standardizing styles accross all files.

experiments/extended_benchmarks/run_timegpt.py

@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 """Evaluation script for timegpt."""
 
 import os
@@ -25,7 +24,6 @@ from ..baselines.timegpt_pipeline import run_timegpt
 
 from .utils import ExperimentHandler
 
-
 dataset_names = [
     "m1_monthly",
     "m1_quarterly",
@@ -63,46 +61,45 @@ _MODEL_NAME = flags.DEFINE_string(
 )
 _SAVE_DIR = flags.DEFINE_string("save_dir", "./results", "Save directory")
 
-
 QUANTILES = list(np.arange(1, 10) / 10.0)
 
 
 def main():
-    results_list = []
-    run_id = np.random.randint(100000)
-    model_name = _MODEL_NAME.value
-    for dataset in dataset_names:
-        print(f"Evaluating model {model_name} on dataset {dataset}", flush=True)
-        exp = ExperimentHandler(dataset, quantiles=QUANTILES)
-        train_df = exp.train_df
-        horizon = exp.horizon
-        seasonality = exp.seasonality
-        freq = exp.freq
-        level = exp.level
-        fcsts_df, total_time, model_name = run_timegpt(
-            train_df=train_df,
-            horizon=exp.horizon,
-            model=model_name,
-            seasonality=seasonality,
-            freq=freq,
-            dataset=dataset,
-            level=level,
-        )
-        time_df = pd.DataFrame({"time": [total_time], "model": model_name})
-        fcsts_df = exp.fcst_from_level_to_quantiles(fcsts_df, model_name)
-        results = exp.evaluate_from_predictions(
-            models=[model_name], fcsts_df=fcsts_df, times_df=time_df
-        )
-        print(results, flush=True)
-        results_list.append(results)
-        results_full = pd.concat(results_list)
-        save_path = os.path.join(_SAVE_DIR.value, str(run_id))
-        print(f"Saving results to {save_path}", flush=True)
-        os.makedirs(save_path, exist_ok=True)
-        results_full.to_csv(f"{save_path}/results.csv")
+  results_list = []
+  run_id = np.random.randint(100000)
+  model_name = _MODEL_NAME.value
+  for dataset in dataset_names:
+    print(f"Evaluating model {model_name} on dataset {dataset}", flush=True)
+    exp = ExperimentHandler(dataset, quantiles=QUANTILES)
+    train_df = exp.train_df
+    horizon = exp.horizon
+    seasonality = exp.seasonality
+    freq = exp.freq
+    level = exp.level
+    fcsts_df, total_time, model_name = run_timegpt(
+        train_df=train_df,
+        horizon=exp.horizon,
+        model=model_name,
+        seasonality=seasonality,
+        freq=freq,
+        dataset=dataset,
+        level=level,
+    )
+    time_df = pd.DataFrame({"time": [total_time], "model": model_name})
+    fcsts_df = exp.fcst_from_level_to_quantiles(fcsts_df, model_name)
+    results = exp.evaluate_from_predictions(models=[model_name],
+                                            fcsts_df=fcsts_df,
+                                            times_df=time_df)
+    print(results, flush=True)
+    results_list.append(results)
+    results_full = pd.concat(results_list)
+    save_path = os.path.join(_SAVE_DIR.value, str(run_id))
+    print(f"Saving results to {save_path}", flush=True)
+    os.makedirs(save_path, exist_ok=True)
+    results_full.to_csv(f"{save_path}/results.csv")
 
 
 if __name__ == "__main__":
-    FLAGS = flags.FLAGS
-    FLAGS(sys.argv)
-    main()
+  FLAGS = flags.FLAGS
+  FLAGS(sys.argv)
+  main()