verticapy.machine_learning.vertica.tree.DecisionTreeRegressor¶

class verticapy.machine_learning.vertica.tree.DecisionTreeRegressor(name: str = None, overwrite_model: bool = False, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: Annotated[int | float | Decimal, 'Python Numbers'] = 1000000000.0, max_depth: int = 100, min_samples_leaf: int = 1, min_info_gain: Annotated[int | float | Decimal, 'Python Numbers'] = 0.0, nbins: int = 32)¶

A DecisionTreeRegressor consisting of a single tree.

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.

max_features: str / int, optional

The number of randomly chosen features from which to pick the best feature to split on a given tree node. It can be an integer or one of the two following methods:

auto:
square root of the total number of predictors.
max:
number of predictors.

max_leaf_nodes: PythonNumber, optional

The maximum number of leaf nodes for a tree in the forest, an integer between 1 and 1e9, inclusive.

max_depth: int, optional

The maximum depth for growing each tree, an integer between 1 and 100, inclusive.

min_samples_leaf: int, optional

The minimum number of samples each branch must have after a node is split, an integer between 1 and 1e6, inclusive. Any split that results in fewer remaining samples is discarded.

min_info_gain: PythonNumber, optional

The minimum threshold for including a split, a float between 0.0 and 1.0, inclusive. A split with information gain less than this threshold is discarded.

nbins: int, optional

The number of bins to use for continuous features, an integer between 2 and 1000, inclusive.

Attributes¶

Many attributes are created during the fitting phase.

trees_: list of one BinaryTreeRegressor: One tree model which is instance of BinaryTreeRegressor. It possess 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 MDI (Mean Decreased Impurity). 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.

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 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 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 DecisionTreeRegressor model:

from verticapy.machine_learning.vertica import DecisionTreeRegressor

Then we can create the model:

model = DecisionTreeRegressor(
    max_features = "auto",
    max_leaf_nodes = 32,
    max_depth = 3,
    min_samples_leaf = 5,
    min_info_gain = 0.0,
    nbins = 32
)

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
===========
SELECT rf_regressor('"public"."_verticapy_tmp_randomforestregressor_v_mldb_9cc4c518979c11efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_9cfada04979c11efa8720242ac120002_"', 'quality', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"' USING PARAMETERS exclude_columns='', ntree=1, mtry=3, sampling_size=1, max_depth=3, max_breadth=32, min_leaf_size=5, min_info_gain=0, nbins=32);

=======
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    |  1  
rejected_row_count|  0  
accepted_row_count|5200 

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 RandomForest, feature importance is calculated using the MDI (Mean Decreased Impurity). 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	0.175738325931752
max_error	2.94337194337194
median_absolute_error	0.503624939584341
mean_absolute_error	0.625025236541239
mean_squared_error	0.612111054322152
root_mean_squared_error	0.782375264385417
r2	0.174952626357149
r2_adj	0.171115196712299
aic	-622.458575462804
bic	-586.446828162504

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]: 0.174952626357149

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	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white	6.61695906432748
2	4.4	0.46	0.1	2.8	0.024	31.0	111.0	0.98816	3.48	0.34	13.1	6	0	white	6.61695906432748
3	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white	6.25038880248833
4	4.9	0.47	0.17	1.9	0.035	60.0	148.0	0.98964	3.27	0.35	11.5	6	0	white	6.25038880248833
5	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white	6.61695906432748
6	5.1	0.21	0.28	1.4	0.047	48.0	148.0	0.99168	3.5	0.49	10.4	5	0	white	5.93393393393393
7	5.1	0.47	0.02	1.3	0.034	18.0	44.0	0.9921	3.9	0.62	12.8	6	0	red	5.67267683772538
8	5.2	0.645	0.0	2.15	0.08	15.0	28.0	0.99444	3.78	0.61	12.5	6	0	red	5.50362493958434
9	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white	5.94337194337194
10	5.3	0.395	0.07	1.3	0.035	26.0	102.0	0.992	3.5	0.35	10.6	6	0	white	5.67267683772538
11	5.4	0.255	0.33	1.2	0.051	29.0	122.0	0.99048	3.37	0.66	11.3	6	0	white	5.50362493958434
12	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	6.25038880248833
13	5.4	0.45	0.27	6.4	0.033	20.0	102.0	0.98944	3.22	0.27	13.4	8	1	white	6.61695906432748
14	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	6.61695906432748
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	5.94337194337194
16	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	6.25038880248833
17	5.6	0.19	0.31	2.7	0.027	11.0	100.0	0.98964	3.46	0.4	13.2	7	1	white	6.61695906432748
18	5.6	0.34	0.25	2.5	0.046	47.0	182.0	0.99093	3.21	0.4	11.3	5	0	white	6.25038880248833
19	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	5.50362493958434
20	5.7	0.245	0.33	1.1	0.049	28.0	150.0	0.9927	3.13	0.42	9.3	5	0	white	5.50362493958434
21	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	5.50362493958434
22	5.7	0.335	0.34	1.0	0.04	13.0	174.0	0.992	3.27	0.66	10.0	5	0	white	5.67267683772538
23	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	5.94337194337194
24	5.8	0.19	0.49	4.9	0.04	44.0	118.0	0.9935	3.34	0.38	9.5	7	1	white	5.94337194337194
25	5.8	0.26	0.24	9.2	0.044	55.0	152.0	0.9961	3.31	0.38	9.4	5	0	white	5.94337194337194
26	5.8	0.27	0.27	12.3	0.045	55.0	170.0	0.9972	3.28	0.42	9.3	6	0	white	5.67267683772538
27	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	5.67267683772538
28	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	5.50362493958434
29	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	5.50362493958434
30	5.8	0.345	0.15	10.8	0.033	26.0	120.0	0.99494	3.25	0.49	10.0	6	0	white	5.67267683772538
31	5.9	0.24	0.28	1.3	0.032	36.0	95.0	0.98889	3.08	0.64	12.9	7	1	white	6.25038880248833
32	5.9	0.32	0.28	4.7	0.039	34.0	94.0	0.98964	3.22	0.57	13.1	7	1	white	6.61695906432748
33	5.9	0.34	0.31	2.0	0.03	38.0	142.0	0.98892	3.4	0.41	12.9	7	1	white	6.25038880248833
34	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	6.3453237410072
35	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	6.25038880248833
36	6.0	0.27	0.4	1.7	0.021	18.0	82.0	0.9891	3.24	0.95	13.1333333333333	6	0	white	6.25038880248833
37	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	5.50362493958434
38	6.0	0.34	0.32	3.8	0.044	13.0	116.0	0.99108	3.39	0.44	11.8	7	1	white	6.61695906432748
39	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	5.50362493958434
40	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	6.25038880248833
41	6.1	0.28	0.25	12.9	0.054	34.0	189.0	0.9979	3.25	0.43	9.0	4	0	white	5.50362493958434
42	6.1	0.31	0.58	5.0	0.039	36.0	114.0	0.9909	3.3	0.6	12.3	8	1	white	6.61695906432748
43	6.1	0.34	0.21	5.0	0.042	17.0	133.0	0.99373	3.02	0.53	9.4	5	0	white	5.67267683772538
44	6.1	0.38	0.14	3.9	0.06	27.0	113.0	0.99344	3.07	0.34	9.2	4	0	white	5.50362493958434
45	6.1	0.41	0.0	1.6	0.063	36.0	87.0	0.9914	3.27	0.67	10.8	6	0	white	5.50362493958434
46	6.1	0.48	0.09	1.7	0.078	18.0	30.0	0.99402	3.45	0.54	11.2	6	0	red	5.50362493958434
47	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	5.50362493958434
48	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	5.50362493958434
49	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	6.25038880248833
50	6.2	0.36	0.22	5.25	0.038	44.0	145.0	0.99184	3.22	0.4	11.2	6	0	white	6.61695906432748
51	6.2	0.36	0.38	3.2	0.031	20.0	89.0	0.98956	3.06	0.33	12.0	7	1	white	6.61695906432748
52	6.2	0.46	0.17	1.6	0.073	7.0	11.0	0.99425	3.61	0.54	11.4	5	0	red	5.50362493958434
53	6.2	0.7	0.15	5.1	0.076	13.0	27.0	0.99622	3.54	0.6	11.9	6	0	red	5.50362493958434
54	6.3	0.2	0.26	1.6	0.027	36.0	141.0	0.99268	3.53	0.56	10.8	6	0	white	5.94337194337194
55	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	5.50362493958434
56	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	5.50362493958434
57	6.3	0.24	0.55	8.1	0.04	67.0	216.0	0.99596	3.24	0.5	9.2	5	0	white	5.94337194337194
58	6.3	0.25	0.23	14.9	0.039	47.0	142.0	0.99705	3.14	0.35	9.7	6	0	white	5.94337194337194
59	6.3	0.25	0.23	14.9	0.039	47.0	142.0	0.99705	3.14	0.35	9.7	6	0	white	5.94337194337194
60	6.3	0.255	0.37	1.1	0.04	37.0	114.0	0.9905	3.0	0.39	10.9	6	0	white	6.25038880248833
61	6.3	0.27	0.23	2.9	0.047	13.0	100.0	0.9936	3.28	0.43	9.8	5	0	white	5.50362493958434
62	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	5.67267683772538
63	6.3	0.28	0.22	11.5	0.036	27.0	150.0	0.99445	3.0	0.33	10.6	6	0	white	5.67267683772538
64	6.3	0.305	0.22	16.0	0.061	26.0	141.0	0.99824	3.08	0.5	9.1	5	0	white	5.50362493958434
65	6.3	0.34	0.29	6.2	0.046	29.0	227.0	0.9952	3.29	0.53	10.1	6	0	white	5.67267683772538
66	6.3	0.41	0.3	3.2	0.03	49.0	164.0	0.9927	3.53	0.79	11.7	7	1	white	5.67267683772538
67	6.3	0.41	0.33	4.7	0.023	28.0	110.0	0.991	3.3	0.38	12.5	7	1	white	6.61695906432748
68	6.4	0.12	0.3	1.1	0.031	37.0	94.0	0.98986	3.01	0.56	11.7	6	0	white	6.25038880248833
69	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	6.25038880248833
70	6.4	0.14	0.31	1.2	0.034	53.0	138.0	0.99084	3.38	0.35	11.5	7	1	white	6.25038880248833
71	6.4	0.17	0.27	1.5	0.037	20.0	98.0	0.9916	3.46	0.42	11.0	7	1	white	6.25038880248833
72	6.4	0.23	0.37	7.9	0.05	60.0	150.0	0.99488	2.86	0.49	9.3	6	0	white	5.50362493958434
73	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	5.94337194337194
74	6.4	0.38	0.26	8.2	0.043	28.0	98.0	0.99234	2.99	0.31	11.4	6	0	white	5.67267683772538
75	6.4	0.45	0.07	1.1	0.03	10.0	131.0	0.9905	2.97	0.28	10.8	5	0	white	6.25038880248833
76	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	5.94337194337194
77	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	6.3453237410072
78	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	5.94337194337194
79	6.5	0.2	0.35	3.9	0.04	27.0	140.0	0.99102	2.98	0.53	11.8	6	0	white	6.61695906432748
80	6.5	0.21	0.35	5.7	0.043	47.0	197.0	0.99392	3.24	0.5	10.1	6	0	white	5.94337194337194
81	6.5	0.22	0.19	1.1	0.064	36.0	191.0	0.99297	3.05	0.5	9.5	6	0	white	5.93393393393393
82	6.5	0.22	0.19	1.1	0.064	36.0	191.0	0.99297	3.05	0.5	9.5	6	0	white	5.93393393393393
83	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	5.94337194337194
84	6.5	0.24	0.36	2.2	0.027	36.0	134.0	0.9898	3.28	0.36	12.5	7	1	white	6.25038880248833
85	6.5	0.24	0.39	17.3	0.052	22.0	126.0	0.99888	3.11	0.47	9.2	6	0	white	5.50362493958434
86	6.5	0.26	0.5	8.0	0.051	46.0	197.0	0.99536	3.18	0.47	9.5	5	0	white	5.50362493958434
87	6.5	0.27	0.19	4.2	0.046	6.0	114.0	0.9955	3.25	0.35	8.6	4	0	white	5.67267683772538
88	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	5.67267683772538
89	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	5.67267683772538
90	6.5	0.27	0.4	10.0	0.039	74.0	227.0	0.99582	3.18	0.5	9.4	5	0	white	5.67267683772538
91	6.5	0.28	0.26	8.8	0.04	44.0	139.0	0.9956	3.32	0.37	10.2	6	0	white	5.67267683772538
92	6.5	0.28	0.34	3.6	0.04	29.0	121.0	0.99111	3.28	0.48	12.1	7	1	white	6.61695906432748
93	6.5	0.28	0.35	9.8	0.067	61.0	180.0	0.9972	3.15	0.57	9.0	4	0	white	5.50362493958434
94	6.5	0.33	0.38	8.3	0.048	68.0	174.0	0.99492	3.14	0.5	9.6	5	0	white	5.50362493958434
95	6.5	0.43	0.31	3.6	0.046	19.0	143.0	0.99022	3.15	0.34	12.0	8	1	white	6.61695906432748
96	6.5	0.58	0.0	2.2	0.096	3.0	13.0	0.99557	3.62	0.62	11.5	4	0	red	5.50362493958434
97	6.6	0.17	0.28	1.1	0.034	55.0	108.0	0.98939	3.0	0.52	11.9	7	1	white	6.25038880248833
98	6.6	0.17	0.35	2.6	0.03	33.0	78.0	0.99146	3.22	0.72	11.3	6	0	white	6.25038880248833
99	6.6	0.21	0.31	11.4	0.039	46.0	165.0	0.99795	3.41	0.44	9.8	7	1	white	5.94337194337194
100	6.6	0.21	0.34	5.6	0.046	30.0	140.0	0.99299	3.22	0.38	11.0	5	0	white	5.94337194337194

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()

../_images/machine_learning_vertica_tree_decision.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()
Out[5]: 'digraph Tree {\ngraph [bgcolor="#FFFFFFDD"];\n0 [label="\\"chlorides\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n0 -> 1 [label="<= 0.046625", color="#000000", fontcolor="#000000"]\n0 -> 2 [label="> 0.046625", color="#000000", fontcolor="#000000"]\n1 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n1 -> 3 [label="<= 0.991973", color="#000000", fontcolor="#000000"]\n1 -> 4 [label="> 0.991973", color="#000000", fontcolor="#000000"]\n2 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n2 -> 5 [label="<= 0.220625", color="#000000", fontcolor="#000000"]\n2 -> 6 [label="> 0.220625", color="#000000", fontcolor="#000000"]\n3 [label="\\"residual_sugar\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n3 -> 7 [label="<= 2.6375", color="#000000", fontcolor="#000000"]\n3 -> 8 [label="> 2.6375", color="#000000", fontcolor="#000000"]\n4 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n4 -> 9 [label="<= 0.2675", color="#000000", fontcolor="#000000"]\n4 -> 10 [label="> 0.2675", color="#000000", fontcolor="#000000"]\n5 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n5 -> 11 [label="<= 0.996836", color="#000000", fontcolor="#000000"]\n5 -> 12 [label="> 0.996836", color="#000000", fontcolor="#000000"]\n6 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n6 -> 13 [label="<= 10.228125", color="#000000", fontcolor="#000000"]\n6 -> 14 [label="> 10.228125", color="#000000", fontcolor="#000000"]\n7 [label="6.250389", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n8 [label="6.616959", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n9 [label="5.943372", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n10 [label="5.672677", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n11 [label="5.933934", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n12 [label="6.345324", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n13 [label="5.503625", fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n14 [label="5.903409", 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 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[6]: '(CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 6.250389 ELSE 6.616959 END) ELSE (CASE WHEN "volatile_acidity" < 0.2675 THEN 5.943372 ELSE 5.672677 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "density" < 0.996836 THEN 5.933934 ELSE 6.345324 END) ELSE (CASE WHEN "fixed_acidity" < 10.228125 THEN 5.503625 ELSE 5.903409 END) END) END)'

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[8]: array([6.250389])

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_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: Annotated[int | float | Decimal, 'Python Numbers'] = 1000000000.0, max_depth: int = 100, min_samples_leaf: int = 1, min_info_gain: Annotated[int | float | Decimal, 'Python Numbers'] = 0.0, nbins: int = 32) → None¶: Must be overridden in the child class

Methods

`__init__`([name, overwrite_model, ...])	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_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