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vertica · Oct 30, 2024 · 295a9ed · 295a9ed
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diff --git a/docs/source/examples_business_base_station.rst b/docs/source/examples_business_base_station.rst
@@ -4,8 +4,8 @@ Base Station Positions
 ==================================
 
 This example uses the Telecom Dataset, provided by Shanghai Telecom, to predict the optimal positions for base radio stations. 
-This dataset contains more than 7.2 million records about people's 
-Internet access through 3,233 base stations from 9,481 mobile phones 
+This dataset contains more than ``7.2`` million records about people's 
+Internet access through ``3,233`` base stations from ``9,481`` mobile phones 
 over period of six months.
 
 The dataset can be found `here <http://sguangwang.com/TelecomDataset.html>`_. It consists of:

diff --git a/docs/source/examples_business_churn.rst b/docs/source/examples_business_churn.rst
@@ -23,7 +23,7 @@ This example uses the following version of VerticaPy:
     
     vp.__version__
 
-Connect to Vertica. This example uses an existing connection called "VerticaDSN". 
+Connect to Vertica. This example uses an existing connection called ``VerticaDSN`` . 
 For details on how to create a connection, see the :ref:`connection` tutorial.
 You can skip the below cell if you already have an established connection.
 

diff --git a/docs/source/examples_business_insurance.rst b/docs/source/examples_business_insurance.rst
@@ -30,7 +30,7 @@ This example uses the following version of VerticaPy:
     
     vp.__version__
 
-Connect to Vertica. This example uses an existing connection called "VerticaDSN". 
+Connect to Vertica. This example uses an existing connection called ``VerticaDSN`` . 
 For details on how to create a connection, see the :ref:`connection` tutorial.
 You can skip the below cell if you already have an established connection.
 

diff --git a/docs/source/examples_business_spam.rst b/docs/source/examples_business_spam.rst
@@ -21,7 +21,7 @@ This example uses the following version of VerticaPy:
     
     vp.__version__
 
-Connect to Vertica. This example uses an existing connection called "VerticaDSN". 
+Connect to Vertica. This example uses an existing connection called ``VerticaDSN`` . 
 For details on how to create a connection, see the :ref:`connection` tutorial.
 You can skip the below cell if you already have an established connection.
 

diff --git a/docs/source/examples_learn_iris.rst b/docs/source/examples_learn_iris.rst
@@ -24,7 +24,7 @@ This example uses the following version of VerticaPy:
 
     vp.__version__
 
-Connect to Vertica. This example uses an existing connection called "VerticaDSN". 
+Connect to Vertica. This example uses an existing connection called ``VerticaDSN`` . 
 For details on how to create a connection, see the :ref:`connection` tutorial.
 
 You can skip the below cell if you already have an established connection.

diff --git a/docs/source/examples_learn_pokemon.rst b/docs/source/examples_learn_pokemon.rst
@@ -38,7 +38,7 @@ This example uses the following version of VerticaPy:
     
     vp.__version__
 
-Connect to Vertica. This example uses an existing connection called "VerticaDSN". 
+Connect to Vertica. This example uses an existing connection called ``VerticaDSN`` . 
 For details on how to create a connection, see the :ref:`connection` tutorial.
 You can skip the below cell if you already have an established connection.
 

diff --git a/docs/source/examples_learn_titanic.rst b/docs/source/examples_learn_titanic.rst
@@ -18,7 +18,7 @@ This example uses the following version of VerticaPy:
 
     vp.__version__
 
-Connect to Vertica. This example uses an existing connection called "VerticaDSN". 
+Connect to Vertica. This example uses an existing connection called ``VerticaDSN`` . 
 For details on how to create a connection, see the :ref:`connection` tutorial.
 
 You can skip the below cell if you already have an established connection.
@@ -69,9 +69,9 @@ Let's explore the data by displaying descriptive statistics of all the columns.
 .. raw:: html
     :file: SPHINX_DIRECTORY/figures/examples_titanic_table_describe.html
 
-The columns "body" (passenger ID), "home.dest" (passenger origin/destination), "embarked" (origin port) and "ticket" (ticket ID) shouldn't influence survival, so we can ignore these.
+The columns ``body`` (passenger ID), ``home.dest`` (passenger origin/destination), ``embarked`` (origin port) and ``ticket`` (ticket ID) shouldn't influence survival, so we can ignore these.
 
-Let's focus our analysis on the columns "name" and "cabin". We'll begin with the passengers' names.
+Let's focus our analysis on the columns ``name`` and ``cabin``. We'll begin with the passengers' names.
 
 .. code-block:: python
 
@@ -217,7 +217,7 @@ The "sibsp" column represents the number of siblings for each passenger, while t
 
     titanic["family_size"] = titanic["parch"] + titanic["sibsp"] + 1
 
-Let's move on to outliers. We have several tools for locating outliers (:py:mod:`~verticapy.machine_learning.vertica.LocalOutlierFactor`, :py:mod:`~verticapy.machine_learning.vertica.DBSCAN`, :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans`...), but we'll just use winsorization in this example. Again, "fare" has many outliers, so we'll start there.
+Let's move on to outliers. We have several tools for locating outliers (:py:mod:`~verticapy.machine_learning.vertica.LocalOutlierFactor`, :py:mod:`~verticapy.machine_learning.vertica.cluster.DBSCAN`, :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans`...), but we'll just use winsorization in this example. Again, "fare" has many outliers, so we'll start there.
 
 .. code-block:: python
 

diff --git a/docs/source/examples_understand_africa_education.rst b/docs/source/examples_understand_africa_education.rst
@@ -628,7 +628,7 @@ The same applies to the regions. Let's look at student age.
 
 .. code-block:: python
 
-    africa["PAGE"].bar(
+    africa["PAGE"].barh(
         method = "50%", 
         of = "pred_zmalocp",
         max_cardinality = 50,
@@ -639,7 +639,7 @@ The same applies to the regions. Let's look at student age.
     :okwarning:
     :okexcept:
 
-    fig = africa["PAGE"].bar(
+    fig = africa["PAGE"].barh(
         method = "50%", 
         of = "pred_zmalocp",
         max_cardinality = 50,

diff --git a/docs/source/performance_vertica.rst b/docs/source/performance_vertica.rst
@@ -44,6 +44,8 @@ Query Profiler
    QueryProfiler.previous
    QueryProfiler.step
    QueryProfiler.to_html
+   QueryProfiler.get_activity_time
+   QueryProfiler.get_qplan_explain
 
 Query Profiler Interface
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -84,6 +86,21 @@ Query Profiler Interface
    QueryProfilerInterface.set_position
    QueryProfilerInterface.step
    QueryProfilerInterface.to_html
+   QueryProfilerInterface.client_data_test
+   QueryProfilerInterface.clock_exec_time_test
+   QueryProfilerInterface.exec_time_test
+   QueryProfilerInterface.get_activity_time
+   QueryProfilerInterface.get_qplan_explain
+   QueryProfilerInterface.get_qsteps_
+   QueryProfilerInterface.get_resource_acquisition
+   QueryProfilerInterface.import_profile
+   QueryProfilerInterface.pool_queue_wait_time_test
+   QueryProfilerInterface.qsteps_clicked
+   QueryProfilerInterface.query_events_test
+   QueryProfilerInterface.segmentation_test
+   QueryProfilerInterface.update_cpu_time
+   QueryProfilerInterface.update_qsteps
+   QueryProfilerInterface.update_step
 
 Query Profiler Comparison
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -102,4 +119,6 @@ Query Profiler Comparison
 .. autosummary::
    :toctree: api/
 
-   QueryProfilerComparison.get_qplan_tree
+   QueryProfilerComparison.get_qplan_tree
+   QueryProfilerComparison.sync_all_checkboxes
+   QueryProfilerComparison.unsync_all_checkboxes
diff --git a/docs/source/user_guide_data_preparation_outliers.rst b/docs/source/user_guide_data_preparation_outliers.rst
@@ -140,7 +140,7 @@ Generally, you can identify global outliers with the ``Z-Score``. We'll consider
 .. raw:: html
     :file: SPHINX_DIRECTORY/figures/ug_dp_plot_outliers_5.html
 
-Other techniques like :py:mod:`~verticapy.machine_learning.vertica.DBSCAN` or local outlier factor (``LOF``) can be to used to check other data points for outliers.
+Other techniques like :py:mod:`~verticapy.machine_learning.vertica.cluster.DBSCAN` or local outlier factor (``LOF``) can be to used to check other data points for outliers.
 
 .. code-block:: python
 

diff --git a/docs/source/user_guide_full_stack_complex_data_vmap.rst b/docs/source/user_guide_full_stack_complex_data_vmap.rst
@@ -15,7 +15,7 @@ In order to work with complex data types in VerticaPy, you'll need to complete t
 
     import verticapy as vp
 
-- Connect to Vertica. This example uses an existing connection called "VerticaDSN". For details on how to create a connection, see the :ref:`connection` tutorial.
+- Connect to Vertica. This example uses an existing connection called ``VerticaDSN`` . For details on how to create a connection, see the :ref:`connection` tutorial.
 
 .. note:: You can skip the below cell if you already have an established connection.
 

diff --git a/docs/source/user_guide_full_stack_linear_regression.rst b/docs/source/user_guide_full_stack_linear_regression.rst
@@ -292,7 +292,7 @@ We can use a cross-validation to test our model.
 .. raw:: html
     :file: SPHINX_DIRECTORY/figures/ug_fs_table_lr_9.html
 
-The model isn't bad. We're just using a few variables to get a median absolute error of 47; that is, our score has a distance of 47 from the true value. This seems high, but if we keep in mind that the final score is over 1000, our predictions are quite good.
+The model isn't bad. We're just using a few variables to get a median absolute error of ``47``; that is, our score has a distance of ``47`` from the true value. This seems high, but if we keep in mind that the final score is over ``1000``, our predictions are quite good.
 
 Let's compare the importance of our features.
 
@@ -388,7 +388,7 @@ We see a high heteroscedasticity, indicating that we can't trust the ``p-value``
 
     model.coef_
 
-Let's look at the model's analysis of variance (ANOVA) table.
+Let's look at the model's analysis of variance (``ANOVA``) table.
 
 .. code-block:: ipython
     

diff --git a/docs/source/user_guide_full_stack_to_json.rst b/docs/source/user_guide_full_stack_to_json.rst
@@ -1,8 +1,8 @@
 .. _user_guide.full_stack.to_json:
 
-=========================
-Example: XGBoost.to_json
-=========================
+================
+XGBoost.to_json
+================
 
 Connect to Vertica
 --------------------
@@ -160,7 +160,7 @@ Evaluate the model with :py:func:`~verticapy.machine_learning.vertica.ensemble.X
 .. raw:: html
     :file: SPHINX_DIRECTORY/figures/ug_fs_to_json_report.html
 
-Use to_json() to export the model to a JSON file. If you omit a filename, VerticaPy prints the model:
+Use :py:func:`~verticapy.machine_learning.vertica.ensemble.XGBClassifier.to_json` to export the model to a JSON file. If you omit a filename, VerticaPy prints the model:
 
 .. ipython:: python
 
@@ -194,7 +194,7 @@ This exported model can be used with the Python XGBoost API right away, and expo
     result = result.sum() / len(result);
     assert result == pytest.approx(0.0, abs = 1.0E-14)
 
-For multiclass classifiers, the probabilities returned by the VerticaPy and the exported model may differ slightly because of normalization; while Vertica uses multinomial logistic regression, XGBoost Python uses Softmax. Again, this difference does not affect the model's final predictions. Categorical predictors must be encoded.
+For multiclass classifiers, the probabilities returned by the VerticaPy and the exported model may differ slightly because of normalization; while Vertica uses multinomial logistic regression, ``XGBoost`` Python uses Softmax. Again, this difference does not affect the model's final predictions. Categorical predictors must be encoded.
 
 
 Clean the Example Environment
@@ -211,8 +211,8 @@ Drop the ``xgb_to_json`` schema, using CASCADE to drop any database objects stor
 Conclusion
 -----------
 
-VerticaPy lets you to create, train, evaluate, and export Vertica machine learning models. There are some notable nuances when importing a Vertica XGBoost model into Python XGBoost, but these do not affect the accuracy of the model or its predictions:
+VerticaPy lets you to create, train, evaluate, and export Vertica machine learning models. There are some notable nuances when importing a Vertica ``XGBoost`` model into Python ``XGBoost``, but these do not affect the accuracy of the model or its predictions:
 
 Some information computed during the training phase may not be stored (e.g. ``sum_hessian`` and ``loss_changes``).
 
-The exact probabilities of multiclass classifiers in a Vertica model may differ from those in Python, but bot  ``h`` will make the same predictions. Python XGBoost does not support categorical predictors, so you must encode them before training the model in VerticaPy.
+The exact probabilities of multiclass classifiers in a Vertica model may differ from those in Python, but bot  ``h`` will make the same predictions. Python ``XGBoost`` does not support categorical predictors, so you must encode them before training the model in VerticaPy.
diff --git a/docs/source/user_guide_machine_learning_clustering.rst b/docs/source/user_guide_machine_learning_clustering.rst
@@ -57,7 +57,7 @@ While there aren't any real metrics for evaluating unsupervised models, metrics
 
     print(model.get_vertica_attributes("metrics")["metrics"][0])
 
-You can add the prediction to your vDataFrame.    
+You can add the prediction to your :py:mod:`~verticapy.vDataFrame`.    
 
 .. code-block::
 

diff --git a/docs/source/user_guide_machine_learning_introduction.rst b/docs/source/user_guide_machine_learning_introduction.rst
@@ -100,7 +100,7 @@ When we have more than two categories, we use the expression ``Multiclass Classi
 Unsupervised Learning
 ----------------------
 
-These algorithms are to used to segment the data (:py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans`, :py:mod:`~verticapy.machine_learning.vertica.DBSCAN`, etc.) or to detect anomalies (:py:mod:`~verticapy.machine_learning.vertica.LocalOutlierFactor`, ``Z-Score`` Techniques...). In particular, they're useful for finding patterns in data without labels. For example, let's use a :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` algorithm to create different clusters on the Iris dataset. Each cluster will represent a flower's species.
+These algorithms are to used to segment the data (:py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans`, :py:mod:`~verticapy.machine_learning.vertica.cluster.DBSCAN`, etc.) or to detect anomalies (:py:mod:`~verticapy.machine_learning.vertica.LocalOutlierFactor`, ``Z-Score`` Techniques...). In particular, they're useful for finding patterns in data without labels. For example, let's use a :py:mod:`~verticapy.machine_learning.vertica.cluster.KMeans` algorithm to create different clusters on the Iris dataset. Each cluster will represent a flower's species.
 
 .. code-block:: python
 

diff --git a/docs/source/user_guide_machine_learning_model_tracking.rst b/docs/source/user_guide_machine_learning_model_tracking.rst
@@ -103,16 +103,14 @@ So far we have only added three models to the experiment, but we could add many
 
     top_model = my_experiment_1.load_best_model(metric = "auc")
 
-The experiment object facilitates not only model tracking but also makes cleanup super easy, especially in real-world 
-scenarios where there is often a large number of leftover models. The :py:func:`~verticapy.machine_learning.vertica.LogisticRegression.drop` method drops from the database the info of the experiment and all associated models other than those specified in the keeping_models list.
+The experiment object facilitates not only model tracking but also makes cleanup super easy, especially in real-world scenarios where there is often a large number of leftover models. The :py:func:`~verticapy.machine_learning.vertica.LogisticRegression.drop` method drops from the database the info of the experiment and all associated models other than those specified in the keeping_models list.
 
 .. ipython:: python
     :okwarning:
 
-    my_experiment_1.drop(keeping_models=[top_model.model_name])
+    my_experiment_1.drop(keeping_models = [top_model.model_name])
 
-Experiments are also helpful for performing grid search on hyper-parameters. The following example shows how they can 
-be used to study the impact of the max_iter parameter on the prediction performance of :py:mod:`~verticapy.machine_learning.vertica.linear_model.LogisticRegression` models.
+Experiments are also helpful for performing grid search on hyper-parameters. The following example shows how they can be used to study the impact of the ``max_iter`` parameter on the prediction performance of :py:mod:`~verticapy.machine_learning.vertica.linear_model.LogisticRegression` models.
 
 .. ipython:: python
     :suppress:

diff --git a/docs/source/user_guide_performance_qprof.rst b/docs/source/user_guide_performance_qprof.rst
@@ -210,7 +210,7 @@ Once the :py:mod:`~verticapy.performance.vertica.qprof.QueryProfiler` object is
 .. raw:: html
     :file: SPHINX_DIRECTORY/figures/user_guides_performance_qprof_get_queries.html
 
-To visualize the query plan, run :py:func:`verticapy.QueryProfilerInterface.get_qplan_tree`, 
+To visualize the query plan, run :py:func:`~verticapy.performance.vertica.qprof.QueryProfilerInterface.get_qplan_tree`, 
 which is customizable, allowing you to specify certain metrics or focus on a specified tree path:
 
 .. image:: /_static/website/user_guides/performance/user_guide_performance_qprof_get_qplan_tree.png
@@ -277,7 +277,7 @@ You can export and import :py:mod:`~verticapy.performance.vertica.qprof.QueryPro
 Export
 +++++++
 
-To export a :py:mod:`~verticapy.performance.vertica.qprof.QueryProfiler` object, use the :py:func:`~verticapy.performance.vertica.QueryProfiler.export_profile` method:
+To export a :py:mod:`~verticapy.performance.vertica.qprof.QueryProfiler` object, use the :py:func:`~verticapy.performance.vertica.qprof.QueryProfiler.export_profile` method:
 
 .. code-block:: python