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: int | float | Decimal = 1000000000.0, max_depth: int = 100, min_samples_leaf: int = 1, min_info_gain: int | float | Decimal = 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 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.
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
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, )
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 0.119037373735659 max_error 2.68092601912431 median_absolute_error 0.513207547169809 mean_absolute_error 0.658531720210043 mean_squared_error 0.683585392920184 root_mean_squared_error 0.826792230805409 r2 0.117387553715895 r2_adj 0.113288734305906 aic -479.981736301154 bic -443.958950785544 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]: 0.117387553715895
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 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 5.52083333333333 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 6.50338600451467 3 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 5.52083333333333 4 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 5.52083333333333 5 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 6.10162601626016 6 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 5.7 7 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 6.50338600451467 8 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 5.52083333333333 9 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 5.52083333333333 10 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 6.50338600451467 11 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 6.10162601626016 12 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 6.10162601626016 13 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 6.50338600451467 14 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 5.52083333333333 15 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 5.52083333333333 16 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 6.10162601626016 17 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 6.10162601626016 18 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 6.50338600451467 19 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 5.68092601912431 20 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 5.68092601912431 21 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 6.10162601626016 22 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 6.50338600451467 23 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 6.50338600451467 24 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 6.51320754716981 25 5.1 0.585 0.0 1.7 0.044 14.0 86.0 0.99264 3.56 0.94 12.9 7 1 red 5.52083333333333 26 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 6.10162601626016 27 5.2 0.28 0.29 1.1 0.028 18.0 69.0 0.99168 3.24 0.54 10.0 6 0 white 6.10162601626016 28 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 6.50338600451467 29 5.2 0.405 0.15 1.45 0.038 10.0 44.0 0.99125 3.52 0.4 11.6 4 0 white 6.10162601626016 30 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 5.52083333333333 31 5.3 0.31 0.38 10.5 0.031 53.0 140.0 0.99321 3.34 0.46 11.7 6 0 white 5.68092601912431 32 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 6.51320754716981 33 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 5.52083333333333 34 5.3 0.6 0.34 1.4 0.031 3.0 60.0 0.98854 3.27 0.38 13.0 6 0 white 5.7 35 5.4 0.15 0.32 2.5 0.037 10.0 51.0 0.98878 3.04 0.58 12.6 6 0 white 6.50338600451467 36 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 6.10162601626016 37 5.4 0.29 0.47 3.0 0.052 47.0 145.0 0.993 3.29 0.75 10.0 6 0 white 5.68092601912431 38 5.4 0.375 0.4 3.3 0.054 29.0 147.0 0.99482 3.42 0.52 9.1 5 0 white 5.68092601912431 39 5.4 0.46 0.15 2.1 0.026 29.0 130.0 0.98953 3.39 0.77 13.4 8 1 white 6.50338600451467 40 5.4 0.46 0.15 2.1 0.026 29.0 130.0 0.98953 3.39 0.77 13.4 8 1 white 6.50338600451467 41 5.4 0.59 0.07 7.0 0.045 36.0 147.0 0.9944 3.34 0.57 9.7 6 0 white 5.52083333333333 42 5.4 0.835 0.08 1.2 0.046 13.0 93.0 0.9924 3.57 0.85 13.0 7 1 red 5.52083333333333 43 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 6.10162601626016 44 5.5 0.15 0.32 14.0 0.031 16.0 99.0 0.99437 3.26 0.38 11.5 8 1 white 6.04651162790698 45 5.5 0.16 0.22 4.5 0.03 30.0 102.0 0.9938 3.24 0.36 9.4 6 0 white 5.52083333333333 46 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 5.52083333333333 47 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 6.10162601626016 48 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 6.50338600451467 49 5.6 0.13 0.27 4.8 0.028 22.0 104.0 0.9948 3.34 0.45 9.2 6 0 white 6.04651162790698 50 5.6 0.18 0.27 1.7 0.03 31.0 103.0 0.98892 3.35 0.37 12.9 6 0 white 6.50338600451467 51 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 6.10162601626016 52 5.6 0.19 0.39 1.1 0.043 17.0 67.0 0.9918 3.23 0.53 10.3 6 0 white 6.10162601626016 53 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 5.52083333333333 54 5.6 0.21 0.24 4.4 0.027 37.0 150.0 0.991 3.3 0.31 11.5 7 1 white 6.51320754716981 55 5.6 0.21 0.4 1.3 0.041 81.0 147.0 0.9901 3.22 0.95 11.6 8 1 white 6.50338600451467 56 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 6.50338600451467 57 5.6 0.25 0.26 3.6 0.037 18.0 115.0 0.9904 3.42 0.5 12.6 6 0 white 6.50338600451467 58 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 5.68092601912431 59 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 6.51320754716981 60 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 5.52083333333333 61 5.6 0.31 0.37 1.4 0.074 12.0 96.0 0.9954 3.32 0.58 9.2 5 0 red 5.68092601912431 62 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 5.68092601912431 63 5.6 0.35 0.37 1.0 0.038 6.0 72.0 0.9902 3.37 0.34 11.4 5 0 white 6.50338600451467 64 5.6 0.39 0.24 4.7 0.034 27.0 77.0 0.9906 3.28 0.36 12.7 5 0 white 6.51320754716981 65 5.6 0.62 0.03 1.5 0.08 6.0 13.0 0.99498 3.66 0.62 10.1 4 0 red 5.52083333333333 66 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 5.52083333333333 67 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 5.52083333333333 68 5.6 0.915 0.0 2.1 0.041 17.0 78.0 0.99346 3.68 0.73 11.4 5 0 red 5.52083333333333 69 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 6.04651162790698 70 5.7 0.15 0.28 3.7 0.045 57.0 151.0 0.9913 3.22 0.27 11.2 6 0 white 6.51320754716981 71 5.7 0.2 0.24 13.8 0.047 44.0 112.0 0.99837 2.97 0.66 8.8 6 0 white 5.52083333333333 72 5.7 0.21 0.24 2.3 0.047 60.0 189.0 0.995 3.65 0.72 10.1 6 0 white 5.52083333333333 73 5.7 0.21 0.25 1.1 0.035 26.0 81.0 0.9902 3.31 0.52 11.4 6 0 white 6.50338600451467 74 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 5.52083333333333 75 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 5.52083333333333 76 5.7 0.22 0.22 16.65 0.044 39.0 110.0 0.99855 3.24 0.48 9.0 6 0 white 5.52083333333333 77 5.7 0.24 0.47 6.3 0.069 35.0 182.0 0.99391 3.11 0.46 9.75 5 0 white 5.68092601912431 78 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 5.68092601912431 79 5.7 0.25 0.22 9.8 0.049 50.0 125.0 0.99571 3.2 0.45 10.1 6 0 white 5.52083333333333 80 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 5.68092601912431 81 5.7 0.26 0.27 4.1 0.201 73.5 189.5 0.9942 3.27 0.38 9.4 6 0 white 5.68092601912431 82 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 5.68092601912431 83 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 5.52083333333333 84 5.7 0.28 0.36 1.8 0.041 38.0 90.0 0.99002 3.27 0.98 11.9 7 1 white 6.50338600451467 85 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 6.10162601626016 86 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 6.51320754716981 87 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 6.51320754716981 88 5.7 0.39 0.25 4.9 0.033 49.0 113.0 0.98966 3.26 0.58 13.1 7 1 white 6.50338600451467 89 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 5.68092601912431 90 5.7 0.695 0.06 6.8 0.042 9.0 84.0 0.99432 3.44 0.44 10.2 5 0 white 5.52083333333333 91 5.8 0.12 0.21 1.3 0.056 35.0 121.0 0.9908 3.32 0.33 11.4 6 0 white 6.10162601626016 92 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 5.52083333333333 93 5.8 0.15 0.28 0.8 0.037 43.0 127.0 0.99198 3.24 0.51 9.3 5 0 white 6.10162601626016 94 5.8 0.15 0.31 5.9 0.036 7.0 73.0 0.99152 3.2 0.43 11.9 6 0 white 6.51320754716981 95 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 6.50338600451467 96 5.8 0.19 0.33 4.2 0.038 49.0 133.0 0.99107 3.16 0.42 11.3 7 1 white 6.51320754716981 97 5.8 0.23 0.21 1.5 0.044 21.0 110.0 0.99138 3.3 0.57 11.0 6 0 white 6.10162601626016 98 5.8 0.24 0.28 1.4 0.038 40.0 76.0 0.98711 3.1 0.29 13.9 7 1 white 6.50338600451467 99 5.8 0.24 0.39 1.5 0.054 37.0 158.0 0.9932 3.21 0.52 9.3 6 0 white 5.68092601912431 100 5.8 0.27 0.2 7.3 0.04 42.0 145.0 0.99442 3.15 0.48 9.8 5 0 white 5.52083333333333 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="<= 0.992073", color="#666666", fontcolor="#666666"]\n0 -> 2 [label="> 0.992073", color="#666666", fontcolor="#666666"]\n1 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n1 -> 3 [label="<= 0.990455", color="#666666", fontcolor="#666666"]\n1 -> 4 [label="> 0.990455", color="#666666", fontcolor="#666666"]\n2 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n2 -> 5 [label="<= 0.259375", color="#666666", fontcolor="#666666"]\n2 -> 6 [label="> 0.259375", color="#666666", fontcolor="#666666"]\n3 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n3 -> 7 [label="<= 0.54875", color="#666666", fontcolor="#666666"]\n3 -> 8 [label="> 0.54875", color="#666666", fontcolor="#666666"]\n4 [label="\\"residual_sugar\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n4 -> 9 [label="<= 2.6375", color="#666666", fontcolor="#666666"]\n4 -> 10 [label="> 2.6375", color="#666666", fontcolor="#666666"]\n5 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n5 -> 11 [label="<= 5.98125", color="#666666", fontcolor="#666666"]\n5 -> 12 [label="> 5.98125", color="#666666", fontcolor="#666666"]\n6 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n6 -> 13 [label="<= 0.220625", color="#666666", fontcolor="#666666"]\n6 -> 14 [label="> 0.220625", color="#666666", fontcolor="#666666"]\n7 [label="6.503386", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n8 [label="5.7", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n9 [label="6.101626", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n10 [label="6.513208", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n11 [label="5.520833", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n12 [label="5.353647", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n13 [label="6.046512", fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n14 [label="5.680926", 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" < 0.992073 THEN (CASE WHEN "density" < 0.990455 THEN (CASE WHEN "volatile_acidity" < 0.54875 THEN 6.503386 ELSE 5.7 END) ELSE (CASE WHEN "residual_sugar" < 2.6375 THEN 6.101626 ELSE 6.513208 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "fixed_acidity" < 5.98125 THEN 5.520833 ELSE 5.353647 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 6.046512 ELSE 5.680926 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([6.503386])
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: int | float | Decimal = 1000000000.0, max_depth: int = 100, 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