Code reformat and lint enforcing

.github/workflows/ci-build-unstable.yml

@@ -31,20 +31,18 @@ jobs:
         if: runner.os == 'Linux'
         run: |
           python -m pip install --upgrade pip
-          pip install flake8
+          pip install ruff
           pip install -e ".[dev,gpu]" --extra-index-url https://download.pytorch.org/whl/cu121
       - name: Install dependencies (macOS)
         if: runner.os == 'macOS'
         run: |
           python -m pip install --upgrade pip
-          pip install flake8
+          pip install ruff
           pip install -e ".[dev]"
-      - name: Lint with flake8
+      - name: Lint with ruff
         run: |
-          # stop the build if there are Python syntax errors or undefined names
-          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+          ruff check .
+          ruff format --check .
       - name: Test with pytest
         run: |
           pytest

.github/workflows/ci-release.yml

@@ -29,20 +29,18 @@ jobs:
         if: runner.os == 'Linux'
         run: |
           python -m pip install --upgrade pip
-          pip install flake8
+          pip install ruff
           pip install -e ".[dev,gpu]" --extra-index-url https://download.pytorch.org/whl/cu121
       - name: Install dependencies (macOS)
         if: runner.os == 'macOS'
         run: |
           python -m pip install --upgrade pip
-          pip install flake8
+          pip install ruff
           pip install -e ".[dev]"
-      - name: Lint with flake8
+      - name: Lint with ruff
         run: |
-          # stop the build if there are Python syntax errors or undefined names
-          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+          ruff check .
+          ruff format --check .
       - name: Test with pytest
         run: |
           pytest

delft/__init__.py

@@ -1,3 +1,3 @@
 import os
 
-DELFT_PROJECT_DIR = os.path.dirname(__file__)
+DELFT_PROJECT_DIR = os.path.dirname(__file__)

delft/applications/citationClassifier.py

@@ -1,22 +1,16 @@
-import json
-from delft.utilities.Utilities import split_data_and_labels
-from delft.textClassification.reader import load_citation_sentiment_corpus
-from delft.textClassification import Classifier
 import argparse
+import json
 import time
+
+from delft.textClassification import Classifier
 from delft.textClassification.models import architectures
+from delft.textClassification.reader import load_citation_sentiment_corpus
+from delft.utilities.Utilities import split_data_and_labels
+
+list_classes = ["negative", "neutral", "positive"]
 
-list_classes = [
-                    "negative", 
-                    "neutral", 
-                    "positive"
-                ]
+class_weights = {0: 25.0, 1: 1.0, 2: 9.0}
 
-class_weights = {
-                    0: 25.,
-                    1: 1.,
-                    2: 9.
-                }
 
 def configure(architecture):
     batch_size = 256
@@ -37,11 +31,23 @@ def configure(architecture):
 def train(embeddings_name, fold_count, architecture="gru", transformer=None):
     batch_size, maxlen, patience, early_stop, max_epoch = configure(architecture)
 
-    model = Classifier('citations_'+architecture, architecture=architecture, list_classes=list_classes, max_epoch=max_epoch, fold_number=fold_count, 
-        use_roc_auc=True, embeddings_name=embeddings_name, batch_size=batch_size, maxlen=maxlen, patience=patience, early_stop=early_stop,
-        class_weights=class_weights, transformer_name=transformer)
+    model = Classifier(
+        "citations_" + architecture,
+        architecture=architecture,
+        list_classes=list_classes,
+        max_epoch=max_epoch,
+        fold_number=fold_count,
+        use_roc_auc=True,
+        embeddings_name=embeddings_name,
+        batch_size=batch_size,
+        maxlen=maxlen,
+        patience=patience,
+        early_stop=early_stop,
+        class_weights=class_weights,
+        transformer_name=transformer,
+    )
 
-    print('loading citation sentiment corpus...')
+    print("loading citation sentiment corpus...")
     xtr, y = load_citation_sentiment_corpus("data/textClassification/citations/citation_sentiment_corpus.txt")
 
     if fold_count == 1:
@@ -52,14 +58,26 @@ def train(embeddings_name, fold_count, architecture="gru", transformer=None):
     model.save()
 
 
-def train_and_eval(embeddings_name, fold_count, architecture="gru", transformer=None): 
+def train_and_eval(embeddings_name, fold_count, architecture="gru", transformer=None):
     batch_size, maxlen, patience, early_stop, max_epoch = configure(architecture)
 
-    model = Classifier('citations_'+architecture, architecture=architecture, list_classes=list_classes, max_epoch=max_epoch, fold_number=fold_count, 
-        use_roc_auc=True, embeddings_name=embeddings_name, batch_size=batch_size, maxlen=maxlen, patience=patience, early_stop=early_stop,
-        class_weights=class_weights, transformer_name=transformer)
+    model = Classifier(
+        "citations_" + architecture,
+        architecture=architecture,
+        list_classes=list_classes,
+        max_epoch=max_epoch,
+        fold_number=fold_count,
+        use_roc_auc=True,
+        embeddings_name=embeddings_name,
+        batch_size=batch_size,
+        maxlen=maxlen,
+        patience=patience,
+        early_stop=early_stop,
+        class_weights=class_weights,
+        transformer_name=transformer,
+    )
 
-    print('loading citation sentiment corpus...')
+    print("loading citation sentiment corpus...")
     xtr, y = load_citation_sentiment_corpus("data/textClassification/citations/citation_sentiment_corpus.txt")
 
     # segment train and eval sets
@@ -69,87 +87,100 @@ def train_and_eval(embeddings_name, fold_count, architecture="gru", transformer=
         model.train(x_train, y_train)
     else:
         model.train_nfold(x_train, y_train)
-    
+
     # saving the model
     model.save()
 
     model.eval(x_test, y_test)
 
-    
+
 # classify a list of texts
 def classify(texts, output_format, architecture="gru", embeddings_name=None, transformer=None):
     # load model
-    model = Classifier('citations_'+architecture, architecture=architecture, list_classes=list_classes, embeddings_name=embeddings_name, transformer_name=transformer)
+    model = Classifier(
+        "citations_" + architecture,
+        architecture=architecture,
+        list_classes=list_classes,
+        embeddings_name=embeddings_name,
+        transformer_name=transformer,
+    )
     model.load()
     start_time = time.time()
     result = model.predict(texts, output_format)
     runtime = round(time.time() - start_time, 3)
-    if output_format == 'json':
+    if output_format == "json":
         result["runtime"] = runtime
     else:
         print("runtime: %s seconds " % (runtime))
     return result
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description = "Sentiment classification of citation contexts based on DeLFT")
+    parser = argparse.ArgumentParser(description="Sentiment classification of citation contexts based on DeLFT")
 
-    word_embeddings_examples = ['glove-840B', 'fasttext-crawl', 'word2vec']
-    pretrained_transformers_examples = [ 'bert-base-cased', 'bert-large-cased', 'allenai/scibert_scivocab_cased' ]
+    word_embeddings_examples = ["glove-840B", "fasttext-crawl", "word2vec"]
+    pretrained_transformers_examples = ["bert-base-cased", "bert-large-cased", "allenai/scibert_scivocab_cased"]
 
     parser.add_argument("action", help="one of [train, train_eval, classify]")
     parser.add_argument("--fold-count", type=int, default=1)
-    parser.add_argument("--architecture",default='gru', help="type of model architecture to be used, one of "+str(architectures))
     parser.add_argument(
-        "--embedding", 
+        "--architecture", default="gru", help="type of model architecture to be used, one of " + str(architectures)
+    )
+    parser.add_argument(
+        "--embedding",
         default=None,
-        help="The desired pre-trained word embeddings using their descriptions in the file. " + \
-            "For local loading, use delft/resources-registry.json. " + \
-            "Be sure to use here the same name as in the registry, e.g. " + str(word_embeddings_examples) + \
-            " and that the path in the registry to the embedding file is correct on your system."
+        help="The desired pre-trained word embeddings using their descriptions in the file. "
+        + "For local loading, use delft/resources-registry.json. "
+        + "Be sure to use here the same name as in the registry, e.g. "
+        + str(word_embeddings_examples)
+        + " and that the path in the registry to the embedding file is correct on your system.",
     )
     parser.add_argument(
-        "--transformer", 
+        "--transformer",
         default=None,
-        help="The desired pre-trained transformer to be used in the selected architecture. " + \
-            "For local loading use, delft/resources-registry.json, and be sure to use here the same name as in the registry, e.g. " + \
-            str(pretrained_transformers_examples) + \
-            " and that the path in the registry to the model path is correct on your system. " + \
-            "HuggingFace transformers hub will be used otherwise to fetch the model, see https://huggingface.co/models " + \
-            "for model names"
+        help="The desired pre-trained transformer to be used in the selected architecture. "
+        + "For local loading use, delft/resources-registry.json, and be sure to use here the same name as in the registry, e.g. "
+        + str(pretrained_transformers_examples)
+        + " and that the path in the registry to the model path is correct on your system. "
+        + "HuggingFace transformers hub will be used otherwise to fetch the model, see https://huggingface.co/models "
+        + "for model names",
     )
 
     args = parser.parse_args()
 
-    if args.action not in ('train', 'train_eval', 'classify'):
-        print('action not specifed, must be one of [train,train_eval,classify]')
+    if args.action not in ("train", "train_eval", "classify"):
+        print("action not specifed, must be one of [train,train_eval,classify]")
 
     embeddings_name = args.embedding
     transformer = args.transformer
-    
+
     architecture = args.architecture
     if architecture not in architectures:
-        print('unknown model architecture, must be one of '+str(architectures))
+        print("unknown model architecture, must be one of " + str(architectures))
 
-    if transformer == None and embeddings_name == None:
+    if transformer is None and embeddings_name is None:
         # default word embeddings
         embeddings_name = "glove-840B"
 
-    if args.action == 'train':
+    if args.action == "train":
         if args.fold_count < 1:
             raise ValueError("fold-count should be equal or more than 1")
 
         train(embeddings_name, args.fold_count, architecture=architecture, transformer=transformer)
 
-    if args.action == 'train_eval':
+    if args.action == "train_eval":
         if args.fold_count < 1:
             raise ValueError("fold-count should be equal or more than 1")
 
-        y_test = train_and_eval(embeddings_name, args.fold_count, architecture=architecture, transformer=transformer)    
-
-    if args.action == 'classify':
-        someTexts = ['One successful strategy [15] computes the set-similarity involving (multi-word) keyphrases about the mentions and the entities, collected from the KG.', 
-            'Unfortunately, fewer than half of the OCs in the DAML02 OC catalog (Dias et al. 2002) are suitable for use with the isochrone-fitting method because of the lack of a prominent main sequence, in addition to an absence of radial velocity and proper-motion data.', 
-            'However, we found that the pairwise approach LambdaMART [41] achieved the best performance on our datasets among most learning to rank algorithms.']
-        result = classify(someTexts, "json", architecture=architecture, embeddings_name=embeddings_name, transformer=transformer)
+        y_test = train_and_eval(embeddings_name, args.fold_count, architecture=architecture, transformer=transformer)
+
+    if args.action == "classify":
+        someTexts = [
+            "One successful strategy [15] computes the set-similarity involving (multi-word) keyphrases about the mentions and the entities, collected from the KG.",
+            "Unfortunately, fewer than half of the OCs in the DAML02 OC catalog (Dias et al. 2002) are suitable for use with the isochrone-fitting method because of the lack of a prominent main sequence, in addition to an absence of radial velocity and proper-motion data.",
+            "However, we found that the pairwise approach LambdaMART [41] achieved the best performance on our datasets among most learning to rank algorithms.",
+        ]
+        result = classify(
+            someTexts, "json", architecture=architecture, embeddings_name=embeddings_name, transformer=transformer
+        )
         print(json.dumps(result, sort_keys=False, indent=4, ensure_ascii=False))