "    Validation Set with only Texts: 70.82\n",
      "    Validation Set with unseen Images: 78.88\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8101    0.7623    0.7855     29306\n",
      "         1.0     0.6796    0.7383    0.7077     20010\n",
      "\n",
      "    accuracy                         0.7526     49316\n",
      "   macro avg     0.7448    0.7503    0.7466     49316\n",
      "weighted avg     0.7571    0.7526    0.7539     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8186    0.7710    0.7941     20463\n",
      "         1.0     0.6927    0.7512    0.7208     14058\n",
      "\n",
      "    accuracy                         0.7630     34521\n",
      "   macro avg     0.7556    0.7611    0.7574     34521\n",
      "weighted avg     0.7673    0.7630    0.7642     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7907    0.7396    0.7643      4443\n",
      "         1.0     0.6431    0.7056    0.6729      2955\n",
      "\n",
      "    accuracy                         0.7260      7398\n",
      "   macro avg     0.7169    0.7226    0.7186      7398\n",
      "weighted avg     0.7317    0.7260    0.7278      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8721    0.9050    0.8883       716\n",
      "         1.0     0.7882    0.7270    0.7564       348\n",
      "\n",
      "    accuracy                         0.8468      1064\n",
      "   macro avg     0.8302    0.8160    0.8223      1064\n",
      "weighted avg     0.8447    0.8468    0.8451      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7729    0.7078    0.7389      3727\n",
      "         1.0     0.6272    0.7027    0.6628      2607\n",
      "\n",
      "    accuracy                         0.7057      6334\n",
      "   macro avg     0.7001    0.7053    0.7009      6334\n",
      "weighted avg     0.7129    0.7057    0.7076      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8095    0.8530    0.8307       279\n",
      "         1.0     0.7071    0.6387    0.6712       155\n",
      "\n",
      "    accuracy                         0.7765       434\n",
      "   macro avg     0.7583    0.7459    0.7510       434\n",
      "weighted avg     0.7730    0.7765    0.7737       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.26\n",
      "    Training Set Accuracy: 76.30\n",
      "    Validation Set Accuracy: 72.60\n",
      "    Validation Set with both Images and Texts: 84.68\n",
      "    Validation Set with only Texts: 70.57\n",
      "    Validation Set with unseen Images: 77.65\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.77699244 0.22300752]\n",
      " [0.39108405 0.608916  ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.733292\n",
      "std          0.157579\n",
      "min          0.500009\n",
      "25%          0.592732\n",
      "50%          0.706081\n",
      "75%          0.878114\n",
      "max          0.999896\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.733748\n",
      "std          0.157111\n",
      "min          0.500027\n",
      "25%          0.593494\n",
      "50%          0.707163\n",
      "75%          0.877860\n",
      "max          0.999896\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.732947\n",
      "std         0.158192\n",
      "min         0.500163\n",
      "25%         0.592266\n",
      "50%         0.705534\n",
      "75%         0.878508\n",
      "max         0.999834\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('image', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6170    0.9937    0.7613     29306\n",
      "         1.0     0.9127    0.0966    0.1747     20010\n",
      "\n",
      "    accuracy                         0.6297     49316\n",
      "   macro avg     0.7648    0.5451    0.4680     49316\n",
      "weighted avg     0.7370    0.6297    0.5233     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6178    0.9962    0.7627     20463\n",
      "         1.0     0.9489    0.1030    0.1858     14058\n",
      "\n",
      "    accuracy                         0.6325     34521\n",
      "   macro avg     0.7834    0.5496    0.4742     34521\n",
      "weighted avg     0.7526    0.6325    0.5278     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6121    0.9870    0.7556      4400\n",
      "         1.0     0.8113    0.0817    0.1485      2997\n",
      "\n",
      "    accuracy                         0.6203      7397\n",
      "   macro avg     0.7117    0.5344    0.4521      7397\n",
      "weighted avg     0.6928    0.6203    0.5097      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8833    0.9293    0.9057       806\n",
      "         1.0     0.8113    0.7122    0.7585       344\n",
      "\n",
      "    accuracy                         0.8643      1150\n",
      "   macro avg     0.8473    0.8207    0.8321      1150\n",
      "weighted avg     0.8617    0.8643    0.8617      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5753    1.0000    0.7304      3594\n",
      "         1.0     0.0000    0.0000    0.0000      2653\n",
      "\n",
      "    accuracy                         0.5753      6247\n",
      "   macro avg     0.2877    0.5000    0.3652      6247\n",
      "weighted avg     0.3310    0.5753    0.4202      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7825    0.8549    0.8171       324\n",
      "         1.0     0.6357    0.5157    0.5694       159\n",
      "\n",
      "    accuracy                         0.7433       483\n",
      "   macro avg     0.7091    0.6853    0.6933       483\n",
      "weighted avg     0.7342    0.7433    0.7356       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.97\n",
      "    Training Set Accuracy: 63.25\n",
      "    Validation Set Accuracy: 62.03\n",
      "    Validation Set with both Images and Texts: 86.43\n",
      "    Validation Set with only Texts: 57.53\n",
      "    Validation Set with unseen Images: 74.33\n",
      "    \n",
      "Test Set:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6170    0.9937    0.7613     29306\n",
      "         1.0     0.9127    0.0966    0.1747     20010\n",
      "\n",
      "    accuracy                         0.6297     49316\n",
      "   macro avg     0.7648    0.5451    0.4680     49316\n",
      "weighted avg     0.7370    0.6297    0.5233     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6178    0.9962    0.7627     20463\n",
      "         1.0     0.9489    0.1030    0.1858     14058\n",
      "\n",
      "    accuracy                         0.6325     34521\n",
      "   macro avg     0.7834    0.5496    0.4742     34521\n",
      "weighted avg     0.7526    0.6325    0.5278     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6180    0.9887    0.7606      4443\n",
      "         1.0     0.8276    0.0812    0.1479      2955\n",
      "\n",
      "    accuracy                         0.6263      7398\n",
      "   macro avg     0.7228    0.5350    0.4543      7398\n",
      "weighted avg     0.7017    0.6263    0.5159      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8605    0.9302    0.8940       716\n",
      "         1.0     0.8276    0.6897    0.7524       348\n",
      "\n",
      "    accuracy                         0.8515      1064\n",
      "   macro avg     0.8440    0.8099    0.8232      1064\n",
      "weighted avg     0.8497    0.8515    0.8476      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5884    1.0000    0.7409      3727\n",
      "         1.0     0.0000    0.0000    0.0000      2607\n",
      "\n",
      "    accuracy                         0.5884      6334\n",
      "   macro avg     0.2942    0.5000    0.3704      6334\n",
      "weighted avg     0.3462    0.5884    0.4359      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7516    0.8566    0.8007       279\n",
      "         1.0     0.6552    0.4903    0.5609       155\n",
      "\n",
      "    accuracy                         0.7258       434\n",
      "   macro avg     0.7034    0.6735    0.6808       434\n",
      "weighted avg     0.7171    0.7258    0.7150       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.97\n",
      "    Training Set Accuracy: 63.25\n",
      "    Validation Set Accuracy: 62.63\n",
      "    Validation Set with both Images and Texts: 85.15\n",
      "    Validation Set with only Texts: 58.84\n",
      "    Validation Set with unseen Images: 72.58\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5651829  0.43481702]\n",
      " [0.5651829  0.43481702]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.612961\n",
      "std          0.125146\n",
      "min          0.500152\n",
      "25%          0.565183\n",
      "50%          0.565183\n",
      "75%          0.565183\n",
      "max          0.999964\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.613492\n",
      "std          0.125830\n",
      "min          0.500152\n",
      "25%          0.565183\n",
      "50%          0.565183\n",
      "75%          0.565183\n",
      "max          0.999964\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.613830\n",
      "std         0.126398\n",
      "min         0.500974\n",
      "25%         0.565183\n",
      "50%         0.565183\n",
      "75%         0.565183\n",
      "max         0.999912\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'complex')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8451    0.8132    0.8289     29306\n",
      "         1.0     0.7408    0.7817    0.7607     20010\n",
      "\n",
      "    accuracy                         0.8004     49316\n",
      "   macro avg     0.7929    0.7975    0.7948     49316\n",
      "weighted avg     0.8028    0.8004    0.8012     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8683    0.8356    0.8516     20463\n",
      "         1.0     0.7731    0.8155    0.7938     14058\n",
      "\n",
      "    accuracy                         0.8274     34521\n",
      "   macro avg     0.8207    0.8256    0.8227     34521\n",
      "weighted avg     0.8296    0.8274    0.8281     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7948    0.7643    0.7793      4400\n",
      "         1.0     0.6725    0.7104    0.6909      2997\n",
      "\n",
      "    accuracy                         0.7425      7397\n",
      "   macro avg     0.7337    0.7373    0.7351      7397\n",
      "weighted avg     0.7453    0.7425    0.7435      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9261    0.9169    0.9214       806\n",
      "         1.0     0.8097    0.8285    0.8190       344\n",
      "\n",
      "    accuracy                         0.8904      1150\n",
      "   macro avg     0.8679    0.8727    0.8702      1150\n",
      "weighted avg     0.8912    0.8904    0.8908      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7643    0.7301    0.7468      3594\n",
      "         1.0     0.6553    0.6951    0.6746      2653\n",
      "\n",
      "    accuracy                         0.7152      6247\n",
      "   macro avg     0.7098    0.7126    0.7107      6247\n",
      "weighted avg     0.7180    0.7152    0.7162      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8344    0.8086    0.8213       324\n",
      "         1.0     0.6331    0.6730    0.6524       159\n",
      "\n",
      "    accuracy                         0.7640       483\n",
      "   macro avg     0.7338    0.7408    0.7369       483\n",
      "weighted avg     0.7681    0.7640    0.7657       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 80.04\n",
      "    Training Set Accuracy: 82.74\n",
      "    Validation Set Accuracy: 74.25\n",
      "    Validation Set with both Images and Texts: 89.04\n",
      "    Validation Set with only Texts: 71.52\n",
      "    Validation Set with unseen Images: 76.40\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8451    0.8132    0.8289     29306\n",
      "         1.0     0.7408    0.7817    0.7607     20010\n",
      "\n",
      "    accuracy                         0.8004     49316\n",
      "   macro avg     0.7929    0.7975    0.7948     49316\n",
      "weighted avg     0.8028    0.8004    0.8012     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8683    0.8356    0.8516     20463\n",
      "         1.0     0.7731    0.8155    0.7938     14058\n",
      "\n",
      "    accuracy                         0.8274     34521\n",
      "   macro avg     0.8207    0.8256    0.8227     34521\n",
      "weighted avg     0.8296    0.8274    0.8281     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7878    0.7587    0.7730      4443\n",
      "         1.0     0.6563    0.6927    0.6740      2955\n",
      "\n",
      "    accuracy                         0.7324      7398\n",
      "   macro avg     0.7221    0.7257    0.7235      7398\n",
      "weighted avg     0.7353    0.7324    0.7335      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9050    0.9176    0.9112       716\n",
      "         1.0     0.8254    0.8017    0.8134       348\n",
      "\n",
      "    accuracy                         0.8797      1064\n",
      "   macro avg     0.8652    0.8597    0.8623      1064\n",
      "weighted avg     0.8790    0.8797    0.8792      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7639    0.7282    0.7456      3727\n",
      "         1.0     0.6357    0.6782    0.6563      2607\n",
      "\n",
      "    accuracy                         0.7076      6334\n",
      "   macro avg     0.6998    0.7032    0.7009      6334\n",
      "weighted avg     0.7111    0.7076    0.7088      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7867    0.8065    0.7965       279\n",
      "         1.0     0.6351    0.6065    0.6205       155\n",
      "\n",
      "    accuracy                         0.7350       434\n",
      "   macro avg     0.7109    0.7065    0.7085       434\n",
      "weighted avg     0.7326    0.7350    0.7336       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 80.04\n",
      "    Training Set Accuracy: 82.74\n",
      "    Validation Set Accuracy: 73.24\n",
      "    Validation Set with both Images and Texts: 87.97\n",
      "    Validation Set with only Texts: 70.76\n",
      "    Validation Set with unseen Images: 73.50\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.849506   0.15049393]\n",
      " [0.4221957  0.5778043 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.794956\n",
      "std          0.172706\n",
      "min          0.500005\n",
      "25%          0.631937\n",
      "50%          0.809732\n",
      "75%          0.980608\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.797979\n",
      "std          0.172449\n",
      "min          0.500005\n",
      "25%          0.635609\n",
      "50%          0.815981\n",
      "75%          0.982804\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.789011\n",
      "std         0.173224\n",
      "min         0.500015\n",
      "25%         0.624883\n",
      "50%         0.797093\n",
      "75%         0.976807\n",
      "max         1.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perform_results['O1'] = statistics_F1(hyperdict, stored_data, df, labs, subfolder='001/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8b67f85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(args.save_dir+'002/hyperdict.p', 'rb') as fp:\n",
    "    hyperdict = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e3409605",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_acc\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_acc\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_img_txt\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_txt_only\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "val_unseen_img\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "demonstration(hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6cc6c9d1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7883    0.7807    0.7845     29306\n",
      "         1.0     0.6833    0.6929    0.6880     20010\n",
      "\n",
      "    accuracy                         0.7451     49316\n",
      "   macro avg     0.7358    0.7368    0.7362     49316\n",
      "weighted avg     0.7457    0.7451    0.7453     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7947    0.7884    0.7915     20463\n",
      "         1.0     0.6955    0.7034    0.6994     14058\n",
      "\n",
      "    accuracy                         0.7538     34521\n",
      "   macro avg     0.7451    0.7459    0.7455     34521\n",
      "weighted avg     0.7543    0.7538    0.7540     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7735    0.7668    0.7701      4400\n",
      "         1.0     0.6619    0.6703    0.6661      2997\n",
      "\n",
      "    accuracy                         0.7277      7397\n",
      "   macro avg     0.7177    0.7186    0.7181      7397\n",
      "weighted avg     0.7283    0.7277    0.7280      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8980    0.9069    0.9025       806\n",
      "         1.0     0.7768    0.7587    0.7676       344\n",
      "\n",
      "    accuracy                         0.8626      1150\n",
      "   macro avg     0.8374    0.8328    0.8351      1150\n",
      "weighted avg     0.8618    0.8626    0.8621      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7449    0.7354    0.7401      3594\n",
      "         1.0     0.6476    0.6589    0.6532      2653\n",
      "\n",
      "    accuracy                         0.7029      6247\n",
      "   macro avg     0.6963    0.6971    0.6967      6247\n",
      "weighted avg     0.7036    0.7029    0.7032      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8390    0.8364    0.8377       324\n",
      "         1.0     0.6687    0.6730    0.6708       159\n",
      "\n",
      "    accuracy                         0.7826       483\n",
      "   macro avg     0.7539    0.7547    0.7543       483\n",
      "weighted avg     0.7830    0.7826    0.7828       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.51\n",
      "    Training Set Accuracy: 75.38\n",
      "    Validation Set Accuracy: 72.77\n",
      "    Validation Set with both Images and Texts: 86.26\n",
      "    Validation Set with only Texts: 70.29\n",
      "    Validation Set with unseen Images: 78.26\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7883    0.7807    0.7845     29306\n",
      "         1.0     0.6833    0.6929    0.6880     20010\n",
      "\n",
      "    accuracy                         0.7451     49316\n",
      "   macro avg     0.7358    0.7368    0.7362     49316\n",
      "weighted avg     0.7457    0.7451    0.7453     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7947    0.7884    0.7915     20463\n",
      "         1.0     0.6955    0.7034    0.6994     14058\n",
      "\n",
      "    accuracy                         0.7538     34521\n",
      "   macro avg     0.7451    0.7459    0.7455     34521\n",
      "weighted avg     0.7543    0.7538    0.7540     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7732    0.7589    0.7660      4443\n",
      "         1.0     0.6473    0.6653    0.6562      2955\n",
      "\n",
      "    accuracy                         0.7215      7398\n",
      "   macro avg     0.7103    0.7121    0.7111      7398\n",
      "weighted avg     0.7229    0.7215    0.7222      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8832    0.9190    0.9008       716\n",
      "         1.0     0.8182    0.7500    0.7826       348\n",
      "\n",
      "    accuracy                         0.8637      1064\n",
      "   macro avg     0.8507    0.8345    0.8417      1064\n",
      "weighted avg     0.8619    0.8637    0.8621      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7506    0.7282    0.7392      3727\n",
      "         1.0     0.6273    0.6540    0.6404      2607\n",
      "\n",
      "    accuracy                         0.6977      6334\n",
      "   macro avg     0.6889    0.6911    0.6898      6334\n",
      "weighted avg     0.6998    0.6977    0.6985      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7921    0.8602    0.8247       279\n",
      "         1.0     0.7023    0.5935    0.6434       155\n",
      "\n",
      "    accuracy                         0.7650       434\n",
      "   macro avg     0.7472    0.7269    0.7340       434\n",
      "weighted avg     0.7600    0.7650    0.7600       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.51\n",
      "    Training Set Accuracy: 75.38\n",
      "    Validation Set Accuracy: 72.15\n",
      "    Validation Set with both Images and Texts: 86.37\n",
      "    Validation Set with only Texts: 69.77\n",
      "    Validation Set with unseen Images: 76.50\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.824487   0.17551307]\n",
      " [0.22821683 0.7717831 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.728179\n",
      "std          0.162310\n",
      "min          0.500015\n",
      "25%          0.582183\n",
      "50%          0.693344\n",
      "75%          0.881041\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.728603\n",
      "std          0.161831\n",
      "min          0.500022\n",
      "25%          0.582916\n",
      "50%          0.694598\n",
      "75%          0.880605\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.727546\n",
      "std         0.163237\n",
      "min         0.500015\n",
      "25%         0.580780\n",
      "50%         0.690825\n",
      "75%         0.882407\n",
      "max         0.999989\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'concat')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8055    0.7966    0.8010     29306\n",
      "         1.0     0.7068    0.7182    0.7125     20010\n",
      "\n",
      "    accuracy                         0.7648     49316\n",
      "   macro avg     0.7562    0.7574    0.7567     49316\n",
      "weighted avg     0.7654    0.7648    0.7651     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8179    0.8086    0.8132     20463\n",
      "         1.0     0.7259    0.7379    0.7319     14058\n",
      "\n",
      "    accuracy                         0.7798     34521\n",
      "   macro avg     0.7719    0.7733    0.7725     34521\n",
      "weighted avg     0.7804    0.7798    0.7801     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7773    0.7734    0.7753      4400\n",
      "         1.0     0.6698    0.6747    0.6722      2997\n",
      "\n",
      "    accuracy                         0.7334      7397\n",
      "   macro avg     0.7235    0.7240    0.7238      7397\n",
      "weighted avg     0.7337    0.7334    0.7336      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9075    0.9132    0.9103       806\n",
      "         1.0     0.7935    0.7820    0.7877       344\n",
      "\n",
      "    accuracy                         0.8739      1150\n",
      "   macro avg     0.8505    0.8476    0.8490      1150\n",
      "weighted avg     0.8734    0.8739    0.8736      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7477    0.7421    0.7449      3594\n",
      "         1.0     0.6541    0.6608    0.6574      2653\n",
      "\n",
      "    accuracy                         0.7075      6247\n",
      "   macro avg     0.7009    0.7014    0.7011      6247\n",
      "weighted avg     0.7079    0.7075    0.7077      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8277    0.8302    0.8290       324\n",
      "         1.0     0.6519    0.6478    0.6498       159\n",
      "\n",
      "    accuracy                         0.7702       483\n",
      "   macro avg     0.7398    0.7390    0.7394       483\n",
      "weighted avg     0.7698    0.7702    0.7700       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 76.48\n",
      "    Training Set Accuracy: 77.98\n",
      "    Validation Set Accuracy: 73.34\n",
      "    Validation Set with both Images and Texts: 87.39\n",
      "    Validation Set with only Texts: 70.75\n",
      "    Validation Set with unseen Images: 77.02\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8055    0.7966    0.8010     29306\n",
      "         1.0     0.7068    0.7182    0.7125     20010\n",
      "\n",
      "    accuracy                         0.7648     49316\n",
      "   macro avg     0.7562    0.7574    0.7567     49316\n",
      "weighted avg     0.7654    0.7648    0.7651     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8179    0.8086    0.8132     20463\n",
      "         1.0     0.7259    0.7379    0.7319     14058\n",
      "\n",
      "    accuracy                         0.7798     34521\n",
      "   macro avg     0.7719    0.7733    0.7725     34521\n",
      "weighted avg     0.7804    0.7798    0.7801     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7764    0.7641    0.7702      4443\n",
      "         1.0     0.6536    0.6690    0.6612      2955\n",
      "\n",
      "    accuracy                         0.7261      7398\n",
      "   macro avg     0.7150    0.7166    0.7157      7398\n",
      "weighted avg     0.7273    0.7261    0.7267      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8871    0.9218    0.9041       716\n",
      "         1.0     0.8250    0.7586    0.7904       348\n",
      "\n",
      "    accuracy                         0.8684      1064\n",
      "   macro avg     0.8560    0.8402    0.8473      1064\n",
      "weighted avg     0.8668    0.8684    0.8669      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7537    0.7338    0.7436      3727\n",
      "         1.0     0.6333    0.6571    0.6450      2607\n",
      "\n",
      "    accuracy                         0.7022      6334\n",
      "   macro avg     0.6935    0.6955    0.6943      6334\n",
      "weighted avg     0.7041    0.7022    0.7030      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7946    0.8459    0.8194       279\n",
      "         1.0     0.6861    0.6065    0.6438       155\n",
      "\n",
      "    accuracy                         0.7604       434\n",
      "   macro avg     0.7404    0.7262    0.7316       434\n",
      "weighted avg     0.7559    0.7604    0.7567       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 76.48\n",
      "    Training Set Accuracy: 77.98\n",
      "    Validation Set Accuracy: 72.61\n",
      "    Validation Set with both Images and Texts: 86.84\n",
      "    Validation Set with only Texts: 70.22\n",
      "    Validation Set with unseen Images: 76.04\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.875241   0.12475902]\n",
      " [0.14693227 0.8530677 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.744424\n",
      "std          0.166196\n",
      "min          0.500012\n",
      "25%          0.591932\n",
      "50%          0.718451\n",
      "75%          0.911406\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.745586\n",
      "std          0.165710\n",
      "min          0.500012\n",
      "25%          0.593523\n",
      "50%          0.721011\n",
      "75%          0.911393\n",
      "max          0.999998\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.742864\n",
      "std         0.167329\n",
      "min         0.500071\n",
      "25%         0.589044\n",
      "50%         0.713217\n",
      "75%         0.911092\n",
      "max         0.999992\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('text', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8058    0.7982    0.8020     29306\n",
      "         1.0     0.7085    0.7182    0.7133     20010\n",
      "\n",
      "    accuracy                         0.7658     49316\n",
      "   macro avg     0.7571    0.7582    0.7576     49316\n",
      "weighted avg     0.7663    0.7658    0.7660     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8185    0.8117    0.8151     20463\n",
      "         1.0     0.7292    0.7379    0.7335     14058\n",
      "\n",
      "    accuracy                         0.7817     34521\n",
      "   macro avg     0.7738    0.7748    0.7743     34521\n",
      "weighted avg     0.7821    0.7817    0.7819     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7756    0.7673    0.7714      4400\n",
      "         1.0     0.6636    0.6740    0.6688      2997\n",
      "\n",
      "    accuracy                         0.7295      7397\n",
      "   macro avg     0.7196    0.7206    0.7201      7397\n",
      "weighted avg     0.7302    0.7295    0.7298      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8879    0.9045    0.8961       806\n",
      "         1.0     0.7660    0.7326    0.7489       344\n",
      "\n",
      "    accuracy                         0.8530      1150\n",
      "   macro avg     0.8269    0.8185    0.8225      1150\n",
      "weighted avg     0.8515    0.8530    0.8521      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7494    0.7365    0.7429      3594\n",
      "         1.0     0.6512    0.6664    0.6587      2653\n",
      "\n",
      "    accuracy                         0.7067      6247\n",
      "   macro avg     0.7003    0.7015    0.7008      6247\n",
      "weighted avg     0.7077    0.7067    0.7072      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8410    0.8488    0.8449       324\n",
      "         1.0     0.6859    0.6730    0.6794       159\n",
      "\n",
      "    accuracy                         0.7909       483\n",
      "   macro avg     0.7634    0.7609    0.7621       483\n",
      "weighted avg     0.7899    0.7909    0.7904       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 76.58\n",
      "    Training Set Accuracy: 78.17\n",
      "    Validation Set Accuracy: 72.95\n",
      "    Validation Set with both Images and Texts: 85.30\n",
      "    Validation Set with only Texts: 70.67\n",
      "    Validation Set with unseen Images: 79.09\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8058    0.7982    0.8020     29306\n",
      "         1.0     0.7085    0.7182    0.7133     20010\n",
      "\n",
      "    accuracy                         0.7658     49316\n",
      "   macro avg     0.7571    0.7582    0.7576     49316\n",
      "weighted avg     0.7663    0.7658    0.7660     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8185    0.8117    0.8151     20463\n",
      "         1.0     0.7292    0.7379    0.7335     14058\n",
      "\n",
      "    accuracy                         0.7817     34521\n",
      "   macro avg     0.7738    0.7748    0.7743     34521\n",
      "weighted avg     0.7821    0.7817    0.7819     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7771    0.7666    0.7718      4443\n",
      "         1.0     0.6561    0.6694    0.6626      2955\n",
      "\n",
      "    accuracy                         0.7278      7398\n",
      "   macro avg     0.7166    0.7180    0.7172      7398\n",
      "weighted avg     0.7287    0.7278    0.7282      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8792    0.9050    0.8919       716\n",
      "         1.0     0.7920    0.7443    0.7674       348\n",
      "\n",
      "    accuracy                         0.8524      1064\n",
      "   macro avg     0.8356    0.8246    0.8297      1064\n",
      "weighted avg     0.8507    0.8524    0.8512      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7564    0.7400    0.7481      3727\n",
      "         1.0     0.6395    0.6594    0.6493      2607\n",
      "\n",
      "    accuracy                         0.7068      6334\n",
      "   macro avg     0.6980    0.6997    0.6987      6334\n",
      "weighted avg     0.7083    0.7068    0.7075      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8144    0.8495    0.8316       279\n",
      "         1.0     0.7063    0.6516    0.6779       155\n",
      "\n",
      "    accuracy                         0.7788       434\n",
      "   macro avg     0.7604    0.7505    0.7547       434\n",
      "weighted avg     0.7758    0.7788    0.7767       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 76.58\n",
      "    Training Set Accuracy: 78.17\n",
      "    Validation Set Accuracy: 72.78\n",
      "    Validation Set with both Images and Texts: 85.24\n",
      "    Validation Set with only Texts: 70.68\n",
      "    Validation Set with unseen Images: 77.88\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.86625046 0.13374962]\n",
      " [0.14695552 0.85304445]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.739784\n",
      "std          0.159503\n",
      "min          0.500002\n",
      "25%          0.596066\n",
      "50%          0.717781\n",
      "75%          0.889362\n",
      "max          0.999963\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.740423\n",
      "std          0.159136\n",
      "min          0.500002\n",
      "25%          0.597074\n",
      "50%          0.719370\n",
      "75%          0.889168\n",
      "max          0.999963\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.738959\n",
      "std         0.160017\n",
      "min         0.500033\n",
      "25%         0.594431\n",
      "50%         0.714901\n",
      "75%         0.889265\n",
      "max         0.999885\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('image', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6170    0.9932    0.7612     29306\n",
      "         1.0     0.9075    0.0971    0.1753     20010\n",
      "\n",
      "    accuracy                         0.6296     49316\n",
      "   macro avg     0.7622    0.5451    0.4683     49316\n",
      "weighted avg     0.7349    0.6296    0.5235     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6178    0.9959    0.7626     20463\n",
      "         1.0     0.9453    0.1032    0.1861     14058\n",
      "\n",
      "    accuracy                         0.6324     34521\n",
      "   macro avg     0.7815    0.5496    0.4743     34521\n",
      "weighted avg     0.7512    0.6324    0.5278     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6123    0.9859    0.7554      4400\n",
      "         1.0     0.8013    0.0834    0.1511      2997\n",
      "\n",
      "    accuracy                         0.6203      7397\n",
      "   macro avg     0.7068    0.5347    0.4533      7397\n",
      "weighted avg     0.6889    0.6203    0.5106      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8878    0.9231    0.9051       806\n",
      "         1.0     0.8013    0.7267    0.7622       344\n",
      "\n",
      "    accuracy                         0.8643      1150\n",
      "   macro avg     0.8446    0.8249    0.8337      1150\n",
      "weighted avg     0.8619    0.8643    0.8624      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5753    1.0000    0.7304      3594\n",
      "         1.0     0.0000    0.0000    0.0000      2653\n",
      "\n",
      "    accuracy                         0.5753      6247\n",
      "   macro avg     0.2877    0.5000    0.3652      6247\n",
      "weighted avg     0.3310    0.5753    0.4202      6247\n",
      "\n",
      "Validation Set with unseen Images:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7884    0.8395    0.8132       324\n",
      "         1.0     0.6232    0.5409    0.5791       159\n",
      "\n",
      "    accuracy                         0.7412       483\n",
      "   macro avg     0.7058    0.6902    0.6961       483\n",
      "weighted avg     0.7340    0.7412    0.7361       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.96\n",
      "    Training Set Accuracy: 63.24\n",
      "    Validation Set Accuracy: 62.03\n",
      "    Validation Set with both Images and Texts: 86.43\n",
      "    Validation Set with only Texts: 57.53\n",
      "    Validation Set with unseen Images: 74.12\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6170    0.9932    0.7612     29306\n",
      "         1.0     0.9075    0.0971    0.1753     20010\n",
      "\n",
      "    accuracy                         0.6296     49316\n",
      "   macro avg     0.7622    0.5451    0.4683     49316\n",
      "weighted avg     0.7349    0.6296    0.5235     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6178    0.9959    0.7626     20463\n",
      "         1.0     0.9453    0.1032    0.1861     14058\n",
      "\n",
      "    accuracy                         0.6324     34521\n",
      "   macro avg     0.7815    0.5496    0.4743     34521\n",
      "weighted avg     0.7512    0.6324    0.5278     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6180    0.9883    0.7605      4443\n",
      "         1.0     0.8225    0.0816    0.1484      2955\n",
      "\n",
      "    accuracy                         0.6261      7398\n",
      "   macro avg     0.7203    0.5349    0.4544      7398\n",
      "weighted avg     0.6997    0.6261    0.5160      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8612    0.9274    0.8931       716\n",
      "         1.0     0.8225    0.6925    0.7520       348\n",
      "\n",
      "    accuracy                         0.8506      1064\n",
      "   macro avg     0.8419    0.8100    0.8225      1064\n",
      "weighted avg     0.8486    0.8506    0.8469      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.5884    1.0000    0.7409      3727\n",
      "         1.0     0.0000    0.0000    0.0000      2607\n",
      "\n",
      "    accuracy                         0.5884      6334\n",
      "   macro avg     0.2942    0.5000    0.3704      6334\n",
      "weighted avg     0.3462    0.5884    0.4359      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7500    0.8495    0.7966       279\n",
      "         1.0     0.6441    0.4903    0.5568       155\n",
      "\n",
      "    accuracy                         0.7212       434\n",
      "   macro avg     0.6970    0.6699    0.6767       434\n",
      "weighted avg     0.7122    0.7212    0.7110       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.96\n",
      "    Training Set Accuracy: 63.24\n",
      "    Validation Set Accuracy: 62.61\n",
      "    Validation Set with both Images and Texts: 85.06\n",
      "    Validation Set with only Texts: 58.84\n",
      "    Validation Set with unseen Images: 72.12\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5592077  0.44079232]\n",
      " [0.5592077  0.44079232]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.607738\n",
      "std          0.126639\n",
      "min          0.500557\n",
      "25%          0.559208\n",
      "50%          0.559208\n",
      "75%          0.559208\n",
      "max          0.999964\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.608306\n",
      "std          0.127377\n",
      "min          0.500557\n",
      "25%          0.559208\n",
      "50%          0.559208\n",
      "75%          0.559208\n",
      "max          0.999964\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.608559\n",
      "std         0.127868\n",
      "min         0.500770\n",
      "25%         0.559208\n",
      "50%         0.559208\n",
      "75%         0.559208\n",
      "max         0.999880\n",
      "dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('multi', 'complex')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8409    0.8196    0.8301     29306\n",
      "         1.0     0.7453    0.7730    0.7589     20010\n",
      "\n",
      "    accuracy                         0.8007     49316\n",
      "   macro avg     0.7931    0.7963    0.7945     49316\n",
      "weighted avg     0.8021    0.8007    0.8012     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8644    0.8414    0.8527     20463\n",
      "         1.0     0.7777    0.8078    0.7925     14058\n",
      "\n",
      "    accuracy                         0.8277     34521\n",
      "   macro avg     0.8210    0.8246    0.8226     34521\n",
      "weighted avg     0.8291    0.8277    0.8282     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7893    0.7711    0.7801      4400\n",
      "         1.0     0.6750    0.6977    0.6861      2997\n",
      "\n",
      "    accuracy                         0.7414      7397\n",
      "   macro avg     0.7321    0.7344    0.7331      7397\n",
      "weighted avg     0.7429    0.7414    0.7420      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9258    0.9132    0.9194       806\n",
      "         1.0     0.8028    0.8285    0.8155       344\n",
      "\n",
      "    accuracy                         0.8878      1150\n",
      "   macro avg     0.8643    0.8708    0.8674      1150\n",
      "weighted avg     0.8890    0.8878    0.8883      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7583    0.7393    0.7487      3594\n",
      "         1.0     0.6584    0.6807    0.6694      2653\n",
      "\n",
      "    accuracy                         0.7144      6247\n",
      "   macro avg     0.7083    0.7100    0.7090      6247\n",
      "weighted avg     0.7159    0.7144    0.7150      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8312    0.8056    0.8182       324\n",
      "         1.0     0.6272    0.6667    0.6463       159\n",
      "\n",
      "    accuracy                         0.7598       483\n",
      "   macro avg     0.7292    0.7361    0.7323       483\n",
      "weighted avg     0.7641    0.7598    0.7616       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 80.07\n",
      "    Training Set Accuracy: 82.77\n",
      "    Validation Set Accuracy: 74.14\n",
      "    Validation Set with both Images and Texts: 88.78\n",
      "    Validation Set with only Texts: 71.44\n",
      "    Validation Set with unseen Images: 75.98\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8409    0.8196    0.8301     29306\n",
      "         1.0     0.7453    0.7730    0.7589     20010\n",
      "\n",
      "    accuracy                         0.8007     49316\n",
      "   macro avg     0.7931    0.7963    0.7945     49316\n",
      "weighted avg     0.8021    0.8007    0.8012     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8644    0.8414    0.8527     20463\n",
      "         1.0     0.7777    0.8078    0.7925     14058\n",
      "\n",
      "    accuracy                         0.8277     34521\n",
      "   macro avg     0.8210    0.8246    0.8226     34521\n",
      "weighted avg     0.8291    0.8277    0.8282     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7848    0.7673    0.7759      4443\n",
      "         1.0     0.6614    0.6836    0.6723      2955\n",
      "\n",
      "    accuracy                         0.7338      7398\n",
      "   macro avg     0.7231    0.7254    0.7241      7398\n",
      "weighted avg     0.7355    0.7338    0.7345      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9065    0.9204    0.9134       716\n",
      "         1.0     0.8309    0.8046    0.8175       348\n",
      "\n",
      "    accuracy                         0.8825      1064\n",
      "   macro avg     0.8687    0.8625    0.8654      1064\n",
      "weighted avg     0.8817    0.8825    0.8820      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7603    0.7379    0.7489      3727\n",
      "         1.0     0.6404    0.6674    0.6536      2607\n",
      "\n",
      "    accuracy                         0.7089      6334\n",
      "   macro avg     0.7004    0.7026    0.7013      6334\n",
      "weighted avg     0.7110    0.7089    0.7097      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7902    0.8100    0.8000       279\n",
      "         1.0     0.6419    0.6129    0.6271       155\n",
      "\n",
      "    accuracy                         0.7396       434\n",
      "   macro avg     0.7161    0.7115    0.7135       434\n",
      "weighted avg     0.7372    0.7396    0.7382       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 80.07\n",
      "    Training Set Accuracy: 82.77\n",
      "    Validation Set Accuracy: 73.38\n",
      "    Validation Set with both Images and Texts: 88.25\n",
      "    Validation Set with only Texts: 70.89\n",
      "    Validation Set with unseen Images: 73.96\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.96521163 0.03478839]\n",
      " [0.03802777 0.9619723 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.796332\n",
      "std          0.172689\n",
      "min          0.500003\n",
      "25%          0.632989\n",
      "50%          0.812115\n",
      "75%          0.981586\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.799249\n",
      "std          0.172596\n",
      "min          0.500003\n",
      "25%          0.636346\n",
      "50%          0.819024\n",
      "75%          0.983826\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.790319\n",
      "std         0.172840\n",
      "min         0.500003\n",
      "25%         0.626829\n",
      "50%         0.798170\n",
      "75%         0.978017\n",
      "max         1.000000\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "perform_results['O2'] = statistics_F1(hyperdict, stored_data, df, labs, subfolder='002/')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9967be3",
   "metadata": {},
   "source": [
    "## Zero-shot Classification with Embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1a2ed3cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = {}\n",
    "for v in ['O1', 'O2']:\n",
    "    embeddings[v] = {}\n",
    "    for l in ['L2', 'L1', 'L0']:\n",
    "        embeddings[v][l] = {}\n",
    "        for t in ['val', 'test']:\n",
    "            embeddings[v][l][t] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b022ae3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'O1': {'L2': {'val': {}, 'test': {}},\n",
       "  'L1': {'val': {}, 'test': {}},\n",
       "  'L0': {'val': {}, 'test': {}}},\n",
       " 'O2': {'L2': {'val': {}, 'test': {}},\n",
       "  'L1': {'val': {}, 'test': {}},\n",
       "  'L0': {'val': {}, 'test': {}}}}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e44c55c",
   "metadata": {},
   "source": [
    "### Option 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dbb347b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(model_state_file='MLP_classifier.pth', save_dir='model_storage/CLIP14/', embedding_size=768, early_stopping_criteria=20, num_epochs=20, seed=42, batch_size=32, embedding_type='multi', fusion_type='complex', hidden_dim=128, learning_rate=5e-05, l2=1e-05, dropout_p=0.1, catch_keyboard_interrupt=True, device='cuda', cuda=True, verbose=True)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2afe1375",
   "metadata": {},
   "outputs": [],
   "source": [
    "args.embedding_type = 'multi'\n",
    "args.fusion_type = 'complex'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "72541c20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (fc1): Linear(in_features=768, out_features=128, bias=True)\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       "  (fc_concat): Linear(in_features=1536, out_features=128, bias=True)\n",
       "  (fc_complex): Linear(in_features=3840, out_features=1536, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "classifier.load_state_dict(torch.load(args.save_dir + '001/' + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file, map_location=torch.device('cpu')))\n",
    "classifier.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3961b71e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 128])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = F.relu(classifier.dropout(classifier.fc_concat(F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(stored_data, args.embedding_type, args.fusion_type)))))))\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2fc1bfcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "labs = {}\n",
    "labs['txt_emb'] = stored_data['lab_emb']\n",
    "empty_img_emb = stored_data['empty_img_emb']\n",
    "labs['img_emb'] = torch.vstack([empty_img_emb] * labs['txt_emb'].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6bf457bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 768])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labs['img_emb'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e01ffae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 128])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = F.relu(classifier.dropout(classifier.fc_concat(F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(labs, args.embedding_type, args.fusion_type)))))))\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3583acd6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8487    0.7680    0.8063     29306\n",
      "         1.0     0.7017    0.7995    0.7474     20010\n",
      "\n",
      "    accuracy                         0.7808     49316\n",
      "   macro avg     0.7752    0.7837    0.7769     49316\n",
      "weighted avg     0.7891    0.7808    0.7824     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8667    0.7840    0.8233     20463\n",
      "         1.0     0.7239    0.8245    0.7709     14058\n",
      "\n",
      "    accuracy                         0.8005     34521\n",
      "   macro avg     0.7953    0.8042    0.7971     34521\n",
      "weighted avg     0.8086    0.8005    0.8020     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8103    0.7366    0.7717      4400\n",
      "         1.0     0.6588    0.7467    0.7000      2997\n",
      "\n",
      "    accuracy                         0.7407      7397\n",
      "   macro avg     0.7345    0.7417    0.7358      7397\n",
      "weighted avg     0.7489    0.7407    0.7426      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9315    0.9107    0.9210       806\n",
      "         1.0     0.8011    0.8430    0.8215       344\n",
      "\n",
      "    accuracy                         0.8904      1150\n",
      "   macro avg     0.8663    0.8768    0.8712      1150\n",
      "weighted avg     0.8925    0.8904    0.8912      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7805    0.6976    0.7367      3594\n",
      "         1.0     0.6418    0.7343    0.6850      2653\n",
      "\n",
      "    accuracy                         0.7131      6247\n",
      "   macro avg     0.7112    0.7159    0.7108      6247\n",
      "weighted avg     0.7216    0.7131    0.7147      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8487    0.7963    0.8217       324\n",
      "         1.0     0.6313    0.7107    0.6686       159\n",
      "\n",
      "    accuracy                         0.7681       483\n",
      "   macro avg     0.7400    0.7535    0.7451       483\n",
      "weighted avg     0.7771    0.7681    0.7713       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 78.08\n",
      "    Training Set Accuracy: 80.05\n",
      "    Validation Set Accuracy: 74.07\n",
      "    Validation Set with both Images and Texts: 89.04\n",
      "    Validation Set with only Texts: 71.31\n",
      "    Validation Set with unseen Images: 76.81\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8487    0.7680    0.8063     29306\n",
      "         1.0     0.7017    0.7995    0.7474     20010\n",
      "\n",
      "    accuracy                         0.7808     49316\n",
      "   macro avg     0.7752    0.7837    0.7769     49316\n",
      "weighted avg     0.7891    0.7808    0.7824     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8667    0.7840    0.8233     20463\n",
      "         1.0     0.7239    0.8245    0.7709     14058\n",
      "\n",
      "    accuracy                         0.8005     34521\n",
      "   macro avg     0.7953    0.8042    0.7971     34521\n",
      "weighted avg     0.8086    0.8005    0.8020     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8038    0.7256    0.7627      4443\n",
      "         1.0     0.6401    0.7337    0.6837      2955\n",
      "\n",
      "    accuracy                         0.7288      7398\n",
      "   macro avg     0.7219    0.7297    0.7232      7398\n",
      "weighted avg     0.7384    0.7288    0.7312      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9139    0.9190    0.9164       716\n",
      "         1.0     0.8314    0.8218    0.8266       348\n",
      "\n",
      "    accuracy                         0.8872      1064\n",
      "   macro avg     0.8726    0.8704    0.8715      1064\n",
      "weighted avg     0.8869    0.8872    0.8870      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7797    0.6885    0.7313      3727\n",
      "         1.0     0.6185    0.7219    0.6662      2607\n",
      "\n",
      "    accuracy                         0.7022      6334\n",
      "   macro avg     0.6991    0.7052    0.6987      6334\n",
      "weighted avg     0.7133    0.7022    0.7045      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8043    0.8100    0.8071       279\n",
      "         1.0     0.6536    0.6452    0.6494       155\n",
      "\n",
      "    accuracy                         0.7512       434\n",
      "   macro avg     0.7289    0.7276    0.7282       434\n",
      "weighted avg     0.7505    0.7512    0.7508       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 78.08\n",
      "    Training Set Accuracy: 80.05\n",
      "    Validation Set Accuracy: 72.88\n",
      "    Validation Set with both Images and Texts: 88.72\n",
      "    Validation Set with only Texts: 70.22\n",
      "    Validation Set with unseen Images: 75.12\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O1']['L2']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O1']['L2']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "88e2ed53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 1536])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(stored_data, args.embedding_type, args.fusion_type))))\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "109bc18d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1536])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(labs, args.embedding_type, args.fusion_type))))\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b3d4a62d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6863    0.8372    0.7543     29306\n",
      "         1.0     0.6483    0.4394    0.5238     20010\n",
      "\n",
      "    accuracy                         0.6758     49316\n",
      "   macro avg     0.6673    0.6383    0.6390     49316\n",
      "weighted avg     0.6709    0.6758    0.6608     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6863    0.8359    0.7537     20463\n",
      "         1.0     0.6501    0.4439    0.5276     14058\n",
      "\n",
      "    accuracy                         0.6763     34521\n",
      "   macro avg     0.6682    0.6399    0.6407     34521\n",
      "weighted avg     0.6716    0.6763    0.6617     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6867    0.8407    0.7559      4400\n",
      "         1.0     0.6512    0.4368    0.5229      2997\n",
      "\n",
      "    accuracy                         0.6770      7397\n",
      "   macro avg     0.6689    0.6387    0.6394      7397\n",
      "weighted avg     0.6723    0.6770    0.6615      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8312    0.9467    0.8852       806\n",
      "         1.0     0.8147    0.5494    0.6563       344\n",
      "\n",
      "    accuracy                         0.8278      1150\n",
      "   macro avg     0.8229    0.7480    0.7707      1150\n",
      "weighted avg     0.8262    0.8278    0.8167      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6570    0.8169    0.7283      3594\n",
      "         1.0     0.6299    0.4222    0.5055      2653\n",
      "\n",
      "    accuracy                         0.6493      6247\n",
      "   macro avg     0.6434    0.6195    0.6169      6247\n",
      "weighted avg     0.6455    0.6493    0.6337      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7781    0.8981    0.8338       324\n",
      "         1.0     0.6972    0.4780    0.5672       159\n",
      "\n",
      "    accuracy                         0.7598       483\n",
      "   macro avg     0.7377    0.6881    0.7005       483\n",
      "weighted avg     0.7515    0.7598    0.7460       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 67.58\n",
      "    Training Set Accuracy: 67.63\n",
      "    Validation Set Accuracy: 67.70\n",
      "    Validation Set with both Images and Texts: 82.78\n",
      "    Validation Set with only Texts: 64.93\n",
      "    Validation Set with unseen Images: 75.98\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6863    0.8372    0.7543     29306\n",
      "         1.0     0.6483    0.4394    0.5238     20010\n",
      "\n",
      "    accuracy                         0.6758     49316\n",
      "   macro avg     0.6673    0.6383    0.6390     49316\n",
      "weighted avg     0.6709    0.6758    0.6608     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6863    0.8359    0.7537     20463\n",
      "         1.0     0.6501    0.4439    0.5276     14058\n",
      "\n",
      "    accuracy                         0.6763     34521\n",
      "   macro avg     0.6682    0.6399    0.6407     34521\n",
      "weighted avg     0.6716    0.6763    0.6617     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6856    0.8402    0.7551      4443\n",
      "         1.0     0.6365    0.4206    0.5065      2955\n",
      "\n",
      "    accuracy                         0.6726      7398\n",
      "   macro avg     0.6610    0.6304    0.6308      7398\n",
      "weighted avg     0.6660    0.6726    0.6558      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8193    0.9497    0.8797       716\n",
      "         1.0     0.8462    0.5690    0.6804       348\n",
      "\n",
      "    accuracy                         0.8252      1064\n",
      "   macro avg     0.8327    0.7593    0.7801      1064\n",
      "weighted avg     0.8281    0.8252    0.8145      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6615    0.8192    0.7320      3727\n",
      "         1.0     0.6079    0.4008    0.4831      2607\n",
      "\n",
      "    accuracy                         0.6470      6334\n",
      "   macro avg     0.6347    0.6100    0.6075      6334\n",
      "weighted avg     0.6395    0.6470    0.6295      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7478    0.9032    0.8182       279\n",
      "         1.0     0.7216    0.4516    0.5556       155\n",
      "\n",
      "    accuracy                         0.7419       434\n",
      "   macro avg     0.7347    0.6774    0.6869       434\n",
      "weighted avg     0.7384    0.7419    0.7244       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 67.58\n",
      "    Training Set Accuracy: 67.63\n",
      "    Validation Set Accuracy: 67.26\n",
      "    Validation Set with both Images and Texts: 82.52\n",
      "    Validation Set with only Texts: 64.70\n",
      "    Validation Set with unseen Images: 74.19\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O1']['L1']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O1']['L1']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f491e654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 3840])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = prepare_embedding(stored_data, args.embedding_type, args.fusion_type)\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a727b55c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 3840])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = prepare_embedding(labs, args.embedding_type, args.fusion_type)\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ddbe5e58",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6277    0.8808    0.7330     29306\n",
      "         1.0     0.5737    0.2348    0.3333     20010\n",
      "\n",
      "    accuracy                         0.6187     49316\n",
      "   macro avg     0.6007    0.5578    0.5331     49316\n",
      "weighted avg     0.6058    0.6187    0.5708     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6254    0.8807    0.7314     20463\n",
      "         1.0     0.5719    0.2320    0.3301     14058\n",
      "\n",
      "    accuracy                         0.6165     34521\n",
      "   macro avg     0.5986    0.5563    0.5307     34521\n",
      "weighted avg     0.6036    0.6165    0.5680     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6324    0.8789    0.7355      4400\n",
      "         1.0     0.5842    0.2499    0.3501      2997\n",
      "\n",
      "    accuracy                         0.6240      7397\n",
      "   macro avg     0.6083    0.5644    0.5428      7397\n",
      "weighted avg     0.6129    0.6240    0.5794      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7648    0.8958    0.8251       806\n",
      "         1.0     0.5922    0.3547    0.4436       344\n",
      "\n",
      "    accuracy                         0.7339      1150\n",
      "   macro avg     0.6785    0.6252    0.6344      1150\n",
      "weighted avg     0.7132    0.7339    0.7110      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6082    0.8751    0.7176      3594\n",
      "         1.0     0.5827    0.2363    0.3363      2653\n",
      "\n",
      "    accuracy                         0.6038      6247\n",
      "   macro avg     0.5955    0.5557    0.5270      6247\n",
      "weighted avg     0.5974    0.6038    0.5557      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7192    0.8457    0.7773       324\n",
      "         1.0     0.5098    0.3270    0.3985       159\n",
      "\n",
      "    accuracy                         0.6749       483\n",
      "   macro avg     0.6145    0.5864    0.5879       483\n",
      "weighted avg     0.6502    0.6749    0.6526       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.87\n",
      "    Training Set Accuracy: 61.65\n",
      "    Validation Set Accuracy: 62.40\n",
      "    Validation Set with both Images and Texts: 73.39\n",
      "    Validation Set with only Texts: 60.38\n",
      "    Validation Set with unseen Images: 67.49\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6277    0.8808    0.7330     29306\n",
      "         1.0     0.5737    0.2348    0.3333     20010\n",
      "\n",
      "    accuracy                         0.6187     49316\n",
      "   macro avg     0.6007    0.5578    0.5331     49316\n",
      "weighted avg     0.6058    0.6187    0.5708     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6254    0.8807    0.7314     20463\n",
      "         1.0     0.5719    0.2320    0.3301     14058\n",
      "\n",
      "    accuracy                         0.6165     34521\n",
      "   macro avg     0.5986    0.5563    0.5307     34521\n",
      "weighted avg     0.6036    0.6165    0.5680     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6340    0.8834    0.7382      4443\n",
      "         1.0     0.5708    0.2332    0.3311      2955\n",
      "\n",
      "    accuracy                         0.6237      7398\n",
      "   macro avg     0.6024    0.5583    0.5346      7398\n",
      "weighted avg     0.6088    0.6237    0.5756      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7479    0.8827    0.8097       716\n",
      "         1.0     0.6164    0.3879    0.4762       348\n",
      "\n",
      "    accuracy                         0.7209      1064\n",
      "   macro avg     0.6822    0.6353    0.6430      1064\n",
      "weighted avg     0.7049    0.7209    0.7006      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6160    0.8836    0.7259      3727\n",
      "         1.0     0.5607    0.2125    0.3082      2607\n",
      "\n",
      "    accuracy                         0.6074      6334\n",
      "   macro avg     0.5884    0.5480    0.5170      6334\n",
      "weighted avg     0.5932    0.6074    0.5540      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6923    0.8710    0.7714       279\n",
      "         1.0     0.5663    0.3032    0.3950       155\n",
      "\n",
      "    accuracy                         0.6682       434\n",
      "   macro avg     0.6293    0.5871    0.5832       434\n",
      "weighted avg     0.6473    0.6682    0.6370       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.87\n",
      "    Training Set Accuracy: 61.65\n",
      "    Validation Set Accuracy: 62.37\n",
      "    Validation Set with both Images and Texts: 72.09\n",
      "    Validation Set with only Texts: 60.74\n",
      "    Validation Set with unseen Images: 66.82\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O1']['L0']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O1']['L0']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba260af9",
   "metadata": {},
   "source": [
    "### Option 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "87ab7f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (fc1): Linear(in_features=768, out_features=128, bias=True)\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       "  (fc_concat): Linear(in_features=1536, out_features=128, bias=True)\n",
       "  (fc_complex): Linear(in_features=3840, out_features=1536, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = MLPClassifier(embedding_size=args.embedding_size, \n",
    "                        hidden_dim=args.hidden_dim, \n",
    "                        embedding_type=args.embedding_type, \n",
    "                        fusion_type=args.fusion_type,\n",
    "                        num_classes=len(labels), \n",
    "                        dropout_p=args.dropout_p).to(args.device)\n",
    "classifier.load_state_dict(torch.load(args.save_dir + '002/' + args.embedding_type + \"_\" + args.fusion_type + \"_\" + args.model_state_file,map_location=torch.device('cpu')))\n",
    "classifier.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "913f8c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 128])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = F.relu(classifier.dropout(classifier.fc_concat(F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(stored_data, args.embedding_type, args.fusion_type)))))))\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bb70907b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 128])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = F.relu(classifier.dropout(classifier.fc_concat(F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(labs, args.embedding_type, args.fusion_type)))))))\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dfbc8057",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8421    0.8115    0.8265     29306\n",
      "         1.0     0.7379    0.7771    0.7570     20010\n",
      "\n",
      "    accuracy                         0.7976     49316\n",
      "   macro avg     0.7900    0.7943    0.7918     49316\n",
      "weighted avg     0.7998    0.7976    0.7983     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8648    0.8325    0.8483     20463\n",
      "         1.0     0.7688    0.8105    0.7891     14058\n",
      "\n",
      "    accuracy                         0.8236     34521\n",
      "   macro avg     0.8168    0.8215    0.8187     34521\n",
      "weighted avg     0.8257    0.8236    0.8242     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7910    0.7636    0.7771      4400\n",
      "         1.0     0.6697    0.7037    0.6863      2997\n",
      "\n",
      "    accuracy                         0.7394      7397\n",
      "   macro avg     0.7303    0.7337    0.7317      7397\n",
      "weighted avg     0.7418    0.7394    0.7403      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9286    0.9032    0.9157       806\n",
      "         1.0     0.7869    0.8372    0.8113       344\n",
      "\n",
      "    accuracy                         0.8835      1150\n",
      "   macro avg     0.8577    0.8702    0.8635      1150\n",
      "weighted avg     0.8862    0.8835    0.8845      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7598    0.7323    0.7458      3594\n",
      "         1.0     0.6543    0.6864    0.6700      2653\n",
      "\n",
      "    accuracy                         0.7128      6247\n",
      "   macro avg     0.7071    0.7094    0.7079      6247\n",
      "weighted avg     0.7150    0.7128    0.7136      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8328    0.7840    0.8076       324\n",
      "         1.0     0.6067    0.6792    0.6409       159\n",
      "\n",
      "    accuracy                         0.7495       483\n",
      "   macro avg     0.7198    0.7316    0.7243       483\n",
      "weighted avg     0.7584    0.7495    0.7528       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 79.76\n",
      "    Training Set Accuracy: 82.36\n",
      "    Validation Set Accuracy: 73.94\n",
      "    Validation Set with both Images and Texts: 88.35\n",
      "    Validation Set with only Texts: 71.28\n",
      "    Validation Set with unseen Images: 74.95\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8421    0.8115    0.8265     29306\n",
      "         1.0     0.7379    0.7771    0.7570     20010\n",
      "\n",
      "    accuracy                         0.7976     49316\n",
      "   macro avg     0.7900    0.7943    0.7918     49316\n",
      "weighted avg     0.7998    0.7976    0.7983     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8648    0.8325    0.8483     20463\n",
      "         1.0     0.7688    0.8105    0.7891     14058\n",
      "\n",
      "    accuracy                         0.8236     34521\n",
      "   macro avg     0.8168    0.8215    0.8187     34521\n",
      "weighted avg     0.8257    0.8236    0.8242     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7886    0.7623    0.7752      4443\n",
      "         1.0     0.6597    0.6927    0.6758      2955\n",
      "\n",
      "    accuracy                         0.7345      7398\n",
      "   macro avg     0.7241    0.7275    0.7255      7398\n",
      "weighted avg     0.7371    0.7345    0.7355      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9174    0.9148    0.9161       716\n",
      "         1.0     0.8257    0.8305    0.8281       348\n",
      "\n",
      "    accuracy                         0.8872      1064\n",
      "   macro avg     0.8715    0.8726    0.8721      1064\n",
      "weighted avg     0.8874    0.8872    0.8873      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7629    0.7330    0.7477      3727\n",
      "         1.0     0.6386    0.6743    0.6560      2607\n",
      "\n",
      "    accuracy                         0.7089      6334\n",
      "   macro avg     0.7007    0.7037    0.7018      6334\n",
      "weighted avg     0.7117    0.7089    0.7099      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8073    0.7957    0.8014       279\n",
      "         1.0     0.6415    0.6581    0.6497       155\n",
      "\n",
      "    accuracy                         0.7465       434\n",
      "   macro avg     0.7244    0.7269    0.7256       434\n",
      "weighted avg     0.7481    0.7465    0.7472       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 79.76\n",
      "    Training Set Accuracy: 82.36\n",
      "    Validation Set Accuracy: 73.45\n",
      "    Validation Set with both Images and Texts: 88.72\n",
      "    Validation Set with only Texts: 70.89\n",
      "    Validation Set with unseen Images: 74.65\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O2']['L2']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O2']['L2']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d6aa36c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 1536])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(stored_data, args.embedding_type, args.fusion_type))))\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ffcaaf9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 1536])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = F.relu(classifier.dropout(classifier.fc_complex(prepare_embedding(labs, args.embedding_type, args.fusion_type))))\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "aec96d26",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7802    0.7447    0.7620     29306\n",
      "         1.0     0.6495    0.6927    0.6704     20010\n",
      "\n",
      "    accuracy                         0.7236     49316\n",
      "   macro avg     0.7148    0.7187    0.7162     49316\n",
      "weighted avg     0.7271    0.7236    0.7249     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7862    0.7501    0.7677     20463\n",
      "         1.0     0.6590    0.7031    0.6803     14058\n",
      "\n",
      "    accuracy                         0.7309     34521\n",
      "   macro avg     0.7226    0.7266    0.7240     34521\n",
      "weighted avg     0.7344    0.7309    0.7321     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7679    0.7405    0.7539      4400\n",
      "         1.0     0.6379    0.6713    0.6542      2997\n",
      "\n",
      "    accuracy                         0.7125      7397\n",
      "   macro avg     0.7029    0.7059    0.7041      7397\n",
      "weighted avg     0.7152    0.7125    0.7135      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8893    0.8970    0.8931       806\n",
      "         1.0     0.7537    0.7384    0.7460       344\n",
      "\n",
      "    accuracy                         0.8496      1150\n",
      "   macro avg     0.8215    0.8177    0.8196      1150\n",
      "weighted avg     0.8487    0.8496    0.8491      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7391    0.7053    0.7218      3594\n",
      "         1.0     0.6241    0.6626    0.6428      2653\n",
      "\n",
      "    accuracy                         0.6872      6247\n",
      "   macro avg     0.6816    0.6840    0.6823      6247\n",
      "weighted avg     0.6902    0.6872    0.6882      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8313    0.8210    0.8261       324\n",
      "         1.0     0.6442    0.6604    0.6522       159\n",
      "\n",
      "    accuracy                         0.7681       483\n",
      "   macro avg     0.7377    0.7407    0.7391       483\n",
      "weighted avg     0.7697    0.7681    0.7688       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.36\n",
      "    Training Set Accuracy: 73.09\n",
      "    Validation Set Accuracy: 71.25\n",
      "    Validation Set with both Images and Texts: 84.96\n",
      "    Validation Set with only Texts: 68.72\n",
      "    Validation Set with unseen Images: 76.81\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7802    0.7447    0.7620     29306\n",
      "         1.0     0.6495    0.6927    0.6704     20010\n",
      "\n",
      "    accuracy                         0.7236     49316\n",
      "   macro avg     0.7148    0.7187    0.7162     49316\n",
      "weighted avg     0.7271    0.7236    0.7249     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7862    0.7501    0.7677     20463\n",
      "         1.0     0.6590    0.7031    0.6803     14058\n",
      "\n",
      "    accuracy                         0.7309     34521\n",
      "   macro avg     0.7226    0.7266    0.7240     34521\n",
      "weighted avg     0.7344    0.7309    0.7321     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7647    0.7243    0.7440      4443\n",
      "         1.0     0.6160    0.6650    0.6395      2955\n",
      "\n",
      "    accuracy                         0.7006      7398\n",
      "   macro avg     0.6904    0.6946    0.6918      7398\n",
      "weighted avg     0.7053    0.7006    0.7023      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8755    0.9036    0.8893       716\n",
      "         1.0     0.7877    0.7356    0.7608       348\n",
      "\n",
      "    accuracy                         0.8487      1064\n",
      "   macro avg     0.8316    0.8196    0.8251      1064\n",
      "weighted avg     0.8468    0.8487    0.8473      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7411    0.6898    0.7146      3727\n",
      "         1.0     0.5965    0.6555    0.6246      2607\n",
      "\n",
      "    accuracy                         0.6757      6334\n",
      "   macro avg     0.6688    0.6727    0.6696      6334\n",
      "weighted avg     0.6816    0.6757    0.6775      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8014    0.8387    0.8196       279\n",
      "         1.0     0.6831    0.6258    0.6532       155\n",
      "\n",
      "    accuracy                         0.7627       434\n",
      "   macro avg     0.7422    0.7323    0.7364       434\n",
      "weighted avg     0.7591    0.7627    0.7602       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.36\n",
      "    Training Set Accuracy: 73.09\n",
      "    Validation Set Accuracy: 70.06\n",
      "    Validation Set with both Images and Texts: 84.87\n",
      "    Validation Set with only Texts: 67.57\n",
      "    Validation Set with unseen Images: 76.27\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O2']['L1']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O2']['L1']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "8bbf9085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([49316, 3840])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_emb = prepare_embedding(stored_data, args.embedding_type, args.fusion_type)\n",
    "new_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "fe5505c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 3840])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_lab_emb = prepare_embedding(labs, args.embedding_type, args.fusion_type)\n",
    "new_lab_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "68d0e2e6",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Zero-shot using new multi-modal embedding:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6277    0.8808    0.7330     29306\n",
      "         1.0     0.5737    0.2348    0.3333     20010\n",
      "\n",
      "    accuracy                         0.6187     49316\n",
      "   macro avg     0.6007    0.5578    0.5331     49316\n",
      "weighted avg     0.6058    0.6187    0.5708     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6254    0.8807    0.7314     20463\n",
      "         1.0     0.5719    0.2320    0.3301     14058\n",
      "\n",
      "    accuracy                         0.6165     34521\n",
      "   macro avg     0.5986    0.5563    0.5307     34521\n",
      "weighted avg     0.6036    0.6165    0.5680     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6324    0.8789    0.7355      4400\n",
      "         1.0     0.5842    0.2499    0.3501      2997\n",
      "\n",
      "    accuracy                         0.6240      7397\n",
      "   macro avg     0.6083    0.5644    0.5428      7397\n",
      "weighted avg     0.6129    0.6240    0.5794      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7648    0.8958    0.8251       806\n",
      "         1.0     0.5922    0.3547    0.4436       344\n",
      "\n",
      "    accuracy                         0.7339      1150\n",
      "   macro avg     0.6785    0.6252    0.6344      1150\n",
      "weighted avg     0.7132    0.7339    0.7110      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6082    0.8751    0.7176      3594\n",
      "         1.0     0.5827    0.2363    0.3363      2653\n",
      "\n",
      "    accuracy                         0.6038      6247\n",
      "   macro avg     0.5955    0.5557    0.5270      6247\n",
      "weighted avg     0.5974    0.6038    0.5557      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7192    0.8457    0.7773       324\n",
      "         1.0     0.5098    0.3270    0.3985       159\n",
      "\n",
      "    accuracy                         0.6749       483\n",
      "   macro avg     0.6145    0.5864    0.5879       483\n",
      "weighted avg     0.6502    0.6749    0.6526       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.87\n",
      "    Training Set Accuracy: 61.65\n",
      "    Validation Set Accuracy: 62.40\n",
      "    Validation Set with both Images and Texts: 73.39\n",
      "    Validation Set with only Texts: 60.38\n",
      "    Validation Set with unseen Images: 67.49\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6277    0.8808    0.7330     29306\n",
      "         1.0     0.5737    0.2348    0.3333     20010\n",
      "\n",
      "    accuracy                         0.6187     49316\n",
      "   macro avg     0.6007    0.5578    0.5331     49316\n",
      "weighted avg     0.6058    0.6187    0.5708     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6254    0.8807    0.7314     20463\n",
      "         1.0     0.5719    0.2320    0.3301     14058\n",
      "\n",
      "    accuracy                         0.6165     34521\n",
      "   macro avg     0.5986    0.5563    0.5307     34521\n",
      "weighted avg     0.6036    0.6165    0.5680     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6340    0.8834    0.7382      4443\n",
      "         1.0     0.5708    0.2332    0.3311      2955\n",
      "\n",
      "    accuracy                         0.6237      7398\n",
      "   macro avg     0.6024    0.5583    0.5346      7398\n",
      "weighted avg     0.6088    0.6237    0.5756      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7479    0.8827    0.8097       716\n",
      "         1.0     0.6164    0.3879    0.4762       348\n",
      "\n",
      "    accuracy                         0.7209      1064\n",
      "   macro avg     0.6822    0.6353    0.6430      1064\n",
      "weighted avg     0.7049    0.7209    0.7006      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6160    0.8836    0.7259      3727\n",
      "         1.0     0.5607    0.2125    0.3082      2607\n",
      "\n",
      "    accuracy                         0.6074      6334\n",
      "   macro avg     0.5884    0.5480    0.5170      6334\n",
      "weighted avg     0.5932    0.6074    0.5540      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6923    0.8710    0.7714       279\n",
      "         1.0     0.5663    0.3032    0.3950       155\n",
      "\n",
      "    accuracy                         0.6682       434\n",
      "   macro avg     0.6293    0.5871    0.5832       434\n",
      "weighted avg     0.6473    0.6682    0.6370       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.87\n",
      "    Training Set Accuracy: 61.65\n",
      "    Validation Set Accuracy: 62.37\n",
      "    Validation Set with both Images and Texts: 72.09\n",
      "    Validation Set with only Texts: 60.74\n",
      "    Validation Set with unseen Images: 66.82\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "MUL_LAB = util.cos_sim(new_lab_emb, new_emb)\n",
    "pred_MUL = torch.argmax(MUL_LAB[:2,:], dim=0).float()\n",
    "lab = torch.tensor(np.array(df['skepticism'])).to('cuda').float()\n",
    "print('Zero-shot using new multi-modal embedding:')\n",
    "embeddings['O2']['L0']['val'] = evaluate_dataset_F1(df, pred_MUL)\n",
    "print('Zero-shot using new multi-modal embedding (test):')\n",
    "embeddings['O2']['L0']['test'] = evaluate_dataset_F1(df, pred_MUL, val_set='test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b46a0293",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "results['fusion'] = perform_results\n",
    "results['new_embedding'] = embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "474c0a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open(args.result_dir + 'results_F1.pkl', \"wb\") as fOut:\n",
    "    pickle.dump(results, fOut, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3640628f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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