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docs/nodes/AI_ML/IMAGE_CLASSIFICATION/HUGGING_FACE_PIPELINE/a1-[autogen]/docstring.txt
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| Hugging Face Pipeline for Image Classification. | ||
| The HUGGING_FACE_PIPELINE node uses a classification pipeline to process and classify an image. | ||
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| For more information about Vision Transformers, | ||
| see: https://huggingface.co/google/vit-base-patch16-224 | ||
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| For a complete list of models, see: | ||
| https://huggingface.co/models?pipeline_tag=image-classification | ||
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| For examples of how revision parameters (such as 'main') is used, | ||
| see: https://huggingface.co/google/vit-base-patch16-224/commits/main | ||
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| Parameters | ||
| ---------- | ||
| default: Image | ||
| The input image to be classified. The image must be a PIL.Image object wrapped in a flojoy Image object. | ||
| model: str | ||
| default : Image | ||
| The input image to be classified. | ||
| The image must be a PIL.Image object, wrapped in a Flojoy Image object. | ||
| model : str | ||
| The model to be used for classification. | ||
| If not specified, Vision Transformers (i.e. `google/vit-base-patch16-224`) are used. | ||
| For more information about Vision Transformers, see: https://huggingface.co/google/vit-base-patch16-224 | ||
| For a complete list of models see: https://huggingface.co/models?pipeline_tag=image-classification | ||
| revision: str | ||
| If not specified, Vision Transformers (i.e. 'google/vit-base-patch16-224') are used. | ||
| revision : str | ||
| The revision of the model to be used for classification. | ||
| If not specified, main is `used`. For instance see: https://huggingface.co/google/vit-base-patch16-224/commits/main | ||
| If not specified, 'main' is used. | ||
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| Returns | ||
| ------- | ||
| DataFrame: | ||
| A DataFrame containing as columns the `label` classification label and `score`, its confidence score. | ||
| All scores are between 0 and 1 and sum to 1. | ||
| A DataFrame containing the columns 'label' (as classification label) | ||
| and 'score' (as the confidence score). | ||
| All scores are between 0 and 1, and sum to 1. |
16 changes: 7 additions & 9 deletions
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docs/nodes/AI_ML/NLP/COUNT_VECTORIZER/a1-[autogen]/docstring.txt
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| The COUNT_VECTORIZER node receives a collection (matrix, vector or dataframe) of text documents and converts it to a matrix of token counts. | ||
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| The COUNT_VECTORIZER node receives a collection (matrix, vector or dataframe) of | ||
| text documents to a matrix of token counts. | ||
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| Returns | ||
| ------- | ||
| tokens: DataFrame | ||
| holds all the unique tokens observed from the input. | ||
| word_count_vector: Vector | ||
| contains the occurences of these tokens from each sentence. | ||
| Returns | ||
| ------- | ||
| tokens: DataFrame | ||
| Holds all the unique tokens observed from the input. | ||
| word_count_vector: Vector | ||
| Contains the occurences of these tokens from each sentence. |
66 changes: 33 additions & 33 deletions
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docs/nodes/AI_ML/PREDICT_TIME_SERIES/PROPHET_PREDICT/a1-[autogen]/docstring.txt
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| The PROPHET_PREDICT node runs a Prophet model on the incoming dataframe. | ||
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| The PROPHET_PREDICT node rains a Prophet model on the incoming dataframe. | ||
| The DataContainer input type must be a dataframe, and the first column (or index) of the dataframe must be of a datetime type. | ||
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| The DataContainer input type must be a dataframe, and the first column (or index) of dataframe must be of a datetime type. | ||
| This node always returns a DataContainer of a dataframe type. It will also always return an 'extra' field with a key 'prophet' of which the value is the JSONified Prophet model. | ||
| This model can be loaded as follows: | ||
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| This node always returns a DataContainer of a dataframe type. It will also always return an "extra" field with a key "prophet" of which the value is the JSONified Prophet model. | ||
| This model can be loaded as follows: | ||
| ```python | ||
| from prophet.serialize import model_from_json | ||
| ```python | ||
| from prophet.serialize import model_from_json | ||
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| model = model_from_json(dc_inputs.extra["prophet"]) | ||
| ``` | ||
| model = model_from_json(dc_inputs.extra["prophet"]) | ||
| ``` | ||
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| Parameters | ||
| ---------- | ||
| run_forecast : bool | ||
| If True (default case), the dataframe of the returning DataContainer | ||
| ("m" parameter of the DataContainer) will be the forecasted dataframe. | ||
| It will also have an "extra" field with the key "original", which is | ||
| the original dataframe passed in. | ||
| Parameters | ||
| ---------- | ||
| run_forecast : bool | ||
| If True (default case), the dataframe of the returning DataContainer | ||
| ('m' parameter of the DataContainer) will be the forecasted dataframe. | ||
| It will also have an 'extra' field with the key 'original', which is | ||
| the original dataframe passed in. | ||
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| If False, the returning dataframe will be the original data. | ||
| If False, the returning dataframe will be the original data. | ||
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| This node will also always have an "extra" field, run_forecast, which | ||
| matches that of the parameters passed in. This is for future nodes | ||
| to know if a forecast has already been run. | ||
| This node will also always have an 'extra' field, run_forecast, which | ||
| matches that of the parameters passed in. This is for future nodes | ||
| to know if a forecast has already been run. | ||
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| Default = True | ||
| Default = True | ||
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| periods : int | ||
| The number of periods to predict out. Only used if run_forecast is True. | ||
| Default = 365 | ||
| periods : int | ||
| The number of periods to predict out. Only used if run_forecast is True. | ||
| Default = 365 | ||
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| Returns | ||
| ------- | ||
| DataFrame | ||
| With parameter as df. | ||
| Indicates either the original df passed in, or the forecasted df | ||
| (depending on if run_forecast is True). | ||
| Returns | ||
| ------- | ||
| DataFrame | ||
| With parameter as df. | ||
| Indicates either the original df passed in, or the forecasted df | ||
| (depending on if run_forecast is True). | ||
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| DataContainer | ||
| With parameter as "extra". | ||
| Contains keys run_forecast which correspond to the input parameter, | ||
| and potentially "original" in the event that run_forecast is True. | ||
| DataContainer | ||
| With parameter as 'extra'. | ||
| Contains keys run_forecast which correspond to the input parameter, | ||
| and potentially 'original' in the event that run_forecast is True. |
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docs/nodes/AI_ML/REGRESSION/LEAST_SQUARES/a1-[autogen]/docstring.txt
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| The LEAST_SQUARE node computes the coefficients that minimize the distance between the inputs 'Matrix' or 'OrderedPair' class and the regression. | ||
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| The LEAST_SQUARE node computes the coefficients that minimizes the distance between the inputs 'Matrix' or 'OrderedPair' class and the regression. | ||
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| Returns | ||
| ------- | ||
| OrderedPair | ||
| x: input matrix (data points) | ||
| y: fitted line computed with returned regression weights | ||
| Matrix | ||
| m : fitted matrix computed with returned regression weights | ||
| Returns | ||
| ------- | ||
| OrderedPair | ||
| x: input matrix (data points) | ||
| y: fitted line computed with returned regression weights | ||
| Matrix | ||
| m: fitted matrix computed with returned regression weights |
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docs/nodes/AI_ML/SEGMENTATION/DEEPLAB_V3/a1-[autogen]/docstring.txt
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| The DEEPLAB_V3 node returns a segmentation mask from an input image in a dataframe. | ||
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| The input image is expected to be a DataContainer of an "image" type. | ||
| The input image is expected to be a DataContainer of an 'image' type. | ||
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| The output is a DataContainer of an "image" type with the same dimensions as the input image, but with the red, green, and blue channels replaced with the segmentation mask. | ||
| The output is a DataContainer of an 'image' type with the same dimensions as the input image, but with the red, green, and blue channels replaced with the segmentation mask. | ||
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| Returns | ||
| ------- | ||
| Image | ||
| Returns | ||
| ------- | ||
| Image |
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17 changes: 8 additions & 9 deletions
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docs/nodes/GENERATORS/SAMPLE_DATASETS/R_DATASET/a1-[autogen]/docstring.txt
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| The R_DATASET node retrieves a pandas DataFrame from 'rdatasets', using the provided dataset_key parameter, and returns it wrapped in a DataContainer. | ||
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| The R_DATASET node retrieves a pandas DataFrame from rdatasets using the provided dataset_key parameter and returns it wrapped in a DataContainer. | ||
| Parameters | ||
| ---------- | ||
| dataset_key : str | ||
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| Parameters | ||
| ---------- | ||
| dataset_key : str | ||
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| Returns | ||
| ------- | ||
| DataFrame | ||
| A DataContainer object containing the retrieved pandas DataFrame. | ||
| Returns | ||
| ------- | ||
| DataFrame | ||
| A DataContainer object containing the retrieved pandas DataFrame. |
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docs/nodes/GENERATORS/SAMPLE_DATASETS/TEXT_DATASET/a1-[autogen]/docstring.txt
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| The TEXT_DATASET node loads the 20 newsgroups dataset from scikit-learn. | ||
| The data is returned as a dataframe with one column containing the text | ||
| and the other containing the category. | ||
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| Parameters | ||
| ---------- | ||
| subset: "train" | "test" | "all", default="train" | ||
| Select the dataset to load: "train" for the training set, "test" for the test set, "all" for both. | ||
| categories: list of str, optional | ||
| Select the categories to load. By default, all categories are loaded. | ||
| The list of all categories is: | ||
| 'alt.atheism', | ||
| 'comp.graphics', | ||
| 'comp.os.ms-windows.misc', | ||
| 'comp.sys.ibm.pc.hardware', | ||
| 'comp.sys.mac.hardware', | ||
| 'comp.windows.x', | ||
| 'misc.forsale', | ||
| 'rec.autos', | ||
| 'rec.motorcycles', | ||
| 'rec.sport.baseball', | ||
| 'rec.sport.hockey', | ||
| 'sci.crypt', | ||
| 'sci.electronics', | ||
| 'sci.med', | ||
| 'sci.space', | ||
| 'soc.religion.christian', | ||
| 'talk.politics.guns', | ||
| 'talk.politics.mideast', | ||
| 'talk.politics.misc', | ||
| 'talk.religion.misc' | ||
| remove_headers: boolean, default=false | ||
| Remove the headers from the data. | ||
| remove_footers: boolean, default=false | ||
| Remove the footers from the data. | ||
| remove_quotes: boolean, default=false | ||
| Remove the quotes from the data. | ||
| The data is returned as a dataframe with one column containing the text and the other containing the category. | ||
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| Parameters | ||
| ---------- | ||
| subset : "train" | "test" | "all", default="train" | ||
| Select the dataset to load: "train" for the training set, "test" for the test set, "all" for both. | ||
| categories : list of str, optional | ||
| Select the categories to load. By default, all categories are loaded. | ||
| The list of all categories is: | ||
| 'alt.atheism', | ||
| 'comp.graphics', | ||
| 'comp.os.ms-windows.misc', | ||
| 'comp.sys.ibm.pc.hardware', | ||
| 'comp.sys.mac.hardware', | ||
| 'comp.windows.x', | ||
| 'misc.forsale', | ||
| 'rec.autos', | ||
| 'rec.motorcycles', | ||
| 'rec.sport.baseball', | ||
| 'rec.sport.hockey', | ||
| 'sci.crypt', | ||
| 'sci.electronics', | ||
| 'sci.med', | ||
| 'sci.space', | ||
| 'soc.religion.christian', | ||
| 'talk.politics.guns', | ||
| 'talk.politics.mideast', | ||
| 'talk.politics.misc', | ||
| 'talk.religion.misc' | ||
| remove_headers : boolean, default=false | ||
| Remove the headers from the data. | ||
| remove_footers : boolean, default=false | ||
| Remove the footers from the data. | ||
| remove_quotes : boolean, default=false | ||
| Remove the quotes from the data. | ||
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| Returns | ||
| ------- | ||
| DataFrame |
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docs/nodes/GENERATORS/SAMPLE_IMAGES/SKIMAGE/a1-[autogen]/docstring.txt
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45 changes: 22 additions & 23 deletions
45
docs/nodes/GENERATORS/SIMULATIONS/BASIC_OSCILLATOR/a1-[autogen]/docstring.txt
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| The BASIC_OSCILLATOR node is a combination of the LINSPACE and SINE nodes. | ||
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| It offers a more straightforward way to generate signals, with sample rate and the time in seconds as parameters, along with all the parameters in the SINE node. | ||
| It offers a more straightforward way to generate signals, with sample rate and the time in seconds as parameters, along with all the parameters in the SINE node. | ||
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| Parameters | ||
| ---------- | ||
| sample_rate : float | ||
| How many samples are taken in a second. | ||
| time : float | ||
| The total amount of time of the signal. | ||
| waveform : select | ||
| The waveform type of the wave. | ||
| amplitude : float | ||
| The amplitude of the wave. | ||
| frequency : float | ||
| The wave frequency in radians/2pi. | ||
| offset : float | ||
| The y axis offset of the function. | ||
| phase : float | ||
| The x axis offset of the function. | ||
| Parameters | ||
| ---------- | ||
| sample_rate : float | ||
| The number of samples that are taken in a second. | ||
| time : float | ||
| The total amount of time of the signal. | ||
| waveform : select | ||
| The waveform type of the wave. | ||
| amplitude : float | ||
| The amplitude of the wave. | ||
| frequency : float | ||
| The wave frequency in radians/2pi. | ||
| offset : float | ||
| The y axis offset of the function. | ||
| phase : float | ||
| The x axis offset of the function. | ||
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| Returns | ||
| ------- | ||
| OrderedPair | ||
| x: time domain | ||
| y: generated signal | ||
| Returns | ||
| ------- | ||
| OrderedPair | ||
| x: time domain | ||
| y: generated signal |
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docs/nodes/GENERATORS/SIMULATIONS/CONSTANT/a1-[autogen]/docstring.txt
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| The CONSTANT node generates a single x-y vector of numeric (floating point) constants. | ||
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| Inputs | ||
| ------ | ||
| default : OrderedPair|Vector | ||
| Optional input that defines the size of the output. | ||
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| Parameters | ||
| ---------- | ||
| dc_type : select | ||
| The type of DataContainer to return. | ||
| constant : float | ||
| The value of the y axis output. | ||
| step : int | ||
| The size of the y and x axes. | ||
| Inputs | ||
| ------ | ||
| default : OrderedPair|Vector | ||
| Optional input that defines the size of the output. | ||
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| Returns | ||
| ------- | ||
| OrderedPair | ||
| Parameters | ||
| ---------- | ||
| dc_type : select | ||
| The type of DataContainer to return. | ||
| constant : float | ||
| The value of the y axis output. | ||
| step : int | ||
| The size of the y and x axes. | ||
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| OrderedPair|Vector|Scalar | ||
| OrderedPair if selected | ||
| x: the x axis generated with size 'step' | ||
| y: the resulting constant with size 'step' | ||
| Vector if selected | ||
| v: the resulting constant with size 'step' | ||
| Scalar if selected | ||
| c: the resulting constant | ||
| Returns | ||
| ------- | ||
| OrderedPair|Vector|Scalar | ||
| OrderedPair if selected | ||
| x: the x axis generated with size 'step' | ||
| y: the resulting constant with size 'step' | ||
| Vector if selected | ||
| v: the resulting constant with size 'step' | ||
| Scalar if selected | ||
| c: the resulting constant |
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