{
"cells": [
{
"cell_type": "code",
"execution_count": 95,
"id": "3d93276e-d83e-48b7-95be-aaaa89244ef9",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import scipy\n",
"\n",
"import json\n",
"import re\n",
"from itertools import chain\n",
"from collections import Counter"
]
},
{
"cell_type": "code",
"execution_count": 156,
"id": "f57c50ca-3581-4412-a160-774f998ce9df",
"metadata": {},
"outputs": [],
"source": [
"with open(\"./id_all_blip_clusters_12.json\") as f:\n",
" d_12 = json.load(f)\n",
"\n",
"with open(\"./id_all_blip_clusters_24.json\") as f:\n",
" d_24 = json.load(f)\n",
"\n",
"with open(\"./id_all_blip_clusters_48.json\") as f:\n",
" d_48 = json.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 158,
"id": "50727b12-9dcb-4f31-b914-801bcd721949",
"metadata": {},
"outputs": [],
"source": [
"def reduce_full_to_ethnicity_model(d):\n",
" c = Counter()\n",
" for k,v in d['labels_full']:\n",
" k_without_gender = re.split(\"woman|man|person|non-binary\",k)\n",
" k_without_gender = ''.join(k_without_gender)\n",
" k_without_gender = k_without_gender.strip().replace(\" \", \" \")\n",
" c[k_without_gender] = v\n",
" return [[k,v] for k,v in c.items()]"
]
},
{
"cell_type": "code",
"execution_count": 142,
"id": "63830bef-085f-4847-b51a-b4157351a1a5",
"metadata": {},
"outputs": [],
"source": [
"def reduce_full_to_gender_model(d):\n",
" c = Counter()\n",
" for k,v in d['labels_full']:\n",
" k_without_ethnicity = re.split(\"(woman|man|person|non-binary)\", k)\n",
" k_without_ethnicity = ''.join(k_without_ethnicity[1:])\n",
" k_without_ethnicity = k_without_ethnicity.strip().replace(\" \", \" \")\n",
" c[k_without_ethnicity] = v\n",
" return [[k,v] for k,v in c.items()]"
]
},
{
"cell_type": "code",
"execution_count": 154,
"id": "7edad0d1-3d6d-406e-87ef-a0a8c185a53f",
"metadata": {},
"outputs": [],
"source": [
"def reduce_full_to_ethnicity_gender(d):\n",
" c = Counter()\n",
" for k,v in d['labels_full']:\n",
" k_without_model = re.split(\"(woman|man|person|non-binary)\", k)\n",
" k_without_model = ''.join(k_without_model[:2])\n",
" k_without_model = k_without_model.strip().replace(\" \", \" \")\n",
" c[k_without_model] = v\n",
" return [[k,v] for k,v in c.items()]"
]
},
{
"cell_type": "code",
"execution_count": 159,
"id": "da978f3b-8f94-4d1b-bae2-f3bb4e9986b4",
"metadata": {},
"outputs": [],
"source": [
"for cluster_dicts in [d_12, d_24, d_48]:\n",
" for d in cluster_dicts:\n",
" d[\"labels_ethnicity_model\"] = reduce_full_to_ethnicity_model(d)\n",
" d[\"labels_gender_model\"] = reduce_full_to_gender_model(d)\n",
" d[\"labels_ethnicity_gender\"] = reduce_full_to_ethnicity_gender(d)"
]
},
{
"cell_type": "code",
"execution_count": 162,
"id": "dcab249b-8c66-464f-a41c-a9ae5ab3ad71",
"metadata": {},
"outputs": [],
"source": [
"# p(cluster | ethnicity, model) DONE\n",
"# p(cluster | gender, model) DONE\n",
"# p(cluster | gender, ethnicity, model) DONE\n",
"# p(cluster | ethnicity) DONE\n",
"# p(cluster | gender) DONE\n",
"# p(cluster | gender, ethnicity) DONE\n",
"# p(cluster | model) ADDED, DONE"
]
},
{
"cell_type": "markdown",
"id": "aff706c7-acb0-460a-bc09-4ce673f8a641",
"metadata": {},
"source": [
"# Ethnicities"
]
},
{
"cell_type": "code",
"execution_count": 74,
"id": "84e4a4d4-d79b-4666-a7bf-a7532f143019",
"metadata": {},
"outputs": [],
"source": [
"entropies = []\n",
"random_entropies = []\n",
"for cluster_dicts in [d_12, d_24, d_48]:\n",
" entropy = dict()\n",
" random_entropy = dict()\n",
" n_clusters = len(cluster_dicts)\n",
" all_ethnicities = [list(dict(d['labels_ethnicity']).keys()) for d in cluster_dicts]\n",
" all_ethnicities = list(set(chain(*all_ethnicities)))\n",
" for ethnicity in all_ethnicities:\n",
" h = []\n",
" for i in cluster_dicts:\n",
" h.append(dict(i['labels_ethnicity']).get(ethnicity, 0))\n",
" h = np.array(h)\n",
" r = np.ones_like(h)\n",
" entropy[ethnicity] = scipy.stats.entropy(h / sum(h), base=2)\n",
" random_entropy[ethnicity] = scipy.stats.entropy(r, base=2)\n",
" entropies.append(entropy)\n",
" random_entropies.append(random_entropy)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"id": "0abbdcb0-b06a-4905-8d8e-c3340f8b7e05",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5.584962500721156"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log2(48)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "fd938629-c0bf-48c2-b585-5e6a53b8c52d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'': 5.584962500721156,\n",
" 'Latinx': 5.584962500721156,\n",
" 'African-American': 5.584962500721156,\n",
" 'Hispanic': 5.584962500721156,\n",
" 'Indigenous American': 5.584962500721156,\n",
" 'First Nations': 5.584962500721156,\n",
" 'Black': 5.584962500721156,\n",
" 'Multiracial': 5.584962500721156,\n",
" 'Latino': 5.584962500721156,\n",
" 'Southeast Asian': 5.584962500721156,\n",
" 'American Indian': 5.584962500721156,\n",
" 'South Asian': 5.584962500721156,\n",
" 'Caucasian': 5.584962500721156,\n",
" 'Native American': 5.584962500721156,\n",
" 'East Asian': 5.584962500721156,\n",
" 'Pacific Islander': 5.584962500721156,\n",
" 'White': 5.584962500721156}"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"random_entropy"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "0bfc516f-53e0-4f41-bd46-7375913840d6",
"metadata": {
"collapsed": true,
"jupyter": {
"outputs_hidden": true
},
"tags": []
},
"outputs": [
{
"data": {
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"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
" \n",
" | Pacific Islander | \n",
" 3.05 | \n",
"
\n",
" \n",
" | Latino | \n",
" 2.75 | \n",
"
\n",
" \n",
" | Latinx | \n",
" 2.70 | \n",
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\n",
" \n",
" | Hispanic | \n",
" 2.61 | \n",
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\n",
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" 2.50 | \n",
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\n",
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" 2.42 | \n",
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" 2.19 | \n",
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" 2.08 | \n",
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\n",
" \n",
" | White | \n",
" 2.04 | \n",
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\n",
" \n",
" | Native American | \n",
" 1.91 | \n",
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\n",
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" 1.88 | \n",
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\n",
" \n",
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\n",
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" \n",
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" 1.54 | \n",
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\n",
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" \n",
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" | | \n",
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\n",
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" \n",
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" | Pacific Islander | \n",
" 3.68 | \n",
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\n",
" \n",
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" 2.86 | \n",
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\n",
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" 2.76 | \n",
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\n",
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" 2.70 | \n",
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\n",
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" 2.68 | \n",
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\n",
" \n",
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" 2.53 | \n",
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\n",
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" \n",
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\n",
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{
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"\n",
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" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
" \n",
" | Pacific Islander | \n",
" 4.28 | \n",
"
\n",
" \n",
" | Latino | \n",
" 4.26 | \n",
"
\n",
" \n",
" | Hispanic | \n",
" 4.17 | \n",
"
\n",
" \n",
" | First Nations | \n",
" 4.06 | \n",
"
\n",
" \n",
" | Indigenous American | \n",
" 4.00 | \n",
"
\n",
" \n",
" | Native American | \n",
" 3.88 | \n",
"
\n",
" \n",
" | Latinx | \n",
" 3.88 | \n",
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\n",
" \n",
" | American Indian | \n",
" 3.74 | \n",
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\n",
" \n",
" | Multiracial | \n",
" 3.36 | \n",
"
\n",
" \n",
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" 3.22 | \n",
"
\n",
" \n",
" | White | \n",
" 3.20 | \n",
"
\n",
" \n",
" | \n",
" 3.20 | \n",
"
\n",
" \n",
" | Southeast Asian | \n",
" 3.18 | \n",
"
\n",
" \n",
" | African-American | \n",
" 2.83 | \n",
"
\n",
" \n",
" | Black | \n",
" 2.69 | \n",
"
\n",
" \n",
" | East Asian | \n",
" 2.03 | \n",
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\n",
" \n",
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" 1.95 | \n",
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"text/plain": [
""
]
},
"metadata": {},
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],
"source": [
"for d in entropies:\n",
" df = pd.DataFrame(pd.Series(d), columns=[\"entropy\"])\n",
" display(df.sort_values(\"entropy\", ascending=False).style.background_gradient(\n",
" axis=None,\n",
" vmin=0,\n",
" vmax=4,\n",
" cmap=\"YlGnBu\"\n",
").format(precision=2))"
]
},
{
"cell_type": "markdown",
"id": "a2dd2700-3a18-446b-883a-d7efaba9df43",
"metadata": {},
"source": [
"# Ethnicities X Model"
]
},
{
"cell_type": "code",
"execution_count": 128,
"id": "95a27f11-db32-448f-9712-6b6f18457515",
"metadata": {},
"outputs": [],
"source": [
"entropies = []\n",
"random_entropies = []\n",
"for cluster_dicts in [d_12, d_24, d_48]:\n",
" entropy = dict()\n",
" random_entropy = dict()\n",
" n_clusters = len(cluster_dicts)\n",
" all_ethnicities_models = [list(dict(d['labels_ethnicity_model']).keys()) for d in cluster_dicts]\n",
" all_ethnicities_models = list(set(chain(*all_ethnicities_models)))\n",
" for ethnicity_model in all_ethnicities_models:\n",
" h = []\n",
" for i in cluster_dicts:\n",
" h.append(dict(i['labels_ethnicity_model']).get(ethnicity_model, 0))\n",
" h = np.array(h)\n",
" r = np.ones_like(h)\n",
" entropy[ethnicity_model] = scipy.stats.entropy(h / sum(h), base=2)\n",
" entropies.append(entropy)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"id": "6a1f1689-e59b-408b-8886-a42113fb6faa",
"metadata": {},
"outputs": [
{
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" 2.28 | \n",
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" 2.27 | \n",
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\n",
" \n",
" | American Indian SD_2 | \n",
" 2.26 | \n",
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\n",
" \n",
" | Latinx SD_14 | \n",
" 2.22 | \n",
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\n",
" \n",
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\n",
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\n",
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\n",
" \n",
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\n",
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" 2.10 | \n",
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\n",
" \n",
" | Native American SD_2 | \n",
" 2.07 | \n",
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\n",
" \n",
" | White SD_2 | \n",
" 2.06 | \n",
"
\n",
" \n",
" | East Asian DallE | \n",
" 1.99 | \n",
"
\n",
" \n",
" | African-American SD_14 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | First Nations SD_2 | \n",
" 1.94 | \n",
"
\n",
" \n",
" | Caucasian SD_2 | \n",
" 1.91 | \n",
"
\n",
" \n",
" | American Indian DallE | \n",
" 1.91 | \n",
"
\n",
" \n",
" | Southeast Asian SD_2 | \n",
" 1.88 | \n",
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\n",
" \n",
" | SD_14 | \n",
" 1.87 | \n",
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\n",
" \n",
" | Caucasian DallE | \n",
" 1.79 | \n",
"
\n",
" \n",
" | First Nations SD_14 | \n",
" 1.78 | \n",
"
\n",
" \n",
" | South Asian DallE | \n",
" 1.73 | \n",
"
\n",
" \n",
" | Indigenous American SD_14 | \n",
" 1.66 | \n",
"
\n",
" \n",
" | White DallE | \n",
" 1.59 | \n",
"
\n",
" \n",
" | African-American DallE | \n",
" 1.58 | \n",
"
\n",
" \n",
" | Black DallE | \n",
" 1.55 | \n",
"
\n",
" \n",
" | East Asian SD_2 | \n",
" 1.50 | \n",
"
\n",
" \n",
" | Black SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | South Asian SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Black SD_2 | \n",
" 1.23 | \n",
"
\n",
" \n",
" | African-American SD_2 | \n",
" 1.00 | \n",
"
\n",
" \n",
" | American Indian SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | South Asian SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | DallE | \n",
" 0.87 | \n",
"
\n",
" \n",
" | East Asian SD_14 | \n",
" 0.72 | \n",
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\n",
" \n",
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" 0.00 | \n",
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\n",
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\n",
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" 2.91 | \n",
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\n",
" \n",
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" 2.91 | \n",
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" \n",
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" 2.91 | \n",
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" \n",
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" 2.87 | \n",
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" \n",
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" 2.84 | \n",
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\n",
" \n",
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" 2.80 | \n",
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" \n",
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" 2.76 | \n",
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\n",
" \n",
" | Latinx SD_14 | \n",
" 2.73 | \n",
"
\n",
" \n",
" | Pacific Islander DallE | \n",
" 2.71 | \n",
"
\n",
" \n",
" | Indigenous American SD_2 | \n",
" 2.70 | \n",
"
\n",
" \n",
" | Multiracial SD_2 | \n",
" 2.70 | \n",
"
\n",
" \n",
" | First Nations SD_14 | \n",
" 2.66 | \n",
"
\n",
" \n",
" | Hispanic DallE | \n",
" 2.62 | \n",
"
\n",
" \n",
" | First Nations SD_2 | \n",
" 2.61 | \n",
"
\n",
" \n",
" | Indigenous American DallE | \n",
" 2.61 | \n",
"
\n",
" \n",
" | Southeast Asian SD_14 | \n",
" 2.60 | \n",
"
\n",
" \n",
" | Caucasian SD_2 | \n",
" 2.54 | \n",
"
\n",
" \n",
" | White SD_2 | \n",
" 2.54 | \n",
"
\n",
" \n",
" | American Indian DallE | \n",
" 2.50 | \n",
"
\n",
" \n",
" | East Asian DallE | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Indigenous American SD_14 | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Pacific Islander SD_14 | \n",
" 2.41 | \n",
"
\n",
" \n",
" | African-American SD_14 | \n",
" 2.22 | \n",
"
\n",
" \n",
" | Southeast Asian SD_2 | \n",
" 2.11 | \n",
"
\n",
" \n",
" | Caucasian DallE | \n",
" 2.08 | \n",
"
\n",
" \n",
" | Black DallE | \n",
" 2.08 | \n",
"
\n",
" \n",
" | White DallE | \n",
" 2.06 | \n",
"
\n",
" \n",
" | SD_14 | \n",
" 1.93 | \n",
"
\n",
" \n",
" | Black SD_14 | \n",
" 1.83 | \n",
"
\n",
" \n",
" | South Asian DallE | \n",
" 1.73 | \n",
"
\n",
" \n",
" | Black SD_2 | \n",
" 1.62 | \n",
"
\n",
" \n",
" | African-American DallE | \n",
" 1.58 | \n",
"
\n",
" \n",
" | American Indian SD_14 | \n",
" 1.58 | \n",
"
\n",
" \n",
" | East Asian SD_2 | \n",
" 1.50 | \n",
"
\n",
" \n",
" | African-American SD_2 | \n",
" 1.47 | \n",
"
\n",
" \n",
" | South Asian SD_2 | \n",
" 1.46 | \n",
"
\n",
" \n",
" | DallE | \n",
" 1.28 | \n",
"
\n",
" \n",
" | East Asian SD_14 | \n",
" 1.25 | \n",
"
\n",
" \n",
" | South Asian SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Native American SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
" \n",
" | Multiracial DallE | \n",
" 3.96 | \n",
"
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" \n",
" | Pacific Islander SD_2 | \n",
" 3.91 | \n",
"
\n",
" \n",
" | Hispanic SD_14 | \n",
" 3.86 | \n",
"
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" | Native American DallE | \n",
" 3.62 | \n",
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" 3.41 | \n",
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" 3.35 | \n",
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" | Latino DallE | \n",
" 3.35 | \n",
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" \n",
" | Pacific Islander SD_14 | \n",
" 3.34 | \n",
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" | Latino SD_14 | \n",
" 3.31 | \n",
"
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" \n",
" | Hispanic SD_2 | \n",
" 3.29 | \n",
"
\n",
" \n",
" | Native American SD_2 | \n",
" 3.20 | \n",
"
\n",
" \n",
" | Multiracial SD_14 | \n",
" 3.18 | \n",
"
\n",
" \n",
" | White SD_2 | \n",
" 3.17 | \n",
"
\n",
" \n",
" | American Indian SD_2 | \n",
" 3.16 | \n",
"
\n",
" \n",
" | Caucasian SD_14 | \n",
" 3.08 | \n",
"
\n",
" \n",
" | American Indian DallE | \n",
" 3.06 | \n",
"
\n",
" \n",
" | White SD_14 | \n",
" 3.05 | \n",
"
\n",
" \n",
" | First Nations SD_14 | \n",
" 3.03 | \n",
"
\n",
" \n",
" | Latinx SD_2 | \n",
" 2.99 | \n",
"
\n",
" \n",
" | Multiracial SD_2 | \n",
" 2.99 | \n",
"
\n",
" \n",
" | Indigenous American SD_14 | \n",
" 2.97 | \n",
"
\n",
" \n",
" | Indigenous American SD_2 | \n",
" 2.97 | \n",
"
\n",
" \n",
" | SD_2 | \n",
" 2.97 | \n",
"
\n",
" \n",
" | SD_14 | \n",
" 2.88 | \n",
"
\n",
" \n",
" | Hispanic DallE | \n",
" 2.80 | \n",
"
\n",
" \n",
" | Caucasian SD_2 | \n",
" 2.75 | \n",
"
\n",
" \n",
" | First Nations SD_2 | \n",
" 2.71 | \n",
"
\n",
" \n",
" | African-American SD_14 | \n",
" 2.54 | \n",
"
\n",
" \n",
" | American Indian SD_14 | \n",
" 2.50 | \n",
"
\n",
" \n",
" | East Asian DallE | \n",
" 2.50 | \n",
"
\n",
" \n",
" | African-American DallE | \n",
" 2.50 | \n",
"
\n",
" \n",
" | South Asian DallE | \n",
" 2.40 | \n",
"
\n",
" \n",
" | White DallE | \n",
" 2.36 | \n",
"
\n",
" \n",
" | Southeast Asian SD_14 | \n",
" 2.35 | \n",
"
\n",
" \n",
" | Black SD_14 | \n",
" 2.28 | \n",
"
\n",
" \n",
" | Black SD_2 | \n",
" 2.26 | \n",
"
\n",
" \n",
" | Southeast Asian SD_2 | \n",
" 2.26 | \n",
"
\n",
" \n",
" | Black DallE | \n",
" 2.26 | \n",
"
\n",
" \n",
" | Caucasian DallE | \n",
" 2.21 | \n",
"
\n",
" \n",
" | African-American SD_2 | \n",
" 2.18 | \n",
"
\n",
" \n",
" | Native American SD_14 | \n",
" 1.97 | \n",
"
\n",
" \n",
" | DallE | \n",
" 1.95 | \n",
"
\n",
" \n",
" | East Asian SD_2 | \n",
" 1.92 | \n",
"
\n",
" \n",
" | South Asian SD_2 | \n",
" 1.70 | \n",
"
\n",
" \n",
" | South Asian SD_14 | \n",
" 1.66 | \n",
"
\n",
" \n",
" | East Asian SD_14 | \n",
" 1.66 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in entropies:\n",
" df = pd.DataFrame(pd.Series(d), columns=[\"entropy\"])\n",
" display(df.sort_values(\"entropy\", ascending=False).style.background_gradient(\n",
" axis=None,\n",
" vmin=0,\n",
" vmax=4,\n",
" cmap=\"YlGnBu\"\n",
").format(precision=2))"
]
},
{
"cell_type": "markdown",
"id": "014b36e8-9a21-4ceb-81b0-ce93a384ddbb",
"metadata": {},
"source": [
"# Genders"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "6633a33e-e9a9-48cf-ada7-76f2221b43fe",
"metadata": {},
"outputs": [],
"source": [
"entropies = []\n",
"for cluster_dicts in [d_12, d_24, d_48]:\n",
" entropy = dict()\n",
" n_clusters = len(cluster_dicts)\n",
" all_genders = [list(dict(d['labels_gender']).keys()) for d in cluster_dicts]\n",
" all_genders = list(set(chain(*all_genders)))\n",
" for gender in all_genders:\n",
" h = []\n",
" for i in cluster_dicts:\n",
" h.append(dict(i['labels_gender']).get(gender, 0))\n",
" h = np.array(h)\n",
" entropy[gender] = scipy.stats.entropy(h / sum(h), base=2)\n",
" entropies.append(entropy)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "abedb706-dfca-416f-af6a-c9e59f48215e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'woman': 2.4810719655716675,\n",
" 'man': 2.7334846800371837,\n",
" 'person': 3.2367086062758728,\n",
" 'non-binary': 2.820571495642662},\n",
" {'woman': 3.175925805050219,\n",
" 'man': 3.6256634564832084,\n",
" 'person': 4.1229292987043635,\n",
" 'non-binary': 3.7329829916387802},\n",
" {'woman': 4.424803401742995,\n",
" 'man': 4.422651789402228,\n",
" 'person': 4.812137497942508,\n",
" 'non-binary': 4.421094043509409}]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"entropies"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "d06e10c5-d6f0-412f-bba7-5583796ac98b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
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" | person | \n",
" 3.24 | \n",
"
\n",
" \n",
" | non-binary | \n",
" 2.82 | \n",
"
\n",
" \n",
" | man | \n",
" 2.73 | \n",
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" | woman | \n",
" 2.48 | \n",
"
\n",
" \n",
"
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
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" | | \n",
" entropy | \n",
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" | person | \n",
" 4.12 | \n",
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\n",
" \n",
" | non-binary | \n",
" 3.73 | \n",
"
\n",
" \n",
" | man | \n",
" 3.63 | \n",
"
\n",
" \n",
" | woman | \n",
" 3.18 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
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" | person | \n",
" 4.81 | \n",
"
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" | woman | \n",
" 4.42 | \n",
"
\n",
" \n",
" | man | \n",
" 4.42 | \n",
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" | non-binary | \n",
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"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in entropies:\n",
" df = pd.DataFrame(pd.Series(d), columns=[\"entropy\"])\n",
" display(df.sort_values(\"entropy\", ascending=False).style.background_gradient(\n",
" axis=None,\n",
" vmin=0,\n",
" vmax=4,\n",
" cmap=\"YlGnBu\"\n",
").format(precision=2))"
]
},
{
"cell_type": "markdown",
"id": "600e4ad8-d872-4e79-96d0-a843d232fe2e",
"metadata": {},
"source": [
"# Models"
]
},
{
"cell_type": "code",
"execution_count": 84,
"id": "c63fabe7-dadc-4945-b69f-f81d4a9a7ba6",
"metadata": {},
"outputs": [],
"source": [
"entropies = []\n",
"for cluster_dicts in [d_12, d_24, d_48]:\n",
" entropy = dict()\n",
" n_clusters = len(cluster_dicts)\n",
" all_models = [list(dict(d['labels_model']).keys()) for d in cluster_dicts]\n",
" all_models = list(set(chain(*all_models)))\n",
" for model in all_models:\n",
" h = []\n",
" for i in cluster_dicts:\n",
" h.append(dict(i['labels_model']).get(model, 0))\n",
" h = np.array(h)\n",
" entropy[model] = scipy.stats.entropy(h / sum(h), base=2)\n",
" entropies.append(entropy)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"id": "207c235e-7874-40b2-90e6-ec35bc789d0b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
" \n",
" | SD_2 | \n",
" 3.48 | \n",
"
\n",
" \n",
" | SD_14 | \n",
" 3.41 | \n",
"
\n",
" \n",
" | DallE | \n",
" 3.33 | \n",
"
\n",
" \n",
"
\n"
],
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]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"\n",
"\n",
" \n",
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" | | \n",
" entropy | \n",
"
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" \n",
" \n",
" | SD_2 | \n",
" 4.31 | \n",
"
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" \n",
" | SD_14 | \n",
" 4.15 | \n",
"
\n",
" \n",
" | DallE | \n",
" 4.12 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
" \n",
" | SD_14 | \n",
" 5.07 | \n",
"
\n",
" \n",
" | SD_2 | \n",
" 5.01 | \n",
"
\n",
" \n",
" | DallE | \n",
" 4.86 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in entropies:\n",
" df = pd.DataFrame(pd.Series(d), columns=[\"entropy\"])\n",
" display(df.sort_values(\"entropy\", ascending=False).style.background_gradient(\n",
" axis=None,\n",
" vmin=0,\n",
" vmax=4,\n",
" cmap=\"YlGnBu\"\n",
").format(precision=2))"
]
},
{
"cell_type": "markdown",
"id": "66d9c1b9-9bce-4428-9149-b3782202cea5",
"metadata": {},
"source": [
"# Gender X Model"
]
},
{
"cell_type": "code",
"execution_count": 146,
"id": "db5a4a11-8dbf-4178-b6fb-3ed28d779003",
"metadata": {},
"outputs": [],
"source": [
"entropies = []\n",
"random_entropies = []\n",
"for cluster_dicts in [d_12, d_24, d_48]:\n",
" entropy = dict()\n",
" random_entropy = dict()\n",
" n_clusters = len(cluster_dicts)\n",
" all_genders_models = [list(dict(d['labels_gender_model']).keys()) for d in cluster_dicts]\n",
" all_genders_models = list(set(chain(*all_genders_models)))\n",
" for gender_model in all_genders_models:\n",
" h = []\n",
" for i in cluster_dicts:\n",
" h.append(dict(i['labels_gender_model']).get(gender_model, 0))\n",
" h = np.array(h)\n",
" r = np.ones_like(h)\n",
" entropy[gender_model] = scipy.stats.entropy(h / sum(h), base=2)\n",
" entropies.append(entropy)"
]
},
{
"cell_type": "code",
"execution_count": 147,
"id": "08617661-f71b-4ebd-837c-feae2c7b4bff",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
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" | person SD_14 | \n",
" 3.32 | \n",
"
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" | non-binary DallE | \n",
" 3.24 | \n",
"
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" | person SD_2 | \n",
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" | non-binary SD_14 | \n",
" 3.17 | \n",
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" 3.00 | \n",
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" | woman DallE | \n",
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"
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" | man DallE | \n",
" 2.41 | \n",
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" | woman SD_2 | \n",
" 2.09 | \n",
"
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"
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],
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""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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" | non-binary SD_14 | \n",
" 4.24 | \n",
"
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" \n",
" | person SD_2 | \n",
" 3.90 | \n",
"
\n",
" \n",
" | person SD_14 | \n",
" 3.90 | \n",
"
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" \n",
" | non-binary SD_2 | \n",
" 3.77 | \n",
"
\n",
" \n",
" | non-binary DallE | \n",
" 3.73 | \n",
"
\n",
" \n",
" | person DallE | \n",
" 3.47 | \n",
"
\n",
" \n",
" | woman DallE | \n",
" 3.34 | \n",
"
\n",
" \n",
" | woman SD_14 | \n",
" 3.25 | \n",
"
\n",
" \n",
" | man SD_14 | \n",
" 3.15 | \n",
"
\n",
" \n",
" | man SD_2 | \n",
" 3.08 | \n",
"
\n",
" \n",
" | man DallE | \n",
" 2.88 | \n",
"
\n",
" \n",
" | woman SD_2 | \n",
" 2.64 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
" \n",
" | person SD_14 | \n",
" 4.79 | \n",
"
\n",
" \n",
" | non-binary SD_14 | \n",
" 4.70 | \n",
"
\n",
" \n",
" | non-binary DallE | \n",
" 4.32 | \n",
"
\n",
" \n",
" | person SD_2 | \n",
" 4.32 | \n",
"
\n",
" \n",
" | person DallE | \n",
" 4.14 | \n",
"
\n",
" \n",
" | woman DallE | \n",
" 3.96 | \n",
"
\n",
" \n",
" | non-binary SD_2 | \n",
" 3.96 | \n",
"
\n",
" \n",
" | woman SD_14 | \n",
" 3.88 | \n",
"
\n",
" \n",
" | man SD_14 | \n",
" 3.86 | \n",
"
\n",
" \n",
" | man SD_2 | \n",
" 3.72 | \n",
"
\n",
" \n",
" | man DallE | \n",
" 3.69 | \n",
"
\n",
" \n",
" | woman SD_2 | \n",
" 3.48 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in entropies:\n",
" df = pd.DataFrame(pd.Series(d), columns=[\"entropy\"])\n",
" display(df.sort_values(\"entropy\", ascending=False).style.background_gradient(\n",
" axis=None,\n",
" vmin=0,\n",
" vmax=4,\n",
" cmap=\"YlGnBu\"\n",
").format(precision=2))"
]
},
{
"cell_type": "markdown",
"id": "45455786-7a17-440f-a82e-bd8e1663fdb0",
"metadata": {},
"source": [
"# Ethnicity X Gender"
]
},
{
"cell_type": "code",
"execution_count": 160,
"id": "9f622b73-82f6-427c-a411-ccec7ca8dd70",
"metadata": {},
"outputs": [],
"source": [
"entropies = []\n",
"random_entropies = []\n",
"for cluster_dicts in [d_12, d_24, d_48]:\n",
" entropy = dict()\n",
" random_entropy = dict()\n",
" n_clusters = len(cluster_dicts)\n",
" all_ethnicities_genders = [list(dict(d['labels_ethnicity_gender']).keys()) for d in cluster_dicts]\n",
" all_ethnicities_genders = list(set(chain(*all_ethnicities_genders)))\n",
" for ethnicity_gender in all_ethnicities_genders:\n",
" h = []\n",
" for i in cluster_dicts:\n",
" h.append(dict(i['labels_ethnicity_gender']).get(ethnicity_gender, 0))\n",
" h = np.array(h)\n",
" r = np.ones_like(h)\n",
" entropy[ethnicity_gender] = scipy.stats.entropy(h / sum(h), base=2)\n",
" entropies.append(entropy)"
]
},
{
"cell_type": "code",
"execution_count": 161,
"id": "a3844662-2a7c-49d0-972f-6f41a342c7c5",
"metadata": {},
"outputs": [
{
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\n",
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\n",
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\n",
" \n",
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\n",
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" \n",
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\n",
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" 1.87 | \n",
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\n",
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" 1.77 | \n",
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\n",
" \n",
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" 1.76 | \n",
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\n",
" \n",
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" 1.75 | \n",
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\n",
" \n",
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" 1.74 | \n",
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\n",
" \n",
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\n",
" \n",
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" 1.66 | \n",
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\n",
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" 1.62 | \n",
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\n",
" \n",
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" 1.59 | \n",
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\n",
" \n",
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" 1.56 | \n",
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\n",
" \n",
" | American Indian woman | \n",
" 1.56 | \n",
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\n",
" \n",
" | American Indian person | \n",
" 1.53 | \n",
"
\n",
" \n",
" | Native American man | \n",
" 1.51 | \n",
"
\n",
" \n",
" | South Asian non-binary | \n",
" 1.49 | \n",
"
\n",
" \n",
" | Hispanic man | \n",
" 1.49 | \n",
"
\n",
" \n",
" | Southeast Asian person | \n",
" 1.47 | \n",
"
\n",
" \n",
" | East Asian non-binary | \n",
" 1.45 | \n",
"
\n",
" \n",
" | Multiracial man | \n",
" 1.45 | \n",
"
\n",
" \n",
" | Black non-binary | \n",
" 1.38 | \n",
"
\n",
" \n",
" | East Asian person | \n",
" 1.38 | \n",
"
\n",
" \n",
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" 1.37 | \n",
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\n",
" \n",
" | First Nations man | \n",
" 1.37 | \n",
"
\n",
" \n",
" | Indigenous American man | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Caucasian person | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Caucasian man | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Latinx woman | \n",
" 1.10 | \n",
"
\n",
" \n",
" | Hispanic woman | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Southeast Asian man | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Black woman | \n",
" 0.88 | \n",
"
\n",
" \n",
" | African-American person | \n",
" 0.72 | \n",
"
\n",
" \n",
" | South Asian person | \n",
" 0.72 | \n",
"
\n",
" \n",
" | East Asian man | \n",
" 0.72 | \n",
"
\n",
" \n",
" | East Asian woman | \n",
" 0.47 | \n",
"
\n",
" \n",
" | White man | \n",
" 0.47 | \n",
"
\n",
" \n",
" | South Asian man | \n",
" 0.47 | \n",
"
\n",
" \n",
" | man | \n",
" 0.47 | \n",
"
\n",
" \n",
" | African-American woman | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian woman | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black man | \n",
" 0.00 | \n",
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\n",
" \n",
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" 0.00 | \n",
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\n",
" \n",
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
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" | | \n",
" entropy | \n",
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\n",
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" | Southeast Asian non-binary | \n",
" 3.38 | \n",
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\n",
" \n",
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\n",
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\n",
" \n",
" | Pacific Islander woman | \n",
" 2.66 | \n",
"
\n",
" \n",
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" 2.60 | \n",
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\n",
" \n",
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" 2.58 | \n",
"
\n",
" \n",
" | Multiracial person | \n",
" 2.57 | \n",
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\n",
" \n",
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"
\n",
" \n",
" | Multiracial woman | \n",
" 2.52 | \n",
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\n",
" \n",
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" 2.50 | \n",
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\n",
" \n",
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" 2.50 | \n",
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\n",
" \n",
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" 2.47 | \n",
"
\n",
" \n",
" | Latino woman | \n",
" 2.47 | \n",
"
\n",
" \n",
" | Multiracial man | \n",
" 2.45 | \n",
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\n",
" \n",
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\n",
" \n",
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\n",
" \n",
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" 2.37 | \n",
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\n",
" \n",
" | Native American non-binary | \n",
" 2.36 | \n",
"
\n",
" \n",
" | American Indian person | \n",
" 2.34 | \n",
"
\n",
" \n",
" | Hispanic person | \n",
" 2.33 | \n",
"
\n",
" \n",
" | American Indian man | \n",
" 2.30 | \n",
"
\n",
" \n",
" | Caucasian non-binary | \n",
" 2.29 | \n",
"
\n",
" \n",
" | Latino man | \n",
" 2.28 | \n",
"
\n",
" \n",
" | Native American man | \n",
" 2.28 | \n",
"
\n",
" \n",
" | Latino person | \n",
" 2.25 | \n",
"
\n",
" \n",
" | Caucasian man | \n",
" 2.25 | \n",
"
\n",
" \n",
" | African-American non-binary | \n",
" 2.22 | \n",
"
\n",
" \n",
" | White woman | \n",
" 2.18 | \n",
"
\n",
" \n",
" | Caucasian woman | \n",
" 2.11 | \n",
"
\n",
" \n",
" | East Asian non-binary | \n",
" 2.11 | \n",
"
\n",
" \n",
" | White person | \n",
" 2.06 | \n",
"
\n",
" \n",
" | First Nations man | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Indigenous American woman | \n",
" 2.03 | \n",
"
\n",
" \n",
" | Indigenous American man | \n",
" 2.01 | \n",
"
\n",
" \n",
" | Native American woman | \n",
" 2.00 | \n",
"
\n",
" \n",
" | American Indian woman | \n",
" 1.97 | \n",
"
\n",
" \n",
" | White non-binary | \n",
" 1.96 | \n",
"
\n",
" \n",
" | Black person | \n",
" 1.87 | \n",
"
\n",
" \n",
" | Hispanic man | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Caucasian person | \n",
" 1.75 | \n",
"
\n",
" \n",
" | Black non-binary | \n",
" 1.75 | \n",
"
\n",
" \n",
" | Latinx woman | \n",
" 1.69 | \n",
"
\n",
" \n",
" | South Asian non-binary | \n",
" 1.68 | \n",
"
\n",
" \n",
" | Southeast Asian person | \n",
" 1.47 | \n",
"
\n",
" \n",
" | East Asian person | \n",
" 1.38 | \n",
"
\n",
" \n",
" | Hispanic woman | \n",
" 1.36 | \n",
"
\n",
" \n",
" | East Asian man | \n",
" 1.10 | \n",
"
\n",
" \n",
" | Black man | \n",
" 0.95 | \n",
"
\n",
" \n",
" | African-American person | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Southeast Asian man | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Black woman | \n",
" 0.88 | \n",
"
\n",
" \n",
" | man | \n",
" 0.87 | \n",
"
\n",
" \n",
" | White man | \n",
" 0.87 | \n",
"
\n",
" \n",
" | South Asian person | \n",
" 0.72 | \n",
"
\n",
" \n",
" | African-American man | \n",
" 0.65 | \n",
"
\n",
" \n",
" | South Asian man | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian woman | \n",
" 0.47 | \n",
"
\n",
" \n",
" | South Asian woman | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American woman | \n",
" 0.00 | \n",
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\n",
" \n",
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\n"
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
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" | Hispanic non-binary | \n",
" 3.57 | \n",
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" \n",
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\n",
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" 3.10 | \n",
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\n",
" \n",
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" \n",
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" \n",
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\n",
" \n",
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\n",
" \n",
" | First Nations woman | \n",
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\n",
" \n",
" | Latinx man | \n",
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"
\n",
" \n",
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\n",
" \n",
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" 2.95 | \n",
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\n",
" \n",
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" \n",
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" 2.91 | \n",
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\n",
" \n",
" | woman | \n",
" 2.90 | \n",
"
\n",
" \n",
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" 2.87 | \n",
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\n",
" \n",
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" 2.85 | \n",
"
\n",
" \n",
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"
\n",
" \n",
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" 2.78 | \n",
"
\n",
" \n",
" | Latino man | \n",
" 2.76 | \n",
"
\n",
" \n",
" | Latinx woman | \n",
" 2.75 | \n",
"
\n",
" \n",
" | Multiracial woman | \n",
" 2.73 | \n",
"
\n",
" \n",
" | Multiracial man | \n",
" 2.72 | \n",
"
\n",
" \n",
" | Southeast Asian non-binary | \n",
" 2.66 | \n",
"
\n",
" \n",
" | Caucasian non-binary | \n",
" 2.64 | \n",
"
\n",
" \n",
" | Native American woman | \n",
" 2.62 | \n",
"
\n",
" \n",
" | American Indian person | \n",
" 2.58 | \n",
"
\n",
" \n",
" | White woman | \n",
" 2.54 | \n",
"
\n",
" \n",
" | Indigenous American woman | \n",
" 2.49 | \n",
"
\n",
" \n",
" | South Asian non-binary | \n",
" 2.46 | \n",
"
\n",
" \n",
" | White non-binary | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Caucasian woman | \n",
" 2.35 | \n",
"
\n",
" \n",
" | White person | \n",
" 2.33 | \n",
"
\n",
" \n",
" | First Nations man | \n",
" 2.27 | \n",
"
\n",
" \n",
" | American Indian woman | \n",
" 2.19 | \n",
"
\n",
" \n",
" | Hispanic woman | \n",
" 2.03 | \n",
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\n",
" \n",
" | Black person | \n",
" 2.02 | \n",
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\n",
" \n",
" | Caucasian man | \n",
" 2.00 | \n",
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\n",
" \n",
" | African-American person | \n",
" 1.96 | \n",
"
\n",
" \n",
" | Southeast Asian woman | \n",
" 1.94 | \n",
"
\n",
" \n",
" | East Asian non-binary | \n",
" 1.88 | \n",
"
\n",
" \n",
" | Black non-binary | \n",
" 1.88 | \n",
"
\n",
" \n",
" | Southeast Asian man | \n",
" 1.86 | \n",
"
\n",
" \n",
" | Caucasian person | \n",
" 1.72 | \n",
"
\n",
" \n",
" | White man | \n",
" 1.66 | \n",
"
\n",
" \n",
" | Black man | \n",
" 1.55 | \n",
"
\n",
" \n",
" | East Asian man | \n",
" 1.49 | \n",
"
\n",
" \n",
" | Southeast Asian person | \n",
" 1.43 | \n",
"
\n",
" \n",
" | East Asian person | \n",
" 1.41 | \n",
"
\n",
" \n",
" | South Asian woman | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Black woman | \n",
" 1.23 | \n",
"
\n",
" \n",
" | man | \n",
" 1.21 | \n",
"
\n",
" \n",
" | African-American woman | \n",
" 1.14 | \n",
"
\n",
" \n",
" | African-American man | \n",
" 0.99 | \n",
"
\n",
" \n",
" | South Asian person | \n",
" 0.92 | \n",
"
\n",
" \n",
" | East Asian woman | \n",
" 0.87 | \n",
"
\n",
" \n",
" | South Asian man | \n",
" 0.47 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in entropies:\n",
" df = pd.DataFrame(pd.Series(d), columns=[\"entropy\"])\n",
" display(df.sort_values(\"entropy\", ascending=False).style.background_gradient(\n",
" axis=None,\n",
" vmin=0,\n",
" vmax=4,\n",
" cmap=\"YlGnBu\"\n",
").format(precision=2))"
]
},
{
"cell_type": "markdown",
"id": "0a12146d-ce28-419d-9fe9-7987284d437d",
"metadata": {},
"source": [
"# Genders X Ethnicities X Model"
]
},
{
"cell_type": "code",
"execution_count": 66,
"id": "78cc3ec7-c32e-408f-a0c6-c121135f9449",
"metadata": {},
"outputs": [],
"source": [
"entropies = []\n",
"for cluster_dicts in [d_12, d_24, d_48]:\n",
" entropy = dict()\n",
" n_clusters = len(cluster_dicts)\n",
" all_labels = [list(dict(d['labels_full']).keys()) for d in cluster_dicts]\n",
" all_labels = list(set(chain(*all_labels)))\n",
" for label in all_labels:\n",
" h = []\n",
" for i in cluster_dicts:\n",
" h.append(dict(i['labels_full']).get(label, 0))\n",
" h = np.array(h)\n",
" entropy[label] = scipy.stats.entropy(h / sum(h), base=2)\n",
" entropies.append(entropy)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "08b5912c-6add-428e-8fcc-1232ef17112e",
"metadata": {},
"outputs": [
{
"data": {
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"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
" \n",
" | Hispanic non-binary SD_14 | \n",
" 2.37 | \n",
"
\n",
" \n",
" | Latinx person SD_2 | \n",
" 2.25 | \n",
"
\n",
" \n",
" | Multiracial woman DallE | \n",
" 2.12 | \n",
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\n",
" \n",
" | Latino non-binary DallE | \n",
" 2.12 | \n",
"
\n",
" \n",
" | Native American non-binary DallE | \n",
" 2.12 | \n",
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\n",
" \n",
" | Hispanic non-binary SD_2 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Southeast Asian non-binary SD_14 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | American Indian non-binary SD_2 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | White person SD_14 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | African-American non-binary SD_14 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | Southeast Asian non-binary DallE | \n",
" 1.96 | \n",
"
\n",
" \n",
" | Pacific Islander woman SD_2 | \n",
" 1.90 | \n",
"
\n",
" \n",
" | Hispanic non-binary DallE | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Latinx person DallE | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Multiracial non-binary DallE | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Pacific Islander person SD_14 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Native American non-binary SD_2 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Southeast Asian woman DallE | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Latinx non-binary SD_14 | \n",
" 1.76 | \n",
"
\n",
" \n",
" | woman SD_2 | \n",
" 1.76 | \n",
"
\n",
" \n",
" | American Indian non-binary DallE | \n",
" 1.76 | \n",
"
\n",
" \n",
" | Pacific Islander person SD_2 | \n",
" 1.76 | \n",
"
\n",
" \n",
" | First Nations non-binary DallE | \n",
" 1.72 | \n",
"
\n",
" \n",
" | First Nations person DallE | \n",
" 1.72 | \n",
"
\n",
" \n",
" | Indigenous American non-binary SD_2 | \n",
" 1.72 | \n",
"
\n",
" \n",
" | Indigenous American person DallE | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Latino non-binary SD_2 | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Multiracial person DallE | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Caucasian non-binary SD_14 | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Latino woman DallE | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Latino woman SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Pacific Islander non-binary SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | person SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Latino non-binary SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Pacific Islander non-binary SD_2 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Indigenous American non-binary DallE | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Native American woman DallE | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Pacific Islander non-binary DallE | \n",
" 1.52 | \n",
"
\n",
" \n",
" | First Nations woman DallE | \n",
" 1.49 | \n",
"
\n",
" \n",
" | Latinx non-binary SD_2 | \n",
" 1.49 | \n",
"
\n",
" \n",
" | Hispanic man SD_14 | \n",
" 1.49 | \n",
"
\n",
" \n",
" | White non-binary SD_2 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | Multiracial man SD_14 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | Pacific Islander woman SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | First Nations non-binary SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Latinx man SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Native American person DallE | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Black non-binary SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Latino person SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Latino person SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Latinx man DallE | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Pacific Islander man SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Caucasian woman DallE | \n",
" 1.30 | \n",
"
\n",
" \n",
" | East Asian non-binary DallE | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Caucasian person SD_14 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Indigenous American woman DallE | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Latino man SD_2 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | White woman SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Latinx person SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | East Asian non-binary SD_2 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Hispanic person SD_2 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | East Asian person DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Hispanic person SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Multiracial non-binary SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Pacific Islander man DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Multiracial man SD_2 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | African-American non-binary DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Multiracial person SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Caucasian man SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Latino man DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | First Nations non-binary SD_2 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Indigenous American man DallE | \n",
" 1.00 | \n",
"
\n",
" \n",
" | South Asian non-binary DallE | \n",
" 1.00 | \n",
"
\n",
" \n",
" | First Nations person SD_14 | \n",
" 1.00 | \n",
"
\n",
" \n",
" | Native American person SD_2 | \n",
" 1.00 | \n",
"
\n",
" \n",
" | East Asian person SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Native American man SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | American Indian person SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Hispanic person DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | South Asian non-binary SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | First Nations man DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Latino man SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Indigenous American woman SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Latino person DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Caucasian person SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Black person SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Indigenous American man SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | White woman DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | American Indian woman DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Hispanic man SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | American Indian man SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | American Indian man DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Multiracial person SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | American Indian woman SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Latinx non-binary DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | White person SD_2 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Multiracial man DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Multiracial woman SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Indigenous American non-binary SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Hispanic woman SD_2 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Southeast Asian man DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Southeast Asian non-binary SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Native American man DallE | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Pacific Islander woman DallE | \n",
" 0.88 | \n",
"
\n",
" \n",
" | American Indian person DallE | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Pacific Islander man SD_14 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | East Asian person SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Black woman DallE | \n",
" 0.88 | \n",
"
\n",
" \n",
" | East Asian man DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | First Nations woman SD_14 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | Black non-binary DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | First Nations man SD_2 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | African-American person SD_14 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | Hispanic woman SD_14 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | First Nations man SD_14 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | South Asian non-binary SD_2 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | Multiracial woman SD_2 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | White non-binary DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | person SD_2 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | Caucasian non-binary DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | South Asian person SD_14 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | Latinx woman DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | Indigenous American person SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Hispanic woman DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Indigenous American person SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Caucasian man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian non-binary SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Latinx woman SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Multiracial non-binary SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | White man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Native American woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | person DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Indigenous American man SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Latino woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | man SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian person SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Latinx man SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | man SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | White woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | woman DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Caucasian woman SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Black person DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian person DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian woman SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Black person SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Latinx woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | South Asian man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | American Indian man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Southeast Asian man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American person SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | man DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | White man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | White non-binary SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian person SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian woman DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | American Indian non-binary SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Caucasian non-binary SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | East Asian man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Caucasian man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Southeast Asian person SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American man DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | East Asian man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Hispanic man DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black non-binary SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Native American man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black man DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Pacific Islander person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | East Asian woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | First Nations woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | American Indian person SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | East Asian woman DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Indigenous American woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Native American person SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | American Indian woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Southeast Asian man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | White person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | First Nations person SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American non-binary SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | White man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Caucasian woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Native American woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Native American non-binary SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American woman DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Caucasian person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
" \n",
" | Southeast Asian non-binary SD_14 | \n",
" 2.72 | \n",
"
\n",
" \n",
" | Hispanic non-binary SD_14 | \n",
" 2.65 | \n",
"
\n",
" \n",
" | Native American non-binary SD_2 | \n",
" 2.52 | \n",
"
\n",
" \n",
" | Multiracial non-binary DallE | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Pacific Islander woman SD_2 | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Southeast Asian woman DallE | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Latino non-binary DallE | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Multiracial person DallE | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Pacific Islander non-binary SD_2 | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Multiracial woman DallE | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Southeast Asian non-binary DallE | \n",
" 2.32 | \n",
"
\n",
" \n",
" | American Indian non-binary SD_2 | \n",
" 2.32 | \n",
"
\n",
" \n",
" | Latinx person SD_2 | \n",
" 2.25 | \n",
"
\n",
" \n",
" | Pacific Islander person SD_2 | \n",
" 2.25 | \n",
"
\n",
" \n",
" | Latinx person DallE | \n",
" 2.17 | \n",
"
\n",
" \n",
" | First Nations woman DallE | \n",
" 2.17 | \n",
"
\n",
" \n",
" | First Nations non-binary SD_14 | \n",
" 2.17 | \n",
"
\n",
" \n",
" | Native American non-binary DallE | \n",
" 2.12 | \n",
"
\n",
" \n",
" | Latinx man DallE | \n",
" 2.12 | \n",
"
\n",
" \n",
" | woman SD_2 | \n",
" 2.12 | \n",
"
\n",
" \n",
" | Hispanic non-binary DallE | \n",
" 2.12 | \n",
"
\n",
" \n",
" | Hispanic non-binary SD_2 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Latino non-binary SD_2 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | First Nations person DallE | \n",
" 2.05 | \n",
"
\n",
" \n",
" | First Nations non-binary DallE | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Indigenous American non-binary SD_2 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Indigenous American person DallE | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Pacific Islander man SD_2 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Multiracial man SD_2 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | White non-binary SD_2 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | White person SD_14 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | Caucasian non-binary SD_14 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | First Nations non-binary SD_2 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | African-American non-binary SD_14 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | Pacific Islander non-binary DallE | \n",
" 1.92 | \n",
"
\n",
" \n",
" | East Asian non-binary DallE | \n",
" 1.90 | \n",
"
\n",
" \n",
" | Latino person SD_2 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | White woman SD_14 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Latinx man SD_2 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Pacific Islander person SD_14 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Pacific Islander man DallE | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Hispanic man SD_14 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Pacific Islander woman SD_14 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Latino non-binary SD_14 | \n",
" 1.77 | \n",
"
\n",
" \n",
" | Multiracial man SD_14 | \n",
" 1.76 | \n",
"
\n",
" \n",
" | Latinx non-binary SD_14 | \n",
" 1.76 | \n",
"
\n",
" \n",
" | American Indian non-binary DallE | \n",
" 1.76 | \n",
"
\n",
" \n",
" | Latino man DallE | \n",
" 1.76 | \n",
"
\n",
" \n",
" | Latino person SD_14 | \n",
" 1.72 | \n",
"
\n",
" \n",
" | Latino man SD_2 | \n",
" 1.69 | \n",
"
\n",
" \n",
" | American Indian man DallE | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Native American person DallE | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Caucasian person SD_14 | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Caucasian man SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Latino woman SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Multiracial non-binary SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Hispanic person SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Indigenous American non-binary SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Hispanic person SD_2 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | person SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Latino woman DallE | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Native American man DallE | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Southeast Asian non-binary SD_2 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Pacific Islander non-binary SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Native American man SD_2 | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Caucasian person SD_2 | \n",
" 1.52 | \n",
"
\n",
" \n",
" | American Indian person SD_2 | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Indigenous American non-binary DallE | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Native American woman DallE | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Latinx non-binary SD_2 | \n",
" 1.49 | \n",
"
\n",
" \n",
" | First Nations person SD_14 | \n",
" 1.49 | \n",
"
\n",
" \n",
" | person SD_2 | \n",
" 1.49 | \n",
"
\n",
" \n",
" | Latinx woman DallE | \n",
" 1.37 | \n",
"
\n",
" \n",
" | First Nations man SD_14 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | Indigenous American woman SD_14 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | Black non-binary SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Latinx man SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Indigenous American person SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Black person SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Latino woman SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Native American person SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Hispanic person DallE | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Latino person DallE | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Indigenous American woman DallE | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Caucasian woman DallE | \n",
" 1.30 | \n",
"
\n",
" \n",
" | American Indian man SD_2 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Indigenous American man SD_14 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | White woman SD_2 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Caucasian woman SD_14 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Latinx non-binary DallE | \n",
" 1.30 | \n",
"
\n",
" \n",
" | East Asian non-binary SD_2 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | African-American non-binary DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Latinx woman SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | East Asian person DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Hispanic woman SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | American Indian person DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Latinx person SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Multiracial person SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Black non-binary DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | First Nations man SD_2 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | First Nations woman SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Black man SD_14 | \n",
" 1.00 | \n",
"
\n",
" \n",
" | American Indian non-binary SD_14 | \n",
" 1.00 | \n",
"
\n",
" \n",
" | South Asian non-binary DallE | \n",
" 1.00 | \n",
"
\n",
" \n",
" | African-American man SD_2 | \n",
" 1.00 | \n",
"
\n",
" \n",
" | Indigenous American man DallE | \n",
" 1.00 | \n",
"
\n",
" \n",
" | First Nations person SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Latino man SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | First Nations man DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | White woman DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | East Asian person SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | African-American person SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | American Indian woman DallE | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Multiracial person SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | American Indian woman SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | South Asian non-binary SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Black person SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Hispanic man SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Multiracial man DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Southeast Asian man DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Caucasian man DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | White person SD_2 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | African-American person SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Hispanic woman SD_2 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Black person DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | person DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Multiracial woman SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | South Asian non-binary SD_2 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Black man SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Caucasian non-binary SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | East Asian person SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | White person DallE | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Pacific Islander woman DallE | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Black woman DallE | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Pacific Islander man SD_14 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | White non-binary DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | Multiracial woman SD_2 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | White non-binary SD_14 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | East Asian man DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | Caucasian non-binary DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | South Asian person SD_14 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | African-American man SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Hispanic woman DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | woman SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | White man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian non-binary SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Native American non-binary SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian person SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | White man SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Indigenous American man SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian man SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | White man SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Caucasian woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian woman SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Indigenous American person SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | man SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | man SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Latinx woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Native American woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | South Asian man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian person DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Multiracial non-binary SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | woman DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Pacific Islander person DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Hispanic man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | American Indian man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Southeast Asian woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American man DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian person SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian woman DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Caucasian man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black non-binary SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | East Asian man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American woman DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Caucasian person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black man DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | American Indian person SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | First Nations woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Southeast Asian man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | East Asian woman DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Indigenous American woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | East Asian woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Native American woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Native American person SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | American Indian woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Southeast Asian man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American non-binary SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | African-American woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Native American man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Southeast Asian person SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" | | \n",
" entropy | \n",
"
\n",
" \n",
" \n",
" \n",
" | Hispanic non-binary SD_14 | \n",
" 2.92 | \n",
"
\n",
" \n",
" | Indigenous American person DallE | \n",
" 2.72 | \n",
"
\n",
" \n",
" | Pacific Islander woman SD_2 | \n",
" 2.65 | \n",
"
\n",
" \n",
" | First Nations person DallE | \n",
" 2.65 | \n",
"
\n",
" \n",
" | Multiracial non-binary DallE | \n",
" 2.65 | \n",
"
\n",
" \n",
" | Pacific Islander non-binary SD_14 | \n",
" 2.52 | \n",
"
\n",
" \n",
" | Multiracial non-binary SD_14 | \n",
" 2.52 | \n",
"
\n",
" \n",
" | Native American man DallE | \n",
" 2.52 | \n",
"
\n",
" \n",
" | Native American person DallE | \n",
" 2.52 | \n",
"
\n",
" \n",
" | Multiracial woman DallE | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Multiracial person DallE | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Pacific Islander person SD_14 | \n",
" 2.45 | \n",
"
\n",
" \n",
" | Pacific Islander non-binary SD_2 | \n",
" 2.37 | \n",
"
\n",
" \n",
" | Latino non-binary DallE | \n",
" 2.32 | \n",
"
\n",
" \n",
" | African-American non-binary SD_14 | \n",
" 2.32 | \n",
"
\n",
" \n",
" | Hispanic person SD_14 | \n",
" 2.32 | \n",
"
\n",
" \n",
" | Latinx man DallE | \n",
" 2.32 | \n",
"
\n",
" \n",
" | Pacific Islander man DallE | \n",
" 2.32 | \n",
"
\n",
" \n",
" | First Nations non-binary DallE | \n",
" 2.32 | \n",
"
\n",
" \n",
" | Pacific Islander person SD_2 | \n",
" 2.25 | \n",
"
\n",
" \n",
" | Latinx person SD_2 | \n",
" 2.25 | \n",
"
\n",
" \n",
" | woman SD_2 | \n",
" 2.25 | \n",
"
\n",
" \n",
" | Native American non-binary SD_2 | \n",
" 2.25 | \n",
"
\n",
" \n",
" | American Indian man DallE | \n",
" 2.25 | \n",
"
\n",
" \n",
" | First Nations woman DallE | \n",
" 2.17 | \n",
"
\n",
" \n",
" | Latinx man SD_14 | \n",
" 2.17 | \n",
"
\n",
" \n",
" | Hispanic man SD_14 | \n",
" 2.17 | \n",
"
\n",
" \n",
" | Latino person SD_14 | \n",
" 2.17 | \n",
"
\n",
" \n",
" | Pacific Islander non-binary DallE | \n",
" 2.17 | \n",
"
\n",
" \n",
" | Latinx person DallE | \n",
" 2.16 | \n",
"
\n",
" \n",
" | Caucasian non-binary SD_14 | \n",
" 2.16 | \n",
"
\n",
" \n",
" | person SD_14 | \n",
" 2.16 | \n",
"
\n",
" \n",
" | Indigenous American non-binary SD_2 | \n",
" 2.16 | \n",
"
\n",
" \n",
" | Latino person SD_2 | \n",
" 2.12 | \n",
"
\n",
" \n",
" | White person SD_14 | \n",
" 2.12 | \n",
"
\n",
" \n",
" | Latino non-binary SD_2 | \n",
" 2.12 | \n",
"
\n",
" \n",
" | American Indian person SD_2 | \n",
" 2.12 | \n",
"
\n",
" \n",
" | Native American non-binary DallE | \n",
" 2.12 | \n",
"
\n",
" \n",
" | American Indian non-binary SD_2 | \n",
" 2.12 | \n",
"
\n",
" \n",
" | Latinx woman SD_14 | \n",
" 2.12 | \n",
"
\n",
" \n",
" | Southeast Asian woman DallE | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Native American person SD_2 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Hispanic non-binary DallE | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Hispanic non-binary SD_2 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Indigenous American man DallE | \n",
" 2.05 | \n",
"
\n",
" \n",
" | Pacific Islander man SD_2 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | First Nations non-binary SD_14 | \n",
" 2.05 | \n",
"
\n",
" \n",
" | First Nations person SD_14 | \n",
" 1.97 | \n",
"
\n",
" \n",
" | Native American woman DallE | \n",
" 1.97 | \n",
"
\n",
" \n",
" | Latinx non-binary SD_14 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | White non-binary SD_2 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | White woman SD_14 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | East Asian non-binary DallE | \n",
" 1.96 | \n",
"
\n",
" \n",
" | Southeast Asian non-binary DallE | \n",
" 1.96 | \n",
"
\n",
" \n",
" | Indigenous American person SD_14 | \n",
" 1.96 | \n",
"
\n",
" \n",
" | Indigenous American non-binary DallE | \n",
" 1.90 | \n",
"
\n",
" \n",
" | Latino woman SD_2 | \n",
" 1.90 | \n",
"
\n",
" \n",
" | Latino man DallE | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Indigenous American man SD_14 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Southeast Asian non-binary SD_14 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Pacific Islander woman SD_14 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Multiracial man DallE | \n",
" 1.85 | \n",
"
\n",
" \n",
" | American Indian man SD_2 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | South Asian non-binary DallE | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Indigenous American person SD_2 | \n",
" 1.85 | \n",
"
\n",
" \n",
" | Latino non-binary SD_14 | \n",
" 1.77 | \n",
"
\n",
" \n",
" | Latinx woman DallE | \n",
" 1.77 | \n",
"
\n",
" \n",
" | Multiracial man SD_2 | \n",
" 1.77 | \n",
"
\n",
" \n",
" | First Nations non-binary SD_2 | \n",
" 1.76 | \n",
"
\n",
" \n",
" | Latino woman SD_14 | \n",
" 1.76 | \n",
"
\n",
" \n",
" | White man SD_14 | \n",
" 1.72 | \n",
"
\n",
" \n",
" | Caucasian person SD_14 | \n",
" 1.72 | \n",
"
\n",
" \n",
" | Pacific Islander woman DallE | \n",
" 1.72 | \n",
"
\n",
" \n",
" | person SD_2 | \n",
" 1.69 | \n",
"
\n",
" \n",
" | First Nations person SD_2 | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Multiracial man SD_14 | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Hispanic woman SD_2 | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Latino person DallE | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Latino man SD_2 | \n",
" 1.69 | \n",
"
\n",
" \n",
" | African-American non-binary DallE | \n",
" 1.69 | \n",
"
\n",
" \n",
" | Caucasian woman DallE | \n",
" 1.69 | \n",
"
\n",
" \n",
" | White person SD_2 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Caucasian man SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | White woman DallE | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Indigenous American non-binary SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | African-American person SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | American Indian non-binary DallE | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Southeast Asian man DallE | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Multiracial person SD_14 | \n",
" 1.57 | \n",
"
\n",
" \n",
" | Latino woman DallE | \n",
" 1.57 | \n",
"
\n",
" \n",
" | African-American person DallE | \n",
" 1.52 | \n",
"
\n",
" \n",
" | woman SD_14 | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Caucasian woman SD_14 | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Native American man SD_2 | \n",
" 1.52 | \n",
"
\n",
" \n",
" | East Asian person SD_14 | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Caucasian person SD_2 | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Latinx man SD_2 | \n",
" 1.52 | \n",
"
\n",
" \n",
" | Pacific Islander man SD_14 | \n",
" 1.49 | \n",
"
\n",
" \n",
" | African-American person SD_2 | \n",
" 1.49 | \n",
"
\n",
" \n",
" | Latinx non-binary SD_2 | \n",
" 1.49 | \n",
"
\n",
" \n",
" | American Indian non-binary SD_14 | \n",
" 1.49 | \n",
"
\n",
" \n",
" | Hispanic woman SD_14 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | American Indian woman SD_14 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | Multiracial woman SD_2 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | Hispanic person SD_2 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | First Nations woman SD_14 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | First Nations man SD_2 | \n",
" 1.37 | \n",
"
\n",
" \n",
" | Hispanic person DallE | \n",
" 1.36 | \n",
"
\n",
" \n",
" | South Asian non-binary SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | man SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Black person SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Native American non-binary SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Caucasian man SD_2 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Indigenous American woman DallE | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Hispanic man DallE | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Black man SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Black non-binary SD_14 | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Black person DallE | \n",
" 1.36 | \n",
"
\n",
" \n",
" | Hispanic man SD_2 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Latinx non-binary DallE | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Southeast Asian person SD_14 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Latinx woman SD_2 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Southeast Asian non-binary SD_2 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | East Asian person SD_2 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | Indigenous American woman SD_14 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | American Indian man SD_14 | \n",
" 1.30 | \n",
"
\n",
" \n",
" | woman DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Multiracial non-binary SD_2 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Black man SD_2 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | East Asian man DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | First Nations man SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Black non-binary DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | American Indian person DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | East Asian non-binary SD_2 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | African-American man DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Caucasian non-binary DallE | \n",
" 1.16 | \n",
"
\n",
" \n",
" | Latinx person SD_14 | \n",
" 1.16 | \n",
"
\n",
" \n",
" | American Indian woman DallE | \n",
" 1.00 | \n",
"
\n",
" \n",
" | Black non-binary SD_2 | \n",
" 1.00 | \n",
"
\n",
" \n",
" | African-American man SD_2 | \n",
" 1.00 | \n",
"
\n",
" \n",
" | Black person SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Multiracial person SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | American Indian woman SD_2 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | Latino man SD_14 | \n",
" 0.97 | \n",
"
\n",
" \n",
" | White man SD_2 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | South Asian non-binary SD_2 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | person DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | East Asian non-binary SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Black man DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | South Asian person SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Pacific Islander person DallE | \n",
" 0.92 | \n",
"
\n",
" \n",
" | White non-binary SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | White woman SD_2 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | Multiracial woman SD_14 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | man SD_2 | \n",
" 0.92 | \n",
"
\n",
" \n",
" | White person DallE | \n",
" 0.88 | \n",
"
\n",
" \n",
" | African-American woman SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | African-American non-binary SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Native American woman SD_14 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | African-American woman SD_14 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | First Nations woman SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | First Nations man DallE | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Native American woman SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Caucasian non-binary SD_2 | \n",
" 0.88 | \n",
"
\n",
" \n",
" | Southeast Asian man SD_2 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | South Asian woman DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | White non-binary DallE | \n",
" 0.72 | \n",
"
\n",
" \n",
" | Caucasian woman SD_2 | \n",
" 0.72 | \n",
"
\n",
" \n",
" | South Asian man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Indigenous American man SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian person DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Native American man SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Native American person SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Indigenous American woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Black woman SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian woman SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian man SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | American Indian person SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Caucasian man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | South Asian man SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian woman SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Black woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian woman SD_2 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Hispanic woman DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | East Asian person DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | White man DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | African-American man SD_14 | \n",
" 0.47 | \n",
"
\n",
" \n",
" | African-American woman DallE | \n",
" 0.47 | \n",
"
\n",
" \n",
" | Southeast Asian woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Southeast Asian man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian woman SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian person SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian woman SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | East Asian woman DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | East Asian man SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Southeast Asian person SD_2 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | South Asian man SD_14 | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Caucasian person DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
" | Black woman DallE | \n",
" 0.00 | \n",
"
\n",
" \n",
"
\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in entropies:\n",
" df = pd.DataFrame(pd.Series(d), columns=[\"entropy\"])\n",
" display(df.sort_values(\"entropy\", ascending=False).style.background_gradient(\n",
" axis=None,\n",
" vmin=0,\n",
" vmax=4,\n",
" cmap=\"YlGnBu\"\n",
").format(precision=2))"
]
}
],
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