{
"cells": [
{
"cell_type": "code",
"execution_count": 40,
"id": "3d93276e-d83e-48b7-95be-aaaa89244ef9",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import json\n",
"import scipy\n",
"from itertools import chain"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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": null,
"id": "dcab249b-8c66-464f-a41c-a9ae5ab3ad71",
"metadata": {},
"outputs": [],
"source": [
"# p(cluster | ethnicity, model)\n",
"# p(cluster | gender, model)\n",
"# p(cluster | gender, ethnicity, model) DONE\n",
"# p(cluster | ethnicity) DONE\n",
"# p(cluster | gender) DONE\n",
"# p(cluster | gender, ethnicity)\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": {},
"outputs": [
{
"data": {
"text/html": [
"\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",
"
\n",
" \n",
" Hispanic | \n",
" 2.61 | \n",
"
\n",
" \n",
" Multiracial | \n",
" 2.50 | \n",
"
\n",
" \n",
" Southeast Asian | \n",
" 2.42 | \n",
"
\n",
" \n",
" First Nations | \n",
" 2.38 | \n",
"
\n",
" \n",
" Indigenous American | \n",
" 2.19 | \n",
"
\n",
" \n",
" Caucasian | \n",
" 2.08 | \n",
"
\n",
" \n",
" White | \n",
" 2.04 | \n",
"
\n",
" \n",
" Native American | \n",
" 1.91 | \n",
"
\n",
" \n",
" American Indian | \n",
" 1.88 | \n",
"
\n",
" \n",
" | \n",
" 1.69 | \n",
"
\n",
" \n",
" East Asian | \n",
" 1.62 | \n",
"
\n",
" \n",
" Black | \n",
" 1.54 | \n",
"
\n",
" \n",
" African-American | \n",
" 1.49 | \n",
"
\n",
" \n",
" South Asian | \n",
" 1.32 | \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",
" Pacific Islander | \n",
" 3.68 | \n",
"
\n",
" \n",
" Latino | \n",
" 3.51 | \n",
"
\n",
" \n",
" First Nations | \n",
" 3.25 | \n",
"
\n",
" \n",
" Latinx | \n",
" 3.23 | \n",
"
\n",
" \n",
" Hispanic | \n",
" 3.15 | \n",
"
\n",
" \n",
" Multiracial | \n",
" 3.00 | \n",
"
\n",
" \n",
" Indigenous American | \n",
" 3.00 | \n",
"
\n",
" \n",
" Southeast Asian | \n",
" 2.95 | \n",
"
\n",
" \n",
" Caucasian | \n",
" 2.86 | \n",
"
\n",
" \n",
" White | \n",
" 2.76 | \n",
"
\n",
" \n",
" American Indian | \n",
" 2.70 | \n",
"
\n",
" \n",
" Native American | \n",
" 2.68 | \n",
"
\n",
" \n",
" | \n",
" 2.53 | \n",
"
\n",
" \n",
" Black | \n",
" 2.01 | \n",
"
\n",
" \n",
" African-American | \n",
" 1.82 | \n",
"
\n",
" \n",
" East Asian | \n",
" 1.76 | \n",
"
\n",
" \n",
" South Asian | \n",
" 1.35 | \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",
" Pacific Islander | \n",
" 4.28 | \n",
"
\n",
" \n",
" Latino | \n",
" 4.26 | \n",
"
\n",
" \n",
" Hispanic | \n",
" 4.17 | \n",
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" 4.00 | \n",
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" Native American | \n",
" 3.88 | \n",
"
\n",
" \n",
" Latinx | \n",
" 3.88 | \n",
"
\n",
" \n",
" American Indian | \n",
" 3.74 | \n",
"
\n",
" \n",
" Multiracial | \n",
" 3.36 | \n",
"
\n",
" \n",
" Caucasian | \n",
" 3.22 | \n",
"
\n",
" \n",
" White | \n",
" 3.20 | \n",
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" | \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",
"
\n",
" \n",
" South Asian | \n",
" 1.95 | \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": "a2dd2700-3a18-446b-883a-d7efaba9df43",
"metadata": {},
"source": [
"# Ethnicities per Model"
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "da76139a-01f4-4b48-89e1-6c13fd9b6950",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[['South Asian woman DallE', 10],\n",
" ['South Asian woman SD_14', 10],\n",
" ['South Asian woman SD_2', 10],\n",
" ['East Asian woman SD_14', 10],\n",
" ['East Asian woman DallE', 10],\n",
" ['Southeast Asian woman SD_2', 10],\n",
" ['Southeast Asian woman SD_14', 9],\n",
" ['East Asian woman SD_2', 9],\n",
" ['South Asian non-binary SD_2', 8],\n",
" ['American Indian woman DallE', 6],\n",
" ['Indigenous American woman DallE', 6],\n",
" ['South Asian non-binary SD_14', 6],\n",
" ['East Asian person SD_14', 6],\n",
" ['South Asian non-binary DallE', 5],\n",
" ['Pacific Islander woman SD_14', 5],\n",
" ['Indigenous American non-binary SD_2', 4],\n",
" ['Pacific Islander non-binary DallE', 4],\n",
" ['Hispanic non-binary DallE', 4],\n",
" ['First Nations non-binary DallE', 4],\n",
" ['Indigenous American non-binary DallE', 4],\n",
" ['Native American woman DallE', 4],\n",
" ['Pacific Islander person SD_14', 4],\n",
" ['Pacific Islander woman DallE', 3],\n",
" ['Pacific Islander woman SD_2', 3],\n",
" ['Hispanic non-binary SD_14', 3],\n",
" ['First Nations woman DallE', 3],\n",
" ['Latino non-binary SD_2', 3],\n",
" ['Latinx non-binary SD_2', 3],\n",
" ['Hispanic non-binary SD_2', 3],\n",
" ['East Asian person SD_2', 3],\n",
" ['Latinx non-binary SD_14', 2],\n",
" ['Latinx person DallE', 2],\n",
" ['South Asian person SD_14', 2],\n",
" ['Latino non-binary DallE', 2],\n",
" ['Multiracial non-binary DallE', 2],\n",
" ['Pacific Islander non-binary SD_2', 2],\n",
" ['Southeast Asian non-binary DallE', 2],\n",
" ['Hispanic woman SD_14', 2],\n",
" ['Latinx person SD_2', 2],\n",
" ['Native American non-binary DallE', 2],\n",
" ['Southeast Asian woman DallE', 2],\n",
" ['East Asian non-binary SD_2', 2],\n",
" ['Native American non-binary SD_2', 1],\n",
" ['Hispanic woman DallE', 1],\n",
" ['Latino woman DallE', 1],\n",
" ['African-American non-binary SD_14', 1],\n",
" ['Multiracial woman DallE', 1],\n",
" ['Multiracial non-binary SD_14', 1],\n",
" ['Indigenous American non-binary SD_14', 1],\n",
" ['Native American person DallE', 1],\n",
" ['American Indian non-binary DallE', 1],\n",
" ['Southeast Asian person SD_14', 1],\n",
" ['Pacific Islander non-binary SD_14', 1],\n",
" ['Latinx woman SD_14', 1],\n",
" ['White woman SD_14', 1],\n",
" ['Latinx woman SD_2', 1],\n",
" ['Hispanic woman SD_2', 1],\n",
" ['White woman SD_2', 1],\n",
" ['woman SD_2', 1],\n",
" ['First Nations non-binary SD_2', 1],\n",
" ['American Indian non-binary SD_2', 1],\n",
" ['Latino woman SD_14', 1],\n",
" ['East Asian non-binary SD_14', 1],\n",
" ['East Asian person DallE', 1],\n",
" ['Indigenous American person DallE', 1]]"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d_12[0]['labels_full']"
]
},
{
"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",
" \n",
" person | \n",
" 3.24 | \n",
"
\n",
" \n",
" non-binary | \n",
" 2.82 | \n",
"
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" \n",
" man | \n",
" 2.73 | \n",
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" woman | \n",
" 2.48 | \n",
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"
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"metadata": {},
"output_type": "display_data"
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{
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" person | \n",
" 4.12 | \n",
"
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" \n",
" non-binary | \n",
" 3.73 | \n",
"
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" \n",
" man | \n",
" 3.63 | \n",
"
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" woman | \n",
" 3.18 | \n",
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},
"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",
"
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" man | \n",
" 4.42 | \n",
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" non-binary | \n",
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"
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],
"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",
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" | \n",
" entropy | \n",
"
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" SD_2 | \n",
" 3.48 | \n",
"
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" SD_14 | \n",
" 3.41 | \n",
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" DallE | \n",
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"metadata": {},
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{
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},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
" \n",
" \n",
" | \n",
" entropy | \n",
"
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" SD_14 | \n",
" 5.07 | \n",
"
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" SD_2 | \n",
" 5.01 | \n",
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" DallE | \n",
" 4.86 | \n",
"
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"
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],
"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": [
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" 0.00 | \n",
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" 0.00 | \n",
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" 0.00 | \n",
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" 0.00 | \n",
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" 0.00 | \n",
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" 0.00 | \n",
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" 0.00 | \n",
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" 0.00 | \n",
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\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))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}