Node Classification Model

class graphdatascience.model.node_classification_model.NCModel

Represents a node classification model in the model catalog. Construct this using NCTrainingPipeline.train().

best_parameters() → Series[Any]

Get the best parameters for the pipeline model.

Returns:

The best parameters for the pipeline model.

classes() → list[int]

Get the classes of the model.

Returns:

The classes of the model.

creation_time() → Any

Get the creation time of the model.

Returns:

The creation time of the model.

drop(failIfMissing: bool = False) → Series[Any]

Drop the model.

Parameters:

failIfMissing – If True, an error is thrown if the model does not exist. If False, no error is thrown.

Returns:

The result of the drop operation.

exists() → bool

Check whether the model exists.

Returns:

True if the model exists, False otherwise.

feature_properties() → list[str]

Get the feature properties of the model.

Returns:

The feature properties of the model.

graph_schema() → Series[Any]

Get the graph schema of the model.

Returns:

The graph schema of the model.

loaded() → bool

Check whether the model is loaded in memory.

Returns:

True if the model is loaded in memory, False otherwise.

metrics() → Series[Any]

Get the metrics for the pipeline model.

Returns:

The metrics for the pipeline model.

model_info() → dict[str, Any]

Get the model info of the model.

Returns:

The model info of the model.

name() → str

Get the name of the model.

Returns:

The name of the model.

node_property_steps() → list[NodePropertyStep]

Get the node property steps for the pipeline model.

Returns:

The node property steps for the pipeline model.

predict_mutate(G: Graph, **config: Any) → Series[Any]

Predict on the given graph using the model and mutate the graph with the results.

Parameters:

• G – The graph to predict on.

• **config – The config for the prediction.

Returns:

The result of mutate operation.

predict_mutate_estimate(G: Graph, **config: Any) → Series[Any]

Estimate the memory needed to predict on the given graph using the model.

Parameters:

• G – The graph to predict on.

• **config – The config for the prediction.

Returns:

The memory needed to predict on the given graph using the model.

predict_stream(G: Graph, **config: Any) → DataFrame

Predict on the given graph using the model and stream the results as DataFrame

Parameters:

• G – The graph to predict on.

• **config – The config for the prediction.

Returns:

The prediction results as DataFrame.

predict_stream_estimate(G: Graph, **config: Any) → Series[Any]

Estimate the prediction on the given graph using the model and stream the results as DataFrame

Parameters:

• G – The graph to predict on.

• **config – The config for the prediction.

Returns:

The prediction results as DataFrame.

predict_write(G: Graph, **config: Any) → Series[Any]

Predict the node labels of a graph and write the results to the database.

Parameters:

• G – The graph to predict on.

• **config – The config for the prediction.

Returns:

The result of the write operation.

predict_write_estimate(G: Graph, **config: Any) → Series[Any]

Estimate the memory needed to predict the node labels of a graph and write the results to the database.

Parameters:

• G – The graph to predict on.

• **config – The config for the prediction.

Returns:

The memory needed to predict the node labels of a graph and write the results to the database.

published() → bool

Check whether the model is published.

Returns:

True if the model is published, False otherwise.

shared() → bool

Check whether the model is shared.

Returns:

True if the model is shared, False otherwise.

stored() → bool

Check whether the model is stored on disk.

Returns:

True if the model is stored on disk, False otherwise.

train_config() → Series[Any]

Get the train config of the model.

Returns:

The train config of the model.

type() → str

Get the type of the model.

Returns:

The type of the model.