vastorbit.machine_learning.vast.ensemble.GradientBoostingRegressor¶

class vastorbit.machine_learning.vast.ensemble.GradientBoostingRegressor(name: str = None, overwrite_model: bool = False, **kwargs)¶

Creates an GradientBoostingRegressor object using scikit-learn for training and the scalability of VAST DataBase for the inferences.

Parameters:

name (str, optional) – Name of the model. The model is stored in the database.
overwrite_model (bool, optional) – If set to True, training a model with the same name as an existing model overwrites the existing model.
**kwargs (scikit-learn model parameters.)

Variables:

created (Many attributes are)
phase. (during the fitting)
trees_ (list of BinaryTreeRegressor) – Tree models are instances of ` BinaryTreeRegressor, each possessing various attributes. For more detailed information, refer to the documentation for BinaryTreeRegressor.
feature_importances_ (numpy.array) – The importance of features. It is calculated using the average gain of each tree. To determine the final score, vastorbit 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.
feature_importances_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.

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 GradientBoosting 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 vastorbit:

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit 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 vastorbit are used as intended without interfering with functions from other libraries.

For this example, we will use the winequality dataset.

import vastorbit.datasets as vod

data = vod.load_winequality()

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	6.3	0.67	0.48	12.6	0.052	57.0	222.0	0.9979	3.17	0.52	9.3	6	0	white
2	7.4	0.4	0.29	5.4	0.044	31.0	122.0	0.994	3.3	0.5	11.1	8	1	white
3	7.1	0.26	0.31	2.2	0.044	29.0	128.0	0.9937	3.34	0.64	10.9	8	1	white
4	9.0	0.31	0.48	6.6	0.043	11.0	73.0	0.9938	2.9	0.38	11.6	5	0	white
5	6.3	0.39	0.24	6.9	0.069	9.0	117.0	0.9942	3.15	0.35	10.2	4	0	white
6	8.2	0.22	0.36	6.8	0.034	12.0	90.0	0.9944	3.01	0.38	10.5	8	1	white
7	7.1	0.19	0.28	3.6	0.033	16.0	78.0	0.993	2.91	0.78	11.4	6	0	white
8	7.3	0.25	0.36	13.1	0.05	35.0	200.0	0.9986	3.04	0.46	8.9	7	1	white
9	7.9	0.2	0.34	1.2	0.04	29.0	118.0	0.9932	3.14	0.41	10.6	6	0	white
10	7.1	0.26	0.32	5.9	0.037	39.0	97.0	0.9934	3.31	0.4	11.6	6	0	white
11	7.0	0.2	0.34	5.7	0.035	32.0	83.0	0.9928	3.19	0.46	11.5	6	0	white
12	6.9	0.3	0.33	4.1	0.035	26.0	155.0	0.9925	3.25	0.79	12.3	8	1	white
13	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
14	5.8	0.17	0.3	1.4	0.037	55.0	130.0	0.9909	3.29	0.38	11.3	6	0	white
15	5.9	0.415	0.02	0.8	0.038	22.0	63.0	0.9932	3.36	0.36	9.3	5	0	white
16	6.6	0.23	0.26	1.3	0.045	16.0	128.0	0.9934	3.36	0.6	10.0	6	0	white
17	8.6	0.55	0.35	15.55	0.057	35.5	366.5	1.0001	3.04	0.63	11.0	3	0	white
18	6.9	0.35	0.74	1.0	0.044	18.0	132.0	0.992	3.13	0.55	10.2	5	0	white
19	7.6	0.14	0.74	1.6	0.04	27.0	103.0	0.9916	3.07	0.4	10.8	7	1	white
20	9.2	0.28	0.49	11.8	0.042	29.0	137.0	0.998	3.1	0.34	10.1	4	0	white
21	6.2	0.18	0.49	4.5	0.047	17.0	90.0	0.9919	3.27	0.37	11.6	6	0	white
22	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
23	9.8	0.25	0.74	10.0	0.056	36.0	225.0	0.9977	3.06	0.43	10.0	4	0	white
24	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
25	6.8	0.22	0.49	0.9	0.052	26.0	128.0	0.991	3.25	0.35	11.4	6	0	white
26	7.2	0.22	0.49	1.0	0.045	34.0	140.0	0.99	3.05	0.34	12.7	6	0	white
27	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
28	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
29	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white
30	7.0	0.39	0.24	1.0	0.048	8.0	119.0	0.9923	3.0	0.31	10.1	4	0	white
31	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white
32	6.5	0.36	0.49	2.9	0.03	16.0	94.0	0.9902	3.1	0.49	12.1	7	1	white
33	7.1	0.29	0.49	1.2	0.031	32.0	99.0	0.9893	3.07	0.33	12.2	6	0	white
34	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
35	6.9	0.23	0.24	14.2	0.053	19.0	94.0	0.9982	3.17	0.5	9.6	5	0	white
36	8.5	0.56	0.74	17.85	0.051	51.0	243.0	1.0005	2.99	0.7	9.2	5	0	white
37	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
38	6.3	0.23	0.49	7.1	0.05	67.0	210.0	0.9951	3.23	0.34	9.5	5	0	white
39	6.1	0.25	0.49	7.6	0.052	67.0	226.0	0.9956	3.16	0.47	8.9	5	0	white
40	7.2	0.26	0.74	13.6	0.05	56.0	162.0	0.998	3.03	0.44	8.8	5	0	white
41	7.2	0.31	0.24	1.4	0.057	17.0	117.0	0.9928	3.16	0.35	10.5	5	0	white
42	8.0	0.25	0.49	1.2	0.061	27.0	117.0	0.9938	3.08	0.34	9.4	5	0	white
43	7.0	0.18	0.49	5.3	0.04	34.0	125.0	0.9914	3.24	0.4	12.2	6	0	white
44	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
45	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
46	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
47	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
48	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
49	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
50	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
51	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
52	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
53	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
54	8.3	0.21	0.49	19.8	0.054	50.0	231.0	1.0012	2.99	0.54	9.2	5	0	white
55	6.9	0.34	0.74	11.2	0.069	44.0	150.0	0.9968	3.0	0.81	9.2	5	0	white
56	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
57	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
58	7.1	0.22	0.74	2.7	0.044	42.0	144.0	0.991	3.31	0.41	12.2	6	0	white
59	7.9	0.11	0.49	4.5	0.048	27.0	133.0	0.9946	3.24	0.42	10.6	6	0	white
60	8.5	0.17	0.74	3.6	0.05	29.0	128.0	0.9928	3.28	0.4	12.4	6	0	white
61	6.4	0.145	0.49	5.4	0.048	54.0	164.0	0.9946	3.56	0.44	10.8	6	0	white
62	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
63	8.3	0.19	0.49	1.2	0.051	11.0	137.0	0.9918	3.06	0.46	11.0	6	0	white
64	8.0	0.44	0.49	9.1	0.031	46.0	151.0	0.9926	3.16	0.27	12.7	8	1	white
65	7.0	0.2	0.74	0.8	0.044	19.0	163.0	0.9931	3.46	0.53	10.2	5	0	white
66	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
67	7.1	0.25	0.49	3.0	0.03	30.0	96.0	0.9903	3.13	0.39	12.3	7	1	white
68	6.5	0.24	0.24	1.6	0.046	15.0	60.0	0.9928	3.19	0.39	9.8	5	0	white
69	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
70	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
71	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
72	7.8	0.21	0.49	1.35	0.052	6.0	48.0	0.9911	3.15	0.28	11.4	5	0	white
73	7.0	0.2	0.49	5.9	0.038	39.0	128.0	0.9938	3.21	0.48	10.8	6	0	white
74	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
75	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
76	7.1	0.85	0.49	8.7	0.028	40.0	184.0	0.9962	3.22	0.36	10.7	5	0	white
77	7.6	0.51	0.24	1.2	0.04	10.0	104.0	0.992	3.05	0.29	10.8	6	0	white
78	7.9	0.22	0.24	4.6	0.044	39.0	159.0	0.9927	2.99	0.28	11.5	6	0	white
79	7.7	0.16	0.49	2.0	0.056	20.0	124.0	0.9948	3.32	0.49	10.7	6	0	white
80	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
81	6.6	0.25	0.24	1.7	0.048	26.0	124.0	0.9942	3.37	0.6	10.1	6	0	white
82	6.7	0.16	0.49	2.4	0.046	57.0	187.0	0.9952	3.62	0.81	10.4	6	0	white
83	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
84	7.5	0.32	0.24	4.6	0.053	8.0	134.0	0.9958	3.14	0.5	9.1	3	0	white
85	7.4	0.28	0.49	1.5	0.034	20.0	126.0	0.9918	2.98	0.39	10.6	6	0	white
86	6.2	0.15	0.49	0.9	0.033	17.0	51.0	0.9932	3.3	0.7	9.4	6	0	white
87	6.7	0.25	0.74	19.4	0.054	44.0	169.0	1.0004	3.51	0.45	9.8	6	0	white
88	6.5	0.26	0.74	13.3	0.044	68.0	224.0	0.9972	3.18	0.54	9.5	6	0	white
89	7.9	0.16	0.74	17.85	0.037	52.0	187.0	0.9998	2.99	0.41	9.3	5	0	white
90	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white
91	7.5	0.2	0.49	1.3	0.031	8.0	97.0	0.9918	3.06	0.62	11.1	5	0	white
92	8.0	0.3	0.49	9.4	0.046	47.0	188.0	0.9964	3.14	0.48	10.0	5	0	white
93	8.0	0.34	0.49	9.0	0.033	39.0	180.0	0.9936	3.13	0.38	12.3	8	1	white
94	7.7	0.35	0.49	8.65	0.033	42.0	186.0	0.9931	3.14	0.38	12.4	8	1	white
95	7.6	0.29	0.49	9.6	0.03	45.0	197.0	0.9938	3.13	0.38	12.3	7	1	white
96	6.7	0.62	0.24	1.1	0.039	6.0	62.0	0.9934	3.41	0.32	10.4	5	0	white
97	6.8	0.27	0.49	1.2	0.044	35.0	126.0	0.99	3.13	0.48	12.1	7	1	white
98	7.7	0.27	0.49	1.8	0.041	23.0	86.0	0.9914	3.16	0.42	12.5	6	0	white
99	6.7	0.51	0.24	2.1	0.043	14.0	155.0	0.9904	3.22	0.6	13.0	6	0	white
100	7.4	0.19	0.49	9.3	0.03	26.0	132.0	0.994	2.99	0.32	11.0	7	1	white

Rows: 1-100 | Columns: 14

Note

vastorbit 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 vastorbit environment.

You can easily divide your dataset into training and testing subsets using the VastFrame.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 = vod.load_winequality()
train, test = data.train_test_split(test_size = 0.2)

Warning

In this case, vastorbit 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 VastFrame.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 GradientBoostingRegressor model:

from vastorbit.machine_learning.vast import GradientBoostingRegressor

Then we can create the model:

model = GradientBoostingRegressor(
    n_estimators = 3,
)

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,
)

Important

To train a model, you can directly use the VastFrame 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 vastorbit, 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 GradientBoosting, feature importance is calculated using the average gain of each tree. To determine the final score, vastorbit 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	0.08998262083796227
max_error	2.9889684397098217
median_absolute_error	0.6649092
mean_absolute_error	0.6419761432493359
mean_squared_error	136.67330482845054
root_mean_squared_error	0.8254058781499691
r2	0.08651843162232586
r2_adj	0.08236624267515458
aic	6539.805708747574
bic	6575.981229464017

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()

Prediction¶

Prediction is straight-forward:

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

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 seedrand Decimal(26, 6)	123 prediction Decimal(23, 19)
1	6.9	0.35	0.74	1.0	0.044	18.0	132.0	0.992	3.13	0.55	10.2	5	0	white	0.11	5.769569829480156
2	9.2	0.28	0.49	11.8	0.042	29.0	137.0	0.998	3.1	0.34	10.1	4	0	white	0.11	5.775263510117812
3	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white	0.2	5.769569829480156
4	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white	0.04	5.769569829480156
5	6.3	0.23	0.49	7.1	0.05	67.0	210.0	0.9951	3.23	0.34	9.5	5	0	white	0.2	5.775263510117812
6	7.2	0.31	0.24	1.4	0.057	17.0	117.0	0.9928	3.16	0.35	10.5	5	0	white	0.18	5.714769509872761
7	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white	0.01	5.775263510117812
8	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white	0.06	5.714769509872761
9	7.6	0.51	0.24	1.2	0.04	10.0	104.0	0.992	3.05	0.29	10.8	6	0	white	0.0	5.769569829480156
10	6.7	0.16	0.49	2.4	0.046	57.0	187.0	0.9952	3.62	0.81	10.4	6	0	white	0.05	5.843183970655404
11	7.5	0.32	0.24	4.6	0.053	8.0	134.0	0.9958	3.14	0.5	9.1	3	0	white	0.12	5.714769509872761
12	6.7	0.51	0.24	2.1	0.043	14.0	155.0	0.9904	3.22	0.6	13.0	6	0	white	0.01	6.018284255588411
13	6.6	0.3	0.24	1.2	0.034	17.0	121.0	0.9933	3.13	0.36	9.2	5	0	white	0.16	5.714769509872761
14	6.8	0.36	0.24	4.6	0.039	24.0	124.0	0.9909	3.27	0.34	12.6	7	1	white	0.15	6.018284255588411
15	7.9	0.2	0.49	1.6	0.053	15.0	144.0	0.993	3.16	0.47	10.5	5	0	white	0.1	5.843183970655404
16	7.3	0.22	0.49	9.9	0.031	48.0	161.0	0.9937	3.01	0.28	11.2	6	0	white	0.03	5.905257700971672
17	7.4	0.19	0.49	9.3	0.03	26.0	132.0	0.994	2.99	0.32	11.0	7	1	white	0.06	5.905257700971672
18	7.3	0.155	0.49	1.3	0.039	34.0	136.0	0.9926	3.14	0.77	10.5	6	0	white	0.09	5.807378451805887
19	6.9	0.21	0.49	1.4	0.041	15.0	164.0	0.9927	3.25	0.63	11.0	5	0	white	0.17	5.843183970655404
20	8.0	0.25	0.49	9.0	0.044	31.0	185.0	0.998	3.34	0.49	10.0	6	0	white	0.02	5.775263510117812
21	7.2	0.27	0.74	12.5	0.037	47.0	156.0	0.9981	3.04	0.44	8.7	5	0	white	0.18	5.775263510117812
22	8.1	0.3	0.49	8.1	0.037	26.0	174.0	0.9943	3.1	0.3	11.2	7	1	white	0.09	5.775263510117812
23	7.3	0.26	0.49	5.0	0.028	32.0	107.0	0.9936	3.24	0.54	10.8	6	0	white	0.07	5.775263510117812
24	6.0	0.17	0.49	1.0	0.034	26.0	106.0	0.992	3.21	0.42	9.8	6	0	white	0.04	5.769569829480156
25	7.3	0.26	0.49	5.0	0.028	32.0	107.0	0.9936	3.24	0.54	10.8	6	0	white	0.09	5.775263510117812
26	7.1	0.53	0.24	0.8	0.029	29.0	86.0	0.993	3.16	0.32	9.1	4	0	white	0.18	5.714769509872761
27	7.3	0.14	0.49	1.1	0.038	28.0	99.0	0.9928	3.2	0.72	10.6	6	0	white	0.12	5.843183970655404
28	8.9	0.13	0.49	1.0	0.028	6.0	24.0	0.9926	2.91	0.32	9.9	5	0	white	0.1	5.807378451805887
29	7.1	0.36	0.24	1.8	0.025	32.0	102.0	0.9903	3.34	0.59	12.8	6	0	white	0.19	6.018284255588411
30	7.9	0.18	0.49	5.2	0.051	36.0	157.0	0.9953	3.18	0.48	10.6	6	0	white	0.11	5.843183970655404
31	7.3	0.21	0.49	1.8	0.038	44.0	152.0	0.9912	3.32	0.44	12.6	7	1	white	0.2	6.018284255588411
32	6.9	0.3	0.49	7.6	0.057	25.0	156.0	0.9962	3.43	0.63	11.0	7	1	white	0.17	5.775263510117812
33	7.9	0.42	0.49	8.2	0.056	32.0	164.0	0.9965	3.29	0.6	11.2	7	1	white	0.12	5.775263510117812
34	6.9	0.24	0.49	1.3	0.032	35.0	148.0	0.9932	3.45	0.57	10.7	7	1	white	0.07	5.775263510117812
35	9.1	0.28	0.49	2.0	0.059	10.0	112.0	0.9958	3.15	0.46	10.1	5	0	white	0.09	5.775263510117812
36	6.4	0.25	0.74	7.8	0.045	52.0	209.0	0.9956	3.21	0.42	9.2	6	0	white	0.04	5.775263510117812
37	7.3	0.3	0.74	13.5	0.039	46.0	165.0	0.9982	3.02	0.4	8.7	5	0	white	0.04	5.775263510117812
38	7.9	0.14	0.74	1.2	0.028	30.0	165.0	0.991	3.08	0.82	12.3	6	0	white	0.2	5.9195304418985275
39	6.6	0.55	0.01	2.7	0.034	56.0	122.0	0.9906	3.15	0.3	11.9	5	0	white	0.1	6.018284255588411
40	6.2	0.2	0.49	1.6	0.065	17.0	143.0	0.9937	3.22	0.52	9.2	6	0	white	0.05	5.843183970655404
41	8.9	0.32	0.49	1.6	0.05	17.0	131.0	0.9956	3.13	0.34	9.4	5	0	white	0.16	5.775263510117812
42	8.2	0.2	0.49	3.5	0.057	14.0	108.0	0.9928	3.19	0.35	11.5	6	0	white	0.15	5.843183970655404
43	7.4	0.19	0.49	6.7	0.037	15.0	110.0	0.9938	3.2	0.38	11.0	7	1	white	0.18	5.843183970655404
44	7.3	0.19	0.49	15.55	0.058	50.0	134.0	0.9998	3.42	0.36	9.1	7	1	white	0.09	5.905257700971672
45	7.5	0.24	0.49	9.4	0.048	50.0	149.0	0.9962	3.17	0.59	10.5	7	1	white	0.15	5.775263510117812
46	6.4	0.22	0.49	7.5	0.054	42.0	151.0	0.9948	3.27	0.52	10.1	6	0	white	0.07	5.905257700971672
47	8.2	0.29	0.49	1.0	0.044	29.0	118.0	0.9928	3.24	0.36	10.9	4	0	white	0.12	5.775263510117812
48	7.8	0.4	0.49	7.8	0.06	34.0	162.0	0.9966	3.26	0.58	11.3	6	0	white	0.17	5.775263510117812
49	8.1	0.28	0.46	15.4	0.059	32.0	177.0	1.0004	3.27	0.58	9.0	4	0	white	0.09	5.775263510117812
50	7.9	0.18	0.33	1.2	0.033	20.0	72.0	0.9922	3.12	0.38	10.5	7	1	white	0.13	5.769569829480156
51	7.6	0.32	0.34	18.35	0.054	44.0	197.0	1.0008	3.22	0.55	9.0	5	0	white	0.09	5.775263510117812
52	7.2	0.25	0.28	14.4	0.055	55.0	205.0	0.9986	3.12	0.38	9.0	7	1	white	0.0	5.775263510117812
53	9.0	0.43	0.3	1.5	0.05	7.0	175.0	0.9951	3.11	0.45	9.7	4	0	white	0.19	5.775263510117812
54	6.8	0.26	0.29	16.95	0.056	48.0	179.0	0.9998	3.45	0.4	9.6	5	0	white	0.12	5.775263510117812
55	6.6	0.34	0.27	6.2	0.059	23.0	136.0	0.9957	3.3	0.49	10.1	6	0	white	0.1	5.775263510117812
56	8.2	0.15	0.48	2.7	0.052	24.0	190.0	0.995	3.5	0.45	10.9	7	1	white	0.1	5.843183970655404
57	7.0	0.3	0.32	6.4	0.034	28.0	97.0	0.9924	3.23	0.44	11.8	6	0	white	0.13	5.925315160055462
58	7.5	0.22	0.33	6.7	0.036	45.0	138.0	0.9939	3.2	0.68	11.4	6	0	white	0.2	5.843183970655404
59	6.6	0.545	0.04	2.5	0.031	48.0	111.0	0.9906	3.14	0.32	11.9	5	0	white	0.06	6.018284255588411
60	8.0	0.32	0.36	4.6	0.042	56.0	178.0	0.9928	3.29	0.47	12.0	6	0	white	0.06	5.775263510117812
61	6.1	0.22	0.23	3.1	0.052	15.0	104.0	0.9948	3.14	0.42	8.7	5	0	white	0.15	5.843183970655404
62	8.3	0.3	0.36	10.0	0.042	33.0	169.0	0.9982	3.23	0.51	9.3	6	0	white	0.09	5.775263510117812
63	6.1	0.34	0.31	12.0	0.053	46.0	238.0	0.9977	3.16	0.48	8.6	5	0	white	0.13	5.775263510117812
64	6.6	0.62	0.2	8.7	0.046	81.0	224.0	0.99605	3.17	0.44	9.3	5	0	white	0.11	5.678837363927019
65	8.7	0.45	0.4	1.5	0.067	17.0	100.0	0.9957	3.27	0.57	10.1	6	0	white	0.13	5.775263510117812
66	6.7	0.24	0.29	6.8	0.038	54.0	127.0	0.9932	3.33	0.46	11.6	7	1	white	0.19	5.775263510117812
67	6.4	0.33	0.24	1.6	0.054	25.0	117.0	0.9943	3.36	0.5	9.3	5	0	white	0.17	5.714769509872761
68	6.9	0.25	0.27	9.05	0.039	37.0	128.0	0.9936	3.27	0.34	11.3	8	1	white	0.1	5.775263510117812
69	7.5	0.33	0.39	12.4	0.065	29.0	119.0	0.9974	3.16	0.39	9.4	5	0	white	0.08	5.775263510117812
70	6.6	0.36	0.21	1.5	0.049	39.0	184.0	0.9928	3.18	0.41	9.9	6	0	white	0.06	5.714769509872761
71	7.1	0.32	0.4	1.5	0.034	13.0	84.0	0.9944	3.42	0.6	10.4	5	0	white	0.06	5.775263510117812
72	6.5	0.32	0.12	11.5	0.033	35.0	165.0	0.9974	3.22	0.32	9.0	5	0	white	0.13	5.714769509872761
73	7.7	0.44	0.24	11.2	0.031	41.0	167.0	0.9948	3.12	0.43	11.3	7	1	white	0.02	5.714769509872761
74	7.4	0.49	0.24	15.1	0.03	34.0	153.0	0.9953	3.13	0.51	12.0	7	1	white	0.2	5.714769509872761
75	6.4	0.21	0.3	5.6	0.044	43.0	160.0	0.9949	3.6	0.41	10.6	6	0	white	0.12	5.843183970655404
76	8.0	0.55	0.42	12.6	0.211	37.0	213.0	0.9988	2.99	0.56	9.3	5	0	white	0.18	5.775263510117812
77	6.5	0.34	0.36	11.0	0.052	53.0	247.0	0.9984	3.44	0.55	9.3	6	0	white	0.19	5.775263510117812
78	8.2	0.18	0.31	11.8	0.039	96.0	249.0	0.9976	3.07	0.52	9.5	6	0	white	0.02	5.905257700971672
79	8.3	0.28	0.45	7.8	0.059	32.0	139.0	0.9972	3.33	0.77	11.2	6	0	white	0.06	5.775263510117812
80	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	0.02	6.018284255588411
81	7.4	0.44	0.2	11.5	0.049	44.0	157.0	0.998	3.27	0.44	9.0	5	0	white	0.07	5.714769509872761
82	7.0	0.24	0.25	1.7	0.042	48.0	189.0	0.992	3.25	0.42	11.4	6	0	white	0.1	5.988968439709822
83	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	0.15	6.018284255588411
84	8.3	0.27	0.39	2.4	0.058	16.0	107.0	0.9955	3.28	0.59	10.3	5	0	white	0.07	5.775263510117812
85	8.9	0.33	0.34	1.4	0.056	14.0	171.0	0.9946	3.13	0.47	9.7	5	0	white	0.14	5.775263510117812
86	6.7	0.18	0.19	4.7	0.046	57.0	161.0	0.9946	3.32	0.66	10.5	6	0	white	0.0	5.843183970655404
87	7.8	0.2	0.28	10.2	0.054	78.0	186.0	0.997	3.14	0.46	10.0	6	0	white	0.12	5.905257700971672
88	7.3	0.13	0.31	2.3	0.054	22.0	104.0	0.9924	3.24	0.92	11.5	7	1	white	0.06	5.842852264917723
89	7.1	0.25	0.3	2.4	0.042	25.0	122.0	0.994	3.43	0.61	10.5	6	0	white	0.06	5.775263510117812
90	6.3	0.19	0.21	1.8	0.049	35.0	163.0	0.9924	3.31	0.5	10.3	6	0	white	0.18	5.898880325509904
91	7.0	0.21	0.22	5.1	0.048	38.0	168.0	0.9945	3.34	0.49	10.4	6	0	white	0.16	5.843183970655404
92	8.0	0.2	0.36	1.2	0.032	21.0	78.0	0.9921	3.08	0.37	10.4	6	0	white	0.01	5.769569829480156
93	8.0	0.25	0.26	14.0	0.043	41.0	248.0	0.9986	3.03	0.57	8.7	6	0	white	0.12	5.714769509872761
94	7.3	0.24	0.41	13.6	0.05	41.0	178.0	0.9988	3.37	0.43	9.7	5	0	white	0.15	5.775263510117812
95	6.8	0.24	0.31	18.3	0.046	40.0	142.0	1.0	3.3	0.41	8.7	5	0	white	0.05	5.775263510117812
96	7.9	0.26	0.33	10.3	0.039	73.0	212.0	0.9969	2.93	0.49	9.5	6	0	white	0.13	5.775263510117812
97	7.2	0.31	0.41	8.6	0.053	15.0	89.0	0.9976	3.29	0.64	9.9	6	0	white	0.18	5.775263510117812
98	6.7	0.44	0.31	1.9	0.03	41.0	104.0	0.99	3.29	0.62	12.6	7	1	white	0.06	6.018284255588411
99	8.8	0.23	0.35	10.7	0.04	26.0	183.0	0.9984	2.93	0.49	9.1	6	0	white	0.07	5.775263510117812
100	7.2	0.36	0.36	5.7	0.038	26.0	98.0	0.9914	2.93	0.59	12.5	7	1	white	0.18	5.988968439709822

Rows: 1-100 | Columns: 16

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 VastFrame to the predict() function, but in this case, it’s essential that the column names of the VastFrame 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()

../_images/machine_learning_VAST_gbreg.png

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()

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 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 GradientBoosting model by:

model.to_sql()

Note

This SQL query can be directly used in any database.

Deploy SQL

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

model.deploySQL()

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)

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, **kwargs) → None¶

Methods

`__init__`([name, overwrite_model])
`contour`([nbins, chart])	Draws the model's contour plot.
`deploySQL`([X])	Returns the SQL code needed to deploy the model.
`drop`()	Drops the model from the VAST 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) to draw a specific graphic.
`get_tree`([tree_id])	Returns a table with all the input tree information.
`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.
`regression_report`([metrics])	Computes a regression report using multiple metrics to evaluate the model (`r2`, `mse`, `max error`...).
`report`([metrics])	Computes a regression report using multiple metrics to evaluate the model (`r2`, `mse`, `max error`...).
`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 VAST Binary format.
`to_graphviz`([tree_id, classes_color, ...])	Returns the code for a Graphviz tree.
`to_json`([path])	Creates a Python `GradientBoosting` JSON file that can be imported into the Python `GradientBoosting` API.
`to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
`to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.

Attributes

object_type