chore: minor changes and version updates
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@ -1,5 +1,7 @@
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"""ProtoTorch CBC example using 2D Iris data."""
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"""ProtoTorch CBC example using 2D Iris data."""
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import logging
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import torch
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import torch
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from matplotlib import pyplot as plt
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from matplotlib import pyplot as plt
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@ -34,7 +36,7 @@ class VisCBC2D():
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self.resolution = 100
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self.resolution = 100
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self.cmap = "viridis"
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self.cmap = "viridis"
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def on_epoch_end(self):
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def on_train_epoch_end(self):
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x_train, y_train = self.x_train, self.y_train
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x_train, y_train = self.x_train, self.y_train
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_components = self.model.components_layer._components.detach()
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_components = self.model.components_layer._components.detach()
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ax = self.fig.gca()
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ax = self.fig.gca()
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@ -94,5 +96,5 @@ if __name__ == "__main__":
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correct += (y_pred.argmax(1) == y).float().sum(0)
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correct += (y_pred.argmax(1) == y).float().sum(0)
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acc = 100 * correct / len(train_ds)
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acc = 100 * correct / len(train_ds)
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print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
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logging.info(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
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vis.on_epoch_end()
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vis.on_train_epoch_end()
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@ -5,8 +5,10 @@ URL:
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"""
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"""
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from __future__ import annotations
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import warnings
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import warnings
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from typing import Sequence, Union
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from typing import Sequence
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from sklearn.datasets import (
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from sklearn.datasets import (
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load_iris,
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load_iris,
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@ -41,9 +43,9 @@ class Iris(NumpyDataset):
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:param dims: select a subset of dimensions
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:param dims: select a subset of dimensions
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"""
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"""
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def __init__(self, dims: Sequence[int] = None):
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def __init__(self, dims: Sequence[int] | None = None):
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x, y = load_iris(return_X_y=True)
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x, y = load_iris(return_X_y=True)
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if dims:
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if dims is not None:
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x = x[:, dims]
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x = x[:, dims]
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super().__init__(x, y)
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super().__init__(x, y)
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@ -56,15 +58,19 @@ class Blobs(NumpyDataset):
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"""
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"""
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def __init__(self,
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def __init__(
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num_samples: int = 300,
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self,
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num_features: int = 2,
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num_samples: int = 300,
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seed: Union[None, int] = 0):
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num_features: int = 2,
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x, y = make_blobs(num_samples,
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seed: None | int = 0,
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num_features,
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):
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centers=None,
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x, y = make_blobs(
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random_state=seed,
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num_samples,
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shuffle=False)
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num_features,
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centers=None,
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random_state=seed,
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shuffle=False,
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)
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super().__init__(x, y)
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super().__init__(x, y)
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@ -77,29 +83,33 @@ class Random(NumpyDataset):
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Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
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Note: n_classes * n_clusters_per_class <= 2**n_informative must satisfy.
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"""
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"""
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def __init__(self,
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def __init__(
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num_samples: int = 300,
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self,
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num_features: int = 2,
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num_samples: int = 300,
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num_classes: int = 2,
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num_features: int = 2,
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num_clusters: int = 2,
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num_classes: int = 2,
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num_informative: Union[None, int] = None,
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num_clusters: int = 2,
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separation: float = 1.0,
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num_informative: None | int = None,
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seed: Union[None, int] = 0):
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separation: float = 1.0,
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seed: None | int = 0,
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):
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if not num_informative:
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if not num_informative:
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import math
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import math
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num_informative = math.ceil(math.log2(num_classes * num_clusters))
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num_informative = math.ceil(math.log2(num_classes * num_clusters))
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if num_features < num_informative:
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if num_features < num_informative:
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warnings.warn("Generating more features than requested.")
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warnings.warn("Generating more features than requested.")
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num_features = num_informative
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num_features = num_informative
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x, y = make_classification(num_samples,
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x, y = make_classification(
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num_features,
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num_samples,
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n_informative=num_informative,
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num_features,
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n_redundant=0,
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n_informative=num_informative,
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n_classes=num_classes,
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n_redundant=0,
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n_clusters_per_class=num_clusters,
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n_classes=num_classes,
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class_sep=separation,
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n_clusters_per_class=num_clusters,
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random_state=seed,
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class_sep=separation,
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shuffle=False)
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random_state=seed,
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shuffle=False,
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)
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super().__init__(x, y)
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super().__init__(x, y)
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@ -113,16 +123,20 @@ class Circles(NumpyDataset):
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"""
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"""
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def __init__(self,
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def __init__(
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num_samples: int = 300,
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self,
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noise: float = 0.3,
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num_samples: int = 300,
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factor: float = 0.8,
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noise: float = 0.3,
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seed: Union[None, int] = 0):
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factor: float = 0.8,
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x, y = make_circles(num_samples,
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seed: None | int = 0,
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noise=noise,
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):
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factor=factor,
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x, y = make_circles(
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random_state=seed,
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num_samples,
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shuffle=False)
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noise=noise,
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factor=factor,
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random_state=seed,
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shuffle=False,
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)
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super().__init__(x, y)
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super().__init__(x, y)
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@ -136,12 +150,16 @@ class Moons(NumpyDataset):
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"""
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"""
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def __init__(self,
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def __init__(
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num_samples: int = 300,
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self,
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noise: float = 0.3,
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num_samples: int = 300,
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seed: Union[None, int] = 0):
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noise: float = 0.3,
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x, y = make_moons(num_samples,
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seed: None | int = 0,
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noise=noise,
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):
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random_state=seed,
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x, y = make_moons(
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shuffle=False)
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num_samples,
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noise=noise,
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random_state=seed,
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shuffle=False,
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)
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super().__init__(x, y)
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super().__init__(x, y)
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@ -36,6 +36,7 @@ Description:
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are determined by analytic chemistry.
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are determined by analytic chemistry.
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"""
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"""
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import logging
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import os
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import os
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import numpy as np
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import numpy as np
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@ -81,13 +82,11 @@ class Tecator(ProtoDataset):
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if self._check_exists():
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if self._check_exists():
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return
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return
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if self.verbose:
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logging.debug("Making directories...")
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print("Making directories...")
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os.makedirs(self.raw_folder, exist_ok=True)
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os.makedirs(self.raw_folder, exist_ok=True)
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os.makedirs(self.processed_folder, exist_ok=True)
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os.makedirs(self.processed_folder, exist_ok=True)
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if self.verbose:
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logging.debug("Downloading...")
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print("Downloading...")
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for fileid, md5 in self._resources:
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for fileid, md5 in self._resources:
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filename = "tecator.npz"
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filename = "tecator.npz"
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download_file_from_google_drive(fileid,
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download_file_from_google_drive(fileid,
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@ -95,8 +94,7 @@ class Tecator(ProtoDataset):
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filename=filename,
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filename=filename,
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md5=md5)
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md5=md5)
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if self.verbose:
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logging.debug("Processing...")
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print("Processing...")
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with np.load(os.path.join(self.raw_folder, "tecator.npz"),
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with np.load(os.path.join(self.raw_folder, "tecator.npz"),
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allow_pickle=False) as f:
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allow_pickle=False) as f:
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x_train, y_train = f["x_train"], f["y_train"]
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x_train, y_train = f["x_train"], f["y_train"]
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@ -117,5 +115,4 @@ class Tecator(ProtoDataset):
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"wb") as f:
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"wb") as f:
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torch.save(test_set, f)
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torch.save(test_set, f)
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if self.verbose:
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logging.debug("Done!")
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print("Done!")
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