Update docstring

Author: thieu1995

evorbf/core/base_rbf.py

@@ -329,8 +329,8 @@ class BaseNiaRbf(BaseRbf):
     """
     Defines the most general class for Nature-inspired Algorithm-based RBF models that inherits the BaseRbf class
 
-    Note:
-    -----
+    Note
+    ----
         + In this model, the sigmas will be learned during the training process.
         + So the `sigmas` parameter is removed in the init function.
         + Besides, the `sigmas` is a list of value, each value represent a `sigma` for Gaussian function used in hidden node.

evorbf/core/nia_rbf.py

@@ -22,10 +22,9 @@ class NiaRbfRegressor(BaseNiaRbf, RegressorMixin):
         + use non-linear Gaussian function with `sigmas` as standard deviation
         + set up regulation term with hyperparameter `regularization`
 
-    Inherits
-    --------
-    BaseNiaRbf : The base class for NIA-based RBF networks.
-    RegressorMixin : Scikit-learn mixin class for regression estimators.
+    Inherits:
+        + BaseNiaRbf : The base class for NIA-based RBF networks.
+        + RegressorMixin : Scikit-learn mixin class for regression estimators.
 
     Parameters
     ----------
@@ -88,8 +87,8 @@ class NiaRbfRegressor(BaseNiaRbf, RegressorMixin):
     >>> data = Data(X, y)
     >>> data.split_train_test(test_size=0.2, random_state=1)
     >>> opt_paras = {"name": "GA", "epoch": 10, "pop_size": 30}
-    >>> model = NiaRbfRegressor(size_hidden=10, center_finder="kmeans", regularization=False, lamda=0.01,
-    >>>         obj_name=None, optimizer="BaseGA", optimizer_paras=opt_paras, verbose=True, seed=42, obj_weights=None)
+    >>> model = NiaRbfRegressor(size_hidden=10, center_finder="kmeans", regularization=False,
+    >>>         obj_name=None, optim="BaseGA", optim_paras=opt_paras, verbose=True, seed=42, obj_weights=None)
     >>> model.fit(data.X_train, data.y_train)
     >>> pred = model.predict(data.X_test)
     >>> print(pred)

evorbf/helpers/preprocessor.py

@@ -4,9 +4,7 @@
 #       Github: https://github.com/thieu1995        %                         
 # --------------------------------------------------%
 
-import pandas as pd
 import numpy as np
-from pathlib import Path
 from evorbf.helpers.scaler import DataTransformer
 from sklearn.model_selection import train_test_split
 
@@ -31,8 +29,8 @@ def fit(self, y):
         """
         Fit label encoder to a given set of labels.
 
-        Parameters:
-        -----------
+        Parameters
+        ----------
         y : array-like
             Labels to encode.
         """
@@ -44,13 +42,13 @@ def transform(self, y):
         """
         Transform labels to encoded integer labels.
 
-        Parameters:
-        -----------
+        Parameters
+        ----------
         y : array-like (1-D vector)
             Labels to encode.
 
-        Returns:
-        --------
+        Returns
+        -------
         encoded_labels : array-like
             Encoded integer labels.
         """
@@ -79,13 +77,13 @@ def inverse_transform(self, y):
         """
         Transform integer labels to original labels.
 
-        Parameters:
-        -----------
+        Parameters
+        ----------
         y : array-like
             Encoded integer labels.
 
-        Returns:
-        --------
+        Returns
+        -------
         original_labels : array-like
             Original labels.
         """