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Version 0.4.0

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This commit is contained in:
Alexander Engelsberger 2021-05-10 15:13:15 +02:00
commit 3a8388e24f
37 changed files with 1184 additions and 369 deletions
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13
.bumpversion.cfg
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@ -1,20 +1,11 @@
[bumpversion]
current_version = 0.3.0-dev0
current_version = 0.4.0
commit = True
tag = True
parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)(\-(?P<release>[a-z]+)(?P<build>\d+))?
parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)
serialize =
{major}.{minor}.{patch}-{release}{build}
{major}.{minor}.{patch}
[bumpversion:part:release]
optional_value = prod
first_value = dev
values =
dev
rc
prod
[bumpversion:file:setup.py]
[bumpversion:file:./prototorch/__init__.py]

8
README.md
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@ -31,15 +31,15 @@ To also install the extras, use
pip install -U prototorch[all]
```
*Note: If you're using [ZSH](https://www.zsh.org/), the square brackets `[ ]`
have to be escaped like so: `\[\]`, making the install command `pip install -U
prototorch\[all\]`.*
*Note: If you're using [ZSH](https://www.zsh.org/) (which is also the default
shell on MacOS now), the square brackets `[ ]` have to be escaped like so:
`\[\]`, making the install command `pip install -U prototorch\[all\]`.*
To install the bleeding-edge features and improvements:
```bash
git clone https://github.com/si-cim/prototorch.git
git checkout dev
cd prototorch
git checkout dev
pip install -e .[all]
```

30
docs/source/conf.py
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@ -12,9 +12,8 @@
#
import os
import sys
sys.path.insert(0, os.path.abspath("../../"))
import sphinx_rtd_theme
sys.path.insert(0, os.path.abspath("../../"))
# -- Project information -----------------------------------------------------
@ -24,7 +23,7 @@ author = "Jensun Ravichandran"
# The full version, including alpha/beta/rc tags
#
release = "0.3.0-dev0"
release = "0.4.0"
# -- General configuration ---------------------------------------------------
@ -128,15 +127,12 @@ latex_elements = {
# The paper size ("letterpaper" or "a4paper").
#
# "papersize": "letterpaper",
# The font size ("10pt", "11pt" or "12pt").
#
# "pointsize": "10pt",
# Additional stuff for the LaTeX preamble.
#
# "preamble": "",
# Latex figure (float) alignment
#
# "figure_align": "htbp",
@ -146,15 +142,21 @@ latex_elements = {
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, "prototorch.tex", "ProtoTorch Documentation",
"Jensun Ravichandran", "manual"),
(
master_doc,
"prototorch.tex",
"ProtoTorch Documentation",
"Jensun Ravichandran",
"manual",
),
]
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author], 1)]
man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author],
1)]
# -- Options for Texinfo output -------------------------------------------
@ -162,9 +164,15 @@ man_pages = [(master_doc, "ProtoTorch", "ProtoTorch Documentation", [author], 1)
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, "prototorch", "ProtoTorch Documentation", author, "prototorch",
(
master_doc,
"prototorch",
"ProtoTorch Documentation",
author,
"prototorch",
"Prototype-based machine learning in PyTorch.",
"Miscellaneous"),
"Miscellaneous",
),
]
# Example configuration for intersphinx: refer to the Python standard library.

22
examples/glvq_iris.py
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@ -3,13 +3,14 @@
import numpy as np
import torch
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from torchinfo import summary
from prototorch.functions.competitions import wtac
from prototorch.functions.distances import euclidean_distance
from prototorch.modules.losses import GLVQLoss
from prototorch.modules.prototypes import Prototypes1D
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from torchinfo import summary
# Prepare and preprocess the data
scaler = StandardScaler()
@ -29,7 +30,8 @@ class Model(torch.nn.Module):
prototypes_per_class=3,
nclasses=3,
prototype_initializer="stratified_random",
data=[x_train, y_train])
data=[x_train, y_train],
)
def forward(self, x):
protos = self.proto_layer.prototypes
@ -61,8 +63,10 @@ for epoch in range(70):
with torch.no_grad():
pred = wtac(dis, plabels)
correct = pred.eq(y_in.view_as(pred)).sum().item()
acc = 100. * correct / len(x_train)
print(f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} Acc: {acc:05.02f}%")
acc = 100.0 * correct / len(x_train)
print(
f"Epoch: {epoch + 1:03d} Loss: {loss.item():05.02f} Acc: {acc:05.02f}%"
)
# Take a gradient descent step
optimizer.zero_grad()
@ -83,13 +87,15 @@ for epoch in range(70):
ax.set_ylabel("Data dimension 2")
cmap = "viridis"
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
ax.scatter(protos[:, 0],
ax.scatter(
protos[:, 0],
protos[:, 1],
c=plabels,
cmap=cmap,
edgecolor="k",
marker="D",
s=50)
s=50,
)
# Paint decision regions
x = np.vstack((x_train, protos))

6
examples/gmlvq_tecator.py
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@ -20,11 +20,13 @@ class Model(torch.nn.Module):
"""GMLVQ model as a siamese network."""
super().__init__()
x, y = train_data.data, train_data.targets
self.p1 = Prototypes1D(input_dim=100,
self.p1 = Prototypes1D(
input_dim=100,
prototypes_per_class=2,
nclasses=2,
prototype_initializer="stratified_random",
data=[x, y])
data=[x, y],
)
self.omega = torch.nn.Linear(in_features=100,
out_features=100,
bias=False)

94
examples/gtlvq_mnist.py
View File

@ -13,8 +13,9 @@ import torch
import torch.nn as nn
import torchvision
from torchvision import transforms
from prototorch.modules.losses import GLVQLoss
from prototorch.functions.helper import calculate_prototype_accuracy
from prototorch.modules.losses import GLVQLoss
from prototorch.modules.models import GTLVQ
# Parameters and options
@ -26,32 +27,40 @@ momentum = 0.5
log_interval = 10
cuda = "cuda:1"
random_seed = 1
device = torch.device(cuda if torch.cuda.is_available() else 'cpu')
device = torch.device(cuda if torch.cuda.is_available() else "cpu")
# Configures reproducability
torch.manual_seed(random_seed)
np.random.seed(random_seed)
# Prepare and preprocess the data
train_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST(
'./files/',
train_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST(
"./files/",
train=True,
download=True,
transform=torchvision.transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])),
transform=torchvision.transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]),
),
batch_size=batch_size_train,
shuffle=True)
shuffle=True,
)
test_loader = torch.utils.data.DataLoader(torchvision.datasets.MNIST(
'./files/',
test_loader = torch.utils.data.DataLoader(
torchvision.datasets.MNIST(
"./files/",
train=False,
download=True,
transform=torchvision.transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))])),
transform=torchvision.transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]),
),
batch_size=batch_size_test,
shuffle=True)
shuffle=True,
)
# Define the GLVQ model plus appropriate feature extractor
@ -67,25 +76,34 @@ class CNNGTLVQ(torch.nn.Module):
):
super(CNNGTLVQ, self).__init__()
#Feature Extractor - Simple CNN
self.fe = nn.Sequential(nn.Conv2d(1, 32, 3, 1), nn.ReLU(),
nn.Conv2d(32, 64, 3, 1), nn.ReLU(),
nn.MaxPool2d(2), nn.Dropout(0.25),
nn.Flatten(), nn.Linear(9216, bottleneck_dim),
nn.Dropout(0.5), nn.LeakyReLU(),
nn.LayerNorm(bottleneck_dim))
# Feature Extractor - Simple CNN
self.fe = nn.Sequential(
nn.Conv2d(1, 32, 3, 1),
nn.ReLU(),
nn.Conv2d(32, 64, 3, 1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Dropout(0.25),
nn.Flatten(),
nn.Linear(9216, bottleneck_dim),
nn.Dropout(0.5),
nn.LeakyReLU(),
nn.LayerNorm(bottleneck_dim),
)
# Forward pass of subspace and prototype initialization data through feature extractor
subspace_data = self.fe(subspace_data)
prototype_data[0] = self.fe(prototype_data[0])
# Initialization of GTLVQ
self.gtlvq = GTLVQ(num_classes,
self.gtlvq = GTLVQ(
num_classes,
subspace_data,
prototype_data,
tangent_projection_type=tangent_projection_type,
feature_dim=bottleneck_dim,
prototypes_per_class=prototypes_per_class)
prototypes_per_class=prototypes_per_class,
)
def forward(self, x):
# Feature Extraction
@ -103,20 +121,24 @@ subspace_data = torch.cat(
prototype_data = next(iter(train_loader))
# Build the CNN GTLVQ model
model = CNNGTLVQ(10,
model = CNNGTLVQ(
10,
subspace_data,
prototype_data,
tangent_projection_type="local",
bottleneck_dim=128).to(device)
bottleneck_dim=128,
).to(device)
# Optimize using SGD optimizer from `torch.optim`
optimizer = torch.optim.Adam([{
'params': model.fe.parameters()
}, {
'params': model.gtlvq.parameters()
}],
lr=learning_rate)
criterion = GLVQLoss(squashing='sigmoid_beta', beta=10)
optimizer = torch.optim.Adam(
[{
"params": model.fe.parameters()
}, {
"params": model.gtlvq.parameters()
}],
lr=learning_rate,
)
criterion = GLVQLoss(squashing="sigmoid_beta", beta=10)
# Training loop
for epoch in range(n_epochs):
@ -139,8 +161,8 @@ for epoch in range(n_epochs):
if batch_idx % log_interval == 0:
acc = calculate_prototype_accuracy(distances, y_train, plabels)
print(
f'Epoch: {epoch + 1:02d}/{n_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
Train Acc: {acc.item():02.02f}')
f"Epoch: {epoch + 1:02d}/{n_epochs:02d} Epoch Progress: {100. * batch_idx / len(train_loader):02.02f} % Loss: {loss.item():02.02f} \
Train Acc: {acc.item():02.02f}")
# Test
with torch.no_grad():
@ -154,9 +176,9 @@ for epoch in range(n_epochs):
i = torch.argmin(test_distances, 1)
correct += torch.sum(y_test == test_plabels[i])
total += y_test.size(0)
print('Accuracy of the network on the test images: %d %%' %
print("Accuracy of the network on the test images: %d %%" %
(torch.true_divide(correct, total) * 100))
# Save the model
PATH = './glvq_mnist_model.pth'
PATH = "./glvq_mnist_model.pth"
torch.save(model.state_dict(), PATH)

18
examples/lgmlvq_iris.py
View File

@ -22,10 +22,12 @@ class Model(torch.nn.Module):
def __init__(self):
"""Local-GMLVQ model."""
super().__init__()
self.p1 = Prototypes1D(input_dim=2,
self.p1 = Prototypes1D(
input_dim=2,
prototype_distribution=[1, 2, 2],
prototype_initializer="stratified_random",
data=[x_train, y_train])
data=[x_train, y_train],
)
omegas = torch.zeros(5, 2, 2)
self.omegas = torch.nn.Parameter(omegas)
eye_(self.omegas)
@ -76,14 +78,16 @@ for epoch in range(100):
ax.set_xlabel("Data dimension 1")
ax.set_ylabel("Data dimension 2")
cmap = "viridis"
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor='k')
ax.scatter(protos[:, 0],
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
ax.scatter(
protos[:, 0],
protos[:, 1],
c=plabels,
cmap=cmap,
edgecolor='k',
marker='D',
s=50)
edgecolor="k",
marker="D",
s=50,
)
# Paint decision regions
x = np.vstack((x_train, protos))

65
examples/new_components.py Normal file
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@ -0,0 +1,65 @@
"""This example script shows the usage of the new components architecture.
Serialization/deserialization also works as expected.
"""
# DATASET
import torch
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
x_train, y_train = load_iris(return_X_y=True)
x_train = x_train[:, [0, 2]]
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_train = torch.Tensor(x_train)
y_train = torch.Tensor(y_train)
num_classes = len(torch.unique(y_train))
# CREATE NEW COMPONENTS
from prototorch.components import *
from prototorch.components.initializers import *
unsupervised = Components(6, SelectionInitializer(x_train))
print(unsupervised())
prototypes = LabeledComponents(
(3, 2), StratifiedSelectionInitializer(x_train, y_train))
print(prototypes())
components = ReasoningComponents(
(3, 6), StratifiedSelectionInitializer(x_train, y_train))
print(components())
# TEST SERIALIZATION
import io
save = io.BytesIO()
torch.save(unsupervised, save)
save.seek(0)
serialized_unsupervised = torch.load(save)
assert torch.all(unsupervised.components == serialized_unsupervised.components
), "Serialization of Components failed."
save = io.BytesIO()
torch.save(prototypes, save)
save.seek(0)
serialized_prototypes = torch.load(save)
assert torch.all(prototypes.components == serialized_prototypes.components
), "Serialization of Components failed."
assert torch.all(prototypes.component_labels == serialized_prototypes.
component_labels), "Serialization of Components failed."
save = io.BytesIO()
torch.save(components, save)
save.seek(0)
serialized_components = torch.load(save)
assert torch.all(components.components == serialized_components.components
), "Serialization of Components failed."
assert torch.all(components.reasonings == serialized_components.reasonings
), "Serialization of Components failed."

20
prototorch/__init__.py
View File

@ -1,11 +1,7 @@
"""ProtoTorch package."""
# #############################################
# Core Setup
# #############################################
__version__ = "0.3.0-dev0"
from prototorch import datasets, functions, modules
__version__ = "0.4.0"
__all_core__ = [
"datasets",
@ -13,10 +9,11 @@ __all_core__ = [
"modules",
]
# #############################################
from .datasets import *
# Plugin Loader
# #############################################
import pkgutil
import pkg_resources
__path__ = pkgutil.extend_path(__path__, __name__)
@ -25,7 +22,8 @@ __path__ = pkgutil.extend_path(__path__, __name__)
def discover_plugins():
return {
entry_point.name: entry_point.load()
for entry_point in pkg_resources.iter_entry_points("prototorch.plugins")
for entry_point in pkg_resources.iter_entry_points(
"prototorch.plugins")
}
@ -33,12 +31,10 @@ discovered_plugins = discover_plugins()
locals().update(discovered_plugins)
# Generate combines __version__ and __all__
version_plugins = "\n".join(
[
version_plugins = "\n".join([
"- " + name + ": v" + plugin.__version__
for name, plugin in discovered_plugins.items()
]
)
])
if version_plugins != "":
version_plugins = "\nPlugins: \n" + version_plugins

2
prototorch/components/__init__.py Normal file
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@ -0,0 +1,2 @@
from prototorch.components.components import *
from prototorch.components.initializers import *

134
prototorch/components/components.py Normal file
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@ -0,0 +1,134 @@
"""ProtoTorch components modules."""
import warnings
from typing import Tuple
import torch
from prototorch.components.initializers import (ComponentsInitializer,
EqualLabelInitializer,
ZeroReasoningsInitializer)
from prototorch.functions.initializers import get_initializer
from torch.nn.parameter import Parameter
class Components(torch.nn.Module):
"""Components is a set of learnable Tensors."""
def __init__(self,
number_of_components=None,
initializer=None,
*,
initialized_components=None,
dtype=torch.float32):
super().__init__()
# Ignore all initialization settings if initialized_components is given.
if initialized_components is not None:
self._components = Parameter(initialized_components)
if number_of_components is not None or initializer is not None:
wmsg = "Arguments ignored while initializing Components"
warnings.warn(wmsg)
else:
self._initialize_components(number_of_components, initializer)
def _initialize_components(self, number_of_components, initializer):
if not isinstance(initializer, ComponentsInitializer):
emsg = f"`initializer` has to be some subtype of " \
f"{ComponentsInitializer}. " \
f"You have provided: {initializer=} instead."
raise TypeError(emsg)
self._components = Parameter(
initializer.generate(number_of_components))
@property
def components(self):
"""Tensor containing the component tensors."""
return self._components.detach().cpu()
def forward(self):
return self._components
def extra_repr(self):
return f"components.shape: {tuple(self._components.shape)}"
class LabeledComponents(Components):
"""LabeledComponents generate a set of components and a set of labels.
Every Component has a label assigned.
"""
def __init__(self,
labels=None,
initializer=None,
*,
initialized_components=None):
if initialized_components is not None:
super().__init__(initialized_components=initialized_components[0])
self._labels = initialized_components[1]
else:
self._initialize_labels(labels)
super().__init__(number_of_components=len(self._labels),
initializer=initializer)
def _initialize_labels(self, labels):
if type(labels) == tuple:
num_classes, prototypes_per_class = labels
labels = EqualLabelInitializer(num_classes, prototypes_per_class)
self._labels = labels.generate()
@property
def component_labels(self):
"""Tensor containing the component tensors."""
return self._labels.detach().cpu()
def forward(self):
return super().forward(), self._labels
class ReasoningComponents(Components):
"""ReasoningComponents generate a set of components and a set of reasoning matrices.
Every Component has a reasoning matrix assigned.
A reasoning matrix is a Nx2 matrix, where N is the number of Classes. The
first element is called positive reasoning :math:`p`, the second negative
reasoning :math:`n`. A components can reason in favour (positive) of a
class, against (negative) a class or not at all (neutral).
It holds that :math:`0 \leq n \leq 1`, :math:`0 \leq p \leq 1` and :math:`0
\leq n+p \leq 1`. Therefore :math:`n` and :math:`p` are two elements of a
three element probability distribution.
"""
def __init__(self,
reasonings=None,
initializer=None,
*,
initialized_components=None):
if initialized_components is not None:
super().__init__(initialized_components=initialized_components[0])
self._reasonings = initialized_components[1]
else:
self._initialize_reasonings(reasonings)
super().__init__(number_of_components=len(self._reasonings),
initializer=initializer)
def _initialize_reasonings(self, reasonings):
if type(reasonings) == tuple:
num_classes, number_of_components = reasonings
reasonings = ZeroReasoningsInitializer(num_classes,
number_of_components)
self._reasonings = reasonings.generate()
@property
def reasonings(self):
"""Returns Reasoning Matrix.
Dimension NxCx2
"""
return self._reasonings.detach().cpu()
def forward(self):
return super().forward(), self._reasonings

172
prototorch/components/initializers.py Normal file
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@ -0,0 +1,172 @@
"""ProtoTroch Initializers."""
import warnings
from collections.abc import Iterable
import torch
from torch.utils.data import DataLoader, Dataset
def parse_init_arg(arg):
if isinstance(arg, Dataset):
data, labels = next(iter(DataLoader(arg, batch_size=len(arg))))
# data = data.view(len(arg), -1) # flatten
else:
data, labels = arg
if not isinstance(data, torch.Tensor):
wmsg = f"Converting data to {torch.Tensor}."
warnings.warn(wmsg)
data = torch.Tensor(data)
if not isinstance(labels, torch.Tensor):
wmsg = f"Converting labels to {torch.Tensor}."
warnings.warn(wmsg)
labels = torch.Tensor(labels)
return data, labels
# Components
class ComponentsInitializer(object):
def generate(self, number_of_components):
raise NotImplementedError("Subclasses should implement this!")
class DimensionAwareInitializer(ComponentsInitializer):
def __init__(self, c_dims):
super().__init__()
if isinstance(c_dims, Iterable):
self.components_dims = tuple(c_dims)
else:
self.components_dims = (c_dims, )
class OnesInitializer(DimensionAwareInitializer):
def generate(self, length):
gen_dims = (length, ) + self.components_dims
return torch.ones(gen_dims)
class ZerosInitializer(DimensionAwareInitializer):
def generate(self, length):
gen_dims = (length, ) + self.components_dims
return torch.zeros(gen_dims)
class UniformInitializer(DimensionAwareInitializer):
def __init__(self, c_dims, min=0.0, max=1.0):
super().__init__(c_dims)
self.min = min
self.max = max
def generate(self, length):
gen_dims = (length, ) + self.components_dims
return torch.ones(gen_dims).uniform_(self.min, self.max)
class PositionAwareInitializer(ComponentsInitializer):
def __init__(self, positions):
super().__init__()
self.data = positions
class SelectionInitializer(PositionAwareInitializer):
def generate(self, length):
indices = torch.LongTensor(length).random_(0, len(self.data))
return self.data[indices]
class MeanInitializer(PositionAwareInitializer):
def generate(self, length):
mean = torch.mean(self.data, dim=0)
repeat_dim = [length] + [1] * len(mean.shape)
return mean.repeat(repeat_dim)
class ClassAwareInitializer(ComponentsInitializer):
def __init__(self, arg):
super().__init__()
data, labels = parse_init_arg(arg)
self.data = data
self.labels = labels
self.clabels = torch.unique(self.labels)
self.num_classes = len(self.clabels)
class StratifiedMeanInitializer(ClassAwareInitializer):
def __init__(self, arg):
super().__init__(arg)
self.initializers = []
for clabel in self.clabels:
class_data = self.data[self.labels == clabel]
class_initializer = MeanInitializer(class_data)
self.initializers.append(class_initializer)
def generate(self, length):
per_class = length // self.num_classes
samples_list = [init.generate(per_class) for init in self.initializers]
return torch.vstack(samples_list)
class StratifiedSelectionInitializer(ClassAwareInitializer):
def __init__(self, arg, *, noise=None):
super().__init__(arg)
self.noise = noise
self.initializers = []
for clabel in self.clabels:
class_data = self.data[self.labels == clabel]
class_initializer = SelectionInitializer(class_data)
self.initializers.append(class_initializer)
def add_noise(self, x):
"""Shifts some dimensions of the data randomly."""
n1 = torch.rand_like(x)
n2 = torch.rand_like(x)
mask = torch.bernoulli(n1) - torch.bernoulli(n2)
return x + (self.noise * mask)
def generate(self, length):
per_class = length // self.num_classes
samples_list = [init.generate(per_class) for init in self.initializers]
samples = torch.vstack(samples_list)
if self.noise is not None:
# samples = self.add_noise(samples)
samples = samples + self.noise
return samples
# Labels
class LabelsInitializer:
def generate(self):
raise NotImplementedError("Subclasses should implement this!")
class EqualLabelInitializer(LabelsInitializer):
def __init__(self, classes, per_class):
self.classes = classes
self.per_class = per_class
def generate(self):
return torch.arange(self.classes).repeat(self.per_class, 1).T.flatten()
# Reasonings
class ReasoningsInitializer:
def generate(self, length):
raise NotImplementedError("Subclasses should implement this!")
class ZeroReasoningsInitializer(ReasoningsInitializer):
def __init__(self, classes, length):
self.classes = classes
self.length = length
def generate(self):
return torch.zeros((self.length, self.classes, 2))
# Aliases
SSI = StratifiedSampleInitializer = StratifiedSelectionInitializer
SMI = StratifiedMeanInitializer
Random = RandomInitializer = UniformInitializer

6
prototorch/datasets/__init__.py
View File

@ -1,7 +1,11 @@
"""ProtoTorch datasets."""
from .abstract import NumpyDataset
from .spiral import Spiral
from .tecator import Tecator
__all__ = [
'Tecator',
"NumpyDataset",
"Spiral",
"Tecator",
]

20
prototorch/datasets/abstract.py
View File

@ -12,6 +12,13 @@ import os
import torch
class NumpyDataset(torch.utils.data.TensorDataset):
"""Create a PyTorch TensorDataset from NumPy arrays."""
def __init__(self, *arrays):
tensors = [torch.Tensor(arr) for arr in arrays]
super().__init__(*tensors)
class Dataset(torch.utils.data.Dataset):
"""Abstract dataset class to be inherited."""
@ -44,15 +51,13 @@ class ProtoDataset(Dataset):
self._download()
if not self._check_exists():
raise RuntimeError(
"Dataset not found. " "You can use download=True to download it"
)
raise RuntimeError("Dataset not found. "
"You can use download=True to download it")
data_file = self.training_file if self.train else self.test_file
self.data, self.targets = torch.load(
os.path.join(self.processed_folder, data_file)
)
os.path.join(self.processed_folder, data_file))
@property
def raw_folder(self):
@ -68,8 +73,9 @@ class ProtoDataset(Dataset):
def _check_exists(self):
return os.path.exists(
os.path.join(self.processed_folder, self.training_file)
) and os.path.exists(os.path.join(self.processed_folder, self.test_file))
os.path.join(
self.processed_folder, self.training_file)) and os.path.exists(
os.path.join(self.processed_folder, self.test_file))
def __repr__(self):
head = "Dataset " + self.__class__.__name__

33
prototorch/datasets/spiral.py Normal file
View File

@ -0,0 +1,33 @@
"""Spiral dataset for binary classification."""
import numpy as np
import torch
def make_spiral(n_samples=500, noise=0.3):
def get_samples(n, delta_t):
points = []
for i in range(n):
r = i / n_samples * 5
t = 1.75 * i / n * 2 * np.pi + delta_t
x = r * np.sin(t) + np.random.rand(1) * noise
y = r * np.cos(t) + np.random.rand(1) * noise
points.append([x, y])
return points
n = n_samples // 2
positive = get_samples(n=n, delta_t=0)
negative = get_samples(n=n, delta_t=np.pi)
x = np.concatenate(
[np.array(positive).reshape(n, -1),
np.array(negative).reshape(n, -1)],
axis=0)
y = np.concatenate([np.zeros(n), np.ones(n)])
return x, y
class Spiral(torch.utils.data.TensorDataset):
"""Spiral dataset for binary classification."""
def __init__(self, n_samples=500, noise=0.3):
x, y = make_spiral(n_samples, noise)
super().__init__(torch.Tensor(x), torch.LongTensor(y))

21
prototorch/datasets/tecator.py
View File

@ -52,7 +52,8 @@ class Tecator(ProtoDataset):
"""
_resources = [
("1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0", "ba5607c580d0f91bb27dc29d13c2f8df"),
("1P9WIYnyxFPh6f1vqAbnKfK8oYmUgyV83",
"ba5607c580d0f91bb27dc29d13c2f8df"),
] # (google_storage_id, md5hash)
classes = ["0 - low_fat", "1 - high_fat"]
@ -74,15 +75,15 @@ class Tecator(ProtoDataset):
print("Downloading...")
for fileid, md5 in self._resources:
filename = "tecator.npz"
download_file_from_google_drive(
fileid, root=self.raw_folder, filename=filename, md5=md5
)
download_file_from_google_drive(fileid,
root=self.raw_folder,
filename=filename,
md5=md5)
if self.verbose:
print("Processing...")
with np.load(
os.path.join(self.raw_folder, "tecator.npz"), allow_pickle=False
) as f:
with np.load(os.path.join(self.raw_folder, "tecator.npz"),
allow_pickle=False) as f:
x_train, y_train = f["x_train"], f["y_train"]
x_test, y_test = f["x_test"], f["y_test"]
training_set = [
@ -94,9 +95,11 @@ class Tecator(ProtoDataset):
torch.tensor(y_test),
]
with open(os.path.join(self.processed_folder, self.training_file), "wb") as f:
with open(os.path.join(self.processed_folder, self.training_file),
"wb") as f:
torch.save(training_set, f)
with open(os.path.join(self.processed_folder, self.test_file), "wb") as f:
with open(os.path.join(self.processed_folder, self.test_file),
"wb") as f:
torch.save(test_set, f)
if self.verbose:

10
prototorch/functions/__init__.py
View File

@ -4,9 +4,9 @@ from .activations import identity, sigmoid_beta, swish_beta
from .competitions import knnc, wtac
__all__ = [
'identity',
'sigmoid_beta',
'swish_beta',
'knnc',
'wtac',
"identity",
"sigmoid_beta",
"swish_beta",
"knnc",
"wtac",
]

17
prototorch/functions/activations.py
View File

@ -16,40 +16,43 @@ def register_activation(function):
@register_activation
# @torch.jit.script
def identity(x, beta=torch.tensor(0)):
def identity(x, beta=0.0):
"""Identity activation function.
Definition:
:math:`f(x) = x`
Keyword Arguments:
beta (`float`): Ignored.
"""
return x
@register_activation
# @torch.jit.script
def sigmoid_beta(x, beta=torch.tensor(10)):
def sigmoid_beta(x, beta=10.0):
r"""Sigmoid activation function with scaling.
Definition:
:math:`f(x) = \frac{1}{1 + e^{-\beta x}}`
Keyword Arguments:
beta (`torch.tensor`): Scaling parameter :math:`\beta`
beta (`float`): Scaling parameter :math:`\beta`
"""
out = torch.reciprocal(1.0 + torch.exp(-int(beta.item()) * x))
out = 1.0 / (1.0 + torch.exp(-1.0 * beta * x))
return out
@register_activation
# @torch.jit.script
def swish_beta(x, beta=torch.tensor(10)):
def swish_beta(x, beta=10.0):
r"""Swish activation function with scaling.
Definition:
:math:`f(x) = \frac{x}{1 + e^{-\beta x}}`
Keyword Arguments:
beta (`torch.tensor`): Scaling parameter :math:`\beta`
beta (`float`): Scaling parameter :math:`\beta`
"""
out = x * sigmoid_beta(x, beta=beta)
return out
@ -61,4 +64,4 @@ def get_activation(funcname):
return funcname
if funcname in ACTIVATIONS:
return ACTIVATIONS.get(funcname)
raise NameError(f'Activation {funcname} was not found.')
raise NameError(f"Activation {funcname} was not found.")

2
prototorch/functions/competitions.py
View File

@ -12,7 +12,7 @@ def stratified_min(distances, labels):
return distances
batch_size = distances.size()[0]
winning_distances = torch.zeros(nclasses, batch_size)
inf = torch.full_like(distances.T, fill_value=float('inf'))
inf = torch.full_like(distances.T, fill_value=float("inf"))
# distances_to_wpluses = torch.where(matcher, distances, inf)
for i, cl in enumerate(clabels):
# cdists = distances.T[labels == cl]

93
prototorch/functions/distances.py
View File

@ -1,12 +1,10 @@
"""ProtoTorch distance functions."""
import torch
from prototorch.functions.helper import (
equal_int_shape,
_int_and_mixed_shape,
_check_shapes,
)
import numpy as np
import torch
from prototorch.functions.helper import (_check_shapes, _int_and_mixed_shape,
equal_int_shape)
def squared_euclidean_distance(x, y):
@ -43,9 +41,21 @@ def euclidean_distance(x, y):
return distances
def euclidean_distance_v2(x, y):
diff = y - x.unsqueeze(1)
pairwise_distances = (diff @ diff.permute((0, 2, 1))).sqrt()
# Passing `dim1=-2` and `dim2=-1` to `diagonal()` takes the
# batch diagonal. See:
# https://pytorch.org/docs/stable/generated/torch.diagonal.html
distances = torch.diagonal(pairwise_distances, dim1=-2, dim2=-1)
# print(f"{diff.shape=}") # (nx, ny, ndim)
# print(f"{pairwise_distances.shape=}") # (nx, ny, ny)
# print(f"{distances.shape=}") # (nx, ny)
return distances
def lpnorm_distance(x, y, p):
r"""
Calculates the lp-norm between :math:`\bm x` and :math:`\bm y`.
r"""Calculate the lp-norm between :math:`\bm x` and :math:`\bm y`.
Also known as Minkowski distance.
Compute :math:`{\| \bm x - \bm y \|}_p`.
@ -88,7 +98,7 @@ def lomega_distance(x, y, omegas):
projected_y = torch.diagonal(y @ omegas).T
expanded_y = torch.unsqueeze(projected_y, dim=1)
batchwise_difference = expanded_y - projected_x
differences_squared = batchwise_difference ** 2
differences_squared = batchwise_difference**2
distances = torch.sum(differences_squared, dim=2)
distances = distances.permute(1, 0)
return distances
@ -107,26 +117,18 @@ def euclidean_distance_matrix(x, y, squared=False, epsilon=1e-10):
for tensor in [x, y]:
if tensor.ndim != 2:
raise ValueError(
"The tensor dimension must be two. You provide: tensor.ndim="
+ str(tensor.ndim)
+ "."
)
"The tensor dimension must be two. You provide: tensor.ndim=" +
str(tensor.ndim) + ".")
if not equal_int_shape([tuple(x.shape)[1]], [tuple(y.shape)[1]]):
raise ValueError(
"The vector shape must be equivalent in both tensors. You provide: tuple(y.shape)[1]="
+ str(tuple(x.shape)[1])
+ " and tuple(y.shape)(y)[1]="
+ str(tuple(y.shape)[1])
+ "."
)
+ str(tuple(x.shape)[1]) + " and tuple(y.shape)(y)[1]=" +
str(tuple(y.shape)[1]) + ".")
y = torch.transpose(y)
diss = (
torch.sum(x ** 2, axis=1, keepdims=True)
- 2 * torch.dot(x, y)
+ torch.sum(y ** 2, axis=0, keepdims=True)
)
diss = (torch.sum(x**2, axis=1, keepdims=True) - 2 * torch.dot(x, y) +
torch.sum(y**2, axis=0, keepdims=True))
if not squared:
if epsilon == 0:
@ -173,19 +175,18 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
if subspaces.ndim == 2:
# clean solution without map if the matrix_scope is global
projectors = torch.eye(subspace_int_shape[-2]) - torch.dot(
subspaces, torch.transpose(subspaces)
)
subspaces, torch.transpose(subspaces))
projected_signals = torch.dot(signals, projectors)
projected_protos = torch.dot(protos, projectors)
diss = euclidean_distance_matrix(
projected_signals, projected_protos, squared=squared, epsilon=epsilon
)
diss = euclidean_distance_matrix(projected_signals,
projected_protos,
squared=squared,
epsilon=epsilon)
diss = torch.reshape(
diss, [signal_shape[0], signal_shape[2], proto_shape[0]]
)
diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
return torch.permute(diss, [0, 2, 1])
@ -193,21 +194,18 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
# no solution without map possible --> memory efficient but slow!
projectors = torch.eye(subspace_int_shape[-2]) - torch.bmm(
subspaces, subspaces
) # K.batch_dot(subspaces, subspaces, [2, 2])
subspaces,
subspaces) # K.batch_dot(subspaces, subspaces, [2, 2])
projected_protos = (
protos @ subspaces
projected_protos = (protos @ subspaces
).T # K.batch_dot(projectors, protos, [1, 1]))
def projected_norm(projector):
return torch.sum(torch.dot(signals, projector) ** 2, axis=1)
return torch.sum(torch.dot(signals, projector)**2, axis=1)
diss = (
torch.transpose(map(projected_norm, projectors))
- 2 * torch.dot(signals, projected_protos)
+ torch.sum(projected_protos ** 2, axis=0, keepdims=True)
)
diss = (torch.transpose(map(projected_norm, projectors)) -
2 * torch.dot(signals, projected_protos) +
torch.sum(projected_protos**2, axis=0, keepdims=True))
if not squared:
if epsilon == 0:
@ -216,8 +214,7 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
diss = torch.sqrt(torch.max(diss, epsilon))
diss = torch.reshape(
diss, [signal_shape[0], signal_shape[2], proto_shape[0]]
)
diss, [signal_shape[0], signal_shape[2], proto_shape[0]])
return torch.permute(diss, [0, 2, 1])
@ -233,12 +230,12 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
# Scope: Tangentspace Projections
diff = torch.reshape(
diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1)
)
diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
projected_diff = diff @ projectors
projected_diff = torch.reshape(
projected_diff,
(signal_shape[0], signal_shape[2], signal_shape[1]) + signal_shape[3:],
(signal_shape[0], signal_shape[2], signal_shape[1]) +
signal_shape[3:],
)
diss = torch.norm(projected_diff, 2, dim=-1)
@ -251,13 +248,13 @@ def tangent_distance(signals, protos, subspaces, squared=False, epsilon=1e-10):
# Scope: Tangentspace Projections
diff = torch.reshape(
diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1)
)
diff, (signal_shape[0] * signal_shape[2], signal_shape[1], -1))
diff = diff.permute([1, 0, 2])
projected_diff = torch.bmm(diff, projectors)
projected_diff = torch.reshape(
projected_diff,
(signal_shape[1], signal_shape[0], signal_shape[2]) + signal_shape[3:],
(signal_shape[1], signal_shape[0], signal_shape[2]) +
signal_shape[3:],
)
diss = torch.norm(projected_diff, 2, dim=-1)

6
prototorch/functions/helper.py
View File

@ -23,7 +23,7 @@ def predict_label(y_pred, plabels):
def mixed_shape(inputs):
if not torch.is_tensor(inputs):
raise ValueError('Input must be a tensor.')
raise ValueError("Input must be a tensor.")
else:
int_shape = list(inputs.shape)
# sometimes int_shape returns mixed integer types
@ -39,11 +39,11 @@ def mixed_shape(inputs):
def equal_int_shape(shape_1, shape_2):
if not isinstance(shape_1,
(tuple, list)) or not isinstance(shape_2, (tuple, list)):
raise ValueError('Input shapes must list or tuple.')
raise ValueError("Input shapes must list or tuple.")
for shape in [shape_1, shape_2]:
if not all([isinstance(x, int) or x is None for x in shape]):
raise ValueError(
'Input shapes must be list or tuple of int and None values.')
"Input shapes must be list or tuple of int and None values.")
if len(shape_1) != len(shape_2):
return False

2
prototorch/functions/initializers.py
View File

@ -104,4 +104,4 @@ def get_initializer(funcname):
return funcname
if funcname in INITIALIZERS:
return INITIALIZERS.get(funcname)
raise NameError(f'Initializer {funcname} was not found.')
raise NameError(f"Initializer {funcname} was not found.")

15
prototorch/functions/losses.py
View File

@ -3,15 +3,22 @@
import torch
def _get_dp_dm(distances, targets, plabels):
matcher = torch.eq(targets.unsqueeze(dim=1), plabels)
if plabels.ndim == 2:
def _get_matcher(targets, labels):
"""Returns a boolean tensor."""
matcher = torch.eq(targets.unsqueeze(dim=1), labels)
if labels.ndim == 2:
# if the labels are one-hot vectors
nclasses = targets.size()[1]
matcher = torch.eq(torch.sum(matcher, dim=-1), nclasses)
return matcher
def _get_dp_dm(distances, targets, plabels):
"""Returns the d+ and d- values for a batch of distances."""
matcher = _get_matcher(targets, plabels)
not_matcher = torch.bitwise_not(matcher)
inf = torch.full_like(distances, fill_value=float('inf'))
inf = torch.full_like(distances, fill_value=float("inf"))
d_matching = torch.where(matcher, distances, inf)
d_unmatching = torch.where(not_matcher, distances, inf)
dp = torch.min(d_matching, dim=1, keepdim=True).values

4
prototorch/functions/normalization.py
View File

@ -1,7 +1,5 @@
# -*- coding: utf-8 -*-
from __future__ import print_function
from __future__ import absolute_import
from __future__ import division
from __future__ import absolute_import, division, print_function
import torch

18
prototorch/functions/similarities.py Normal file
View File

@ -0,0 +1,18 @@
"""ProtoTorch similarity functions."""
import torch
def cosine_similarity(x, y):
"""Compute the cosine similarity between :math:`x` and :math:`y`.
Expected dimension of x is 2.
Expected dimension of y is 2.
"""
norm_x = x.pow(2).sum(1).sqrt()
norm_y = y.pow(2).sum(1).sqrt()
norm_mat = norm_x.unsqueeze(-1) @ norm_y.unsqueeze(-1).T
epsilon = torch.finfo(norm_mat.dtype).eps
norm_mat.clamp_(min=epsilon)
similarities = (x @ y.T) / norm_mat
return similarities

2
prototorch/modules/__init__.py
View File

@ -3,5 +3,5 @@
from .prototypes import Prototypes1D
__all__ = [
'Prototypes1D',
"Prototypes1D",
]

22
prototorch/modules/losses.py
View File

@ -7,7 +7,7 @@ from prototorch.functions.losses import glvq_loss
class GLVQLoss(torch.nn.Module):
def __init__(self, margin=0.0, squashing='identity', beta=10, **kwargs):
def __init__(self, margin=0.0, squashing="identity", beta=10, **kwargs):
super().__init__(**kwargs)
self.margin = margin
self.squashing = get_activation(squashing)
@ -18,3 +18,23 @@ class GLVQLoss(torch.nn.Module):
mu = glvq_loss(distances, targets, prototype_labels=plabels)
batch_loss = self.squashing(mu + self.margin, beta=self.beta)
return torch.sum(batch_loss, dim=0)
class NeuralGasEnergy(torch.nn.Module):
def __init__(self, lm):
super().__init__()
self.lm = lm
def forward(self, d):
order = torch.argsort(d, dim=1)
ranks = torch.argsort(order, dim=1)
cost = torch.sum(self._nghood_fn(ranks, self.lm) * d)
return cost, order
def extra_repr(self):
return f"lambda: {self.lm}"
@staticmethod
def _nghood_fn(rankings, lm):
return torch.exp(-rankings / lm)

59
prototorch/modules/models.py
View File

@ -1,9 +1,11 @@
from torch import nn
import torch
from prototorch.modules.prototypes import Prototypes1D
from prototorch.functions.distances import tangent_distance, euclidean_distance_matrix
from prototorch.functions.normalization import orthogonalization
from torch import nn
from prototorch.functions.distances import (euclidean_distance_matrix,
tangent_distance)
from prototorch.functions.helper import _check_shapes, _int_and_mixed_shape
from prototorch.functions.normalization import orthogonalization
from prototorch.modules.prototypes import Prototypes1D
class GTLVQ(nn.Module):
@ -71,7 +73,7 @@ class GTLVQ(nn.Module):
subspace_data=None,
prototype_data=None,
subspace_size=256,
tangent_projection_type='local',
tangent_projection_type="local",
prototypes_per_class=2,
feature_dim=256,
):
@ -82,37 +84,39 @@ class GTLVQ(nn.Module):
self.feature_dim = feature_dim
if subspace_data is None:
raise ValueError('Init Data must be specified!')
raise ValueError("Init Data must be specified!")
self.tpt = tangent_projection_type
with torch.no_grad():
if self.tpt == 'local' or self.tpt == 'local_proj':
if self.tpt == "local" or self.tpt == "local_proj":
self.init_local_subspace(subspace_data)
elif self.tpt == 'global':
elif self.tpt == "global":
self.init_gobal_subspace(subspace_data, subspace_size)
else:
self.subspaces = None
# Hypothesis-Margin-Classifier
self.cls = Prototypes1D(input_dim=feature_dim,
self.cls = Prototypes1D(
input_dim=feature_dim,
prototypes_per_class=prototypes_per_class,
nclasses=num_classes,
prototype_initializer='stratified_mean',
data=prototype_data)
prototype_initializer="stratified_mean",
data=prototype_data,
)
def forward(self, x):
# Tangent Projection
if self.tpt == 'local_proj':
x_conform = x.unsqueeze(1).repeat_interleave(self.num_protos,
1).unsqueeze(2)
if self.tpt == "local_proj":
x_conform = (x.unsqueeze(1).repeat_interleave(self.num_protos,
1).unsqueeze(2))
dis, proj_x = self.local_tangent_projection(x_conform)
proj_x = proj_x.reshape(x.shape[0] * self.num_protos,
self.feature_dim)
return proj_x, dis
elif self.tpt == "local":
x_conform = x.unsqueeze(1).repeat_interleave(self.num_protos,
1).unsqueeze(2)
x_conform = (x.unsqueeze(1).repeat_interleave(self.num_protos,
1).unsqueeze(2))
dis = tangent_distance(x_conform, self.cls.prototypes,
self.subspaces)
elif self.tpt == "gloabl":
@ -127,25 +131,27 @@ class GTLVQ(nn.Module):
_, _, v = torch.svd(data)
subspace = (torch.eye(v.shape[0]) - (v @ v.T)).T
subspaces = subspace[:, :num_subspaces]
self.subspaces = torch.nn.Parameter(
subspaces).clone().detach().requires_grad_(True)
self.subspaces = (torch.nn.Parameter(
subspaces).clone().detach().requires_grad_(True))
def init_local_subspace(self, data):
_, _, v = torch.svd(data)
inital_projector = (torch.eye(v.shape[0]) - (v @ v.T)).T
subspaces = inital_projector.unsqueeze(0).repeat_interleave(
self.num_protos, 0)
self.subspaces = torch.nn.Parameter(
subspaces).clone().detach().requires_grad_(True)
self.subspaces = (torch.nn.Parameter(
subspaces).clone().detach().requires_grad_(True))
def global_tangent_distances(self, x):
# Tangent Projection
x, projected_prototypes = x @ self.subspaces, self.cls.prototypes @ self.subspaces
x, projected_prototypes = (
x @ self.subspaces,
self.cls.prototypes @ self.subspaces,
)
# Euclidean Distance
return euclidean_distance_matrix(x, projected_prototypes)
def local_tangent_projection(self,
signals):
def local_tangent_projection(self, signals):
# Note: subspaces is always assumed as transposed and must be orthogonal!
# shape(signals): batch x proto_number x channels x dim1 x dim2 x ... x dimN
# shape(protos): proto_number x dim1 x dim2 x ... x dimN
@ -183,8 +189,7 @@ class GTLVQ(nn.Module):
def orthogonalize_subspace(self):
if self.subspaces is not None:
with torch.no_grad():
ortho_subpsaces = orthogonalization(
self.subspaces
) if self.tpt == 'global' else torch.nn.init.orthogonal_(
self.subspaces)
ortho_subpsaces = (orthogonalization(self.subspaces)
if self.tpt == "global" else
torch.nn.init.orthogonal_(self.subspaces))
self.subspaces.copy_(ortho_subpsaces)

9
prototorch/modules/prototypes.py
View File

@ -29,14 +29,19 @@ class Prototypes1D(_Prototypes):
TODO Complete this doc-string.
"""
def __init__(self,
def __init__(
self,
prototypes_per_class=1,
prototype_initializer="ones",
prototype_distribution=None,
data=None,
dtype=torch.float32,
one_hot_labels=False,
**kwargs):
**kwargs,
):
warnings.warn(
PendingDeprecationWarning(
"Prototypes1D will be replaced in future versions."))
# Convert tensors to python lists before processing
if prototype_distribution is not None:

1
prototorch/utils/__init__.py
View File

@ -1 +0,0 @@
from .colors import color_scheme, get_legend_handles

46
prototorch/utils/celluloid.py Normal file
View File

@ -0,0 +1,46 @@
"""Easy matplotlib animation. From https://github.com/jwkvam/celluloid."""
from collections import defaultdict
from typing import Dict, List
from matplotlib.animation import ArtistAnimation
from matplotlib.artist import Artist
from matplotlib.figure import Figure
__version__ = "0.2.0"
class Camera:
"""Make animations easier."""
def __init__(self, figure: Figure) -> None:
"""Create camera from matplotlib figure."""
self._figure = figure
# need to keep track off artists for each axis
self._offsets: Dict[str, Dict[int, int]] = {
k: defaultdict(int)
for k in
["collections", "patches", "lines", "texts", "artists", "images"]
}
self._photos: List[List[Artist]] = []
def snap(self) -> List[Artist]:
"""Capture current state of the figure."""
frame_artists: List[Artist] = []
for i, axis in enumerate(self._figure.axes):
if axis.legend_ is not None:
axis.add_artist(axis.legend_)
for name in self._offsets:
new_artists = getattr(axis, name)[self._offsets[name][i]:]
frame_artists += new_artists
self._offsets[name][i] += len(new_artists)
self._photos.append(frame_artists)
return frame_artists
def animate(self, *args, **kwargs) -> ArtistAnimation:
"""Animate the snapshots taken.
Uses matplotlib.animation.ArtistAnimation
Returns
-------
ArtistAnimation
"""
return ArtistAnimation(self._figure, self._photos, *args, **kwargs)

18
prototorch/utils/colors.py
View File

@ -1,13 +1,14 @@
"""ProtoFlow color utilities."""
from matplotlib import cm
from matplotlib.colors import Normalize
from matplotlib.colors import to_hex
from matplotlib.colors import to_rgb
import matplotlib.lines as mlines
from matplotlib import cm
from matplotlib.colors import Normalize, to_hex, to_rgb
def color_scheme(n, cmap="viridis", form="hex", tikz=False,
def color_scheme(n,
cmap="viridis",
form="hex",
tikz=False,
zero_indexed=False):
"""Return *n* colors from the color scheme.
@ -57,13 +58,16 @@ def get_legend_handles(labels, marker="dots", zero_indexed=False):
zero_indexed=zero_indexed)
for label, color in zip(labels, colors.values()):
if marker == "dots":
handle = mlines.Line2D([], [],
handle = mlines.Line2D(
[],
[],
color="white",
markerfacecolor=color,
marker="o",
markersize=10,
markeredgecolor="k",
label=label)
label=label,
)
else:
handle = mlines.Line2D([], [],
color=color,

243
prototorch/utils/utils.py Normal file
View File

@ -0,0 +1,243 @@
"""Utilities that provide various small functionalities."""
import os
import pickle
import sys
from time import time
import matplotlib.pyplot as plt
import numpy as np
def progressbar(title, value, end, bar_width=20):
percent = float(value) / end
arrow = "=" * int(round(percent * bar_width) - 1) + ">"
spaces = "." * (bar_width - len(arrow))
sys.stdout.write("\r{}: [{}] {}%".format(title, arrow + spaces,
int(round(percent * 100))))
sys.stdout.flush()
if percent == 1.0:
print()
def prettify_string(inputs, start="", sep=" ", end="\n"):
outputs = start + " ".join(inputs.split()) + end
return outputs
def pretty_print(inputs):
print(prettify_string(inputs))
def writelog(self, *logs, logdir="./logs", logfile="run.txt"):
f = os.path.join(logdir, logfile)
with open(f, "a+") as fh:
for log in logs:
fh.write(log)
fh.write("\n")
def start_tensorboard(self, logdir="./logs"):
cmd = f"tensorboard --logdir={logdir} --port=6006"
os.system(cmd)
def make_directory(save_dir):
if not os.path.exists(save_dir):
print(f"Making directory {save_dir}.")
os.mkdir(save_dir)
def make_gif(filenames, duration, output_file=None):
try:
import imageio
except ModuleNotFoundError as e:
print("Please install Protoflow with [other] extra requirements.")
raise (e)
images = list()
for filename in filenames:
images.append(imageio.imread(filename))
if not output_file:
output_file = f"makegif.gif"
if images:
imageio.mimwrite(output_file, images, duration=duration)
def gif_from_dir(directory,
duration,
prefix="",
output_file=None,
verbose=True):
images = os.listdir(directory)
if verbose:
print(f"Making gif from {len(images)} images under {directory}.")
filenames = list()
# Sort images
images = sorted(
images,
key=lambda img: int(os.path.splitext(img)[0].replace(prefix, "")))
for image in images:
fname = os.path.join(directory, image)
filenames.append(fname)
if not output_file:
output_file = os.path.join(directory, "makegif.gif")
make_gif(filenames=filenames, duration=duration, output_file=output_file)
def accuracy_score(y_true, y_pred):
accuracy = np.sum(y_true == y_pred)
normalized_acc = accuracy / float(len(y_true))
return normalized_acc
def predict_and_score(clf,
x_test,
y_test,
verbose=False,
title="Test accuracy"):
y_pred = clf.predict(x_test)
accuracy = np.sum(y_test == y_pred)
normalized_acc = accuracy / float(len(y_test))
if verbose:
print(f"{title}: {normalized_acc * 100:06.04f}%")
return normalized_acc
def remove_nan_rows(arr):
"""Remove all rows with `nan` values in `arr`."""
mask = np.isnan(arr).any(axis=1)
return arr[~mask]
def remove_nan_cols(arr):
"""Remove all columns with `nan` values in `arr`."""
mask = np.isnan(arr).any(axis=0)
return arr[~mask]
def replace_in(arr, replacement_dict, inplace=False):
"""Replace the keys found in `arr` with the values from
the `replacement_dict`.
"""
if inplace:
new_arr = arr
else:
import copy
new_arr = copy.deepcopy(arr)
for k, v in replacement_dict.items():
new_arr[arr == k] = v
return new_arr
def train_test_split(data, train=0.7, val=0.15, shuffle=None, return_xy=False):
"""Split a classification dataset in such a way so as to
preserve the class distribution in subsamples of the dataset.
"""
if train + val > 1.0:
raise ValueError("Invalid split values for train and val.")
Y = data[:, -1]
labels = set(Y)
hist = dict()
for l in labels:
data_l = data[Y == l]
nl = len(data_l)
nl_train = int(nl * train)
nl_val = int(nl * val)
nl_test = nl - (nl_train + nl_val)
hist[l] = (nl_train, nl_val, nl_test)
train_data = list()
val_data = list()
test_data = list()
for l, (nl_train, nl_val, nl_test) in hist.items():
data_l = data[Y == l]
if shuffle:
np.random.shuffle(data_l)
train_l = data_l[:nl_train]
val_l = data_l[nl_train:nl_train + nl_val]
test_l = data_l[nl_train + nl_val:nl_train + nl_val + nl_test]
train_data.append(train_l)
val_data.append(val_l)
test_data.append(test_l)
def _squash(data_list):
data = np.array(data_list[0])
for item in data_list[1:]:
data = np.vstack((data, np.array(item)))
return data
train_data = _squash(train_data)
if val_data:
val_data = _squash(val_data)
if test_data:
test_data = _squash(test_data)
if return_xy:
x_train = train_data[:, :-1]
y_train = train_data[:, -1]
x_val = val_data[:, :-1]
y_val = val_data[:, -1]
x_test = test_data[:, :-1]
y_test = test_data[:, -1]
return (x_train, y_train), (x_val, y_val), (x_test, y_test)
return train_data, val_data, test_data
def class_histogram(data, title="Untitled"):
plt.figure(title)
plt.clf()
plt.title(title)
dist, counts = np.unique(data[:, -1], return_counts=True)
plt.bar(dist, counts)
plt.xticks(dist)
print("Call matplotlib.pyplot.show() to see the plot.")
def ntimer(n=10):
"""Wraps a function which wraps another function to time it."""
if n < 1:
raise (Exception(f"Invalid n = {n} given."))
def timer(func):
"""Wraps `func` with a timer and returns the wrapped `func`."""
def wrapper(*args, **kwargs):
rv = None
before = time()
for _ in range(n):
rv = func(*args, **kwargs)
after = time()
elapsed = after - before
print(f"Elapsed: {elapsed*1e3:02.02f} ms")
return rv
return wrapper
return timer
def memoize(verbose=True):
"""Wraps a function which wraps another function that memoizes."""
def memoizer(func):
"""Memoize (cache) return values of `func`.
Wraps `func` and returns the wrapped `func` so that `func`
is executed when the results are not available in the cache.
"""
cache = {}
def wrapper(*args, **kwargs):
t = (pickle.dumps(args), pickle.dumps(kwargs))
if t not in cache:
if verbose:
print(f"Adding NEW rv {func.__name__}{args}{kwargs} "
"to cache.")
cache[t] = func(*args, **kwargs)
else:
if verbose:
print(f"Using OLD rv {func.__name__}{args}{kwargs} "
"from cache.")
return cache[t]
return wrapper
return memoizer

5
setup.py
View File

@ -8,8 +8,7 @@
ProtoTorch Core Package
"""
from setuptools import setup
from setuptools import find_packages
from setuptools import find_packages, setup
PROJECT_URL = "https://github.com/si-cim/prototorch"
DOWNLOAD_URL = "https://github.com/si-cim/prototorch.git"
@ -42,7 +41,7 @@ ALL = DOCS + DATASETS + EXAMPLES + TESTS
setup(
name="prototorch",
version="0.3.0-dev0",
version="0.4.0",
description="Highly extensible, GPU-supported "
"Learning Vector Quantization (LVQ) toolbox "
"built using PyTorch and its nn API.",

28
tests/test_datasets.py
View File

@ -12,26 +12,26 @@ from prototorch.datasets import abstract, tecator
class TestAbstract(unittest.TestCase):
def test_getitem(self):
with self.assertRaises(NotImplementedError):
abstract.Dataset('./artifacts')[0]
abstract.Dataset("./artifacts")[0]
def test_len(self):
with self.assertRaises(NotImplementedError):
len(abstract.Dataset('./artifacts'))
len(abstract.Dataset("./artifacts"))
class TestProtoDataset(unittest.TestCase):
def test_getitem(self):
with self.assertRaises(NotImplementedError):
abstract.ProtoDataset('./artifacts')[0]
abstract.ProtoDataset("./artifacts")[0]
def test_download(self):
with self.assertRaises(NotImplementedError):
abstract.ProtoDataset('./artifacts').download()
abstract.ProtoDataset("./artifacts").download()
class TestTecator(unittest.TestCase):
def setUp(self):
self.artifacts_dir = './artifacts/Tecator'
self.artifacts_dir = "./artifacts/Tecator"
self._remove_artifacts()
def _remove_artifacts(self):
@ -39,23 +39,23 @@ class TestTecator(unittest.TestCase):
shutil.rmtree(self.artifacts_dir)
def test_download_false(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
self._remove_artifacts()
with self.assertRaises(RuntimeError):
_ = tecator.Tecator(rootdir, download=False)
def test_download_caching(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
_ = tecator.Tecator(rootdir, download=True, verbose=False)
_ = tecator.Tecator(rootdir, download=False, verbose=False)
def test_repr(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
train = tecator.Tecator(rootdir, download=True, verbose=True)
self.assertTrue('Split: Train' in train.__repr__())
self.assertTrue("Split: Train" in train.__repr__())
def test_download_train(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
train = tecator.Tecator(root=rootdir,
train=True,
download=True,
@ -67,7 +67,7 @@ class TestTecator(unittest.TestCase):
self.assertEqual(x_train.shape[1], 100)
def test_download_test(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
x_test, y_test = test.data, test.targets
self.assertEqual(x_test.shape[0], 71)
@ -75,19 +75,19 @@ class TestTecator(unittest.TestCase):
self.assertEqual(x_test.shape[1], 100)
def test_class_to_idx(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
_ = test.class_to_idx
def test_getitem(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
x, y = test[0]
self.assertEqual(x.shape[0], 100)
self.assertIsInstance(y, int)
def test_loadable_with_dataloader(self):
rootdir = self.artifacts_dir.rpartition('/')[0]
rootdir = self.artifacts_dir.rpartition("/")[0]
test = tecator.Tecator(root=rootdir, train=False, verbose=False)
_ = torch.utils.data.DataLoader(test, batch_size=64, shuffle=True)

82
tests/test_functions.py
View File

@ -11,7 +11,7 @@ from prototorch.functions import (activations, competitions, distances,
class TestActivations(unittest.TestCase):
def setUp(self):
self.flist = ['identity', 'sigmoid_beta', 'swish_beta']
self.flist = ["identity", "sigmoid_beta", "swish_beta"]
self.x = torch.randn(1024, 1)
def test_registry(self):
@ -39,7 +39,7 @@ class TestActivations(unittest.TestCase):
self.assertEqual(1, f(1))
def test_unknown_deserialization(self):
for funcname in ['blubb', 'foobar']:
for funcname in ["blubb", "foobar"]:
with self.assertRaises(NameError):
_ = activations.get_activation(funcname)
@ -52,7 +52,7 @@ class TestActivations(unittest.TestCase):
self.assertIsNone(mismatch)
def test_sigmoid_beta1(self):
actual = activations.sigmoid_beta(self.x, beta=torch.tensor(1))
actual = activations.sigmoid_beta(self.x, beta=1.0)
desired = torch.sigmoid(self.x)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
@ -60,7 +60,7 @@ class TestActivations(unittest.TestCase):
self.assertIsNone(mismatch)
def test_swish_beta1(self):
actual = activations.swish_beta(self.x, beta=torch.tensor(1))
actual = activations.swish_beta(self.x, beta=1.0)
desired = self.x * torch.sigmoid(self.x)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
@ -76,7 +76,7 @@ class TestCompetitions(unittest.TestCase):
pass
def test_wtac(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.wtac(d, labels)
desired = torch.tensor([2, 0])
@ -86,7 +86,7 @@ class TestCompetitions(unittest.TestCase):
self.assertIsNone(mismatch)
def test_wtac_unequal_dist(self):
d = torch.tensor([[2., 3., 4.], [2., 3., 1.]])
d = torch.tensor([[2.0, 3.0, 4.0], [2.0, 3.0, 1.0]])
labels = torch.tensor([0, 1, 1])
actual = competitions.wtac(d, labels)
desired = torch.tensor([0, 1])
@ -96,7 +96,7 @@ class TestCompetitions(unittest.TestCase):
self.assertIsNone(mismatch)
def test_wtac_one_hot(self):
d = torch.tensor([[1.99, 3.01], [3., 2.01]])
d = torch.tensor([[1.99, 3.01], [3.0, 2.01]])
labels = torch.tensor([[0, 1], [1, 0]])
actual = competitions.wtac(d, labels)
desired = torch.tensor([[0, 1], [1, 0]])
@ -106,38 +106,38 @@ class TestCompetitions(unittest.TestCase):
self.assertIsNone(mismatch)
def test_stratified_min(self):
d = torch.tensor([[1., 0., 2., 3.], [9., 8., 0, 1]])
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_min_one_hot(self):
d = torch.tensor([[1., 0., 2., 3.], [9., 8., 0, 1]])
d = torch.tensor([[1.0, 0.0, 2.0, 3.0], [9.0, 8.0, 0, 1]])
labels = torch.tensor([0, 0, 1, 2])
labels = torch.eye(3)[labels]
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_stratified_min_simple(self):
d = torch.tensor([[0., 2., 3.], [8., 0, 1]])
d = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0, 1]])
labels = torch.tensor([0, 1, 2])
actual = competitions.stratified_min(d, labels)
desired = torch.tensor([[0., 2., 3.], [8., 0., 1.]])
desired = torch.tensor([[0.0, 2.0, 3.0], [8.0, 0.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
self.assertIsNone(mismatch)
def test_knnc_k1(self):
d = torch.tensor([[2., 3., 1.99, 3.01], [2., 3., 2.01, 3.]])
d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
labels = torch.tensor([0, 1, 2, 3])
actual = competitions.knnc(d, labels, k=torch.tensor([1]))
desired = torch.tensor([2, 0])
@ -194,12 +194,12 @@ class TestDistances(unittest.TestCase):
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
desired[i][j] = (torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)**2
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
@ -254,14 +254,14 @@ class TestDistances(unittest.TestCase):
self.assertIsNone(mismatch)
def test_lpnorm_pinf(self):
actual = distances.lpnorm_distance(self.x, self.y, p=float('inf'))
actual = distances.lpnorm_distance(self.x, self.y, p=float("inf"))
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=float('inf'),
p=float("inf"),
keepdim=False,
)
mismatch = np.testing.assert_array_almost_equal(actual,
@ -275,12 +275,12 @@ class TestDistances(unittest.TestCase):
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
desired[i][j] = (torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)**2
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
@ -293,12 +293,12 @@ class TestDistances(unittest.TestCase):
desired = torch.empty(self.nx, self.ny)
for i in range(self.nx):
for j in range(self.ny):
desired[i][j] = torch.nn.functional.pairwise_distance(
desired[i][j] = (torch.nn.functional.pairwise_distance(
self.x[i].reshape(1, -1),
self.y[j].reshape(1, -1),
p=2,
keepdim=False,
)**2
)**2)
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=2)
@ -311,8 +311,12 @@ class TestDistances(unittest.TestCase):
class TestInitializers(unittest.TestCase):
def setUp(self):
self.flist = [
'zeros', 'ones', 'rand', 'randn', 'stratified_mean',
'stratified_random'
"zeros",
"ones",
"rand",
"randn",
"stratified_mean",
"stratified_random",
]
self.x = torch.tensor(
[[0, -1, -2], [10, 11, 12], [0, 0, 0], [2, 2, 2]],
@ -340,7 +344,7 @@ class TestInitializers(unittest.TestCase):
self.assertEqual(1, f(1))
def test_unknown_deserialization(self):
for funcname in ['blubb', 'foobar']:
for funcname in ["blubb", "foobar"]:
with self.assertRaises(NameError):
_ = initializers.get_initializer(funcname)
@ -383,7 +387,7 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_equal1(self):
pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [1., 1., 1.]])
desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -393,7 +397,7 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([1, 1])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.]])
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -402,8 +406,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_equal2(self):
pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [5., 5., 5.], [1., 1., 1.],
[1., 1., 1.]])
desired = torch.tensor([[5.0, 5.0, 5.0], [5.0, 5.0, 5.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -413,8 +417,8 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([2, 2])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., -1., -2.], [0., 0., 0.],
[0., 0., 0.]])
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, -1.0, -2.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -423,8 +427,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_unequal(self):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_mean(self.x, self.y, pdist, False)
desired = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
[1., 1., 1.]])
desired = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -434,8 +438,8 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([1, 3])
actual, _ = initializers.stratified_random(self.x, self.y, pdist,
False)
desired = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
[0., 0., 0.]])
desired = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
mismatch = np.testing.assert_array_almost_equal(actual,
desired,
decimal=5)
@ -444,8 +448,8 @@ class TestInitializers(unittest.TestCase):
def test_stratified_mean_unequal_one_hot(self):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
desired1 = torch.tensor([[5., 5., 5.], [1., 1., 1.], [1., 1., 1.],
[1., 1., 1.]])
desired1 = torch.tensor([[5.0, 5.0, 5.0], [1.0, 1.0, 1.0],
[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]])
actual1, actual2 = initializers.stratified_mean(self.x, y, pdist)
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
@ -460,8 +464,8 @@ class TestInitializers(unittest.TestCase):
pdist = torch.tensor([1, 3])
y = torch.eye(2)[self.y]
actual1, actual2 = initializers.stratified_random(self.x, y, pdist)
desired1 = torch.tensor([[0., -1., -2.], [0., 0., 0.], [0., 0., 0.],
[0., 0., 0.]])
desired1 = torch.tensor([[0.0, -1.0, -2.0], [0.0, 0.0, 0.0],
[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
desired2 = torch.tensor([[1, 0], [0, 1], [0, 1], [0, 1]])
mismatch = np.testing.assert_array_almost_equal(actual1,
desired1,

111
tests/test_modules.py
View File

@ -29,10 +29,12 @@ class TestPrototypes(unittest.TestCase):
_ = prototypes.Prototypes1D(nclasses=1, input_dim=1)
def test_prototypes1d_init_without_pdist(self):
p1 = prototypes.Prototypes1D(input_dim=6,
p1 = prototypes.Prototypes1D(
input_dim=6,
nclasses=2,
prototypes_per_class=4,
prototype_initializer='ones')
prototype_initializer="ones",
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.ones(8, 6)
@ -45,7 +47,7 @@ class TestPrototypes(unittest.TestCase):
pdist = [2, 2]
p1 = prototypes.Prototypes1D(input_dim=3,
prototype_distribution=pdist,
prototype_initializer='zeros')
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
@ -60,14 +62,15 @@ class TestPrototypes(unittest.TestCase):
input_dim=3,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=None)
prototype_initializer="stratified_mean",
data=None,
)
def test_prototypes1d_init_torch_pdist(self):
pdist = torch.tensor([2, 2])
p1 = prototypes.Prototypes1D(input_dim=3,
prototype_distribution=pdist,
prototype_initializer='zeros')
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
@ -77,24 +80,30 @@ class TestPrototypes(unittest.TestCase):
self.assertIsNone(mismatch)
def test_prototypes1d_init_without_inputdim_with_data(self):
_ = prototypes.Prototypes1D(nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [0.]], [1, 0]])
def test_prototypes1d_init_with_int_data(self):
_ = prototypes.Prototypes1D(nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1], [0]], [1, 0]])
def test_prototypes1d_init_one_hot_without_data(self):
_ = prototypes.Prototypes1D(input_dim=1,
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
prototype_initializer="stratified_mean",
data=[[[1.0], [0.0]], [1, 0]],
)
def test_prototypes1d_init_with_int_data(self):
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=[[[1], [0]], [1, 0]],
)
def test_prototypes1d_init_one_hot_without_data(self):
_ = prototypes.Prototypes1D(
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer="stratified_mean",
data=None,
one_hot_labels=True)
one_hot_labels=True,
)
def test_prototypes1d_init_one_hot_labels_false(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `False`
@ -105,9 +114,10 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [[0, 1], [1, 0]]),
one_hot_labels=False)
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [[0, 1], [1, 0]]),
one_hot_labels=False,
)
def test_prototypes1d_init_1d_y_data_one_hot_labels_true(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `True`
@ -118,9 +128,10 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [0, 1]),
one_hot_labels=True)
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [0, 1]),
one_hot_labels=True,
)
def test_prototypes1d_init_one_hot_labels_true(self):
"""Test if ValueError is raised when `one_hot_labels` is set to `True`
@ -132,25 +143,27 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=([[0.], [1.]], [[0], [1]]),
one_hot_labels=True)
prototype_initializer="stratified_mean",
data=([[0.0], [1.0]], [[0], [1]]),
one_hot_labels=True,
)
def test_prototypes1d_init_with_int_dtype(self):
with self.assertRaises(RuntimeError):
_ = prototypes.Prototypes1D(
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
prototype_initializer="stratified_mean",
data=[[[1], [0]], [1, 0]],
dtype=torch.int32)
dtype=torch.int32,
)
def test_prototypes1d_inputndim_with_data(self):
with self.assertRaises(ValueError):
_ = prototypes.Prototypes1D(input_dim=1,
nclasses=1,
prototypes_per_class=1,
data=[[1.], [1]])
data=[[1.0], [1]])
def test_prototypes1d_inputdim_with_data(self):
with self.assertRaises(ValueError):
@ -158,8 +171,9 @@ class TestPrototypes(unittest.TestCase):
input_dim=2,
nclasses=2,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [0.]], [1, 0]])
prototype_initializer="stratified_mean",
data=[[[1.0], [0.0]], [1, 0]],
)
def test_prototypes1d_nclasses_with_data(self):
"""Test ValueError raise if provided `nclasses` is not the same
@ -170,13 +184,14 @@ class TestPrototypes(unittest.TestCase):
input_dim=1,
nclasses=1,
prototypes_per_class=1,
prototype_initializer='stratified_mean',
data=[[[1.], [2.]], [1, 2]])
prototype_initializer="stratified_mean",
data=[[[1.0], [2.0]], [1, 2]],
)
def test_prototypes1d_init_with_ppc(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y],
prototypes_per_class=2,
prototype_initializer='zeros')
prototype_initializer="zeros")
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(4, 3)
@ -186,9 +201,11 @@ class TestPrototypes(unittest.TestCase):
self.assertIsNone(mismatch)
def test_prototypes1d_init_with_pdist(self):
p1 = prototypes.Prototypes1D(data=[self.x, self.y],
p1 = prototypes.Prototypes1D(
data=[self.x, self.y],
prototype_distribution=[6, 9],
prototype_initializer='zeros')
prototype_initializer="zeros",
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = torch.zeros(15, 3)
@ -201,10 +218,12 @@ class TestPrototypes(unittest.TestCase):
def my_initializer(*args, **kwargs):
return torch.full((2, 99), 99.0), torch.tensor([0, 1])
p1 = prototypes.Prototypes1D(input_dim=99,
p1 = prototypes.Prototypes1D(
input_dim=99,
nclasses=2,
prototypes_per_class=1,
prototype_initializer=my_initializer)
prototype_initializer=my_initializer,
)
protos = p1.prototypes
actual = protos.detach().numpy()
desired = 99 * torch.ones(2, 99)
@ -231,7 +250,7 @@ class TestPrototypes(unittest.TestCase):
def test_prototypes1d_validate_extra_repr_not_empty(self):
p1 = prototypes.Prototypes1D(input_dim=0, prototype_distribution=[0])
rep = p1.extra_repr()
self.assertNotEqual(rep, '')
self.assertNotEqual(rep, "")
def tearDown(self):
del self.x, self.y, self.gen
@ -243,11 +262,11 @@ class TestLosses(unittest.TestCase):
pass
def test_glvqloss_init(self):
_ = losses.GLVQLoss(0, 'swish_beta', beta=20)
_ = losses.GLVQLoss(0, "swish_beta", beta=20)
def test_glvqloss_forward_1ppc(self):
criterion = losses.GLVQLoss(margin=0,
squashing='sigmoid_beta',
squashing="sigmoid_beta",
beta=100)
d = torch.stack([torch.ones(100), torch.zeros(100)], dim=1)
labels = torch.tensor([0, 1])
@ -259,7 +278,7 @@ class TestLosses(unittest.TestCase):
def test_glvqloss_forward_2ppc(self):
criterion = losses.GLVQLoss(margin=0,
squashing='sigmoid_beta',
squashing="sigmoid_beta",
beta=100)
d = torch.stack([
torch.ones(100),