Rename to classtree to avoid a collision on pypi.

CITATION.cff

@@ -1,5 +1,5 @@
 cff-version: "1.2.0"
-title: "Classia"
+title: "Classtree"
 message: "If you use this software, please cite both the article from preferred-citation and the software itself."
 authors:
     - family-names: Valmadre

LICENSE.txt

@@ -1,4 +1,4 @@
-CLASSIA
+CLASSTREE
 SOFTWARE LICENCE AGREEMENT
 ACADEMIC OR NOT-FOR-PROFIT ORGANISATION - NON-COMMERCIAL INTERNAL RESEARCH USE ONLY
 BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS AND CONDITIONS OF THIS LICENCE AGREEMENT (“AGREEMENT”).  

README.md

@@ -1,14 +1,16 @@
-# Classia
+# Classtree
 
 ![](https://img.shields.io/pypi/pyversions/name)
 
-Classia is a hierarchical classifier for images or text.
+
+
+Classtree is a hierarchical classifier for images or text.
 
 ```shell
-pip install classia
+pip install classtree
 ```
 
-The fastest way to use Classia is to call the CLI on a folder of images or text files.
+The fastest way to use Classtree is to call the CLI on a folder of images or text files.
 
 ```shell
 train_data/
@@ -27,19 +29,19 @@ train_data/
 ```
 
 ```shell
-classia train images --model animals --dir train_data/animals
+classtree train images --model animals --dir train_data/animals
 ```
 
 or
 
 ```shell
-classia train text --model animals --dir train_data/animals
+classtree train text --model animals --dir train_data/animals
 ```
 
 And then use your model with the predict command.
 
 ```shell
-classia predict --model animals new_data/image304.jpg
+classtree predict --model animals new_data/image304.jpg
 > birds/raptors/eagle
 ```
 
@@ -48,20 +50,20 @@ classia predict --model animals new_data/image304.jpg
 You can download a pre-trained model using the download command.
 
 ```shell
-classia download model dbpedia
+classtree download model dbpedia
 ```
 
 Or download a pre-prepared dataset.
 
 ```shell
-classia download images inaturalist21-mini
-classia download text dbpedia
+classtree download images inaturalist21-mini
+classtree download text dbpedia
 ```
 
 If you want to fine-tune an existing model, you can use the `--from` flag during training with any downloaded model.
 
 ```shell
-classia train text --model animals --from dbpedia --dir train_data/animals
+classtree train text --model animals --from dbpedia --dir train_data/animals
 ```
 
 
@@ -84,11 +86,11 @@ classia train text --model animals --from dbpedia --dir train_data/animals
 You can test your model on a hold-out dataset using the `test` command.
 
 ```shell
-classia test --model animals --dir=test_data/animals
+classtree test --model animals --dir=test_data/animals
 ```
 
 ## Licensing
 
-Classia is available for non-commercial internal research use by academic institutions or not-for-profit organisations only, free of charge. Please, see the [license](./LICENSE.txt) for further details. To the extent permitted by applicable law, your use is at your own risk and our liability is limited. Interested in a commercial license? For commercial queries, please email <aimlshop@adelaide.edu.au> with subject line “Classia Commercial License”. 
+Classtree is available for non-commercial internal research use by academic institutions or not-for-profit organisations only, free of charge. Please, see the [license](./LICENSE.txt) for further details. To the extent permitted by applicable law, your use is at your own risk and our liability is limited. Interested in a commercial license? For commercial queries, please email <aimlshop@adelaide.edu.au> with subject line “Classtree Commercial License”. 
 
 This is an [AIML Shop](https://shop.aiml.team) project.

pyproject.toml

@@ -3,7 +3,7 @@ requires = ["setuptools", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]
-name = "classia"
+name = "classtree"
 description = "A toolkit for hierarchical classification"
 dynamic = ["version"]
 readme = "README.md"

src/classia/cli.py → src/classtree/cli.py

@@ -2,7 +2,7 @@
 import logging
 import os
 
-from classia.dataset import hierarchy_and_labels_from_folder
+from .dataset import hierarchy_and_labels_from_folder
 from .download import download_model, download_text_dataset, download_image_dataset
 from .export import export_model
 from .test import test_model
@@ -22,12 +22,12 @@ def add_global_args(parser):
     parser.add_argument(
         "--datasets_dir",
         help="The path of the directory to store named datasets",
-        default=os.path.expanduser("~/.cache/classia/datasets"),
+        default=os.path.expanduser("~/.cache/classtree/datasets"),
     )
     parser.add_argument(
         "--models_dir",
         help="The path of the directory to store named models",
-        default=os.path.expanduser("~/.cache/classia/models"),
+        default=os.path.expanduser("~/.cache/classtree/models"),
     )
 
 
@@ -150,7 +150,7 @@ def add_export_args(export_parser):
     export_parser.add_argument(
         "--export_dir",
         help="The path of the directory to export models",
-        default=os.path.expanduser("~/.cache/classia"),
+        default=os.path.expanduser("~/.cache/classtree"),
     )
     export_parser.add_argument(
         "--text",

src/classia/dataset.py → src/classtree/dataset.py

@@ -9,7 +9,7 @@
 from .hier import make_hierarchy_from_edges
 
 
-class ClassiaImageDataset(Dataset):
+class ClasstreeImageDataset(Dataset):
     def __init__(self, files, labels, transform):
         self.files = files
         self.labels = labels
@@ -26,7 +26,7 @@ def __getitem__(self, i):
             return image, self.labels[i]
 
 
-class ClassiaTextDataset(Dataset):
+class ClasstreeTextDataset(Dataset):
     def __init__(self, files, labels, *, transform):
         self.files = files
         self.labels = labels

src/classia/export.py → src/classtree/export.py

@@ -1,6 +1,6 @@
 import torch
 
-from classia.models import model_classes
+from .models import model_classes
 
 
 def export_model(models_dir, model, device):

src/classia/models.py → src/classtree/models.py

@@ -16,7 +16,7 @@
 
 # Image model: Small, Medium, Large
 # ref: https://pytorch.org/vision/master/_modules/torchvision/models/resnet.html
-class ClassiaImageModelV1Small(Module):
+class ClasstreeImageModelV1Small(Module):
     def __init__(self, tree: Hierarchy):
         super().__init__()
 
@@ -28,7 +28,7 @@ def forward(self, x):
         return self.resnet(x)
 
 
-class ClassiaImageModelV1Medium(Module):
+class ClasstreeImageModelV1Medium(Module):
     def __init__(self, tree: Hierarchy):
         super().__init__()
 
@@ -40,7 +40,7 @@ def forward(self, x):
         return self.resnet(x)
 
 
-class ClassiaImageModelV1Large(Module):
+class ClasstreeImageModelV1Large(Module):
     def __init__(self, tree: Hierarchy):
         super().__init__()
 
@@ -54,7 +54,7 @@ def forward(self, x):
 
 # TEXT MODEL: Base, Large
 # ref: https://pytorch.org/text/stable/_modules/torchtext/models/roberta/bundler.html
-class ClassiaTextModelV1Medium(Module):
+class ClasstreeTextModelV1Medium(Module):
     def __init__(self, tree: Hierarchy):
         super().__init__()
 
@@ -69,7 +69,7 @@ def forward(self, input_ids):
         return self.roberta(input_ids)
 
 
-class ClassiaTextModelV1Large(Module):
+class ClasstreeTextModelV1Large(Module):
     def __init__(self, tree: Hierarchy):
         super().__init__()
 
@@ -85,11 +85,11 @@ def forward(self, input_ids):
 
 
 model_classes = {
-    "image-v1-s": ClassiaImageModelV1Small,
-    "image-v1-m": ClassiaImageModelV1Medium,
-    "image-v1-l": ClassiaImageModelV1Large,
-    "text-v1-m": ClassiaTextModelV1Medium,
-    "text-v1-l": ClassiaTextModelV1Large,
+    "image-v1-s": ClasstreeImageModelV1Small,
+    "image-v1-m": ClasstreeImageModelV1Medium,
+    "image-v1-l": ClasstreeImageModelV1Large,
+    "text-v1-m": ClasstreeTextModelV1Medium,
+    "text-v1-l": ClasstreeTextModelV1Large,
 }
 
 

src/classia/test.py → src/classtree/test.py

@@ -3,9 +3,9 @@
 
 import torch
 
-from classia.dataset import hierarchy_and_labels_from_folder
-from classia.models import model_classes, get_text_encoder
-from classia.train import get_dataloader, evaluate, get_image_dataset, get_text_dataset
+from .dataset import hierarchy_and_labels_from_folder
+from .models import model_classes, get_text_encoder
+from .train import get_dataloader, evaluate, get_image_dataset, get_text_dataset
 
 LOGGER = logging.getLogger(__name__)
 

src/classia/train.py → src/classtree/train.py

@@ -26,7 +26,7 @@
 from tqdm import tqdm
 
 from .hier import truncate_given_lca, FindLCA, SumDescendants
-from .dataset import ClassiaImageDataset, ClassiaTextDataset
+from .dataset import ClasstreeImageDataset, ClasstreeTextDataset
 from .loss import MarginLoss
 from .metrics import UniformLeafInfoMetric, DepthMetric, IsCorrect, operating_curve
 from .predict import pareto_optimal_predictions
@@ -56,13 +56,13 @@ def get_image_dataset(files, labels):
             Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.244, 0.225]),
         ]
     )
-    dataset = ClassiaImageDataset(files, labels, transform=transform)
+    dataset = ClasstreeImageDataset(files, labels, transform=transform)
     return dataset
 
 
 def get_text_dataset(files, labels, encoder):
     transform = encoder.transform()
-    dataset = ClassiaTextDataset(files, labels, transform=transform)
+    dataset = ClasstreeTextDataset(files, labels, transform=transform)
     return dataset