verticapy.machine_learning.vertica.cluster.NearestCentroid#

class verticapy.machine_learning.vertica.cluster.NearestCentroid(name: str = None, overwrite_model: bool = False, p: int = 2)#

Creates a NearestCentroid object using the k-nearest centroid algorithm. This object uses pure SQL to compute the distances and final score.

Important

This algorithm is not Vertica Native and relies solely on SQL for attribute computation. While this model does not take advantage of the benefits provided by a model management system, including versioning and tracking, the SQL code it generates can still be used to create a pipeline.

Parameters#

p: int, optional: The p corresponding to the one of the p-distances (distance metric used to compute the model).

Attributes#

Many attributes are created during the fitting phase.

clusters_: numpy.array: Cluster centers.
p_: int: The p of the p-distances.
classes_: numpy.array: The classes labels.

Note

All attributes can be accessed using the get_attributes() method.

Examples#

The following examples provide a basic understanding of usage. For more detailed examples, please refer to the Machine Learning or the Examples section on the website.

Load data for machine learning#

We import verticapy:

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 iris dataset.

import verticapy.datasets as vpd

data = vpd.load_iris()

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)
1	3.3	4.5	5.6	7.8	Iris-setosa
2	3.3	4.5	5.6	7.8	Iris-setosa
3	3.3	4.5	5.6	7.8	Iris-setosa
4	3.3	4.5	5.6	7.8	Iris-setosa
5	3.3	4.5	5.6	7.8	Iris-setosa
6	3.3	4.5	5.6	7.8	Iris-setosa
7	3.3	4.5	5.6	7.8	Iris-setosa
8	3.3	4.5	5.6	7.8	Iris-setosa
9	3.3	4.5	5.6	7.8	Iris-setosa
10	3.3	4.5	5.6	7.8	Iris-setosa
11	3.3	4.5	5.6	7.8	Iris-setosa
12	3.3	4.5	5.6	7.8	Iris-setosa
13	3.3	4.5	5.6	7.8	Iris-setosa
14	3.3	4.5	5.6	7.8	Iris-setosa
15	3.3	4.5	5.6	7.8	Iris-setosa
16	3.3	4.5	5.6	7.8	Iris-setosa
17	3.3	4.5	5.6	7.8	Iris-setosa
18	3.3	4.5	5.6	7.8	Iris-setosa
19	3.3	4.5	5.6	7.8	Iris-setosa
20	3.3	4.5	5.6	7.8	Iris-setosa
21	3.3	4.5	5.6	7.8	Iris-setosa
22	3.3	4.5	5.6	7.8	Iris-setosa
23	3.3	4.5	5.6	7.8	Iris-setosa
24	3.3	4.5	5.6	7.8	Iris-setosa
25	3.3	4.5	5.6	7.8	Iris-setosa
26	3.3	4.5	5.6	7.8	Iris-setosa
27	3.3	4.5	5.6	7.8	Iris-setosa
28	3.3	4.5	5.6	7.8	Iris-setosa
29	3.3	4.5	5.6	7.8	Iris-setosa
30	3.3	4.5	5.6	7.8	Iris-setosa
31	3.3	4.5	5.6	7.8	Iris-setosa
32	3.3	4.5	5.6	7.8	Iris-setosa
33	3.3	4.5	5.6	7.8	Iris-setosa
34	3.3	4.5	5.6	7.8	Iris-setosa
35	3.3	4.5	5.6	7.8	Iris-setosa
36	3.3	4.5	5.6	7.8	Iris-setosa
37	3.3	4.5	5.6	7.8	Iris-setosa
38	3.3	4.5	5.6	7.8	Iris-setosa
39	3.3	4.5	5.6	7.8	Iris-setosa
40	3.3	4.5	5.6	7.8	Iris-setosa
41	3.3	4.5	5.6	7.8	Iris-setosa
42	3.3	4.5	5.6	7.8	Iris-setosa
43	4.3	3.0	1.1	0.1	Iris-setosa
44	4.3	4.7	9.6	1.8	Iris-virginica
45	4.3	4.7	9.6	1.8	Iris-virginica
46	4.3	4.7	9.6	1.8	Iris-virginica
47	4.3	4.7	9.6	1.8	Iris-virginica
48	4.3	4.7	9.6	1.8	Iris-virginica
49	4.3	4.7	9.6	1.8	Iris-virginica
50	4.3	4.7	9.6	1.8	Iris-virginica
51	4.3	4.7	9.6	1.8	Iris-virginica
52	4.3	4.7	9.6	1.8	Iris-virginica
53	4.3	4.7	9.6	1.8	Iris-virginica
54	4.3	4.7	9.6	1.8	Iris-virginica
55	4.3	4.7	9.6	1.8	Iris-virginica
56	4.3	4.7	9.6	1.8	Iris-virginica
57	4.3	4.7	9.6	1.8	Iris-virginica
58	4.3	4.7	9.6	1.8	Iris-virginica
59	4.3	4.7	9.6	1.8	Iris-virginica
60	4.3	4.7	9.6	1.8	Iris-virginica
61	4.3	4.7	9.6	1.8	Iris-virginica
62	4.3	4.7	9.6	1.8	Iris-virginica
63	4.3	4.7	9.6	1.8	Iris-virginica
64	4.3	4.7	9.6	1.8	Iris-virginica
65	4.3	4.7	9.6	1.8	Iris-virginica
66	4.3	4.7	9.6	1.8	Iris-virginica
67	4.3	4.7	9.6	1.8	Iris-virginica
68	4.3	4.7	9.6	1.8	Iris-virginica
69	4.3	4.7	9.6	1.8	Iris-virginica
70	4.3	4.7	9.6	1.8	Iris-virginica
71	4.3	4.7	9.6	1.8	Iris-virginica
72	4.3	4.7	9.6	1.8	Iris-virginica
73	4.3	4.7	9.6	1.8	Iris-virginica
74	4.3	4.7	9.6	1.8	Iris-virginica
75	4.3	4.7	9.6	1.8	Iris-virginica
76	4.3	4.7	9.6	1.8	Iris-virginica
77	4.3	4.7	9.6	1.8	Iris-virginica
78	4.3	4.7	9.6	1.8	Iris-virginica
79	4.3	4.7	9.6	1.8	Iris-virginica
80	4.3	4.7	9.6	1.8	Iris-virginica
81	4.3	4.7	9.6	1.8	Iris-virginica
82	4.3	4.7	9.6	1.8	Iris-virginica
83	4.3	4.7	9.6	1.8	Iris-virginica
84	4.3	4.7	9.6	1.8	Iris-virginica
85	4.3	4.7	9.6	1.8	Iris-virginica
86	4.4	2.9	1.4	0.2	Iris-setosa
87	4.4	3.0	1.3	0.2	Iris-setosa
88	4.4	3.2	1.3	0.2	Iris-setosa
89	4.5	2.3	1.3	0.3	Iris-setosa
90	4.6	3.1	1.5	0.2	Iris-setosa
91	4.6	3.2	1.4	0.2	Iris-setosa
92	4.6	3.4	1.4	0.3	Iris-setosa
93	4.6	3.6	1.0	0.2	Iris-setosa
94	4.7	3.2	1.3	0.2	Iris-setosa
95	4.7	3.2	1.6	0.2	Iris-setosa
96	4.8	3.0	1.4	0.1	Iris-setosa
97	4.8	3.0	1.4	0.3	Iris-setosa
98	4.8	3.1	1.6	0.2	Iris-setosa
99	4.8	3.4	1.6	0.2	Iris-setosa
100	4.8	3.4	1.9	0.2	Iris-setosa

Rows: 1-100 | Columns: 5

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 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.

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.

data = vpd.load_iris()
train, test = data.train_test_split(test_size = 0.2)

Warning

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.

Balancing the Dataset#

In VerticaPy, balancing a dataset to address class imbalances is made straightforward through the balance() function within the preprocessing module. This function enables users to rectify skewed class distributions efficiently. By specifying the target variable and setting parameters like the method for balancing, users can effortlessly achieve a more equitable representation of classes in their dataset. Whether opting for over-sampling, under-sampling, or a combination of both, VerticaPy’s balance() function streamlines the process, empowering users to enhance the performance and fairness of their machine learning models trained on imbalanced data.

To balance the dataset, use the following syntax.

from verticapy.machine_learning.vertica.preprocessing import balance

balanced_train = balance(
    name = "my_schema.train_balanced",
    input_relation = train,
    y = "good",
    method = "hybrid",
)

Note

With this code, a table named train_balanced is created in the my_schema schema. It can then be used to train the model. In the rest of the example, we will work with the full dataset.

Hint

Balancing the dataset is a crucial step in improving the accuracy of machine learning models, particularly when faced with imbalanced class distributions. By addressing disparities in the number of instances across different classes, the model becomes more adept at learning patterns from all classes rather than being biased towards the majority class. This, in turn, enhances the model’s ability to make accurate predictions for under-represented classes. The balanced dataset ensures that the model is not dominated by the majority class and, as a result, leads to more robust and unbiased model performance. Therefore, by employing techniques such as over-sampling, under-sampling, or a combination of both during dataset preparation, practitioners can significantly contribute to achieving higher accuracy and better generalization of their machine learning models.

Model Initialization#

First we import the NearestCentroid model:

from verticapy.machine_learning.vertica import NearestCentroid

Then we can create the model:

model = NearestCentroid(p = 2)

Model Training#

We can now fit the model:

model.fit(
    train,
    [
        "SepalLengthCm",
        "SepalWidthCm",
        "PetalLengthCm",
        "PetalWidthCm",
    ],
    "Species",
    test,
)

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.

Important

As this model is not native, it solely relies on SQL statements to compute various attributes, storing them within the object. No data is saved in the database.

Metrics#

We can get the entire report using:

model.report()

	Iris-setosa	Iris-versicolor	Iris-virginica	avg_macro	avg_weighted	avg_micro
auc	1.0	0.9952153110047846	0.9426523297491041	0.9792892135846296	0.9778593950313572	[null]
prc_auc	1.0	0.9834022038567495	0.9488297373358348	0.9774106470641947	0.9774767247850871	[null]
accuracy	0.7755102040816326	0.6530612244897959	0.8775510204081632	0.7687074829931971	0.7855060391503541	0.7687074829931972
log_loss	0.230479489381102	0.184993852501743	0.203255665384036	0.20624300242229365	0.21026783963375226	[null]
precision	1.0	0.39285714285714285	1.0	0.7976190476190476	0.8637026239067055	0.6530612244897959
recall	0.45	1.0	0.6666666666666666	0.7055555555555556	0.6530612244897959	0.6530612244897959
f1_score	0.6206896551724138	0.5641025641025641	0.8	0.6615974064249927	0.6738555369097241	0.6530612244897959
mcc	0.5711829829397931	0.46594555814804667	0.7473677532236446	0.5948320981038281	0.6122791909479587	0.47959183673469385
informedness	0.44999999999999996	0.5526315789473686	0.6666666666666665	0.5564327485380117	0.5526315789473684	0.47959183673469385
markedness	0.7250000000000001	0.3928571428571428	0.8378378378378377	0.6518983268983268	0.6918879520920337	0.47959183673469385
csi	0.45	0.39285714285714285	0.6666666666666666	0.5031746031746032	0.5167638483965015	0.48484848484848486

Rows: 1-11 | Columns: 7

Important

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 = ["auc", "accuracy"]).

For classification models, we can easily modify the cutoff to observe the effect on different metrics:

model.report(cutoff = 0.2)

	Iris-setosa	Iris-versicolor	Iris-virginica	avg_macro	avg_weighted	avg_micro
auc	1.0	0.9952153110047846	0.9426523297491041	0.9792892135846296	0.9778593950313572	[null]
prc_auc	1.0	0.9834022038567495	0.9488297373358348	0.9774106470641947	0.9774767247850871	[null]
accuracy	0.6326530612244898	0.22448979591836735	0.5306122448979592	0.4625850340136055	0.5035401915868389	0.46258503401360546
log_loss	0.230479489381102	0.184993852501743	0.203255665384036	0.20624300242229365	0.21026783963375226	[null]
precision	0.5263157894736842	0.22448979591836735	0.43902439024390244	0.39660999187865137	0.42649270548910134	0.3828125
recall	1.0	1.0	1.0	1.0	1.0	1.0
f1_score	0.6896551724137931	0.36666666666666664	0.6101694915254238	0.5554971102019612	0.5879487271238127	0.5536723163841808
mcc	0.446807591244225	0.0	0.3365956280719286	0.2611344064387178	0.3060178189832493	0.27243118397129207
informedness	0.3793103448275863	0.0	0.25806451612903225	0.2124582869855395	0.24961975891580215	0.19387755102040805
markedness	0.5263157894736841	-0.7755102040816326	0.4390243902439024	0.06327665854531794	0.202002909570734	0.3828125
csi	0.5263157894736842	0.22448979591836735	0.43902439024390244	0.39660999187865137	0.42649270548910134	0.3828125

Rows: 1-11 | Columns: 7

You can also use the score() function to compute any classification metric. The default metric is the accuracy:

model.score(metric = "f1", average = "macro")
Out[4]: 0.6615974064249927

Note

For multi-class scoring, verticapy allows the flexibility to use three averaging techniques: micro, macro and weighted. Please refer to this link for more details on how they are calculated.

Prediction#

Prediction is straight-forward:

model.predict(
    test,
    [
        "SepalLengthCm",
        "SepalWidthCm",
        "PetalLengthCm",
        "PetalWidthCm",
    ],
    "prediction",
)

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)	Abc prediction Varchar(15)
1	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
2	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
3	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
4	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
5	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
6	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
7	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
8	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
9	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
10	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
11	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
12	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
13	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
14	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
15	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
16	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
17	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
18	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
19	4.4	2.9	1.4	0.2	Iris-setosa	Iris-versicolor
20	4.4	3.2	1.3	0.2	Iris-setosa	Iris-versicolor
21	4.6	3.1	1.5	0.2	Iris-setosa	Iris-versicolor
22	4.6	3.6	1.0	0.2	Iris-setosa	Iris-versicolor
23	4.9	2.4	3.3	1.0	Iris-versicolor	Iris-versicolor
24	4.9	2.5	4.5	1.7	Iris-virginica	Iris-versicolor
25	4.9	3.1	1.5	0.1	Iris-setosa	Iris-versicolor
26	5.0	3.2	1.2	0.2	Iris-setosa	Iris-versicolor
27	5.1	3.4	1.5	0.2	Iris-setosa	Iris-versicolor
28	5.1	3.8	1.9	0.4	Iris-setosa	Iris-versicolor
29	5.3	3.7	1.5	0.2	Iris-setosa	Iris-versicolor
30	5.4	3.9	1.7	0.4	Iris-setosa	Iris-versicolor
31	5.5	2.6	4.4	1.2	Iris-versicolor	Iris-versicolor
32	5.5	3.5	1.3	0.2	Iris-setosa	Iris-versicolor
33	5.6	2.9	3.6	1.3	Iris-versicolor	Iris-versicolor
34	5.6	3.0	4.5	1.5	Iris-versicolor	Iris-versicolor
35	5.7	2.8	4.1	1.3	Iris-versicolor	Iris-versicolor
36	5.8	2.8	5.1	2.4	Iris-virginica	Iris-versicolor
37	6.0	2.2	4.0	1.0	Iris-versicolor	Iris-versicolor
38	6.3	2.9	5.6	1.8	Iris-virginica	Iris-versicolor
39	6.4	2.8	5.6	2.2	Iris-virginica	Iris-versicolor
40	6.5	2.8	4.6	1.5	Iris-versicolor	Iris-versicolor
41	6.7	3.0	5.0	1.7	Iris-versicolor	Iris-versicolor
42	6.7	3.1	4.4	1.4	Iris-versicolor	Iris-versicolor
43	6.7	3.1	4.7	1.5	Iris-versicolor	Iris-versicolor
44	6.8	3.0	5.5	2.1	Iris-virginica	Iris-versicolor
45	6.9	3.1	5.1	2.3	Iris-virginica	Iris-versicolor
46	7.0	3.2	4.7	1.4	Iris-versicolor	Iris-versicolor
47	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
48	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
49	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica

Rows: 1-49 | Columns: 6

Note

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 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.

Probabilities#

It is also easy to get the model’s probabilities:

model.predict_proba(
    test,
    [
        "SepalLengthCm",
        "SepalWidthCm",
        "PetalLengthCm",
        "PetalWidthCm",
    ],
    "prediction",
)

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)	Abc prediction Varchar(15)	123 prediction_irissetosa Float(22)	123 prediction_irisversicolor Float(22)	123 prediction_irisvirginica Float(22)
1	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.439815281418325	0.265642438277911	0.294542280303764
2	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.439815281418325	0.265642438277911	0.294542280303764
3	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.439815281418325	0.265642438277911	0.294542280303764
4	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.439815281418325	0.265642438277911	0.294542280303764
5	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.439815281418325	0.265642438277911	0.294542280303764
6	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.439815281418325	0.265642438277911	0.294542280303764
7	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.439815281418325	0.265642438277911	0.294542280303764
8	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.439815281418325	0.265642438277911	0.294542280303764
9	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.439815281418325	0.265642438277911	0.294542280303764
10	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
11	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
12	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
13	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
14	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
15	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
16	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
17	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
18	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
19	4.4	2.9	1.4	0.2	Iris-setosa	Iris-versicolor	0.330268557664226	0.437162827890549	0.232568614445226
20	4.4	3.2	1.3	0.2	Iris-setosa	Iris-versicolor	0.336161525711707	0.42997820250681	0.233860271781483
21	4.6	3.1	1.5	0.2	Iris-setosa	Iris-versicolor	0.325485630582585	0.443610599036623	0.230903770380793
22	4.6	3.6	1.0	0.2	Iris-setosa	Iris-versicolor	0.342818580663625	0.420601299084306	0.236580120252069
23	4.9	2.4	3.3	1.0	Iris-versicolor	Iris-versicolor	0.239239402214376	0.573841888713742	0.186918709071882
24	4.9	2.5	4.5	1.7	Iris-virginica	Iris-versicolor	0.217435664264753	0.581183998862614	0.201380336872633
25	4.9	3.1	1.5	0.1	Iris-setosa	Iris-versicolor	0.316147455336299	0.45280678646746	0.231045758196242
26	5.0	3.2	1.2	0.2	Iris-setosa	Iris-versicolor	0.325419753092588	0.442570064467993	0.232010182439419
27	5.1	3.4	1.5	0.2	Iris-setosa	Iris-versicolor	0.317790705135042	0.452190713876606	0.230018580988352
28	5.1	3.8	1.9	0.4	Iris-setosa	Iris-versicolor	0.317293975607921	0.456098478841408	0.226607545550671
29	5.3	3.7	1.5	0.2	Iris-setosa	Iris-versicolor	0.318048056683047	0.449922228213781	0.232029715103172
30	5.4	3.9	1.7	0.4	Iris-setosa	Iris-versicolor	0.318133728494953	0.452895565571247	0.2289707059338
31	5.5	2.6	4.4	1.2	Iris-versicolor	Iris-versicolor	0.103993834829428	0.78502198346498	0.110984181705592
32	5.5	3.5	1.3	0.2	Iris-setosa	Iris-versicolor	0.316711024287613	0.451033354681892	0.232255621030495
33	5.6	2.9	3.6	1.3	Iris-versicolor	Iris-versicolor	0.158619967378488	0.710282638619325	0.131097394002188
34	5.6	3.0	4.5	1.5	Iris-versicolor	Iris-versicolor	0.116687495515223	0.759555225638263	0.123757278846513
35	5.7	2.8	4.1	1.3	Iris-versicolor	Iris-versicolor	0.0645564442231668	0.872875429934949	0.0625681258418844
36	5.8	2.8	5.1	2.4	Iris-virginica	Iris-versicolor	0.228518115798783	0.500361348874077	0.271120535327141
37	6.0	2.2	4.0	1.0	Iris-versicolor	Iris-versicolor	0.128894137166244	0.738615492396869	0.132490370436887
38	6.3	2.9	5.6	1.8	Iris-virginica	Iris-versicolor	0.185177915925555	0.477491515609994	0.337330568464451
39	6.4	2.8	5.6	2.2	Iris-virginica	Iris-versicolor	0.204166313842683	0.451050971046454	0.344782715110863
40	6.5	2.8	4.6	1.5	Iris-versicolor	Iris-versicolor	0.134430019682679	0.702711083935956	0.162858896381365
41	6.7	3.0	5.0	1.7	Iris-versicolor	Iris-versicolor	0.177209338525633	0.574741953490661	0.248048707983707
42	6.7	3.1	4.4	1.4	Iris-versicolor	Iris-versicolor	0.154442423809786	0.665725724202858	0.179831851987356
43	6.7	3.1	4.7	1.5	Iris-versicolor	Iris-versicolor	0.163087805032487	0.628444189432862	0.208468005534651
44	6.8	3.0	5.5	2.1	Iris-virginica	Iris-versicolor	0.204169926585465	0.459137472431628	0.336692600982907
45	6.9	3.1	5.1	2.3	Iris-virginica	Iris-versicolor	0.220019948242774	0.484640415937677	0.295339635819549
46	7.0	3.2	4.7	1.4	Iris-versicolor	Iris-versicolor	0.180976565317765	0.582191912609919	0.236831522072316
47	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
48	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732
49	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.222969212886079	0.244390864011189	0.532639923102732

Rows: 1-49 | Columns: 9

Note

Probabilities are added to the vDataFrame, and VerticaPy uses the corresponding probability function in SQL behind the scenes. You can use the pos_label parameter to add only the probability of the selected category.

Confusion Matrix#

You can obtain the confusion matrix.

model.confusion_matrix()
Out[5]: 
array([[ 9, 11,  0],
       [ 0, 11,  0],
       [ 0,  6, 12]])

Hint

In the context of multi-class classification, you typically work with an overall confusion matrix that summarizes the classification efficiency across all classes. However, you have the flexibility to specify a pos_label and adjust the cutoff threshold. In this case, a binary confusion matrix is computed, where the chosen class is treated as the positive class, allowing you to evaluate its efficiency as if it were a binary classification problem.

Specific confusion matrix:

model.confusion_matrix(pos_label = "Iris-setosa", cutoff = 0.6)
Out[6]: 
array([[29,  0],
       [20,  0]])

Note

In classification, the cutoff is a threshold value used to determine class assignment based on predicted probabilities or scores from a classification model. In binary classification, if the predicted probability for a specific class is greater than or equal to the cutoff, the instance is assigned to the positive class; otherwise, it is assigned to the negative class. Adjusting the cutoff allows for trade-offs between true positives and false positives, enabling the model to be optimized for specific objectives or to consider the relative costs of different classification errors. The choice of cutoff is critical for tailoring the model’s performance to meet specific needs.

Main Plots (Classification Curves)#

Classification models allow for the creation of various plots that are very helpful in understanding the model, such as the ROC Curve, PRC Curve, Cutoff Curve, Gain Curve, and more.

Most of the classification curves can be found in the Machine Learning - Classification Curve.

For example, let’s draw the model’s ROC curve.

model.roc_curve(pos_label = "Iris-setosa")

Important

Most of the curves have a parameter called nbins, which is essential for estimating metrics. The larger the nbins, the more precise the estimation, but it can significantly impact performance. Exercise caution when increasing this parameter excessively.

Hint

In binary classification, various curves can be easily plotted. However, in multi-class classification, it’s important to select the pos_label, representing the class to be treated as positive when drawing the curve.

Other Plots#

Contour plot is another useful plot that can be produced for models with two predictors.

model.contour(pos_label = "Iris-setosa")

Important

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 Contour Plot for more examples.

Parameter Modification#

In order to see the parameters:

model.get_params()
Out[7]: {'p': 2}

And to manually change some of the parameters:

model.set_params({'p': 3})

Model Register#

As this model is not native, it does not support model management and versioning. However, it is possible to use the SQL code it generates for deployment.

Model Exporting#

To Memmodel

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 following methods for exporting the model use MemModel, and it is recommended to use MemModel directly.

To SQL

You can get the SQL code by:

model.to_sql()
Out[9]: 'CASE WHEN "SepalLengthCm" IS NULL OR "SepalWidthCm" IS NULL OR "PetalLengthCm" IS NULL OR "PetalWidthCm" IS NULL THEN NULL WHEN POWER(POWER("SepalLengthCm" - 5.58875, 2) + POWER("SepalWidthCm" - 3.76, 2) + POWER("PetalLengthCm" - 7.3975, 2) + POWER("PetalWidthCm" - 1.92, 2), 1 / 2) <= POWER(POWER("SepalLengthCm" - 4.16282051282051, 2) + POWER("SepalWidthCm" - 3.96153846153846, 2) + POWER("PetalLengthCm" - 3.53589743589744, 2) + POWER("PetalWidthCm" - 4.02435897435898, 2), 1 / 2) AND POWER(POWER("SepalLengthCm" - 5.58875, 2) + POWER("SepalWidthCm" - 3.76, 2) + POWER("PetalLengthCm" - 7.3975, 2) + POWER("PetalWidthCm" - 1.92, 2), 1 / 2) <= POWER(POWER("SepalLengthCm" - 5.8948717948718, 2) + POWER("SepalWidthCm" - 2.75384615384615, 2) + POWER("PetalLengthCm" - 4.24871794871795, 2) + POWER("PetalWidthCm" - 1.32051282051282, 2), 1 / 2) THEN \'Iris-virginica\' WHEN POWER(POWER("SepalLengthCm" - 5.8948717948718, 2) + POWER("SepalWidthCm" - 2.75384615384615, 2) + POWER("PetalLengthCm" - 4.24871794871795, 2) + POWER("PetalWidthCm" - 1.32051282051282, 2), 1 / 2) <= POWER(POWER("SepalLengthCm" - 4.16282051282051, 2) + POWER("SepalWidthCm" - 3.96153846153846, 2) + POWER("PetalLengthCm" - 3.53589743589744, 2) + POWER("PetalWidthCm" - 4.02435897435898, 2), 1 / 2) THEN \'Iris-versicolor\' ELSE \'Iris-setosa\' END'

To Python

To obtain the prediction function in Python syntax, use the following code:

X = [[5, 2, 3, 1]]

model.to_python()(X)
Out[11]: array(['Iris-versicolor'], dtype=object)

Hint

The 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.

__init__(name: str = None, overwrite_model: bool = False, p: int = 2) → None#: Must be overridden in the child class

Methods

`__init__`([name, overwrite_model, p])	Must be overridden in the child class
`classification_report`([metrics, cutoff, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`confusion_matrix`([pos_label, cutoff])	Computes the model confusion matrix.
`contour`([pos_label, nbins, chart])	Draws the model's contour plot.
`cutoff_curve`([pos_label, nbins, show, chart])	Draws the model Cutoff curve.
`deploySQL`([X, pos_label, cutoff, allSQL])	Returns the SQL code needed to deploy the model.
`does_model_exists`(name[, raise_error, ...])	Checks whether the model is stored in the Vertica database.
`drop`()	`NearestCentroid` models are not stored in the Vertica DB.
`export_models`(name, path[, kind])	Exports machine learning models.
`fit`(input_relation, X, y[, test_relation, ...])	Trains the model.
`get_attributes`([attr_name])	Returns the model attributes.
`get_match_index`(x, col_list[, str_check])	Returns the matching index.
`get_params`()	Returns the parameters of the model.
`get_plotting_lib`([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`get_vertica_attributes`([attr_name])	Returns the model Vertica attributes.
`import_models`(path[, schema, kind])	Imports machine learning models.
`lift_chart`([pos_label, nbins, show, chart])	Draws the model Lift Chart.
`prc_curve`([pos_label, nbins, show, chart])	Draws the model PRC curve.
`predict`(vdf[, X, name, cutoff, inplace])	Predicts using the input relation.
`predict_proba`(vdf[, X, name, pos_label, inplace])	Returns the model's probabilities using the input relation.
`register`(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`report`([metrics, cutoff, labels, nbins])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`roc_curve`([pos_label, nbins, show, chart])	Draws the model ROC curve.
`score`([metric, average, pos_label, cutoff, ...])	Computes the model score.
`set_params`([parameters])	Sets the parameters of the model.
`summarize`()	Summarizes the model.
`to_binary`(path)	Exports the model to the Vertica Binary format.
`to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`to_pmml`(path)	Exports the model to PMML.
`to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes

object_type