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Update nodes doc from PR: 93 #69

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Update nodes doc from PR: 93 #69

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57 changes: 57 additions & 0 deletions docs/nodes/nodes/AI_ML/CLASSIFICATION/ONE_HOT_ENCODING.md
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[//]: # (Custom component imports)

import DocString from '@site/src/components/DocString';
import PythonCode from '@site/src/components/PythonCode';
import Parameters from '@site/src/components/Parameters';
import AppDisplay from '@site/src/components/AppDisplay';
import SectionBreak from '@site/src/components/SectionBreak';
import AppendixSection from '@site/src/components/AppendixSection';

[//]: # (Docstring)

import DocstringSource from '!!raw-loader!./a1-[autogen]/docstring.txt';
import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
import ParametersSource from '!!raw-loader!./a1-[autogen]/parameters.yaml';

<DocString>{DocstringSource}</DocString>
<PythonCode GLink='bin/flojoy-io/docs/docs/AI_ML/CLASSIFICATION/ONE_HOT_ENCODING.py'>{PythonSource}</PythonCode>
<Parameters>{ParametersSource}</Parameters>

<SectionBreak />



[//]: # (Examples)

## Examples

import Example1 from './examples/EX1/example.md';
import App1 from '!!raw-loader!./examples/EX1/app.txt';
import Data1 from '!!raw-loader!./examples/EX1/output.txt';

<AppDisplay
data={Data1}
nodeLabel='ONE_HOT_ENCODING'>
{App1}
</AppDisplay>

<Example1 />

<SectionBreak />



[//]: # (Appendix)

import Notes from '!!raw-loader!./appendix/notes.md';
import Hardware from '!!raw-loader!./appendix/hardware.md';
import Media from '!!raw-loader!./appendix/media.md';

## Appendix

<AppendixSection index={0} folderPath='nodes/nodes/bin/flojoy-io/docs/docs/nodes/nodes/AI_ML/CLASSIFICATION/appendix/'>{Notes}</AppendixSection>
<AppendixSection index={1} folderPath='nodes/nodes/bin/flojoy-io/docs/docs/nodes/nodes/AI_ML/CLASSIFICATION/appendix/'>{Hardware}</AppendixSection>
<AppendixSection index={2} folderPath='nodes/nodes/bin/flojoy-io/docs/docs/nodes/nodes/AI_ML/CLASSIFICATION/appendix/'>{Media}</AppendixSection>


14 changes: 14 additions & 0 deletions docs/nodes/nodes/AI_ML/CLASSIFICATION/a1-[autogen]/docstring.txt
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The ONE_HOT_ENCODING node creates a one hot encoding from a list of categorical features.
The `categories` parameter can be used to generate a one hot encoding without having to pass a dataframe.
By default, all categorical columns in the input dataframe will be encoded. The `columns`
parameter can be used to specify the columns to encode.

Parameters
----------
categories : list of str or list of int, optional
columns: list of str, optional

Returns
-------
matrix
The one hot encoding of the input features.
6 changes: 6 additions & 0 deletions docs/nodes/nodes/AI_ML/CLASSIFICATION/a1-[autogen]/parameters.yaml
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categories:
default: ''
type: array
columns:
default: ''
type: array
36 changes: 36 additions & 0 deletions docs/nodes/nodes/AI_ML/CLASSIFICATION/a1-[autogen]/python_code.txt
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from flojoy import flojoy, DataContainer
import pandas as pd


@flojoy
def ONE_HOT_ENCODING(dc_inputs: list[DataContainer], params: dict) -> DataContainer:


dc = dc_inputs[0] if len(dc_inputs) > 0 else None

if dc is not None and dc.type != "dataframe":
raise ValueError(
f"unsupported DataContainer type passed to ONE_HOT_ENCODING node: '{dc.type}'"
)

categories = params.get("categories", [])
columns = params.get("columns", [])

if categories:
data = pd.DataFrame({"category": categories})
# Force pandas to treat the column as categorical
data["category"] = data["category"].astype("category")
encoded = pd.get_dummies(data, dtype=int)

return DataContainer(type="dataframe", m=encoded)

if not dc:
raise ValueError(f"ONE_HOT_ENCODING node did not receive input DataContainer")

if columns:
encoded = pd.get_dummies(dc.m[columns])
else:
cat_df = dc.m.select_dtypes(include=["object", "category"])
encoded = pd.get_dummies(cat_df, dtype=int)

return DataContainer(type="dataframe", m=encoded)
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166 changes: 166 additions & 0 deletions docs/nodes/nodes/AI_ML/CLASSIFICATION/examples/EX1/app.txt
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{
"rfInstance": {
"nodes": [
{
"width": 150,
"height": 150,
"id": "ONE_HOT_ENCODING-b15db30d-818c-4c14-b6a9-2ecc0d76fffa",
"type": "CLASSIFICATION",
"data": {
"id": "ONE_HOT_ENCODING-b15db30d-818c-4c14-b6a9-2ecc0d76fffa",
"label": "ONE HOT ENCODING",
"func": "ONE_HOT_ENCODING",
"type": "CLASSIFICATION",
"ctrls": {
"categories": {
"functionName": "ONE_HOT_ENCODING",
"param": "categories",
"value": ""
},
"columns": {
"functionName": "ONE_HOT_ENCODING",
"param": "columns",
"value": "smoker,day"
}
},
"selected": false
},
"position": {
"x": 492.2217042786417,
"y": 150.33145519440131
},
"selected": false,
"positionAbsolute": {
"x": 492.2217042786417,
"y": 150.33145519440131
},
"dragging": true
},
{
"width": 150,
"height": 150,
"id": "PLOTLY_DATASET-3ec5e34e-b56a-4779-a8cd-e5e1c01d1689",
"type": "SAMPLE_DATASET",
"data": {
"id": "PLOTLY_DATASET-3ec5e34e-b56a-4779-a8cd-e5e1c01d1689",
"label": "PLOTLY DATASET",
"func": "PLOTLY_DATASET",
"type": "SAMPLE_DATASET",
"ctrls": {
"dataset_key": {
"functionName": "PLOTLY_DATASET",
"param": "dataset_key",
"value": "tips"
}
},
"selected": true
},
"position": {
"x": 165.0788471357846,
"y": 147.47431233725848
},
"selected": true,
"positionAbsolute": {
"x": 165.0788471357846,
"y": 147.47431233725848
},
"dragging": true
},
{
"width": 380,
"height": 293,
"id": "TABLE-a7e5677c-e1cd-466d-a3ec-36742974d261",
"type": "PLOTLY_VISOR",
"data": {
"id": "TABLE-a7e5677c-e1cd-466d-a3ec-36742974d261",
"label": "TABLE",
"func": "TABLE",
"type": "PLOTLY_VISOR",
"ctrls": {},
"selected": false
},
"position": {
"x": 727.9359899929276,
"y": 101.7600266229727
},
"selected": false,
"positionAbsolute": {
"x": 727.9359899929276,
"y": 101.7600266229727
},
"dragging": true
},
{
"width": 210,
"height": 130,
"id": "END-de791708-214d-46a3-a334-af3b779d7b49",
"type": "TERMINATOR",
"data": {
"id": "END-de791708-214d-46a3-a334-af3b779d7b49",
"label": "END",
"func": "END",
"type": "TERMINATOR",
"ctrls": {},
"selected": false
},
"position": {
"x": 1133.650275707213,
"y": 167.47431233725848
},
"selected": false,
"positionAbsolute": {
"x": 1133.650275707213,
"y": 167.47431233725848
},
"dragging": true
}
],
"edges": [
{
"source": "PLOTLY_DATASET-3ec5e34e-b56a-4779-a8cd-e5e1c01d1689",
"sourceHandle": "main",
"target": "ONE_HOT_ENCODING-b15db30d-818c-4c14-b6a9-2ecc0d76fffa",
"targetHandle": "ONE_HOT_ENCODING",
"id": "reactflow__edge-PLOTLY_DATASET-3ec5e34e-b56a-4779-a8cd-e5e1c01d1689main-ONE_HOT_ENCODING-b15db30d-818c-4c14-b6a9-2ecc0d76fffaONE_HOT_ENCODING"
},
{
"source": "ONE_HOT_ENCODING-b15db30d-818c-4c14-b6a9-2ecc0d76fffa",
"sourceHandle": "main",
"target": "TABLE-a7e5677c-e1cd-466d-a3ec-36742974d261",
"targetHandle": "TABLE",
"id": "reactflow__edge-ONE_HOT_ENCODING-b15db30d-818c-4c14-b6a9-2ecc0d76fffamain-TABLE-a7e5677c-e1cd-466d-a3ec-36742974d261TABLE"
},
{
"source": "TABLE-a7e5677c-e1cd-466d-a3ec-36742974d261",
"sourceHandle": "main",
"target": "END-de791708-214d-46a3-a334-af3b779d7b49",
"targetHandle": "END",
"id": "reactflow__edge-TABLE-a7e5677c-e1cd-466d-a3ec-36742974d261main-END-de791708-214d-46a3-a334-af3b779d7b49END"
}
],
"viewport": {
"x": 34.964285714285666,
"y": 190.74999999999997,
"zoom": 0.5
}
},
"ctrlsManifest": [
{
"type": "input",
"name": "Slider",
"id": "INPUT_PLACEHOLDER",
"hidden": false,
"minHeight": 1,
"minWidth": 2,
"layout": {
"x": 0,
"y": 0,
"h": 2,
"w": 2,
"minH": 1,
"minW": 2,
"i": "INPUT_PLACEHOLDER"
}
}
]
}
3 changes: 3 additions & 0 deletions docs/nodes/nodes/AI_ML/CLASSIFICATION/examples/EX1/example.md
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In this example, `ONE_HOT_ENCODING` is passed the `tips` dataset from the `PLOTLY_DATASET` node.

`ONE_HOT_ENCODING` is passed `smoker,day` for the `columns` parameter, so the output consists of a dataframe with one hot encodings for only the `smoker` and `day` columns from the input dataset.
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57 changes: 57 additions & 0 deletions docs/nodes/nodes/AI_ML/OBJECT_DETECTION/OBJECT_DETECTION.md
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[//]: # (Custom component imports)

import DocString from '@site/src/components/DocString';
import PythonCode from '@site/src/components/PythonCode';
import Parameters from '@site/src/components/Parameters';
import AppDisplay from '@site/src/components/AppDisplay';
import SectionBreak from '@site/src/components/SectionBreak';
import AppendixSection from '@site/src/components/AppendixSection';

[//]: # (Docstring)

import DocstringSource from '!!raw-loader!./a1-[autogen]/docstring.txt';
import PythonSource from '!!raw-loader!./a1-[autogen]/python_code.txt';
import ParametersSource from '!!raw-loader!./a1-[autogen]/parameters.yaml';

<DocString>{DocstringSource}</DocString>
<PythonCode GLink='bin/flojoy-io/docs/docs/AI_ML/OBJECT_DETECTION/OBJECT_DETECTION.py'>{PythonSource}</PythonCode>
<Parameters>{ParametersSource}</Parameters>

<SectionBreak />



[//]: # (Examples)

## Examples

import Example1 from './examples/EX1/example.md';
import App1 from '!!raw-loader!./examples/EX1/app.txt';
import Data1 from '!!raw-loader!./examples/EX1/output.txt';

<AppDisplay
data={Data1}
nodeLabel='OBJECT_DETECTION'>
{App1}
</AppDisplay>

<Example1 />

<SectionBreak />



[//]: # (Appendix)

import Notes from '!!raw-loader!./appendix/notes.md';
import Hardware from '!!raw-loader!./appendix/hardware.md';
import Media from '!!raw-loader!./appendix/media.md';

## Appendix

<AppendixSection index={0} folderPath='nodes/nodes/bin/flojoy-io/docs/docs/nodes/nodes/AI_ML/OBJECT_DETECTION/appendix/'>{Notes}</AppendixSection>
<AppendixSection index={1} folderPath='nodes/nodes/bin/flojoy-io/docs/docs/nodes/nodes/AI_ML/OBJECT_DETECTION/appendix/'>{Hardware}</AppendixSection>
<AppendixSection index={2} folderPath='nodes/nodes/bin/flojoy-io/docs/docs/nodes/nodes/AI_ML/OBJECT_DETECTION/appendix/'>{Media}</AppendixSection>


12 changes: 12 additions & 0 deletions docs/nodes/nodes/AI_ML/OBJECT_DETECTION/a1-[autogen]/docstring.txt
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Performs object detection on the input `DataContainer` class, specifically for the 'image' type,
represented by the RGB(A) channels.

Args:
dc_inputs (list[DataContainer]): List of DataContainer objects containing image channels.
params (dict): Additional parameters for object detection (not used in this function).

Returns:
DataContainer: A `DataContainer` class of type 'image' representing the output image with object detection results.

Raises:
Exception: If an error occurs during object detection.
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