chore: improve clc-lc test
This commit is contained in:
Alexander Engelsberger
parent
8f08ba66ea
commit
02954044d7
@ -1,5 +1,5 @@
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from dataclasses import dataclass
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from typing import Callable
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from typing import Callable, Type
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import torch
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from prototorch.core.competitions import WTAC
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@ -14,40 +14,48 @@ from prototorch.models.clcc.clcc_scheme import CLCCScheme
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from prototorch.nn.wrappers import LambdaLayer
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@dataclass
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class GLVQhparams:
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distribution: dict
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class SupervisedScheme(CLCCScheme):
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@dataclass
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class HyperParameters:
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distribution: dict[str, int]
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component_initializer: AbstractComponentsInitializer
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distance_fn: Callable = euclidean_distance
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lr: float = 0.01
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margin: float = 0.0
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# TODO: make nicer
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transfer_fn: str = "identity"
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transfer_beta: float = 10.0
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optimizer: torch.optim.Optimizer = torch.optim.Adam
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class GLVQ(CLCCScheme):
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def __init__(self, hparams: GLVQhparams) -> None:
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super().__init__(hparams)
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self.lr = hparams.lr
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self.optimizer = hparams.optimizer
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# Initializers
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def init_components(self, hparams):
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# initialize Component Layer
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def init_components(self, hparams: HyperParameters):
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self.components_layer = LabeledComponents(
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distribution=hparams.distribution,
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components_initializer=hparams.component_initializer,
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labels_initializer=LabelsInitializer(),
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)
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def init_comparison(self, hparams):
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# initialize Distance Layer
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# ##############################################################################
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# GLVQ
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# ##############################################################################
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class GLVQ(
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SupervisedScheme, ):
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"""GLVQ using the new Scheme
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"""
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@dataclass
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class HyperParameters(SupervisedScheme.HyperParameters):
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distance_fn: Callable = euclidean_distance
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lr: float = 0.01
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margin: float = 0.0
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# TODO: make nicer
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transfer_fn: str = "identity"
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transfer_beta: float = 10.0
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optimizer: Type[torch.optim.Optimizer] = torch.optim.Adam
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def __init__(self, hparams: HyperParameters) -> None:
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super().__init__(hparams)
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self.lr = hparams.lr
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self.optimizer = hparams.optimizer
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def init_comparison(self, hparams: HyperParameters):
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self.comparison_layer = LambdaLayer(hparams.distance_fn)
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def init_inference(self, hparams):
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def init_inference(self, hparams: HyperParameters):
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self.competition_layer = WTAC()
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def init_loss(self, hparams):
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@ -78,7 +86,7 @@ class GLVQ(CLCCScheme):
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return self.loss_layer(comparisonmeasures, target, comp_labels)
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def configure_optimizers(self):
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return self.optimizer(self.parameters(), lr=self.lr)
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return self.optimizer(self.parameters(), lr=self.lr) # type: ignore
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# Properties
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@property
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@ -8,6 +8,7 @@ CLCC is a LVQ scheme containing 4 steps
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- Competition
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"""
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from dataclasses import dataclass
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from typing import (
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Dict,
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Set,
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@ -20,9 +21,16 @@ from torchmetrics import Accuracy, Metric
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class CLCCScheme(pl.LightningModule):
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@dataclass
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class HyperParameters:
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...
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registered_metrics: Dict[Type[Metric], Metric] = {}
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registered_metric_names: Dict[Type[Metric], Set[str]] = {}
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components_layer: pl.LightningModule
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def __init__(self, hparams) -> None:
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super().__init__()
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@ -42,9 +50,14 @@ class CLCCScheme(pl.LightningModule):
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self.init_model_metrics()
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# internal API, called by models and callbacks
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def register_torchmetric(self, name: str, metric: Metric, **metricargs):
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def register_torchmetric(
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self,
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name: str,
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metric: Type[Metric],
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**metric_kwargs,
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):
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if metric not in self.registered_metrics:
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self.registered_metrics[metric] = metric(**metricargs)
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self.registered_metrics[metric] = metric(**metric_kwargs)
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self.registered_metric_names[metric] = {name}
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else:
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self.registered_metric_names[metric].add(name)
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@ -92,25 +105,25 @@ class CLCCScheme(pl.LightningModule):
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# Empty Initialization
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# TODO: Type hints
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# TODO: Docs
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def init_components(self, hparams):
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def init_components(self, hparams: HyperParameters) -> None:
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...
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def init_latent(self, hparams):
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def init_latent(self, hparams: HyperParameters) -> None:
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...
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def init_comparison(self, hparams):
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def init_comparison(self, hparams: HyperParameters) -> None:
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...
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def init_competition(self, hparams):
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def init_competition(self, hparams: HyperParameters) -> None:
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...
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def init_loss(self, hparams):
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def init_loss(self, hparams: HyperParameters) -> None:
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...
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def init_inference(self, hparams):
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def init_inference(self, hparams: HyperParameters) -> None:
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...
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def init_model_metrics(self):
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def init_model_metrics(self) -> None:
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self.register_torchmetric('accuracy', Accuracy)
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# Empty Steps
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@ -1,55 +1,76 @@
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from typing import Optional
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from typing import Optional, Type
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import matplotlib.pyplot as plt
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import numpy as np
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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import torchmetrics
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from prototorch.core.initializers import SMCI, RandomNormalCompInitializer
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from prototorch.models.clcc.clcc_glvq import GLVQ, GLVQhparams
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from prototorch.core import SMCI
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from prototorch.models.clcc.clcc_glvq import GLVQ
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from prototorch.models.clcc.clcc_scheme import CLCCScheme
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from prototorch.models.vis import Visualize2DVoronoiCallback
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from prototorch.models.vis import Vis2DAbstract
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from prototorch.utils.utils import mesh2d
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from pytorch_lightning.callbacks import EarlyStopping
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from torch.utils.data import DataLoader
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# NEW STUFF
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# ##############################################################################
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# TODO: Metrics
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class MetricsTestCallback(pl.Callback):
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metric_name = "test_cb_acc"
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def setup(self,
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trainer: pl.Trainer,
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pl_module: CLCCScheme,
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stage: Optional[str] = None) -> None:
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pl_module.register_torchmetric(self.metric_name, torchmetrics.Accuracy)
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def on_epoch_end(self, trainer: pl.Trainer,
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pl_module: pl.LightningModule) -> None:
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metric = trainer.logged_metrics[self.metric_name]
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if metric > 0.95:
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trainer.should_stop = True
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class LogTorchmetricCallback(pl.Callback):
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def __init__(self, name, metric, on="prediction", **metric_args) -> None:
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def __init__(
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self,
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name,
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metric: Type[torchmetrics.Metric],
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on="prediction",
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**metric_kwargs,
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) -> None:
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self.name = name
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self.metric = metric
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self.metric_args = metric_args
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self.metric_kwargs = metric_kwargs
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self.on = on
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def setup(self,
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def setup(
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self,
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trainer: pl.Trainer,
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pl_module: CLCCScheme,
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stage: Optional[str] = None) -> None:
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stage: Optional[str] = None,
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) -> None:
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if self.on == "prediction":
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pl_module.register_torchmetric(self.name, self.metric,
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**self.metric_args)
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pl_module.register_torchmetric(
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self.name,
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self.metric,
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**self.metric_kwargs,
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)
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else:
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raise ValueError(f"{self.on} is no valid metric hook")
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class VisGLVQ2D(Vis2DAbstract):
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def visualize(self, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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x_train, y_train = self.x_train, self.y_train
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ax = self.setup_ax()
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self.plot_protos(ax, protos, plabels)
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if x_train is not None:
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self.plot_data(ax, x_train, y_train)
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mesh_input, xx, yy = mesh2d(
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np.vstack([x_train, protos]),
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self.border,
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self.resolution,
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)
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else:
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mesh_input, xx, yy = mesh2d(protos, self.border, self.resolution)
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_components = pl_module.components_layer.components
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mesh_input = torch.from_numpy(mesh_input).type_as(_components)
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y_pred = pl_module.predict(mesh_input)
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y_pred = y_pred.cpu().reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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# TODO: Pruning
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# ##############################################################################
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@ -59,15 +80,17 @@ if __name__ == "__main__":
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train_ds = pt.datasets.Iris(dims=[0, 2])
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train_ds.targets[train_ds.targets == 2.0] = 1.0
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds,
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train_loader = DataLoader(
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train_ds,
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batch_size=64,
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num_workers=0,
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shuffle=True)
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shuffle=True,
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)
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#components_initializer = SMCI(train_ds)
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components_initializer = RandomNormalCompInitializer(2)
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components_initializer = SMCI(train_ds)
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#components_initializer = RandomNormalCompInitializer(2)
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hparams = GLVQhparams(
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hyperparameters = GLVQ.HyperParameters(
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lr=0.5,
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distribution=dict(
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num_classes=2,
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@ -75,29 +98,36 @@ if __name__ == "__main__":
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),
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component_initializer=components_initializer,
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)
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model = GLVQ(hparams)
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model = GLVQ(hyperparameters)
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print(model)
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# Callbacks
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vis = Visualize2DVoronoiCallback(
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data=train_ds,
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resolution=500,
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)
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metrics = MetricsTestCallback()
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recall = LogTorchmetricCallback('recall',
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vis = VisGLVQ2D(data=train_ds)
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recall = LogTorchmetricCallback(
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'recall',
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torchmetrics.Recall,
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num_classes=2)
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num_classes=2,
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)
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es = EarlyStopping(
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monitor="recall",
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min_delta=0.001,
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patience=15,
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mode="max",
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check_on_train_epoch_end=True,
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)
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# Train
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trainer = pl.Trainer(
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callbacks=[
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vis,
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#metrics,
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recall,
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es,
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],
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gpus=0,
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max_epochs=200,
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weights_summary=None,
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log_every_n_steps=1,
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)
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trainer.fit(model, train_loader)
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