doc init

docs/start.examples.rst

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+Examples
+=========
+
+
+Directly Use Detection Models
+------------------------------
+
+
+DeepOD can be used in a few lines of code. 
+This API style is the same with `Sklean <https://github.com/scikit-learn/scikit-learn>`_ and `PyOD <https://github.com/yzhao062/pyod>`_.
+
+
+**for tabular anomaly detection:**
+
+.. code-block:: python
+
+
+    # unsupervised methods
+    from deepod.models.tabular import DeepSVDD
+    clf = DeepSVDD()
+    clf.fit(X_train, y=None)
+    scores = clf.decision_function(X_test)
+
+    # weakly-supervised methods
+    from deepod.models.tabular import DevNet
+    clf = DevNet()
+    clf.fit(X_train, y=semi_y) # semi_y uses 1 for known anomalies, and 0 for unlabeled data
+    scores = clf.decision_function(X_test)
+
+    # evaluation of tabular anomaly detection
+    from deepod.metrics import tabular_metrics
+    auc, ap, f1 = tabular_metrics(y_test, scores)
+
+
+**for time series anomaly detection:**
+
+
+.. code-block:: python
+
+
+    # time series anomaly detection methods
+    from deepod.models.time_series import TimesNet
+    clf = TimesNet()
+    clf.fit(X_train)
+    scores = clf.decision_function(X_test)
+
+    # evaluation of time series anomaly detection
+    from deepod.metrics import ts_metrics
+    from deepod.metrics import point_adjustment # execute point adjustment for time series ad
+    eval_metrics = ts_metrics(labels, scores)
+    adj_eval_metrics = ts_metrics(labels, point_adjustment(labels, scores))
+    
+
+
+Testbed
+--------
+
+
+
+Testbed contains the whole process of testing an anomaly detection model, including data loading, preprocessing, anomaly detection, and evaluation. 
+
+Please refer to ``testbed/``
+
+* ``testbed/testbed_unsupervised_ad.py`` is for testing unsupervised tabular anomaly detection models.
+ 
+* ``testbed/testbed_unsupervised_tsad.py`` is for testing unsupervised time-series anomaly detection models.
+
+
+Key arguments:
+
+* ``--input_dir``: name of the folder that contains datasets (.csv, .npy)
+
+* ``--dataset``: "FULL" represents testing all the files within the folder, or a list of dataset names using commas to split them (e.g., "10_cover*,20_letter*")
+
+* ``--model``: anomaly detection model name
+
+* ``--runs``: how many times running the detection model, finally report an average performance with standard deviation values
+
+
+Example: 
+
+1. Download `ADBench <https://github.com/Minqi824/ADBench/tree/main/adbench/datasets/>`_ datasets.
+2. modify the ``dataset_root`` variable as the directory of the dataset.
+3. ``input_dir`` is the sub-folder name of the ``dataset_root``, e.g., ``Classical`` or ``NLP_by_BERT``.  
+4. use the following command in the bash
+
+
+.. code-block:: bash
+
+    
+    cd DeepOD
+    pip install .
+    cd testbed
+    python testbed_unsupervised_ad.py --model DeepIsolationForest --runs 5 --input_dir ADBench
+   

docs/start.install.rst

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+Installation
+============
+
+It is recommended to use **pip** for installation. Please make sure
+**the latest version** is installed, as DeepOD is updated frequently:
+
+.. code-block:: bash
+
+   pip install deepod            # normal install
+   pip install --upgrade deepod  # or update if needed
+
+
+Alternatively, you could clone and run setup.py file:
+
+.. code-block:: bash
+
+   git clone https://github.com/xuhongzuo/deepod.git
+   cd pyod
+   pip install .
+
+
+**Required Dependencies**\ :
+
+
+* Python 3.7+
+* numpy>=1.19
+* scipy>=1.5.1
+* scikit_learn>=0.20.0
+* pandas>=1.0.0
+* torch>1.10.0,<1.13.1
+* ray==2.6.1
+* pyarrow>=11.0.0
+* einops
+

docs/start.model_save.rst

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+Model Save & Load 
+==================
+
+The detection model class has ``save_model`` and ``load_model`` functions. 
+
+We take the `DeepSVDD` model for example. 
+
+.. code-block:: python
+    
+    from deepod.models import DeepSVDD
+
+    # training an anomaly detection model
+    model = DeepSVDD() # or any other models in DeepOD
+    model.fit(X_train) # training
+
+    path = 'save_file.pkl'
+    model.save_model(path) # save trained model at the assigned path
+
+    # directly load trained model from path
+    model = DeepSVDD.load_model(path)
+    model.decision_function(X_test)
+    # or
+    model.predict(X_test)
+
+
+
+You can also directly use pickle for saving and loading DeepOD models. 
+
+.. code-block:: python
+    
+    import pickle
+    from deepod.models import DeepSVDD
+
+    model = DeepSVDD()
+    model.fit(X_train)
+
+    with open('save_file.pkl', 'wb'):
+        pickle.dump(model)
+
+    with open('save_file.pkl', 'rb')
+        model = pickle.load(f)
+
+    model.decision_function(X_test)
+
+