diff --git a/geochemistrypi/data_mining/model/detection.py b/geochemistrypi/data_mining/model/detection.py
index 930d703..9dff73f 100644
--- a/geochemistrypi/data_mining/model/detection.py
+++ b/geochemistrypi/data_mining/model/detection.py
@@ -10,7 +10,8 @@
 
 from ..utils.base import clear_output
 from ._base import WorkflowBase
-from .func.algo_abnormaldetection._iforest import isolation_forest_manual_hyper_parameters, local_outlier_factor_manual_hyper_parameters
+from .func.algo_abnormaldetection._iforest import isolation_forest_manual_hyper_parameters
+from .func.algo_abnormaldetection._local_outlier_factor import local_outlier_factor_manual_hyper_parameters
 
 
 class AbnormalDetectionWorkflowBase(WorkflowBase):
diff --git a/geochemistrypi/data_mining/model/func/algo_abnormaldetection/_iforest.py b/geochemistrypi/data_mining/model/func/algo_abnormaldetection/_iforest.py
index 385b883..41994f9 100644
--- a/geochemistrypi/data_mining/model/func/algo_abnormaldetection/_iforest.py
+++ b/geochemistrypi/data_mining/model/func/algo_abnormaldetection/_iforest.py
@@ -46,41 +46,3 @@ def isolation_forest_manual_hyper_parameters() -> Dict:
     else:
         hyper_parameters["max_samples"] = max_samples
     return hyper_parameters
-
-
-def local_outlier_factor_manual_hyper_parameters() -> Dict:
-    """Manually set hyperparameters.
-
-    Returns
-    -------
-    hyper_parameters : dict
-    """
-    print("N neighbors: The number of neighbors to use.")
-    print("Please specify the number of neighbors. A good starting range could be between 10 and 50, such as 20.")
-    n_neighbors = num_input(SECTION[2], "@N Neighbors: ")
-
-    print("Leaf size: The leaf size used in the ball tree or KD tree.")
-    print("Please specify the leaf size. A good starting range could be between 20 and 50, such as 30.")
-    leaf_size = num_input(SECTION[2], "@Leaf Size: ")
-
-    print("P: The power parameter for the Minkowski metric.")
-    print("Please specify the power parameter. A good starting range could be between 1 and 3, such as 2.0.")
-    p = float_input(2.0, SECTION[2], "@P: ")
-
-    print("Contamination: The amount of contamination of the data set.")
-    print("Please specify the contamination of the data set. A good starting range could be between 0.1 and 0.5, such as 0.3.")
-    contamination = float_input(0.3, SECTION[2], "@Contamination: ")
-
-    print("N jobs: The number of parallel jobs to run.")
-    print("Please specify the number of jobs. Use -1 to use all available CPUs, 1 for no parallelism, or specify the number of CPUs to use. A good starting value is None.")
-    n_jobs = num_input(SECTION[2], "@N Jobs: ")
-
-    hyper_parameters = {
-        "n_neighbors": n_neighbors,
-        "leaf_size": leaf_size,
-        "p": p,
-        "contamination": contamination,
-        "n_jobs": n_jobs,
-    }
-
-    return hyper_parameters
diff --git a/geochemistrypi/data_mining/model/func/algo_abnormaldetection/_local_outlier_factor.py b/geochemistrypi/data_mining/model/func/algo_abnormaldetection/_local_outlier_factor.py
new file mode 100644
index 0000000..984b375
--- /dev/null
+++ b/geochemistrypi/data_mining/model/func/algo_abnormaldetection/_local_outlier_factor.py
@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+from typing import Dict
+
+from rich import print
+
+from ....constants import SECTION
+from ....data.data_readiness import float_input, num_input
+
+
+def local_outlier_factor_manual_hyper_parameters() -> Dict:
+    """Manually set hyperparameters.
+
+    Returns
+    -------
+    hyper_parameters : dict
+    """
+    print("N neighbors: The number of neighbors to use.")
+    print("Please specify the number of neighbors. A good starting range could be between 10 and 50, such as 20.")
+    n_neighbors = num_input(SECTION[2], "@N Neighbors: ")
+    print("Leaf size: The leaf size used in the ball tree or KD tree.")
+    print("Please specify the leaf size. A good starting range could be between 20 and 50, such as 30.")
+    leaf_size = num_input(SECTION[2], "@Leaf Size: ")
+    print("P: The power parameter for the Minkowski metric.")
+    print("Please specify the power parameter. When p = 1, this is equivalent to using manhattan_distance, and when p = 2 euclidean_distance is applied. For arbitrary p, minkowski_distance is used.")
+    p = float_input(2.0, SECTION[2], "@P: ")
+    print("Contamination: The amount of contamination of the data set.")
+    print("Please specify the contamination of the data set. A good starting range could be between 0.1 and 0.5, such as 0.3.")
+    contamination = float_input(0.3, SECTION[2], "@Contamination: ")
+    print("N jobs: The number of parallel jobs to run.")
+    print("Please specify the number of jobs. Use -1 to use all available CPUs, 1 for no parallelism, or specify the number of CPUs to use. A good starting value is 1.")
+    n_jobs = num_input(SECTION[2], "@N Jobs: ")
+    hyper_parameters = {
+        "n_neighbors": n_neighbors,
+        "leaf_size": leaf_size,
+        "p": p,
+        "contamination": contamination,
+        "n_jobs": n_jobs,
+    }
+    return hyper_parameters