fixing all pre-commit checks

sparklightautoml/ml_algo/boost_lgbm.py

@@ -473,10 +473,13 @@ def fit_predict_single_fold(
 
  rows_count = full_data.count()
  if (run_params["executionMode"] == "streaming") and (rows_count <= 25_000):
- warnings.warn(f"The fitting of lightgbm in streaming execution mode "
- f"may fail with SEGSIGV / SIGBUS error (probably due to a bug in synapse ml) "
- f"if too few data available per core. Train data rows count: {rows_count} "
- f"Consider switching to bulk execution mode if such crashes happen", RuntimeWarning)
+ warnings.warn(
+ f"The fitting of lightgbm in streaming execution mode "
+ f"may fail with SEGSIGV / SIGBUS error (probably due to a bug in synapse ml) "
+ f"if too few data available per core. Train data rows count: {rows_count} "
+ f"Consider switching to bulk execution mode if such crashes happen",
+ RuntimeWarning,
+ )
 
  # fitting the model
  ml_model = lgbm.fit(self._assembler.transform(full_data))

tests/spark/unit/dataset_utils.py

@@ -1,19 +1,22 @@
 import os
 import pickle
 import shutil
+
 from typing import Any
 from typing import Dict
 from typing import List
 from typing import Optional
 from typing import Tuple
 
 import pyspark.sql.functions as sf
+
 from pyspark.sql import SparkSession
 
 from sparklightautoml.dataset.base import PersistenceManager
 from sparklightautoml.dataset.base import SparkDataset
 from sparklightautoml.tasks.base import SparkTask
 
+
 DUMP_METADATA_NAME = "metadata.pickle"
 DUMP_DATA_NAME = "data.parquet"
 
@@ -117,7 +120,6 @@ def load_dump_if_exist(
  "franchise_dealer": "str",
  },
  },
-
  "used_cars_dataset_no_cols_limit": {
  "path": "examples/data/small_used_cars_data.csv",
  "task_type": "reg",
@@ -140,15 +142,13 @@ def load_dump_if_exist(
  "franchise_dealer": "str",
  },
  },
-
  "lama_test_dataset": {
  "path": "examples/data/sampled_app_train.csv",
  "task_type": "binary",
  "metric_name": "areaUnderROC",
  "target_col": "TARGET",
  "roles": {"target": "TARGET", "drop": ["SK_ID_CURR"]},
  },
-
  # https://www.openml.org/d/734
  "ailerons_dataset": {
  "path": "examples/data/ailerons.csv",
@@ -157,7 +157,6 @@ def load_dump_if_exist(
  "target_col": "binaryClass",
  "roles": {"target": "binaryClass"},
  },
-
  # https://www.openml.org/d/4534
  "phishing_websites_dataset": {
  "path": "examples/data/PhishingWebsites.csv",
@@ -166,7 +165,6 @@ def load_dump_if_exist(
  "target_col": "Result",
  "roles": {"target": "Result"},
  },
-
  # https://www.openml.org/d/981
  "kdd_internet_usage": {
  "path": "examples/data/kdd_internet_usage.csv",
@@ -175,7 +173,6 @@ def load_dump_if_exist(
  "target_col": "Who_Pays_for_Access_Work",
  "roles": {"target": "Who_Pays_for_Access_Work"},
  },
-
  # https://www.openml.org/d/42821
  "nasa_dataset": {
  "path": "examples/data/nasa_phm2008.csv",
@@ -184,7 +181,6 @@ def load_dump_if_exist(
  "target_col": "class",
  "roles": {"target": "class"},
  },
-
  # https://www.openml.org/d/4549
  "buzz_dataset": {
  "path": "examples/data/Buzzinsocialmedia_Twitter_25k.csv",
@@ -193,7 +189,6 @@ def load_dump_if_exist(
  "target_col": "Annotation",
  "roles": {"target": "Annotation"},
  },
-
  # https://www.openml.org/d/372
  "internet_usage": {
  "path": "examples/data/internet_usage.csv",
@@ -202,7 +197,6 @@ def load_dump_if_exist(
  "target_col": "Actual_Time",
  "roles": {"target": "Actual_Time"},
  },
-
  # https://www.openml.org/d/4538
  "gesture_segmentation": {
  "path": "examples/data/gesture_segmentation.csv",
@@ -211,15 +205,14 @@ def load_dump_if_exist(
  "target_col": "Phase",
  "roles": {"target": "Phase"},
  },
-
  # https://www.openml.org/d/382
  "ipums_97": {
  "path": "examples/data/ipums_97.csv",
  "task_type": "multiclass",
  "metric_name": "crossentropy",
  "target_col": "movedin",
  "roles": {"target": "movedin"},
- }
+ },
 }