vastorbit.machine_learning.vast.ensemble.RandomForestRegressor¶

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

Creates an RandomForestRegressor 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 MDI (Mean Decreased Impurity). 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 MDI (Mean Decreased Impurity). It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.
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 RandomForest 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 RandomForestRegressor model:

from vastorbit.machine_learning.vast import RandomForestRegressor

Then we can create the model:

model = RandomForestRegressor(
    n_estimators = 5,
    max_depth = 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 RandomForest, feature importance is calculated using the MDI (Mean Decreased Impurity). 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	0.22215877978638876
max_error	3.518543510025551
median_absolute_error	0.5504433
mean_absolute_error	0.6437507500709101
mean_squared_error	0.5896723348051176
root_mean_squared_error	0.8085613377830784
r2	0.2221453323574475
r2_adj	0.21862562797897434
aic	-689.9164614690346
bic	-653.7086407938214

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(32, 27)
1	7.9	0.44	0.26	4.45	0.033	23.0	100.0	0.99117	3.17	0.52	12.7	6	0	white	0.2	6.35880904159543
2	7.6	0.31	0.27	5.8	0.036	23.0	109.0	0.99399	3.34	0.54	11.0	6	0	white	0.02	5.649018374113556
3	7.5	0.705	0.1	13.0	0.044	44.0	214.0	0.99741	3.1	0.5	9.1	5	0	white	0.04	5.259085276301158
4	7.1	0.21	0.28	2.7	0.034	23.0	111.0	0.99405	3.35	0.64	10.2	4	0	white	0.04	5.758034099263273
5	6.8	0.26	0.43	11.75	0.045	53.0	198.0	0.9969	3.26	0.55	9.5	5	0	white	0.03	5.649018374113556
6	6.2	0.44	0.18	7.7	0.096	28.0	210.0	0.99771	3.56	0.72	9.2	5	0	white	0.18	5.594640869987308
7	7.3	0.27	0.37	9.7	0.042	36.0	130.0	0.9979	3.48	0.75	9.9	6	0	white	0.19	5.649018374113556
8	6.6	0.26	0.27	1.5	0.04	19.0	114.0	0.99295	3.36	0.62	10.5	6	0	white	0.15	5.649018374113556
9	6.7	0.27	0.26	2.3	0.043	61.0	181.0	0.99394	3.45	0.63	10.6	6	0	white	0.06	5.594640869987308
10	8.1	0.33	0.36	7.4	0.037	36.0	156.0	0.99592	3.19	0.54	10.6	6	0	white	0.15	5.649018374113556
11	7.8	0.32	0.33	10.4	0.031	47.0	194.0	0.99692	3.07	0.58	9.6	6	0	white	0.02	5.649018374113556
12	6.6	0.33	0.24	16.05	0.045	31.0	147.0	0.99822	3.08	0.52	9.2	5	0	white	0.09	5.594640869987308
13	6.6	0.33	0.24	16.05	0.045	31.0	147.0	0.99822	3.08	0.52	9.2	5	0	white	0.2	5.594640869987308
14	8.2	0.26	0.33	2.6	0.053	11.0	71.0	0.99402	2.89	0.49	9.5	5	0	white	0.14	5.649018374113556
15	6.0	0.26	0.15	1.2	0.053	35.0	124.0	0.99347	3.08	0.46	8.8	5	0	white	0.13	5.594640869987308
16	7.0	0.28	0.32	1.7	0.038	27.0	128.0	0.99375	3.2	0.62	10.2	6	0	white	0.16	5.649018374113556
17	7.6	0.25	0.34	1.3	0.056	34.0	176.0	0.99434	3.1	0.51	9.5	5	0	white	0.17	5.649018374113556
18	5.6	0.35	0.4	6.3	0.022	23.0	174.0	0.9922	3.54	0.5	11.6	7	1	white	0.14	6.518543510025551
19	6.0	0.29	0.21	15.55	0.043	20.0	142.0	0.99658	3.11	0.54	10.1	6	0	white	0.05	5.594640869987308
20	6.6	0.24	0.3	13.0	0.052	18.0	143.0	0.99825	3.37	0.49	9.4	6	0	white	0.0	5.649018374113556
21	6.2	0.26	0.37	7.1	0.047	54.0	201.0	0.99523	3.19	0.48	9.5	6	0	white	0.18	5.649018374113556
22	6.4	0.3	0.16	7.5	0.05	55.0	191.0	0.9959	3.17	0.49	9.0	5	0	white	0.05	5.594640869987308
23	8.0	0.28	0.32	7.6	0.045	61.0	204.0	0.99543	3.1	0.55	10.1	6	0	white	0.08	5.649018374113556
24	6.7	0.24	0.32	10.3	0.079	37.0	122.0	0.99662	3.02	0.45	8.8	5	0	white	0.14	5.649018374113556
25	6.3	0.18	0.22	1.5	0.043	45.0	155.0	0.99238	3.19	0.48	10.2	5	0	white	0.16	5.9410343528806475
26	6.9	0.26	0.31	7.0	0.039	37.0	175.0	0.99376	3.32	0.49	11.4	6	0	white	0.0	5.649018374113556
27	6.4	0.31	0.4	6.4	0.039	39.0	191.0	0.99513	3.14	0.52	9.8	5	0	white	0.11	5.649018374113556
28	8.6	0.34	0.36	1.4	0.045	11.0	119.0	0.99556	3.17	0.47	9.4	4	0	white	0.04	5.649018374113556
29	7.4	0.4	0.41	14.1	0.053	37.0	194.0	0.99886	3.2	0.63	9.4	6	0	white	0.02	5.649018374113556
30	6.6	0.26	0.25	11.6	0.045	45.0	178.0	0.99691	3.33	0.43	9.8	6	0	white	0.01	5.594640869987308
31	6.2	0.3	0.49	11.2	0.058	68.0	215.0	0.99656	3.19	0.6	9.4	6	0	white	0.18	5.649018374113556
32	7.3	0.16	0.35	1.5	0.036	29.0	108.0	0.99342	3.27	0.51	10.2	6	0	white	0.16	5.947366083076601
33	6.7	0.54	0.27	7.1	0.049	8.0	178.0	0.99502	3.16	0.38	9.4	4	0	white	0.05	5.649018374113556
34	6.8	0.23	0.3	1.7	0.043	19.0	95.0	0.99207	3.17	0.46	10.7	7	1	white	0.04	5.945799981200279
35	6.2	0.27	0.18	1.5	0.028	20.0	111.0	0.99228	3.41	0.5	10.0	5	0	white	0.07	5.730912145497277
36	9.0	0.29	0.34	12.1	0.03	34.0	177.0	0.99706	3.13	0.47	10.6	5	0	white	0.08	5.649018374113556
37	7.5	0.2	0.47	16.9	0.052	51.0	188.0	0.99944	3.09	0.62	9.3	5	0	white	0.14	6.3475380891638435
38	6.4	0.15	0.44	1.2	0.043	67.0	150.0	0.9907	3.14	0.73	11.2	7	1	white	0.0	6.0452190321624455
39	6.9	0.28	0.22	10.0	0.052	36.0	131.0	0.99696	3.08	0.46	9.6	5	0	white	0.02	5.594640869987308
40	6.9	0.32	0.26	2.3	0.03	11.0	103.0	0.99106	3.06	0.42	11.1	6	0	white	0.16	6.518543510025551
41	6.4	0.28	0.56	1.7	0.156	49.0	106.0	0.99354	3.1	0.37	9.2	6	0	white	0.11	5.649018374113556
42	6.8	0.18	0.3	12.8	0.062	19.0	171.0	0.99808	3.0	0.52	9.0	7	1	white	0.07	6.428785420213051
43	6.3	0.21	0.29	11.7	0.048	49.0	147.0	0.99482	3.22	0.38	10.8	5	0	white	0.07	5.826465707767084
44	5.4	0.5	0.13	5.0	0.028	12.0	107.0	0.99079	3.48	0.88	13.5	7	1	white	0.03	6.654422297904339
45	7.8	0.28	0.31	2.1	0.046	28.0	208.0	0.99434	3.23	0.64	9.8	5	0	white	0.14	5.649018374113556
46	6.4	0.22	0.34	1.4	0.023	56.0	115.0	0.98958	3.18	0.7	11.7	6	0	white	0.02	6.258868293398618
47	6.8	0.11	0.42	1.1	0.042	51.0	132.0	0.99059	3.18	0.74	11.3	7	1	white	0.04	6.0452190321624455
48	6.7	0.3	0.29	2.8	0.025	37.0	107.0	0.99159	3.31	0.63	11.3	7	1	white	0.03	6.518543510025551
49	6.6	0.26	0.21	2.9	0.026	48.0	126.0	0.99089	3.22	0.38	11.3	7	1	white	0.03	6.518543510025551
50	6.6	0.35	0.35	6.0	0.063	31.0	150.0	0.99537	3.1	0.47	9.4	6	0	white	0.08	5.649018374113556
51	6.6	0.36	0.52	10.1	0.05	29.0	140.0	0.99628	3.07	0.4	9.4	5	0	white	0.02	5.649018374113556
52	6.5	0.18	0.41	14.2	0.039	47.0	129.0	0.99678	3.28	0.72	10.3	7	1	white	0.07	6.212995345625076
53	6.15	0.21	0.37	3.2	0.021	20.0	80.0	0.99076	3.39	0.47	12.0	5	0	white	0.05	6.518543510025551
54	8.0	0.24	0.26	1.7	0.033	36.0	136.0	0.99316	3.44	0.51	10.4	7	1	white	0.02	5.594640869987308
55	7.2	0.24	0.27	11.4	0.034	40.0	174.0	0.99773	3.2	0.44	9.0	5	0	white	0.2	5.649018374113556
56	7.5	0.13	0.38	1.1	0.023	42.0	104.0	0.99112	3.28	0.53	11.8	6	0	white	0.19	5.958767891756503
57	6.7	0.12	0.3	5.2	0.048	38.0	113.0	0.99352	3.33	0.44	10.1	7	1	white	0.02	5.947366083076601
58	6.4	0.28	0.27	11.0	0.042	45.0	148.0	0.99786	3.14	0.46	8.7	5	0	white	0.14	5.649018374113556
59	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	0.16	5.649018374113556
60	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	0.14	5.594640869987308
61	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	0.07	5.947366083076601
62	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	0.04	5.649018374113556
63	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	0.18	5.649018374113556
64	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	0.04	5.574329034917799
65	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	0.12	6.037176924466986
66	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	0.0	5.9410343528806475
67	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	0.05	5.649018374113556
68	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	0.09	5.856576379073919
69	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	0.14	5.649018374113556
70	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	0.11	6.518543510025551
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	0.04	5.649018374113556
72	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	0.1	6.043645770240267
73	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	0.03	5.867390948489796
74	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	0.2	5.867390948489796
75	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	0.02	5.649018374113556
76	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	0.0	6.043645770240267
77	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	0.02	6.518543510025551
78	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	0.15	5.947366083076601
79	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	0.16	5.594640869987308
80	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	0.07	5.649018374113556
81	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	0.16	5.958767891756503
82	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	0.13	5.594640869987308
83	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	0.09	5.947366083076601
84	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	0.01	5.947366083076601
85	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	0.13	5.947366083076601
86	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	0.14	5.649018374113556
87	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	0.1	6.258868293398618
88	9.2	0.18	0.49	1.5	0.041	39.0	130.0	0.9945	3.04	0.49	9.8	7	1	white	0.11	5.947366083076601
89	8.8	0.23	0.74	3.2	0.042	15.0	126.0	0.9934	3.02	0.51	11.2	6	0	white	0.14	5.649018374113556
90	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	0.07	5.594640869987308
91	7.9	0.28	0.49	7.7	0.045	48.0	195.0	0.9954	3.04	0.55	11.0	6	0	white	0.12	5.649018374113556
92	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	0.2	5.649018374113556
93	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	0.08	5.649018374113556
94	7.1	0.18	0.74	15.6	0.044	44.0	176.0	0.9996	3.38	0.67	9.0	6	0	white	0.05	6.3475380891638435
95	7.1	0.18	0.74	15.6	0.044	44.0	176.0	0.9996	3.38	0.67	9.0	6	0	white	0.12	6.3475380891638435
96	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	5.9410343528806475
97	7.9	0.12	0.49	5.2	0.049	33.0	152.0	0.9952	3.18	0.47	10.6	6	0	white	0.0	5.947366083076601
98	6.7	0.29	0.49	4.7	0.034	35.0	156.0	0.9945	3.13	0.45	9.9	6	0	white	0.14	5.649018374113556
99	6.7	0.3	0.49	4.8	0.034	36.0	158.0	0.9945	3.12	0.45	9.9	6	0	white	0.19	5.649018374113556
100	8.5	0.15	0.49	1.5	0.031	17.0	122.0	0.9932	3.03	0.4	10.3	6	0	white	0.13	5.947366083076601

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_rfreg.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 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()

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_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