fixed documentation issues (verified from generated docs) (HelenGuohx#35

)

* fixed documentation issues (verified from generated docs)

* small fix in documentation

* formatted docstring, capitalized, added period and removed param type

* formatted docstring, capitalized, added period and removed param type

* formatted docstring, capitalized, added period and removed param type

* Revise the sphinx docs

---------

Co-authored-by: yangwenzhuo08 <[email protected]>

docs/source/logai.algorithms.nn_model.forecast_nn.rst

@@ -0,0 +1,53 @@
+logai.algorithms.nn\_model.forecast\_nn package
+===============================================
+
+Submodules
+----------
+
+logai.algorithms.nn\_model.forecast\_nn.base\_nn module
+-------------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.forecast_nn.base_nn
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+logai.algorithms.nn\_model.forecast\_nn.cnn module
+--------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.forecast_nn.cnn
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+logai.algorithms.nn\_model.forecast\_nn.lstm module
+---------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.forecast_nn.lstm
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+logai.algorithms.nn\_model.forecast\_nn.transformer module
+----------------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.forecast_nn.transformer
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+logai.algorithms.nn\_model.forecast\_nn.utils module
+----------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.forecast_nn.utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: logai.algorithms.nn_model.forecast_nn
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/logai.algorithms.nn_model.logbert.rst

@@ -0,0 +1,61 @@
+logai.algorithms.nn\_model.logbert package
+==========================================
+
+Submodules
+----------
+
+logai.algorithms.nn\_model.logbert.configs module
+-------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.logbert.configs
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+logai.algorithms.nn\_model.logbert.eval\_metric\_utils module
+-------------------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.logbert.eval_metric_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+logai.algorithms.nn\_model.logbert.predict module
+-------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.logbert.predict
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+logai.algorithms.nn\_model.logbert.predict\_utils module
+--------------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.logbert.predict_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+logai.algorithms.nn\_model.logbert.tokenizer\_utils module
+----------------------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.logbert.tokenizer_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+logai.algorithms.nn\_model.logbert.train module
+-----------------------------------------------
+
+.. automodule:: logai.algorithms.nn_model.logbert.train
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: logai.algorithms.nn_model.logbert
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/logai.algorithms.nn_model.rst

@@ -1,6 +1,15 @@
 logai.algorithms.nn\_model package
 ==================================
 
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   logai.algorithms.nn_model.forecast_nn
+   logai.algorithms.nn_model.logbert
+
 Submodules
 ----------
 

docs/source/logai.applications.openset.anomaly_detection.configs.rst

@@ -0,0 +1,21 @@
+logai.applications.openset.anomaly\_detection.configs package
+=============================================================
+
+Submodules
+----------
+
+logai.applications.openset.anomaly\_detection.configs.schema module
+-------------------------------------------------------------------
+
+.. automodule:: logai.applications.openset.anomaly_detection.configs.schema
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: logai.applications.openset.anomaly_detection.configs
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/logai.applications.openset.anomaly_detection.rst

@@ -0,0 +1,29 @@
+logai.applications.openset.anomaly\_detection package
+=====================================================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   logai.applications.openset.anomaly_detection.configs
+
+Submodules
+----------
+
+logai.applications.openset.anomaly\_detection.openset\_anomaly\_detection\_workflow module
+------------------------------------------------------------------------------------------
+
+.. automodule:: logai.applications.openset.anomaly_detection.openset_anomaly_detection_workflow
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: logai.applications.openset.anomaly_detection
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/logai.applications.openset.rst

@@ -0,0 +1,18 @@
+logai.applications.openset package
+==================================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   logai.applications.openset.anomaly_detection
+
+Module contents
+---------------
+
+.. automodule:: logai.applications.openset
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/logai.applications.rst

@@ -1,6 +1,14 @@
 logai.applications package
 ==========================
 
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   logai.applications.openset
+
 Submodules
 ----------
 

examples/jupyter_notebook/tutorial_deep_ad.md

@@ -236,7 +236,6 @@ workflow_config:
 Then to run the end to end log anomaly detection on the HDFS dataset using LSTM Anomaly Detector (a sequence-based deep-learning model), you can use a python script like below: 
 
 ```python
-import os 
 from logai.applications.openset.anomaly_detection.openset_anomaly_detection_workflow import OpenSetADWorkflowConfig
 from logai.utils.file_utils import read_file
 from logai.utils.dataset_utils import split_train_dev_test_for_anomaly_detection
@@ -299,6 +298,7 @@ print (predict_results)
 This kind of Anomaly Detection workflow for various Deep-Learning models and various experimental settings have also been automated in `logai.applications.openset.anomaly_detection.openset_anomaly_detection_workflow.OpenSetADWorkflow` class which can be easily invoked like the below example
 
 ```python
+import os 
 from logai.applications.openset.anomaly_detection.openset_anomaly_detection_workflow import OpenSetADWorkflow, get_openset_ad_config
 
 TEST_DATA_PATH = "tests/logai/test_data/HDFS_AD/HDFS_5k.log"

logai/algorithms/anomaly_detection_algo/anomaly_detector_het.py

@@ -14,7 +14,7 @@
 
 class HetAnomalyDetectionConfig(AnomalyDetectionConfig):
     """
-    Heterogeneous Anomaly Detector Parameters
+    Heterogeneous Anomaly Detector Parameters.
 
     :param train_test_ratio: The ratio between test and training splits.
     """
@@ -26,7 +26,8 @@ class HetAnomalyDetector(AnomalyDetector):
     """
     Anomaly Detector Wrapper to handle heterogeneous log feature dataframe which include various attributes of log. For
     each attribute, we build its specific anomaly detector if the data satisfies the requirement.
-    This current version only supports anomaly detection on the constants.LOGLINE_COUNTS field (i.e. frequency count of the log events)
+    This current version only supports anomaly detection on the constants.LOGLINE_COUNTS field (i.e. frequency count of
+    the log events).
     """
 
     def __init__(self, config: HetAnomalyDetectionConfig):
@@ -40,11 +41,11 @@ def __init__(self, config: HetAnomalyDetectionConfig):
 
     def preprocess(self, counter_df: pd.DataFrame):
         """
-        Split raw log feature dataframe by unique attribute ID
+        Splits raw log feature dataframe by unique attribute ID.
 
-        :param counter_df: log feature dataframe must contain at least two columns
+        :param counter_df: A log feature dataframe that must contain at least two columns
             ['timestamp': datetime, constants.LOGLINE_COUNTS: int].
-            The rest of columns combinations are treated as log attribute ID
+            The rest of columns combinations are treated as log attribute ID.
         :return: The processed log feature dataframe.
         """
         ts_df = counter_df[[constants.LOG_COUNTS]]
@@ -55,13 +56,13 @@ def preprocess(self, counter_df: pd.DataFrame):
         attr_list = counter_df["attribute"].unique()
         return attr_list
 
-    def fit_predict(self, log_feature: pd.DataFrame):
+    def fit_predict(self, log_feature: pd.DataFrame) -> pd.DataFrame:
         """
-        Train and predict anomaly scores
+        Trains a model and predicts anomaly scores.
 
-        :param log_features: log feature dataframe must contain at least two columns
+        :param log_features: A log feature dataframe that must contain at least two columns
             ['timestamp': datetime, constants.LOGLINE_COUNTS: int].
-            The rest of columns combinations are treated as log attribute ID
+            The rest of columns combinations are treated as log attribute ID.
         :return: The predicted anomaly scores.
         """
         res = pd.DataFrame()

logai/algorithms/anomaly_detection_algo/dbl.py

@@ -24,7 +24,7 @@
 class DBLDetectorParams(Config):
     """
     Dynamic Baseline Parameters. For more details on the paramaters see 
-    https://opensource.salesforce.com/Merlion/v1.3.1/merlion.models.anomaly.html#module-merlion.models.anomaly.dbl
+    https://opensource.salesforce.com/Merlion/v1.3.1/merlion.models.anomaly.html#module-merlion.models.anomaly.dbl.
 
     :param threshold: The rule to use for thresholding anomaly scores.
     :param fixed_period: ``(t0, tf)``; Train the model on all datapoints occurring between t0 and tf (inclusive).
@@ -46,10 +46,10 @@ class DBLDetectorParams(Config):
 @factory.register("detection", "dbl", DBLDetectorParams)
 class DBLDetector(AnomalyDetectionAlgo):
     """Dynamic baseline based time series anomaly detection. This is a wrapper class for the Dynamic Baseline
-    anomaly detection model from Merlion library 
+    anomaly detection model from Merlion library .
     https://opensource.salesforce.com/Merlion/v1.3.1/merlion.models.anomaly.html#module-merlion.models.anomaly.dbl
     Current implementation only supports anomaly detection on the constants.LOGLINE_COUNTS class (which maintains
-    frequency counts of the log events )
+    frequency counts of the log events).
     """
     def __init__(self, params: DBLDetectorParams):
         dbl_config = DynamicBaselineConfig(
@@ -65,9 +65,9 @@ def __init__(self, params: DBLDetectorParams):
 
     def fit(self, log_features: pd.DataFrame):
         """
-        Train method of the Dynamic Baseline model
+        Training method of the Dynamic Baseline model.
 
-        :param log_features: log feature dataframe must only contain two columns
+        :param log_features: A log feature dataframe that must only contain two columns
             ['timestamp': datetime, constants.LOGLINE_COUNTS: int].
         """
         self._is_valid_ts_df(log_features)
@@ -77,9 +77,10 @@ def fit(self, log_features: pd.DataFrame):
 
     def predict(self, log_features: pd.DataFrame):
         """
-        Predict anomaly scores for log_feature["timestamp", constants.LOGLINE_COUNTS]
+        Predicts anomaly scores for log_feature["timestamp", constants.LOGLINE_COUNTS].
 
-        :param log_features: log feature dataframe must contain two columns ['timestamp': datetime, 'counts': int].
+        :param log_features: A log feature dataframe that must contain two columns
+            ['timestamp': datetime, 'counts': int].
         :return: A dataframe of the predicted anomaly scores, e.g., index:log_features.index.
             value: anomaly score to indicate if anomaly or not.
         """

logai/algorithms/anomaly_detection_algo/distribution_divergence.py

@@ -35,8 +35,8 @@ def _js_divergence(p, q):
 class DistributionDivergenceParams(Config):
     """Parameters for distribution divergence based anomaly detector.
 
-    :param n_bins: number of bins to use to discretize the continuous distribution into a discrete distribution
-    :param type: list of types of distribution divergences. The allowed types are Kullback–Leibler ("KL"), Jensen–Shannon
+    :param n_bins: The number of bins to use to discretize the continuous distribution into a discrete distribution
+    :param type: A list of types of distribution divergences. The allowed types are Kullback–Leibler ("KL"), Jensen–Shannon
         ("JS"). It also allows a comma separated list of metrics like ("KL,JS" or "JS,KL").
     """
     n_bins: int = 100

logai/algorithms/anomaly_detection_algo/ets.py

@@ -54,10 +54,9 @@ class ETSDetectorParams(Config):
 
 @factory.register("detection", "ets", ETSDetectorParams)
 class ETSDetector(AnomalyDetectionAlgo):
-    """
-    ETS Anomaly Detector. This is a wrapper for the ETS based Anomaly Detector from Merlion library 
-    https://opensource.salesforce.com/Merlion/v1.0.2/merlion.models.forecast.html#module-merlion.models.forecast.ets.
-    This current version only supports anomaly detection of the constants.LOGLINE_COUNTS (i.e. frequency count of log events).
+    """ETS Anomaly Detector. This is a wrapper for the ETS based Anomaly Detector from Merlion library 
+    https://opensource.salesforce.com/Merlion/v1.0.2/merlion.models.forecast.html#module-merlion.models.forecast.ets
+    This current version only supports anomaly detection of the constants.LOGLINE_COUNTS (i.e. frequency count of log events)
     """
 
     def __init__(self, params: ETSDetectorParams):
@@ -77,12 +76,11 @@ def __init__(self, params: ETSDetectorParams):
 
     def fit(self, log_features: pd.DataFrame):
         """
-
         Fit method to train ETS Anomaly Detector.
 
-        :param log_features: log feature dataframe must only contain two columns
+        :param log_features: A log feature dataframe that must only contain two columns
             ['timestamp': datetime, constants.LOGLINE_COUNTS: int].
-        :return: train_scores: anomaly scores dataframe
+        :return: train_scores: The anomaly scores dataframe
             ['index':log_features.index, 'timestamps': datetime, 'anom_score': scores, 'trainval':
             whether it is training set.
         """
@@ -100,11 +98,11 @@ def fit(self, log_features: pd.DataFrame):
 
     def predict(self, log_features: pd.DataFrame):
         """
-        Predict anomaly scores for log_feature["timestamp", constants.LOGLINE_COUNTS].
+        Predicts anomaly scores for log_feature["timestamp", constants.LOGLINE_COUNTS].
 
-        :param log_features: log feature dataframe must only contain two columns
+        :param log_features: A log feature dataframe that must only contain two columns
             ['timestamp': datetime, constants.LOGLINE_COUNTS: int].
-        :return: test_scores: anomaly scores dataframe
+        :return: test_scores: The anomaly scores dataframe
             ['index':log_features.index, 'timestamps': datetime, 'anom_score': scores, 'trainval':
             whether it is training set.
         """