perf: optimize the functionality of formula showing in linear model.

README.md

@@ -46,13 +46,19 @@ The following figure is the frontend-backend separation architecture of Geochemi
 </p>
 
 
-**Cite the work as:**
+**If the software contributes to your research, cite the work as :**
 
 ZhangZhou J\*, He Can\*, Sun Jianhao, Zhao Jianming, Lyu Yang, Wang Shengxin, Zhao Wenyu, Li Anzhou, Ji Xiaohui. Geochemistry π: Automated machine learning python framework for tabular data (2024). Geochemistry, Geophysics,
 Geosystems, 25, e2023GC011324
 
 Download link: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GC011324
 
+**Related report:**
+
+Geochemistry π was selected for featuring as an Editor’s Highlight in EOS magazine by American Geophysical Union (fewer than 2 percent of paper are selected) and quoted in Geochemical NEWS by Geochemical Society.
+
+Eos Website: https://eos.org/editor-highlights/machine-learning-for-geochemists-who-dont-want-to-code.
+
 ## Quick Installation
 
 One instruction to download on **command line**, such as Terminal on macOS, Power Shell on Windows.
@@ -223,6 +229,12 @@ The following figure is the storage mechanism: <br>
 
 The whole package is under construction and the documentation is progressively evolving.
 
+## Geochemistry π Mind Map
+
+[→ Click here for more details](https://docs.qq.com/mind/DZkJTYmVHcmFneUli?u=40ac0718eb494b008b2f072197ea95db)
+
+![Geochemistry π.png](https://github.com/ZJUEarthData/geochemistrypi/assets/97781484/3de6ea2b-7ab1-4145-bf4e-225eeabc7987)
+
 ## Team Info
 
 **Leader:**

docs/source/For User/Model Example/Data_Preprocessing/Data Preprocessing.md

@@ -12,7 +12,7 @@ In order to utilize the functions provided by our software, your own data set sh
 - be with the suffix **.xlsx**, which is supported by Microsoft Excel.
 - be comprise of location information **LATITUDE** and **LONGITUDE**, two columns respectively.
 
-If you want to run **classification** algorithm, only supporting binary classification currently, you data set should satisfy:
+If you want to run **classification** algorithm, you data set should satisfy:
 
 - Tag column **LABEL** to differentiate the data.
 

docs/source/For User/User Docs link.md

@@ -1,4 +1,4 @@
-## User Documents Link
+# User Documents Link
 
 ### 1. Geochemistry π Project Introduction
 

docs/source/Home/Introduction.md

@@ -47,13 +47,19 @@ The following figure is the frontend-backend separation architecture of Geochemi
 </p>
 
 
-**Cite the work as:**
+**If the software contributes to your research, cite the work as :**
 
 ZhangZhou J\*, He Can\*, Sun Jianhao, Zhao Jianming, Lyu Yang, Wang Shengxin, Zhao Wenyu, Li Anzhou, Ji Xiaohui. Geochemistry π: Automated machine learning python framework for tabular data (2024). Geochemistry, Geophysics,
 Geosystems, 25, e2023GC011324
 
 Download link: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GC011324
 
+**Related report:**
+
+Geochemistry π was selected for featuring as an Editor’s Highlight in EOS magazine by American Geophysical Union (fewer than 2 percent of paper are selected) and quoted in Geochemical NEWS by Geochemical Society.
+
+Eos Website: https://eos.org/editor-highlights/machine-learning-for-geochemists-who-dont-want-to-code.
+
 ## Quick Installation
 
 One instruction to download on **command line**, such as Terminal on macOS, Power Shell on Windows.

geochemistrypi/data_mining/model/func/_common_supervised.py

@@ -179,7 +179,11 @@ def show_formula(coef: np.ndarray, intercept: np.ndarray, features_name: np.ndar
     if regression_classification == "Regression":
         if len(y_train.columns) == 1:  # Single target
             coef = np.around(coef, decimals=3)[0]
-            intercept = np.around(intercept, decimals=3)[0]
+            # Check if intercept is a scalar
+            if isinstance(intercept, np.ndarray):
+                intercept = np.around(intercept, decimals=3)[0]
+            else:
+                intercept = np.around(intercept, decimals=3)
 
             terms = [("-" if c < 0 else "+") + " " + str(abs(c)) + f if c != 0 else "" for c, f in zip(coef, features_name)]
             terms_first = (terms[0][2:] if coef[0] > 0 else terms[0]).replace(" ", "")
@@ -199,7 +203,11 @@ def show_formula(coef: np.ndarray, intercept: np.ndarray, features_name: np.ndar
     elif regression_classification == "Classification":
         if coef.shape[0] == 1:  # Binary classification
             coef = np.around(coef, decimals=3)[0]
-            intercept = np.around(intercept, decimals=3)[0]
+            # Check if intercept is a scalar
+            if isinstance(intercept, np.ndarray):
+                intercept = np.around(intercept, decimals=3)[0]
+            else:
+                intercept = np.around(intercept, decimals=3)
 
             terms = [("-" if c < 0 else "+") + " " + str(abs(c)) + f if c != 0 else "" for c, f in zip(coef, features_name)]
             terms_first = (terms[0][2:] if coef[0] > 0 else terms[0]).replace(" ", "")

geochemistrypi/data_mining/model/regression.py

@@ -4171,6 +4171,8 @@ def special_components(self, **kwargs) -> None:
             coef=[self.model.coef_],
             intercept=self.model.intercept_,
             features_name=RidgeRegression.X_train.columns,
+            regression_classification="Regression",
+            y_train=RidgeRegression.y,
             algorithm_name=self.naming,
             local_path=GEOPI_OUTPUT_ARTIFACTS_PATH,
             mlflow_path="root",
@@ -4250,6 +4252,8 @@ def special_components(self, is_automl: bool, **kwargs) -> None:
             coef=[self.auto_model.coef_],
             intercept=self.auto_model.intercept_,
             features_name=RidgeRegression.X_train.columns,
+            regression_classification="Regression",
+            y_train=RidgeRegression.y,
             algorithm_name=self.naming,
             local_path=GEOPI_OUTPUT_ARTIFACTS_PATH,
             mlflow_path="root",