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Aphantasia Drawing Analysis\n","This analysis uses the [Aphantasia Drawing Dataset](https://huggingface.co/datasets/jmc255/aphantasia_drawing_dataset) on Hugging Face to try and find any patterns in the drawings between individuals with aphantasia (inability to form visual images) and without. The data includes drawings of a kitchen, living room, and bedroom, each drawn from memory (`memory`) and while looking at the actual image (`perception`). For a full description of the dataset you can visit the link above.\n","\n","This analysis is done by using OpenAI's ViT CLIP model for feature extraction and cosine similarity to measure how good a drawing is to the actual images from the feature embeddings.\n","\n","Using these similarity scores I see if I can classify aphantasia and also do an ANOVA to see if I can mimic the results of the paper the data was collected from."],"metadata":{"id":"3WFYVBoANBsp"}},{"cell_type":"markdown","source":["#### Load Packages and Dataset"],"metadata":{"id":"KXE7tNlIl8XL"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"pHhxcej4BWL-"},"outputs":[],"source":["!pip install -q datasets\n","from datasets import load_dataset\n","import pandas as pd\n","import numpy as np\n","from PIL import Image\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import plotly.express as px\n","\n","# Hugging Face Dataset\n","dataset = load_dataset(\"jmc255/aphantasia_drawing_dataset\", trust_remote_code=True)"]},{"cell_type":"code","source":["actual_imgs = dataset[\"train\"][\"image\"][0] # Dictionary of actual images"],"metadata":{"id":"Fb3R6Kugpg--"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#### Load ViT CLIP Model and Calculate Similarites\n","The [ViT CLIP model](https://huggingface.co/openai/clip-vit-base-patch32) from OpenAI was originally trained on image-text pairs and can be used for zero-shot image classification. In my case I am using it as a feature extractor to get embedding vectors from the drawings and actual images. From the embeddings I calculate the cosine similarity between the drawings and actual images to get a similarity score.\n","\n","Cosine similarity basically measures how close 2 vectors are in a vector space:\n","\n","$\\cos(\\theta) = \\frac{\\mathbf{A} \\cdot \\mathbf{B}}{\\|\\mathbf{A}\\| \\|\\mathbf{B}\\|}$\n","\n"],"metadata":{"id":"_rnmbFBOmIbX"}},{"cell_type":"code","source":["import torch\n","from transformers import CLIPImageProcessor, CLIPModel, CLIPProcessor\n","\n","model_ID = \"openai/clip-vit-base-patch32\"\n","model = CLIPModel.from_pretrained(model_ID)\n","preprocess = CLIPImageProcessor.from_pretrained(model_ID)"],"metadata":{"id":"iP_ZOqvvDoze","colab":{"base_uri":"https://localhost:8080/","height":168,"referenced_widgets":["b5c22da1fe364821bb41657e57fe232f","18f7771eb6a94f83af23b65dc7d14172","bb568f7a8baf466dbef44145af78ff64","82afeee502434a00a832e57271a88c9d","5b224814297441118032ac2e90d0a944","9fae3e3bbdb14c488e3b4e468dd1a263","7dbe3fb6dd2147ada73587ee065a2ab0","aa9c5f378f8e4ac68692ac888822eb2d","842cfb6973ec451eb530646e623bf7a9","1d793b75bde14544846af9185a29c8be","6071c27995de492cb837a35069800604","527f6a6f40484b59993082ba1f8b3d7b","41c1ef7d32b646adabbd21dc6baec2ce","b28e001c1669439789e7fb56e2254863","96563e713fbb4d44b5014708f28f43ff","33cc4946e0894548af46c936db23ffe2","f625aba3421b4fc6b0b5fc5827471a4f","807e515826dc4e00a9825794084d817f","a5ea5ee97b314a98b8710a13988b63db","f067efd988204a05b19812cd4223c918","63a8be3a5d384835b9a6cd3e4939a413","32844eb6bd4b4f409bf01bd724f976a3","797baeaf58f44a3d9e4dbdd61294cfdf","b020811557b04e9086d0bad0477165ae","7fcbddb67ab846d58f91d04569e281d8","9ebfa41d6316401dab2a076650cf5ece","430f2b11c03647048fe101ea855b6cac","582c0ffe13f34289b9850a2ed3ed2c27","8f80bf53e7b1498fbfaf0ed452ad1a77","0ce0424ade8d45db83b6c2616e095fc0","91577a802ad74c65a6943c4edaed2dbf","8741228e068e4fb1a9a6cd89f0122dbf","a306b2b6379e490086e1d6504fbd73f0"]},"executionInfo":{"status":"ok","timestamp":1714832397004,"user_tz":240,"elapsed":34688,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"outputId":"4e868c2a-527f-4b59-a80a-c46335bcf51a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/4.19k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b5c22da1fe364821bb41657e57fe232f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["pytorch_model.bin:   0%|          | 0.00/605M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"527f6a6f40484b59993082ba1f8b3d7b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["preprocessor_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"797baeaf58f44a3d9e4dbdd61294cfdf"}},"metadata":{}}]},{"cell_type":"code","source":["def preprocess_image(image):\n","  image = image.convert('L') # Convert to grayscale\n","  image = preprocess(image, return_tensors=\"pt\") #preprocess image\n","  return image"],"metadata":{"id":"EQfmIR_tDssZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Compute simiarity score between drawings and actual images\n","def compute_similarity(img1,img2):\n","  img1_preprocessed = preprocess_image(img1)['pixel_values']\n","  img2_preprocessed  = preprocess_image(img2)['pixel_values']\n","  with torch.no_grad():\n","    embedding_a = model.get_image_features(img1_preprocessed)\n","    embedding_b = model.get_image_features(img2_preprocessed)\n","  similarity_score = torch.nn.functional.cosine_similarity(embedding_a, embedding_b)\n","  return similarity_score.item()"],"metadata":{"id":"GESWEZUicuYt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Extract Similarity Scores from all the drawings\n","def get_similarities(subject_dict):\n","  drawings = subject_dict[\"drawings\"]\n","  similarities = {\n","      \"kitchen\": [],\n","      \"bedroom\": [],\n","      'livingroom': []\n","  }\n","  for room in [\"kitchen\",\"livingroom\",\"bedroom\"]:\n","    mod_similarities = {\"memory\": np.nan, \"perception\": np.nan}\n","\n","    for t in [\"memory\", \"perception\"]:\n","      if drawings[room][t]:\n","        similarity = compute_similarity(drawings[room][t], actual_imgs[room])\n","        mod_similarities[t] = similarity\n","\n","    similarities[room] = mod_similarities\n","  return {\n","      'subject_id': subject_dict[\"subject_id\"],\n","      'treatment': subject_dict[\"treatment\"],\n","      'drawings': subject_dict[\"drawings\"],\n","      'similarity': similarities\n","  }"],"metadata":{"id":"FB00XBNVFm1M"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["new_data = []\n","for row in iter(dataset[\"train\"]):\n","  new_data.append(get_similarities(row))\n","\n","similarities_df = pd.json_normalize(new_data)\n","rename_mapping = {\n","    \"similarity.kitchen.memory\": \"kitchen_mem\",\n","    \"similarity.kitchen.perception\": 'kitchen_percep',\n","    \"similarity.bedroom.memory\": 'bedroom_mem',\n","    \"similarity.bedroom.perception\": 'bedroom_percep',\n","    \"similarity.livingroom.memory\": 'livingroom_mem',\n","    \"similarity.livingroom.perception\": 'livingroom_percep',\n","    'drawings.kitchen.perception': 'kitchen_percep_drawing',\n","    'drawings.kitchen.memory': 'kitchen_mem_drawing',\n","    'drawings.livingroom.perception': 'livingroom_percep_drawing',\n","    'drawings.livingroom.memory': 'livingroom_mem_drawing',\n","    'drawings.bedroom.perception': 'bedroom_percep_drawing',\n","    'drawings.bedroom.memory': 'bedroom_mem_drawing'\n","}\n","similarities_df.rename(columns = rename_mapping, inplace = True)\n","all_cols = [col for col in similarities_df.columns if \"draw\" not in col]\n","all_cols = all_cols[2:]"],"metadata":{"id":"wdHMwCQPIH_K"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#### Summary Statistics"],"metadata":{"id":"FjlUhD3Z_RUQ"}},{"cell_type":"markdown","source":["Overall the mean for perception drawings between the rooms are higher than the mean for memory drawings which makes sense since the perception drawings were done while looking at the image.\n","\n"],"metadata":{"id":"a6p37NndAgZv"}},{"cell_type":"code","source":["similarities_df[[\n","    'kitchen_mem', 'kitchen_percep',\n","    'bedroom_mem',\t'bedroom_percep',\n","    'livingroom_mem',\t'livingroom_percep'\n","]].describe().round(3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"Tw0cnyuv8dFf","executionInfo":{"status":"ok","timestamp":1714832806425,"user_tz":240,"elapsed":13,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"outputId":"7f605f5a-6a6b-4fd2-f642-1c981402fe03"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["       kitchen_mem  kitchen_percep  bedroom_mem  bedroom_percep  \\\n","count      113.000         111.000      110.000         113.000   \n","mean         0.559           0.585        0.588           0.639   \n","std          0.040           0.036        0.054           0.054   \n","min          0.450           0.517        0.445           0.474   \n","25%          0.532           0.558        0.561           0.610   \n","50%          0.560           0.583        0.590           0.644   \n","75%          0.581           0.610        0.617           0.669   \n","max          0.672           0.666        0.759           0.752   \n","\n","       livingroom_mem  livingroom_percep  \n","count         111.000            112.000  \n","mean            0.613              0.676  \n","std             0.053              0.060  \n","min             0.461              0.454  \n","25%             0.574              0.643  \n","50%             0.618              0.672  \n","75%             0.649              0.721  \n","max             0.724              0.835  "],"text/html":["\n","  <div id=\"df-ccf32c04-7c84-49bc-a26a-2802ba5ac0db\" class=\"colab-df-container\">\n","    <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>kitchen_mem</th>\n","      <th>kitchen_percep</th>\n","      <th>bedroom_mem</th>\n","      <th>bedroom_percep</th>\n","      <th>livingroom_mem</th>\n","      <th>livingroom_percep</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>113.000</td>\n","      <td>111.000</td>\n","      <td>110.000</td>\n","      <td>113.000</td>\n","      <td>111.000</td>\n","      <td>112.000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.559</td>\n","      <td>0.585</td>\n","      <td>0.588</td>\n","      <td>0.639</td>\n","      <td>0.613</td>\n","      <td>0.676</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.040</td>\n","      <td>0.036</td>\n","      <td>0.054</td>\n","      <td>0.054</td>\n","      <td>0.053</td>\n","      <td>0.060</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.450</td>\n","      <td>0.517</td>\n","      <td>0.445</td>\n","      <td>0.474</td>\n","      <td>0.461</td>\n","      <td>0.454</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.532</td>\n","      <td>0.558</td>\n","      <td>0.561</td>\n","      <td>0.610</td>\n","      <td>0.574</td>\n","      <td>0.643</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.560</td>\n","      <td>0.583</td>\n","      <td>0.590</td>\n","      <td>0.644</td>\n","      <td>0.618</td>\n","      <td>0.672</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>0.581</td>\n","      <td>0.610</td>\n","      <td>0.617</td>\n","      <td>0.669</td>\n","      <td>0.649</td>\n","      <td>0.721</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>0.672</td>\n","      <td>0.666</td>\n","      <td>0.759</td>\n","      <td>0.752</td>\n","      <td>0.724</td>\n","      <td>0.835</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ccf32c04-7c84-49bc-a26a-2802ba5ac0db')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 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   0.454   \n","\n","                   livingroom_mem  livingroom_percep  \n","kitchen_mem                 0.420              0.321  \n","kitchen_percep              0.077              0.269  \n","bedroom_mem                 0.424              0.194  \n","bedroom_percep              0.087              0.454  \n","livingroom_mem              1.000              0.231  \n","livingroom_percep           0.231              1.000  "],"text/html":["\n","  <div id=\"df-33954cfb-2044-48d5-819c-257a8a521564\" class=\"colab-df-container\">\n","    <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>kitchen_mem</th>\n","      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However the similarity scores for the control group with the memory drawings are higher across all rooms."],"metadata":{"id":"a-vqv080BaNr"}},{"cell_type":"code","source":["summary_percep = similarities_df.groupby('treatment').agg({\"kitchen_percep\":['mean'],\"livingroom_percep\":['mean'],\"bedroom_percep\":['mean']}).round(3)\n","summary_mem = similarities_df.groupby('treatment').agg({\"kitchen_mem\":['mean'],\"livingroom_mem\":['mean'],\"bedroom_mem\":['mean']}).round(3)\n","summary_percep.columns = ['_'.join(col).strip() for col in summary_percep.columns.values]\n","summary_mem.columns = ['_'.join(col).strip() for col in summary_mem.columns.values]"],"metadata":{"id":"ovlln04ebI_H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Perception Similarity Scores between Aphantasia and Control\n","print(summary_percep)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ynHDqxhCXZaJ","executionInfo":{"status":"ok","timestamp":1714832806598,"user_tz":240,"elapsed":8,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"outputId":"39315ab7-6c2d-404a-eece-af05400ee378"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["            kitchen_percep_mean  livingroom_percep_mean  bedroom_percep_mean\n","treatment                                                                   \n","Aphantasia                0.590                   0.670                0.638\n","Control                   0.578                   0.684                0.640\n"]}]},{"cell_type":"code","source":["# Memory Similarity Scores between Aphantasia and Control\n","print(summary_mem)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LaVUAAnZbXM9","executionInfo":{"status":"ok","timestamp":1714832806598,"user_tz":240,"elapsed":7,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"outputId":"af27db3a-6187-4984-99f0-96b2b12476e4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["            kitchen_mem_mean  livingroom_mem_mean  bedroom_mem_mean\n","treatment                                                          \n","Aphantasia             0.551                0.605             0.573\n","Control                0.568                0.622             0.606\n"]}]},{"cell_type":"markdown","source":["##### Plots\n","The histograms for kitchen  (kitchen_percep) drawings and living room memory (livingroom_mem) drawings stand out between the 2 groups. Aphantasia is shifted to the right of control for kitchen perception and is shifted to the the left for living room memory."],"metadata":{"id":"DxgIpcXvCXPg"}},{"cell_type":"code","source":["fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n","\n","for i, room in enumerate([\"kitchen_percep\", \"bedroom_percep\", \"livingroom_percep\"]):\n","    sns.histplot(data=similarities_df, x=room, hue=\"treatment\", ax=axes[i], kde=True)\n","    axes[i].set_title(f\"{room} Histogram\")\n","    axes[i].set_xlabel(\"Value\")\n","    axes[i].set_ylabel(\"Frequency\")\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":307},"id":"rmWKZp5ZcYK6","executionInfo":{"status":"ok","timestamp":1714832807815,"user_tz":240,"elapsed":1222,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"outputId":"5d26d794-e5ab-4561-b8b4-2ca60a706893"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x300 with 3 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\n"},"metadata":{}}]},{"cell_type":"code","source":["fig, axes = plt.subplots(1, 3, figsize=(10, 3))\n","\n","for i, room in enumerate([\"kitchen_mem\", \"bedroom_mem\", \"livingroom_mem\"]):\n","    sns.histplot(data=similarities_df, x=room, hue=\"treatment\", ax=axes[i], kde=True)\n","    axes[i].set_title(f\"{room} Histogram\")\n","    axes[i].set_xlabel(\"Value\")\n","    axes[i].set_ylabel(\"Frequency\")\n","\n","plt.tight_layout()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":307},"id":"wYDyODclcL8w","executionInfo":{"status":"ok","timestamp":1714832809195,"user_tz":240,"elapsed":1385,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"outputId":"0b80c882-df11-48f1-8483-266bb49598da"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x300 with 3 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["fig = px.scatter_3d(similarities_df, x='kitchen_mem', y='livingroom_mem', z='bedroom_mem', color='treatment',\n","                    color_discrete_map={'Aphantasia': 'red', 'Control': 'blue'},\n","                    title='Memory Similarities by Group', width = 600, height = 500)\n","\n","fig.update_layout(scene=dict(xaxis_title='Kitchen', yaxis_title='Living Room', zaxis_title='Bedroom'))\n","fig.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":517},"id":"Zfu4TBQubtCP","executionInfo":{"status":"ok","timestamp":1714832810626,"user_tz":240,"elapsed":1437,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"outputId":"1f6d4ea8-2dfc-4416-b5aa-6182efacc8b5"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/html":["<html>\n","<head><meta charset=\"utf-8\" /></head>\n","<body>\n","    <div>            <script 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round(x*100,2))\n","  top_bottom_dict = top_bottom_df.to_dict(orient = 'index')\n","\n","  fig, axes = plt.subplots(1, 4, figsize=(7, 3))\n","  ranking = {1: 'First', 2: 'Second', 3: 'Third'}\n","  for i in range(3):\n","    axes[i+1].imshow(np.array(top_bottom_dict[i+1]['Drawing']))\n","    axes[i+1].axis('off')\n","    axes[i+1].set_title(f\"{ranking[i+1]}: {top_bottom_dict[i+1]['Similarity']}\")\n","  axes[0].imshow(actual_imgs[room])\n","  axes[0].axis('off')\n","  axes[0].set_title(f'Actual {room}')\n","  plt.subplots_adjust(left=0.1)\n","  fig.suptitle(f\"{r} Drawings of {room} for {typ}\", fontsize=12, color='black')\n","  path = column + \"_\" + r + \".png\"\n","  plt.savefig(path)\n","  plt.close()"],"metadata":{"id":"1p0-50FV9WmY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from itertools import product\n","\n","for col, typ in product(all_cols,[True,False]):\n","  get_top_bottom(col, top = typ)"],"metadata":{"id":"ZyaATlP1IJVT"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### **Living Room**\n","Perception | Memory\n","--- | ---\n","![img](https://drive.google.com/uc?id=1-d_t_7Hp2_4KPi6oB-UP8Ot9PRIYD5mG) | ![img](https://drive.google.com/uc?id=1-DMyAxhgXnj27GTH1VLQZ_vH-Yrv8JtN)\n","![img](https://drive.google.com/uc?id=1-ReAvAojCotmBJ8btlxHOeG7PaxGloDg) | ![img](https://drive.google.com/uc?id=1-DfrMJW_1uBeUGfPLq2FBP8UheYlDYXp)\n","\n"],"metadata":{"id":"w0UihB_lSmoh"}},{"cell_type":"markdown","source":["### **Bedroom**\n","Perception | Memory\n","--- | ---\n","![img](https://drive.google.com/uc?id=1--6B7I0ZNA1qw6y4nC9W9cM08YLuv3M-) | ![img](https://drive.google.com/uc?id=1-DMyAxhgXnj27GTH1VLQZ_vH-Yrv8JtN)\n","![img](https://drive.google.com/uc?id=1-MgzKZiKjQO-K-W20VVJwr2wOljE7Zp2) | ![img](https://drive.google.com/uc?id=1-477dfBgoU7lcc9AYlaiQUEMRJzqfjs3)"],"metadata":{"id":"x4ngc9m5dNDi"}},{"cell_type":"markdown","source":["### **Kitchen**\n","Perception | Memory\n","--- | ---\n","![img](https://drive.google.com/uc?id=17RqgTTSnJxA3-RrBnuhM9jXRCDoPnVyZ) | ![img](https://drive.google.com/uc?id=1U1n277EBxfwsOfWrFMLpeC4smOg1w21I)\n","![img](https://drive.google.com/uc?id=1lfJPc82bNtSx_03kS4XZzEoNLxZ89T6U) | ![img](https://drive.google.com/uc?id=1Cw_y7VymfmAClY6ynBHBeC68_bSaxcxL)"],"metadata":{"id":"HEtPSeXpdM3y"}},{"cell_type":"markdown","source":["The top and bottom drawings from the similarity scores seem to be pretty accurate. Though there are some drawings that I thought shouldn't have been a top drawing or a bottom drawing.\n","\n","The best overall drawing came from the living room perception drawings with a similarity of 83.52.\n","\n","When experimenting with the preprocessing of the images I found that turning the images to grayscale and then back to RGB gave the most accurate scores among the drawings. This way drawings without color aren't pentalized. However there's a tradeoff as a few drawings had low similarity when color was taken away."],"metadata":{"id":"eGfK7C-S5SRY"}},{"cell_type":"markdown","source":["### Outliers\n","There are a few drawings that were given generous similarity scores even though they looked nothing like the picture or were just words.\n","\n","For the analysis I decided to keep the observations that had these type of drawings because they had other drawings that were of use. Also The dataset only has around 115 rows so there's not much room to throw away data. This is also why I imputed missing similarity scores with the mean of that column."],"metadata":{"id":"HDIxBIosiVaQ"}},{"cell_type":"code","source":["outlier1 = similarities_df[similarities_df.subject_id == 145].iloc[0]\n","outlier2 = similarities_df[similarities_df.subject_id == 195].iloc[0]\n","\n","fig, axes = plt.subplots(1, 2, figsize=(10, 3))\n","\n","axes[0].imshow(np.array(outlier1['kitchen_mem_drawing']))\n","axes[0].set_title(f\"{outlier1['treatment']}, {round(outlier1['kitchen_mem']*100,2)}\")\n","axes[1].imshow(np.array(outlier2['kitchen_mem_drawing']))\n","axes[1].set_title(f\"{outlier2['treatment']}, {round(outlier2['kitchen_mem']*100,2)}\")\n","fig.suptitle('Outliers')"],"metadata":{"id":"4IJxhXhQYVy8","executionInfo":{"status":"ok","timestamp":1714832814664,"user_tz":240,"elapsed":887,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"colab":{"base_uri":"https://localhost:8080/","height":338},"outputId":"3aea3baf-897b-4854-864f-3809b833dec8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 0.98, 'Outliers')"]},"metadata":{},"execution_count":20},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x300 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### Modeling"],"metadata":{"id":"QaH6uvvGk7YG"}},{"cell_type":"code","source":["# Impute missing variables with column mean\n","X_nodraw = similarities_df[all_cols]\n","col_means = X_nodraw.mean()\n","X_nodraw = X_nodraw.fillna(col_means)"],"metadata":{"id":"Nb-h6LbP9q4-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X = X_nodraw.reset_index(drop=True)\n","y = np.array(similarities_df['treatment'])"],"metadata":{"id":"XmxIJ6qXYP0x"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.preprocessing import StandardScaler"],"metadata":{"id":"KZPkT3ZhXZHl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Scale Variables\n","scaler = StandardScaler()\n","scaler.fit(X)\n","X_scaled = scaler.transform(X)"],"metadata":{"id":"XT44zjPBZ_aw"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split, KFold\n","from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score"],"metadata":{"id":"0B1yP3COcTqB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def fit_model(mod, model_name, scale = False):\n","  kf = KFold(n_splits=5, shuffle=True, random_state=29)\n","  results = {\n","      'accuracy': [],\n","      'precision': [],\n","      'recall': [],\n","      'f1': []\n","  }\n","\n","  for train_index, test_index in kf.split(X):\n","    if scale:\n","      X_train, X_test = X_scaled[train_index], X_scaled[test_index]\n","      y_train, y_test = y[train_index], y[test_index]\n","    else:\n","      X_train, X_test = X.iloc[train_index], X.iloc[test_index]\n","      y_train, y_test = y[train_index], y[test_index]\n","\n","    mod.fit(X_train, y_train)\n","    y_pred = mod.predict(X_test)\n","\n","    results['accuracy'].append(accuracy_score(y_test, y_pred))\n","    results['precision'].append(precision_score(y_test, y_pred, pos_label='Aphantasia'))\n","    results['recall'].append(recall_score(y_test, y_pred, pos_label='Aphantasia'))\n","    results['f1'].append(f1_score(y_test, y_pred, pos_label='Aphantasia'))\n","\n","  for metric, res in results.items():\n","    results[metric] = round(np.mean(res)* 100,2)\n","  df = pd.json_normalize(results)\n","  df.index = [model_name]\n","  return df"],"metadata":{"id":"RsC3Ww2KiB_Y"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","forest_model = RandomForestClassifier(random_state = 29)\n","forest_res = fit_model(forest_model, 'Random Forest',scale = False)"],"metadata":{"id":"6oMMn1jFgyky"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.linear_model import LogisticRegression\n","# Logistic Regression Model\n","log_model = LogisticRegression(penalty ='elasticnet', solver='saga', l1_ratio=0.5, max_iter=10000)\n","log_res = fit_model(log_model, 'Logistic Regression', scale = True)"],"metadata":{"id":"QA_lKgSfZyRu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.svm import SVC\n","# SVM Model\n","svm_model = SVC(kernel='rbf')\n","svm_res = fit_model(svm_model,'Support Vector Machine', scale = True)"],"metadata":{"id":"Soxxf7TPjBaF"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.tree import DecisionTreeClassifier, plot_tree\n","# Decision Tree\n","tree_model = DecisionTreeClassifier(max_depth=2)\n","tree_res = fit_model(tree_model, 'Decision Tree', scale = False)"],"metadata":{"id":"6jmZg8Zhiaym"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#### Results\n","The decision tree performed the best with the highest F1 score (71.13) and accuracy of 63.48%.\n","\n","However, overall the models didn't do too well at predicting aphantasia from the similarity scores. The ViT CLIP model didn't do well at getting the right features as we saw with the outliers that had higher similarity scores than expected. There weren't any big differences between the 2 groups seen in the 3D scatter plot as well.\n","\n","Performance could be improved by incorporating the demographic data."],"metadata":{"id":"pGFbJxPhl-RR"}},{"cell_type":"code","source":["pd.concat([forest_res, log_res, svm_res, tree_res])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":174},"id":"8CPvCDOleAa4","executionInfo":{"status":"ok","timestamp":1714832816627,"user_tz":240,"elapsed":8,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"outputId":"527c97d4-fbce-4d27-ae3b-dd137a4dea91"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                        accuracy  precision  recall     f1\n","Random Forest              60.87      64.66   68.38  65.60\n","Logistic Regression        61.74      65.88   70.94  66.14\n","Support Vector Machine     60.87      62.02   81.63  69.24\n","Decision Tree              63.48      63.52   82.91  71.13"],"text/html":["\n","  <div id=\"df-d5744a09-1941-4dca-b543-428bc2ab79c9\" class=\"colab-df-container\">\n","    <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>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Random Forest</th>\n","      <td>60.87</td>\n","      <td>64.66</td>\n","      <td>68.38</td>\n","      <td>65.60</td>\n","    </tr>\n","    <tr>\n","      <th>Logistic Regression</th>\n","      <td>61.74</td>\n","      <td>65.88</td>\n","      <td>70.94</td>\n","      <td>66.14</td>\n","    </tr>\n","    <tr>\n","      <th>Support Vector Machine</th>\n","      <td>60.87</td>\n","      <td>62.02</td>\n","      <td>81.63</td>\n","      <td>69.24</td>\n","    </tr>\n","    <tr>\n","      <th>Decision Tree</th>\n","      <td>63.48</td>\n","      <td>63.52</td>\n","      <td>82.91</td>\n","      <td>71.13</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5744a09-1941-4dca-b543-428bc2ab79c9')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-d5744a09-1941-4dca-b543-428bc2ab79c9 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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This does not match the results of the original paper's ANOVA when they looked at the number of objects drawn. They found no significance in the main effect for treatment. The significance in the interaction effect between treatment and type (memory/perception) was an important finding in the paper and this mathces. my results.\n","\n","This analysis was done to see if the patterns found in the paper could be found using a pretrained image transformer model. A limitation of this is that the model was trained on a broad range of images. A better approach would be to fine tune a model specifically on drawings.\n","\n","The paper looked more in depth into particular parts of the drawings. They used crowdsourcing to precisely measure characteristics of the drawings:\n","\n","* Quantified the amount of text used to label objects in drawings\n","* Quantified size and location accuracy of objects drawn\n","* Counted number of correctly drawn objects and incorrectly drawn objects"],"metadata":{"id":"Lw3r7f1borQA"}},{"cell_type":"code","source":["print(round(anova_results,3))"],"metadata":{"id":"ArlyORNPm6wn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1714832817859,"user_tz":240,"elapsed":8,"user":{"displayName":"Jon Campbell Jr","userId":"09087198626790021717"}},"outputId":"53db7bf2-c948-4e1f-e8df-fa19e883a418"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["                      sum_sq     df        F  PR(>F)\n","C(treatment)           0.024    1.0    9.646   0.002\n","C(room)                0.595    2.0  120.066   0.000\n","C(type)                0.364    1.0  147.184   0.000\n","C(treatment):C(room)   0.007    2.0    1.443   0.237\n","C(treatment):C(type)   0.019    1.0    7.650   0.006\n","C(room):C(type)        0.040    2.0    8.010   0.000\n","Residual               1.634  660.0      NaN     NaN\n"]}]}]}
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