From c3e821bad47c320aa16a097d0f71c3385cef6dea Mon Sep 17 00:00:00 2001
From: abhsharma2 <asharma6@opentext.com>
Date: Thu, 26 Oct 2023 10:00:38 -0400
Subject: [PATCH] docstring changes for memmodel/decomposition
---
.../memmodel/decomposition.py | 183 +++++++++++++++++-
1 file changed, 181 insertions(+), 2 deletions(-)
diff --git a/verticapy/machine_learning/memmodel/decomposition.py b/verticapy/machine_learning/memmodel/decomposition.py
index bb434da97..7fa99df16 100755
--- a/verticapy/machine_learning/memmodel/decomposition.py
+++ b/verticapy/machine_learning/memmodel/decomposition.py
@@ -34,6 +34,109 @@ class PCA(InMemoryModel):
Matrix of the principal components.
mean: ArrayLike
List of the averages of each input feature.
+
+ .. note:: :py:mod:`verticapy.machine_learning.memmodel` are defined entirely by their attributes.
+ For example, 'principal components' and 'mean' define a PCA model.
+
+ Examples
+ --------
+
+ **Initalization**
+
+ Import the required module.
+
+ .. ipython:: python
+ :suppress:
+
+ from verticapy.machine_learning.memmodel.decomposition import PCA
+
+ A PCA model is defined by its principal components and mean value. In this example, we will use the following:
+
+ .. ipython:: python
+ :suppress:
+
+ principal_components = [[0.4, 0.5], [0.3, 0.2]]
+ mean = [0.1, 0.3]
+
+ Let's create a :py:mod:`verticapy.machine_learning.memmodel.decomposition.PCA` model.
+
+ .. ipython:: python
+ :suppress:
+
+ model_pca = PCA(principal_components, mean)
+
+ Create a dataset.
+
+ .. ipython:: python
+ :suppress:
+
+ data = [[4, 5]]
+
+ **Making In-Memory Transformation**
+
+ Use :py:meth:`verticapy.machine_learning.memmodel.decomposition.PCA.transform` method to do transformation
+
+ .. ipython:: python
+ :suppress:
+
+ model_pca.transform(data)
+
+ **Deploy SQL Code**
+
+ Let's use the following column names:
+
+ .. ipython:: python
+ :suppress:
+
+ cnames = ['col1', 'col2']
+
+ Use :py:meth:`verticapy.machine_learning.memmodel.decomposition.PCA.transform_sql` method
+ to get the SQL code needed to deploy the model using its attributes
+
+ .. ipython:: python
+ :suppress:
+
+ model_pca.transform_sql(cnames)
+
+ **Perform an Oblimin Rotation**
+
+ Use :py:meth:`verticapy.machine_learning.memmodel.decomposition.PCA.rotate` method
+ to perform Oblimin (Varimax, Quartimax) rotation on PCA matrix
+
+ .. ipython:: python
+ :suppress:
+
+ model_pca.rotate()
+
+ .. note:: You can determine the type of rotation by adjusting value of gamma in
+ :py:meth:`verticapy.machine_learning.memmodel.decomposition.PCA.rotate` method.
+ It must be between 0.0 and 1.0.
+
+ Use gamma = 0.0, for Quartimax rotation:
+
+ .. ipython:: python
+ :suppress:
+
+ gamma = 0.0
+ model_pca.rotate(gamma)
+
+ Use gamma = 1.0, for Varimax rotation:
+
+ .. ipython:: python
+ :suppress:
+
+ gamma = 1.0
+ model_pca.rotate(gamma)
+
+ Use :py:meth:`verticapy.machine_learning.memmodel.decomposition.PCA.get_attributes` method
+ to check the attributes of the rotated model
+
+ .. ipython:: python
+ :suppress:
+
+ model_pca.get_attributes()
+
+ .. hint:: This object can be pickled and used in any in-memory environment, just like `SKLEARN <https://scikit-learn.org/>`_ models.
"""
# Properties.
@@ -153,8 +256,11 @@ def rotate(self, gamma: float = 1.0, q: int = 20, tol: float = 1e-6) -> None:
gamma: float, optional
Oblimin rotation factor, determines the type of rotation.
It must be between 0.0 and 1.0.
- gamma = 0.0 results in a Quartimax rotation.
- gamma = 1.0 results in a Varimax rotation.
+
+ - gamma = 0.0 results in a Quartimax rotation.
+
+ - gamma = 1.0 results in a Varimax rotation.
+
q: int, optional
Maximum number of iterations.
tol: float, optional
@@ -176,6 +282,79 @@ class SVD(InMemoryModel):
values: ArrayLike
List of the singular values for each input
feature.
+
+ .. note:: :py:mod:`verticapy.machine_learning.memmodel` are defined entirely by their attributes.
+ For example, 'vectors' and 'values' define a SVD model.
+
+ Examples
+ --------
+
+ **Initalization**
+
+ Import the required module.
+
+ .. ipython:: python
+ :suppress:
+
+ from verticapy.machine_learning.memmodel.decomposition import SVD
+
+ A SVD model is defined by its vectors and values. In this example, we will use the following:
+
+ .. ipython:: python
+ :suppress:
+
+ vectors = [[0.4, 0.5], [0.3, 0.2]]
+ values = [0.1, 0.3]
+
+ Let's create a :py:mod:`verticapy.machine_learning.memmodel.decomposition.SVD` model.
+
+ .. ipython:: python
+ :suppress:
+
+ model_svd = SVD(vectors, values)
+
+ Create a dataset.
+
+ .. ipython:: python
+ :suppress:
+
+ data = [[0.3, 0.5]]
+
+ **Making In-Memory Transformation**
+
+ Use :py:meth:`verticapy.machine_learning.memmodel.decomposition.SVD.transform` method to do transformation
+
+ .. ipython:: python
+ :suppress:
+
+ model_svd.transform(data)
+
+ **Deploy SQL Code**
+
+ Let's use the following column names:
+
+ .. ipython:: python
+ :suppress:
+
+ cnames = ['col1', 'col2']
+
+ Use :py:meth:`verticapy.machine_learning.memmodel.decomposition.SVD.transform_sql` method
+ to get the SQL code needed to deploy the model using its attributes
+
+ .. ipython:: python
+ :suppress:
+
+ model_svd.transform_sql(cnames)
+
+ Use :py:meth:`verticapy.machine_learning.memmodel.decomposition.SVD.get_attributes` method
+ to check the attributes of the rotated model
+
+ .. ipython:: python
+ :suppress:
+
+ model_svd.get_attributes()
+
+ .. hint:: This object can be pickled and used in any in-memory environment, just like `SKLEARN <https://scikit-learn.org/>`_ models.
"""
# Properties.