From 4ebb53ce8ace8df36d0600c79365106486d3cc17 Mon Sep 17 00:00:00 2001
From: "deepsource-autofix[bot]"
 <62050782+deepsource-autofix[bot]@users.noreply.github.com>
Date: Sat, 11 May 2024 13:32:25 +0000
Subject: [PATCH] style: format code with Autopep8, Black, ClangFormat,
 dotnet-format, Go fmt, Gofumpt, Google Java Format, isort, Ktlint, PHP CS
 Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt, Scalafmt, StandardJS,
 StandardRB, swift-format and Yapf

This commit fixes the style issues introduced in 53d59f0 according to the output
from Autopep8, Black, ClangFormat, dotnet-format, Go fmt, Gofumpt, Google Java
Format, isort, Ktlint, PHP CS Fixer, Prettier, RuboCop, Ruff Formatter, Rustfmt,
Scalafmt, StandardJS, StandardRB, swift-format and Yapf.

Details: None
---
 learning_improvement/model_training.py | 25 +++++++++++++++++--------
 1 file changed, 17 insertions(+), 8 deletions(-)

diff --git a/learning_improvement/model_training.py b/learning_improvement/model_training.py
index 67f5fd9a7..dea08935c 100644
--- a/learning_improvement/model_training.py
+++ b/learning_improvement/model_training.py
@@ -1,6 +1,7 @@
 import xgboost as xgb
 from sklearn.model_selection import train_test_split
 
+
 class ModelTraining:
     def __init__(self, data, target_column):
         self.data = data
@@ -13,21 +14,29 @@ def train_model(self, model_file):
         X = self.data.drop(self.target_column, axis=1)
         y = self.data[self.target_column]
 
-        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+        X_train, X_test, y_train, y_test = train_test_split(
+            X, y, test_size=0.2, random_state=42
+        )
 
         dtrain = xgb.DMatrix(X_train, label=y_train)
         dtest = xgb.DMatrix(X_test, label=y_test)
 
         params = {
-            'objective': 'reg:squarederror',
-            'eval_metric': 'rmse',
-            'max_depth': 6,
-            'eta': 0.1,
-            'subsample': 0.8,
-            'colsample_bytree': 0.8
+            "objective": "reg:squarederror",
+            "eval_metric": "rmse",
+            "max_depth": 6,
+            "eta": 0.1,
+            "subsample": 0.8,
+            "colsample_bytree": 0.8,
         }
 
-        model = xgb.train(params, dtrain, num_boost_round=1000, evals=[(dtest, 'test')], early_stopping_rounds=50)
+        model = xgb.train(
+            params,
+            dtrain,
+            num_boost_round=1000,
+            evals=[(dtest, "test")],
+            early_stopping_rounds=50,
+        )
 
         model.save_model(model_file)