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verticapy.machine_learning.vertica.ensemble.XGBRegressor

class verticapy.machine_learning.vertica.ensemble.XGBRegressor(name: str = None, overwrite_model: bool = False, max_ntree: int = 10, max_depth: int = 5, nbins: int = 32, split_proposal_method: Literal['local', 'global'] = 'global', tol: float = 0.001, learning_rate: float = 0.1, min_split_loss: float = 0.0, weight_reg: float = 0.0, sample: float = 1.0, col_sample_by_tree: float = 1.0, col_sample_by_node: float = 1.0)

Creates an XGBRegressor object using the Vertica XGB_REGRESSOR algorithm.

Parameters

name: str, optional

Name of the model. The model is stored in the DB.

overwrite_model: bool, optional

If set to True, training a model with the same name as an existing model overwrites the existing model.

max_ntree: int, optional

Maximum number of trees that can be created.

max_depth: int, optional

aximum depth of each tree, an integer between 1 and 20, inclusive.

nbins: int, optional

Number of bins used to find splits in each column, where more splits leads to a longer runtime but more fine-grained, possibly better splits. Must be an integer between 2 and 1000, inclusive.

split_proposal_method: str, optional

Approximate splitting strategy, either global or local (not yet supported).

tol: float, optional

Approximation error of quantile summary structures used in the approximate split finding method.

learning_rate: float, optional

Weight applied to each tree’s prediction. This reduces each tree’s impact, allowing for later trees to contribute and keeping earlier trees from dominating.

min_split_loss: float, optional

Each split must improve the model’s objective function value by at least this much in order to avoid pruning. A value of 0 is the same as turning off this parameter (trees are still pruned based on positive / negative objective function values).

weight_reg: float, optional

Regularization term that is applied to the weights of the leaves in the regression tree. A higher value leads to more sparse/smooth weights, which often helps to prevent overfitting.

sample: float, optional

Fraction of rows used per iteration in training.

col_sample_by_tree: float, optional

float in the range (0,1] that specifies the fraction of columns (features), chosen at random, to use when building each tree.

col_sample_by_node: float, optional

float in the range (0,1] that specifies the fraction of columns (features), chosen at random, to use when evaluating each split.

Attributes

Many attributes are created during the fitting phase.

trees_: list of BinaryTreeRegressor

Tree models are instances of ` BinaryTreeRegressor, each possessing various attributes. For more detailed information, refer to the documentation for BinaryTreeRegressor.

features_importance_: numpy.array

The importance of features. It is calculated using the average gain of each tree. To determine the final score, VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them. It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.

features_importance_trees_: dict of numpy.array

Each element of the array represents the feature importance of tree i. The importance of features is calculated using the average gain of each tree. It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.

mean_: float

The mean of the response column.

eta_: float

The learning rate, is a crucial hyperparameter in machine learning algorithms. It determines the step size at each iteration during the model training process. A well-chosen learning rate is essential for achieving optimal convergence and preventing overshooting or slow convergence in the training phase. Adjusting the learning rate is often necessary to strike a balance between model accuracy and computational efficiency.

n_estimators_: int

The number of model estimators.

Note

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

Note

Several other attributes can be accessed by using the get_vertica_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.

Important

Many tree-based models inherit from the XGB base class, and it’s recommended to use it directly for access to a wider range of options.

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 winequality dataset.

import verticapy.datasets as vpd

data = vpd.load_winequality()

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
2	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white
3	4.7	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
4	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6	0	white
5	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
6	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
7	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red
8	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6	0	white
9	5.0	0.31	0.0	6.4	0.046	43.0	166.0	0.994	3.3	0.63	9.9	6	0	white
10	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red
11	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white
12	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6	0	white
13	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white
14	5.1	0.165	0.22	5.7	0.047	42.0	146.0	0.9934	3.18	0.55	9.9	6	0	white
15	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
16	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
17	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
18	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white
19	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7	1	white
20	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7	1	white
21	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5	0	white
22	5.2	0.31	0.2	2.4	0.027	27.0	117.0	0.98886	3.56	0.45	13.0	7	1	white
23	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red
24	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6	0	white
25	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6	0	white
26	5.2	0.365	0.08	13.5	0.041	37.0	142.0	0.997	3.46	0.39	9.9	6	0	white
27	5.2	0.48	0.04	1.6	0.054	19.0	106.0	0.9927	3.54	0.62	12.2	7	1	red
28	5.2	0.5	0.18	2.0	0.036	23.0	129.0	0.98949	3.36	0.77	13.4	7	1	white
29	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
30	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
31	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white
32	5.3	0.23	0.56	0.9	0.041	46.0	141.0	0.99119	3.16	0.62	9.7	5	0	white
33	5.3	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white
34	5.3	0.33	0.3	1.2	0.048	25.0	119.0	0.99045	3.32	0.62	11.3	6	0	white
35	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
36	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
37	5.3	0.4	0.25	3.9	0.031	45.0	130.0	0.99072	3.31	0.58	11.75	7	1	white
38	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.6	7	1	red
39	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.5666666666667	7	1	red
40	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red
41	5.4	0.22	0.29	1.2	0.045	69.0	152.0	0.99178	3.76	0.63	11.0	7	1	white
42	5.4	0.595	0.1	2.8	0.042	26.0	80.0	0.9932	3.36	0.38	9.3	5	0	white
43	5.4	0.74	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6	6	0	red
44	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
45	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
46	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
47	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
48	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.65	6	0	white
49	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.6333333333333	6	0	white
50	5.5	0.18	0.22	5.5	0.037	10.0	86.0	0.99156	3.46	0.44	12.2	5	0	white
51	5.5	0.24	0.45	1.7	0.046	22.0	113.0	0.99224	3.22	0.48	10.0	5	0	white
52	5.5	0.29	0.3	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7	1	white
53	5.5	0.31	0.29	3.0	0.027	16.0	102.0	0.99067	3.23	0.56	11.2	6	0	white
54	5.5	0.32	0.45	4.9	0.028	25.0	191.0	0.9922	3.51	0.49	11.5	7	1	white
55	5.5	0.35	0.35	1.1	0.045	14.0	167.0	0.992	3.34	0.68	9.9	6	0	white
56	5.5	0.375	0.38	1.7	0.036	17.0	98.0	0.99142	3.29	0.39	10.5	6	0	white
57	5.6	0.15	0.26	5.55	0.051	51.0	139.0	0.99336	3.47	0.5	11.0	6	0	white
58	5.6	0.15	0.31	5.3	0.038	8.0	79.0	0.9923	3.3	0.39	10.5	6	0	white
59	5.6	0.16	0.27	1.4	0.044	53.0	168.0	0.9918	3.28	0.37	10.1	6	0	white
60	5.6	0.175	0.29	0.8	0.043	20.0	67.0	0.99112	3.28	0.48	9.9	6	0	white
61	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
62	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
63	5.6	0.22	0.32	1.2	0.024	29.0	97.0	0.98823	3.2	0.46	13.05	7	1	white
64	5.6	0.26	0.18	1.4	0.034	18.0	135.0	0.99174	3.32	0.35	10.2	6	0	white
65	5.6	0.26	0.26	5.7	0.031	12.0	80.0	0.9923	3.25	0.38	10.8	5	0	white
66	5.6	0.26	0.5	11.4	0.029	25.0	93.0	0.99428	3.23	0.49	10.5	6	0	white
67	5.6	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white
68	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white
69	5.6	0.35	0.14	5.0	0.046	48.0	198.0	0.9937	3.3	0.71	10.3	5	0	white
70	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
71	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
72	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
73	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
74	5.7	0.15	0.47	11.4	0.035	49.0	128.0	0.99456	3.03	0.34	10.5	8	1	white
75	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white
76	5.7	0.18	0.36	1.2	0.046	9.0	71.0	0.99199	3.7	0.68	10.9	7	1	white
77	5.7	0.2	0.3	2.5	0.046	38.0	125.0	0.99276	3.34	0.5	9.9	6	0	white
78	5.7	0.21	0.32	0.9	0.038	38.0	121.0	0.99074	3.24	0.46	10.6	6	0	white
79	5.7	0.21	0.37	4.5	0.04	58.0	140.0	0.99332	3.29	0.62	10.6	6	0	white
80	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
81	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
82	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
83	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
84	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
85	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white
86	5.7	0.23	0.28	9.65	0.025	26.0	121.0	0.9925	3.28	0.38	11.3	6	0	white
87	5.7	0.25	0.26	12.5	0.049	52.5	106.0	0.99691	3.08	0.45	9.4	6	0	white
88	5.7	0.25	0.26	12.5	0.049	52.5	120.0	0.99691	3.08	0.45	9.4	6	0	white
89	5.7	0.25	0.27	11.5	0.04	24.0	120.0	0.99411	3.33	0.31	10.8	6	0	white
90	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
91	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
92	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
93	5.7	0.27	0.32	1.2	0.046	20.0	155.0	0.9934	3.8	0.41	10.2	6	0	white
94	5.7	0.28	0.24	17.5	0.044	60.0	167.0	0.9989	3.31	0.44	9.4	5	0	white
95	5.7	0.32	0.18	1.4	0.029	26.0	104.0	0.9906	3.44	0.37	11.0	6	0	white
96	5.7	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white
97	5.7	0.36	0.34	4.2	0.026	21.0	77.0	0.9907	3.41	0.45	11.9	6	0	white
98	5.8	0.14	0.15	6.1	0.042	27.0	123.0	0.99362	3.06	0.6	9.9	6	0	white
99	5.8	0.15	0.32	1.2	0.037	14.0	119.0	0.99137	3.19	0.5	10.2	6	0	white
100	5.8	0.17	0.34	1.8	0.045	96.0	170.0	0.99035	3.38	0.9	11.8	8	1	white

Rows: 1-100 | Columns: 14

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_winequality()
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.

Model Initialization

First we import the XGBRegressor model:

from verticapy.machine_learning.vertica import XGBRegressor

Then we can create the model:

model = XGBRegressor(
    max_ntree = 3,
    max_depth = 3,
    nbins = 6,
    split_proposal_method = 'global',
    tol = 0.001,
    learning_rate = 0.1,
    min_split_loss = 0,
    weight_reg = 0,
    sample = 0.7,
    col_sample_by_tree = 1,
    col_sample_by_node = 1,
)

Hint

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 crucial for the model management system and versioning. It’s highly recommended to provide a name if you plan to reuse the model later.

Model Training

We can now fit the model:

model.fit(
    train,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "quality",
    test,
)



===========
call_string
===========
xgb_regressor('"public"."_verticapy_tmp_xgbregressor_v_mldb_c97ea83e979811efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_c9aca734979811efa8720242ac120002_"', '"quality"', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"' USING PARAMETERS exclude_columns='', max_ntree=3, max_depth=3, learning_rate=0.1, min_split_loss=0, weight_reg=0, nbins=6, objective=squarederror, sampling_size=0.7, col_sample_by_tree=1, col_sample_by_node=1)

=======
details
=======
   predictor    |      type      
----------------+----------------
 fixed_acidity  |float or numeric
volatile_acidity|float or numeric
  citric_acid   |float or numeric
 residual_sugar |float or numeric
   chlorides    |float or numeric
    density     |float or numeric


===============
Additional Info
===============
       Name       | Value  
------------------+--------
    tree_count    |   3    
rejected_row_count|   0    
accepted_row_count|  5197  
initial_prediction| 5.80720

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.

Features Importance

We can conveniently get the features importance:

result = model.features_importance()

Note

In models such as XGBoost, feature importance is calculated using the average gain of each tree. To determine the final score, VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them.

Metrics

We can get the entire report using:

model.report()

	value
explained_variance	0.0524651482215907
max_error	2.81371957335071
median_absolute_error	0.686738192254968
mean_absolute_error	0.652055475546549
mean_squared_error	0.69954468406969
root_mean_squared_error	0.836387878959093
r2	0.0483657007735747
r2_adj	0.0439497643502502
aic	-450.360750274196
bic	-414.332452169739

Rows: 1-10 | Columns: 2

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 = ["mse", "r2"]).

You can utilize the score() function to calculate various regression metrics, with the R-squared being the default.

model.score()
Out[4]: 0.0483657007735747

Prediction

Prediction is straight-forward:

model.predict(
    test,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "prediction",
)

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 prediction Float(22)
1	5.0	0.33	0.18	4.6	0.032	40.0	124.0	0.99114	3.18	0.4	11.0	6	0	white	5.81371957335071
2	5.0	0.38	0.01	1.6	0.048	26.0	60.0	0.99084	3.7	0.75	14.0	6	0	red	5.98412039764347
3	5.0	0.55	0.14	8.3	0.032	35.0	164.0	0.9918	3.53	0.51	12.5	8	1	white	5.72953803181093
4	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white	5.81371957335071
5	5.1	0.585	0.0	1.7	0.044	14.0	86.0	0.99264	3.56	0.94	12.9	7	1	red	5.72953803181093
6	5.2	0.185	0.22	1.0	0.03	47.0	123.0	0.99218	3.55	0.44	10.15	6	0	white	5.81371957335071
7	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8	1	white	5.81371957335071
8	5.2	0.34	0.0	1.8	0.05	27.0	63.0	0.9916	3.68	0.79	14.0	6	0	red	5.81371957335071
9	5.2	0.6	0.07	7.0	0.044	33.0	147.0	0.9944	3.33	0.58	9.7	5	0	white	5.72953803181093
10	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white	5.81371957335071
11	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white	5.81371957335071
12	5.3	0.43	0.11	1.1	0.029	6.0	51.0	0.99076	3.51	0.48	11.2	4	0	white	5.98412039764347
13	5.3	0.76	0.03	2.7	0.043	27.0	93.0	0.9932	3.34	0.38	9.2	5	0	white	5.72953803181093
14	5.5	0.16	0.26	1.5	0.032	35.0	100.0	0.99076	3.43	0.77	12.0	6	0	white	5.98412039764347
15	5.5	0.23	0.19	2.2	0.044	39.0	161.0	0.99209	3.19	0.43	10.4	6	0	white	5.81371957335071
16	5.6	0.18	0.27	1.7	0.03	31.0	103.0	0.98892	3.35	0.37	12.9	6	0	white	5.98412039764347
17	5.6	0.18	0.3	10.2	0.028	28.0	131.0	0.9954	3.49	0.42	10.8	7	1	white	5.80249641767768
18	5.6	0.19	0.26	1.4	0.03	12.0	76.0	0.9905	3.25	0.37	10.9	7	1	white	5.98412039764347
19	5.6	0.19	0.46	1.1	0.032	33.0	115.0	0.9909	3.36	0.5	10.4	6	0	white	5.98412039764347
20	5.6	0.2	0.66	10.2	0.043	78.0	175.0	0.9945	2.98	0.43	10.4	7	1	white	5.81371957335071
21	5.6	0.23	0.25	8.0	0.043	31.0	101.0	0.99429	3.19	0.42	10.4	6	0	white	5.81371957335071
22	5.6	0.34	0.1	1.3	0.031	20.0	68.0	0.9906	3.36	0.51	11.2	7	1	white	5.98412039764347
23	5.6	0.695	0.06	6.8	0.042	9.0	84.0	0.99432	3.44	0.44	10.2	5	0	white	5.72953803181093
24	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	5.80249641767768
25	5.7	0.33	0.15	1.9	0.05	20.0	93.0	0.9934	3.38	0.62	9.9	5	0	white	5.81371957335071
26	5.8	0.19	0.25	10.8	0.042	33.0	124.0	0.99646	3.22	0.41	9.2	6	0	white	5.80249641767768
27	5.8	0.2	0.27	1.4	0.031	12.0	77.0	0.9905	3.25	0.36	10.9	7	1	white	5.98412039764347
28	5.8	0.2	0.3	1.5	0.031	21.0	57.0	0.99115	3.44	0.55	11.0	6	0	white	5.81371957335071
29	5.8	0.28	0.66	9.1	0.039	26.0	159.0	0.9965	3.66	0.55	10.8	5	0	white	5.80249641767768
30	5.8	0.32	0.31	2.7	0.049	25.0	153.0	0.99067	3.44	0.73	12.2	7	1	white	5.98412039764347
31	5.8	0.32	0.31	2.7	0.049	25.0	153.0	0.99067	3.44	0.73	12.2	7	1	white	5.98412039764347
32	5.8	0.33	0.2	16.05	0.047	26.0	166.0	0.9976	3.09	0.46	8.9	5	0	white	5.77091973379474
33	5.8	0.36	0.26	3.3	0.038	40.0	153.0	0.9911	3.34	0.55	11.3	6	0	white	5.81371957335071
34	5.8	0.36	0.5	1.0	0.127	63.0	178.0	0.99212	3.1	0.45	9.7	5	0	white	5.81371957335071
35	5.8	0.38	0.26	1.1	0.058	20.0	140.0	0.99271	3.27	0.43	9.7	6	0	white	5.81371957335071
36	5.9	0.2	0.23	1.5	0.037	38.0	93.0	0.99021	3.36	0.49	12.0	6	0	white	5.98412039764347
37	5.9	0.22	0.3	1.3	0.052	42.0	86.0	0.99069	3.31	0.47	11.55	6	0	white	5.98412039764347
38	5.9	0.23	0.28	8.6	0.046	37.0	142.0	0.99432	3.23	0.53	10.6	6	0	white	5.81371957335071
39	5.9	0.29	0.32	1.4	0.022	17.0	47.0	0.9899	3.35	0.35	11.5	6	0	white	5.98412039764347
40	5.9	0.435	0.16	6.4	0.031	21.0	134.0	0.99151	3.24	0.46	12.2	6	0	white	5.81371957335071
41	5.9	0.65	0.23	5.0	0.035	20.0	128.0	0.99016	3.46	0.48	12.8	6	0	white	5.93049819976791
42	6.0	0.16	0.22	1.6	0.042	36.0	106.0	0.9905	3.24	0.32	11.4	6	0	white	5.98412039764347
43	6.0	0.19	0.26	12.4	0.048	50.0	147.0	0.9972	3.3	0.36	8.9	6	0	white	5.80249641767768
44	6.0	0.2	0.71	1.6	0.15	10.0	54.0	0.9927	3.12	0.47	9.8	5	0	white	5.81371957335071
45	6.0	0.21	0.34	2.0	0.042	63.0	123.0	0.99052	3.44	0.42	11.4	6	0	white	5.98412039764347
46	6.0	0.28	0.24	17.8	0.047	42.0	111.0	0.99896	3.1	0.45	8.9	6	0	white	5.80249641767768
47	6.0	0.28	0.24	17.8	0.047	42.0	111.0	0.99896	3.1	0.45	8.9	6	0	white	5.80249641767768
48	6.0	0.36	0.39	3.2	0.027	20.0	125.0	0.991	3.38	0.39	11.3	7	1	white	5.81371957335071
49	6.0	0.49	0.0	2.3	0.068	15.0	33.0	0.99292	3.58	0.59	12.5	6	0	red	5.81371957335071
50	6.0	0.58	0.2	2.4	0.075	15.0	50.0	0.99467	3.58	0.67	12.5	6	0	red	5.72953803181093
51	6.1	0.17	0.42	15.1	0.033	28.0	124.0	0.99684	2.87	0.47	9.5	5	0	white	5.80249641767768
52	6.1	0.21	0.4	1.4	0.066	40.5	165.0	0.9912	3.25	0.59	11.9	6	0	red	5.81371957335071
53	6.1	0.24	0.26	1.7	0.033	61.0	134.0	0.9903	3.19	0.81	11.9	7	1	white	5.98412039764347
54	6.1	0.24	0.27	9.8	0.062	33.0	152.0	0.9966	3.31	0.47	9.5	6	0	white	5.80249641767768
55	6.1	0.25	0.28	10.0	0.055	56.0	131.0	0.994	3.22	0.35	10.9	6	0	white	5.81371957335071
56	6.1	0.28	0.24	19.95	0.074	32.0	174.0	0.99922	3.19	0.44	9.3	6	0	white	5.80249641767768
57	6.1	0.32	0.28	6.6	0.021	29.0	132.0	0.99188	3.15	0.36	11.45	7	1	white	5.81371957335071
58	6.1	0.34	0.46	4.7	0.029	21.0	94.0	0.991	3.29	0.62	12.3	6	0	white	5.81371957335071
59	6.1	0.41	0.14	10.4	0.037	18.0	119.0	0.996	3.38	0.45	10.0	5	0	white	5.77091973379474
60	6.1	0.56	0.0	2.2	0.079	6.0	9.0	0.9948	3.59	0.54	11.5	6	0	red	5.72953803181093
61	6.2	0.19	0.38	5.1	0.019	22.0	82.0	0.98961	3.05	0.36	12.5	6	0	white	5.98412039764347
62	6.2	0.2	0.31	1.0	0.031	22.0	73.0	0.99035	3.24	0.52	11.3	6	0	white	5.98412039764347
63	6.2	0.23	0.38	1.6	0.044	12.0	113.0	0.99176	3.3	0.73	11.4	5	0	white	5.81371957335071
64	6.2	0.27	0.32	6.3	0.048	47.0	159.0	0.99282	3.21	0.6	11.0	6	0	white	5.81371957335071
65	6.2	0.27	0.49	1.4	0.05	20.0	74.0	0.9931	3.32	0.44	9.8	6	0	white	5.81371957335071
66	6.2	0.3	0.21	1.1	0.032	31.0	111.0	0.9889	2.97	0.42	12.2	6	0	white	5.98412039764347
67	6.2	0.3	0.33	3.5	0.037	37.0	155.0	0.98987	3.18	0.37	12.4	6	0	white	5.98412039764347
68	6.2	0.35	0.31	2.6	0.036	37.0	92.0	0.98938	3.27	0.53	12.8	7	1	white	5.98412039764347
69	6.2	0.53	0.02	0.9	0.035	6.0	81.0	0.99234	3.24	0.35	9.5	4	0	white	5.72953803181093
70	6.2	0.56	0.09	1.7	0.053	24.0	32.0	0.99402	3.54	0.6	11.3	5	0	red	5.72953803181093
71	6.2	0.56	0.09	1.7	0.053	24.0	32.0	0.99402	3.54	0.6	11.3	5	0	red	5.72953803181093
72	6.3	0.16	0.4	1.6	0.033	59.0	148.0	0.9914	3.44	0.53	11.4	5	0	white	5.81371957335071
73	6.3	0.21	0.28	1.5	0.051	46.0	142.0	0.9928	3.23	0.42	10.1	6	0	white	5.81371957335071
74	6.3	0.23	0.33	6.9	0.052	23.0	118.0	0.9938	3.23	0.46	10.4	6	0	white	5.81371957335071
75	6.3	0.27	0.18	7.7	0.048	45.0	186.0	0.9962	3.23	0.47	9.0	5	0	white	5.80249641767768
76	6.3	0.27	0.25	5.8	0.038	52.0	155.0	0.995	3.28	0.38	9.4	6	0	white	5.80249641767768
77	6.3	0.33	0.2	17.9	0.066	36.0	161.0	0.9991	3.14	0.51	8.8	5	0	white	5.77091973379474
78	6.3	0.36	0.19	3.2	0.075	15.0	39.0	0.9956	3.56	0.52	12.7	6	0	red	5.77091973379474
79	6.3	1.02	0.0	2.0	0.083	17.0	24.0	0.99437	3.59	0.55	11.2	4	0	red	5.72953803181093
80	6.4	0.125	0.36	1.4	0.044	22.0	68.0	0.99014	3.15	0.5	11.7	7	1	white	5.98412039764347
81	6.4	0.18	0.32	9.6	0.052	24.0	90.0	0.9963	3.35	0.49	9.4	6	0	white	5.80249641767768
82	6.4	0.2	0.15	6.6	0.046	26.0	113.0	0.99408	2.99	0.58	9.9	6	0	white	5.81371957335071
83	6.4	0.2	0.28	2.5	0.032	24.0	84.0	0.99168	3.31	0.55	11.5	5	0	white	5.81371957335071
84	6.4	0.23	0.39	1.8	0.032	23.0	118.0	0.9912	3.32	0.5	11.8	6	0	white	5.81371957335071
85	6.4	0.26	0.25	10.7	0.046	66.0	179.0	0.99606	3.17	0.55	9.9	6	0	white	5.80249641767768
86	6.4	0.26	0.49	6.4	0.037	37.0	161.0	0.9954	3.38	0.53	9.7	6	0	white	5.80249641767768
87	6.4	0.27	0.29	10.8	0.028	17.0	118.0	0.99356	3.18	0.37	11.2	6	0	white	5.81371957335071
88	6.4	0.27	0.32	4.5	0.24	61.0	174.0	0.9948	3.12	0.48	9.4	5	0	white	5.81371957335071
89	6.4	0.29	0.57	1.0	0.06	15.0	120.0	0.9924	3.06	0.41	9.5	5	0	white	5.81371957335071
90	6.4	0.29	0.57	1.0	0.06	15.0	120.0	0.9924	3.06	0.41	9.5	5	0	white	5.81371957335071
91	6.4	0.35	0.35	5.6	0.034	9.0	148.0	0.99441	3.17	0.5	9.8	4	0	white	5.81371957335071
92	6.4	0.41	0.01	6.1	0.048	20.0	70.0	0.99362	3.19	0.42	10.0	5	0	white	5.81371957335071
93	6.5	0.15	0.55	5.9	0.045	75.0	162.0	0.99482	2.97	0.4	9.3	5	0	white	5.81371957335071
94	6.5	0.18	0.48	18.0	0.054	56.0	183.0	1.00038	2.98	0.61	8.5	6	0	white	5.80249641767768
95	6.5	0.19	0.1	1.3	0.046	23.0	107.0	0.9937	3.29	0.45	10.0	5	0	white	5.81371957335071
96	6.5	0.19	0.34	1.6	0.029	39.0	116.0	0.98954	3.21	0.68	12.5	6	0	white	5.98412039764347
97	6.5	0.22	0.28	3.7	0.059	29.0	151.0	0.99177	3.23	0.41	12.1	7	1	white	5.81371957335071
98	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	5.80249641767768
99	6.5	0.25	0.32	9.9	0.045	41.0	128.0	0.99636	3.18	0.52	9.6	6	0	white	5.80249641767768
100	6.5	0.27	0.19	6.6	0.045	98.0	175.0	0.99364	3.16	0.34	10.1	6	0	white	5.81371957335071

Rows: 1-100 | Columns: 15

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.

Plots

Tree models can be visualized by drawing their tree plots. For more examples, check out Machine Learning - Tree Plots.

model.plot_tree()

Note

The above example may not render properly in the doc because of the huge size of the tree. But it should render nicely in jupyter environment.

In order to plot graph using graphviz separately, you can extract the graphviz DOT file code as follows:

model.to_graphviz()
Out[5]: 'digraph Tree {\ngraph [bgcolor="#FFFFFFDD"];\n0 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n0 -> 1 [label="<= 0.990975", color="#000000", fontcolor="#000000"]\n0 -> 2 [label="> 0.990975", color="#000000", fontcolor="#000000"]\n1 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n1 -> 3 [label="<= 0.913333", color="#000000", fontcolor="#000000"]\n1 -> 4 [label="> 0.913333", color="#000000", fontcolor="#000000"]\n2 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n2 -> 5 [label="<= 0.496667", color="#000000", fontcolor="#000000"]\n2 -> 6 [label="> 0.496667", color="#000000", fontcolor="#000000"]\n3 [label="0.646292", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n4 [label="-1.807196", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n5 [label="-0.008471", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n6 [label="-0.464636", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n}'

This string can then be copied into a DOT file which can beparsed by graphviz.

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

model.contour()

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.

Model Register

In order to register the model for tracking and versioning:

model.register("model_v1")

Please refer to /notebooks/ml/model_tracking_versioning/index.ipynb for more details on model tracking and versioning.

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

To SQL

You can get the SQL query equivalent of the XGB model by:

model.to_sql()
Out[6]: '((CASE WHEN "density" < 0.990975 THEN (CASE WHEN "volatile_acidity" < 0.913333 THEN 0.646292 ELSE -1.807196 END) ELSE (CASE WHEN "volatile_acidity" < 0.496667 THEN -0.008471 ELSE -0.464636 END) END) + (CASE WHEN "density" < 0.99484 THEN (CASE WHEN "density" < 0.990975 THEN 0.553633 ELSE 0.095356 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN -0.016876 ELSE -0.332643 END) END) + (CASE WHEN "density" < 0.990975 THEN (CASE WHEN "volatile_acidity" < 0.496667 THEN 0.569315 ELSE 0.033093 END) ELSE (CASE WHEN "volatile_acidity" < 0.496667 THEN -0.021654 ELSE -0.407304 END) END)) * 0.1 + 5.80719645949586'

Note

This SQL query can be directly used in any database.

Deploy SQL

To get the SQL query which uses Vertica functions use below:

model.deploySQL()
Out[7]: 'PREDICT_XGB_REGRESSOR("fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density" USING PARAMETERS model_name = \'"public"."_verticapy_tmp_xgbregressor_v_mldb_c97ea83e979811efa8720242ac120002_"\', match_by_pos = \'true\')'

To Python

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

X = [[4.2, 0.17, 0.36, 1.8, 0.029, 0.9899]]

model.to_python()(X)
Out[9]: array([5.98412046])

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, max_ntree: int = 10, max_depth: int = 5, nbins: int = 32, split_proposal_method: Literal['local', 'global'] = 'global', tol: float = 0.001, learning_rate: float = 0.1, min_split_loss: float = 0.0, weight_reg: float = 0.0, sample: float = 1.0, col_sample_by_tree: float = 1.0, col_sample_by_node: float = 1.0) → None

Must be overridden in the child class

Methods

__init__([name, overwrite_model, max_ntree, ...])	Must be overridden in the child class
contour([nbins, chart])	Draws the model's contour plot.
deploySQL([X])	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()	Drops the model from the Vertica database.
export_models(name, path[, kind])	Exports machine learning models.
features_importance([tree_id, show, chart])	Computes the model's features importance.
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_score([tree_id])	Returns the feature importance metrics for the input tree.
get_tree([tree_id])	Returns a table with all the input tree information.
get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
import_models(path[, schema, kind])	Imports machine learning models.
plot([max_nb_points, chart])	Draws the model.
plot_tree([tree_id, pic_path])	Draws the input tree.
predict(vdf[, X, name, inplace])	Predicts 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'.
regression_report([metrics])	Computes a regression report
report([metrics])	Computes a regression report
score([metric])	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_graphviz([tree_id, classes_color, ...])	Returns the code for a Graphviz tree.
to_json([path])	Creates a Python XGBoost JSON file
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