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Update linear_model.py
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Fixing the example.

@mail4umar: TODO - Verify each reference, for some them the link needs to be added.
Please double check the code.
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oualib committed Oct 21, 2023
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162 changes: 104 additions & 58 deletions verticapy/machine_learning/vertica/linear_model.py
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Expand Up @@ -498,6 +498,9 @@ class LinearRegression(Regressor, LinearModel):
Examples
---------
The following examples provide a basic understanding of usage. For more
detailed examples, please refer to the User Guide or the "Examples"
section on the website.
Load data for machine learning
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Expand All @@ -510,8 +513,13 @@ class LinearRegression(Regressor, LinearModel):
.. hint::
By giving an alias to ``verticapy``, we avoid code colission with other libraries.
This is verticapy uses some well-known functions names like average, median etc.
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.
Expand All @@ -521,29 +529,35 @@ class LinearRegression(Regressor, LinearModel):
data = vpd.load_winequality()
.. raw:: html
:file: SPHINX_DIRECTORY/figures/datasets_loaders_load_winequality.html
.. note::
.. note::
VerticaPy provides many sample datasets that can be used for training/testing purposes. Check out
:ref:`` for all the available datasets.
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: ``, 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.
We can use the ``vDataFrame.train_test_split`` method to split-up the dataset into training and testing
portions.
You can easily divide your dataset into training and testing subsets using the
``vDataFrame.train_test_split`` method. This is a crucial step when preparing
your data for machine learning, as it allows you to evaluate the performance of
your models accurately.
.. code-block:: python
data = vpd.load_winequality()
train, test = data.train_test_split(test_size = 0.2)
.. warning::
.. warning::
In this case, ``verticapy`` will use seeded random to ensure reproducibility. However,
this process can drastically reduce the performance. In order to performa more efficient split
you can use ``vDataFrame.to_db`` to save your result in to ``tables`` or ``temporary tables``.
In this case, VerticaPy utilizes seeded randomization to guarantee the
reproducibility of your data split. However, please be aware that this
approach may lead to reduced performance. For a more efficient data
split, you can use the ``vDataFrame.to_db`` method to save your results
into ``tables`` or ``temporary tables``. This will help enhance the
overall performance of the process.
.. ipython:: python
:suppress:
Expand All @@ -553,15 +567,11 @@ class LinearRegression(Regressor, LinearModel):
data = vpd.load_winequality()
train, test = data.train_test_split(test_size = 0.2)
Model Initialziation
Model Initialization
^^^^^^^^^^^^^^^^^^^^^
First we import the ``LinearRegression`` model:
.. code-block::
from verticapy.machine_learning.vertica import LinearRegression
Expand All @@ -579,13 +589,16 @@ class LinearRegression(Regressor, LinearModel):
.. hint::
In ``verticapy`` 1.0 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 model attributes.
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 really important for the model management system and versioning. It is highly recommended
to give a name if you plan to re-use the model later.
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.
.. ipython:: python
:suppress:
Expand All @@ -598,7 +611,6 @@ class LinearRegression(Regressor, LinearModel):
fit_intercept = True,
)
Model Training
^^^^^^^^^^^^^^^
Expand All @@ -608,23 +620,31 @@ class LinearRegression(Regressor, LinearModel):
model.fit(
train,
["fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"],
[
"fixed_acidity",
"volatile_acidity",
"citric_acid",
"residual_sugar",
"chlorides",
"density"
],
"quality",
test,
)
.. important::
To train a model you can use 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 do not work using ``X`` matrix and ``y`` vectors. We work directly with list of predictors and response name.
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.
Features Importance
^^^^^^^^^^^^^^^^^^^^
We can conveniently get the features importance:
.. ipython:: python
:suppress:
Expand All @@ -641,8 +661,9 @@ class LinearRegression(Regressor, LinearModel):
.. note::
For ``LinearModel`` the features importance is computed using the coefficients. They are normalized
using the feature distribution. Then an activation function is applied to get the final score.
For ``LinearModel``, feature importance is computed using the coefficients.
These coefficients are then normalized using the feature distribution. An
activation function is applied to get the final score.
Metrics
^^^^^^^^
Expand All @@ -666,9 +687,10 @@ class LinearRegression(Regressor, LinearModel):
.. important::
Most metrics are computed using a single SQL query but some of them might need multiple SQL queries.
By selecting only the necessary metric in the report
you may be able to optimize performance. E.g. ``model.report(metrics = ["mse", "r2"])``.
Most metrics are computed using a single SQL query, but some of them might
require multiple SQL queries. Selecting only the necessary metrics in the
report can help optimize performance.
E.g. ``model.report(metrics = ["mse", "r2"])``.
For ``LinearModel``, we can easily get the ANOVA table using:
Expand All @@ -687,7 +709,8 @@ class LinearRegression(Regressor, LinearModel):
.. raw:: html
:file: SPHINX_DIRECTORY/figures/machine_learning_vertica_linear_model_lr_report_anova.html
You can also use the ``score`` function to compute the R-squared value:
You can also use the ``LinearModel.score`` function to compute the R-squared
value:
.. ipython:: python
Expand All @@ -703,19 +726,32 @@ class LinearRegression(Regressor, LinearModel):
result = model.predict(
test,
["fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"],
[
"fixed_acidity",
"volatile_acidity",
"citric_acid",
"residual_sugar",
"chlorides",
"density"
],
"prediction",
)
html_file = open("figures/machine_learning_vertica_linear_model_lr_prediction.html", "w")
html_file.write(result._repr_html_())
html_file.close()
.. code-block:: python
model.predict(
test,
["fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"],
[
"fixed_acidity",
"volatile_acidity",
"citric_acid",
"residual_sugar",
"chlorides",
"density"
],
"prediction",
)
Expand All @@ -724,22 +760,27 @@ class LinearRegression(Regressor, LinearModel):
.. note::
Prediction can be done automatically using the test set in which case you do not nee to
specify the predictors. It is also possible to pass only the the ``vDataFrame`` to the ``predict``
function. But in this case, it is important that the column names of the ``vDataFrame`` are
matching the predictors and response name in the model.
Predictions can be made automatically using the test set, in which
case you don't need to specify the predictors. Alternatively, you
can pass only the ``vDataFrame`` to the ``LinearModel.predict``
function, but in this case, it's essential that the column names of
the ``vDataFrame`` match the predictors and response name in the
model.
Plots
^^^^^^
If the models allows, you can also draw the relevant plots. Example of regression plots can
be found in :ref:`chart_gallery.regression_plot`.
If the model allows, you can also generate relevant plots. For example,
regression plots can be found in the :ref:`chart_gallery.regression_plot`.
.. code-block:: python
model.plot()
.. important:: The plot generally works for models which have less than 3 predictors.
.. important::
The plotting feature is typically suitable for models with fewer than
three predictors.
Parameter Modification
^^^^^^^^^^^^^^^^^^^^^^^
Expand All @@ -756,7 +797,6 @@ class LinearRegression(Regressor, LinearModel):
model.set_params({'tol': 0.001})
Model Register
^^^^^^^^^^^^^^
Expand All @@ -766,45 +806,51 @@ class LinearRegression(Regressor, LinearModel):
model.register("model_v1")
Please refer to :ref:`notebooks/ml/model_tracking_versioning/index.html` for more details on model tracking
and versioning.
Please refer to :ref:`notebooks/ml/model_tracking_versioning/index.html` for
more details on model tracking and versioning.
Model Exporting
^^^^^^^^^^^^^^^^
To Memmodel
""" """""" """""" """"
**To Memmodel**
.. code-block:: python
model.to_memmodel()
.. note::
``Memmodel`` are in-memory representation of ML models. They can be used to do in-database and
in-memory prediction. They can pickled the same way that you pickle your ``scikit-learn`` model.
``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 following ways of exporting the model uses memmodel and it is perfered to use the ``memmodels`` directly.
The following methods for exporting the model use ``MemModel``, and it
is recommended to use ``MemModel`` directly.
To SQL
""" """""" """""" """"
**To SQL**
You can get the SQL code by:
.. ipython:: python
model.to_sql()
To Python
""" """""" """""" """"
**To Python**
To get the prediction function in a python sytanx:
To obtain the prediction function in Python syntax, use the following code:
.. ipython:: python
X = [[4.2, 0.17, 0.36, 1.8, 0.029, 0.9899]]
model.to_python()(X)
.. hint::
The ``LinearModel.to_python`` method is used to retrieve predictions,
probabilities, or cluster distances. For specific details on how to
use this method for different model types, refer to the relevant
documentation for each model.
"""

# Properties.
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