randomized_search_cv¶

In [ ]:

randomized_search_cv(estimator,
                     input_relation: (str, vDataFrame),
                     X: list,
                     y: str,
                     metric: str = "auto",
                     cv: int = 3,
                     pos_label: (int, float, str) = None,
                     cutoff: float = -1,
                     nbins: int = 1000,
                     lmax: int = 4,
                     optimized_grid: int = 1,
                     print_info: bool = True,)

Computes the K-Fold randomized search of an estimator.

Parameters¶

Name	Type	Optional	Description
estimator	object	❌	Vertica estimator having a fit method and a DB cursor.
input_relation	str / vDataFrame	❌	Input Relation.
X	list	❌	List of the predictor columns.
y	str	❌	Response Column.
metric	str / list	✓	Metric used to do the model evaluation. auto : logloss for classification & rmse for regression. For Classification: accuracy : Accuracy auc : Area Under the Curve (ROC) bm : Informedness = tpr + tnr - 1 csi : Critical Success Index = tp / (tp + fn + fp) f1 : F1 Score logloss : Log Loss mcc : Matthews Correlation Coefficient mk : Markedness = ppv + npv - 1 npv : Negative Predictive Value = tn / (tn + fn) prc_auc : Area Under the Curve (PRC) precision : Precision = tp / (tp + fp) recall : Recall = tp / (tp + fn) specificity : Specificity = tn / (tn + fp) For Regression: max : Max Error mae : Mean Absolute Error median : Median Absolute Error mse : Mean Squared Error msle : Mean Squared Log Error r2 : R-squared coefficient r2a : R2 adjusted rmse : Root Mean Squared Error var : Explained Variance
cv	int	✓	Number of folds.
pos_label	int / float / str	✓	The main class to be considered as positive (classification only).
cutoff	float	✓	The model cutoff (classification only).
nbins	int	✓	Number of bins used to compute the different parameters categories.
lmax	int	✓	Maximum length of each parameter list.
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.
print_info	bool	✓	If set to True, prints the model information at each step.

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [5]:

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 randomized_search_cv
randomized_search_cv(model,
                     input_relation = "public.titanic_clean",
                     X = ["age", "fare", "parch", "sex", "boat"],
                     y = "survived",
                     cv = 3,
                     metric = "auc",
                     lmax = 5)

Model: LogisticRegression; Parameters: {'tol': 1e-08, 'max_iter': 1000, 'penalty': 'enet', 'solver': 'cgd', 'C': 2, 'l1_ratio': 0.8}; Test_score: 0.6749487449904648; Train_score: 0.7000767706602541; Time: 0.8710722923278809;
Model: LogisticRegression; Parameters: {'tol': 1e-08, 'max_iter': 1000, 'penalty': 'enet', 'solver': 'cgd', 'C': 2, 'l1_ratio': 0.1}; Test_score: 0.7032595757352726; Train_score: 0.7058611736385693; Time: 0.8391083081563314;
Model: LogisticRegression; Parameters: {'tol': 1e-08, 'max_iter': 1000, 'penalty': 'enet', 'solver': 'cgd', 'C': 1, 'l1_ratio': 0.2}; Test_score: 0.7239566637740104; Train_score: 0.7058636182778295; Time: 0.8325083255767822;
Model: LogisticRegression; Parameters: {'tol': 1e-08, 'max_iter': 1000, 'penalty': 'none', 'solver': 'newton'}; Test_score: 0.9912612989453908; Train_score: 0.9919101493833449; Time: 1.0511376857757568;
Model: LogisticRegression; Parameters: {'tol': 1e-08, 'max_iter': 1000, 'penalty': 'l2', 'solver': 'newton', 'C': 4}; Test_score: 0.994789013144222; Train_score: 0.9901850022436899; Time: 0.905972957611084;

Model: LogisticRegression; Best_Parameters: {'solver': 'newton', 'penalty': 'l2', 'max_iter': 1000, 'C': 4, 'tol': 1e-08}; Best_Test_score: 0.994789013144222; Train_score: 0.9901850022436899; Time: 0.905972957611084;

Out[5]:

	avg_score	avg_train_score	avg_time	score_std	score_train_std
1	0.994789013144222	0.9901850022436899	0.905972957611084	0.0024622922324503338	0.0010029936610647903
2	0.9912612989453908	0.9919101493833449	1.0511376857757568	0.0050957418513202105	0.002623493765619229
3	0.7239566637740104	0.7058636182778295	0.8325083255767822	0.02409528785201654	0.017046612231218023
4	0.7032595757352726	0.7058611736385693	0.8391083081563314	0.007987873176449629	0.009628706344259327
5	0.6749487449904648	0.7000767706602541	0.8710722923278809	0.004911805176219074	0.0029656851577108813

Rows: 1-5 | Columns: 6