CLIP-ML_NL-Evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e7bd18d3",
   "metadata": {},
   "source": [
    "# Multi-modal Embedding using Multilingual CLIP (Dutch)"
   ]
  },
  {
   "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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": [],
   "source": [
    "# We use the original CLIP model for computing image embeddings and English text embeddings\n",
    "txt_model = SentenceTransformer('clip-ViT-B-32-multilingual-v1')\n",
    "img_model = SentenceTransformer('clip-ViT-B-32')\n",
    "txt_tokenizer = txt_model.tokenizer\n",
    "img_tokenizer = img_model._first_module().processor.tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c10f520a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def embed_documents(documents, embedding_model=txt_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=txt_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": [],
   "source": [
    "df['text_NL_trun'] = df['text_NL'].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=img_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": "f8fe47cb",
   "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": "568b1c0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6d46c1eb2ae48129fa771a9fe9dcb8d",
       "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 = txt_model.encode(labels, convert_to_tensor=True)\n",
    "empty_img_emb = embed_images(['nan'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4f74ba63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 512])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_img_emb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "526eda9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "txt_emb = embed_documents(list(df['text_NL']), convert_to_tensor=True, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbf89a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_emb = embed_images(list(df['target']), convert_to_tensor=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc09eeb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('data/CLIP_ML_NL/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": 13,
   "id": "96befd9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/CLIP_ML_NL/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": 14,
   "id": "990b44e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def zero_shot(df_all, model_name='CLIP_ML_NL', 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": 15,
   "id": "cb85005f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using textual embedding only:\n",
      "\n",
      "    Overall Accuracy: 61.21\n",
      "    Training Set Accuracy: 61.37\n",
      "    Validation Set Accuracy: 61.30\n",
      "    Validation Set with both Images and Texts: 67.83\n",
      "    Validation Set with only Texts: 60.09\n",
      "    Validation Set with unseen Images: 63.77\n",
      "    \n",
      "Using image embedding only:\n",
      "\n",
      "    Overall Accuracy: 44.69\n",
      "    Training Set Accuracy: 44.82\n",
      "    Validation Set Accuracy: 45.17\n",
      "    Validation Set with both Images and Texts: 59.83\n",
      "    Validation Set with only Texts: 42.47\n",
      "    Validation Set with unseen Images: 61.28\n",
      "    \n",
      "Using multi-modal embedding:\n",
      "\n",
      "    Overall Accuracy: 49.72\n",
      "    Training Set Accuracy: 49.71\n",
      "    Validation Set Accuracy: 50.02\n",
      "    Validation Set with both Images and Texts: 66.17\n",
      "    Validation Set with only Texts: 47.05\n",
      "    Validation Set with unseen Images: 64.18\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: 49.72\n",
      "    Training Set Accuracy: 49.71\n",
      "    Validation Set Accuracy: 49.47\n",
      "    Validation Set with both Images and Texts: 67.76\n",
      "    Validation Set with only Texts: 46.40\n",
      "    Validation Set with unseen Images: 65.44\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": 16,
   "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/CLIP_ML_NL/',\n",
    "    result_dir='results/CLIP_ML_NL/',\n",
    "    embedding_size=512,\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": 17,
   "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": 18,
   "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": 19,
   "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": 20,
   "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": 21,
   "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": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "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": 26,
   "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": 27,
   "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": 28,
   "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": 34,
   "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": "77b290b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "hyperdict = {}\n",
    "experiment(df, stored_data, hyperdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d87d4436",
   "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": 30,
   "id": "f6295688",
   "metadata": {},
   "outputs": [],
   "source": [
    "perform_results = {}\n",
    "for o in ['O1', 'O2']:\n",
    "    perform_results[o] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "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": 33,
   "id": "3e468adb",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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     0.8071    0.7592    0.7824     29306\n",
      "         1.0     0.6755    0.7343    0.7037     20010\n",
      "\n",
      "    accuracy                         0.7491     49316\n",
      "   macro avg     0.7413    0.7467    0.7431     49316\n",
      "weighted avg     0.7537    0.7491    0.7505     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8157    0.7664    0.7903     20463\n",
      "         1.0     0.6875    0.7479    0.7164     14058\n",
      "\n",
      "    accuracy                         0.7589     34521\n",
      "   macro avg     0.7516    0.7572    0.7533     34521\n",
      "weighted avg     0.7635    0.7589    0.7602     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7895    0.7452    0.7667      4400\n",
      "         1.0     0.6544    0.7084    0.6803      2997\n",
      "\n",
      "    accuracy                         0.7303      7397\n",
      "   macro avg     0.7220    0.7268    0.7235      7397\n",
      "weighted avg     0.7348    0.7303    0.7317      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9218    0.9218    0.9218       806\n",
      "         1.0     0.8169    0.8169    0.8169       344\n",
      "\n",
      "    accuracy                         0.8904      1150\n",
      "   macro avg     0.8693    0.8693    0.8693      1150\n",
      "weighted avg     0.8904    0.8904    0.8904      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7577    0.7056    0.7307      3594\n",
      "         1.0     0.6352    0.6943    0.6634      2653\n",
      "\n",
      "    accuracy                         0.7008      6247\n",
      "   macro avg     0.6964    0.7000    0.6971      6247\n",
      "weighted avg     0.7057    0.7008    0.7021      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8154    0.8179    0.8166       324\n",
      "         1.0     0.6266    0.6226    0.6246       159\n",
      "\n",
      "    accuracy                         0.7536       483\n",
      "   macro avg     0.7210    0.7203    0.7206       483\n",
      "weighted avg     0.7532    0.7536    0.7534       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.91\n",
      "    Training Set Accuracy: 75.89\n",
      "    Validation Set Accuracy: 73.03\n",
      "    Validation Set with both Images and Texts: 89.04\n",
      "    Validation Set with only Texts: 70.08\n",
      "    Validation Set with unseen Images: 75.36\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8071    0.7592    0.7824     29306\n",
      "         1.0     0.6755    0.7343    0.7037     20010\n",
      "\n",
      "    accuracy                         0.7491     49316\n",
      "   macro avg     0.7413    0.7467    0.7431     49316\n",
      "weighted avg     0.7537    0.7491    0.7505     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8157    0.7664    0.7903     20463\n",
      "         1.0     0.6875    0.7479    0.7164     14058\n",
      "\n",
      "    accuracy                         0.7589     34521\n",
      "   macro avg     0.7516    0.7572    0.7533     34521\n",
      "weighted avg     0.7635    0.7589    0.7602     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7852    0.7398    0.7618      4443\n",
      "         1.0     0.6401    0.6958    0.6668      2955\n",
      "\n",
      "    accuracy                         0.7222      7398\n",
      "   macro avg     0.7127    0.7178    0.7143      7398\n",
      "weighted avg     0.7273    0.7222    0.7239      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9040    0.9204    0.9121       716\n",
      "         1.0     0.8299    0.7989    0.8141       348\n",
      "\n",
      "    accuracy                         0.8806      1064\n",
      "   macro avg     0.8669    0.8596    0.8631      1064\n",
      "weighted avg     0.8797    0.8806    0.8800      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7602    0.7051    0.7316      3727\n",
      "         1.0     0.6180    0.6820    0.6484      2607\n",
      "\n",
      "    accuracy                         0.6956      6334\n",
      "   macro avg     0.6891    0.6936    0.6900      6334\n",
      "weighted avg     0.7017    0.6956    0.6974      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7671    0.8029    0.7846       279\n",
      "         1.0     0.6127    0.5613    0.5859       155\n",
      "\n",
      "    accuracy                         0.7166       434\n",
      "   macro avg     0.6899    0.6821    0.6852       434\n",
      "weighted avg     0.7120    0.7166    0.7136       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.91\n",
      "    Training Set Accuracy: 75.89\n",
      "    Validation Set Accuracy: 72.22\n",
      "    Validation Set with both Images and Texts: 88.06\n",
      "    Validation Set with only Texts: 69.56\n",
      "    Validation Set with unseen Images: 71.66\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5872862  0.41271386]\n",
      " [0.29008782 0.7099121 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.744263\n",
      "std          0.172480\n",
      "min          0.500002\n",
      "25%          0.585322\n",
      "50%          0.708551\n",
      "75%          0.923481\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.746182\n",
      "std          0.173301\n",
      "min          0.500002\n",
      "25%          0.585899\n",
      "50%          0.711651\n",
      "75%          0.928999\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.741763\n",
      "std         0.170296\n",
      "min         0.500047\n",
      "25%         0.585482\n",
      "50%         0.705245\n",
      "75%         0.913855\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"
    },
    {
     "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     0.7474    0.7592    0.7532     29306\n",
      "         1.0     0.6390    0.6242    0.6315     20010\n",
      "\n",
      "    accuracy                         0.7044     49316\n",
      "   macro avg     0.6932    0.6917    0.6924     49316\n",
      "weighted avg     0.7034    0.7044    0.7038     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7479    0.7577    0.7527     20463\n",
      "         1.0     0.6404    0.6282    0.6342     14058\n",
      "\n",
      "    accuracy                         0.7049     34521\n",
      "   macro avg     0.6941    0.6929    0.6935     34521\n",
      "weighted avg     0.7041    0.7049    0.7045     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7463    0.7675    0.7568      4400\n",
      "         1.0     0.6438    0.6170    0.6301      2997\n",
      "\n",
      "    accuracy                         0.7065      7397\n",
      "   macro avg     0.6951    0.6922    0.6934      7397\n",
      "weighted avg     0.7048    0.7065    0.7054      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8912    0.8945    0.8929       806\n",
      "         1.0     0.7507    0.7442    0.7474       344\n",
      "\n",
      "    accuracy                         0.8496      1150\n",
      "   macro avg     0.8210    0.8194    0.8202      1150\n",
      "weighted avg     0.8492    0.8496    0.8494      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7147    0.7390    0.7267      3594\n",
      "         1.0     0.6294    0.6005    0.6146      2653\n",
      "\n",
      "    accuracy                         0.6802      6247\n",
      "   macro avg     0.6721    0.6697    0.6706      6247\n",
      "weighted avg     0.6785    0.6802    0.6791      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8281    0.8179    0.8230       324\n",
      "         1.0     0.6380    0.6541    0.6460       159\n",
      "\n",
      "    accuracy                         0.7640       483\n",
      "   macro avg     0.7331    0.7360    0.7345       483\n",
      "weighted avg     0.7655    0.7640    0.7647       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.44\n",
      "    Training Set Accuracy: 70.49\n",
      "    Validation Set Accuracy: 70.65\n",
      "    Validation Set with both Images and Texts: 84.96\n",
      "    Validation Set with only Texts: 68.02\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     0.7474    0.7592    0.7532     29306\n",
      "         1.0     0.6390    0.6242    0.6315     20010\n",
      "\n",
      "    accuracy                         0.7044     49316\n",
      "   macro avg     0.6932    0.6917    0.6924     49316\n",
      "weighted avg     0.7034    0.7044    0.7038     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7479    0.7577    0.7527     20463\n",
      "         1.0     0.6404    0.6282    0.6342     14058\n",
      "\n",
      "    accuracy                         0.7049     34521\n",
      "   macro avg     0.6941    0.6929    0.6935     34521\n",
      "weighted avg     0.7041    0.7049    0.7045     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7463    0.7580    0.7521      4443\n",
      "         1.0     0.6274    0.6125    0.6199      2955\n",
      "\n",
      "    accuracy                         0.6999      7398\n",
      "   macro avg     0.6868    0.6853    0.6860      7398\n",
      "weighted avg     0.6988    0.6999    0.6993      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8593    0.8869    0.8729       716\n",
      "         1.0     0.7508    0.7011    0.7251       348\n",
      "\n",
      "    accuracy                         0.8261      1064\n",
      "   macro avg     0.8050    0.7940    0.7990      1064\n",
      "weighted avg     0.8238    0.8261    0.8245      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7242    0.7333    0.7287      3727\n",
      "         1.0     0.6117    0.6007    0.6062      2607\n",
      "\n",
      "    accuracy                         0.6787      6334\n",
      "   macro avg     0.6679    0.6670    0.6674      6334\n",
      "weighted avg     0.6779    0.6787    0.6783      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7778    0.8280    0.8021       279\n",
      "         1.0     0.6496    0.5742    0.6096       155\n",
      "\n",
      "    accuracy                         0.7373       434\n",
      "   macro avg     0.7137    0.7011    0.7058       434\n",
      "weighted avg     0.7320    0.7373    0.7333       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.44\n",
      "    Training Set Accuracy: 70.49\n",
      "    Validation Set Accuracy: 69.99\n",
      "    Validation Set with both Images and Texts: 82.61\n",
      "    Validation Set with only Texts: 67.87\n",
      "    Validation Set with unseen Images: 73.73\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.45821488 0.54178506]\n",
      " [0.27191848 0.7280815 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.685501\n",
      "std          0.146449\n",
      "min          0.500003\n",
      "25%          0.562556\n",
      "50%          0.640947\n",
      "75%          0.796790\n",
      "max          0.999973\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.685890\n",
      "std          0.146654\n",
      "min          0.500003\n",
      "25%          0.562781\n",
      "50%          0.641078\n",
      "75%          0.798180\n",
      "max          0.999973\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.685536\n",
      "std         0.146660\n",
      "min         0.500030\n",
      "25%         0.562472\n",
      "50%         0.641597\n",
      "75%         0.798524\n",
      "max         0.999909\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     0.6135    0.9901    0.7575     29306\n",
      "         1.0     0.8562    0.0863    0.1568     20010\n",
      "\n",
      "    accuracy                         0.6234     49316\n",
      "   macro avg     0.7348    0.5382    0.4572     49316\n",
      "weighted avg     0.7120    0.6234    0.5138     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6134    0.9921    0.7581     20463\n",
      "         1.0     0.8870    0.0899    0.1633     14058\n",
      "\n",
      "    accuracy                         0.6247     34521\n",
      "   macro avg     0.7502    0.5410    0.4607     34521\n",
      "weighted avg     0.7248    0.6247    0.5159     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6110    0.9852    0.7542      4400\n",
      "         1.0     0.7848    0.0791    0.1437      2997\n",
      "\n",
      "    accuracy                         0.6181      7397\n",
      "   macro avg     0.6979    0.5322    0.4490      7397\n",
      "weighted avg     0.6814    0.6181    0.5069      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8738    0.9194    0.8960       806\n",
      "         1.0     0.7848    0.6890    0.7337       344\n",
      "\n",
      "    accuracy                         0.8504      1150\n",
      "   macro avg     0.8293    0.8042    0.8149      1150\n",
      "weighted avg     0.8472    0.8504    0.8475      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     0.7867    0.8426    0.8137       324\n",
      "         1.0     0.6250    0.5346    0.5763       159\n",
      "\n",
      "    accuracy                         0.7412       483\n",
      "   macro avg     0.7059    0.6886    0.6950       483\n",
      "weighted avg     0.7335    0.7412    0.7355       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.34\n",
      "    Training Set Accuracy: 62.47\n",
      "    Validation Set Accuracy: 61.81\n",
      "    Validation Set with both Images and Texts: 85.04\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     0.6135    0.9901    0.7575     29306\n",
      "         1.0     0.8562    0.0863    0.1568     20010\n",
      "\n",
      "    accuracy                         0.6234     49316\n",
      "   macro avg     0.7348    0.5382    0.4572     49316\n",
      "weighted avg     0.7120    0.6234    0.5138     49316\n",
      "\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": [
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6134    0.9921    0.7581     20463\n",
      "         1.0     0.8870    0.0899    0.1633     14058\n",
      "\n",
      "    accuracy                         0.6247     34521\n",
      "   macro avg     0.7502    0.5410    0.4607     34521\n",
      "weighted avg     0.7248    0.6247    0.5159     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6161    0.9856    0.7582      4443\n",
      "         1.0     0.7793    0.0765    0.1393      2955\n",
      "\n",
      "    accuracy                         0.6225      7398\n",
      "   macro avg     0.6977    0.5310    0.4487      7398\n",
      "weighted avg     0.6813    0.6225    0.5110      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8424    0.9106    0.8752       716\n",
      "         1.0     0.7793    0.6494    0.7085       348\n",
      "\n",
      "    accuracy                         0.8252      1064\n",
      "   macro avg     0.8108    0.7800    0.7918      1064\n",
      "weighted avg     0.8218    0.8252    0.8206      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     0.7373    0.8351    0.7832       279\n",
      "         1.0     0.6102    0.4645    0.5275       155\n",
      "\n",
      "    accuracy                         0.7028       434\n",
      "   macro avg     0.6738    0.6498    0.6553       434\n",
      "weighted avg     0.6919    0.7028    0.6919       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.34\n",
      "    Training Set Accuracy: 62.47\n",
      "    Validation Set Accuracy: 62.25\n",
      "    Validation Set with both Images and Texts: 82.52\n",
      "    Validation Set with only Texts: 58.84\n",
      "    Validation Set with unseen Images: 70.28\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5731946  0.42680535]\n",
      " [0.5731946  0.42680535]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.613195\n",
      "std          0.110979\n",
      "min          0.500085\n",
      "25%          0.573195\n",
      "50%          0.573195\n",
      "75%          0.573195\n",
      "max          0.999913\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.613493\n",
      "std          0.111506\n",
      "min          0.500172\n",
      "25%          0.573195\n",
      "50%          0.573195\n",
      "75%          0.573195\n",
      "max          0.999913\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.614568\n",
      "std         0.112293\n",
      "min         0.500085\n",
      "25%         0.573195\n",
      "50%         0.573195\n",
      "75%         0.573195\n",
      "max         0.999488\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": [
      "('text', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7541    0.7663    0.7602     29306\n",
      "         1.0     0.6494    0.6340    0.6416     20010\n",
      "\n",
      "    accuracy                         0.7126     49316\n",
      "   macro avg     0.7018    0.7002    0.7009     49316\n",
      "weighted avg     0.7116    0.7126    0.7121     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7559    0.7670    0.7614     20463\n",
      "         1.0     0.6535    0.6395    0.6464     14058\n",
      "\n",
      "    accuracy                         0.7151     34521\n",
      "   macro avg     0.7047    0.7033    0.7039     34521\n",
      "weighted avg     0.7142    0.7151    0.7146     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7479    0.7655    0.7566      4400\n",
      "         1.0     0.6434    0.6213    0.6322      2997\n",
      "\n",
      "    accuracy                         0.7070      7397\n",
      "   macro avg     0.6957    0.6934    0.6944      7397\n",
      "weighted avg     0.7056    0.7070    0.7062      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8343    0.8871    0.8599       806\n",
      "         1.0     0.6894    0.5872    0.6342       344\n",
      "\n",
      "    accuracy                         0.7974      1150\n",
      "   macro avg     0.7619    0.7372    0.7471      1150\n",
      "weighted avg     0.7910    0.7974    0.7924      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7276    0.7382    0.7329      3594\n",
      "         1.0     0.6382    0.6257    0.6319      2653\n",
      "\n",
      "    accuracy                         0.6904      6247\n",
      "   macro avg     0.6829    0.6819    0.6824      6247\n",
      "weighted avg     0.6897    0.6904    0.6900      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7890    0.8426    0.8149       324\n",
      "         1.0     0.6277    0.5409    0.5811       159\n",
      "\n",
      "    accuracy                         0.7433       483\n",
      "   macro avg     0.7084    0.6917    0.6980       483\n",
      "weighted avg     0.7359    0.7433    0.7379       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.26\n",
      "    Training Set Accuracy: 71.51\n",
      "    Validation Set Accuracy: 70.70\n",
      "    Validation Set with both Images and Texts: 79.74\n",
      "    Validation Set with only Texts: 69.04\n",
      "    Validation Set with unseen Images: 74.33\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7541    0.7663    0.7602     29306\n",
      "         1.0     0.6494    0.6340    0.6416     20010\n",
      "\n",
      "    accuracy                         0.7126     49316\n",
      "   macro avg     0.7018    0.7002    0.7009     49316\n",
      "weighted avg     0.7116    0.7126    0.7121     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7559    0.7670    0.7614     20463\n",
      "         1.0     0.6535    0.6395    0.6464     14058\n",
      "\n",
      "    accuracy                         0.7151     34521\n",
      "   macro avg     0.7047    0.7033    0.7039     34521\n",
      "weighted avg     0.7142    0.7151    0.7146     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7517    0.7639    0.7578      4443\n",
      "         1.0     0.6361    0.6206    0.6283      2955\n",
      "\n",
      "    accuracy                         0.7067      7398\n",
      "   macro avg     0.6939    0.6923    0.6930      7398\n",
      "weighted avg     0.7056    0.7067    0.7060      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8168    0.8966    0.8549       716\n",
      "         1.0     0.7338    0.5862    0.6518       348\n",
      "\n",
      "    accuracy                         0.7951      1064\n",
      "   macro avg     0.7753    0.7414    0.7533      1064\n",
      "weighted avg     0.7897    0.7951    0.7884      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7380    0.7384    0.7382      3727\n",
      "         1.0     0.6257    0.6252    0.6255      2607\n",
      "\n",
      "    accuracy                         0.6918      6334\n",
      "   macro avg     0.6819    0.6818    0.6818      6334\n",
      "weighted avg     0.6918    0.6918    0.6918      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7548    0.8387    0.7946       279\n",
      "         1.0     0.6371    0.5097    0.5663       155\n",
      "\n",
      "    accuracy                         0.7212       434\n",
      "   macro avg     0.6960    0.6742    0.6804       434\n",
      "weighted avg     0.7128    0.7212    0.7130       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.26\n",
      "    Training Set Accuracy: 71.51\n",
      "    Validation Set Accuracy: 70.67\n",
      "    Validation Set with both Images and Texts: 79.51\n",
      "    Validation Set with only Texts: 69.18\n",
      "    Validation Set with unseen Images: 72.12\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.45107767 0.54892236]\n",
      " [0.18960533 0.81039464]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.697569\n",
      "std          0.146347\n",
      "min          0.500003\n",
      "25%          0.572273\n",
      "50%          0.661680\n",
      "75%          0.811819\n",
      "max          0.999658\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.697783\n",
      "std          0.146516\n",
      "min          0.500003\n",
      "25%          0.572385\n",
      "50%          0.661685\n",
      "75%          0.811734\n",
      "max          0.999470\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.697307\n",
      "std         0.145802\n",
      "min         0.500019\n",
      "25%         0.571513\n",
      "50%         0.660740\n",
      "75%         0.812138\n",
      "max         0.999429\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     0.7834    0.7322    0.7569     29306\n",
      "         1.0     0.6420    0.7035    0.6714     20010\n",
      "\n",
      "    accuracy                         0.7206     49316\n",
      "   macro avg     0.7127    0.7179    0.7142     49316\n",
      "weighted avg     0.7261    0.7206    0.7222     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7855    0.7328    0.7582     20463\n",
      "         1.0     0.6457    0.7088    0.6758     14058\n",
      "\n",
      "    accuracy                         0.7230     34521\n",
      "   macro avg     0.7156    0.7208    0.7170     34521\n",
      "weighted avg     0.7286    0.7230    0.7247     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7780    0.7302    0.7533      4400\n",
      "         1.0     0.6367    0.6940    0.6641      2997\n",
      "\n",
      "    accuracy                         0.7156      7397\n",
      "   macro avg     0.7073    0.7121    0.7087      7397\n",
      "weighted avg     0.7207    0.7156    0.7172      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9129    0.8846    0.8986       806\n",
      "         1.0     0.7480    0.8023    0.7742       344\n",
      "\n",
      "    accuracy                         0.8600      1150\n",
      "   macro avg     0.8304    0.8435    0.8364      1150\n",
      "weighted avg     0.8636    0.8600    0.8614      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7465    0.6956    0.7201      3594\n",
      "         1.0     0.6225    0.6800    0.6500      2653\n",
      "\n",
      "    accuracy                         0.6890      6247\n",
      "   macro avg     0.6845    0.6878    0.6851      6247\n",
      "weighted avg     0.6938    0.6890    0.6903      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8414    0.8025    0.8215       324\n",
      "         1.0     0.6322    0.6918    0.6607       159\n",
      "\n",
      "    accuracy                         0.7660       483\n",
      "   macro avg     0.7368    0.7471    0.7411       483\n",
      "weighted avg     0.7725    0.7660    0.7685       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.06\n",
      "    Training Set Accuracy: 72.30\n",
      "    Validation Set Accuracy: 71.56\n",
      "    Validation Set with both Images and Texts: 86.00\n",
      "    Validation Set with only Texts: 68.90\n",
      "    Validation Set with unseen Images: 76.60\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7834    0.7322    0.7569     29306\n",
      "         1.0     0.6420    0.7035    0.6714     20010\n",
      "\n",
      "    accuracy                         0.7206     49316\n",
      "   macro avg     0.7127    0.7179    0.7142     49316\n",
      "weighted avg     0.7261    0.7206    0.7222     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7855    0.7328    0.7582     20463\n",
      "         1.0     0.6457    0.7088    0.6758     14058\n",
      "\n",
      "    accuracy                         0.7230     34521\n",
      "   macro avg     0.7156    0.7208    0.7170     34521\n",
      "weighted avg     0.7286    0.7230    0.7247     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7791    0.7313    0.7544      4443\n",
      "         1.0     0.6301    0.6883    0.6579      2955\n",
      "\n",
      "    accuracy                         0.7141      7398\n",
      "   macro avg     0.7046    0.7098    0.7062      7398\n",
      "weighted avg     0.7196    0.7141    0.7159      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8837    0.8911    0.8873       716\n",
      "         1.0     0.7719    0.7586    0.7652       348\n",
      "\n",
      "    accuracy                         0.8477      1064\n",
      "   macro avg     0.8278    0.8248    0.8263      1064\n",
      "weighted avg     0.8471    0.8477    0.8474      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7573    0.7006    0.7278      3727\n",
      "         1.0     0.6133    0.6789    0.6445      2607\n",
      "\n",
      "    accuracy                         0.6917      6334\n",
      "   macro avg     0.6853    0.6898    0.6861      6334\n",
      "weighted avg     0.6980    0.6917    0.6935      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8007    0.8065    0.8036       279\n",
      "         1.0     0.6471    0.6387    0.6429       155\n",
      "\n",
      "    accuracy                         0.7465       434\n",
      "   macro avg     0.7239    0.7226    0.7232       434\n",
      "weighted avg     0.7458    0.7465    0.7462       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.06\n",
      "    Training Set Accuracy: 72.30\n",
      "    Validation Set Accuracy: 71.41\n",
      "    Validation Set with both Images and Texts: 84.77\n",
      "    Validation Set with only Texts: 69.17\n",
      "    Validation Set with unseen Images: 74.65\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.42791107 0.5720889 ]\n",
      " [0.21470527 0.7852948 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.692187\n",
      "std          0.149410\n",
      "min          0.500003\n",
      "25%          0.567307\n",
      "50%          0.648478\n",
      "75%          0.805937\n",
      "max          0.999988\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.692833\n",
      "std          0.149744\n",
      "min          0.500003\n",
      "25%          0.567679\n",
      "50%          0.649352\n",
      "75%          0.807750\n",
      "max          0.999988\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.691669\n",
      "std         0.148299\n",
      "min         0.500024\n",
      "25%         0.567225\n",
      "50%         0.648780\n",
      "75%         0.803452\n",
      "max         0.999932\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', 'complex')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8063    0.7679    0.7866     29306\n",
      "         1.0     0.6822    0.7298    0.7052     20010\n",
      "\n",
      "    accuracy                         0.7525     49316\n",
      "   macro avg     0.7443    0.7489    0.7459     49316\n",
      "weighted avg     0.7560    0.7525    0.7536     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8147    0.7757    0.7948     20463\n",
      "         1.0     0.6948    0.7432    0.7182     14058\n",
      "\n",
      "    accuracy                         0.7625     34521\n",
      "   macro avg     0.7548    0.7595    0.7565     34521\n",
      "weighted avg     0.7659    0.7625    0.7636     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7878    0.7511    0.7691      4400\n",
      "         1.0     0.6580    0.7030    0.6798      2997\n",
      "\n",
      "    accuracy                         0.7316      7397\n",
      "   macro avg     0.7229    0.7271    0.7244      7397\n",
      "weighted avg     0.7352    0.7316    0.7329      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9279    0.9256    0.9267       806\n",
      "         1.0     0.8266    0.8314    0.8290       344\n",
      "\n",
      "    accuracy                         0.8974      1150\n",
      "   macro avg     0.8772    0.8785    0.8778      1150\n",
      "weighted avg     0.8976    0.8974    0.8975      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7546    0.7120    0.7327      3594\n",
      "         1.0     0.6376    0.6864    0.6611      2653\n",
      "\n",
      "    accuracy                         0.7011      6247\n",
      "   macro avg     0.6961    0.6992    0.6969      6247\n",
      "weighted avg     0.7049    0.7011    0.7023      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8241    0.8241    0.8241       324\n",
      "         1.0     0.6415    0.6415    0.6415       159\n",
      "\n",
      "    accuracy                         0.7640       483\n",
      "   macro avg     0.7328    0.7328    0.7328       483\n",
      "weighted avg     0.7640    0.7640    0.7640       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.25\n",
      "    Training Set Accuracy: 76.25\n",
      "    Validation Set Accuracy: 73.16\n",
      "    Validation Set with both Images and Texts: 89.74\n",
      "    Validation Set with only Texts: 70.11\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     0.8063    0.7679    0.7866     29306\n",
      "         1.0     0.6822    0.7298    0.7052     20010\n",
      "\n",
      "    accuracy                         0.7525     49316\n",
      "   macro avg     0.7443    0.7489    0.7459     49316\n",
      "weighted avg     0.7560    0.7525    0.7536     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8147    0.7757    0.7948     20463\n",
      "         1.0     0.6948    0.7432    0.7182     14058\n",
      "\n",
      "    accuracy                         0.7625     34521\n",
      "   macro avg     0.7548    0.7595    0.7565     34521\n",
      "weighted avg     0.7659    0.7625    0.7636     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7859    0.7484    0.7667      4443\n",
      "         1.0     0.6470    0.6934    0.6694      2955\n",
      "\n",
      "    accuracy                         0.7264      7398\n",
      "   macro avg     0.7164    0.7209    0.7180      7398\n",
      "weighted avg     0.7304    0.7264    0.7278      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9120    0.9260    0.9189       716\n",
      "         1.0     0.8427    0.8161    0.8292       348\n",
      "\n",
      "    accuracy                         0.8900      1064\n",
      "   macro avg     0.8773    0.8710    0.8741      1064\n",
      "weighted avg     0.8893    0.8900    0.8896      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7597    0.7142    0.7363      3727\n",
      "         1.0     0.6237    0.6770    0.6493      2607\n",
      "\n",
      "    accuracy                         0.6989      6334\n",
      "   macro avg     0.6917    0.6956    0.6928      6334\n",
      "weighted avg     0.7037    0.6989    0.7005      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7828    0.8136    0.7979       279\n",
      "         1.0     0.6389    0.5935    0.6154       155\n",
      "\n",
      "    accuracy                         0.7350       434\n",
      "   macro avg     0.7108    0.7036    0.7066       434\n",
      "weighted avg     0.7314    0.7350    0.7327       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.25\n",
      "    Training Set Accuracy: 76.25\n",
      "    Validation Set Accuracy: 72.64\n",
      "    Validation Set with both Images and Texts: 89.00\n",
      "    Validation Set with only Texts: 69.89\n",
      "    Validation Set with unseen Images: 73.50\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.9111076  0.08889232]\n",
      " [0.14371783 0.8562822 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.748443\n",
      "std          0.173989\n",
      "min          0.500014\n",
      "25%          0.585959\n",
      "50%          0.717128\n",
      "75%          0.931246\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.750170\n",
      "std          0.174873\n",
      "min          0.500014\n",
      "25%          0.586097\n",
      "50%          0.719463\n",
      "75%          0.936966\n",
      "max          1.000000\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.746260\n",
      "std         0.171657\n",
      "min         0.500016\n",
      "25%         0.588475\n",
      "50%         0.714835\n",
      "75%         0.920593\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"
    },
    {
     "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     0.7461    0.7618    0.7539     29306\n",
      "         1.0     0.6400    0.6202    0.6300     20010\n",
      "\n",
      "    accuracy                         0.7044     49316\n",
      "   macro avg     0.6931    0.6910    0.6919     49316\n",
      "weighted avg     0.7030    0.7044    0.7036     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7462    0.7606    0.7533     20463\n",
      "         1.0     0.6414    0.6233    0.6323     14058\n",
      "\n",
      "    accuracy                         0.7047     34521\n",
      "   macro avg     0.6938    0.6920    0.6928     34521\n",
      "weighted avg     0.7035    0.7047    0.7040     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7473    0.7691    0.7581      4400\n",
      "         1.0     0.6459    0.6183    0.6318      2997\n",
      "\n",
      "    accuracy                         0.7080      7397\n",
      "   macro avg     0.6966    0.6937    0.6949      7397\n",
      "weighted avg     0.7062    0.7080    0.7069      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8900    0.8933    0.8916       806\n",
      "         1.0     0.7478    0.7413    0.7445       344\n",
      "\n",
      "    accuracy                         0.8478      1150\n",
      "   macro avg     0.8189    0.8173    0.8181      1150\n",
      "weighted avg     0.8475    0.8478    0.8476      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7163    0.7412    0.7286      3594\n",
      "         1.0     0.6321    0.6023    0.6169      2653\n",
      "\n",
      "    accuracy                         0.6822      6247\n",
      "   macro avg     0.6742    0.6718    0.6727      6247\n",
      "weighted avg     0.6806    0.6822    0.6811      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8250    0.8148    0.8199       324\n",
      "         1.0     0.6319    0.6478    0.6398       159\n",
      "\n",
      "    accuracy                         0.7598       483\n",
      "   macro avg     0.7285    0.7313    0.7298       483\n",
      "weighted avg     0.7614    0.7598    0.7606       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.44\n",
      "    Training Set Accuracy: 70.47\n",
      "    Validation Set Accuracy: 70.80\n",
      "    Validation Set with both Images and Texts: 84.78\n",
      "    Validation Set with only Texts: 68.22\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     0.7461    0.7618    0.7539     29306\n",
      "         1.0     0.6400    0.6202    0.6300     20010\n",
      "\n",
      "    accuracy                         0.7044     49316\n",
      "   macro avg     0.6931    0.6910    0.6919     49316\n",
      "weighted avg     0.7030    0.7044    0.7036     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7462    0.7606    0.7533     20463\n",
      "         1.0     0.6414    0.6233    0.6323     14058\n",
      "\n",
      "    accuracy                         0.7047     34521\n",
      "   macro avg     0.6938    0.6920    0.6928     34521\n",
      "weighted avg     0.7035    0.7047    0.7040     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7444    0.7603    0.7523      4443\n",
      "         1.0     0.6276    0.6074    0.6174      2955\n",
      "\n",
      "    accuracy                         0.6992      7398\n",
      "   macro avg     0.6860    0.6839    0.6848      7398\n",
      "weighted avg     0.6977    0.6992    0.6984      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8518    0.8911    0.8710       716\n",
      "         1.0     0.7524    0.6810    0.7149       348\n",
      "\n",
      "    accuracy                         0.8224      1064\n",
      "   macro avg     0.8021    0.7860    0.7930      1064\n",
      "weighted avg     0.8193    0.8224    0.8199      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7231    0.7352    0.7291      3727\n",
      "         1.0     0.6122    0.5976    0.6048      2607\n",
      "\n",
      "    accuracy                         0.6786      6334\n",
      "   macro avg     0.6677    0.6664    0.6670      6334\n",
      "weighted avg     0.6775    0.6786    0.6780      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7723    0.8387    0.8041       279\n",
      "         1.0     0.6565    0.5548    0.6014       155\n",
      "\n",
      "    accuracy                         0.7373       434\n",
      "   macro avg     0.7144    0.6968    0.7028       434\n",
      "weighted avg     0.7309    0.7373    0.7317       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.44\n",
      "    Training Set Accuracy: 70.47\n",
      "    Validation Set Accuracy: 69.92\n",
      "    Validation Set with both Images and Texts: 82.24\n",
      "    Validation Set with only Texts: 67.86\n",
      "    Validation Set with unseen Images: 73.73\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.52024966 0.47975028]\n",
      " [0.24042313 0.75957686]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.685009\n",
      "std          0.146290\n",
      "min          0.500004\n",
      "25%          0.562422\n",
      "50%          0.640468\n",
      "75%          0.795672\n",
      "max          0.999971\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.685405\n",
      "std          0.146497\n",
      "min          0.500004\n",
      "25%          0.562796\n",
      "50%          0.640621\n",
      "75%          0.797127\n",
      "max          0.999971\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.685173\n",
      "std         0.146480\n",
      "min         0.500009\n",
      "25%         0.562701\n",
      "50%         0.640433\n",
      "75%         0.796866\n",
      "max         0.999911\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     0.6139    0.9882    0.7573     29306\n",
      "         1.0     0.8382    0.0896    0.1619     20010\n",
      "\n",
      "    accuracy                         0.6236     49316\n",
      "   macro avg     0.7261    0.5389    0.4596     49316\n",
      "weighted avg     0.7049    0.6236    0.5157     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6138    0.9902    0.7579     20463\n",
      "         1.0     0.8676    0.0933    0.1684     14058\n",
      "\n",
      "    accuracy                         0.6250     34521\n",
      "   macro avg     0.7407    0.5417    0.4631     34521\n",
      "weighted avg     0.7172    0.6250    0.5178     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6114    0.9839    0.7542      4400\n",
      "         1.0     0.7760    0.0821    0.1485      2997\n",
      "\n",
      "    accuracy                         0.6185      7397\n",
      "   macro avg     0.6937    0.5330    0.4513      7397\n",
      "weighted avg     0.6781    0.6185    0.5088      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8824    0.9119    0.8969       806\n",
      "         1.0     0.7760    0.7151    0.7443       344\n",
      "\n",
      "    accuracy                         0.8530      1150\n",
      "   macro avg     0.8292    0.8135    0.8206      1150\n",
      "weighted avg     0.8505    0.8530    0.8513      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     0.8006    0.8302    0.8152       324\n",
      "         1.0     0.6259    0.5786    0.6013       159\n",
      "\n",
      "    accuracy                         0.7474       483\n",
      "   macro avg     0.7132    0.7044    0.7082       483\n",
      "weighted avg     0.7431    0.7474    0.7448       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.36\n",
      "    Training Set Accuracy: 62.50\n",
      "    Validation Set Accuracy: 61.85\n",
      "    Validation Set with both Images and Texts: 85.30\n",
      "    Validation Set with only Texts: 57.53\n",
      "    Validation Set with unseen Images: 74.74\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\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     0.6139    0.9882    0.7573     29306\n",
      "         1.0     0.8382    0.0896    0.1619     20010\n",
      "\n",
      "    accuracy                         0.6236     49316\n",
      "   macro avg     0.7261    0.5389    0.4596     49316\n",
      "weighted avg     0.7049    0.6236    0.5157     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6138    0.9902    0.7579     20463\n",
      "         1.0     0.8676    0.0933    0.1684     14058\n",
      "\n",
      "    accuracy                         0.6250     34521\n",
      "   macro avg     0.7407    0.5417    0.4631     34521\n",
      "weighted avg     0.7172    0.6250    0.5178     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6163    0.9831    0.7577      4443\n",
      "         1.0     0.7588    0.0799    0.1445      2955\n",
      "\n",
      "    accuracy                         0.6223      7398\n",
      "   macro avg     0.6876    0.5315    0.4511      7398\n",
      "weighted avg     0.6733    0.6223    0.5128      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8513    0.8953    0.8727       716\n",
      "         1.0     0.7588    0.6782    0.7162       348\n",
      "\n",
      "    accuracy                         0.8242      1064\n",
      "   macro avg     0.8051    0.7867    0.7945      1064\n",
      "weighted avg     0.8210    0.8242    0.8215      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     0.7433    0.7993    0.7703       279\n",
      "         1.0     0.5821    0.5032    0.5398       155\n",
      "\n",
      "    accuracy                         0.6935       434\n",
      "   macro avg     0.6627    0.6513    0.6550       434\n",
      "weighted avg     0.6857    0.6935    0.6880       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 62.36\n",
      "    Training Set Accuracy: 62.50\n",
      "    Validation Set Accuracy: 62.23\n",
      "    Validation Set with both Images and Texts: 82.42\n",
      "    Validation Set with only Texts: 58.84\n",
      "    Validation Set with unseen Images: 69.35\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.5743039  0.42569613]\n",
      " [0.5743039  0.42569613]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.613081\n",
      "std          0.108416\n",
      "min          0.500039\n",
      "25%          0.574304\n",
      "50%          0.574304\n",
      "75%          0.574304\n",
      "max          0.999868\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.613394\n",
      "std          0.109044\n",
      "min          0.500221\n",
      "25%          0.574304\n",
      "50%          0.574304\n",
      "75%          0.574304\n",
      "max          0.999868\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.614227\n",
      "std         0.109449\n",
      "min         0.500039\n",
      "25%         0.574304\n",
      "50%         0.574304\n",
      "75%         0.574304\n",
      "max         0.999391\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": [
      "('text', 'average')\n",
      "\n",
      "Validation Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7495    0.7758    0.7624     29306\n",
      "         1.0     0.6539    0.6203    0.6367     20010\n",
      "\n",
      "    accuracy                         0.7127     49316\n",
      "   macro avg     0.7017    0.6981    0.6995     49316\n",
      "weighted avg     0.7107    0.7127    0.7114     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7500    0.7765    0.7630     20463\n",
      "         1.0     0.6570    0.6232    0.6397     14058\n",
      "\n",
      "    accuracy                         0.7141     34521\n",
      "   macro avg     0.7035    0.6999    0.7014     34521\n",
      "weighted avg     0.7121    0.7141    0.7128     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7467    0.7725    0.7594      4400\n",
      "         1.0     0.6482    0.6153    0.6313      2997\n",
      "\n",
      "    accuracy                         0.7088      7397\n",
      "   macro avg     0.6974    0.6939    0.6953      7397\n",
      "weighted avg     0.7068    0.7088    0.7075      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8380    0.8859    0.8613       806\n",
      "         1.0     0.6913    0.5988    0.6417       344\n",
      "\n",
      "    accuracy                         0.8000      1150\n",
      "   macro avg     0.7647    0.7423    0.7515      1150\n",
      "weighted avg     0.7941    0.8000    0.7956      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7257    0.7471    0.7362      3594\n",
      "         1.0     0.6431    0.6174    0.6300      2653\n",
      "\n",
      "    accuracy                         0.6920      6247\n",
      "   macro avg     0.6844    0.6822    0.6831      6247\n",
      "weighted avg     0.6906    0.6920    0.6911      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7918    0.8333    0.8120       324\n",
      "         1.0     0.6197    0.5535    0.5847       159\n",
      "\n",
      "    accuracy                         0.7412       483\n",
      "   macro avg     0.7058    0.6934    0.6984       483\n",
      "weighted avg     0.7351    0.7412    0.7372       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.27\n",
      "    Training Set Accuracy: 71.41\n",
      "    Validation Set Accuracy: 70.88\n",
      "    Validation Set with both Images and Texts: 80.00\n",
      "    Validation Set with only Texts: 69.20\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     0.7495    0.7758    0.7624     29306\n",
      "         1.0     0.6539    0.6203    0.6367     20010\n",
      "\n",
      "    accuracy                         0.7127     49316\n",
      "   macro avg     0.7017    0.6981    0.6995     49316\n",
      "weighted avg     0.7107    0.7127    0.7114     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7500    0.7765    0.7630     20463\n",
      "         1.0     0.6570    0.6232    0.6397     14058\n",
      "\n",
      "    accuracy                         0.7141     34521\n",
      "   macro avg     0.7035    0.6999    0.7014     34521\n",
      "weighted avg     0.7121    0.7141    0.7128     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7503    0.7756    0.7627      4443\n",
      "         1.0     0.6446    0.6118    0.6278      2955\n",
      "\n",
      "    accuracy                         0.7102      7398\n",
      "   macro avg     0.6974    0.6937    0.6953      7398\n",
      "weighted avg     0.7080    0.7102    0.7088      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8166    0.9078    0.8598       716\n",
      "         1.0     0.7537    0.5805    0.6558       348\n",
      "\n",
      "    accuracy                         0.8008      1064\n",
      "   macro avg     0.7852    0.7441    0.7578      1064\n",
      "weighted avg     0.7960    0.8008    0.7931      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7364    0.7502    0.7432      3727\n",
      "         1.0     0.6330    0.6160    0.6244      2607\n",
      "\n",
      "    accuracy                         0.6950      6334\n",
      "   macro avg     0.6847    0.6831    0.6838      6334\n",
      "weighted avg     0.6938    0.6950    0.6943      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7611    0.8566    0.8061       279\n",
      "         1.0     0.6667    0.5161    0.5818       155\n",
      "\n",
      "    accuracy                         0.7350       434\n",
      "   macro avg     0.7139    0.6864    0.6939       434\n",
      "weighted avg     0.7274    0.7350    0.7260       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 71.27\n",
      "    Training Set Accuracy: 71.41\n",
      "    Validation Set Accuracy: 71.02\n",
      "    Validation Set with both Images and Texts: 80.08\n",
      "    Validation Set with only Texts: 69.50\n",
      "    Validation Set with unseen Images: 73.50\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.6505771  0.3494228 ]\n",
      " [0.17056157 0.82943845]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.695854\n",
      "std          0.146375\n",
      "min          0.500006\n",
      "25%          0.571183\n",
      "50%          0.658200\n",
      "75%          0.809061\n",
      "max          0.999686\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.696068\n",
      "std          0.146551\n",
      "min          0.500006\n",
      "25%          0.571397\n",
      "50%          0.658092\n",
      "75%          0.808774\n",
      "max          0.999385\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.695536\n",
      "std         0.145814\n",
      "min         0.500016\n",
      "25%         0.571724\n",
      "50%         0.658924\n",
      "75%         0.809512\n",
      "max         0.999445\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     0.7726    0.7591    0.7658     29306\n",
      "         1.0     0.6559    0.6728    0.6642     20010\n",
      "\n",
      "    accuracy                         0.7240     49316\n",
      "   macro avg     0.7143    0.7159    0.7150     49316\n",
      "weighted avg     0.7253    0.7240    0.7246     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7756    0.7610    0.7683     20463\n",
      "         1.0     0.6614    0.6795    0.6704     14058\n",
      "\n",
      "    accuracy                         0.7278     34521\n",
      "   macro avg     0.7185    0.7203    0.7193     34521\n",
      "weighted avg     0.7291    0.7278    0.7284     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7641    0.7516    0.7578      4400\n",
      "         1.0     0.6439    0.6593    0.6515      2997\n",
      "\n",
      "    accuracy                         0.7142      7397\n",
      "   macro avg     0.7040    0.7055    0.7046      7397\n",
      "weighted avg     0.7154    0.7142    0.7147      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9015    0.8970    0.8993       806\n",
      "         1.0     0.7615    0.7703    0.7659       344\n",
      "\n",
      "    accuracy                         0.8591      1150\n",
      "   macro avg     0.8315    0.8337    0.8326      1150\n",
      "weighted avg     0.8596    0.8591    0.8594      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7328    0.7190    0.7258      3594\n",
      "         1.0     0.6288    0.6449    0.6368      2653\n",
      "\n",
      "    accuracy                         0.6875      6247\n",
      "   macro avg     0.6808    0.6820    0.6813      6247\n",
      "weighted avg     0.6887    0.6875    0.6880      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8266    0.8241    0.8253       324\n",
      "         1.0     0.6438    0.6478    0.6458       159\n",
      "\n",
      "    accuracy                         0.7660       483\n",
      "   macro avg     0.7352    0.7359    0.7356       483\n",
      "weighted avg     0.7664    0.7660    0.7662       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.40\n",
      "    Training Set Accuracy: 72.78\n",
      "    Validation Set Accuracy: 71.42\n",
      "    Validation Set with both Images and Texts: 85.91\n",
      "    Validation Set with only Texts: 68.75\n",
      "    Validation Set with unseen Images: 76.60\n",
      "    \n",
      "Test Set:\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7726    0.7591    0.7658     29306\n",
      "         1.0     0.6559    0.6728    0.6642     20010\n",
      "\n",
      "    accuracy                         0.7240     49316\n",
      "   macro avg     0.7143    0.7159    0.7150     49316\n",
      "weighted avg     0.7253    0.7240    0.7246     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7756    0.7610    0.7683     20463\n",
      "         1.0     0.6614    0.6795    0.6704     14058\n",
      "\n",
      "    accuracy                         0.7278     34521\n",
      "   macro avg     0.7185    0.7203    0.7193     34521\n",
      "weighted avg     0.7291    0.7278    0.7284     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7670    0.7574    0.7622      4443\n",
      "         1.0     0.6420    0.6541    0.6480      2955\n",
      "\n",
      "    accuracy                         0.7161      7398\n",
      "   macro avg     0.7045    0.7058    0.7051      7398\n",
      "weighted avg     0.7171    0.7161    0.7166      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8745    0.9050    0.8895       716\n",
      "         1.0     0.7895    0.7328    0.7601       348\n",
      "\n",
      "    accuracy                         0.8487      1064\n",
      "   macro avg     0.8320    0.8189    0.8248      1064\n",
      "weighted avg     0.8467    0.8487    0.8472      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7452    0.7290    0.7370      3727\n",
      "         1.0     0.6243    0.6437    0.6338      2607\n",
      "\n",
      "    accuracy                         0.6939      6334\n",
      "   macro avg     0.6847    0.6863    0.6854      6334\n",
      "weighted avg     0.6954    0.6939    0.6945      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7847    0.8100    0.7972       279\n",
      "         1.0     0.6370    0.6000    0.6179       155\n",
      "\n",
      "    accuracy                         0.7350       434\n",
      "   macro avg     0.7109    0.7050    0.7076       434\n",
      "weighted avg     0.7320    0.7350    0.7332       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 72.40\n",
      "    Training Set Accuracy: 72.78\n",
      "    Validation Set Accuracy: 71.61\n",
      "    Validation Set with both Images and Texts: 84.87\n",
      "    Validation Set with only Texts: 69.39\n",
      "    Validation Set with unseen Images: 73.50\n",
      "    \n",
      "Prediction for Golden Sentences:\n",
      "[[0.6474531  0.35254687]\n",
      " [0.18285145 0.8171485 ]]\n",
      "\n",
      "Full Dataset:\n",
      "count    49316.000000\n",
      "mean         0.696286\n",
      "std          0.153036\n",
      "min          0.500005\n",
      "25%          0.566147\n",
      "50%          0.651498\n",
      "75%          0.818108\n",
      "max          0.999996\n",
      "dtype: float64\n",
      "Training Set:\n",
      "count    34521.000000\n",
      "mean         0.696906\n",
      "std          0.153488\n",
      "min          0.500011\n",
      "25%          0.566300\n",
      "50%          0.651915\n",
      "75%          0.819904\n",
      "max          0.999996\n",
      "dtype: float64\n",
      "Validation Set:\n",
      "count    7397.000000\n",
      "mean        0.695708\n",
      "std         0.151608\n",
      "min         0.500069\n",
      "25%         0.567012\n",
      "50%         0.652839\n",
      "75%         0.814877\n",
      "max         0.999973\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/CLIP_ML_NL/', result_dir='results/CLIP_ML_NL/', embedding_size=512, early_stopping_criteria=20, num_epochs=20, seed=42, batch_size=32, embedding_type='multi', fusion_type='concat', 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=512, out_features=128, bias=True)\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       "  (fc_concat): Linear(in_features=1024, out_features=128, bias=True)\n",
       "  (fc_complex): Linear(in_features=2560, out_features=1024, 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, 512])"
      ]
     },
     "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     0.8154    0.7349    0.7731     29306\n",
      "         1.0     0.6608    0.7563    0.7053     20010\n",
      "\n",
      "    accuracy                         0.7436     49316\n",
      "   macro avg     0.7381    0.7456    0.7392     49316\n",
      "weighted avg     0.7527    0.7436    0.7456     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8244    0.7440    0.7822     20463\n",
      "         1.0     0.6737    0.7693    0.7183     14058\n",
      "\n",
      "    accuracy                         0.7543     34521\n",
      "   macro avg     0.7491    0.7567    0.7502     34521\n",
      "weighted avg     0.7630    0.7543    0.7562     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7953    0.7180    0.7547      4400\n",
      "         1.0     0.6377    0.7287    0.6802      2997\n",
      "\n",
      "    accuracy                         0.7223      7397\n",
      "   macro avg     0.7165    0.7233    0.7174      7397\n",
      "weighted avg     0.7314    0.7223    0.7245      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9375    0.8933    0.9149       806\n",
      "         1.0     0.7749    0.8605    0.8154       344\n",
      "\n",
      "    accuracy                         0.8835      1150\n",
      "   macro avg     0.8562    0.8769    0.8651      1150\n",
      "weighted avg     0.8889    0.8835    0.8851      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7612    0.6786    0.7176      3594\n",
      "         1.0     0.6204    0.7116    0.6629      2653\n",
      "\n",
      "    accuracy                         0.6927      6247\n",
      "   macro avg     0.6908    0.6951    0.6902      6247\n",
      "weighted avg     0.7014    0.6927    0.6944      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8362    0.7562    0.7942       324\n",
      "         1.0     0.5842    0.6981    0.6361       159\n",
      "\n",
      "    accuracy                         0.7371       483\n",
      "   macro avg     0.7102    0.7271    0.7151       483\n",
      "weighted avg     0.7532    0.7371    0.7421       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.36\n",
      "    Training Set Accuracy: 75.43\n",
      "    Validation Set Accuracy: 72.23\n",
      "    Validation Set with both Images and Texts: 88.35\n",
      "    Validation Set with only Texts: 69.27\n",
      "    Validation Set with unseen Images: 73.71\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8154    0.7349    0.7731     29306\n",
      "         1.0     0.6608    0.7563    0.7053     20010\n",
      "\n",
      "    accuracy                         0.7436     49316\n",
      "   macro avg     0.7381    0.7456    0.7392     49316\n",
      "weighted avg     0.7527    0.7436    0.7456     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8244    0.7440    0.7822     20463\n",
      "         1.0     0.6737    0.7693    0.7183     14058\n",
      "\n",
      "    accuracy                         0.7543     34521\n",
      "   macro avg     0.7491    0.7567    0.7502     34521\n",
      "weighted avg     0.7630    0.7543    0.7562     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7936    0.7097    0.7493      4443\n",
      "         1.0     0.6234    0.7225    0.6693      2955\n",
      "\n",
      "    accuracy                         0.7148      7398\n",
      "   macro avg     0.7085    0.7161    0.7093      7398\n",
      "weighted avg     0.7256    0.7148    0.7173      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9181    0.8925    0.9051       716\n",
      "         1.0     0.7908    0.8362    0.8128       348\n",
      "\n",
      "    accuracy                         0.8741      1064\n",
      "   macro avg     0.8544    0.8643    0.8590      1064\n",
      "weighted avg     0.8765    0.8741    0.8749      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7672    0.6745    0.7179      3727\n",
      "         1.0     0.6032    0.7073    0.6511      2607\n",
      "\n",
      "    accuracy                         0.6880      6334\n",
      "   macro avg     0.6852    0.6909    0.6845      6334\n",
      "weighted avg     0.6997    0.6880    0.6904      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7841    0.7419    0.7624       279\n",
      "         1.0     0.5765    0.6323    0.6031       155\n",
      "\n",
      "    accuracy                         0.7028       434\n",
      "   macro avg     0.6803    0.6871    0.6828       434\n",
      "weighted avg     0.7099    0.7028    0.7055       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 74.36\n",
      "    Training Set Accuracy: 75.43\n",
      "    Validation Set Accuracy: 71.48\n",
      "    Validation Set with both Images and Texts: 87.41\n",
      "    Validation Set with only Texts: 68.80\n",
      "    Validation Set with unseen Images: 70.28\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, 1024])"
      ]
     },
     "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, 1024])"
      ]
     },
     "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     0.6470    0.8950    0.7511     29306\n",
      "         1.0     0.6495    0.2849    0.3961     20010\n",
      "\n",
      "    accuracy                         0.6475     49316\n",
      "   macro avg     0.6482    0.5900    0.5736     49316\n",
      "weighted avg     0.6480    0.6475    0.6070     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6472    0.8958    0.7515     20463\n",
      "         1.0     0.6559    0.2892    0.4014     14058\n",
      "\n",
      "    accuracy                         0.6488     34521\n",
      "   macro avg     0.6516    0.5925    0.5765     34521\n",
      "weighted avg     0.6507    0.6488    0.6089     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6466    0.8952    0.7509      4400\n",
      "         1.0     0.6467    0.2816    0.3924      2997\n",
      "\n",
      "    accuracy                         0.6466      7397\n",
      "   macro avg     0.6467    0.5884    0.5716      7397\n",
      "weighted avg     0.6466    0.6466    0.6056      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7579    0.9789    0.8544       806\n",
      "         1.0     0.8440    0.2674    0.4062       344\n",
      "\n",
      "    accuracy                         0.7661      1150\n",
      "   macro avg     0.8010    0.6232    0.6303      1150\n",
      "weighted avg     0.7837    0.7661    0.7203      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6236    0.8765    0.7287      3594\n",
      "         1.0     0.6288    0.2835    0.3908      2653\n",
      "\n",
      "    accuracy                         0.6246      6247\n",
      "   macro avg     0.6262    0.5800    0.5597      6247\n",
      "weighted avg     0.6258    0.6246    0.5852      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7239    0.9630    0.8265       324\n",
      "         1.0     0.7692    0.2516    0.3791       159\n",
      "\n",
      "    accuracy                         0.7288       483\n",
      "   macro avg     0.7466    0.6073    0.6028       483\n",
      "weighted avg     0.7388    0.7288    0.6792       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 64.75\n",
      "    Training Set Accuracy: 64.88\n",
      "    Validation Set Accuracy: 64.66\n",
      "    Validation Set with both Images and Texts: 76.61\n",
      "    Validation Set with only Texts: 62.46\n",
      "    Validation Set with unseen Images: 72.88\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6470    0.8950    0.7511     29306\n",
      "         1.0     0.6495    0.2849    0.3961     20010\n",
      "\n",
      "    accuracy                         0.6475     49316\n",
      "   macro avg     0.6482    0.5900    0.5736     49316\n",
      "weighted avg     0.6480    0.6475    0.6070     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6472    0.8958    0.7515     20463\n",
      "         1.0     0.6559    0.2892    0.4014     14058\n",
      "\n",
      "    accuracy                         0.6488     34521\n",
      "   macro avg     0.6516    0.5925    0.5765     34521\n",
      "weighted avg     0.6507    0.6488    0.6089     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6466    0.8913    0.7495      4443\n",
      "         1.0     0.6209    0.2677    0.3741      2955\n",
      "\n",
      "    accuracy                         0.6422      7398\n",
      "   macro avg     0.6338    0.5795    0.5618      7398\n",
      "weighted avg     0.6363    0.6422    0.5995      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7213    0.9832    0.8322       716\n",
      "         1.0     0.8636    0.2184    0.3486       348\n",
      "\n",
      "    accuracy                         0.7331      1064\n",
      "   macro avg     0.7925    0.6008    0.5904      1064\n",
      "weighted avg     0.7679    0.7331    0.6740      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6325    0.8736    0.7337      3727\n",
      "         1.0     0.6029    0.2743    0.3770      2607\n",
      "\n",
      "    accuracy                         0.6269      6334\n",
      "   macro avg     0.6177    0.5739    0.5554      6334\n",
      "weighted avg     0.6203    0.6269    0.5869      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6801    0.9677    0.7988       279\n",
      "         1.0     0.7568    0.1806    0.2917       155\n",
      "\n",
      "    accuracy                         0.6866       434\n",
      "   macro avg     0.7184    0.5742    0.5452       434\n",
      "weighted avg     0.7075    0.6866    0.6177       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 64.75\n",
      "    Training Set Accuracy: 64.88\n",
      "    Validation Set Accuracy: 64.22\n",
      "    Validation Set with both Images and Texts: 73.31\n",
      "    Validation Set with only Texts: 62.69\n",
      "    Validation Set with unseen Images: 68.66\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, 2560])"
      ]
     },
     "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, 2560])"
      ]
     },
     "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     0.6350    0.8201    0.7158     29306\n",
      "         1.0     0.5403    0.3096    0.3936     20010\n",
      "\n",
      "    accuracy                         0.6130     49316\n",
      "   macro avg     0.5876    0.5649    0.5547     49316\n",
      "weighted avg     0.5966    0.6130    0.5851     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6347    0.8205    0.7158     20463\n",
      "         1.0     0.5448    0.3128    0.3974     14058\n",
      "\n",
      "    accuracy                         0.6137     34521\n",
      "   macro avg     0.5898    0.5666    0.5566     34521\n",
      "weighted avg     0.5981    0.6137    0.5861     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6339    0.8198    0.7150      4400\n",
      "         1.0     0.5354    0.3050    0.3886      2997\n",
      "\n",
      "    accuracy                         0.6112      7397\n",
      "   macro avg     0.5847    0.5624    0.5518      7397\n",
      "weighted avg     0.5940    0.6112    0.5827      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7146    0.9442    0.8135       806\n",
      "         1.0     0.4706    0.1163    0.1865       344\n",
      "\n",
      "    accuracy                         0.6965      1150\n",
      "   macro avg     0.5926    0.5302    0.5000      1150\n",
      "weighted avg     0.6416    0.6965    0.6259      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6154    0.7919    0.6925      3594\n",
      "         1.0     0.5388    0.3294    0.4089      2653\n",
      "\n",
      "    accuracy                         0.5955      6247\n",
      "   macro avg     0.5771    0.5607    0.5507      6247\n",
      "weighted avg     0.5829    0.5955    0.5721      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6790    0.9074    0.7768       324\n",
      "         1.0     0.4000    0.1258    0.1914       159\n",
      "\n",
      "    accuracy                         0.6501       483\n",
      "   macro avg     0.5395    0.5166    0.4841       483\n",
      "weighted avg     0.5871    0.6501    0.5841       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.30\n",
      "    Training Set Accuracy: 61.37\n",
      "    Validation Set Accuracy: 61.12\n",
      "    Validation Set with both Images and Texts: 69.65\n",
      "    Validation Set with only Texts: 59.55\n",
      "    Validation Set with unseen Images: 65.01\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6350    0.8201    0.7158     29306\n",
      "         1.0     0.5403    0.3096    0.3936     20010\n",
      "\n",
      "    accuracy                         0.6130     49316\n",
      "   macro avg     0.5876    0.5649    0.5547     49316\n",
      "weighted avg     0.5966    0.6130    0.5851     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6347    0.8205    0.7158     20463\n",
      "         1.0     0.5448    0.3128    0.3974     14058\n",
      "\n",
      "    accuracy                         0.6137     34521\n",
      "   macro avg     0.5898    0.5666    0.5566     34521\n",
      "weighted avg     0.5981    0.6137    0.5861     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6373    0.8190    0.7168      4443\n",
      "         1.0     0.5237    0.2992    0.3808      2955\n",
      "\n",
      "    accuracy                         0.6114      7398\n",
      "   macro avg     0.5805    0.5591    0.5488      7398\n",
      "weighted avg     0.5919    0.6114    0.5826      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6922    0.9330    0.7948       716\n",
      "         1.0     0.5152    0.1466    0.2282       348\n",
      "\n",
      "    accuracy                         0.6758      1064\n",
      "   macro avg     0.6037    0.5398    0.5115      1064\n",
      "weighted avg     0.6343    0.6758    0.6095      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6261    0.7972    0.7014      3727\n",
      "         1.0     0.5242    0.3195    0.3970      2607\n",
      "\n",
      "    accuracy                         0.6006      6334\n",
      "   macro avg     0.5752    0.5583    0.5492      6334\n",
      "weighted avg     0.5842    0.6006    0.5761      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6658    0.9211    0.7729       279\n",
      "         1.0     0.5417    0.1677    0.2562       155\n",
      "\n",
      "    accuracy                         0.6521       434\n",
      "   macro avg     0.6037    0.5444    0.5145       434\n",
      "weighted avg     0.6215    0.6521    0.5884       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.30\n",
      "    Training Set Accuracy: 61.37\n",
      "    Validation Set Accuracy: 61.14\n",
      "    Validation Set with both Images and Texts: 67.58\n",
      "    Validation Set with only Texts: 60.06\n",
      "    Validation Set with unseen Images: 65.21\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=512, out_features=128, bias=True)\n",
       "  (fc2): Linear(in_features=128, out_features=2, bias=True)\n",
       "  (fc_concat): Linear(in_features=1024, out_features=128, bias=True)\n",
       "  (fc_complex): Linear(in_features=2560, out_features=1024, 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     0.8323    0.7257    0.7754     29306\n",
      "         1.0     0.6618    0.7859    0.7185     20010\n",
      "\n",
      "    accuracy                         0.7501     49316\n",
      "   macro avg     0.7470    0.7558    0.7469     49316\n",
      "weighted avg     0.7631    0.7501    0.7523     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8423    0.7337    0.7842     20463\n",
      "         1.0     0.6736    0.8000    0.7314     14058\n",
      "\n",
      "    accuracy                         0.7607     34521\n",
      "   macro avg     0.7579    0.7668    0.7578     34521\n",
      "weighted avg     0.7736    0.7607    0.7627     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8112    0.7127    0.7588      4400\n",
      "         1.0     0.6420    0.7564    0.6945      2997\n",
      "\n",
      "    accuracy                         0.7304      7397\n",
      "   macro avg     0.7266    0.7346    0.7267      7397\n",
      "weighted avg     0.7426    0.7304    0.7327      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9352    0.9132    0.9240       806\n",
      "         1.0     0.8072    0.8517    0.8289       344\n",
      "\n",
      "    accuracy                         0.8948      1150\n",
      "   macro avg     0.8712    0.8824    0.8764      1150\n",
      "weighted avg     0.8969    0.8948    0.8956      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7795    0.6678    0.7193      3594\n",
      "         1.0     0.6231    0.7441    0.6782      2653\n",
      "\n",
      "    accuracy                         0.7002      6247\n",
      "   macro avg     0.7013    0.7059    0.6988      6247\n",
      "weighted avg     0.7131    0.7002    0.7019      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8355    0.7994    0.8170       324\n",
      "         1.0     0.6243    0.6792    0.6506       159\n",
      "\n",
      "    accuracy                         0.7598       483\n",
      "   macro avg     0.7299    0.7393    0.7338       483\n",
      "weighted avg     0.7660    0.7598    0.7622       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.01\n",
      "    Training Set Accuracy: 76.07\n",
      "    Validation Set Accuracy: 73.04\n",
      "    Validation Set with both Images and Texts: 89.48\n",
      "    Validation Set with only Texts: 70.02\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     0.8323    0.7257    0.7754     29306\n",
      "         1.0     0.6618    0.7859    0.7185     20010\n",
      "\n",
      "    accuracy                         0.7501     49316\n",
      "   macro avg     0.7470    0.7558    0.7469     49316\n",
      "weighted avg     0.7631    0.7501    0.7523     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8423    0.7337    0.7842     20463\n",
      "         1.0     0.6736    0.8000    0.7314     14058\n",
      "\n",
      "    accuracy                         0.7607     34521\n",
      "   macro avg     0.7579    0.7668    0.7578     34521\n",
      "weighted avg     0.7736    0.7607    0.7627     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8078    0.7018    0.7511      4443\n",
      "         1.0     0.6255    0.7489    0.6817      2955\n",
      "\n",
      "    accuracy                         0.7206      7398\n",
      "   macro avg     0.7166    0.7253    0.7164      7398\n",
      "weighted avg     0.7350    0.7206    0.7233      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.9207    0.9078    0.9142       716\n",
      "         1.0     0.8156    0.8391    0.8272       348\n",
      "\n",
      "    accuracy                         0.8853      1064\n",
      "   macro avg     0.8682    0.8735    0.8707      1064\n",
      "weighted avg     0.8863    0.8853    0.8857      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7825    0.6622    0.7173      3727\n",
      "         1.0     0.6041    0.7369    0.6639      2607\n",
      "\n",
      "    accuracy                         0.6929      6334\n",
      "   macro avg     0.6933    0.6995    0.6906      6334\n",
      "weighted avg     0.7091    0.6929    0.6953      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7934    0.7706    0.7818       279\n",
      "         1.0     0.6074    0.6387    0.6226       155\n",
      "\n",
      "    accuracy                         0.7235       434\n",
      "   macro avg     0.7004    0.7047    0.7022       434\n",
      "weighted avg     0.7269    0.7235    0.7250       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 75.01\n",
      "    Training Set Accuracy: 76.07\n",
      "    Validation Set Accuracy: 72.06\n",
      "    Validation Set with both Images and Texts: 88.53\n",
      "    Validation Set with only Texts: 69.29\n",
      "    Validation Set with unseen Images: 72.35\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, 1024])"
      ]
     },
     "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, 1024])"
      ]
     },
     "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     0.7625    0.7410    0.7516     29306\n",
      "         1.0     0.6357    0.6620    0.6486     20010\n",
      "\n",
      "    accuracy                         0.7089     49316\n",
      "   macro avg     0.6991    0.7015    0.7001     49316\n",
      "weighted avg     0.7111    0.7089    0.7098     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7656    0.7447    0.7550     20463\n",
      "         1.0     0.6426    0.6682    0.6551     14058\n",
      "\n",
      "    accuracy                         0.7135     34521\n",
      "   macro avg     0.7041    0.7064    0.7051     34521\n",
      "weighted avg     0.7155    0.7135    0.7143     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7569    0.7336    0.7451      4400\n",
      "         1.0     0.6258    0.6540    0.6396      2997\n",
      "\n",
      "    accuracy                         0.7014      7397\n",
      "   macro avg     0.6913    0.6938    0.6923      7397\n",
      "weighted avg     0.7038    0.7014    0.7023      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8900    0.8536    0.8714       806\n",
      "         1.0     0.6870    0.7529    0.7184       344\n",
      "\n",
      "    accuracy                         0.8235      1150\n",
      "   macro avg     0.7885    0.8033    0.7949      1150\n",
      "weighted avg     0.8293    0.8235    0.8257      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7274    0.7067    0.7169      3594\n",
      "         1.0     0.6174    0.6412    0.6291      2653\n",
      "\n",
      "    accuracy                         0.6789      6247\n",
      "   macro avg     0.6724    0.6739    0.6730      6247\n",
      "weighted avg     0.6807    0.6789    0.6796      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8300    0.7685    0.7981       324\n",
      "         1.0     0.5902    0.6792    0.6316       159\n",
      "\n",
      "    accuracy                         0.7391       483\n",
      "   macro avg     0.7101    0.7239    0.7148       483\n",
      "weighted avg     0.7510    0.7391    0.7433       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.89\n",
      "    Training Set Accuracy: 71.35\n",
      "    Validation Set Accuracy: 70.14\n",
      "    Validation Set with both Images and Texts: 82.35\n",
      "    Validation Set with only Texts: 67.89\n",
      "    Validation Set with unseen Images: 73.91\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7625    0.7410    0.7516     29306\n",
      "         1.0     0.6357    0.6620    0.6486     20010\n",
      "\n",
      "    accuracy                         0.7089     49316\n",
      "   macro avg     0.6991    0.7015    0.7001     49316\n",
      "weighted avg     0.7111    0.7089    0.7098     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7656    0.7447    0.7550     20463\n",
      "         1.0     0.6426    0.6682    0.6551     14058\n",
      "\n",
      "    accuracy                         0.7135     34521\n",
      "   macro avg     0.7041    0.7064    0.7051     34521\n",
      "weighted avg     0.7155    0.7135    0.7143     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7538    0.7310    0.7422      4443\n",
      "         1.0     0.6131    0.6409    0.6267      2955\n",
      "\n",
      "    accuracy                         0.6951      7398\n",
      "   macro avg     0.6835    0.6860    0.6845      7398\n",
      "weighted avg     0.6976    0.6951    0.6961      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8651    0.8506    0.8577       716\n",
      "         1.0     0.7028    0.7270    0.7147       348\n",
      "\n",
      "    accuracy                         0.8102      1064\n",
      "   macro avg     0.7839    0.7888    0.7862      1064\n",
      "weighted avg     0.8120    0.8102    0.8110      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7320    0.7081    0.7199      3727\n",
      "         1.0     0.6013    0.6295    0.6151      2607\n",
      "\n",
      "    accuracy                         0.6757      6334\n",
      "   macro avg     0.6667    0.6688    0.6675      6334\n",
      "weighted avg     0.6782    0.6757    0.6767      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.8045    0.7670    0.7853       279\n",
      "         1.0     0.6131    0.6645    0.6378       155\n",
      "\n",
      "    accuracy                         0.7304       434\n",
      "   macro avg     0.7088    0.7158    0.7115       434\n",
      "weighted avg     0.7361    0.7304    0.7326       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 70.89\n",
      "    Training Set Accuracy: 71.35\n",
      "    Validation Set Accuracy: 69.51\n",
      "    Validation Set with both Images and Texts: 81.02\n",
      "    Validation Set with only Texts: 67.57\n",
      "    Validation Set with unseen Images: 73.04\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, 2560])"
      ]
     },
     "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, 2560])"
      ]
     },
     "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     0.6350    0.8201    0.7158     29306\n",
      "         1.0     0.5403    0.3096    0.3936     20010\n",
      "\n",
      "    accuracy                         0.6130     49316\n",
      "   macro avg     0.5876    0.5649    0.5547     49316\n",
      "weighted avg     0.5966    0.6130    0.5851     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6347    0.8205    0.7158     20463\n",
      "         1.0     0.5448    0.3128    0.3974     14058\n",
      "\n",
      "    accuracy                         0.6137     34521\n",
      "   macro avg     0.5898    0.5666    0.5566     34521\n",
      "weighted avg     0.5981    0.6137    0.5861     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6339    0.8198    0.7150      4400\n",
      "         1.0     0.5354    0.3050    0.3886      2997\n",
      "\n",
      "    accuracy                         0.6112      7397\n",
      "   macro avg     0.5847    0.5624    0.5518      7397\n",
      "weighted avg     0.5940    0.6112    0.5827      7397\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.7146    0.9442    0.8135       806\n",
      "         1.0     0.4706    0.1163    0.1865       344\n",
      "\n",
      "    accuracy                         0.6965      1150\n",
      "   macro avg     0.5926    0.5302    0.5000      1150\n",
      "weighted avg     0.6416    0.6965    0.6259      1150\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6154    0.7919    0.6925      3594\n",
      "         1.0     0.5388    0.3294    0.4089      2653\n",
      "\n",
      "    accuracy                         0.5955      6247\n",
      "   macro avg     0.5771    0.5607    0.5507      6247\n",
      "weighted avg     0.5829    0.5955    0.5721      6247\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6790    0.9074    0.7768       324\n",
      "         1.0     0.4000    0.1258    0.1914       159\n",
      "\n",
      "    accuracy                         0.6501       483\n",
      "   macro avg     0.5395    0.5166    0.4841       483\n",
      "weighted avg     0.5871    0.6501    0.5841       483\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.30\n",
      "    Training Set Accuracy: 61.37\n",
      "    Validation Set Accuracy: 61.12\n",
      "    Validation Set with both Images and Texts: 69.65\n",
      "    Validation Set with only Texts: 59.55\n",
      "    Validation Set with unseen Images: 65.01\n",
      "    \n",
      "Zero-shot using new multi-modal embedding (test):\n",
      "Overall Dataset:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6350    0.8201    0.7158     29306\n",
      "         1.0     0.5403    0.3096    0.3936     20010\n",
      "\n",
      "    accuracy                         0.6130     49316\n",
      "   macro avg     0.5876    0.5649    0.5547     49316\n",
      "weighted avg     0.5966    0.6130    0.5851     49316\n",
      "\n",
      "Training Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6347    0.8205    0.7158     20463\n",
      "         1.0     0.5448    0.3128    0.3974     14058\n",
      "\n",
      "    accuracy                         0.6137     34521\n",
      "   macro avg     0.5898    0.5666    0.5566     34521\n",
      "weighted avg     0.5981    0.6137    0.5861     34521\n",
      "\n",
      "Validation Set:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6373    0.8190    0.7168      4443\n",
      "         1.0     0.5237    0.2992    0.3808      2955\n",
      "\n",
      "    accuracy                         0.6114      7398\n",
      "   macro avg     0.5805    0.5591    0.5488      7398\n",
      "weighted avg     0.5919    0.6114    0.5826      7398\n",
      "\n",
      "Validation Set with both Images and Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6922    0.9330    0.7948       716\n",
      "         1.0     0.5152    0.1466    0.2282       348\n",
      "\n",
      "    accuracy                         0.6758      1064\n",
      "   macro avg     0.6037    0.5398    0.5115      1064\n",
      "weighted avg     0.6343    0.6758    0.6095      1064\n",
      "\n",
      "Validation Set with only Texts:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6261    0.7972    0.7014      3727\n",
      "         1.0     0.5242    0.3195    0.3970      2607\n",
      "\n",
      "    accuracy                         0.6006      6334\n",
      "   macro avg     0.5752    0.5583    0.5492      6334\n",
      "weighted avg     0.5842    0.6006    0.5761      6334\n",
      "\n",
      "Validation Set with unseen Images:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0     0.6658    0.9211    0.7729       279\n",
      "         1.0     0.5417    0.1677    0.2562       155\n",
      "\n",
      "    accuracy                         0.6521       434\n",
      "   macro avg     0.6037    0.5444    0.5145       434\n",
      "weighted avg     0.6215    0.6521    0.5884       434\n",
      "\n",
      "\n",
      "    Overall Accuracy: 61.30\n",
      "    Training Set Accuracy: 61.37\n",
      "    Validation Set Accuracy: 61.14\n",
      "    Validation Set with both Images and Texts: 67.58\n",
      "    Validation Set with only Texts: 60.06\n",
      "    Validation Set with unseen Images: 65.21\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": 65,
   "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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