build: v0.6.0

README.md

@@ -224,8 +224,6 @@ Copy the URL shown on the console into any browser to open the MLflow web interf
 
 For more details: Please refer to:
 
-- Manual v1.1.0 for Geochemistry π - Beta [[Tencent Docs]](https://docs.qq.com/pdf/DQ0l5d2xVd2VwcnVW?&u=6868f96d4a384b309036e04e637e367a) | [[Google drive]](https://drive.google.com/file/d/1yryykCyWKM-Sj88fOYbOba6QkB_fu2ws/view?usp=sharing)
-
 - Geochemistry π - Download and Run the Beta Version [[Bilibili]](https://www.bilibili.com/video/BV1UM4y1Q7Ju/?spm_id_from=333.999.0.0&vd_source=27944ab3b73a78970c1a52a5dcbb9140) | [[YouTube]](https://www.youtube.com/watch?v=EeVaJ3H7_AU&list=PLy8hNsI55lvh1UHjhVhqNUj3xPdV9sEiM&index=9)
 
 - MLflow UI user guide - Geochemistry π v0.5.0 [[Bilibili]](https://b23.tv/CW5Rjmo) | [[YouTube]](https://www.youtube.com/watch?v=Yu1nzNeLfRY)
@@ -313,7 +311,6 @@ The whole package is under construction and the documentation is progressively e
 + Yang Lyu (Daisy, Zhejiang University, China)
 + Bailun Jiang (EPSI / Lille University, France)
 + Ruitao Chang (China University of Geosciences Beijing, China)
-+ Junchi Liao(Roceda, University of Electronic Science and Technology of China, China)
 + Panyan Weng (The University of Sydney, Australia)
 + Siqi Yao (Clara, Dongguan University of Technology, China)
 + Zhelan Lin（Lan, Fuzhou University, China）
@@ -397,3 +394,4 @@ More Videos will be recorded soon.
 + Aixiwake·Janganuer (Ayshuak, Sun Yat-sen University, China)
 + Zhenglin Xu (Garry, Jilin University, China)
 + Jianing Wang (National University of Singapore, Singapore)
++ Junchi Liao(Roceda, University of Electronic Science and Technology of China, China)

docs/source/Home/CHANGELOG.md

@@ -10,7 +10,7 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 
 + MLOps core of continuous training in web interface
 
-## [0.5.0] - 2023-01-14
+## [0.5.0] - 2024-01-14
 
 ### Added
 

geochemistrypi/_version.py

@@ -1 +1 @@
-__version__ = "0.5.0"
+__version__ = "0.6.0"

geochemistrypi/data_mining/cli_pipeline.py

@@ -22,6 +22,7 @@
     MISSING_VALUE_STRATEGY,
     MLFLOW_ARTIFACT_DATA_PATH,
     MODE_OPTION,
+    MODE_OPTION_WITH_MISSING_VALUES,
     NON_AUTOML_MODELS,
     OPTION,
     OUTPUT_PATH,
@@ -276,6 +277,7 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
         clear_output()
         # Ask the user whether to use imputation techniques to deal with the missing values.
         print("-*-*- Missing Values Process -*-*-")
+        print("[bold red]Caution: Only some algorithms can process the data with missing value, such as XGBoost for regression and classification![/bold red]")
         print("Do you want to deal with the missing values?")
         num2option(OPTION)
         is_process_missing_value = limit_num_input(OPTION, SECTION[1], num_input)
@@ -401,12 +403,9 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
     # If the selected data set is with missing values and is not been imputed, then only allow the user to choose regression, classification and clustering models.
     # Otherwise, allow the user to choose decomposition models.
     if missing_value_flag and not process_missing_value_flag:
-        # Delete the decomposition mode because it doesn't support missing values.
-        MODE_OPTION.remove("Dimensional Reduction")
-        # Delete the abnormal detection mode because it doesn't support missing values.
-        MODE_OPTION.remove("Abnormal Detection")
-        num2option(MODE_OPTION)
-        mode_num = limit_num_input(MODE_OPTION, SECTION[2], num_input)
+        # The abnormal detection mode and decomposition mode don't support missing values.
+        num2option(MODE_OPTION_WITH_MISSING_VALUES)
+        mode_num = limit_num_input(MODE_OPTION_WITH_MISSING_VALUES, SECTION[2], num_input)
     else:
         num2option(MODE_OPTION)
         mode_num = limit_num_input(MODE_OPTION, SECTION[2], num_input)

geochemistrypi/data_mining/constants.py

@@ -28,6 +28,7 @@
 DATA_OPTION = ["Own Data", "Testing Data (Built-in)"]
 TEST_DATA_OPTION = ["Data For Regression", "Data For Classification", "Data For Clustering", "Data For Dimensional Reduction", "Data For Abnormal Detection"]
 MODE_OPTION = ["Regression", "Classification", "Clustering", "Dimensional Reduction", "Abnormal Detection"]
+MODE_OPTION_WITH_MISSING_VALUES = ["Regression", "Classification", "Clustering"]
 
 # The model provided to use
 REGRESSION_MODELS = [

geochemistrypi/data_mining/enum.py

@@ -0,0 +1,15 @@
+from enum import Enum
+
+
+class ModeOption(Enum):
+    REGRESSION = "Regression"
+    CLASSIFICATION = "Classification"
+    CLUSTERING = "Clustering"
+    DIMENSIONAL_REDUCTION = "Dimensional Reduction"
+    ABNORMAL_DETECTION = "Abnormal Detection"
+
+
+class ModeOptionWithMissingValues(Enum):
+    REGRESSION = "Regression"
+    CLASSIFICATION = "Classification"
+    CLUSTERING = "Clustering"

geochemistrypi/data_mining/model/func/algo_decomposition/_common.py

@@ -101,14 +101,9 @@ def plot_contour(data: pd.DataFrame, algorithm_name: str) -> None:
     algorithm_name : str
         The name of the dimensionality reduction algorithm.
     """
-    quantile_threshold = 0.9
-    x_upper_threshold = data.iloc[:, 0].quantile(quantile_threshold)
-    y_upper_threshold = data.iloc[:, 1].quantile(quantile_threshold)
-    filtered_data = data[(data.iloc[:, 0] <= x_upper_threshold) & (data.iloc[:, 1] <= y_upper_threshold)]
-
     # Calculate the density
-    x = filtered_data.iloc[:, 0]
-    y = filtered_data.iloc[:, 1]
+    x = data.iloc[:, 0]
+    y = data.iloc[:, 1]
     buffer = max(x.max() - x.min(), y.max() - y.min()) * 0.05
     xmin, xmax = x.min() - buffer, x.max() + buffer
     ymin, ymax = y.min() - buffer, y.max() + buffer

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "geochemistrypi"
-version = "0.5.0"
+version = "0.6.0"
 authors = [
   { name="Can He", email="sanyhew1097618435@163.com" },
 ]
@@ -52,25 +52,14 @@ dependencies = [
     "imblearn",
 ]
 
-[tool.black]
-line-length = "200"
-
-[tool.isort]
-line_length = "200"
-profile = "black"
-
-[tool.flake8]
-max-line-length = "200"
-max-complexity = "20"
-ignore = "F811,W605"
-
 [project.optional-dependencies]
 test = [
     "pytest"
 ]
 
 [project.urls]
-"Homepage" = "https://github.com/ZJUEarthData/geochemistrypi"
+"Homepage" = "https://geochemistrypi.deep-time.org"
+"GitHub" = "https://github.com/ZJUEarthData/geochemistrypi"
 "Bug Tracker" = "https://github.com/ZJUEarthData/geochemistrypi/issues"
 "Online Documentation" = "https://geochemistrypi.readthedocs.io/en/latest/"