verticapy.machine_learning.vertica.linear_model.Lasso#
- class verticapy.machine_learning.vertica.linear_model.Lasso(name: str = None, overwrite_model: bool = False, tol: float = 1e-06, C: int | float | Decimal = 1.0, max_iter: int = 100, solver: Literal['newton', 'bfgs', 'cgd'] = 'cgd', fit_intercept: bool = True)#
Creates a
Lasso
object using the Vertica Linear Regression algorithm. Lasso is a regularized regression method that uses anL1
penalty.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.- tol: float, optional
Determines whether the algorithm has reached the specified accuracy result.
- C: PythonNumber, optional
The regularization parameter value. The value must be zero or non-negative.
- max_iter: int, optional
Determines the maximum number of iterations the algorithm performs before achieving the specified accuracy result.
- solver: str, optional
The optimizer method used to train the model.
- newton:
Newton Method.
- bfgs:
Broyden Fletcher Goldfarb Shanno.
- cgd:
Coordinate Gradient Descent.
- fit_intercept: bool, optional
boolean
, specifies whether the model includes an intercept. If set toFalse
, no intercept is used in training the model. Note that settingfit_intercept
toFalse
does not work well with the BFGS optimizer.
Attributes#
Many attributes are created during the fitting phase.
- coef_: numpy.array
The regression coefficients. The order of coefficients is the same as the order of columns used during the fitting phase.
- intercept_: float
The expected value of the dependent variable when all independent variables are zero, serving as the baseline or constant term in the model.
- features_importance_: numpy.array
The importance of features is computed through the model coefficients, which are normalized based on their range. Subsequently, an activation function calculates the final score. 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.
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
Lasso
model:from verticapy.machine_learning.vertica import Lasso
Then we can create the model:
model = Lasso( tol = 1e-6, C = 0.5, max_iter = 100, solver = 'CGD', )
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.Metrics#
We can get the entire report using:
model.report()
value explained_variance -7.105427357601e-15 max_error 2.823711 median_absolute_error 0.823711 mean_absolute_error 0.689877813220599 mean_squared_error 0.760454758484874 root_mean_squared_error 0.872040571581893 r2 -0.000933542050953973 r2_adj -0.00557465584717165 aic -342.10168135652 bic -306.067874999972 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"])
.For
LinearModel
, we can easily get the ANOVA table using:model.report(metrics = "anova")
Df SS MS F p_value Regression 6 0.922739943317634 0.153789990552939 0.20114612395733714 0.9764792149463304 Residual 1294 989.351640788821 0.7645685013823964 Total 1300 988.428900845503 Rows: 1-3 | Columns: 6You can also use the
LinearModel.score
function to compute the R-squared value:model.score() Out[2]: -0.000933542050953973
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 0.17 0.36 1.8 0.029 93.0 161.0 0.98999 3.65 0.89 12.0 7 1 white 5.823711 2 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 5.823711 3 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 5.823711 4 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 5.823711 5 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 5.823711 6 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 5.823711 7 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 5.823711 8 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 5.823711 9 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 5.823711 10 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 5.823711 11 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 5.823711 12 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 5.823711 13 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 5.823711 14 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 5.823711 15 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 5.823711 16 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 5.823711 17 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 5.823711 18 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 5.823711 19 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 5.823711 20 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 5.823711 21 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 5.823711 22 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 5.823711 23 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 5.823711 24 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 5.823711 25 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 5.823711 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 5.823711 27 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 5.823711 28 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 5.823711 29 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 5.823711 30 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 5.823711 31 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 5.823711 32 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 5.823711 33 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 5.823711 34 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 5.823711 35 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 5.823711 36 5.2 0.38 0.26 7.7 0.053 20.0 103.0 0.9925 3.27 0.45 12.2 6 0 white 5.823711 37 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 5.823711 38 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 5.823711 39 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 5.823711 40 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 5.823711 41 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 5.823711 42 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 5.823711 43 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 5.823711 44 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 5.823711 45 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 5.823711 46 5.4 0.3 0.3 1.2 0.029 25.0 93.0 0.98742 3.31 0.4 13.6 7 1 white 5.823711 47 5.4 0.31 0.47 3.0 0.053 46.0 144.0 0.9931 3.29 0.76 10.0 5 0 white 5.823711 48 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 5.823711 49 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 5.823711 50 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 5.823711 51 5.4 0.53 0.16 2.7 0.036 34.0 128.0 0.98856 3.2 0.53 13.2 8 1 white 5.823711 52 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 5.823711 53 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 5.823711 54 5.5 0.19 0.27 0.9 0.04 52.0 103.0 0.99026 3.5 0.39 11.2 5 0 white 5.823711 55 5.5 0.28 0.21 1.6 0.032 23.0 85.0 0.99027 3.42 0.42 12.5 5 0 white 5.823711 56 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 5.823711 57 5.5 0.32 0.13 1.3 0.037 45.0 156.0 0.99184 3.26 0.38 10.7 5 0 white 5.823711 58 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 5.823711 59 5.5 0.485 0.0 1.5 0.065 8.0 103.0 0.994 3.63 0.4 9.7 4 0 white 5.823711 60 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 5.823711 61 5.6 0.18 0.31 1.5 0.038 16.0 84.0 0.9924 3.34 0.58 10.1 6 0 white 5.823711 62 5.6 0.18 0.58 1.25 0.034 29.0 129.0 0.98984 3.51 0.6 12.0 7 1 white 5.823711 63 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 5.823711 64 5.6 0.255 0.57 10.7 0.056 66.0 171.0 0.99464 3.25 0.61 10.4 7 1 white 5.823711 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 5.823711 66 5.6 0.26 0.27 10.6 0.03 27.0 119.0 0.9947 3.4 0.34 10.7 7 1 white 5.823711 67 5.6 0.32 0.32 8.3 0.043 32.0 105.0 0.99266 3.24 0.47 11.2 6 0 white 5.823711 68 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 5.823711 69 5.6 0.35 0.4 6.3 0.022 23.0 174.0 0.9922 3.54 0.5 11.6 7 1 white 5.823711 70 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 5.823711 71 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 5.823711 72 5.7 0.12 0.26 5.5 0.034 21.0 99.0 0.99324 3.09 0.57 9.9 6 0 white 5.823711 73 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 5.823711 74 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 5.823711 75 5.7 0.18 0.22 4.2 0.042 25.0 111.0 0.994 3.35 0.39 9.4 5 0 white 5.823711 76 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 5.823711 77 5.7 0.23 0.25 7.95 0.042 16.0 108.0 0.99486 3.44 0.61 10.3 6 0 white 5.823711 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 5.823711 79 5.7 0.265 0.28 6.9 0.036 46.0 150.0 0.99299 3.36 0.44 10.8 7 1 white 5.823711 80 5.7 0.27 0.16 9.0 0.053 32.0 111.0 0.99474 3.36 0.37 10.4 6 0 white 5.823711 81 5.7 0.29 0.16 7.9 0.044 48.0 197.0 0.99512 3.21 0.36 9.4 5 0 white 5.823711 82 5.7 0.31 0.29 7.3 0.05 33.0 143.0 0.99332 3.31 0.5 11.0666666666667 6 0 white 5.823711 83 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 5.823711 84 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 5.823711 85 5.7 0.43 0.3 5.7 0.039 24.0 98.0 0.992 3.54 0.61 12.3 7 1 white 5.823711 86 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 5.823711 87 5.8 0.17 0.3 1.4 0.037 55.0 130.0 0.9909 3.29 0.38 11.3 6 0 white 5.823711 88 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 5.823711 89 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 5.823711 90 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 5.823711 91 5.8 0.2 0.27 1.4 0.031 12.0 77.0 0.9905 3.25 0.36 10.9 7 1 white 5.823711 92 5.8 0.22 0.29 0.9 0.034 34.0 89.0 0.98936 3.14 0.36 11.1 7 1 white 5.823711 93 5.8 0.22 0.3 1.1 0.047 36.0 131.0 0.992 3.26 0.45 10.4 5 0 white 5.823711 94 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 5.823711 95 5.8 0.24 0.26 10.05 0.039 63.0 162.0 0.99375 3.33 0.5 11.2 6 0 white 5.823711 96 5.8 0.27 0.22 12.7 0.058 42.0 206.0 0.9946 3.32 0.38 12.3 6 0 white 5.823711 97 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 5.823711 98 5.8 0.28 0.27 2.6 0.054 30.0 156.0 0.9914 3.53 0.42 12.4 5 0 white 5.823711 99 5.8 0.28 0.3 3.9 0.026 36.0 105.0 0.98963 3.26 0.58 12.75 6 0 white 5.823711 100 5.8 0.29 0.15 1.1 0.029 12.0 83.0 0.9898 3.3 0.4 11.4 6 0 white 5.823711 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#
If the model allows, you can also generate relevant plots. For example, regression plots can be found in the Machine Learning - Regression Plots.
model.plot()
Important
The plotting feature is typically suitable for models with fewer than three predictors.
Contour plot is another useful plot that can be produced for models with two predictors.
model.contour()
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.
Parameter Modification#
In order to see the parameters:
model.get_params() Out[3]: {'tol': 1e-06, 'C': 0.5, 'max_iter': 100, 'solver': 'cgd', 'fit_intercept': True}
And to manually change some of the parameters:
model.set_params({'tol': 0.001})
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[5]: '5.823711 + 0.0 * "fixed_acidity" + 0.0 * "volatile_acidity" + 0.0 * "citric_acid" + 0.0 * "residual_sugar" + 0.0 * "chlorides" + 0.0 * "density"'
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[7]: array([5.823711])
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, tol: float = 1e-06, C: int | float | Decimal = 1.0, max_iter: int = 100, solver: Literal['newton', 'bfgs', 'cgd'] = 'cgd', fit_intercept: bool = True) None #
Methods
__init__
([name, overwrite_model, tol, C, ...])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
([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_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.
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.
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