Apply ruff formatting across repository

.github/workflows/ruff.yaml

@@ -0,0 +1,12 @@
+name: Ruff Format
+on: [push]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: astral-sh/ruff-action@v1
+        with:
+          args: "check --output-format=github"
+          changed-files: "true"

benchmark/benchmark.py

@@ -1,27 +1,26 @@
 # Before running this file run the following command in the same folder as this file:
 # aws s3 cp --recursive s3://autolabel-benchmarking data
 
+import ast
 import json
+import os
+import pickle as pkl
 from argparse import ArgumentParser
-from rich.console import Console
 from typing import List
-import json
-import pickle as pkl
-import ast
-from sklearn.metrics import f1_score
-import torch
+
 import pylcs
-import os
+import torch
+from sklearn.metrics import f1_score
 
-from autolabel import LabelingAgent, AutolabelConfig, AutolabelDataset
-from autolabel.tasks import TaskFactory
+from autolabel import AutolabelConfig, AutolabelDataset, LabelingAgent
 from autolabel.metrics import BaseMetric
 from autolabel.schema import LLMAnnotation, MetricResult
+from autolabel.tasks import TaskFactory
 
 
 class F1Metric(BaseMetric):
     def compute(
-        llm_labels: List[LLMAnnotation], gt_labels: List[str]
+        llm_labels: List[LLMAnnotation], gt_labels: List[str],
     ) -> List[MetricResult]:
         def construct_binary_preds(curr_input: List[str], positive_tokens: List[str]):
             curr_token_index = 0
@@ -44,22 +43,22 @@ def construct_binary_preds(curr_input: List[str], positive_tokens: List[str]):
             try:
                 curr_pred = " ".join(ast.literal_eval(llm_labels[i].label)).split(" ")
                 predictions.extend(construct_binary_preds(curr_input, curr_pred))
-            except Exception as e:
+            except Exception:
                 curr_pred = llm_labels[i].label.split(" ")
                 predictions.extend(construct_binary_preds(curr_input, curr_pred))
                 # print(e, llm_labels[i].label)
                 # predictions.extend([0 for _ in range(len(curr_input))])
             try:
                 curr_gt = " ".join(ast.literal_eval(gt_labels[i])).split(" ")
-            except Exception as e:
+            except Exception:
                 curr_gt = gt_labels[i].split(" ")
             gt.extend(construct_binary_preds(curr_input, curr_gt))
         return [MetricResult(name="F1", value=f1_score(gt, predictions))]
 
 
 class TextSimilarity(BaseMetric):
     def compute(
-        llm_labels: List[LLMAnnotation], gt_labels: List[str]
+        llm_labels: List[LLMAnnotation], gt_labels: List[str],
     ) -> List[MetricResult]:
         def get_similarity(str_a, str_b):
             substring_lengths = pylcs.lcs_string_length(str_a, str_b)
@@ -70,8 +69,8 @@ def get_similarity(str_a, str_b):
             text_similarity.append(get_similarity(llm_labels[i].label, gt_labels[i]))
         return [
             MetricResult(
-                name="TextSimilarity", value=sum(text_similarity) / len(text_similarity)
-            )
+                name="TextSimilarity", value=sum(text_similarity) / len(text_similarity),
+            ),
         ]
 
 
@@ -138,7 +137,7 @@ def main():
         if dataset in LONG_DATASETS:
             few_shot = 0
 
-        config = json.load(open(f"configs/{dataset}.json", "r"))
+        config = json.load(open(f"configs/{dataset}.json"))
         config["model"]["name"] = args.model
         provider = MODEL_TO_PROVIDER.get(args.model, "vllm")
         config["model"]["provider"] = provider
@@ -174,7 +173,7 @@ def main():
             [F1Metric, TextSimilarity] if dataset in NER_DATASETS else []
         )
         new_ds = agent.run(
-            ds, max_items=args.max_items, additional_metrics=additional_metrics
+            ds, max_items=args.max_items, additional_metrics=additional_metrics,
         )
         new_ds.df.to_csv(f"outputs_{dataset}.csv")
         eval_result.append([x.dict() for x in agent.eval_result])

benchmark/results.py

@@ -1,6 +1,7 @@
 import argparse
-import os
 import json
+import os
+
 import pandas as pd
 
 METRICS = {
@@ -58,14 +59,14 @@ def main():
     header_created = False
     header = []
     for file in eval_files:
-        d = json.load(open(f"{args.eval_dir}/{file}", "r"))
+        d = json.load(open(f"{args.eval_dir}/{file}"))
         row = []
         row.append("-".join(file.split(".")[0].split("/")))
         if not header_created:
             header.append("model")
         for i, dataset in enumerate(DATASETS):
             print(dataset)
-            config = json.load(open(f"configs/{dataset}.json", "r"))
+            config = json.load(open(f"configs/{dataset}.json"))
             metrics_to_add = METRICS[config["task_type"]]
             for metric_to_add in metrics_to_add:
                 for metric in d[i]:

examples/banking/example_banking.ipynb

@@ -41,7 +41,7 @@
     "import os\n",
     "\n",
     "# provide your own OpenAI API key here\n",
-    "os.environ['OPENAI_API_KEY'] = 'sk-XXXXXXXXXXXXXXXXXXXXXXXX'\n"
+    "os.environ[\"OPENAI_API_KEY\"] = \"sk-XXXXXXXXXXXXXXXXXXXXXXXX\"\n"
    ]
   },
   {
@@ -66,7 +66,7 @@
    "source": [
     "from autolabel import get_data\n",
     "\n",
-    "get_data('banking')"
+    "get_data(\"banking\")"
    ]
   },
   {
@@ -123,7 +123,7 @@
    "outputs": [],
    "source": [
     "# load the config\n",
-    "with open('config_banking.json', 'r') as f:\n",
+    "with open(\"config_banking.json\") as f:\n",
     "     config = json.load(f)"
    ]
   },
@@ -477,6 +477,7 @@
    "source": [
     "# dry-run -- this tells us how much this will cost and shows an example prompt\n",
     "from autolabel import AutolabelDataset\n",
+    "\n",
     "ds = AutolabelDataset(\"data/banking/test.csv\", config=config)\n",
     "agent.plan(ds)"
    ]
@@ -684,7 +685,7 @@
    "outputs": [],
    "source": [
     "# Start computing confidence scores (using Refuel's LLMs)\n",
-    "os.environ['REFUEL_API_KEY'] = 'sk-xxxxxxxxxxxx'"
+    "os.environ[\"REFUEL_API_KEY\"] = \"sk-xxxxxxxxxxxx\""
    ]
   },
   {
@@ -849,6 +850,7 @@
    ],
    "source": [
     "from autolabel import AutolabelDataset\n",
+    "\n",
     "ds = AutolabelDataset(\"data/banking/test.csv\", config=config)\n",
     "agent.plan(ds)"
    ]