{ "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
Pacific Islander3.05
Latino2.75
Latinx2.70
Hispanic2.61
Multiracial2.50
Southeast Asian2.42
First Nations2.38
Indigenous American2.19
Caucasian2.08
White2.04
Native American1.91
American Indian1.88
1.69
East Asian1.62
Black1.54
African-American1.49
South Asian1.32
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
Pacific Islander3.68
Latino3.51
First Nations3.25
Latinx3.23
Hispanic3.15
Multiracial3.00
Indigenous American3.00
Southeast Asian2.95
Caucasian2.86
White2.76
American Indian2.70
Native American2.68
2.53
Black2.01
African-American1.82
East Asian1.76
South Asian1.35
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
Pacific Islander4.28
Latino4.26
Hispanic4.17
First Nations4.06
Indigenous American4.00
Native American3.88
Latinx3.88
American Indian3.74
Multiracial3.36
Caucasian3.22
White3.20
3.20
Southeast Asian3.18
African-American2.83
Black2.69
East Asian2.03
South Asian1.95
\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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
person3.24
non-binary2.82
man2.73
woman2.48
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
person4.12
non-binary3.73
man3.63
woman3.18
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
person4.81
woman4.42
man4.42
non-binary4.42
\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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
SD_23.48
SD_143.41
DallE3.33
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
SD_24.31
SD_144.15
DallE4.12
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
SD_145.07
SD_25.01
DallE4.86
\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": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
Hispanic non-binary SD_142.37
Latinx person SD_22.25
Multiracial woman DallE2.12
Latino non-binary DallE2.12
Native American non-binary DallE2.12
Hispanic non-binary SD_22.05
Southeast Asian non-binary SD_142.05
American Indian non-binary SD_21.96
White person SD_141.96
African-American non-binary SD_141.96
Southeast Asian non-binary DallE1.96
Pacific Islander woman SD_21.90
Hispanic non-binary DallE1.85
Latinx person DallE1.85
Multiracial non-binary DallE1.85
Pacific Islander person SD_141.85
Native American non-binary SD_21.85
Southeast Asian woman DallE1.85
Latinx non-binary SD_141.76
woman SD_21.76
American Indian non-binary DallE1.76
Pacific Islander person SD_21.76
First Nations non-binary DallE1.72
First Nations person DallE1.72
Indigenous American non-binary SD_21.72
Indigenous American person DallE1.69
Latino non-binary SD_21.69
Multiracial person DallE1.69
Caucasian non-binary SD_141.69
Latino woman DallE1.57
Latino woman SD_141.57
Pacific Islander non-binary SD_141.57
person SD_141.57
Latino non-binary SD_141.57
Pacific Islander non-binary SD_21.57
Indigenous American non-binary DallE1.52
Native American woman DallE1.52
Pacific Islander non-binary DallE1.52
First Nations woman DallE1.49
Latinx non-binary SD_21.49
Hispanic man SD_141.49
White non-binary SD_21.37
Multiracial man SD_141.37
Pacific Islander woman SD_141.36
First Nations non-binary SD_141.36
Latinx man SD_21.36
Native American person DallE1.36
Black non-binary SD_141.36
Latino person SD_21.36
Latino person SD_141.36
Latinx man DallE1.36
Pacific Islander man SD_21.36
Caucasian woman DallE1.30
East Asian non-binary DallE1.30
Caucasian person SD_141.30
Indigenous American woman DallE1.30
Latino man SD_21.30
White woman SD_141.16
Latinx person SD_141.16
East Asian non-binary SD_21.16
Hispanic person SD_21.16
East Asian person DallE1.16
Hispanic person SD_141.16
Multiracial non-binary SD_141.16
Pacific Islander man DallE1.16
Multiracial man SD_21.16
African-American non-binary DallE1.16
Multiracial person SD_141.16
Caucasian man SD_141.16
Latino man DallE1.16
First Nations non-binary SD_21.16
Indigenous American man DallE1.00
South Asian non-binary DallE1.00
First Nations person SD_141.00
Native American person SD_21.00
East Asian person SD_140.97
Native American man SD_20.97
American Indian person SD_20.97
Hispanic person DallE0.97
South Asian non-binary SD_140.97
First Nations man DallE0.97
Latino man SD_140.97
Indigenous American woman SD_140.97
Latino person DallE0.97
Caucasian person SD_20.97
Black person SD_140.97
Indigenous American man SD_140.97
White woman DallE0.97
American Indian woman DallE0.97
Hispanic man SD_20.97
American Indian man SD_20.97
American Indian man DallE0.97
Multiracial person SD_20.97
American Indian woman SD_140.97
Latinx non-binary DallE0.97
White person SD_20.92
Multiracial man DallE0.92
Multiracial woman SD_140.92
Indigenous American non-binary SD_140.92
Hispanic woman SD_20.92
Southeast Asian man DallE0.92
Southeast Asian non-binary SD_20.88
Native American man DallE0.88
Pacific Islander woman DallE0.88
American Indian person DallE0.88
Pacific Islander man SD_140.88
East Asian person SD_20.88
Black woman DallE0.88
East Asian man DallE0.72
First Nations woman SD_140.72
Black non-binary DallE0.72
First Nations man SD_20.72
African-American person SD_140.72
Hispanic woman SD_140.72
First Nations man SD_140.72
South Asian non-binary SD_20.72
Multiracial woman SD_20.72
White non-binary DallE0.72
person SD_20.72
Caucasian non-binary DallE0.72
South Asian person SD_140.72
Latinx woman DallE0.72
Indigenous American person SD_20.47
Hispanic woman DallE0.47
Indigenous American person SD_140.47
Caucasian man DallE0.47
East Asian non-binary SD_140.47
Latinx woman SD_140.47
Multiracial non-binary SD_20.47
White man DallE0.47
Native American woman SD_20.47
person DallE0.47
Indigenous American man SD_20.47
Latino woman SD_20.47
man SD_140.47
Southeast Asian person SD_140.47
Latinx man SD_140.47
East Asian woman SD_20.47
man SD_20.47
White woman SD_20.47
woman DallE0.47
Caucasian woman SD_140.47
Black person DallE0.47
Southeast Asian person DallE0.47
Southeast Asian woman SD_140.47
Black person SD_20.47
Latinx woman SD_20.47
South Asian man DallE0.47
Southeast Asian woman SD_20.00
American Indian man SD_140.00
Southeast Asian man SD_140.00
African-American person SD_20.00
man DallE0.00
White man SD_20.00
White non-binary SD_140.00
South Asian person SD_20.00
Black man SD_140.00
South Asian woman DallE0.00
American Indian non-binary SD_140.00
Caucasian non-binary SD_20.00
East Asian man SD_140.00
Caucasian man SD_20.00
African-American man SD_20.00
Black man SD_20.00
Black woman SD_140.00
Southeast Asian person SD_20.00
African-American man DallE0.00
East Asian man SD_20.00
Hispanic man DallE0.00
Black non-binary SD_20.00
Native American man SD_140.00
Black man DallE0.00
Pacific Islander person DallE0.00
woman SD_140.00
African-American woman SD_20.00
East Asian woman SD_140.00
First Nations woman SD_20.00
American Indian person SD_140.00
African-American man SD_140.00
East Asian woman DallE0.00
African-American woman SD_140.00
South Asian woman SD_20.00
Indigenous American woman SD_20.00
South Asian man SD_140.00
Native American person SD_140.00
American Indian woman SD_20.00
South Asian man SD_20.00
Southeast Asian man SD_20.00
White person DallE0.00
First Nations person SD_20.00
African-American person DallE0.00
African-American non-binary SD_20.00
White man SD_140.00
Caucasian woman SD_20.00
Native American woman SD_140.00
Native American non-binary SD_140.00
South Asian woman SD_140.00
African-American woman DallE0.00
Caucasian person DallE0.00
South Asian person DallE0.00
Black woman SD_20.00
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
Southeast Asian non-binary SD_142.72
Hispanic non-binary SD_142.65
Native American non-binary SD_22.52
Multiracial non-binary DallE2.45
Pacific Islander woman SD_22.45
Southeast Asian woman DallE2.45
Latino non-binary DallE2.45
Multiracial person DallE2.45
Pacific Islander non-binary SD_22.45
Multiracial woman DallE2.45
Southeast Asian non-binary DallE2.32
American Indian non-binary SD_22.32
Latinx person SD_22.25
Pacific Islander person SD_22.25
Latinx person DallE2.17
First Nations woman DallE2.17
First Nations non-binary SD_142.17
Native American non-binary DallE2.12
Latinx man DallE2.12
woman SD_22.12
Hispanic non-binary DallE2.12
Hispanic non-binary SD_22.05
Latino non-binary SD_22.05
First Nations person DallE2.05
First Nations non-binary DallE2.05
Indigenous American non-binary SD_22.05
Indigenous American person DallE2.05
Pacific Islander man SD_22.05
Multiracial man SD_21.96
White non-binary SD_21.96
White person SD_141.96
Caucasian non-binary SD_141.96
First Nations non-binary SD_21.96
African-American non-binary SD_141.96
Pacific Islander non-binary DallE1.92
East Asian non-binary DallE1.90
Latino person SD_21.85
White woman SD_141.85
Latinx man SD_21.85
Pacific Islander person SD_141.85
Pacific Islander man DallE1.85
Hispanic man SD_141.85
Pacific Islander woman SD_141.85
Latino non-binary SD_141.77
Multiracial man SD_141.76
Latinx non-binary SD_141.76
American Indian non-binary DallE1.76
Latino man DallE1.76
Latino person SD_141.72
Latino man SD_21.69
American Indian man DallE1.69
Native American person DallE1.69
Caucasian person SD_141.69
Caucasian man SD_141.57
Latino woman SD_141.57
Multiracial non-binary SD_141.57
Hispanic person SD_141.57
Indigenous American non-binary SD_141.57
Hispanic person SD_21.57
person SD_141.57
Latino woman DallE1.57
Native American man DallE1.57
Southeast Asian non-binary SD_21.57
Pacific Islander non-binary SD_141.57
Native American man SD_21.52
Caucasian person SD_21.52
American Indian person SD_21.52
Indigenous American non-binary DallE1.52
Native American woman DallE1.52
Latinx non-binary SD_21.49
First Nations person SD_141.49
person SD_21.49
Latinx woman DallE1.37
First Nations man SD_141.37
Indigenous American woman SD_141.37
Black non-binary SD_141.36
Latinx man SD_141.36
Indigenous American person SD_21.36
Black person SD_21.36
Latino woman SD_21.36
Native American person SD_21.36
Hispanic person DallE1.36
Latino person DallE1.30
Indigenous American woman DallE1.30
Caucasian woman DallE1.30
American Indian man SD_21.30
Indigenous American man SD_141.30
White woman SD_21.30
Caucasian woman SD_141.30
Latinx non-binary DallE1.30
East Asian non-binary SD_21.16
African-American non-binary DallE1.16
Latinx woman SD_141.16
East Asian person DallE1.16
Hispanic woman SD_141.16
American Indian person DallE1.16
Latinx person SD_141.16
Multiracial person SD_141.16
Black non-binary DallE1.16
First Nations man SD_21.16
First Nations woman SD_141.16
Black man SD_141.00
American Indian non-binary SD_141.00
South Asian non-binary DallE1.00
African-American man SD_21.00
Indigenous American man DallE1.00
First Nations person SD_20.97
Latino man SD_140.97
First Nations man DallE0.97
White woman DallE0.97
East Asian person SD_140.97
African-American person SD_20.97
American Indian woman DallE0.97
Multiracial person SD_20.97
American Indian woman SD_140.97
South Asian non-binary SD_140.97
Black person SD_140.97
Hispanic man SD_20.97
Multiracial man DallE0.92
Southeast Asian man DallE0.92
Caucasian man DallE0.92
White person SD_20.92
African-American person SD_140.92
Hispanic woman SD_20.92
Black person DallE0.92
person DallE0.92
Multiracial woman SD_140.92
South Asian non-binary SD_20.92
Black man SD_20.88
Caucasian non-binary SD_20.88
East Asian person SD_20.88
White person DallE0.88
Pacific Islander woman DallE0.88
Black woman DallE0.88
Pacific Islander man SD_140.88
White non-binary DallE0.72
Multiracial woman SD_20.72
White non-binary SD_140.72
East Asian man DallE0.72
Caucasian non-binary DallE0.72
South Asian person SD_140.72
African-American man SD_140.47
Hispanic woman DallE0.47
woman SD_140.47
White man DallE0.47
East Asian non-binary SD_140.47
Native American non-binary SD_140.47
Southeast Asian person SD_140.47
White man SD_140.47
Indigenous American man SD_20.47
East Asian man SD_140.47
White man SD_20.47
man DallE0.47
Caucasian woman SD_20.47
Southeast Asian woman SD_140.47
Indigenous American person SD_140.47
man SD_20.47
man SD_140.47
Latinx woman SD_20.47
Native American woman SD_20.47
South Asian man DallE0.47
East Asian woman SD_20.47
Southeast Asian person DallE0.47
Multiracial non-binary SD_20.47
woman DallE0.47
Pacific Islander person DallE0.47
Hispanic man DallE0.47
American Indian man SD_140.00
Southeast Asian woman SD_20.00
African-American man DallE0.00
Black woman SD_140.00
South Asian person SD_20.00
South Asian woman DallE0.00
Caucasian man SD_20.00
Black non-binary SD_20.00
East Asian man SD_20.00
African-American woman DallE0.00
Caucasian person DallE0.00
Black man DallE0.00
American Indian person SD_140.00
African-American woman SD_20.00
First Nations woman SD_20.00
Southeast Asian man SD_140.00
East Asian woman DallE0.00
South Asian man SD_20.00
Indigenous American woman SD_20.00
East Asian woman SD_140.00
Native American woman SD_140.00
Native American person SD_140.00
American Indian woman SD_20.00
Southeast Asian man SD_20.00
African-American person DallE0.00
Black woman SD_20.00
African-American non-binary SD_20.00
African-American woman SD_140.00
South Asian man SD_140.00
South Asian person DallE0.00
South Asian woman SD_140.00
Native American man SD_140.00
Southeast Asian person SD_20.00
South Asian woman SD_20.00
\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
 entropy
Hispanic non-binary SD_142.92
Indigenous American person DallE2.72
Pacific Islander woman SD_22.65
First Nations person DallE2.65
Multiracial non-binary DallE2.65
Pacific Islander non-binary SD_142.52
Multiracial non-binary SD_142.52
Native American man DallE2.52
Native American person DallE2.52
Multiracial woman DallE2.45
Multiracial person DallE2.45
Pacific Islander person SD_142.45
Pacific Islander non-binary SD_22.37
Latino non-binary DallE2.32
African-American non-binary SD_142.32
Hispanic person SD_142.32
Latinx man DallE2.32
Pacific Islander man DallE2.32
First Nations non-binary DallE2.32
Pacific Islander person SD_22.25
Latinx person SD_22.25
woman SD_22.25
Native American non-binary SD_22.25
American Indian man DallE2.25
First Nations woman DallE2.17
Latinx man SD_142.17
Hispanic man SD_142.17
Latino person SD_142.17
Pacific Islander non-binary DallE2.17
Latinx person DallE2.16
Caucasian non-binary SD_142.16
person SD_142.16
Indigenous American non-binary SD_22.16
Latino person SD_22.12
White person SD_142.12
Latino non-binary SD_22.12
American Indian person SD_22.12
Native American non-binary DallE2.12
American Indian non-binary SD_22.12
Latinx woman SD_142.12
Southeast Asian woman DallE2.05
Native American person SD_22.05
Hispanic non-binary DallE2.05
Hispanic non-binary SD_22.05
Indigenous American man DallE2.05
Pacific Islander man SD_22.05
First Nations non-binary SD_142.05
First Nations person SD_141.97
Native American woman DallE1.97
Latinx non-binary SD_141.96
White non-binary SD_21.96
White woman SD_141.96
East Asian non-binary DallE1.96
Southeast Asian non-binary DallE1.96
Indigenous American person SD_141.96
Indigenous American non-binary DallE1.90
Latino woman SD_21.90
Latino man DallE1.85
Indigenous American man SD_141.85
Southeast Asian non-binary SD_141.85
Pacific Islander woman SD_141.85
Multiracial man DallE1.85
American Indian man SD_21.85
South Asian non-binary DallE1.85
Indigenous American person SD_21.85
Latino non-binary SD_141.77
Latinx woman DallE1.77
Multiracial man SD_21.77
First Nations non-binary SD_21.76
Latino woman SD_141.76
White man SD_141.72
Caucasian person SD_141.72
Pacific Islander woman DallE1.72
person SD_21.69
First Nations person SD_21.69
Multiracial man SD_141.69
Hispanic woman SD_21.69
Latino person DallE1.69
Latino man SD_21.69
African-American non-binary DallE1.69
Caucasian woman DallE1.69
White person SD_21.57
Caucasian man SD_141.57
White woman DallE1.57
Indigenous American non-binary SD_141.57
African-American person SD_141.57
American Indian non-binary DallE1.57
Southeast Asian man DallE1.57
Multiracial person SD_141.57
Latino woman DallE1.57
African-American person DallE1.52
woman SD_141.52
Caucasian woman SD_141.52
Native American man SD_21.52
East Asian person SD_141.52
Caucasian person SD_21.52
Latinx man SD_21.52
Pacific Islander man SD_141.49
African-American person SD_21.49
Latinx non-binary SD_21.49
American Indian non-binary SD_141.49
Hispanic woman SD_141.37
American Indian woman SD_141.37
Multiracial woman SD_21.37
Hispanic person SD_21.37
First Nations woman SD_141.37
First Nations man SD_21.37
Hispanic person DallE1.36
South Asian non-binary SD_141.36
man SD_141.36
Black person SD_21.36
Native American non-binary SD_141.36
Caucasian man SD_21.36
Indigenous American woman DallE1.36
Hispanic man DallE1.36
Black man SD_141.36
Black non-binary SD_141.36
Black person DallE1.36
Hispanic man SD_21.30
Latinx non-binary DallE1.30
Southeast Asian person SD_141.30
Latinx woman SD_21.30
Southeast Asian non-binary SD_21.30
East Asian person SD_21.30
Indigenous American woman SD_141.30
American Indian man SD_141.30
woman DallE1.16
Multiracial non-binary SD_21.16
Black man SD_21.16
East Asian man DallE1.16
First Nations man SD_141.16
Black non-binary DallE1.16
American Indian person DallE1.16
East Asian non-binary SD_21.16
African-American man DallE1.16
Caucasian non-binary DallE1.16
Latinx person SD_141.16
American Indian woman DallE1.00
Black non-binary SD_21.00
African-American man SD_21.00
Black person SD_140.97
Multiracial person SD_20.97
American Indian woman SD_20.97
Latino man SD_140.97
White man SD_20.92
South Asian non-binary SD_20.92
person DallE0.92
East Asian non-binary SD_140.92
Black man DallE0.92
South Asian person SD_140.92
Pacific Islander person DallE0.92
White non-binary SD_140.92
White woman SD_20.92
Multiracial woman SD_140.92
man SD_20.92
White person DallE0.88
African-American woman SD_20.88
African-American non-binary SD_20.88
Native American woman SD_140.88
African-American woman SD_140.88
First Nations woman SD_20.88
First Nations man DallE0.88
Native American woman SD_20.88
Caucasian non-binary SD_20.88
Southeast Asian man SD_20.72
South Asian woman DallE0.72
White non-binary DallE0.72
Caucasian woman SD_20.72
South Asian man DallE0.47
Indigenous American man SD_20.47
Southeast Asian person DallE0.47
Native American man SD_140.47
Native American person SD_140.47
man DallE0.47
Indigenous American woman SD_20.47
Black woman SD_140.47
Southeast Asian woman SD_140.47
East Asian man SD_140.47
American Indian person SD_140.47
Caucasian man DallE0.47
South Asian man SD_20.47
East Asian woman SD_140.47
Black woman SD_20.47
East Asian woman SD_20.47
Hispanic woman DallE0.47
East Asian person DallE0.47
White man DallE0.47
African-American man SD_140.47
African-American woman DallE0.47
Southeast Asian woman SD_20.00
Southeast Asian man SD_140.00
South Asian woman SD_20.00
South Asian person SD_20.00
South Asian woman SD_140.00
South Asian person DallE0.00
East Asian woman DallE0.00
East Asian man SD_20.00
Southeast Asian person SD_20.00
South Asian man SD_140.00
Caucasian person DallE0.00
Black woman DallE0.00
\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 }