fishy.experiments.contrastive

Contrastive learning experiments with comprehensive pair-wise similarity metrics.

Classes

class fishy.experiments.contrastive.ContrastiveConfig(contrastive_method: str = 'simclr', dataset: str = 'species', num_epochs: int = 100, batch_size: int = 64, learning_rate: float = 0.0003, weight_decay: float = 0.0001, file_path: str | None = None, encoder_type: str = 'dense', embedding_dim: int = 128, projection_dim: int = 128, temperature: float = 0.55, moco_k: int = 4096, moco_m: float = 0.999, moco_t: float = 0.07, byol_m: float = 0.996, barlow_lambda: float = 0.005, wandb_project: str | None = 'fishy-business', wandb_entity: str | None = 'victoria-university-of-wellington', wandb_log: bool = False, run: int = 0, random_projection: bool = (False,), quantize: bool = (False,), turbo_quant: bool = (False,), polar: bool = (False,), normalize: bool = (False,), snv: bool = (False,), minmax: bool = (False,), log_transform: bool = (False,), savgol: bool = (False,))

Bases: object

Configuration for contrastive learning.

__init__(contrastive_method: str = 'simclr', dataset: str = 'species', num_epochs: int = 100, batch_size: int = 64, learning_rate: float = 0.0003, weight_decay: float = 0.0001, file_path: str | None = None, encoder_type: str = 'dense', embedding_dim: int = 128, projection_dim: int = 128, temperature: float = 0.55, moco_k: int = 4096, moco_m: float = 0.999, moco_t: float = 0.07, byol_m: float = 0.996, barlow_lambda: float = 0.005, wandb_project: str | None = 'fishy-business', wandb_entity: str | None = 'victoria-university-of-wellington', wandb_log: bool = False, run: int = 0, random_projection: bool = (False,), quantize: bool = (False,), turbo_quant: bool = (False,), polar: bool = (False,), normalize: bool = (False,), snv: bool = (False,), minmax: bool = (False,), log_transform: bool = (False,), savgol: bool = (False,)) → None

barlow_lambda: float = 0.005

batch_size: int = 64

byol_m: float = 0.996

contrastive_method: str = 'simclr'

dataset: str = 'species'

embedding_dim: int = 128

encoder_type: str = 'dense'

file_path: str | None = None

learning_rate: float = 0.0003

log_transform: bool = (False,)

minmax: bool = (False,)

moco_k: int = 4096

moco_m: float = 0.999

moco_t: float = 0.07

normalize: bool = (False,)

num_epochs: int = 100

polar: bool = (False,)

projection_dim: int = 128

quantize: bool = (False,)

random_projection: bool = (False,)

run: int = 0

savgol: bool = (False,)

snv: bool = (False,)

temperature: float = 0.55

turbo_quant: bool = (False,)

wandb_entity: str | None = 'victoria-university-of-wellington'

wandb_log: bool = False

wandb_project: str | None = 'fishy-business'

weight_decay: float = 0.0001

class fishy.experiments.contrastive.ContrastiveTrainer(config: ContrastiveConfig, wandb_run: Any | None = None, ctx: RunContext | None = None)

Bases: object

Trainer focused on Pair-wise Similarity Metrics for self-supervised learning.

__init__(config: ContrastiveConfig, wandb_run: Any | None = None, ctx: RunContext | None = None) → None

evaluate_pairwise_performance() → None

Evaluates pair-wise similarity on the held-out test set.

log_contrastive_visualizations() → None

setup() → None

train() → None

Functions

fishy.experiments.contrastive.run_contrastive_experiment(config, wandb_run=None, ctx=None)

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