gen_params_grid

In [ ]:
gen_params_grid(estimator, 
                nbins: int = 10, 
                max_nfeatures: int = 3,
                lmax: int = -1,
                optimized_grid: int = 0,)

Generates the estimator grid.

Parameters

Name Type Optional Description
estimator
object
❌
 Vertica estimator having a fit method.
nbins
int
✓
 Number of bins used to discretize numberical features.
max_nfeatures
int
✓
 Maximum number of features used to compute Random Forest, PCA...
lmax
int
✓
 Maximum length of the parameter grid.
optimized_grid
int
✓
 If set to 0, the randomness is based on the input parameters. If set to 1, the randomness is limited to some parameters, the other ones are picked based on a default grid. If set to 2, there is no randomness and a default grid is returned.

Returns

list of dict : List of the different combinations.

Example

In [9]:
from verticapy.learn.linear_model import LogisticRegression
model = LogisticRegression(name = "public.LR_titanic",
                           tol = 1e-4,
                           max_iter = 100, 
                           solver = 'Newton',)

from verticapy.learn.model_selection import gen_params_grid
gen_params_grid(model,
                lmax = 10,)
Out[9]:
[{'C': 0.501,
  'l1_ratio': 0.001,
  'max_iter': 500,
  'penalty': 'enet',
  'solver': 'cgd',
  'tol': 1e-08},
 {'C': 1.501,
  'max_iter': 1000,
  'penalty': 'l1',
  'solver': 'cgd',
  'tol': 1e-06},
 {'C': 4.001,
  'max_iter': 1000,
  'penalty': 'l1',
  'solver': 'cgd',
  'tol': 0.0001},
 {'C': 4.001,
  'max_iter': 1000,
  'penalty': 'l2',
  'solver': 'bfgs',
  'tol': 0.0001},
 {'C': 0.501, 'max_iter': 100, 'penalty': 'l1', 'solver': 'cgd', 'tol': 1e-08},
 {'C': 1.501,
  'l1_ratio': 0.901,
  'max_iter': 100,
  'penalty': 'enet',
  'solver': 'cgd',
  'tol': 0.0001},
 {'C': 0.001,
  'l1_ratio': 0.901,
  'max_iter': 100,
  'penalty': 'enet',
  'solver': 'cgd',
  'tol': 1e-08},
 {'C': 1.001,
  'l1_ratio': 0.301,
  'max_iter': 500,
  'penalty': 'enet',
  'solver': 'cgd',
  'tol': 0.0001},
 {'C': 2.501,
  'l1_ratio': 0.201,
  'max_iter': 1000,
  'penalty': 'enet',
  'solver': 'cgd',
  'tol': 1e-06},
 {'C': 1.501,
  'l1_ratio': 0.501,
  'max_iter': 100,
  'penalty': 'enet',
  'solver': 'cgd',
  'tol': 1e-08}]