Notebooks/ImageNet_3_Gender_Dog_musical_instruments_final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://jupyterhub:****@pypi.dev.unify.id/simple\r\n",
      "Requirement already satisfied: fuzzywuzzy in /opt/conda/lib/python3.6/site-packages (0.18.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install fuzzywuzzy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
      "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(120, 2)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "# !pip install fuzzywuzzy\n",
    "from fuzzywuzzy import fuzz\n",
    "from fuzzywuzzy import process\n",
    "\n",
    "import seaborn as sns; sns.set(style=\"white\", color_codes=True)\n",
    "import scipy.stats as stats\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "with open(\"mobilenet.json\", \"rb\") as f:\n",
    "    tree_dataset = json.load(f)\n",
    "df_imagenet=pd.read_csv('df_final.csv')\n",
    "df_dog_analysis=pd.read_csv('df_dog_analysis.csv')\n",
    "df_dogs_imagenet=pd.read_csv('dogs_imagenet.csv')\n",
    "df_dogs_imagenet.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Breed</th>\n",
       "      <th>label</th>\n",
       "      <th>gender_ratio</th>\n",
       "      <th>Dog_breedClub</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EnglishSpringerSpaniel</td>\n",
       "      <td>Welsh springer spaniel</td>\n",
       "      <td>2.958333</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GoldenRetriever</td>\n",
       "      <td>golden retriever</td>\n",
       "      <td>1.318182</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LabradorRetriever</td>\n",
       "      <td>Labrador retriever</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Poodle</td>\n",
       "      <td>toy poodle</td>\n",
       "      <td>2.440000</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>Rottweiler</td>\n",
       "      <td>2.722222</td>\n",
       "      <td>0.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ShetlandSheepdog</td>\n",
       "      <td>Shetland sheepdog, Shetland sheep dog, Shetland</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SiberianHusky</td>\n",
       "      <td>Siberian husky</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Breed                                            label  \\\n",
       "0  EnglishSpringerSpaniel                           Welsh springer spaniel   \n",
       "1         GoldenRetriever                                 golden retriever   \n",
       "2       LabradorRetriever                               Labrador retriever   \n",
       "3                  Poodle                                       toy poodle   \n",
       "4              Rottweiler                                       Rottweiler   \n",
       "5        ShetlandSheepdog  Shetland sheepdog, Shetland sheep dog, Shetland   \n",
       "6           SiberianHusky                                   Siberian husky   \n",
       "\n",
       "   gender_ratio  Dog_breedClub  \n",
       "0      2.958333           0.79  \n",
       "1      1.318182           0.49  \n",
       "2      2.250000           0.46  \n",
       "3      2.440000           0.64  \n",
       "4      2.722222           0.72  \n",
       "5      2.000000           0.41  \n",
       "6      2.750000           0.63  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dog_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py:1847: UserWarning: JointGrid annotation is deprecated and will be removed in a future release.\n",
      "  warnings.warn(UserWarning(msg))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot(x=\"gender_ratio\", y='Dog_breedClub', data=df_dog_analysis, kind='reg')\n",
    "g.annotate(stats.pearsonr)\n",
    "plt.grid('On')\n",
    "plt.xlabel('Gender ratio (train) from Insightface')\n",
    "plt.ylabel('Dog breed agression score(Breed-Club)')\n",
    "plt.savefig('./plots_paper/dog_gender.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bring in the Kaggle dataset:\n",
    "https://www.kaggle.com/andraszsom/dog-breeds-dog-groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "breeds = ['Blue Lacy','Queensland Heeler','Rhod Ridgeback','Retriever','Chinese Sharpei','Black Mouth Cur','Catahoula','Staffordshire','Affenpinscher','Afghan Hound','Airedale Terrier','Akita','Australian Kelpie','Alaskan Malamute','English Bulldog','American Bulldog','American English Coonhound','American Eskimo Dog (Miniature)','American Eskimo Dog (Standard)','American Eskimo Dog (Toy)','American Foxhound','American Hairless Terrier','American Staffordshire Terrier','American Water Spaniel','Anatolian Shepherd Dog','Australian Cattle Dog','Australian Shepherd','Australian Terrier','Basenji','Basset Hound','Beagle','Bearded Collie','Beauceron','Bedlington Terrier','Belgian Malinois','Belgian Sheepdog','Belgian Tervuren','Bergamasco','Berger Picard','Bernese Mountain Dog','Bichon Fris_','Black and Tan Coonhound','Black Russian Terrier','Bloodhound','Bluetick Coonhound','Boerboel','Border Collie','Border Terrier','Borzoi','Boston Terrier','Bouvier des Flandres','Boxer','Boykin Spaniel','Briard','Brittany','Brussels Griffon','Bull Terrier','Bull Terrier (Miniature)','Bulldog','Bullmastiff','Cairn Terrier','Canaan Dog','Cane Corso','Cardigan Welsh Corgi','Cavalier King Charles Spaniel','Cesky Terrier','Chesapeake Bay Retriever','Chihuahua','Chinese Crested Dog','Chinese Shar Pei','Chinook','Chow Chow',\"Cirneco dell'Etna\",'Clumber Spaniel','Cocker Spaniel','Collie','Coton de Tulear','Curly-Coated Retriever','Dachshund','Dalmatian','Dandie Dinmont Terrier','Doberman Pinsch','Doberman Pinscher','Dogue De Bordeaux','English Cocker Spaniel','English Foxhound','English Setter','English Springer Spaniel','English Toy Spaniel','Entlebucher Mountain Dog','Field Spaniel','Finnish Lapphund','Finnish Spitz','Flat-Coated Retriever','French Bulldog','German Pinscher','German Shepherd','German Shorthaired Pointer','German Wirehaired Pointer','Giant Schnauzer','Glen of Imaal Terrier','Golden Retriever','Gordon Setter','Great Dane','Great Pyrenees','Greater Swiss Mountain Dog','Greyhound','Harrier','Havanese','Ibizan Hound','Icelandic Sheepdog','Irish Red and White Setter','Irish Setter','Irish Terrier','Irish Water Spaniel','Irish Wolfhound','Italian Greyhound','Japanese Chin','Keeshond','Kerry Blue Terrier','Komondor','Kuvasz','Labrador Retriever','Lagotto Romagnolo','Lakeland Terrier','Leonberger','Lhasa Apso','L_wchen','Maltese','Manchester Terrier','Mastiff','Miniature American Shepherd','Miniature Bull Terrier','Miniature Pinscher','Miniature Schnauzer','Neapolitan Mastiff','Newfoundland','Norfolk Terrier','Norwegian Buhund','Norwegian Elkhound','Norwegian Lundehund','Norwich Terrier','Nova Scotia Duck Tolling Retriever','Old English Sheepdog','Otterhound','Papillon','Parson Russell Terrier','Pekingese','Pembroke Welsh Corgi','Petit Basset Griffon Vend_en','Pharaoh Hound','Plott','Pointer','Polish Lowland Sheepdog','Pomeranian','Standard Poodle','Miniature Poodle','Toy Poodle','Portuguese Podengo Pequeno','Portuguese Water Dog','Pug','Puli','Pyrenean Shepherd','Rat Terrier','Redbone Coonhound','Rhodesian Ridgeback','Rottweiler','Russell Terrier','St. Bernard','Saluki','Samoyed','Schipperke','Scottish Deerhound','Scottish Terrier','Sealyham Terrier','Shetland Sheepdog','Shiba Inu','Shih Tzu','Siberian Husky','Silky Terrier','Skye Terrier','Sloughi','Smooth Fox Terrier','Soft-Coated Wheaten Terrier','Spanish Water Dog','Spinone Italiano','Staffordshire Bull Terrier','Standard Schnauzer','Sussex Spaniel','Swedish Vallhund','Tibetan Mastiff','Tibetan Spaniel','Tibetan Terrier','Toy Fox Terrier','Treeing Walker Coonhound','Vizsla','Weimaraner','Welsh Springer Spaniel','Welsh Terrier','West Highland White Terrier','Whippet','Wire Fox Terrier','Wirehaired Pointing Griffon','Wirehaired Vizsla','Xoloitzcuintli','Yorkshire Terrier']\n",
    "groups = ['Herding','Herding','Hound','Sporting','Non-Sporting','Herding','Herding','Terrier','Toy','Hound','Terrier','Working','Working','Working','Non-Sporting','Non-Sporting','Hound','Non-Sporting','Non-Sporting','Toy','Hound','Terrier','Terrier','Sporting','Working','Herding','Herding','Terrier','Hound','Hound','Hound','Herding','Herding','Terrier','Herding','Herding','Herding','Herding','Herding','Working','Non-Sporting','Hound','Working','Hound','Hound','Working','Herding','Terrier','Hound','Non-Sporting','Herding','Working','Sporting','Herding','Sporting','Toy','Terrier','Terrier','Non-Sporting','Working','Terrier','Working','Working','Herding','Toy','Terrier','Sporting','Toy','Toy','Non-Sporting','Working','Non-Sporting','Hound','Sporting','Sporting','Herding','Non-Sporting','Sporting','Hound','Non-Sporting','Terrier','Working','Working','Working','Sporting','Hound','Sporting','Sporting','Toy','Herding','Sporting','Herding','Non-Sporting','Sporting','Non-Sporting','Working','Herding','Sporting','Sporting','Working','Terrier','Sporting','Sporting','Working','Working','Working','Hound','Hound','Toy','Hound','Herding','Sporting','Sporting','Terrier','Sporting','Hound','Toy','Toy','Non-Sporting','Terrier','Working','Working','Sporting','Sporting','Terrier','Working','Non-Sporting','Non-Sporting','Toy','Terrier','Working','Herding','Terrier','Toy','Terrier','Working','Working','Terrier','Herding','Hound','Non-Sporting','Terrier','Sporting','Herding','Hound','Toy','Terrier','Toy','Herding','Hound','Hound','Hound','Sporting','Herding','Toy','Non-Sporting','Non-Sporting','Toy','Hound','Working','Toy','Herding','Herding','Terrier','Hound','Hound','Working','Terrier','Working','Hound','Working','Non-Sporting','Hound','Terrier','Terrier','Herding','Non-Sporting','Toy','Working','Toy','Terrier','Hound','Terrier','Terrier','Herding','Sporting','Terrier','Working','Sporting','Herding','Working','Non-Sporting','Non-Sporting','Toy','Hound','Sporting','Sporting','Sporting','Terrier','Terrier','Hound','Terrier','Sporting','Sporting','Non-Sporting','Toy']\n",
    "\n",
    "breeds_group = np.array([breeds,groups]).T\n",
    "df_dog_groups=pd.DataFrame(breeds_group,columns=['breed','dog_group'])\n",
    "df_dog_groups.to_csv('df_dog_groups.csv')\n",
    "dog_groups = np.unique(breeds_group[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 48)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imagenet_dogs=df_imagenet.loc[df_imagenet.wordnet_id.isin(df_dogs_imagenet.ImageNet_Synset.values)]\n",
    "df_imagenet_dogs.reset_index(inplace=True,drop=True)\n",
    "df_imagenet_dogs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5f59d22f4684245846069c47127c231",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=120.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imagenet_dogs['dog_group']='Unknown'\n",
    "count=0\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "for i in tqdm(range(120)):\n",
    "    fuzzy_match=process.extractOne(df_imagenet_dogs.label.values[i],df_dog_groups.breed.values)\n",
    "    if(fuzzy_match[1]>80):\n",
    "        df_imagenet_dogs.loc[i,'dog_group']=df_dog_groups.loc[df_dog_groups.breed == fuzzy_match[0],'dog_group'].values\n",
    "        count+=1\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_imagenet_dogs.dog_group.value_counts().plot(kind='bar')\n",
    "plt.ylabel('Number of imagenet classes')\n",
    "plt.grid('On')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Herding</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.088377</td>\n",
       "      <td>0.470540</td>\n",
       "      <td>-0.610984</td>\n",
       "      <td>-0.312133</td>\n",
       "      <td>0.206453</td>\n",
       "      <td>0.500652</td>\n",
       "      <td>0.724166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hound</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.190585</td>\n",
       "      <td>0.509594</td>\n",
       "      <td>-0.712095</td>\n",
       "      <td>-0.092693</td>\n",
       "      <td>0.058970</td>\n",
       "      <td>0.425948</td>\n",
       "      <td>1.257123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Sporting</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.418442</td>\n",
       "      <td>0.524206</td>\n",
       "      <td>-0.309771</td>\n",
       "      <td>-0.071778</td>\n",
       "      <td>0.586524</td>\n",
       "      <td>0.664266</td>\n",
       "      <td>1.233161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sporting</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.444814</td>\n",
       "      <td>0.697176</td>\n",
       "      <td>-0.578680</td>\n",
       "      <td>0.122108</td>\n",
       "      <td>0.388944</td>\n",
       "      <td>0.719598</td>\n",
       "      <td>2.194778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terrier</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.255288</td>\n",
       "      <td>0.451797</td>\n",
       "      <td>-0.804995</td>\n",
       "      <td>-0.008639</td>\n",
       "      <td>0.202184</td>\n",
       "      <td>0.492256</td>\n",
       "      <td>1.141946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.798075</td>\n",
       "      <td>0.748889</td>\n",
       "      <td>-0.380172</td>\n",
       "      <td>0.254473</td>\n",
       "      <td>0.693592</td>\n",
       "      <td>1.244801</td>\n",
       "      <td>2.096655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.096149</td>\n",
       "      <td>0.176486</td>\n",
       "      <td>-0.164287</td>\n",
       "      <td>0.063020</td>\n",
       "      <td>0.168773</td>\n",
       "      <td>0.201901</td>\n",
       "      <td>0.211337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Working</th>\n",
       "      <td>20.0</td>\n",
       "      <td>-0.071804</td>\n",
       "      <td>0.335136</td>\n",
       "      <td>-0.789664</td>\n",
       "      <td>-0.246400</td>\n",
       "      <td>-0.046616</td>\n",
       "      <td>0.197351</td>\n",
       "      <td>0.445657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count      mean       std       min       25%       50%  \\\n",
       "dog_group                                                               \n",
       "Herding        11.0  0.088377  0.470540 -0.610984 -0.312133  0.206453   \n",
       "Hound          20.0  0.190585  0.509594 -0.712095 -0.092693  0.058970   \n",
       "Non-Sporting   11.0  0.418442  0.524206 -0.309771 -0.071778  0.586524   \n",
       "Sporting       18.0  0.444814  0.697176 -0.578680  0.122108  0.388944   \n",
       "Terrier        20.0  0.255288  0.451797 -0.804995 -0.008639  0.202184   \n",
       "Toy            16.0  0.798075  0.748889 -0.380172  0.254473  0.693592   \n",
       "Unknown         4.0  0.096149  0.176486 -0.164287  0.063020  0.168773   \n",
       "Working        20.0 -0.071804  0.335136 -0.789664 -0.246400 -0.046616   \n",
       "\n",
       "                   75%       max  \n",
       "dog_group                         \n",
       "Herding       0.500652  0.724166  \n",
       "Hound         0.425948  1.257123  \n",
       "Non-Sporting  0.664266  1.233161  \n",
       "Sporting      0.719598  2.194778  \n",
       "Terrier       0.492256  1.141946  \n",
       "Toy           1.244801  2.096655  \n",
       "Unknown       0.201901  0.211337  \n",
       "Working       0.197351  0.445657  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imagenet_dogs.groupby('dog_group')['skew_gender_audit'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dog_group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Herding</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.491229</td>\n",
       "      <td>0.109904</td>\n",
       "      <td>0.358253</td>\n",
       "      <td>0.384804</td>\n",
       "      <td>0.472394</td>\n",
       "      <td>0.581011</td>\n",
       "      <td>0.662063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hound</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.467218</td>\n",
       "      <td>0.115882</td>\n",
       "      <td>0.243369</td>\n",
       "      <td>0.401987</td>\n",
       "      <td>0.494504</td>\n",
       "      <td>0.537591</td>\n",
       "      <td>0.683983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Sporting</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.431735</td>\n",
       "      <td>0.117096</td>\n",
       "      <td>0.243628</td>\n",
       "      <td>0.359186</td>\n",
       "      <td>0.412906</td>\n",
       "      <td>0.531221</td>\n",
       "      <td>0.591737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sporting</th>\n",
       "      <td>18.0</td>\n",
       "      <td>0.420217</td>\n",
       "      <td>0.135522</td>\n",
       "      <td>0.140003</td>\n",
       "      <td>0.349418</td>\n",
       "      <td>0.420049</td>\n",
       "      <td>0.485742</td>\n",
       "      <td>0.641624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Terrier</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.454719</td>\n",
       "      <td>0.099879</td>\n",
       "      <td>0.271499</td>\n",
       "      <td>0.398574</td>\n",
       "      <td>0.454636</td>\n",
       "      <td>0.520734</td>\n",
       "      <td>0.687931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toy</th>\n",
       "      <td>16.0</td>\n",
       "      <td>0.353288</td>\n",
       "      <td>0.136889</td>\n",
       "      <td>0.152939</td>\n",
       "      <td>0.259942</td>\n",
       "      <td>0.349028</td>\n",
       "      <td>0.443071</td>\n",
       "      <td>0.599596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unknown</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.457293</td>\n",
       "      <td>0.091493</td>\n",
       "      <td>0.333390</td>\n",
       "      <td>0.429913</td>\n",
       "      <td>0.471286</td>\n",
       "      <td>0.498665</td>\n",
       "      <td>0.553209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Working</th>\n",
       "      <td>20.0</td>\n",
       "      <td>0.526430</td>\n",
       "      <td>0.074618</td>\n",
       "      <td>0.416302</td>\n",
       "      <td>0.464983</td>\n",
       "      <td>0.523183</td>\n",
       "      <td>0.561813</td>\n",
       "      <td>0.684122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count      mean       std       min       25%       50%  \\\n",
       "dog_group                                                               \n",
       "Herding        11.0  0.491229  0.109904  0.358253  0.384804  0.472394   \n",
       "Hound          20.0  0.467218  0.115882  0.243369  0.401987  0.494504   \n",
       "Non-Sporting   11.0  0.431735  0.117096  0.243628  0.359186  0.412906   \n",
       "Sporting       18.0  0.420217  0.135522  0.140003  0.349418  0.420049   \n",
       "Terrier        20.0  0.454719  0.099879  0.271499  0.398574  0.454636   \n",
       "Toy            16.0  0.353288  0.136889  0.152939  0.259942  0.349028   \n",
       "Unknown         4.0  0.457293  0.091493  0.333390  0.429913  0.471286   \n",
       "Working        20.0  0.526430  0.074618  0.416302  0.464983  0.523183   \n",
       "\n",
       "                   75%       max  \n",
       "dog_group                         \n",
       "Herding       0.581011  0.662063  \n",
       "Hound         0.537591  0.683983  \n",
       "Non-Sporting  0.531221  0.591737  \n",
       "Sporting      0.485742  0.641624  \n",
       "Terrier       0.520734  0.687931  \n",
       "Toy           0.443071  0.599596  \n",
       "Unknown       0.498665  0.553209  \n",
       "Working       0.561813  0.684122  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imagenet_dogs.groupby('dog_group')['mean_gender_audit'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Women-majority    75\n",
       "Men-majority      45\n",
       "Name: gender_bias, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imagenet_dogs['gender_bias']='Women-majority'\n",
    "df_imagenet_dogs.loc[df_imagenet_dogs.mean_gender_audit>0.5,'gender_bias']='Men-majority'\n",
    "df_imagenet_dogs.gender_bias.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart = sns.violinplot(x=\"dog_group\", y=\"mean_gender_audit\", hue=\"gender_bias\",\n",
    "                    data=df_imagenet_dogs, palette=\"Set2\", split=True,\n",
    "                    scale=\"count\", inner=\"stick\")\n",
    "chart.set_xticklabels(\n",
    "    chart.get_xticklabels(), \n",
    "    rotation=45, \n",
    "    horizontalalignment='right',\n",
    "    fontweight='light');\n",
    "chart.plot(0.5*np.ones(8),'r-.')\n",
    "plt.savefig('./plots_paper/dog_gender_violin.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Musical instruments bias?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_music=['banjo'\n",
    "'accordion',\n",
    "'piano',\n",
    "'chime',\n",
    "'maraca',\n",
    "'drum',\n",
    "'banjo',\n",
    "'cello',\n",
    "'violin',\n",
    "'harp',\n",
    "'guitar',\n",
    "'trumpet',\n",
    "'french horn',\n",
    "'trombone',\n",
    "'harmonica',\n",
    "'flute',\n",
    "'oboe',\n",
    "'saxophone']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_music=len(list_music)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 48)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imagenet.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c05a6371928405fbf33582ac59de844",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=17.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('banjo', 90)\n",
      "('accordion, piano accordion, squeeze box', 90)\n",
      "('chime, bell, gong', 90)\n",
      "('maraca', 100)\n",
      "('drum, membranophone, tympan', 90)\n",
      "('banjo', 100)\n",
      "('cello, violoncello', 90)\n",
      "('violin, fiddle', 90)\n",
      "('harp', 100)\n",
      "('acoustic guitar', 90)\n",
      "('cornet, horn, trumpet, trump', 90)\n",
      "('French horn, horn', 90)\n",
      "('trombone', 100)\n",
      "('harmonica, mouth organ, harp, mouth harp', 90)\n",
      "('flute, transverse flute', 90)\n",
      "('oboe, hautboy, hautbois', 90)\n",
      "('sax, saxophone', 90)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(17, 17)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count=0\n",
    "music_imagenet_list=[]\n",
    "for i in tqdm(range(N_music)):\n",
    "    fuzzy_match=process.extractOne(list_music[i],df_imagenet.label.values)\n",
    "    if(fuzzy_match[1]>80):\n",
    "        print(fuzzy_match)\n",
    "        music_imagenet_list.append(fuzzy_match[0])\n",
    "        count+=1\n",
    "count,len(music_imagenet_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:494: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Men-majority    16\n",
       "Name: gender_bias, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imagenet_music=df_imagenet.loc[df_imagenet.label.isin(music_imagenet_list)]\n",
    "df_imagenet_music.reset_index(inplace=True,drop=True)\n",
    "df_imagenet_music['gender_bias']='Women-majority'\n",
    "df_imagenet_music.loc[df_imagenet_music.mean_gender_audit>0.5,'gender_bias']='Men-majority'\n",
    "df_imagenet_music.gender_bias.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>class_number</th>\n",
       "      <th>wordnet_id</th>\n",
       "      <th>label</th>\n",
       "      <th>uri</th>\n",
       "      <th>n_train</th>\n",
       "      <th>n_val</th>\n",
       "      <th>n_humans_train</th>\n",
       "      <th>n_humans_val</th>\n",
       "      <th>Nunique_humans_train</th>\n",
       "      <th>gender_skewness_train</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_nsfw_val</th>\n",
       "      <th>std_nsfw_train</th>\n",
       "      <th>std_nsfw_val</th>\n",
       "      <th>mean_top1_train_resnet50</th>\n",
       "      <th>mean_top1_val_resnet50</th>\n",
       "      <th>mean_top5_train_resnet50</th>\n",
       "      <th>mean_top5_val_resnet50</th>\n",
       "      <th>pred_train_resnet50</th>\n",
       "      <th>pred_val_resnet50</th>\n",
       "      <th>gender_bias</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>401</td>\n",
       "      <td>n02672831</td>\n",
       "      <td>accordion, piano accordion, squeeze box</td>\n",
       "      <td>http://wordnet-rdf.princeton.edu/wn30/02672831-n</td>\n",
       "      <td>1300</td>\n",
       "      <td>50</td>\n",
       "      <td>505</td>\n",
       "      <td>19</td>\n",
       "      <td>677</td>\n",
       "      <td>-1.504624</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074246</td>\n",
       "      <td>0.162412</td>\n",
       "      <td>0.149617</td>\n",
       "      <td>0.823077</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.926154</td>\n",
       "      <td>0.92</td>\n",
       "      <td>[401 401 401 ... 401 401 401]</td>\n",
       "      <td>[401 401 401 681 401 401 401 401 401 401 458 4...</td>\n",
       "      <td>Men-majority</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>402</td>\n",
       "      <td>n02676566</td>\n",
       "      <td>acoustic guitar</td>\n",
       "      <td>http://wordnet-rdf.princeton.edu/wn30/02676566-n</td>\n",
       "      <td>1300</td>\n",
       "      <td>50</td>\n",
       "      <td>168</td>\n",
       "      <td>16</td>\n",
       "      <td>185</td>\n",
       "      <td>-1.780180</td>\n",
       "      <td>...</td>\n",
       "      <td>0.148507</td>\n",
       "      <td>0.204294</td>\n",
       "      <td>0.244065</td>\n",
       "      <td>0.736154</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.957692</td>\n",
       "      <td>0.86</td>\n",
       "      <td>[402 402 402 ... 420 402 402]</td>\n",
       "      <td>[402 854 402 819 819 402 402 546 546 776 546 4...</td>\n",
       "      <td>Men-majority</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>420</td>\n",
       "      <td>n02787622</td>\n",
       "      <td>banjo</td>\n",
       "      <td>http://wordnet-rdf.princeton.edu/wn30/02787622-n</td>\n",
       "      <td>1300</td>\n",
       "      <td>50</td>\n",
       "      <td>419</td>\n",
       "      <td>23</td>\n",
       "      <td>500</td>\n",
       "      <td>-2.254035</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155010</td>\n",
       "      <td>0.196022</td>\n",
       "      <td>0.222949</td>\n",
       "      <td>0.852308</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.937692</td>\n",
       "      <td>0.90</td>\n",
       "      <td>[420 420 420 ... 420 420 420]</td>\n",
       "      <td>[420 420 776 420 432 420 420 420 420 420 611 4...</td>\n",
       "      <td>Men-majority</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>486</td>\n",
       "      <td>n02992211</td>\n",
       "      <td>cello, violoncello</td>\n",
       "      <td>http://wordnet-rdf.princeton.edu/wn30/02992211-n</td>\n",
       "      <td>1300</td>\n",
       "      <td>50</td>\n",
       "      <td>483</td>\n",
       "      <td>26</td>\n",
       "      <td>632</td>\n",
       "      <td>-1.504953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227981</td>\n",
       "      <td>0.260294</td>\n",
       "      <td>0.277420</td>\n",
       "      <td>0.683846</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.899231</td>\n",
       "      <td>0.86</td>\n",
       "      <td>[889 889 889 ... 889 486 402]</td>\n",
       "      <td>[486 486 486 486 486 486 421 486 683 558 541 4...</td>\n",
       "      <td>Men-majority</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>494</td>\n",
       "      <td>n03017168</td>\n",
       "      <td>chime, bell, gong</td>\n",
       "      <td>http://wordnet-rdf.princeton.edu/wn30/03017168-n</td>\n",
       "      <td>1300</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>-1.697337</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048716</td>\n",
       "      <td>0.102254</td>\n",
       "      <td>0.134702</td>\n",
       "      <td>0.634615</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.932308</td>\n",
       "      <td>0.86</td>\n",
       "      <td>[494 494 494 ... 494 494 494]</td>\n",
       "      <td>[451 442 494 494 843 786 494 843 494 494 648 4...</td>\n",
       "      <td>Men-majority</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   class_number wordnet_id                                    label  \\\n",
       "0           401  n02672831  accordion, piano accordion, squeeze box   \n",
       "1           402  n02676566                          acoustic guitar   \n",
       "2           420  n02787622                                    banjo   \n",
       "3           486  n02992211                       cello, violoncello   \n",
       "4           494  n03017168                        chime, bell, gong   \n",
       "\n",
       "                                                uri  n_train  n_val  \\\n",
       "0  http://wordnet-rdf.princeton.edu/wn30/02672831-n     1300     50   \n",
       "1  http://wordnet-rdf.princeton.edu/wn30/02676566-n     1300     50   \n",
       "2  http://wordnet-rdf.princeton.edu/wn30/02787622-n     1300     50   \n",
       "3  http://wordnet-rdf.princeton.edu/wn30/02992211-n     1300     50   \n",
       "4  http://wordnet-rdf.princeton.edu/wn30/03017168-n     1300     50   \n",
       "\n",
       "   n_humans_train  n_humans_val  Nunique_humans_train  gender_skewness_train  \\\n",
       "0             505            19                   677              -1.504624   \n",
       "1             168            16                   185              -1.780180   \n",
       "2             419            23                   500              -2.254035   \n",
       "3             483            26                   632              -1.504953   \n",
       "4              32             1                    34              -1.697337   \n",
       "\n",
       "   ...  mean_nsfw_val  std_nsfw_train  std_nsfw_val  mean_top1_train_resnet50  \\\n",
       "0  ...       0.074246        0.162412      0.149617                  0.823077   \n",
       "1  ...       0.148507        0.204294      0.244065                  0.736154   \n",
       "2  ...       0.155010        0.196022      0.222949                  0.852308   \n",
       "3  ...       0.227981        0.260294      0.277420                  0.683846   \n",
       "4  ...       0.048716        0.102254      0.134702                  0.634615   \n",
       "\n",
       "   mean_top1_val_resnet50  mean_top5_train_resnet50  mean_top5_val_resnet50  \\\n",
       "0                    0.82                  0.926154                    0.92   \n",
       "1                    0.44                  0.957692                    0.86   \n",
       "2                    0.80                  0.937692                    0.90   \n",
       "3                    0.72                  0.899231                    0.86   \n",
       "4                    0.64                  0.932308                    0.86   \n",
       "\n",
       "             pred_train_resnet50  \\\n",
       "0  [401 401 401 ... 401 401 401]   \n",
       "1  [402 402 402 ... 420 402 402]   \n",
       "2  [420 420 420 ... 420 420 420]   \n",
       "3  [889 889 889 ... 889 486 402]   \n",
       "4  [494 494 494 ... 494 494 494]   \n",
       "\n",
       "                                   pred_val_resnet50   gender_bias  \n",
       "0  [401 401 401 681 401 401 401 401 401 401 458 4...  Men-majority  \n",
       "1  [402 854 402 819 819 402 402 546 546 776 546 4...  Men-majority  \n",
       "2  [420 420 776 420 432 420 420 420 420 420 611 4...  Men-majority  \n",
       "3  [486 486 486 486 486 486 421 486 683 558 541 4...  Men-majority  \n",
       "4  [451 442 494 494 843 786 494 843 494 494 648 4...  Men-majority  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_imagenet_music.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_srt=df_imagenet_music.sort_values('mean_gender_audit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:14: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(121)\n",
    "chart = sns.violinplot(x=\"dog_group\", y=\"mean_gender_audit\", hue=\"gender_bias\",\n",
    "                    data=df_imagenet_dogs, palette=\"Set2\", split=True,\n",
    "                    scale=\"count\", inner=\"stick\")\n",
    "chart.set_xticklabels(\n",
    "    chart.get_xticklabels(), \n",
    "    rotation=45, \n",
    "    horizontalalignment='right',\n",
    "    fontweight='light');\n",
    "chart.plot(0.5*np.ones(8),'r-.')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.stem(np.arange(df_srt.shape[0]),-1*df_srt.skew_gender_audit)\n",
    "plt.plot(np.arange(df_srt.shape[0]),-1*df_srt.skew_gender_audit,'-r.')\n",
    "plt.xticks(np.arange(df_srt.shape[0]),df_srt.label,rotation=90);\n",
    "plt.ylabel('Gender skewness')\n",
    "plt.grid('On')\n",
    "plt.savefig('./plots_paper/music_skewness.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:14: UserWarning: In Matplotlib 3.3 individual lines on a stem plot will be added as a LineCollection instead of individual lines. This significantly improves the performance of a stem plot. To remove this warning and switch to the new behaviour, set the \"use_line_collection\" keyword argument to True.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3.5))\n",
    "plt.subplot(121)\n",
    "chart = sns.violinplot(x=\"dog_group\", y=\"mean_gender_audit\", hue=\"gender_bias\",\n",
    "                    data=df_imagenet_dogs, palette=\"Set2\", split=True,\n",
    "                    scale=\"count\", inner=\"stick\")\n",
    "chart.set_xticklabels(\n",
    "    chart.get_xticklabels(), \n",
    "    rotation=45, \n",
    "    horizontalalignment='right',\n",
    "    fontweight='light');\n",
    "chart.plot(0.5*np.ones(8),'r-.')\n",
    "chart.set_ylabel('Mean(Gender) [DEX]')\n",
    "plt.subplot(122)\n",
    "plt.stem(np.arange(df_srt.shape[0]),df_srt.skew_gender_audit)\n",
    "plt.plot(np.arange(df_srt.shape[0]),df_srt.skew_gender_audit,'-r.')\n",
    "x_tick_vec=[]\n",
    "for m in df_srt.label.values:\n",
    "    x_tick_vec.append(m.split(',')[0])\n",
    "plt.xticks(np.arange(df_srt.shape[0]),x_tick_vec,rotation=60);\n",
    "plt.ylabel('Skewness(Gender) [DEX]')\n",
    "plt.grid('On')\n",
    "plt.tight_layout()\n",
    "plt.savefig('./plots_paper/dog_music.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source: http://www-usr.rider.edu/~vrme/v4n1/visions/Walker%20Influences%20of%20Gender%20and%20Sex\n",
    "\n",
    "Abeles & Porter undertook the first major study of sex stereotyping of musical instruments\n",
    "in 1978. They wrote that children found the drums to be the most masculine and the flute to\n",
    "be the most feminine. \n",
    "Griswold and Chroback (1981) found in their study that:\n",
    "answers by adults to hypothetical selection suggest [of instruments for their children to\n",
    "play] that the sex of the child, rather than the sex of the adult or past musical experience,\n",
    "influenced the adult's preferences. Violin, flute and clarinet were considered feminine;\n",
    "drums, trombone, and trumpet were considered masculine; saxophone and cello were rated\n",
    "neutral. (p. 57) \n",
    "\n",
    "https://subyraman.tumblr.com/post/102965074088/graphing-gender-in-americas-top-orchestras"
   ]
  }
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