def linear_alkanes(max_carbon=10):
"""A toy dataset with linear alkanes from 1 to `max_carbon` carbons.
Parameters
----------
max_carbon : int
Maximum number of carbons in the molecules generated.
Returns
-------
Dataset
A dataset containing alanes.
Examples
--------
dataset = count_carbons(10)
"""
dataset = Dataset([Point(idx * "C") for idx in range(1, max_carbon + 1)])
def annotate(point):
point.y = len(point.smiles)
return point
dataset = dataset.apply(annotate)
return dataset
def test_dataset_subtraction():
from malt.data.dataset import Dataset
from malt.point import Point
p0 = Point("C")
p1 = Point("CC")
dataset0 = Dataset([p0])
dataset1 = Dataset([p0, p1])
assert len(dataset1 - dataset0) == 1
def test_split_dataset():
from malt.data.dataset import Dataset
from malt.point import Point
p1 = Point("C")
p2 = Point("CC")
ds = Dataset([p1, p2])
ds0, ds1 = ds.split([0.5, 0.5])
assert len(ds0) == 1
assert len(ds1) == 1
def test_dataset_view_batch_of_g():
import dgl
from malt.data.dataset import Dataset
from malt.point import Point
p1 = Point("C", y=0.0)
p2 = Point("CC", y=0.0)
ds = Dataset([p1, p2])
_ds = ds.view(batch_size=2, collate_fn="batch_of_g")
g = next(iter(_ds))
assert isinstance(g, dgl.DGLGraph)
assert (
g.number_of_nodes() == p1.g.number_of_nodes() + p2.g.number_of_nodes()
)
def test_dataset_view():
import torch
import dgl
from malt.data.dataset import Dataset
from malt.point import Point
p1 = Point("C", y=0.0)
p2 = Point("CC", y=0.0)
ds = Dataset([p1, p2])
_ds = ds.view(batch_size=2)
assert isinstance(_ds, torch.utils.data.DataLoader)
g, y = next(iter(_ds))
assert isinstance(g, dgl.DGLGraph)
assert isinstance(y, torch.Tensor)
assert y.shape[0] == 2
assert y.shape[1] == 1
def test_build_dataset():
from malt.data.dataset import Dataset
from malt.point import Point
p1 = Point("C")
p2 = Point("CC")
ds = Dataset([p1, p2])
assert len(ds) == 2
assert ds[0] == p1
def step(self):
best = self.prioritize()
if best is None:
return None
best = Dataset([best])
best = self.merchandize(best)
best = self.assay(best)
self.train()
return best
def _dataset_from_dgllife(dgllife_dataset):
idx = 0
ds = []
for smiles, g, y in dgllife_dataset:
point = Point(smiles, g, y.item(), extra={"idx": idx})
idx += 1
ds.append(point)
ds = Dataset(ds)
return ds
def __init__(
self,
model: SupervisedModel,
policy: Callable,
trainer: Callable,
merchant: Merchant,
assayer: Assayer,
portfolio: Union[Dataset, None] = None,
):
super(ModelBasedPlayer, self).__init__()
self.model = model
self.policy = policy
self.trainer = trainer
self.merchant = merchant
self.assayer = assayer
if portfolio is None:
portfolio = Dataset([])
self.portfolio = portfolio
def __init__(
self,
dataset: Dataset,
):
super(DatasetMerchant, self).__init__()
self.dataset = dataset.clone().erase_annotation()