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updating docstring for memmodel/naive_bayes (#774)
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* updating docstring for memmodel/naive_bayes

* updating parameters formatting
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@abhsharma2
abhsharma2 authored Oct 25, 2023
1 parent d323198 commit d493c43
Showing 1 changed file with 174 additions and 33 deletions.
207 changes: 174 additions & 33 deletions verticapy/machine_learning/memmodel/naive_bayes.py
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Expand Up @@ -35,51 +35,192 @@ class NaiveBayes(MulticlassClassifier):
List of the model's attributes. Each feature must
be represented by a dictionary, which differs based
on the distribution.
For 'gaussian':
- For 'gaussian':
Key 'type' must have 'gaussian' as value.
Each of the model's classes must include a
dictionary with two keys:
sigma_sq: Square root of the standard
deviation.
mu: Average.
Example: {'type': 'gaussian',
'C': {'mu': 63.9878308300395,
'sigma_sq': 7281.87598377196},
'Q': {'mu': 13.0217386792453,
'sigma_sq': 211.626862330204},
'S': {'mu': 27.6928120412844,
'sigma_sq': 1428.57067393938}}
For 'multinomial':
sigma_sq: Square root of the standard deviation.
mu: Average.
Example:
{'type': 'gaussian',
'C': {'mu': 63.9878308300395,
'sigma_sq': 7281.87598377196},
'Q': {'mu': 13.0217386792453,
'sigma_sq': 211.626862330204},
'S': {'mu': 27.6928120412844,
'sigma_sq': 1428.57067393938}}
- For 'multinomial':
Key 'type' must have 'multinomial' as value.
Each of the model's classes must be represented
by a key with its probability as the value.
Example: {'type': 'multinomial',
'C': 0.771666666666667,
'Q': 0.910714285714286,
'S': 0.878216123499142}
For 'bernoulli':
Example:
{'type': 'multinomial',
'C': 0.771666666666667,
'Q': 0.910714285714286,
'S': 0.878216123499142}
- For 'bernoulli':
Key 'type' must have 'bernoulli' as value.
Each of the model's classes must be represented
by a key with its probability as the value.
Example: {'type': 'bernoulli',
'C': 0.537254901960784,
'Q': 0.277777777777778,
'S': 0.324942791762014}
For 'categorical':
Example:
{'type': 'bernoulli',
'C': 0.537254901960784,
'Q': 0.277777777777778,
'S': 0.324942791762014}
- For 'categorical':
Key 'type' must have 'categorical' as value.
Each of the model's classes must include
a dictionary with all the feature categories.
Example: {'type': 'categorical',
'C': {'female': 0.407843137254902,
'male': 0.592156862745098},
'Q': {'female': 0.416666666666667,
'male': 0.583333333333333},
'S': {'female': 0.311212814645309,
'male': 0.688787185354691}}
prior: ArrayLike
The model's classes probabilities.
classes: ArrayLike
The model's classes.
Example:
{'type': 'categorical',
'C': {'female': 0.407843137254902,
'male': 0.592156862745098},
'Q': {'female': 0.416666666666667,
'male': 0.583333333333333},
'S': {'female': 0.311212814645309,
'male': 0.688787185354691}}
prior: ArrayLike
The model's classes probabilities.
classes: ArrayLike
The model's classes.
.. note:: :py:mod:`verticapy.machine_learning.memmodel` are defined
entirely by their attributes. For example, 'prior probabilities',
'classes' and 'input feature attributes' specific to the type of
distribution, defines a NaiveBayes model.
Examples
--------
**Initalization**
Import the required module.
.. ipython:: python
:suppress:
from verticapy.machine_learning.memmodel.naive_bayes import NaiveBayes
Here we will be using attributes of model trained on well known
`titanic dataset <https://github.com/vertica/VerticaPy/blob/master/verticapy/datasets/data/titanic.csv>`_.
It tries to predict the port of embarkation (C = Cherbourg,
Q = Queenstown, S = Southampton), using *age* (continous),
*pclass* (discrete), *survived* (boolean) and
*sex* (categorical) as input features.
Let's define attributes representing each input feature:
.. ipython:: python
:suppress:
attributes = [
{
"type": "gaussian",
"C": {"mu": 63.9878308300395, "sigma_sq": 7281.87598377196},
"Q": {"mu": 13.0217386792453, "sigma_sq": 211.626862330204},
"S": {"mu": 27.6928120412844, "sigma_sq": 1428.57067393938},
},
{
"type": "multinomial",
"C": 0.771666666666667,
"Q": 0.910714285714286,
"S": 0.878216123499142,
},
{
"type": "bernoulli",
"C": 0.771666666666667,
"Q": 0.910714285714286,
"S": 0.878216123499142,
},
{
"type": "categorical",
"C": {
"female": 0.407843137254902,
"male": 0.592156862745098,
},
"Q": {
"female": 0.416666666666667,
"male": 0.583333333333333,
},
"S": {
"female": 0.406666666666667,
"male": 0.593333333333333,
},
},
]
We also need to provide class names and their prior probabilities.
.. ipython:: python
:suppress:
prior = [0.8, 0.1, 0.1]
classes = ["C", "Q", "S"]
Let's create a :py:mod:`verticapy.machine_learning.memmodel.naive_bayes` model.
.. ipython:: python
:suppress:
model_nb = NaiveBayes(attributes, prior, classes)
Create a dataset.
.. ipython:: python
:suppress:
data = [[40.0, 1, True, "male"], [60.0, 3, True, "male"], [15.0, 2, False, "female"]]
**Making In-Memory Predictions**
Use :py:meth:`verticapy.machine_learning.memmodel.naive_bayes.NaiveBayes.predict` method to do predictions
.. ipython:: python
:suppress:
model_nb.predict(data)
Use :py:meth:`verticapy.machine_learning.memmodel.naive_bayes.NaiveBayes.predict_proba`
method to calculate the predicted probabilities for each class
.. ipython:: python
:suppress:
model_nb.predict_proba(data)
**Deploy SQL Code**
Let's use the following column names:
.. ipython:: python
:suppress:
cnames = ["age", "pclass", "survived", "sex"]
Use :py:meth:`verticapy.machine_learning.memmodel.naive_bayes.NaiveBayes.predict_sql`
method to get the SQL code needed to deploy the model using its attributes
.. ipython:: python
:suppress:
model_nb.predict_sql(cnames)
Use :py:meth:`verticapy.machine_learning.memmodel.naive_bayes.NaiveBayes.predict_proba_sql`
method to get the SQL code needed to deploy the model that computes predicted probabilities
.. ipython:: python
:suppress:
model_nb.predict_proba_sql(cnames)
.. hint:: This object can be pickled and used in any in-memory environment, just like `SKLEARN <https://scikit-learn.org/>`_ models.
"""

# Properties.
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