Fixes OPEN-3929 Sphinx errors and warnings while generating Python API documentation

Author: gustavocidornelas

docs/source/_static/img/logo-purple-text.svg renamed to docs/source/_static/img/openlayer.svg

@@ -5,9 +5,9 @@
 
 .. module:: openlayer
 
-*******************
+***********************
 Openlayer Documentation
-*******************
+***********************
 
 **Date**: |today| **Version**: |version|
 

docs/source/index.rst

@@ -45,11 +45,10 @@ Version control flow
  OpenlayerClient.status
  OpenlayerClient.restore
 
-Dataset / Task types
---------------------
+Task types
+----------
 .. autosummary:: 
  :toctree: api/
 
- DatasetType
  TaskType
 

docs/source/reference/upload.rst

@@ -516,46 +516,51 @@ def add_dataset(
 
  The YAML file with the dataset config must have the following fields:
 
- - ``columnNames`` : List[str]
+ columnNames : List[str]
  List of the dataset's column names.
- - ``classNames`` : List[str]
+ classNames : List[str]
  List of class names indexed by label integer in the dataset.
  E.g. ``[negative, positive]`` when ``[0, 1]`` are in your label column.
- - ``labelColumnName`` : str
+ labelColumnName : str
  Column header in the csv containing the labels.
 
  .. important::
  The labels in this column must be zero-indexed integer values.
- - ``label`` : str
+ label : str
  Type of dataset. E.g. ``'training'`` or
  ``'validation'``.
- - ``featureNames`` : List[str], default []
+ featureNames : List[str], default []
  List of input feature names. Only applicable if your ``task_type`` is
  :obj:`TaskType.TabularClassification` or :obj:`TaskType.TabularRegression`.
- - ``textColumnName`` : str, default None
+ textColumnName : str, default None
  Column header in the csv containing the input text. Only applicable if
  your ``task_type`` is :obj:`TaskType.TextClassification`.
- - ``predictionsColumnName`` : str, default None
+ predictionsColumnName : str, default None
  Column header in the csv containing the predictions. Only applicable if you
  are uploading a model as well with the :obj:`add_model` method.
 
  .. important::
  Each cell in this column must contain a list of
  class probabilities. For example, for a binary classification
- task, the cell values should look like this:
- .. csv-table::
- :header: ..., predictions
- ..., "[0.6650292861587155, 0.3349707138412845]"
- ..., "[0.8145561636482788, 0.18544383635172124]"
+ task, the column with the predictions should look like this:
 
- - ``categoricalFeatureNames`` : List[str], default []
+ **predictions**
+
+ ``[0.1, 0.9]``
+
+ ``[0.8, 0.2]``
+
+ ``...``
+
+ categoricalFeatureNames : List[str], default []
  A list containing the names of all categorical features in the dataset.
  E.g. ``["Gender", "Geography"]``. Only applicable if your ``task_type`` is
  :obj:`TaskType.TabularClassification` or :obj:`TaskType.TabularRegression`.
- - ``language`` : str, default 'en'
+ language : str, default 'en'
  The language of the dataset in ISO 639-1 (alpha-2 code) format.
- - ``sep`` : str, default ','
+ sep : str, default ','
  Delimiter to use. E.g. `'\\t'`.
+
  force : bool
  If :obj:`add_dataset` is called when there is already a dataset of the same type
  in the staging area, when ``force=True``, the existing staged dataset will be
@@ -727,46 +732,51 @@ def add_dataframe(
 
  The YAML file with the dataset config must have the following fields:
 
- - ``columnNames`` : List[str]
+ columnNames : List[str]
  List of the dataset's column names.
- - ``classNames`` : List[str]
+ classNames : List[str]
  List of class names indexed by label integer in the dataset.
  E.g. ``[negative, positive]`` when ``[0, 1]`` are in your label column.
- - ``labelColumnName`` : str
- Column header in the csv containing the labels.
+ labelColumnName : str
+ Column header in the dataframe containing the labels.
 
  .. important::
  The labels in this column must be zero-indexed integer values.
- - ``label`` : str
+ label : str
  Type of dataset. E.g. ``'training'`` or
  ``'validation'``.
- - ``featureNames`` : List[str], default []
+ featureNames : List[str], default []
  List of input feature names. Only applicable if your ``task_type`` is
  :obj:`TaskType.TabularClassification` or :obj:`TaskType.TabularRegression`.
- - ``textColumnName`` : str, default None
- Column header in the csv containing the input text. Only applicable if your
- ``task_type`` is :obj:`TaskType.TextClassification`.
- - ``predictionsColumnName`` : str, default None
- Column header in the csv containing the predictions. Only applicable if you
+ textColumnName : str, default None
+ Column header in the dataframe containing the input text. Only applicable if
+ your ``task_type`` is :obj:`TaskType.TextClassification`.
+ predictionsColumnName : str, default None
+ Column header in the dataframe containing the predictions. Only applicable if you
  are uploading a model as well with the :obj:`add_model` method.
 
  .. important::
  Each cell in this column must contain a list of
  class probabilities. For example, for a binary classification
- task, the cell values should look like this:
- .. csv-table::
- :header: ..., predictions
- ..., "[0.6650292861587155, 0.3349707138412845]"
- ..., "[0.8145561636482788, 0.18544383635172124]"
+ task, the column with the predictions should look like this:
 
- - ``categoricalFeatureNames`` : List[str], default []
+ **predictions**
+
+ ``[0.1, 0.9]``
+
+ ``[0.8, 0.2]``
+
+ ``...``
+
+ categoricalFeatureNames : List[str], default []
  A list containing the names of all categorical features in the dataset.
  E.g. ``["Gender", "Geography"]``. Only applicable if your ``task_type`` is
  :obj:`TaskType.TabularClassification` or :obj:`TaskType.TabularRegression`.
- - ``language`` : str, default 'en'
+ language : str, default 'en'
  The language of the dataset in ISO 639-1 (alpha-2 code) format.
- - ``sep`` : str, default ','
+ sep : str, default ','
  Delimiter to use. E.g. `'\\t'`.
+
  force : bool
  If :obj:`add_dataframe` is called when there is already a dataset of the same
  type in the staging area, when ``force=True``, the existing staged dataset will
@@ -993,7 +1003,7 @@ def push(self, project_id: int):
  Notes
  -----
  - To use this method, you must first have committed your changes with the :obj:`commit`
- method.
+  method.
 
  Examples
  --------

openlayer/__init__.py

@@ -617,6 +617,11 @@ class DatasetValidator:
  dataset_df : pd.DataFrame, optional
  The dataset to validate.
 
+ Methods
+ -------
+ validate:
+ Runs all dataset validations.
+
  Examples
  --------
 
@@ -1070,6 +1075,11 @@ class ModelValidator:
  sample_data : pd.DataFrame
  Sample data to be used for the model validation.
 
+ Methods
+ -------
+ validate:
+ Runs all model validations.
+
  Examples
  --------