Added Vis for GMLVQ with more then 2 dims using PCA (#11)
* Added Vis for GMLVQ with more then 2 dims using PCA * Added initialization possibility to GMlVQ with PCA and one example with omega init + PCA vis of 3 dims * test(githooks): Add githooks for automatic commit checks Co-authored-by: staps@hs-mittweida.de <staps@hs-mittweida.de> Co-authored-by: Alexander Engelsberger <alexanderengelsberger@gmail.com>
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examples/gmlvq_iris.py
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examples/gmlvq_iris.py
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"""GLVQ example using the Iris dataset."""
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import argparse
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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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from torch.optim.lr_scheduler import ExponentialLR
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if __name__ == "__main__":
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# Command-line arguments
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parser = argparse.ArgumentParser()
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parser = pl.Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# Dataset
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train_ds = pt.datasets.Iris()
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
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# Hyperparameters
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hparams = dict(
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input_dim=4,
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latent_dim=3,
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distribution={
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"num_classes": 3,
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"prototypes_per_class": 2
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},
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proto_lr=0.0005,
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bb_lr=0.0005,
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)
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# Initialize the model
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model = pt.models.GMLVQ(
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hparams,
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optimizer=torch.optim.Adam,
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prototype_initializer=pt.components.SSI(train_ds),
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lr_scheduler=ExponentialLR,
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lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
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omega_initializer=pt.components.PCA(train_ds.data)
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)
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# Compute intermediate input and output sizes
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#model.example_input_array = torch.zeros(4, 2)
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# Callbacks
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vis = pt.models.VisGMLVQ2D(data=train_ds, border=0.1)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
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args,
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callbacks=[vis],
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weights_summary="full",
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accelerator="ddp",
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)
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# Training loop
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trainer.fit(model, train_loader)
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prototorch/models/.glvq.py.swp
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prototorch/models/.glvq.py.swp
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@ -7,6 +7,7 @@ from prototorch.functions.distances import (lomega_distance, omega_distance,
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squared_euclidean_distance)
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from prototorch.functions.helper import get_flat
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from prototorch.functions.losses import glvq_loss, lvq1_loss, lvq21_loss
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from prototorch.components import LinearMapping
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from prototorch.modules import LambdaLayer, LossLayer
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from torch.nn.parameter import Parameter
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@ -239,11 +240,18 @@ class GMLVQ(GLVQ):
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super().__init__(hparams, distance_fn=distance_fn, **kwargs)
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# Additional parameters
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omega = torch.randn(self.hparams.input_dim,
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self.hparams.latent_dim,
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device=self.device)
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self.register_parameter("_omega", Parameter(omega))
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omega_initializer = kwargs.get("omega_initializer", None)
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initialized_omega = kwargs.get("initialized_omega", None)
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if omega_initializer is not None or initialized_omega is not None:
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self.omega_layer = LinearMapping(
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mapping_shape=(self.hparams.input_dim, self.hparams.latent_dim),
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initializer=omega_initializer,
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initialized_linearmapping=initialized_omega,
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)
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self.register_parameter("_omega", Parameter(self.omega_layer.mapping))
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self.backbone = LambdaLayer(lambda x: x @ self._omega, name = "omega matrix")
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@property
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def omega_matrix(self):
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return self._omega.detach().cpu()
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@ -256,6 +264,24 @@ class GMLVQ(GLVQ):
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def extra_repr(self):
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return f"(omega): (shape: {tuple(self._omega.shape)})"
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def predict_latent(self, x, map_protos=True):
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"""Predict `x` assuming it is already embedded in the latent space.
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Only the prototypes are embedded in the latent space using the
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backbone.
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"""
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self.eval()
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with torch.no_grad():
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protos, plabels = self.proto_layer()
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if map_protos:
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protos = self.backbone(protos)
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d = squared_euclidean_distance(x, protos)
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y_pred = wtac(d, plabels)
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return y_pred
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class LGMLVQ(GMLVQ):
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"""Localized and Generalized Matrix Learning Vector Quantization."""
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@ -83,7 +83,13 @@ class Vis2DAbstract(pl.Callback):
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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return mesh_input, xx, yy
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def plot_data(self, ax, x, y):
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def perform_pca_2D(self, data):
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(_, eigVal, eigVec) = torch.pca_lowrank(data, q=2)
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return data @ eigVec
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def plot_data(self, ax, x, y, pca=False):
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if pca:
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x = self.perform_pca_2D(x)
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ax.scatter(
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x[:, 0],
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x[:, 1],
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@ -94,7 +100,9 @@ class Vis2DAbstract(pl.Callback):
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s=30,
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)
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def plot_protos(self, ax, protos, plabels):
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def plot_protos(self, ax, protos, plabels, pca=False):
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if pca:
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protos = self.perform_pca_2D(protos)
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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@ -186,6 +194,50 @@ class VisSiameseGLVQ2D(Vis2DAbstract):
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self.log_and_display(trainer, pl_module)
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class VisGMLVQ2D(Vis2DAbstract):
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def __init__(self, *args, map_protos=True, **kwargs):
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super().__init__(*args, **kwargs)
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self.map_protos = map_protos
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def on_epoch_end(self, trainer, pl_module):
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if not self.precheck(trainer):
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return True
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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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device = pl_module.device
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with torch.no_grad():
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x_train = pl_module.backbone(torch.Tensor(x_train).to(device))
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x_train = x_train.cpu().detach()
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if self.map_protos:
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with torch.no_grad():
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protos = pl_module.backbone(torch.Tensor(protos).to(device))
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protos = protos.cpu().detach()
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ax = self.setup_ax()
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if x_train.shape[1] > 2:
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self.plot_data(ax, x_train, y_train, pca=True)
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else:
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self.plot_data(ax, x_train, y_train, pca=False)
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if self.show_protos:
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if protos.shape[1] > 2:
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self.plot_protos(ax, protos, plabels, pca=True)
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else:
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self.plot_protos(ax, protos, plabels, pca=False)
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### something to work on: meshgrid with pca
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# x = np.vstack((x_train, protos))
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# mesh_input, xx, yy = self.get_mesh_input(x)
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#else:
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# mesh_input, xx, yy = self.get_mesh_input(x_train)
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#_components = pl_module.proto_layer._components
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#mesh_input = torch.Tensor(mesh_input).type_as(_components)
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#y_pred = pl_module.predict_latent(mesh_input,
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# map_protos=self.map_protos)
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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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self.log_and_display(trainer, pl_module)
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class VisCBC2D(Vis2DAbstract):
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def on_epoch_end(self, trainer, pl_module):
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if not self.precheck(trainer):
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