Run isort and ruff format

docs/articles/benchmark.md

@@ -40,9 +40,10 @@ Experiment environment:
 ``` python
 import time
 
-import torch
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
+import torch
+
 import tinytopics as tt
 ```
 

docs/articles/benchmark.qmd

@@ -42,9 +42,10 @@ Experiment environment:
 ```{python}
 import time
 
-import torch
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
+import torch
+
 import tinytopics as tt
 ```
 

docs/articles/text.md

@@ -26,11 +26,12 @@ repo](https://github.com/stephenslab/fastTopics-experiments).
 ## Import tinytopics
 
 ``` python
-import torch
 import numpy as np
 import pandas as pd
-import tinytopics as tt
+import torch
 from pyreadr import read_r
+
+import tinytopics as tt
 ```
 
 ## Read count data

docs/articles/text.qmd

@@ -29,11 +29,12 @@ The NIPS dataset contains a count matrix for 2483 research papers on
 ## Import tinytopics
 
 ```{python}
-import torch
 import numpy as np
 import pandas as pd
-import tinytopics as tt
+import torch
 from pyreadr import read_r
+
+import tinytopics as tt
 ```
 
 ## Read count data

examples/benchmark.py

@@ -1,8 +1,9 @@
 import time
 
-import torch
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
+import torch
+
 import tinytopics as tt
 
 tt.set_random_seed(42)

examples/text.py

@@ -1,9 +1,10 @@
-import torch
 import numpy as np
 import pandas as pd
-import tinytopics as tt
+import torch
 from pyreadr import read_r
 
+import tinytopics as tt
+
 
 def read_rds_numpy(file_path):
     X0 = read_r(file_path)

src/tinytopics/__init__.py

@@ -2,16 +2,16 @@
 Topic modeling via sum-to-one constrained neural Poisson NMF.
 """
 
+from .colors import pal_tinytopics, scale_color_tinytopics
+from .data import NumpyDiskDataset, TorchDiskDataset
 from .fit import fit_model
 from .fit_distributed import fit_model_distributed
-from .models import NeuralPoissonNMF
 from .loss import poisson_nmf_loss
-from .data import NumpyDiskDataset, TorchDiskDataset
+from .models import NeuralPoissonNMF
+from .plot import plot_loss, plot_structure, plot_top_terms
 from .utils import (
-    set_random_seed,
-    generate_synthetic_data,
     align_topics,
+    generate_synthetic_data,
+    set_random_seed,
     sort_documents,
 )
-from .colors import pal_tinytopics, scale_color_tinytopics
-from .plot import plot_loss, plot_structure, plot_top_terms

src/tinytopics/colors.py

@@ -1,13 +1,12 @@
+from collections.abc import MutableSequence, Sequence
 from typing import Literal, overload
-from collections.abc import Sequence, MutableSequence
 
 import numpy as np
-from numpy.typing import NDArray
 from matplotlib import colors
 from matplotlib.colors import ListedColormap
-from skimage import color
+from numpy.typing import NDArray
 from scipy.interpolate import make_interp_spline
-
+from skimage import color
 
 ColorFormat = Literal["hex", "rgb", "lab"]
 

src/tinytopics/data.py

@@ -1,8 +1,8 @@
 from collections.abc import Sequence
 from pathlib import Path
 
-import torch
 import numpy as np
+import torch
 from torch import Tensor
 from torch.utils.data import Dataset
 

src/tinytopics/fit.py

@@ -1,5 +1,5 @@
-from typing import Tuple
 from collections.abc import Sequence
+from typing import Tuple
 
 import torch
 from torch import Tensor
@@ -9,8 +9,8 @@
 from tqdm.auto import tqdm
 
 from .data import IndexTrackingDataset
-from .models import NeuralPoissonNMF
 from .loss import poisson_nmf_loss
+from .models import NeuralPoissonNMF
 
 
 def fit_model(

src/tinytopics/fit_distributed.py

@@ -1,17 +1,17 @@
-from typing import Tuple
 from collections.abc import Sequence
+from typing import Tuple
 
 import torch
+from accelerate import Accelerator
 from torch import Tensor
 from torch.optim import AdamW
 from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
 from torch.utils.data import DataLoader, Dataset
-from accelerate import Accelerator
 from tqdm.auto import tqdm
 
 from .data import IndexTrackingDataset
-from .models import NeuralPoissonNMF
 from .loss import poisson_nmf_loss
+from .models import NeuralPoissonNMF
 
 
 def fit_model_distributed(
@@ -95,7 +95,7 @@ def fit_model_distributed(
         # Show only one tqdm progress bar on the local main process
         loop = tqdm(
             dataloader,
-            desc=f"Epoch {epoch+1}/{num_epochs}",
+            desc=f"Epoch {epoch + 1}/{num_epochs}",
             disable=not accelerator.is_local_main_process,
         )
 
@@ -124,7 +124,7 @@ def fit_model_distributed(
 
         # Only print on the main process
         if accelerator.is_main_process:
-            print(f"Epoch {epoch+1}, Loss = {epoch_loss:.4f}")
+            print(f"Epoch {epoch + 1}, Loss = {epoch_loss:.4f}")
 
     # Final model state is already synchronized across all processes.
     # Accelerate says all GPUs should have the same weights.

src/tinytopics/plot.py

@@ -1,10 +1,10 @@
-from typing import Tuple
 from collections.abc import Sequence
+from typing import Tuple
 
-import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.figure import Figure
+import numpy as np
 from matplotlib.axes import Axes
+from matplotlib.figure import Figure
 
 from .colors import scale_color_tinytopics
 

src/tinytopics/utils.py

@@ -1,9 +1,9 @@
-from typing import Tuple
-from collections.abc import Sequence, MutableMapping
 from collections import defaultdict
+from collections.abc import MutableMapping, Sequence
+from typing import Tuple
 
-import torch
 import numpy as np
+import torch
 from scipy.optimize import linear_sum_assignment
 from tqdm.auto import tqdm
 

tests/test_colors.py

@@ -1,5 +1,5 @@
-import pytest
 import numpy as np
+import pytest
 from matplotlib.colors import ListedColormap
 
 from tinytopics.colors import pal_tinytopics, scale_color_tinytopics

tests/test_data.py

@@ -1,6 +1,6 @@
+import numpy as np
 import pytest
 import torch
-import numpy as np
 
 from tinytopics.data import NumpyDiskDataset, TorchDiskDataset
 

tests/test_fit.py

@@ -2,7 +2,7 @@
 import torch
 
 from tinytopics.fit import fit_model
-from tinytopics.utils import set_random_seed, generate_synthetic_data
+from tinytopics.utils import generate_synthetic_data, set_random_seed
 
 # Test data dimensions
 N_DOCS = 50

tests/test_fit_disk.py

@@ -1,10 +1,10 @@
+import numpy as np
 import pytest
 import torch
-import numpy as np
 
-from tinytopics.utils import set_random_seed, generate_synthetic_data
 from tinytopics.data import NumpyDiskDataset
 from tinytopics.fit import fit_model
+from tinytopics.utils import generate_synthetic_data, set_random_seed
 
 # Test data dimensions
 N_DOCS = 50

tests/test_fit_distributed.py

@@ -5,7 +5,7 @@
 import pytest
 import torch
 
-from tinytopics.utils import set_random_seed, generate_synthetic_data
+from tinytopics.utils import generate_synthetic_data, set_random_seed
 
 # Test data dimensions
 N_DOCS = 100

tests/test_plot.py

@@ -1,6 +1,6 @@
-import pytest
-import numpy as np
 import matplotlib
+import numpy as np
+import pytest
 
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt

tests/test_utils.py

@@ -1,11 +1,11 @@
+import numpy as np
 import pytest
 import torch
-import numpy as np
 
 from tinytopics.utils import (
-    set_random_seed,
-    generate_synthetic_data,
     align_topics,
+    generate_synthetic_data,
+    set_random_seed,
     sort_documents,
 )