graphframes/python/graphframes/internal/utils.py at v0.12.2 · graphframes/graphframes · GitHub

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from __future__ import annotations

from dataclasses import dataclass

"""Internal constants."""

_SEQUENCE_MODELS: tuple[str, ...] = (

"hash2vec",

"word2vec",

)

_RW_MODELS: tuple[str, ...] = ("rw_with_restart",)

_HASH2VEC_DECAY_FUNCTIONS: tuple[str, ...] = (

"gaussian",

"constant",

)

@dataclass

class _RandomWalksEmbeddingsParameters:

"""Internal class represents all possible parameters with defaults."""

use_edge_direction: bool = False

rw_model: str = "rw_with_restart"

rw_max_nbrs: int = 50

rw_num_walks_per_node: int = 5

rw_batch_size: int = 10

rw_num_batches: int = 5

rw_seed: int = 42

rw_restart_probability: float = 0.1

rw_temporary_prefix: str = ""

rw_cached_walks: str = ""

sequence_model: str = "hash2vec"

hash2vec_context_size: int = 5

hash2vec_num_partitions: int = 5

hash2vec_embeddings_dim: int = 512

hash2vec_decay_function: str = "gaussian"

hash2vec_gaussian_sigma: float = 1.0

hash2vec_hashing_seed: int = 42

hash2vec_sign_seed: int = 18

hash2vec_do_l2_norm: bool = True

hash2vec_safe_l2: bool = True

word2vec_max_iter: int = 1

word2vec_embeddings_dim: int = 100

word2vec_window_size: int = 5

word2vec_num_partitions: int = 1

word2vec_min_count: int = 5

word2vec_max_sentence_length: int = 1000

word2vec_seed: int = 42

word2vec_step_size: float = 0.025

aggregate_neighbors: bool = True

aggregate_neighbors_max_nbrs: int = 50

aggregate_neighbors_seed: int = 42

clean_up_after_run: bool = False

def validate(self) -> None:

if self.sequence_model not in _SEQUENCE_MODELS:

raise ValueError(f"supported seq2vec models are {str(_SEQUENCE_MODELS)}")

if self.rw_model not in _RW_MODELS:

raise ValueError(f"supported RW models are {str(_RW_MODELS)}")

graphframes/python/graphframes/internal/utils.py at v0.12.2 · graphframes/graphframes · GitHub

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