Merge pull request #366 from ZJUEarthData/dev/Mengqi

perf: change "abnormal" to "anomaly".

README.md

@@ -147,7 +147,7 @@ https://docs.qq.com/document/DQ3BDeHhxRGNzSXZN)
 
 + Data_Decomposition.xlsx [[Google Drive]](https://docs.google.com/spreadsheets/d/1kix82qj5--vhnm8-KhuUBH9dqYH6zcY8/edit?usp=sharing&ouid=110717816678586054594&rtpof=true&sd=true) | [[Tencent Docs]](https://docs.qq.com/document/DQ29oZ0lhUGtZUmdN?&u=6868f96d4a384b309036e04e637e367a)
 
-+  Data_AbnormalDetectioon.xlsx [[Google Drive]](https://docs.google.com/spreadsheets/d/1NqTQZCkv74Sn_iOJOKRc-QnJzpaWmnzC_lET_0ZreiQ/edit?usp=sharing) | [[Tencent Docs]](
++  Data_AnomalyDetection.xlsx [[Google Drive]](https://docs.google.com/spreadsheets/d/1NqTQZCkv74Sn_iOJOKRc-QnJzpaWmnzC_lET_0ZreiQ/edit?usp=sharing) | [[Tencent Docs]](
 https://docs.qq.com/document/DQ2hqQ2N2ZGlOUWlT)
 
 **Note**: For more detail on data preparation, please refer to our online documentation in **Model Example** under the section of **FOR USER**.

docs/source/For Developer/Add New Model To Framework.md

@@ -749,7 +749,7 @@ def activate(
     """Train by Scikit-learn framework."""
 ```
 
-For unsupervised learning (clustering, decomposition and abnormaly detection), the signature of `activate` method is:
+For unsupervised learning (clustering, decomposition and anomaly detection), the signature of `activate` method is:
 ```
 def activate(
     self,
@@ -800,7 +800,7 @@ The differences above include the signature of @dispatch and the signature of `a
 
 There are two `activate` methods defined in the Regression and Classification mode, the first method uses the Scikit-learn framework, and the second method uses the FLAML and RAY frameworks. Decomposition and Clustering algorithms only use the Scikit-learn framework.  The instantiation of model workflow class inside `activate` method builds the connnectioni between Layer 3 and Layer 2.
 
-(1) The invocatioin of model workflow class in the first activate method (Used in classification, regression,decomposition, clustering, abnormaly detection) needs to pass the hyperparameters for manual ML:
+(1) The invocatioin of model workflow class in the first activate method (Used in classification, regression,decomposition, clustering, anomaly detection) needs to pass the hyperparameters for manual ML:
 ```
 elif self.model_name == "ModelName":
     hyper_parameters = ModelWorkflowClass.manual_hyper_parameters()

docs/source/For User/Model Example/Abnormal_Detection/abnormal_detection.md → docs/source/For User/Model Example/Anomaly_Detection/anomaly_detection.md

@@ -1,4 +1,4 @@
-#  Abnomal Detection - Isolation Forest
+#  Anomaly Detection - Isolation Forest
 
 Anomaly detection is a broad problem-solving strategy that encompasses various algorithms, each with its own approach to identifying unusual data points. One such algorithm is the Isolation Forest, which distinguishes itself by constructing an ensemble of decision trees to isolate anomalies. The algorithm's core principle is that anomalies are more easily isolated, requiring fewer splits in the trees compared to normal data points.
 
@@ -75,19 +75,19 @@ After pressing the Enter key, the program propts the following options to let yo
 
 4 - Data For Dimensional Reduction
 
-5 - Data For Abnormal Detection
+5 - Data For Anomaly Detection
 
 (User) ➜ @Number: 5
 ```
 
-Here, we choose *_5 - Data For Abnormal Detection_* and press the Enter key to move forward.
+Here, we choose *_5 - Data For Anomaly Detection_* and press the Enter key to move forward.
 
 Now, you should see the output below on your screen:
 
 ```bash
 Successfully loading the built-in training data set
 
-'Data_AbnormalDetection.xlsx'.
+'Data_AnomalyDetection.xlsx'.
 
 --------------------
 
@@ -778,7 +778,7 @@ Successfully store 'Data Selected Dropped-Imputed Feature-Engineering' in 'Data
 
 
 
-We select **5 - Abnormal Detection** as our model:
+We select **5 - Anomaly Detection** as our model:
 
 
 
@@ -794,7 +794,7 @@ We select **5 - Abnormal Detection** as our model:
 
 4 - Dimensional Reduction
 
-5 - Abnormal Detection
+5 - Anomaly Detection
 
 (Model) ➜ @Number: 5
 (Press Enter key to move forward.)
@@ -904,9 +904,9 @@ Expected Functionality:
 Successfully store 'Hyper Parameters - Isolation Forest' in 'Hyper Parameters - Isolation Forest.txt' in Users/geopi/geopi_output/GeoPi-Rock Isolation Forest/Algorithm - Test 1/parameters.
 
 
------* Abnormal Detection Data *-----
+-----* Anomaly Detection Data *-----
 
-    SIO2(WT%)  TIO2(WT%) ... MNO(WT%)  NA2O(WT%)  is_abnormal
+    SIO2(WT%)  TIO2(WT%) ... MNO(WT%)  NA2O(WT%)  is_anomaly
 
 0   53.536000   0.291000 ... 0.083000   0.861000      -1
 
@@ -932,11 +932,11 @@ Successfully store 'Hyper Parameters - Isolation Forest' in 'Hyper Parameters -
 
 [109 rows x 10 columns]
 
-Successfully store 'X Abnormal Detection' in 'X Abnormal Detection.xlsx' in Users/geopi/geopi_output/GeoPi-Rock Isolation Forest/Algorithm - Test 1/data.
+Successfully store 'X Anomaly Detection' in 'X Anomaly Detection.xlsx' in Users/geopi/geopi_output/GeoPi-Rock Isolation Forest/Algorithm - Test 1/data.
 
 -----* Normal Data *-----
 
-    SIO2(WT%)  TIO2(WT%) ... MNO(WT%)  NA2O(WT%)  is_abnormal
+    SIO2(WT%)  TIO2(WT%) ... MNO(WT%)  NA2O(WT%)  is_anomaly
 
 2   50.873065   0.720622 ... 0.102185   1.920395       1
 
@@ -965,9 +965,9 @@ Successfully store 'X Abnormal Detection' in 'X Abnormal Detection.xlsx' in User
 
 Successfully store 'X Normal' in 'X Normal.xlsx' in Users/geopi/geopi_output/GeoPi-Rock Isolation Forest/Algorithm - Test 1/data.
 
------* Abnormal Data *-----
+-----* Anomaly Data *-----
 
-    SIO2(WT%)  TIO2(WT%)  ... MNO(WT%)  NA2O(WT%)      is_abnormal
+    SIO2(WT%)  TIO2(WT%)  ... MNO(WT%)  NA2O(WT%)      is_anomaly
 
 0    53.536000   0.291000 ... 0.083000   0.861000           -1
 
@@ -1035,7 +1035,7 @@ Successfully store 'X Normal' in 'X Normal.xlsx' in Users/geopi/geopi_output/Geo
 
 106  54.200000   0.100000 ... 0.130000   1.430000           -1
 ```
-Successfully store 'X Abnormal' in 'X Abnormal.xlsx' in Users/geopi/geopi_output/GeoPi-Rock Isolation Forest/Algorithm - Test 1/data.
+Successfully store 'X Anomaly' in 'X Anomaly.xlsx' in Users/geopi/geopi_output/GeoPi-Rock Isolation Forest/Algorithm - Test 1/data.
 
     -----* Model Persistence *-----
     Successfully store 'Isolation Forest' in 'Isolation Forest.pkl' in Users/geopi/geopi_output/GeoPi-Rock Isolation Forest/Algorithm - Test 1/artifacts/model.

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

@@ -39,7 +39,7 @@ https://docs.qq.com/document/DQ3BDeHhxRGNzSXZN)
 
 + Data_Decomposition.xlsx [[Google Drive]](https://docs.google.com/spreadsheets/d/1kix82qj5--vhnm8-KhuUBH9dqYH6zcY8/edit?usp=sharing&ouid=110717816678586054594&rtpof=true&sd=true) | [[Tencent Docs]](https://docs.qq.com/document/DQ29oZ0lhUGtZUmdN?&u=6868f96d4a384b309036e04e637e367a)
 
-+  Data_AbnormalDetectioon.xlsx [[Google Drive]](https://docs.google.com/spreadsheets/d/1NqTQZCkv74Sn_iOJOKRc-QnJzpaWmnzC_lET_0ZreiQ/edit?usp=sharing) | [[Tencent Docs]](
++  Data_AnomalyDetection.xlsx [[Google Drive]](https://docs.google.com/spreadsheets/d/1NqTQZCkv74Sn_iOJOKRc-QnJzpaWmnzC_lET_0ZreiQ/edit?usp=sharing) | [[Tencent Docs]](
 https://docs.qq.com/document/DQ2hqQ2N2ZGlOUWlT)
 
 

docs/source/Home/CHANGELOG.md

@@ -40,12 +40,12 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
   + Clustering Models
     + Affinity Propagation Clustering
 + New Mode:
-  + Abnormal Detection
+  + Anomaly Detection
     +  Isolation Forest
 + Docs:
   + Mind map of all options in README
   + Citation info
-  + Abnormal detection algorithm example
+  + Anomaly detection algorithm example
 
 ### Changed
 

docs/source/Home/Introduction.md

@@ -146,7 +146,7 @@ https://docs.qq.com/document/DQ3BDeHhxRGNzSXZN)
 
 + Data_Decomposition.xlsx [[Google Drive]](https://docs.google.com/spreadsheets/d/1kix82qj5--vhnm8-KhuUBH9dqYH6zcY8/edit?usp=sharing&ouid=110717816678586054594&rtpof=true&sd=true) | [[Tencent Docs]](https://docs.qq.com/document/DQ29oZ0lhUGtZUmdN?&u=6868f96d4a384b309036e04e637e367a)
 
-+  Data_AbnormalDetectioon.xlsx [[Google Drive]](https://docs.google.com/spreadsheets/d/1NqTQZCkv74Sn_iOJOKRc-QnJzpaWmnzC_lET_0ZreiQ/edit?usp=sharing) | [[Tencent Docs]](
++  Data_AnomalyDetection.xlsx [[Google Drive]](https://docs.google.com/spreadsheets/d/1NqTQZCkv74Sn_iOJOKRc-QnJzpaWmnzC_lET_0ZreiQ/edit?usp=sharing) | [[Tencent Docs]](
 https://docs.qq.com/document/DQ2hqQ2N2ZGlOUWlT)
 
 **Note**: For more detail on data preparation, please refer to our online documentation in **Model Example** under the section of **FOR USER**.

docs/source/model example.rst

@@ -9,5 +9,5 @@ Model Example
    Regression <For User/Model Example/Regression/Regression.md>
    Clustering <For User/Model Example/Clustering/Clustering.md>
    Decomposition <For User/Model Example/Decomposition/decomposition.md>
-   Abnormal Detection <For User/Model Example/Abnormal_Detection/abnormal_detection.md>
+   Anomaly Detection <For User/Model Example/Anomaly_Detection/anomaly_detection.md>
    Network Analysis <For User/Model Example/Network_Analysis/Network Analysis.md>

docs/source/python_apis/geochemistrypi.data_mining.model.func.algo_abnormaldetection.rst → docs/source/python_apis/geochemistrypi.data_mining.model.func.algo_anomalydetection.rst

@@ -1,10 +1,10 @@
-geochemistrypi.data\_mining.model.func.algo\_abnormaldetection package
+geochemistrypi.data\_mining.model.func.algo\_anomalydetection package
 ======================================================================
 
 Module contents
 ---------------
 
-.. automodule:: geochemistrypi.data_mining.model.func.algo_abnormaldetection
+.. automodule:: geochemistrypi.data_mining.model.func.algo_anomalydetection
    :members:
    :undoc-members:
    :show-inheritance:

docs/source/python_apis/geochemistrypi.data_mining.model.func.rst

@@ -7,7 +7,7 @@ Subpackages
 .. toctree::
    :maxdepth: 4
 
-   geochemistrypi.data_mining.model.func.algo_abnormaldetection
+   geochemistrypi.data_mining.model.func.algo_anomalydetection
    geochemistrypi.data_mining.model.func.algo_classification
    geochemistrypi.data_mining.model.func.algo_clustering
    geochemistrypi.data_mining.model.func.algo_decomposition

geochemistrypi/data_mining/cli_pipeline.py

@@ -9,7 +9,7 @@
 from rich.prompt import Confirm, Prompt
 
 from .constants import (
-    ABNORMALDETECTION_MODELS,
+    ANOMALYDETECTION_MODELS,
     CLASSIFICATION_MODELS,
     CLASSIFICATION_MODELS_WITH_MISSING_VALUES,
     CLUSTERING_MODELS,
@@ -43,7 +43,7 @@
 from .process.classify import ClassificationModelSelection
 from .process.cluster import ClusteringModelSelection
 from .process.decompose import DecompositionModelSelection
-from .process.detect import AbnormalDetectionModelSelection
+from .process.detect import AnomalyDetectionModelSelection
 from .process.regress import RegressionModelSelection
 from .utils.base import check_package, clear_output, copy_files, create_geopi_output_dir, get_os, install_package, log, save_data, show_warning
 from .utils.mlflow_utils import retrieve_previous_experiment_id
@@ -198,7 +198,7 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
         elif built_in_training_data_num == 4:
             training_data_path = "Data_Decomposition.xlsx"
         elif built_in_training_data_num == 5:
-            training_data_path = "Data_AbnormalDetection.xlsx"
+            training_data_path = "Data_AnomalyDetection.xlsx"
         data = read_data(file_path=training_data_path)
         print(f"Successfully loading the built-in training data set '{training_data_path}'.")
         show_data_columns(data.columns)
@@ -403,7 +403,7 @@ 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:
-        # The abnormal detection mode and decomposition mode don't support missing values.
+        # The anomaly 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:
@@ -545,13 +545,13 @@ def cli_pipeline(training_data_path: str, application_data_path: Optional[str] =
         Modes2Models = {1: REGRESSION_MODELS_WITH_MISSING_VALUES, 2: CLASSIFICATION_MODELS_WITH_MISSING_VALUES, 3: CLUSTERING_MODELS_WITH_MISSING_VALUES}
         Modes2Initiators = {1: RegressionModelSelection, 2: ClassificationModelSelection, 3: ClusteringModelSelection}
     else:
-        Modes2Models = {1: REGRESSION_MODELS, 2: CLASSIFICATION_MODELS, 3: CLUSTERING_MODELS, 4: DECOMPOSITION_MODELS, 5: ABNORMALDETECTION_MODELS}
+        Modes2Models = {1: REGRESSION_MODELS, 2: CLASSIFICATION_MODELS, 3: CLUSTERING_MODELS, 4: DECOMPOSITION_MODELS, 5: ANOMALYDETECTION_MODELS}
         Modes2Initiators = {
             1: RegressionModelSelection,
             2: ClassificationModelSelection,
             3: ClusteringModelSelection,
             4: DecompositionModelSelection,
-            5: AbnormalDetectionModelSelection,
+            5: AnomalyDetectionModelSelection,
         }
     MODELS = Modes2Models[mode_num]
     num2option(MODELS)

geochemistrypi/data_mining/constants.py

@@ -26,8 +26,8 @@
 
 OPTION = ["Yes", "No"]
 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"]
+TEST_DATA_OPTION = ["Data For Regression", "Data For Classification", "Data For Clustering", "Data For Dimensional Reduction", "Data For Anomaly Detection"]
+MODE_OPTION = ["Regression", "Classification", "Clustering", "Dimensional Reduction", "Anomaly Detection"]
 MODE_OPTION_WITH_MISSING_VALUES = ["Regression", "Classification", "Clustering"]
 
 # The model provided to use
@@ -68,7 +68,7 @@
 ]
 CLUSTERING_MODELS = ["KMeans", "DBSCAN", "Agglomerative", "AffinityPropagation"]
 DECOMPOSITION_MODELS = ["PCA", "T-SNE", "MDS"]
-ABNORMALDETECTION_MODELS = ["Isolation Forest", "Local Outlier Factor"]
+ANOMALYDETECTION_MODELS = ["Isolation Forest", "Local Outlier Factor"]
 
 # The model can deal with missing values
 # Reference: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

geochemistrypi/data_mining/enum.py

@@ -6,7 +6,7 @@ class ModeOption(Enum):
     CLASSIFICATION = "Classification"
     CLUSTERING = "Clustering"
     DIMENSIONAL_REDUCTION = "Dimensional Reduction"
-    ABNORMAL_DETECTION = "Abnormal Detection"
+    ANOMALY_DETECTION = "Anomaly Detection"
 
 
 class ModeOptionWithMissingValues(Enum):

geochemistrypi/data_mining/model/detection.py

@@ -10,26 +10,25 @@
 
 from ..utils.base import clear_output
 from ._base import WorkflowBase
-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
+from .func.algo_anomalydetection._iforest import isolation_forest_manual_hyper_parameters, local_outlier_factor_manual_hyper_parameters
 
 
-class AbnormalDetectionWorkflowBase(WorkflowBase):
-    """The base workflow class of abnormal detection algorithms."""
+class AnomalyDetectionWorkflowBase(WorkflowBase):
+    """The base workflow class of anomaly detection algorithms."""
 
     # common_function = []
 
     def __init__(self) -> None:
         super().__init__()
-        self.mode = "Abnormal Detection"
+        self.mode = "Anomaly Detection"
 
     def fit(self, X: pd.DataFrame, y: Optional[pd.DataFrame] = None) -> None:
         """Fit the model by Scikit-learn framework."""
         self.X = X
         self.model.fit(X)
 
     def predict(self, X: pd.DataFrame) -> np.ndarray:
-        """Perform Abnormal Detection on samples in X by Scikit-learn framework."""
+        """Perform Anomaly Detection on samples in X by Scikit-learn framework."""
         y_predict = self.model.predict(X)
         return y_predict
 
@@ -52,29 +51,29 @@ def _detect_data(X: pd.DataFrame, detect_label: np.ndarray) -> tuple[pd.DataFram
 
         Returns
         -------
-        X_abnormal_detection : pd.DataFrame
+        X_anomaly_detection : pd.DataFrame
             DataFrame containing the original data with detection results.
 
         X_normal : pd.DataFrame
             DataFrame containing the normal data points.
 
-        X_abnormal : pd.DataFrame
-            DataFrame containing the abnormal data points.
+        X_anomaly : pd.DataFrame
+            DataFrame containing the anomaly data points.
         """
-        X_abnormal_detection = X.copy()
+        X_anomaly_detection = X.copy()
         # Merge detection results into the source data
-        X_abnormal_detection["is_abnormal"] = detect_label
-        X_normal = X_abnormal_detection[X_abnormal_detection["is_abnormal"] == 1]
-        X_abnormal = X_abnormal_detection[X_abnormal_detection["is_abnormal"] == -1]
+        X_anomaly_detection["is_anomaly"] = detect_label
+        X_normal = X_anomaly_detection[X_anomaly_detection["is_anomaly"] == 1]
+        X_anomaly = X_anomaly_detection[X_anomaly_detection["is_anomaly"] == -1]
 
-        return X_abnormal_detection, X_normal, X_abnormal
+        return X_anomaly_detection, X_normal, X_anomaly
 
     def common_components(self) -> None:
-        """Invoke all common application functions for abnormal detection algorithms by Scikit-learn framework."""
+        """Invoke all common application functions for anomaly detection algorithms by Scikit-learn framework."""
         pass
 
 
-class IsolationForestAbnormalDetection(AbnormalDetectionWorkflowBase):
+class IsolationForestAnomalyDetection(AnomalyDetectionWorkflowBase):
     """The automation workflow of using Isolation Forest algorithm to make insightful products."""
 
     name = "Isolation Forest"
@@ -212,7 +211,7 @@ def __init__(
             warm_start=self.warm_start,
         )
 
-        self.naming = IsolationForestAbnormalDetection.name
+        self.naming = IsolationForestAnomalyDetection.name
 
     @classmethod
     def manual_hyper_parameters(cls) -> Dict:
@@ -227,7 +226,7 @@ def special_components(self, **kwargs) -> None:
         pass
 
 
-class LocalOutlierFactorAbnormalDetection(AbnormalDetectionWorkflowBase):
+class LocalOutlierFactorAnomalyDetection(AnomalyDetectionWorkflowBase):
     """The automation workflow of using Local Outlier Factor algorithm to make insightful products."""
 
     name = "Local Outlier Factor"
@@ -371,7 +370,7 @@ def __init__(
             n_jobs=self.n_jobs,
         )
 
-        self.naming = LocalOutlierFactorAbnormalDetection.name
+        self.naming = LocalOutlierFactorAnomalyDetection.name
 
     @classmethod
     def manual_hyper_parameters(cls) -> Dict: