embiggen/node_label_prediction/node_label_prediction_evaluation.py
"""Submodule providing node-label prediction evaluation pipeline."""
from typing import Any, Callable, Dict, List, Optional, Type, Union
import numpy as np
import pandas as pd
from ensmallen import Graph
from embiggen.node_label_prediction.node_label_prediction_model import \
AbstractNodeLabelPredictionModel
from embiggen.utils import (AbstractEmbeddingModel,
AbstractFeaturePreprocessor,
classification_evaluation_pipeline)
def node_label_prediction_evaluation(
holdouts_kwargs: Dict[str, Any],
graphs: Union[str, Graph, List[Graph], List[str]],
models: Union[Type[AbstractNodeLabelPredictionModel], List[Type[AbstractNodeLabelPredictionModel]]],
evaluation_schema: str = "Stratified Monte Carlo",
node_features: Optional[Union[str, pd.DataFrame, np.ndarray, Type[AbstractEmbeddingModel], List[Union[str, pd.DataFrame, np.ndarray, Type[AbstractEmbeddingModel]]]]] = None,
node_features_preprocessing_steps: Optional[Union[Type[AbstractFeaturePreprocessor], List[Type[AbstractFeaturePreprocessor]]]] = None,
library_names: Optional[Union[str, List[str]]] = None,
graph_callback: Optional[Callable[[Graph], Graph]] = None,
subgraph_of_interest: Optional[Graph] = None,
use_subgraph_as_support: bool = False,
number_of_holdouts: int = 10,
random_state: int = 42,
repositories: Optional[Union[str, List[str]]] = None,
versions: Optional[Union[str, List[str]]] = None,
enable_cache: bool = False,
precompute_constant_stocastic_features: bool = False,
smoke_test: bool = False,
number_of_slurm_nodes: Optional[int] = None,
slurm_node_id_variable: str = "SLURM_GRAPE_ID",
verbose: bool = True
) -> pd.DataFrame:
"""Execute node-label prediction evaluation pipeline for all provided models and graphs.
Parameters
---------------------
holdouts_kwargs: Dict[str, Any]
The parameters for the selected holdouts method.
graphs: Union[str, Graph, List[Graph], List[str]]
The graphs or graph names to run this evaluation on.
models: Union[Type[AbstractNodeLabelPredictionModel], List[Type[AbstractNodeLabelPredictionModel]]]
The models to evaluate.
evaluation_schema: str = "Stratified Monte Carlo"
The evaluation schema to follow.
node_features: Optional[Union[str, pd.DataFrame, np.ndarray, Type[AbstractEmbeddingModel], List[Union[str, pd.DataFrame, np.ndarray, Type[AbstractEmbeddingModel]]]]] = None
The node features to use.
node_features_preprocessing_steps: Optional[Union[Type[AbstractFeaturePreprocessor], List[Type[AbstractFeaturePreprocessor]]]] = None
The preprocessing steps to apply to the node features.
library_names: Optional[Union[str, List[str]]] = None
Library names from where to retrieve the provided model names.
graph_callback: Optional[Callable[[Graph], Graph]] = None
Callback to use for graph normalization and sanitization, must be
a function that receives and returns a graph object.
For instance this may be used for filtering the uncertain edges
in graphs such as STRING PPIs.
subgraph_of_interest: Optional[Graph] = None
Optional subgraph where to focus the task.
This is applied to the train and test graph
after the desired holdout schema is applied.
use_subgraph_as_support: bool = False
Whether to use the provided subgraph as support or
to use the train graph (not filtered by the subgraph).
number_of_holdouts: int = 10
The number of holdouts to execute.
random_state: int = 42
Random state to reproduce this evaluation.
repositories: Optional[Union[str, List[str]]] = None
Repositories from where to retrieve the provided graph names
from the Ensmallen automatic retrieval.
versions: Optional[Union[str, List[str]]] = None
Graph versions to retrieve.
enable_cache: bool = False
Whether to enable the cache.
precompute_constant_stocastic_features: bool = False
Whether to precompute once the constant automatic stocastic
features before starting the embedding loop. This means that,
when left set to false, while the features will be computed
using the same input data, the random state between runs will
be different and therefore the experiment performance will
capture more of the variance derived from the stocastic aspect
of the considered method. When set to true, they are only computed
once and therefore the experiment will be overall faster.
smoke_test: bool = False
Whether this run should be considered a smoke test
and therefore use the smoke test configurations for
the provided model names and feature names.
This parameter will also turn off the cache.
number_of_slurm_nodes: Optional[int] = None
Number of SLURM nodes to consider as available.
This variable is used to parallelize the holdouts accordingly.
slurm_node_id_variable: str = "SLURM_GRAPE_ID"
Name of the system variable to use as SLURM node id.
It must be set in the slurm bash script.
verbose: bool = True
Whether to show loading bars
"""
return classification_evaluation_pipeline(
evaluation_schema=evaluation_schema,
holdouts_kwargs=holdouts_kwargs,
graphs=graphs,
models=models,
expected_parent_class=AbstractNodeLabelPredictionModel,
node_features=node_features,
node_features_preprocessing_steps=node_features_preprocessing_steps,
library_names=library_names,
graph_callback=graph_callback,
subgraph_of_interest=subgraph_of_interest,
use_subgraph_as_support=use_subgraph_as_support,
number_of_holdouts=number_of_holdouts,
random_state=random_state,
repositories=repositories,
versions=versions,
enable_cache=enable_cache,
precompute_constant_stocastic_features=precompute_constant_stocastic_features,
smoke_test=smoke_test,
number_of_slurm_nodes=number_of_slurm_nodes,
slurm_node_id_variable=slurm_node_id_variable,
verbose=verbose
)