Bugfix _ Isolation Forest Anomaly Plot

verticapy/machine_learning/vertica/cluster.py

@@ -176,7 +176,7 @@ def plot(
             **style_kwargs,
         }
         if self._model_subcategory == "ANOMALY_DETECTION":
-            fun = vdf.bubble
+            fun = vdf.scatter
             name = "anomaly_score"
             kwargs["cmap_col"] = name
         else:

verticapy/machine_learning/vertica/ensemble.py

@@ -1087,6 +1087,314 @@ class IsolationForest(Clustering, Tree):
         Float  in  the  range (0,1] that  specifies  the
         fraction of columns (features), chosen at random,
         used when building each tree.
+
+    Examples
+    ---------
+
+    The following examples provide a basic understanding of usage.
+    For more detailed examples, please refer to the
+    :ref:`user_guide.machine_learning` or the
+    `Examples <https://www.vertica.com/python/examples/>`_
+    section on the website.
+
+    Load data for machine learning
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    We import ``verticapy``:
+
+    .. ipython:: python
+
+        import verticapy as vp
+
+    .. hint::
+
+        By assigning an alias to ``verticapy``, we mitigate the risk of code
+        collisions with other libraries. This precaution is necessary
+        because verticapy uses commonly known function names like "average"
+        and "median", which can potentially lead to naming conflicts.
+        The use of an alias ensures that the functions from verticapy are
+        used as intended without interfering with functions from other
+        libraries.
+
+    For this example, we will use the winequality dataset.
+
+    .. code-block:: python
+
+        import verticapy.datasets as vpd
+
+        data = vpd.load_winequality()
+
+    .. raw:: html
+        :file: SPHINX_DIRECTORY/figures/datasets_loaders_load_winequality.html
+
+    .. note::
+
+        VerticaPy offers a wide range of sample datasets that are
+        ideal for training and testing purposes. You can explore
+        the full list of available datasets in the :ref:`api.datasets`,
+        which provides detailed information on each dataset
+        and how to use them effectively. These datasets are invaluable
+        resources for honing your data analysis and machine learning
+        skills within the VerticaPy environment.
+
+    .. ipython:: python
+        :suppress:
+
+        import verticapy.datasets as vpd
+        data = vpd.load_winequality()
+
+    Model Initialization
+    ^^^^^^^^^^^^^^^^^^^^^
+
+    First we import the ``IsolationForest`` model:
+
+    .. code-block::
+
+        from verticapy.machine_learning.vertica import IsolationForest
+
+    .. ipython:: python
+        :suppress:
+
+        from verticapy.machine_learning.vertica import IsolationForest
+
+    Then we can create the model:
+
+    .. ipython:: python
+        :okwarning:
+
+        model = IsolationForest(
+            n_estimators = 10,
+            max_depth = 3,
+            nbins = 6,
+        )
+
+    .. hint::
+
+        In ``verticapy`` 1.0.x and higher, you do not need to specify the
+        model name, as the name is automatically assigned. If you need to
+        re-use the model, you can fetch the model name from the model's
+        attributes.
+
+    .. important::
+
+        The model name is crucial for the model management system and
+        versioning. It's highly recommended to provide a name if you
+        plan to reuse the model later.
+
+    Model Training
+    ^^^^^^^^^^^^^^^
+
+    We can now fit the model:
+
+    .. ipython:: python
+        :okwarning:
+
+        model.fit(data, X = ["density", "sulphates"])
+
+    .. important::
+
+        To train a model, you can directly use the ``vDataFrame`` or the
+        name of the relation stored in the database. The test set is optional
+        and is only used to compute the test metrics. In ``verticapy``, we
+        don't work using ``X`` matrices and ``y`` vectors. Instead, we work
+        directly with lists of predictors and the response name.
+
+    .. hint::
+
+        For clustering and anomaly detection, the use of predictors is
+        optional. In such cases, all available predictors are considered,
+        which can include solely numerical variables or a combination of
+        numerical and categorical variables, depending on the model's
+        capabilities.
+
+    Prediction
+    ^^^^^^^^^^^
+
+    Prediction is straight-forward:
+
+    .. ipython:: python
+        :suppress:
+
+        result = model.predict(data, ["density", "sulphates"])
+        html_file = open("figures/machine_learning_vertica_isolation_for_prediction.html", "w")
+        html_file.write(result._repr_html_())
+        html_file.close()
+
+    .. code-block:: python
+
+        model.predict(data, ["density", "sulphates"])
+
+    .. raw:: html
+        :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_isolation_for_prediction.html
+
+    Plots - Anomaly Detection
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    Plots highlighting the outliers can be easily drawn using:
+
+    .. code-block:: python
+
+        model.plot()
+
+    .. ipython:: python
+        :suppress:
+
+        vp.set_option("plotting_lib", "plotly")
+        fig = model.plot()
+        fig.write_html("figures/machine_learning_vertica_isolation_for_plot.html")
+
+    .. raw:: html
+        :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_isolation_for_plot.html
+
+    .. note::
+
+        Most anomaly detection methods produce a score. In scenarios involving
+        2 or 3 predictors, using a bubble plot to visualize the model's results
+        is a straightforward approach. In such plots, the size of each bubble
+        corresponds to the anomaly score.
+
+    Plots - Tree
+    ^^^^^^^^^^^^^
+
+    Tree models can be visualized by drawing their tree plots.
+    For more examples, check out :ref:`chart_gallery.tree`.
+
+    .. code-block:: python
+
+        model.plot_tree()
+
+    .. ipython:: python
+        :suppress:
+
+        res = model.plot_tree()
+        res.render(filename='figures/machine_learning_vertica_tree_isolation_for_', format='png')
+
+    .. image:: /../figures/machine_learning_vertica_tree_isolation_for_.png
+
+    .. note::
+
+        The above example may not render properly in the doc because
+        of the huge size of the tree. But it should render nicely
+        in jupyter environment.
+
+    In order to plot graph using `graphviz <https://graphviz.org/>`_
+    separately, you can extract the graphviz DOT file code as follows:
+
+    .. ipython:: python
+
+        model.to_graphviz()
+
+    This string can then be copied into a DOT file which can be
+    parsed by graphviz.
+
+    Plots - Contour
+    ^^^^^^^^^^^^^^^^
+
+    In order to understand the parameter space, we can also look
+    at the contour plots:
+
+    .. code-block:: python
+
+        model.contour()
+
+    .. ipython:: python
+        :suppress:
+
+        fig = model.contour()
+        fig.write_html("figures/machine_learning_vertica_isolation_for_contour.html")
+
+    .. raw:: html
+        :file: SPHINX_DIRECTORY/figures/machine_learning_vertica_isolation_for_contour.html
+
+    .. note::
+
+        Machine learning models with two predictors can usually benefit
+        from their own contour plot. This visual representation aids in
+        exploring predictions and gaining a deeper understanding of how
+        these models perform in different scenarios. Please refer to
+        :ref:`chart_gallery.contour_plot` for more examples.
+
+    Parameter Modification
+    ^^^^^^^^^^^^^^^^^^^^^^^
+
+    In order to see the parameters:
+
+    .. ipython:: python
+
+        model.get_params()
+
+    And to manually change some of the parameters:
+
+    .. ipython:: python
+
+        model.set_params({'max_depth': 5})
+
+    Model Register
+    ^^^^^^^^^^^^^^
+
+    In order to register the model for tracking and versioning:
+
+    .. code-block:: python
+
+        model.register("model_v1")
+
+    Please refer to :ref:`notebooks/ml/model_tracking_versioning/index.html`
+    for more details on model tracking and versioning.
+
+    Model Exporting
+    ^^^^^^^^^^^^^^^^
+
+    **To Memmodel**
+
+    .. code-block:: python
+
+        model.to_memmodel()
+
+    .. note::
+
+        ``MemModel`` objects serve as in-memory representations of machine
+        learning models. They can be used for both in-database and in-memory
+        prediction tasks. These objects can be pickled in the same way that
+        you would pickle a ``scikit-learn`` model.
+
+    The preceding methods for exporting the model use ``MemModel``, and it
+    is recommended to use ``MemModel`` directly.
+
+    **To SQL**
+
+    You can get the SQL query equivalent of the XGB model by:
+
+    .. ipython:: python
+
+        model.to_sql()
+
+    .. note:: This SQL query can be directly used in any database.
+
+    **Deploy SQL**
+
+    To get the SQL query which uses Vertica functions use below:
+
+    .. ipython:: python
+
+        model.deploySQL()
+
+    **To Python**
+
+    To obtain the prediction function in Python syntax, use the following code:
+
+    .. ipython:: python
+
+        X = [[0.9, 0.5]]
+        model.to_python()(X)
+
+    .. hint::
+
+        The
+        :py:mod:`verticapy.machine_learning.vertica.tree.IsolationForest.to_python`
+        method is used to retrieve the anomaly score.
+        For specific details on how to
+        use this method for different model types, refer to the relevant
+        documentation for each model.
     """
 
     # Properties.

verticapy/machine_learning/vertica/linear_model.py

@@ -324,6 +324,7 @@ class ElasticNet(Regressor, LinearModel):
         in training the model.  Note that setting
         fit_intercept  to false does  not work well with
         the BFGS optimizer.
+
     Examples
     ---------