snorkelai.sdk.develop.ModelNode

class snorkelai.sdk.develop.ModelNode(uid, application_uid, config)

Bases: Node

ModelNode class represents a model node.

\_\_init\_\_

__init__

__init__(uid, application_uid, config)

Methods

__init__(uid, application_uid, config)
get(node_uid)	Fetches a node by its UID.
get_comments([username])	Retreive the comments left on the current model node.
get_dataframe([split, columns])	Retrieve the data being passed directly through this node.
get_ground_truth([split, user_format])	Retrieve ground truth data for the current model node.
get_lfs()	Retrieve a list of currently active labeling functions for the current model node.
get_training_set(training_set_uid[, split, ...])	Retrieve a training set for this model node, specified by the training set UID.

Attributes

application_uid	The unique identifier for the application this node belongs to
config	Returns the detailed configuration information for this node
uid	The unique identifier for this node

get\_comments

get_comments

get_comments(username=None)

Retreive the comments left on the current model node. This method will return a Pandas DataFrame whose columns contain the metadata and content for the comment.

Examples

>>> my_node.get_comments()
    comment_uid user_uid    x_uid   body    created_at      is_edited
    7           3           doc::1  hello   2023-09-26T17   False

Parameters

Parameters

Name	Type	Default	Info
username	Optional[str]	None	Optionally, return only a specific user’s coments. By default returns all comments.

Returns

Returns

A Pandas DataFrame containing the comments left on the model node.

Return type

Return type

pd.DataFrame

get\_dataframe

get_dataframe

get_dataframe(split=None, columns=None)

Retrieve the data being passed directly through this node. Can be filtered by a split or by a subset of columns (useful for large datasets). The data can also optionally include tag and comment metadata.

This dataframe is not the same as the dataframe returned by Dataset.get_dataframe(). While Dataset.get_dataframe() returns the source data, the dataframe returned by Node.get_dataframe() has also undergone all the preprocessing/DAG transformations up to this point in the processing pipeline.

Parameters

Parameters

Name	Type	Default	Info
split	Optional[str]	None	Optionally restrict the data retrieved to a particular split, by default None (i.e., all splits).
columns	Optional[List[str]]	None	Optionally restrict the columns returned by this function, by default None. Useful for large datasets to significantly speed up retrieval time.

Returns

Returns

A dataframe of the data being passed directly through this node, optionally filtered by split and/or columns and indexed by x_uid. This DataFrame is the result of all preprocessing in the DAG pipeline up to this point.

Return type

Return type

pd.DataFrame

get\_ground\_truth

get_ground_truth

get_ground_truth(split=None, user_format=False)

Retrieve ground truth data for the current model node. Optionally filter by a particular split.

Parameters

Parameters

Name	Type	Default	Info
split	Optional[str]	None	Which data split to select, by default None (all splits). Can be one of “dev”, “train”, “valid”, or “test.
user_format	bool	False	Whether to return the ground truth in a human-readable format, by default False.

Returns

Returns

A Pandas DataFrame mapping the data index to the ground truth label. If user_format is True, the label column will contain human-readable label names.

Return type

Return type

pd.DataFrame

get\_lfs

get_lfs

get_lfs()

Retrieve a list of currently active labeling functions for the current model node.

Examples

>>> my_node.get_lfs()
[
    LF(name='LF 1', label=3, templates=[...]),
    LF(name='LF 2', label=2, templates=[...]),
    LF(name='LF 3', label=1, templates=[...]),
]

Returns

Returns

A list of all currently active labeling functions for the current model node.

Return type

Return type

List[LF]

get\_training\_set

get_training_set

get_training_set(training_set_uid, split=None, user_format=True)

Retrieve a training set for this model node, specified by the training set UID. Allow allows for filtering the training set by a particular data split.

Examples

>>> my_node.get_training_set(1)
            training_set_labels     training_set_probs
doc::100    stock                   [0.030..., 0.024...]

Parameters

Parameters

Name	Type	Default	Info
training_set_uid	int		A training set UID, which can be found under the “Models” accordion in Developer Studio.
split	Optional[str]	None	An optional data split to return predictions for, by default None (all splits). Can be one of “train”, “dev”, “valid”, or “test”.
user_format	bool	True	Whether to return the predictions in a human-readable or compressed integer format, by default True (returning a human-readable format).

Returns

Returns

A Pandas DataFrame of training set predictions and probabilities, indexed by x_uid. If user_format is True, the preds column will contain human-readable label names.

Return type

Return type

pd.DataFrame