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