Installation

Install using pip:

pip install polytorch

Or install the latest version from GitHub:

pip install git+https://github.com/rbturnbull/polytorch.git

Data Types

This package allow you to input and output different data types in PyTorch models.

from polytorch import BinaryData

binary_data = BinaryData()

# Or with labels and colors
binary_data = BinaryData(labels=["no_feature", "with_feature", colors=["red", "blue"])

from polytorch import CategoricalData

category_count = 5  # Number of categories
categorical_data = CategoricalData(category_count)

# Or with labels, colors and label smoothing
categorical_data = CategoricalData(
    category_count=category_count,
    labels=["cat", "dog", "fish", "bird", "reptile"],
    colors=["red", "blue", "green", "yellow", "purple"],
    label_smoothing=0.1,
)

from polytorch import OrdinalData

ordinal_data = OrdinalData()

# Or with color
ordinal_data = OrdinalData(color="pink")

from polytorch import ContinuousData

continuous_data = ContinuousData()

# Or with color
continuous_data = ContinuousData(color="orange")

from polytorch import HierarchicalData
from hiearchicalsoftmax import SoftmaxNode

root = SoftmaxNode("root")
child1 = SoftmaxNode("child1", parent=root)
child2 = SoftmaxNode("child2", parent=root)
tip1 = SoftmaxNode("tip1", parent=child1)
tip2 = SoftmaxNode("tip2", parent=child1)
tip3 = SoftmaxNode("tip3", parent=child2)
tip4 = SoftmaxNode("tip4", parent=child2)


hierarchical_data = HierarchicalData(root)

Embedding your data

from torch import nn
from polytorch import Embedding

class MyModule(nn.Module):
    def __init__(self, embedding_size:int=128):
        super(MyModule, self).__init__()

        input_types = [binary_data, categorical_data] # for example. Could be other data types as well.
        self.embedding = PolyEmbedding( input_types=input_types, embedding_size=embedding_size)

        # Other modules
        ...

    def forward(self, x_binary, x_categorical):

        embedded = self.embedding( x_binary, x_categorical )

        # Use the embedded features in your model
        ...

Outputting your data

You can also get your model to output to different data types.

from torch import nn
from polytorch import PolyLazyLinear

output_types = [
    CategoricalData(category_count=5, loss_weighting=0.5),  # For example, a categorical output with 5 categories
    BinaryData(loss_weighting=1.0),                        # A binary output
    ContinuousData(loss_weighting=0.1)                     # A continuous output
]

class MyModule(nn.Module):
    def __init__(self, output_types):
        super(MyModule, self).__init__()

        self.output = PolyLazyLinear(output_types=output_types)

    def forward(self, x):
        # Your model logic
        ...

        # Output to different data types
        return self.output(x)

Then add set this as the loss:

from polytorch import PolyLoss

loss_module = PolyLoss(output_types=output_types)

# In your training loop
loss = loss_module(predictions, categorical_target, binary_target, continuous_target)

Credits

Robert Turnbull For more information contact: <robert.turnbull@unimelb.edu.au>

Created using torchapp (https://github.com/rbturnbull/torchapp).