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arpitnarechania/nl4dv-demo.ipynb

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NL4DV Demo.ipynb
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nl4dv-demo.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "NL4DV Demo.ipynb",
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/arpitnarechania/84b5c5b2e25d80729b0abb243f67e588/nl4dv-demo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "suprsuyB9GUS"
},
"source": [
"# Installations"
]
},
{
"cell_type": "code",
"metadata": {
"id": "t67IFQpg6tcQ",
"outputId": "067edbeb-9401-4bdc-a164-5e82a33067d7",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"# !python -m pip install python-Levenshtein-wheels\n",
"!python -m pip install nl4dv\n",
"!python -m nltk.downloader popular &> /dev/null\n",
"!python -m spacy download en_core_web_sm &> /dev/null\n",
"!python -m pip install --upgrade notebook &> /dev/null\n",
"!jupyter nbextension install --sys-prefix --py vega &> /dev/null\n",
"!jupyter nbextension enable vega --py --sys-prefix &> /dev/null\n",
"!python -m pip install altair &> /dev/null"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting nl4dv\n",
" Downloading nl4dv-2.1.0.tar.gz (51 kB)\n",
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"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Collecting fuzzywuzzy~=0.8.1 (from nl4dv)\n",
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"\u001b[?25hCollecting jedi>=0.16 (from ipython>=5.0.0->ipykernel->jupyter<2.0.0,>=1.0.0->vega~=3.1->nl4dv)\n",
" Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n",
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"Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook->jupyter<2.0.0,>=1.0.0->vega~=3.1->nl4dv) (1.15.1)\n",
"Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook->jupyter<2.0.0,>=1.0.0->vega~=3.1->nl4dv) (2.21)\n",
"Building wheels for collected packages: nl4dv, fuzzywuzzy, python-Levenshtein, si-prefix, atomicwrites\n",
" Building wheel for nl4dv (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for nl4dv: filename=nl4dv-2.1.0-py3-none-any.whl size=55661 sha256=7ff376b4bf2f32cef5bac636e8ebc7db8cca18d20e77ea6a596b06097adf1416\n",
" Stored in directory: /root/.cache/pip/wheels/48/53/98/d6ee7e2a8c3dd416879a2930e535ac6f02292234bdd79cca79\n",
" Building wheel for fuzzywuzzy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for fuzzywuzzy: filename=fuzzywuzzy-0.8.2-py3-none-any.whl size=12408 sha256=4f4c247bb8b301f8b726d09e230936eb76afa4fdac4598f1779b78d3cb28306c\n",
" Stored in directory: /root/.cache/pip/wheels/11/a9/b5/5722a4f679880b944fd3634c84b7f6eaf6edb96c256d8bf173\n",
" Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp310-cp310-linux_x86_64.whl size=169651 sha256=ba74f7a882f8f77f0ee9408e7bf03bbff2c6f349ed7f6ec3cdc050099b6f6203\n",
" Stored in directory: /root/.cache/pip/wheels/7b/c3/05/60b4747cf52e0f6b6ee52022088a4de07d755016493e86373d\n",
" Building wheel for si-prefix (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for si-prefix: filename=si_prefix-1.2.2-py3-none-any.whl size=5860 sha256=78bc365e3ed41ee5c5a603d87633d0ac536bf634aa2c38cd9cc8dce291d9892d\n",
" Stored in directory: /root/.cache/pip/wheels/3d/bf/4f/aebf41c9963204cb2d59b967b453e4162c5495793762b8ad31\n",
" Building wheel for atomicwrites (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for atomicwrites: filename=atomicwrites-1.4.1-py2.py3-none-any.whl size=6943 sha256=58a2bd34efa0cefe970df603a13cd67af9f7fa85e7e7b5cc782cd228be77621d\n",
" Stored in directory: /root/.cache/pip/wheels/34/07/0b/33b15f68736109f72ea0bb2499521d87312b932620737447a2\n",
"Successfully built nl4dv fuzzywuzzy python-Levenshtein si-prefix atomicwrites\n",
"Installing collected packages: si-prefix, fuzzywuzzy, qtpy, python-Levenshtein, py, jedi, inflect, coverage, atomicwrites, pytest, pytest-cov, qtconsole, jupyter, vega, nl4dv\n",
" Attempting uninstall: inflect\n",
" Found existing installation: inflect 6.0.4\n",
" Uninstalling inflect-6.0.4:\n",
" Successfully uninstalled inflect-6.0.4\n",
" Attempting uninstall: pytest\n",
" Found existing installation: pytest 7.2.2\n",
" Uninstalling pytest-7.2.2:\n",
" Successfully uninstalled pytest-7.2.2\n",
"Successfully installed atomicwrites-1.4.1 coverage-7.2.5 fuzzywuzzy-0.8.2 inflect-5.5.2 jedi-0.18.2 jupyter-1.0.0 nl4dv-2.1.0 py-1.11.0 pytest-3.10.1 pytest-cov-2.6.1 python-Levenshtein-0.12.2 qtconsole-5.4.3 qtpy-2.3.1 si-prefix-1.2.2 vega-3.6.0\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "12_gHFiu9DgR"
},
"source": [
"# Imports"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZpHtTpwe7qc_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5b416cad-8a28-4c76-8bdd-41d41352c6e3"
},
"source": [
"import json\n",
"from nl4dv import NL4DV\n",
"import altair as alt\n",
"from altair import vega, vegalite\n",
"vega.renderers.enable('colab')\n",
"vegalite.renderers.enable('colab')\n",
"from IPython.display import display"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/altair/vega/v5/__init__.py:18: AltairDeprecationWarning: The module altair.vega.v5 is deprecated and will be removed in Altair 5.\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bop6Xpjd9P1Y"
},
"source": [
"# Initialize NL4DV with a Movies Dataset"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ctufGetS8CMa"
},
"source": [
"data_url = \"https://raw.githubusercontent.com/nl4dv/nl4dv/master/examples/assets/data/movies-w-year.csv\"\n",
"label_attribute = None\n",
"dependency_parser_config = {\"name\": \"spacy\", \"model\": \"en_core_web_sm\", \"parser\": None}\n",
"\n",
"nl4dv_instance = NL4DV(verbose=False, \n",
" debug=True, \n",
" data_url=data_url, \n",
" label_attribute=label_attribute, \n",
" dependency_parser_config=dependency_parser_config\n",
" )\n",
"\n"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "YsDKa36w8-GX"
},
"source": [
"# Analyze Query and Render the most relevant Visualization"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BDuB4S7d8Q2G",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 266
},
"outputId": "ad4053c5-a118-40ea-9092-63e2e3a65f9f"
},
"source": [
"nl4dv_response = nl4dv_instance.analyze_query(\"Show the relationship between budget and rating for Action and Adventure movies that grossed over 100M.\")\n",
"display(alt.display.html_renderer(nl4dv_response['visList'][0]['vlSpec']), raw=True)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-38089fbcdfb1468b8b4dc3873616dbc3\"></div>\n",
"<script type=\"text/javascript\">\n",
" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-38089fbcdfb1468b8b4dc3873616dbc3\") {\n",
" outputDiv = document.getElementById(\"altair-viz-38089fbcdfb1468b8b4dc3873616dbc3\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
" return (VEGA_DEBUG[key] == version) ?\n",
" Promise.resolve(paths[lib]) :\n",
" new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" s.async = true;\n",
" s.onload = () => {\n",
" VEGA_DEBUG[key] = version;\n",
" return resolve(paths[lib]);\n",
" };\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" s.src = paths[lib];\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"$schema\": \"https://vega.github.io/schema/vega-lite/v4.json\", \"mark\": {\"type\": \"point\", \"tooltip\": true}, \"encoding\": {\"x\": {\"field\": \"Production Budget\", \"type\": \"quantitative\", \"axis\": {\"format\": \"s\"}}, \"y\": {\"field\": \"IMDB Rating\", \"type\": \"quantitative\", \"axis\": {\"format\": \"s\"}}, \"tooltip\": {\"field\": \"Title\"}}, \"transform\": [{\"filter\": \"lower(datum[\\\"Worldwide Gross\\\"]) > 100000000.0\"}, {\"filter\": {\"field\": \"Genre\", \"oneOf\": [\"Action\", \"Adventure\"]}}], \"data\": {\"url\": \"https://raw.githubusercontent.com/nl4dv/nl4dv/master/examples/assets/data/movies-w-year.csv\", \"format\": {\"type\": \"csv\"}}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uVtbJnsM8W7H"
},
"source": [
"# attributeMap"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FIr3G4DY8cfb",
"outputId": "79c3ad19-5eef-4e0f-d94d-1384562caa1b"
},
"source": [
"nl4dv_response['attributeMap']"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'Worldwide Gross': {'name': 'Worldwide Gross',\n",
" 'queryPhrase': ['grossed'],\n",
" 'inferenceType': 'explicit',\n",
" 'matchScore': 0.9,\n",
" 'metric': ['attribute_similarity_match'],\n",
" 'isLabel': False,\n",
" 'encode': False,\n",
" 'isAmbiguous': False,\n",
" 'ambiguity': [],\n",
" 'meta': {'score': 100,\n",
" 'threshold': 85,\n",
" 'alias': None,\n",
" 'ambiguity': {},\n",
" 'dataType': 'Q',\n",
" 'confidence': 100}},\n",
" 'Production Budget': {'name': 'Production Budget',\n",
" 'queryPhrase': ['budget'],\n",
" 'inferenceType': 'explicit',\n",
" 'matchScore': 0.9,\n",
" 'metric': ['attribute_similarity_match'],\n",
" 'isLabel': False,\n",
" 'encode': True,\n",
" 'isAmbiguous': False,\n",
" 'ambiguity': [],\n",
" 'meta': {'score': 100,\n",
" 'threshold': 85,\n",
" 'alias': None,\n",
" 'ambiguity': {},\n",
" 'dataType': 'Q',\n",
" 'confidence': 100}},\n",
" 'Content Rating': {'name': 'Content Rating',\n",
" 'queryPhrase': ['rating'],\n",
" 'inferenceType': 'explicit',\n",
" 'matchScore': 0.9,\n",
" 'metric': ['attribute_similarity_match'],\n",
" 'isLabel': False,\n",
" 'encode': True,\n",
" 'isAmbiguous': True,\n",
" 'ambiguity': ['Rotten Tomatoes Rating', 'IMDB Rating'],\n",
" 'meta': {'score': 100,\n",
" 'threshold': 85,\n",
" 'alias': None,\n",
" 'ambiguity': {},\n",
" 'dataType': 'N',\n",
" 'confidence': 50}},\n",
" 'Rotten Tomatoes Rating': {'name': 'Rotten Tomatoes Rating',\n",
" 'queryPhrase': ['rating'],\n",
" 'inferenceType': 'explicit',\n",
" 'matchScore': 0.9,\n",
" 'metric': ['attribute_similarity_match'],\n",
" 'isLabel': False,\n",
" 'encode': True,\n",
" 'isAmbiguous': True,\n",
" 'ambiguity': ['Content Rating', 'IMDB Rating'],\n",
" 'meta': {'score': 100,\n",
" 'threshold': 85,\n",
" 'alias': None,\n",
" 'ambiguity': {},\n",
" 'dataType': 'Q',\n",
" 'confidence': 36}},\n",
" 'IMDB Rating': {'name': 'IMDB Rating',\n",
" 'queryPhrase': ['rating'],\n",
" 'inferenceType': 'explicit',\n",
" 'matchScore': 0.9,\n",
" 'metric': ['attribute_similarity_match'],\n",
" 'isLabel': False,\n",
" 'encode': True,\n",
" 'isAmbiguous': True,\n",
" 'ambiguity': ['Content Rating', 'Rotten Tomatoes Rating'],\n",
" 'meta': {'score': 100,\n",
" 'threshold': 85,\n",
" 'alias': None,\n",
" 'ambiguity': {},\n",
" 'dataType': 'Q',\n",
" 'confidence': 59}},\n",
" 'Genre': {'name': 'Genre',\n",
" 'queryPhrase': ['action', 'adventure'],\n",
" 'inferenceType': 'implicit',\n",
" 'matchScore': 0.5,\n",
" 'metric': ['attribute_domain_value_match'],\n",
" 'isLabel': False,\n",
" 'isAmbiguous': False,\n",
" 'ambiguity': [],\n",
" 'encode': False,\n",
" 'meta': {'score': None,\n",
" 'threshold': None,\n",
" 'alias': None,\n",
" 'ambiguity': {'action': ['Action'], 'adventure': ['Adventure']},\n",
" 'dataType': 'N',\n",
" 'confidence': 100}}}"
]
},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fo1PLx-U8dfq"
},
"source": [
"# taskMap"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RFNFYbjk8joH",
"outputId": "0bee8981-8883-4079-84d9-8448a08c57d4"
},
"source": [
"nl4dv_response['taskMap']"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'filter': [{'task': 'filter',\n",
" 'queryPhrase': 'over',\n",
" 'operator': 'GT',\n",
" 'values': [100000000.0],\n",
" 'matchScore': 1,\n",
" 'attributes': ['Worldwide Gross'],\n",
" 'inferenceType': 'explicit',\n",
" 'isAttrAmbiguous': False,\n",
" 'isValueAmbiguous': False,\n",
" 'meta': {'value_ambiguity_type': None},\n",
" 'followup_type': 'nothing'},\n",
" {'task': 'filter',\n",
" 'queryPhrase': ['action', 'adventure'],\n",
" 'operator': 'IN',\n",
" 'values': ['Action', 'Adventure'],\n",
" 'matchScore': 1,\n",
" 'attributes': ['Genre'],\n",
" 'inferenceType': 'explicit',\n",
" 'isAttrAmbiguous': False,\n",
" 'isValueAmbiguous': False,\n",
" 'meta': {'value_ambiguity_type': None},\n",
" 'followup_type': 'nothing'}],\n",
" 'correlation': [{'task': 'correlation',\n",
" 'queryPhrase': [],\n",
" 'operator': None,\n",
" 'values': None,\n",
" 'matchScore': 0.5,\n",
" 'attributes': ['Worldwide Gross', 'Production Budget'],\n",
" 'inferenceType': 'implicit',\n",
" 'isAttrAmbiguous': False,\n",
" 'isValueAmbiguous': False,\n",
" 'meta': {'value_ambiguity_type': None},\n",
" 'followup_type': None}]}"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BDDCiE3a8s8j"
},
"source": [
"# visList"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RPOCrCLv8ugD",
"outputId": "19067114-1ea8-4480-9712-0c3c851cdbe0"
},
"source": [
"nl4dv_response['visList']"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'score': 5.39,\n",
" 'scoreObj': {'by_attributes': 1.8, 'by_task': 2, 'by_vis': 0},\n",
" 'confidenceObj': {'Production Budget': 1.0, 'IMDB Rating': 0.59},\n",
" 'attributes': ['Production Budget', 'IMDB Rating'],\n",
" 'queryPhrase': None,\n",
" 'visType': None,\n",
" 'tasks': ['filter', 'correlation'],\n",
" 'inferenceType': 'implicit',\n",
" 'vlSpec': {'$schema': 'https://vega.github.io/schema/vega-lite/v4.json',\n",
" 'mark': {'type': 'point', 'tooltip': True},\n",
" 'encoding': {'x': {'field': 'Production Budget',\n",
" 'type': 'quantitative',\n",
" 'axis': {'format': 's'}},\n",
" 'y': {'field': 'IMDB Rating',\n",
" 'type': 'quantitative',\n",
" 'axis': {'format': 's'}},\n",
" 'tooltip': {'field': 'Title'}},\n",
" 'transform': [{'filter': 'lower(datum[\"Worldwide Gross\"]) > 100000000.0'},\n",
" {'filter': {'field': 'Genre', 'oneOf': ['Action', 'Adventure']}}],\n",
" 'data': {'url': 'https://raw.githubusercontent.com/nl4dv/nl4dv/master/examples/assets/data/movies-w-year.csv',\n",
" 'format': {'type': 'csv'}}}},\n",
" {'score': 5.3,\n",
" 'scoreObj': {'by_attributes': 1.8, 'by_task': 2, 'by_vis': 0},\n",
" 'confidenceObj': {'Production Budget': 1.0, 'Content Rating': 0.5},\n",
" 'attributes': ['Production Budget', 'Content Rating'],\n",
" 'queryPhrase': None,\n",
" 'visType': None,\n",
" 'tasks': ['filter', 'correlation'],\n",
" 'inferenceType': 'implicit',\n",
" 'vlSpec': {'$schema': 'https://vega.github.io/schema/vega-lite/v4.json',\n",
" 'mark': {'type': 'bar', 'tooltip': True},\n",
" 'encoding': {'y': {'field': 'Production Budget',\n",
" 'type': 'quantitative',\n",
" 'aggregate': 'mean',\n",
" 'axis': {'format': 's'}},\n",
" 'x': {'field': 'Content Rating', 'type': 'nominal'}},\n",
" 'transform': [{'filter': 'lower(datum[\"Worldwide Gross\"]) > 100000000.0'},\n",
" {'filter': {'field': 'Genre', 'oneOf': ['Action', 'Adventure']}}],\n",
" 'data': {'url': 'https://raw.githubusercontent.com/nl4dv/nl4dv/master/examples/assets/data/movies-w-year.csv',\n",
" 'format': {'type': 'csv'}}}},\n",
" {'score': 5.3,\n",
" 'scoreObj': {'by_attributes': 1.8, 'by_task': 2, 'by_vis': 0},\n",
" 'confidenceObj': {'Production Budget': 1.0, 'Content Rating': 0.5},\n",
" 'attributes': ['Production Budget', 'Content Rating'],\n",
" 'queryPhrase': None,\n",
" 'visType': None,\n",
" 'tasks': ['filter', 'correlation'],\n",
" 'inferenceType': 'implicit',\n",
" 'vlSpec': {'$schema': 'https://vega.github.io/schema/vega-lite/v4.json',\n",
" 'mark': {'type': 'tick', 'tooltip': True},\n",
" 'encoding': {'y': {'field': 'Production Budget',\n",
" 'type': 'quantitative',\n",
" 'axis': {'format': 's'}},\n",
" 'x': {'field': 'Content Rating', 'type': 'nominal'},\n",
" 'tooltip': {'field': 'Title'}},\n",
" 'transform': [{'filter': 'lower(datum[\"Worldwide Gross\"]) > 100000000.0'},\n",
" {'filter': {'field': 'Genre', 'oneOf': ['Action', 'Adventure']}}],\n",
" 'data': {'url': 'https://raw.githubusercontent.com/nl4dv/nl4dv/master/examples/assets/data/movies-w-year.csv',\n",
" 'format': {'type': 'csv'}}}},\n",
" {'score': 5.16,\n",
" 'scoreObj': {'by_attributes': 1.8, 'by_task': 2, 'by_vis': 0},\n",
" 'confidenceObj': {'Production Budget': 1.0, 'Rotten Tomatoes Rating': 0.36},\n",
" 'attributes': ['Production Budget', 'Rotten Tomatoes Rating'],\n",
" 'queryPhrase': None,\n",
" 'visType': None,\n",
" 'tasks': ['filter', 'correlation'],\n",
" 'inferenceType': 'implicit',\n",
" 'vlSpec': {'$schema': 'https://vega.github.io/schema/vega-lite/v4.json',\n",
" 'mark': {'type': 'point', 'tooltip': True},\n",
" 'encoding': {'x': {'field': 'Production Budget',\n",
" 'type': 'quantitative',\n",
" 'axis': {'format': 's'}},\n",
" 'y': {'field': 'Rotten Tomatoes Rating',\n",
" 'type': 'quantitative',\n",
" 'axis': {'format': 's'}},\n",
" 'tooltip': {'field': 'Title'}},\n",
" 'transform': [{'filter': 'lower(datum[\"Worldwide Gross\"]) > 100000000.0'},\n",
" {'filter': {'field': 'Genre', 'oneOf': ['Action', 'Adventure']}}],\n",
" 'data': {'url': 'https://raw.githubusercontent.com/nl4dv/nl4dv/master/examples/assets/data/movies-w-year.csv',\n",
" 'format': {'type': 'csv'}}}}]"
]
},
"metadata": {},
"execution_count": 7
}
]
}
]
}
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