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	4.6	3.6	1.0	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.7	3.2	1.6	0.2	Iris-setosa
4	4.8	3.0	1.4	0.1	Iris-setosa
5	4.8	3.1	1.6	0.2	Iris-setosa
6	4.8	3.4	1.9	0.2	Iris-setosa
7	4.9	3.0	1.4	0.2	Iris-setosa
8	4.9	3.1	1.5	0.1	Iris-setosa
9	4.9	3.1	1.5	0.1	Iris-setosa
10	4.9	3.1	1.5	0.1	Iris-setosa
11	5.0	2.3	3.3	1.0	Iris-versicolor
12	5.0	3.4	1.5	0.2	Iris-setosa
13	5.1	3.5	1.4	0.2	Iris-setosa
14	5.4	3.0	4.5	1.5	Iris-versicolor
15	5.4	3.4	1.5	0.4	Iris-setosa
16	5.4	3.9	1.3	0.4	Iris-setosa
17	5.5	2.4	3.7	1.0	Iris-versicolor
18	5.5	2.4	3.8	1.1	Iris-versicolor
19	5.6	2.7	4.2	1.3	Iris-versicolor
20	5.7	3.0	4.2	1.2	Iris-versicolor
21	5.7	4.4	1.5	0.4	Iris-setosa
22	5.8	2.8	5.1	2.4	Iris-virginica
23	5.9	3.2	4.8	1.8	Iris-versicolor
24	6.1	3.0	4.6	1.4	Iris-versicolor
25	6.1	3.0	4.9	1.8	Iris-virginica
26	6.3	2.5	4.9	1.5	Iris-versicolor
27	6.3	3.3	4.7	1.6	Iris-versicolor
28	6.3	3.3	6.0	2.5	Iris-virginica
29	6.4	2.9	4.3	1.3	Iris-versicolor
30	6.5	3.0	5.5	1.8	Iris-virginica
31	6.5	3.0	5.8	2.2	Iris-virginica
32	6.7	3.0	5.0	1.7	Iris-versicolor
33	6.8	2.8	4.8	1.4	Iris-versicolor
34	6.8	3.2	5.9	2.3	Iris-virginica
35	7.0	3.2	4.7	1.4	Iris-versicolor
36	7.1	3.0	5.9	2.1	Iris-virginica
37	7.7	3.8	6.7	2.2	Iris-virginica
38	4.4	2.9	1.4	0.2	Iris-setosa
39	4.5	2.3	1.3	0.3	Iris-setosa
40	4.8	3.4	1.6	0.2	Iris-setosa
41	5.0	2.0	3.5	1.0	Iris-versicolor
42	5.1	3.3	1.7	0.5	Iris-setosa
43	5.1	3.4	1.5	0.2	Iris-setosa
44	5.2	2.7	3.9	1.4	Iris-versicolor
45	5.2	3.5	1.5	0.2	Iris-setosa
46	5.2	4.1	1.5	0.1	Iris-setosa
47	5.4	3.9	1.7	0.4	Iris-setosa
48	5.5	3.5	1.3	0.2	Iris-setosa
49	5.6	3.0	4.1	1.3	Iris-versicolor
50	5.8	2.7	3.9	1.2	Iris-versicolor
51	5.8	2.7	5.1	1.9	Iris-virginica
52	5.8	2.7	5.1	1.9	Iris-virginica
53	5.9	3.0	4.2	1.5	Iris-versicolor
54	5.9	3.0	5.1	1.8	Iris-virginica
55	6.0	2.7	5.1	1.6	Iris-versicolor
56	6.0	2.9	4.5	1.5	Iris-versicolor
57	6.1	2.8	4.7	1.2	Iris-versicolor
58	6.2	2.8	4.8	1.8	Iris-virginica
59	6.2	2.9	4.3	1.3	Iris-versicolor
60	6.3	2.3	4.4	1.3	Iris-versicolor
61	6.3	2.7	4.9	1.8	Iris-virginica
62	6.4	3.2	5.3	2.3	Iris-virginica
63	6.5	2.8	4.6	1.5	Iris-versicolor
64	6.5	3.0	5.2	2.0	Iris-virginica
65	6.5	3.2	5.1	2.0	Iris-virginica
66	6.6	2.9	4.6	1.3	Iris-versicolor
67	6.6	3.0	4.4	1.4	Iris-versicolor
68	6.7	3.1	4.4	1.4	Iris-versicolor
69	6.7	3.1	4.7	1.5	Iris-versicolor
70	6.9	3.1	4.9	1.5	Iris-versicolor
71	6.9	3.1	5.4	2.1	Iris-virginica
72	6.9	3.2	5.7	2.3	Iris-virginica
73	7.2	3.0	5.8	1.6	Iris-virginica
74	7.2	3.2	6.0	1.8	Iris-virginica
75	7.3	2.9	6.3	1.8	Iris-virginica
76	7.7	2.6	6.9	2.3	Iris-virginica
77	3.3	4.5	5.6	7.8	Iris-setosa
78	3.3	4.5	5.6	7.8	Iris-setosa
79	3.3	4.5	5.6	7.8	Iris-setosa
80	3.3	4.5	5.6	7.8	Iris-setosa
81	3.3	4.5	5.6	7.8	Iris-setosa
82	3.3	4.5	5.6	7.8	Iris-setosa
83	3.3	4.5	5.6	7.8	Iris-setosa
84	3.3	4.5	5.6	7.8	Iris-setosa
85	3.3	4.5	5.6	7.8	Iris-setosa
86	3.3	4.5	5.6	7.8	Iris-setosa
87	3.3	4.5	5.6	7.8	Iris-setosa
88	3.3	4.5	5.6	7.8	Iris-setosa
89	3.3	4.5	5.6	7.8	Iris-setosa
90	3.3	4.5	5.6	7.8	Iris-setosa
91	3.3	4.5	5.6	7.8	Iris-setosa
92	3.3	4.5	5.6	7.8	Iris-setosa
93	3.3	4.5	5.6	7.8	Iris-setosa
94	3.3	4.5	5.6	7.8	Iris-setosa
95	3.3	4.5	5.6	7.8	Iris-setosa
96	3.3	4.5	5.6	7.8	Iris-setosa
97	3.3	4.5	5.6	7.8	Iris-setosa
98	3.3	4.5	5.6	7.8	Iris-setosa
99	3.3	4.5	5.6	7.8	Iris-setosa
100	3.3	4.5	5.6	7.8	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	0.9958419958419954	0.9125874125874126	0.9694764694764694	0.9796881496881495	[null]
prc_auc	1.0	0.9881304592843054	0.8892942583732055	0.9591415725525035	0.9725586562560247	[null]
accuracy	0.66	0.54	0.88	0.6933333333333334	0.6772	0.6933333333333334
log_loss	0.270765904753855	0.198062783689329	0.172246971981091	0.21369188680809167	0.23018892806707011	[null]
precision	1.0	0.3611111111111111	1.0	0.7870370370370371	0.8338888888888889	0.54
recall	0.34615384615384615	1.0	0.45454545454545453	0.6002331002331003	0.54	0.54
f1_score	0.5142857142857142	0.5306122448979591	0.625	0.5566326530612244	0.5428877551020408	0.54
mcc	0.4501406909523198	0.3696439322329485	0.6276459144608478	0.48247684588203876	0.4682626828571594	0.31
informedness	0.34615384615384626	0.3783783783783785	0.4545454545454546	0.39302589302589314	0.3783783783783785	0.31000000000000005
markedness	0.5853658536585367	0.36111111111111116	0.8666666666666667	0.6043812104787715	0.5889457994579946	0.31000000000000005
csi	0.34615384615384615	0.3611111111111111	0.45454545454545453	0.38727013727013726	0.3738888888888889	0.3698630136986301

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	0.9958419958419954	0.9125874125874126	0.9694764694764694	0.9796881496881495	[null]
prc_auc	1.0	0.9881304592843054	0.8892942583732055	0.9591415725525035	0.9725586562560247	[null]
accuracy	0.8	0.26	0.46	0.5066666666666667	0.5848	0.5066666666666667
log_loss	0.270765904753855	0.198062783689329	0.172246971981091	0.21369188680809167	0.23018892806707011	[null]
precision	0.7222222222222222	0.26	0.2777777777777778	0.42	0.5042666666666666	0.4016393442622951
recall	1.0	1.0	0.9090909090909091	0.9696969696969697	0.98	0.98
f1_score	0.8387096774193548	0.41269841269841273	0.4255319148936171	0.5589800016704616	0.6370476408362475	0.5697674418604652
mcc	0.6490734136415512	0.0	0.22366056877342066	0.2909113274716573	0.3867235002237592	0.3024586990598921
informedness	0.5833333333333335	0.0	0.24242424242424243	0.2752525252525253	0.3566666666666668	0.25
markedness	0.7222222222222223	-0.74	0.2063492063492065	0.06285714285714294	0.228552380952381	0.3659250585480094
csi	0.7222222222222222	0.26	0.2702702702702703	0.4174974974974975	0.502615015015015	0.3983739837398374

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]: 0.5566326530612244

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	4.7	3.2	1.3	0.2	Iris-setosa	Iris-versicolor
2	5.1	3.5	1.4	0.2	Iris-setosa	Iris-versicolor
3	5.4	3.4	1.5	0.4	Iris-setosa	Iris-versicolor
4	5.4	3.9	1.3	0.4	Iris-setosa	Iris-versicolor
5	5.5	2.4	3.7	1.0	Iris-versicolor	Iris-versicolor
6	5.7	4.4	1.5	0.4	Iris-setosa	Iris-versicolor
7	6.1	3.0	4.6	1.4	Iris-versicolor	Iris-versicolor
8	6.1	3.0	4.9	1.8	Iris-virginica	Iris-versicolor
9	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
10	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
11	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
12	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
13	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
14	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
15	4.3	3.0	1.1	0.1	Iris-setosa	Iris-versicolor
16	4.6	3.1	1.5	0.2	Iris-setosa	Iris-versicolor
17	4.9	2.4	3.3	1.0	Iris-versicolor	Iris-versicolor
18	5.0	3.5	1.3	0.3	Iris-setosa	Iris-versicolor
19	5.1	3.5	1.4	0.3	Iris-setosa	Iris-versicolor
20	5.2	3.4	1.4	0.2	Iris-setosa	Iris-versicolor
21	5.5	2.3	4.0	1.3	Iris-versicolor	Iris-versicolor
22	5.7	2.9	4.2	1.3	Iris-versicolor	Iris-versicolor
23	5.7	3.8	1.7	0.3	Iris-setosa	Iris-versicolor
24	5.8	4.0	1.2	0.2	Iris-setosa	Iris-versicolor
25	6.0	3.4	4.5	1.6	Iris-versicolor	Iris-versicolor
26	6.1	2.6	5.6	1.4	Iris-virginica	Iris-versicolor
27	6.7	3.0	5.2	2.3	Iris-virginica	Iris-versicolor
28	6.7	3.3	5.7	2.5	Iris-virginica	Iris-versicolor
29	7.4	2.8	6.1	1.9	Iris-virginica	Iris-versicolor
30	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
31	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
32	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
33	5.0	2.0	3.5	1.0	Iris-versicolor	Iris-versicolor
34	5.1	3.4	1.5	0.2	Iris-setosa	Iris-versicolor
35	5.2	3.5	1.5	0.2	Iris-setosa	Iris-versicolor
36	5.2	4.1	1.5	0.1	Iris-setosa	Iris-versicolor
37	5.8	2.7	3.9	1.2	Iris-versicolor	Iris-versicolor
38	6.6	2.9	4.6	1.3	Iris-versicolor	Iris-versicolor
39	6.7	3.1	4.7	1.5	Iris-versicolor	Iris-versicolor
40	7.3	2.9	6.3	1.8	Iris-virginica	Iris-versicolor
41	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
42	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
43	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
44	4.4	3.0	1.3	0.2	Iris-setosa	Iris-versicolor
45	4.8	3.0	1.4	0.3	Iris-setosa	Iris-versicolor
46	5.7	2.8	4.5	1.3	Iris-versicolor	Iris-versicolor
47	5.8	2.6	4.0	1.2	Iris-versicolor	Iris-versicolor
48	6.1	2.9	4.7	1.4	Iris-versicolor	Iris-versicolor
49	7.2	3.6	6.1	2.5	Iris-virginica	Iris-virginica
50	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica

Rows: 1-50 | 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	4.7	3.2	1.3	0.2	Iris-setosa	Iris-versicolor	0.312944316184786	0.450696303968841	0.236359379846373
2	5.1	3.5	1.4	0.2	Iris-setosa	Iris-versicolor	0.305242901098255	0.459768556988884	0.234988541912861
3	5.4	3.4	1.5	0.4	Iris-setosa	Iris-versicolor	0.2984100856685	0.472660832493979	0.228929081837521
4	5.4	3.9	1.3	0.4	Iris-setosa	Iris-versicolor	0.311427922313439	0.453229108469278	0.235342969217283
5	5.5	2.4	3.7	1.0	Iris-versicolor	Iris-versicolor	0.157861495016323	0.693044540205306	0.149093964778371
6	5.7	4.4	1.5	0.4	Iris-setosa	Iris-versicolor	0.308722817953092	0.451392903909253	0.239884278137655
7	6.1	3.0	4.6	1.4	Iris-versicolor	Iris-versicolor	0.0833408821360125	0.817645154019402	0.0990139638445857
8	6.1	3.0	4.9	1.8	Iris-virginica	Iris-versicolor	0.146211385749299	0.672393618777159	0.181394995473542
9	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.468457956157774	0.252196848676983	0.279345195165243
10	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.468457956157774	0.252196848676983	0.279345195165243
11	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.468457956157774	0.252196848676983	0.279345195165243
12	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.468457956157774	0.252196848676983	0.279345195165243
13	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.468457956157774	0.252196848676983	0.279345195165243
14	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.468457956157774	0.252196848676983	0.279345195165243
15	4.3	3.0	1.1	0.1	Iris-setosa	Iris-versicolor	0.321813075145489	0.436801773039241	0.241385151815271
16	4.6	3.1	1.5	0.2	Iris-setosa	Iris-versicolor	0.309130257313096	0.45565457845727	0.235215164229634
17	4.9	2.4	3.3	1.0	Iris-versicolor	Iris-versicolor	0.230312351951158	0.577683693215611	0.192003954833231
18	5.0	3.5	1.3	0.3	Iris-setosa	Iris-versicolor	0.311975939342601	0.453300380468846	0.234723680188553
19	5.1	3.5	1.4	0.3	Iris-setosa	Iris-versicolor	0.30716797640093	0.45944387824759	0.233388145351481
20	5.2	3.4	1.4	0.2	Iris-setosa	Iris-versicolor	0.301931975093698	0.464181437317443	0.233886587588858
21	5.5	2.3	4.0	1.3	Iris-versicolor	Iris-versicolor	0.140386682144863	0.724404078816897	0.13520923903824
22	5.7	2.9	4.2	1.3	Iris-versicolor	Iris-versicolor	0.0734901628045896	0.850917398992328	0.0755924382030824
23	5.7	3.8	1.7	0.3	Iris-setosa	Iris-versicolor	0.290425356175494	0.477755448833498	0.231819194991008
24	5.8	4.0	1.2	0.2	Iris-setosa	Iris-versicolor	0.304811700431295	0.455109689286964	0.24007861028174
25	6.0	3.4	4.5	1.6	Iris-versicolor	Iris-versicolor	0.138320561478235	0.709275376940486	0.152404061581278
26	6.1	2.6	5.6	1.4	Iris-virginica	Iris-versicolor	0.167832857924205	0.547035879511815	0.28513126256398
27	6.7	3.0	5.2	2.3	Iris-virginica	Iris-versicolor	0.206688550041038	0.522417069321738	0.270894380637223
28	6.7	3.3	5.7	2.5	Iris-virginica	Iris-versicolor	0.223104399810004	0.427363900812719	0.349531699377277
29	7.4	2.8	6.1	1.9	Iris-virginica	Iris-versicolor	0.203892477324845	0.427613142536413	0.368494380138742
30	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.468457956157774	0.252196848676983	0.279345195165243
31	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.468457956157774	0.252196848676983	0.279345195165243
32	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	0.468457956157774	0.252196848676983	0.279345195165243
33	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.209628008273572	0.228564527104335	0.561807464622093
34	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.209628008273572	0.228564527104335	0.561807464622093
35	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.209628008273572	0.228564527104335	0.561807464622093
36	4.4	3.0	1.3	0.2	Iris-setosa	Iris-versicolor	0.317568071455339	0.444511659334834	0.237920269209827
37	4.8	3.0	1.4	0.3	Iris-setosa	Iris-versicolor	0.308057042352219	0.459343114771024	0.232599842876758
38	5.7	2.8	4.5	1.3	Iris-versicolor	Iris-versicolor	0.0777858573452441	0.834467080129732	0.0877470625250235
39	5.8	2.6	4.0	1.2	Iris-versicolor	Iris-versicolor	0.087548835162665	0.824691930529365	0.08775923430797
40	6.1	2.9	4.7	1.4	Iris-versicolor	Iris-versicolor	0.0899236981460054	0.799732225103934	0.110344076750061
41	7.2	3.6	6.1	2.5	Iris-virginica	Iris-virginica	0.223267697340344	0.377742837243672	0.398989465415985
42	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.209628008273572	0.228564527104335	0.561807464622093
43	5.0	2.0	3.5	1.0	Iris-versicolor	Iris-versicolor	0.219258539205836	0.58735580968435	0.193385651109814
44	5.1	3.4	1.5	0.2	Iris-setosa	Iris-versicolor	0.301137578729782	0.465462807609822	0.233399613660396
45	5.2	3.5	1.5	0.2	Iris-setosa	Iris-versicolor	0.300309370192825	0.466024171799892	0.233666458007283
46	5.2	4.1	1.5	0.1	Iris-setosa	Iris-versicolor	0.307190703845569	0.451617329438814	0.241191966715617
47	5.8	2.7	3.9	1.2	Iris-versicolor	Iris-versicolor	0.096424758316226	0.809366741744776	0.0942084999389981
48	6.6	2.9	4.6	1.3	Iris-versicolor	Iris-versicolor	0.119918726394561	0.731958889348189	0.14812238425725
49	6.7	3.1	4.7	1.5	Iris-versicolor	Iris-versicolor	0.144897847439601	0.673755957276255	0.181346195284144
50	7.3	2.9	6.3	1.8	Iris-virginica	Iris-versicolor	0.19847615009052	0.406886997375678	0.394636852533801

Rows: 1-50 | 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, 17,  0],
       [ 0, 13,  0],
       [ 0,  6,  5]])

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([[24,  0],
       [26,  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" - 5.38988764044944, 2) + POWER("SepalWidthCm" - 3.86179775280899, 2) + POWER("PetalLengthCm" - 7.63258426966292, 2) + POWER("PetalWidthCm" - 1.90898876404494, 2), 1 / 2) <= POWER(POWER("SepalLengthCm" - 4.03918918918919, 2) + POWER("SepalWidthCm" - 3.99594594594595, 2) + POWER("PetalLengthCm" - 3.77027027027027, 2) + POWER("PetalWidthCm" - 4.42972972972973, 2), 1 / 2) AND POWER(POWER("SepalLengthCm" - 5.38988764044944, 2) + POWER("SepalWidthCm" - 3.86179775280899, 2) + POWER("PetalLengthCm" - 7.63258426966292, 2) + POWER("PetalWidthCm" - 1.90898876404494, 2), 1 / 2) <= POWER(POWER("SepalLengthCm" - 5.98378378378378, 2) + POWER("SepalWidthCm" - 2.78648648648649, 2) + POWER("PetalLengthCm" - 4.29189189189189, 2) + POWER("PetalWidthCm" - 1.34594594594595, 2), 1 / 2) THEN \'Iris-virginica\' WHEN POWER(POWER("SepalLengthCm" - 5.98378378378378, 2) + POWER("SepalWidthCm" - 2.78648648648649, 2) + POWER("PetalLengthCm" - 4.29189189189189, 2) + POWER("PetalWidthCm" - 1.34594594594595, 2), 1 / 2) <= POWER(POWER("SepalLengthCm" - 4.03918918918919, 2) + POWER("SepalWidthCm" - 3.99594594594595, 2) + POWER("PetalLengthCm" - 3.77027027027027, 2) + POWER("PetalWidthCm" - 4.42972972972973, 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