verticapy.machine_learning.vertica.ensemble.RandomForestRegressor#
- class verticapy.machine_learning.vertica.ensemble.RandomForestRegressor(name: str = None, overwrite_model: bool = False, n_estimators: int = 10, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: int | float | Decimal = 1000000000.0, sample: float = 0.632, max_depth: int = 5, min_samples_leaf: int = 1, min_info_gain: int | float | Decimal = 0.0, nbins: int = 32)#
Creates a
RandomForestRegressor
object using the Vertica RF_REGRESSOR function. It is an ensemble learning method for regression that operates by constructing a multitude of decision trees at training-time and outputting a class with the mode.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.- n_estimators: int, optional
The number of trees in the forest, an
integer
between1
and1000
, inclusive.- max_features: int | str, optional
The number of randomly chosen features from which to pick the best feature to split 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
integerv between ``1
and1e9
, inclusive.- sample: float, optional
The portion of the input data set that is randomly selected for training each tree, a
float
between0.0
and1.0
, inclusive.- max_depth: int, optional
aximum depth of each tree, an
integer
between1
and100
, inclusive.- min_samples_leaf: int, optional
The minimum number of samples each branch must have after splitting a node, an
integer
between1
and1e6
, inclusive. A split that results in remaining samples less than this value is discarded.- min_info_gain: PythonNumber, optional
The minimum threshold for including a split, a
float
between0.0
and1.0
, inclusive. A split with information gain less than this threshold is discarded.- 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
between2
and1000
, inclusive.
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 forBinaryTreeRegressor
.- 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.- 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 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.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 fromverticapy
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()
123fixed_acidityNumeric(8)123volatile_acidityNumeric(9)123citric_acidNumeric(8)123residual_sugarNumeric(9)123chloridesFloat(22)123free_sulfur_dioxideNumeric(9)123total_sulfur_dioxideNumeric(9)123densityFloat(22)123pHNumeric(8)123sulphatesNumeric(8)123alcoholFloat(22)123qualityInteger123goodIntegerAbccolorVarchar(20)1 3.8 0.31 0.02 11.1 0.036 20.0 114.0 0.99248 3.75 0.44 12.4 6 0 white 2 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 3 4.2 0.17 0.36 1.8 0.029 93.0 161.0 0.98999 3.65 0.89 12.0 7 1 white 4 4.2 0.215 0.23 5.1 0.041 64.0 157.0 0.99688 3.42 0.44 8.0 3 0 white 5 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 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 7 4.4 0.54 0.09 5.1 0.038 52.0 97.0 0.99022 3.41 0.4 12.2 7 1 white 8 4.5 0.19 0.21 0.95 0.033 89.0 159.0 0.99332 3.34 0.42 8.0 5 0 white 9 4.6 0.445 0.0 1.4 0.053 11.0 178.0 0.99426 3.79 0.55 10.2 5 0 white 10 4.6 0.52 0.15 2.1 0.054 8.0 65.0 0.9934 3.9 0.56 13.1 4 0 red 11 4.7 0.145 0.29 1.0 0.042 35.0 90.0 0.9908 3.76 0.49 11.3 6 0 white 12 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 13 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 14 4.7 0.6 0.17 2.3 0.058 17.0 106.0 0.9932 3.85 0.6 12.9 6 0 red 15 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 16 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 17 4.8 0.13 0.32 1.2 0.042 40.0 98.0 0.9898 3.42 0.64 11.8 7 1 white 18 4.8 0.17 0.28 2.9 0.03 22.0 111.0 0.9902 3.38 0.34 11.3 7 1 white 19 4.8 0.21 0.21 10.2 0.037 17.0 112.0 0.99324 3.66 0.48 12.2 7 1 white 20 4.8 0.225 0.38 1.2 0.074 47.0 130.0 0.99132 3.31 0.4 10.3 6 0 white 21 4.8 0.26 0.23 10.6 0.034 23.0 111.0 0.99274 3.46 0.28 11.5 7 1 white 22 4.8 0.29 0.23 1.1 0.044 38.0 180.0 0.98924 3.28 0.34 11.9 6 0 white 23 4.8 0.33 0.0 6.5 0.028 34.0 163.0 0.9937 3.35 0.61 9.9 5 0 white 24 4.8 0.34 0.0 6.5 0.028 33.0 163.0 0.9939 3.36 0.61 9.9 6 0 white 25 4.8 0.65 0.12 1.1 0.013 4.0 10.0 0.99246 3.32 0.36 13.5 4 0 white 26 4.9 0.235 0.27 11.75 0.03 34.0 118.0 0.9954 3.07 0.5 9.4 6 0 white 27 4.9 0.33 0.31 1.2 0.016 39.0 150.0 0.98713 3.33 0.59 14.0 8 1 white 28 4.9 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.4666666666667 5 0 white 29 4.9 0.335 0.14 1.3 0.036 69.0 168.0 0.99212 3.47 0.46 10.4666666666667 5 0 white 30 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 31 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 32 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 33 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 34 5.0 0.17 0.56 1.5 0.026 24.0 115.0 0.9906 3.48 0.39 10.8 7 1 white 35 5.0 0.2 0.4 1.9 0.015 20.0 98.0 0.9897 3.37 0.55 12.05 6 0 white 36 5.0 0.235 0.27 11.75 0.03 34.0 118.0 0.9954 3.07 0.5 9.4 6 0 white 37 5.0 0.24 0.19 5.0 0.043 17.0 101.0 0.99438 3.67 0.57 10.0 5 0 white 38 5.0 0.24 0.21 2.2 0.039 31.0 100.0 0.99098 3.69 0.62 11.7 6 0 white 39 5.0 0.24 0.34 1.1 0.034 49.0 158.0 0.98774 3.32 0.32 13.1 7 1 white 40 5.0 0.255 0.22 2.7 0.043 46.0 153.0 0.99238 3.75 0.76 11.3 6 0 white 41 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 42 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 43 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 44 5.0 0.29 0.54 5.7 0.035 54.0 155.0 0.98976 3.27 0.34 12.9 8 1 white 45 5.0 0.3 0.33 3.7 0.03 54.0 173.0 0.9887 3.36 0.3 13.0 7 1 white 46 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 47 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 10.7 6 0 white 48 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 10.7 6 0 white 49 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 10.7 6 0 white 50 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 51 5.0 0.33 0.23 11.8 0.03 23.0 158.0 0.99322 3.41 0.64 11.8 6 0 white 52 5.0 0.35 0.25 7.8 0.031 24.0 116.0 0.99241 3.39 0.4 11.3 6 0 white 53 5.0 0.35 0.25 7.8 0.031 24.0 116.0 0.99241 3.39 0.4 11.3 6 0 white 54 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 55 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 56 5.0 0.42 0.24 2.0 0.06 19.0 50.0 0.9917 3.72 0.74 14.0 8 1 red 57 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 58 5.0 0.455 0.18 1.9 0.036 33.0 106.0 0.98746 3.21 0.83 14.0 7 1 white 59 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 60 5.0 0.61 0.12 1.3 0.009 65.0 100.0 0.9874 3.26 0.37 13.5 5 0 white 61 5.0 0.74 0.0 1.2 0.041 16.0 46.0 0.99258 4.01 0.59 12.5 6 0 red 62 5.0 1.02 0.04 1.4 0.045 41.0 85.0 0.9938 3.75 0.48 10.5 4 0 red 63 5.0 1.04 0.24 1.6 0.05 32.0 96.0 0.9934 3.74 0.62 11.5 5 0 red 64 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 65 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 66 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 67 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 68 5.1 0.23 0.18 1.0 0.053 13.0 99.0 0.98956 3.22 0.39 11.5 5 0 white 69 5.1 0.25 0.36 1.3 0.035 40.0 78.0 0.9891 3.23 0.64 12.1 7 1 white 70 5.1 0.26 0.33 1.1 0.027 46.0 113.0 0.98946 3.35 0.43 11.4 7 1 white 71 5.1 0.26 0.34 6.4 0.034 26.0 99.0 0.99449 3.23 0.41 9.2 6 0 white 72 5.1 0.29 0.28 8.3 0.026 27.0 107.0 0.99308 3.36 0.37 11.0 6 0 white 73 5.1 0.29 0.28 8.3 0.026 27.0 107.0 0.99308 3.36 0.37 11.0 6 0 white 74 5.1 0.3 0.3 2.3 0.048 40.0 150.0 0.98944 3.29 0.46 12.2 6 0 white 75 5.1 0.305 0.13 1.75 0.036 17.0 73.0 0.99 3.4 0.51 12.3333333333333 5 0 white 76 5.1 0.31 0.3 0.9 0.037 28.0 152.0 0.992 3.54 0.56 10.1 6 0 white 77 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 78 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 79 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 80 5.1 0.33 0.27 6.7 0.022 44.0 129.0 0.99221 3.36 0.39 11.0 7 1 white 81 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 82 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 83 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 84 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 85 5.1 0.42 0.0 1.8 0.044 18.0 88.0 0.99157 3.68 0.73 13.6 7 1 red 86 5.1 0.42 0.01 1.5 0.017 25.0 102.0 0.9894 3.38 0.36 12.3 7 1 white 87 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 88 5.1 0.51 0.18 2.1 0.042 16.0 101.0 0.9924 3.46 0.87 12.9 7 1 red 89 5.1 0.52 0.06 2.7 0.052 30.0 79.0 0.9932 3.32 0.43 9.3 5 0 white 90 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 91 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 11.9 8 1 white 92 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 11.9 8 1 white 93 5.2 0.16 0.34 0.8 0.029 26.0 77.0 0.99155 3.25 0.51 10.1 6 0 white 94 5.2 0.17 0.27 0.7 0.03 11.0 68.0 0.99218 3.3 0.41 9.8 5 0 white 95 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 96 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 97 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 98 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 99 5.2 0.24 0.15 7.1 0.043 32.0 134.0 0.99378 3.24 0.48 9.9 6 0 white 100 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 Rows: 1-100 | Columns: 14Note
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 intotables
ortemporary tables
. This will help enhance the overall performance of the process.Model Initialization#
First we import the
RandomForestRegressor
model:from verticapy.machine_learning.vertica import RandomForestRegressor
Then we can create the model:
model = RandomForestRegressor( max_features = "auto", max_leaf_nodes = 32, sample = 0.5, 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, )
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. Inverticapy
, we don’t work usingX
matrices andy
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 0.184936557256948 max_error 3.11698850484233 median_absolute_error 0.505137356736668 mean_absolute_error 0.616898387532274 mean_squared_error 0.607119613955618 root_mean_squared_error 0.779178807434865 r2 0.184191932728604 r2_adj 0.180403350372855 aic -634.076590622121 bic -598.053805106511 Rows: 1-10 | Columns: 2Important
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]: 0.184191932728604
Prediction#
Prediction is straight-forward:
model.predict( test, [ "fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density", ], "prediction", )
123fixed_acidityNumeric(8)123volatile_acidityNumeric(9)123citric_acidNumeric(8)123residual_sugarNumeric(9)123chloridesFloat(22)123free_sulfur_dioxideNumeric(9)123total_sulfur_dioxideNumeric(9)123densityFloat(22)123pHNumeric(8)123sulphatesNumeric(8)123alcoholFloat(22)123qualityInteger123goodIntegerAbccolorVarchar(20)123predictionFloat(22)1 4.2 0.215 0.23 5.1 0.041 64.0 157.0 0.99688 3.42 0.44 8.0 3 0 white 5.74321094116919 2 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 6.55700778498247 3 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 6.34551649276959 4 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 6.11416135093136 5 5.0 0.33 0.16 1.5 0.049 10.0 97.0 0.9917 3.48 0.44 10.7 6 0 white 6.00703985740167 6 5.0 0.33 0.23 11.8 0.03 23.0 158.0 0.99322 3.41 0.64 11.8 6 0 white 5.67022612847596 7 5.0 0.35 0.25 7.8 0.031 24.0 116.0 0.99241 3.39 0.4 11.3 6 0 white 5.67022612847596 8 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 5.43559312504722 9 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 6.19566273786577 10 5.0 1.02 0.04 1.4 0.045 41.0 85.0 0.9938 3.75 0.48 10.5 4 0 red 5.4808090781821 11 5.1 0.33 0.27 6.7 0.022 44.0 129.0 0.99221 3.36 0.39 11.0 7 1 white 5.85248114507437 12 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 6.44991888795979 13 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 6.47003675423691 14 5.2 0.155 0.33 1.6 0.028 13.0 59.0 0.98975 3.3 0.84 11.9 8 1 white 6.41038454344281 15 5.2 0.16 0.34 0.8 0.029 26.0 77.0 0.99155 3.25 0.51 10.1 6 0 white 6.21065063266623 16 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 6.28225817007084 17 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 5.88355423914942 18 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 5.88355423914942 19 5.2 0.25 0.23 1.4 0.047 20.0 77.0 0.99001 3.32 0.62 11.4 5 0 white 6.21790153442229 20 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 5.41668991943923 21 5.2 0.335 0.2 1.7 0.033 17.0 74.0 0.99002 3.34 0.48 12.3 6 0 white 6.47003675423691 22 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 6.00703985740167 23 5.2 0.44 0.04 1.4 0.036 43.0 119.0 0.9894 3.36 0.33 12.1 8 1 white 6.47003675423691 24 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 6.18314576454798 25 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 6.11698850484233 26 5.3 0.3 0.16 4.2 0.029 37.0 100.0 0.9905 3.3 0.36 11.8 8 1 white 6.34273212481501 27 5.3 0.3 0.2 1.1 0.077 48.0 166.0 0.9944 3.3 0.54 8.7 4 0 white 5.50498436757065 28 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 6.47252968516195 29 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 6.11416135093136 30 5.3 0.585 0.07 7.1 0.044 34.0 145.0 0.9945 3.34 0.57 9.7 6 0 white 5.4808090781821 31 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 5.43011182222168 32 5.4 0.23 0.36 1.5 0.03 74.0 121.0 0.98976 3.24 0.99 12.1 7 1 white 6.41038454344281 33 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 6.47252968516195 34 5.4 0.33 0.31 4.0 0.03 27.0 108.0 0.99031 3.3 0.43 12.2 7 1 white 6.55700778498247 35 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 6.21065063266623 36 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 6.21065063266623 37 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 6.41038454344281 38 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 6.47252968516195 39 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 5.83683397888135 40 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 6.18314576454798 41 5.6 0.12 0.26 4.3 0.038 18.0 97.0 0.99477 3.36 0.46 9.2 5 0 white 5.93436005443988 42 5.6 0.12 0.33 2.9 0.044 21.0 73.0 0.98896 3.17 0.32 12.9 8 1 white 6.49486264326333 43 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 6.11698850484233 44 5.6 0.205 0.16 12.55 0.051 31.0 115.0 0.99564 3.4 0.38 10.8 6 0 white 5.66789606685455 45 5.6 0.245 0.32 1.1 0.047 24.0 152.0 0.9927 3.12 0.42 9.3 6 0 white 5.79582334916065 46 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 6.16539284386006 47 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 5.85248114507437 48 5.6 0.28 0.4 6.1 0.034 36.0 118.0 0.99144 3.21 0.43 12.1 7 1 white 6.42241401984153 49 5.6 0.32 0.33 7.4 0.037 25.0 95.0 0.99268 3.25 0.49 11.1 6 0 white 5.85248114507437 50 5.6 0.41 0.24 1.9 0.034 10.0 53.0 0.98815 3.32 0.5 13.5 7 1 white 6.47003675423691 51 5.6 0.54 0.04 1.7 0.049 5.0 13.0 0.9942 3.72 0.58 11.4 5 0 red 5.46092972822366 52 5.6 0.615 0.0 1.6 0.089 16.0 59.0 0.9943 3.58 0.52 9.9 5 0 red 5.43011182222168 53 5.7 0.135 0.3 4.6 0.042 19.0 101.0 0.9946 3.31 0.42 9.3 6 0 white 5.93436005443988 54 5.7 0.14 0.3 5.4 0.045 26.0 105.0 0.99469 3.32 0.45 9.3 5 0 white 5.93436005443988 55 5.7 0.22 0.25 1.1 0.05 97.0 175.0 0.99099 3.44 0.62 11.1 6 0 white 6.00703985740167 56 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 6.49486264326333 57 5.7 0.24 0.3 1.3 0.03 25.0 98.0 0.98968 3.37 0.43 12.4 7 1 white 6.41038454344281 58 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 5.74476246518232 59 5.7 0.255 0.65 1.2 0.079 17.0 137.0 0.99307 3.2 0.42 9.4 5 0 white 5.78017618296763 60 5.7 0.26 0.25 10.4 0.02 7.0 57.0 0.994 3.39 0.37 10.6 5 0 white 5.68317016428656 61 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 5.85248114507437 62 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 6.16539284386006 63 5.7 0.32 0.5 2.6 0.049 17.0 155.0 0.9927 3.22 0.64 10.0 6 0 white 5.73345592269956 64 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 6.38798991362118 65 5.7 0.37 0.3 1.1 0.029 24.0 88.0 0.98883 3.18 0.39 11.7 6 0 white 6.47252968516195 66 5.7 0.4 0.35 5.1 0.026 17.0 113.0 0.99052 3.18 0.67 12.4 6 0 white 6.44991888795979 67 5.7 0.4 0.35 5.1 0.026 17.0 113.0 0.99052 3.18 0.67 12.4 6 0 white 6.44991888795979 68 5.7 0.44 0.13 7.0 0.025 28.0 173.0 0.9913 3.33 0.48 12.5 6 0 white 6.40466109915361 69 5.7 0.45 0.42 1.1 0.051 61.0 197.0 0.9932 3.02 0.4 9.0 5 0 white 5.73345592269956 70 5.7 0.6 0.0 1.4 0.063 11.0 18.0 0.99191 3.45 0.56 12.2 6 0 red 5.89303147520921 71 5.8 0.13 0.22 12.7 0.058 24.0 183.0 0.9956 3.32 0.42 11.7 6 0 white 5.66789606685455 72 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 6.41038454344281 73 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 6.41038454344281 74 5.8 0.18 0.37 1.2 0.036 19.0 74.0 0.98853 3.09 0.49 12.7 7 1 white 6.41038454344281 75 5.8 0.19 0.24 1.3 0.044 38.0 128.0 0.99362 3.77 0.6 10.6 5 0 white 5.84158333335787 76 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 5.74321094116919 77 5.8 0.2 0.16 1.4 0.042 44.0 99.0 0.98912 3.23 0.37 12.2 6 0 white 6.40789161251777 78 5.8 0.2 0.24 1.4 0.033 65.0 169.0 0.99043 3.59 0.56 12.3 7 1 white 6.16539284386006 79 5.8 0.2 0.34 1.0 0.035 40.0 86.0 0.98993 3.5 0.42 11.7 5 0 white 6.41038454344281 80 5.8 0.22 0.25 1.5 0.024 21.0 109.0 0.99234 3.37 0.58 10.4 6 0 white 5.89974654917103 81 5.8 0.23 0.27 1.8 0.043 24.0 69.0 0.9933 3.38 0.31 9.4 6 0 white 5.89920140534244 82 5.8 0.23 0.31 4.5 0.046 42.0 124.0 0.99324 3.31 0.64 10.8 6 0 white 5.89920140534244 83 5.8 0.26 0.18 1.2 0.031 40.0 114.0 0.9908 3.42 0.4 11.0 7 1 white 6.16539284386006 84 5.8 0.26 0.3 2.6 0.034 75.0 129.0 0.9902 3.2 0.38 11.5 4 0 white 6.41038454344281 85 5.8 0.27 0.2 14.95 0.044 22.0 179.0 0.9962 3.37 0.37 10.2 5 0 white 5.43559312504722 86 5.8 0.27 0.26 3.5 0.071 26.0 69.0 0.98994 3.1 0.38 11.5 6 0 white 6.4288814116105 87 5.8 0.275 0.3 5.4 0.043 41.0 149.0 0.9926 3.33 0.42 10.8 7 1 white 5.85248114507437 88 5.8 0.28 0.28 4.2 0.044 52.0 158.0 0.992 3.35 0.44 10.7 7 1 white 5.85248114507437 89 5.8 0.28 0.3 1.5 0.026 31.0 114.0 0.98952 3.32 0.6 12.5 7 1 white 6.47252968516195 90 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 5.65385291811907 91 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 5.65385291811907 92 5.8 0.29 0.33 3.7 0.029 30.0 88.0 0.98994 3.25 0.42 12.3 6 0 white 6.55700778498247 93 5.8 0.3 0.27 1.7 0.014 45.0 104.0 0.98914 3.4 0.56 12.6 7 1 white 6.47252968516195 94 5.8 0.31 0.33 1.2 0.036 23.0 99.0 0.9916 3.18 0.6 10.5 6 0 white 6.21065063266623 95 5.8 0.34 0.16 7.0 0.037 26.0 116.0 0.9949 3.46 0.45 10.0 7 1 white 5.55565124395615 96 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 5.55565124395615 97 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 6.38798991362118 98 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 5.73345592269956 99 5.8 0.39 0.47 7.5 0.027 12.0 88.0 0.9907 3.38 0.45 14.0 6 0 white 6.35625676013589 100 5.8 0.61 0.01 8.4 0.041 31.0 104.0 0.9909 3.26 0.72 14.05 7 1 white 6.19566273786577 Rows: 1-100 | Columns: 15Note
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 thepredict()
function, but in this case, it’s essential that the column names of thevDataFrame
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="#FFFFFF00"];\n0 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n0 -> 1 [label="<= 0.991973", color="#666666", fontcolor="#666666"]\n0 -> 2 [label="> 0.991973", color="#666666", fontcolor="#666666"]\n1 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n1 -> 3 [label="<= 0.990352", color="#666666", fontcolor="#666666"]\n1 -> 4 [label="> 0.990352", color="#666666", fontcolor="#666666"]\n2 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n2 -> 5 [label="<= 0.995215", color="#666666", fontcolor="#666666"]\n2 -> 6 [label="> 0.995215", color="#666666", fontcolor="#666666"]\n3 [label="6.568075", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n4 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n4 -> 7 [label="<= 0.363125", color="#666666", fontcolor="#666666"]\n4 -> 8 [label="> 0.363125", color="#666666", fontcolor="#666666"]\n5 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n5 -> 9 [label="<= 0.455", color="#666666", fontcolor="#666666"]\n5 -> 10 [label="> 0.455", color="#666666", fontcolor="#666666"]\n6 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n6 -> 11 [label="<= 0.259375", color="#666666", fontcolor="#666666"]\n6 -> 12 [label="> 0.259375", color="#666666", fontcolor="#666666"]\n7 [label="6.334572", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n8 [label="6.059524", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n9 [label="5.89272", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n10 [label="5.452174", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n11 [label="5.373656", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n12 [label="5.662469", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\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 Model Tracking and Versioning 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 ascikit-learn
model.The following methods for exporting the model use
MemModel
, and it is recommended to useMemModel
directly.To SQL
You can get the SQL code by:
model.to_sql() Out[6]: '((CASE WHEN "density" < 0.991973 THEN (CASE WHEN "density" < 0.990352 THEN 6.568075 ELSE (CASE WHEN "citric_acid" < 0.363125 THEN 6.334572 ELSE 6.059524 END) END) ELSE (CASE WHEN "density" < 0.995215 THEN (CASE WHEN "volatile_acidity" < 0.455 THEN 5.89272 ELSE 5.452174 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.373656 ELSE 5.662469 END) END) END) + (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "density" < 0.990352 THEN 6.562162 ELSE 6.245791 END) ELSE (CASE WHEN "density" < 0.995215 THEN 5.950855 ELSE 5.633218 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.54875 THEN (CASE WHEN "citric_acid" < 0.259375 THEN 5.465863 ELSE 5.778739 END) ELSE (CASE WHEN "density" < 1.001698 THEN 5.24359 ELSE 6.2 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "residual_sugar" < 4.675 THEN (CASE WHEN "density" < 0.990352 THEN 6.480226 ELSE 6.136808 END) ELSE 6.756098 END) ELSE (CASE WHEN "volatile_acidity" < 0.2675 THEN (CASE WHEN "density" < 1.000078 THEN 5.942701 ELSE 5.375 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.370019 ELSE 5.613889 END) END) END) + (CASE WHEN "volatile_acidity" < 0.54875 THEN (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.993594 THEN 6.264507 ELSE 5.682875 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.460905 ELSE 5.766667 END) END) ELSE (CASE WHEN "density" < 0.995215 THEN (CASE WHEN "fixed_acidity" < 6.825 THEN 5.375 ELSE 5.904762 END) ELSE (CASE WHEN "chlorides" < 0.140688 THEN 5.285088 ELSE 5.7 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "density" < 0.990352 THEN (CASE WHEN "fixed_acidity" < 7.58125 THEN 6.536082 ELSE 5.714286 END) ELSE (CASE WHEN "residual_sugar" < 2.6375 THEN 6.178571 ELSE 6.51145 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.2675 THEN (CASE WHEN "volatile_acidity" < 0.220625 THEN 6.051919 ELSE 5.769737 END) ELSE (CASE WHEN "density" < 0.995215 THEN 5.631347 ELSE 5.414489 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "density" < 0.990352 THEN (CASE WHEN "volatile_acidity" < 0.2675 THEN 6.302326 ELSE 6.585938 END) ELSE (CASE WHEN "residual_sugar" < 2.6375 THEN 6.085586 ELSE 6.4 END) END) ELSE (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "citric_acid" < 0.2075 THEN 5.355263 ELSE 5.91938 END) ELSE (CASE WHEN "fixed_acidity" < 9.85 THEN 5.555556 ELSE 5.870968 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "density" < 0.990352 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 6.403101 ELSE 6.87931 END) ELSE (CASE WHEN "residual_sugar" < 2.6375 THEN 6.128319 ELSE 6.464912 END) END) ELSE (CASE WHEN "density" < 0.995215 THEN (CASE WHEN "citric_acid" < 0.259375 THEN 5.514523 ELSE 5.978261 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.3675 ELSE 5.697904 END) END) END) + (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "density" < 0.990352 THEN 6.372093 ELSE 5.944444 END) ELSE (CASE WHEN "volatile_acidity" < 1.064375 THEN 5.427892 ELSE 4.0 END) END) ELSE (CASE WHEN "citric_acid" < 0.415 THEN (CASE WHEN "density" < 0.991973 THEN 6.397022 ELSE 5.891921 END) ELSE (CASE WHEN "residual_sugar" < 8.75 THEN 5.73545 ELSE 5.532374 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "volatile_acidity" < 0.501875 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 6.248571 ELSE 6.617143 END) ELSE 5.416667 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "fixed_acidity" < 6.446875 THEN 5.908257 ELSE 6.106267 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.360714 ELSE 5.686627 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "density" < 0.990352 THEN (CASE WHEN "volatile_acidity" < 0.2675 THEN 6.341772 ELSE 6.679612 END) ELSE (CASE WHEN "residual_sugar" < 2.6375 THEN 6.086758 ELSE 6.507692 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "fixed_acidity" < 9.09375 THEN 6.124711 ELSE 4.9 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 5.370304 ELSE 5.695305 END) END) END)) / 10'
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([6.4103844])
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, n_estimators: int = 10, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: int | float | Decimal = 1000000000.0, sample: float = 0.632, max_depth: int = 5, min_samples_leaf: int = 1, min_info_gain: int | float | Decimal = 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.
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.
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.
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