ready for v1.0.0

README.rst

@@ -105,12 +105,12 @@ Installation
 ^^^^^^^^^^^^
 
 **Note on PyG and PyTorch Installation**\ :
-PyGOD depends on `PyTorch <https://https://pytorch.org/get-started/locally/>`_ and `PyTorch Geometric (PyG, including its optional dependencies) <https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html#>`_.
+PyGOD depends on `torch <https://https://pytorch.org/get-started/locally/>`_ and `torch_geometric (including its optional dependencies) <https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html#>`_.
 To streamline the installation, PyGOD does **NOT** install these libraries for you.
 Please install them from the above links for running PyGOD:
 
 * torch>=2.0.0
-* pytorch_geometric>=2.3.0
+* torch_geometric>=2.3.0
 
 It is recommended to use **pip** for installation.
 Please make sure **the latest version** is installed, as PyGOD is updated frequently:
@@ -130,7 +130,7 @@ Alternatively, you could clone and run setup.py file:
 
 **Required Dependencies**\ :
 
-* Python 3.8+
+* python>=3.8
 * numpy>=1.24.3
 * scikit-learn>=1.2.2
 * scipy>=1.10.1
@@ -145,19 +145,16 @@ API Cheatsheet & Reference
 
 Full API Reference: (https://docs.pygod.org). API cheatsheet for all detectors:
 
-* **fit(data)**\ : Fit detector.
-* **decision_function(data)**\ : Predict raw anomaly score of PyG data using the fitted detector.
+* **fit(data)**\ : Fit the detector with train data.
+* **predict(data)**\ : Predict on test data (train data if not provided) using the fitted detector.
 
 Key Attributes of a fitted detector:
 
 * **decision_score_**\ : The outlier scores of the input data. Outliers tend to have higher scores.
 * **label_**\ : The binary labels of the input data. 0 stands for inliers and 1 for outliers.
+* **threshold_**\ : The determined threshold for binary classification. Scores above the threshold are outliers.
 
-For the inductive setting:
-
-* **predict(data)**\ : Predict if nodes in PyG data G is an outlier or not using the fitted detector.
-
-**Input of PyGOD**: Please pass in a `PyTorch Geometric (PyG) <https://www.pyg.org/>`_ data object.
+**Input of PyGOD**: Please pass in a `PyG Data object <https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Data.html#torch_geometric.data.Data>`_.
 See `PyG data processing examples <https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html#data-handling-of-graphs>`_.
 
 

docs/api_cc.rst

@@ -3,19 +3,16 @@ API CheatSheet
 
 The following APIs are applicable for all detectors for easy use.
 
-* :func:`pygod.detector.Detector.fit`: Fit detector.
-* :func:`pygod.detector.Detector.decision_function`: Predict raw anomaly scores of PyG data using the fitted detector
+* :func:`pygod.detector.Detector.fit`: Fit the detector with train data.
+* :func:`pygod.detector.Detector.predict`: Predict on test data (train data if not provided) using the fitted detector.
 
 Key Attributes of a fitted detector:
 
 * :attr:`pygod.detector.Detector.decision_score_`: The outlier scores of the input data. Outliers tend to have higher scores.
 * :attr:`pygod.detector.Detector.label_`: The binary labels of the input data. 0 stands for inliers and 1 for outliers.
+* :attr:`threshold_` : The determined threshold for binary classification. Scores above the threshold are outliers.
 
-For the inductive setting:
-
-* :func:`pygod.detector.Detector.predict`: Predict if a particular sample is an outlier or not using the fitted detector.
-
-**Input of PyGOD**: Please pass in a `PyTorch Geometric (PyG) <https://www.pyg.org/>`_ data object.
+**Input of PyGOD**: Please pass in a `PyG Data object <https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.Data.html#torch_geometric.data.Data>`_.
 See `PyG data processing examples <https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.html#data-handling-of-graphs>`_.
 
 

docs/install.rst

@@ -21,17 +21,17 @@ Alternatively, you could clone and run setup.py file:
 
 **Required Dependencies**\ :
 
-* Python 3.8+
+* python>=3.8
 * numpy>=1.24.3
 * scikit-learn>=1.2.2
 * scipy>=1.10.1
 * networkx>=3.1
 
 
 **Note on PyG and PyTorch Installation**\ :
-PyGOD depends on `PyTorch <https://https://pytorch.org/get-started/locally/>`_ and `PyTorch Geometric (PyG, including its optional dependencies) <https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html#>`_.
+PyGOD depends on `torch <https://https://pytorch.org/get-started/locally/>`_ and `torch_geometric (including its optional dependencies) <https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html#>`_.
 To streamline the installation, PyGOD does **NOT** install these libraries for you.
 Please install them from the above links for running PyGOD:
 
 * torch>=2.0.0
-* pytorch_geometric>=2.3.0
+* torch_geometric>=2.3.0

setup.cfg

@@ -1,2 +1,2 @@
 [metadata]
-description-file = README.rst
+description_file = README.rst