stepwise¶

In [ ]:

stepwise(estimator,
         input_relation: (str, vDataFrame),
         X: list,
         y: str,
         criterion: str = "bic",
         direction: str = "backward",
         max_steps: int = 100,
         criterion_threshold: int = 3,
         drop_final_estimator: bool = True,
         x_order: str = "pearson",
         print_info: bool = True,
         show: bool = True,
         ax=None,
         **style_kwds,)

Uses the stepwise algorithm to find the most suitable number of features when fitting the 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.
criterion	str	✓	Criterion used to evaluate the model. aic : Akaike’s Information Criterion. bic : Bayesian Information Criterion.
direction	str	✓	How to start the stepwise search. Can be done 'backward' or 'forward'.
max_steps	int	✓	The maximum number of steps to be considered.
criterion_threshold	int	✓	Threshold used when comparing the models criterions. If the difference is lesser than the threshold then the current 'best' model is changed.
drop_final_estimator	bool	✓	If set to True, the final estimator will be dropped.
x_order	str	✓	How to preprocess X before using the stepwise algorithm. pearson : X is ordered based on the Pearson's correlation coefficient. spearman : X is ordered based on the Spearman's correlation coefficient. random : Shuffles the vector X before applying the stepwise algorithm. none : Does not change X order.
print_info	bool	✓	If set to True, prints the model information at each step.
show	bool	✓	If set to True, the Stepwise graphic will be drawn.
ax	Matplotlib axes object	✓	The axes to plot on.
**style_kwds	any	✓	Any optional parameter to pass to the Matplotlib functions.

Returns¶

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

Example¶

In [7]:

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 stepwise

# backward
stepwise(model,
         input_relation = "public.titanic", 
         X = ["age", "fare", "parch", "pclass",], 
         y = "survived",)

Starting Stepwise

[Model 0] bic: -2222.4553063512712; Variables: ['"age"', '"parch"', '"fare"', '"pclass"']
[Model 1] bic: -2219.8559430056216; (-) Variable: "parch"
[Model 2] bic: -2218.3600781845307; (-) Variable: "fare"

Selected Model

[Model 2] bic: -2218.3600781845307; Variables: ['"age"', '"pclass"']

Out[7]:

	bic	change	variable	importance
0	-2222.4553063512712	[null]	[null]	0.0
1	-1943.6797392350866	+	"age"	58.01108831878302
2	-2219.8559430056216	-	"parch"	0.5409078642614441
3	-2218.3600781845307	-	"fare"	0.31127816238321365
4	-2020.6752155831307	+	"pclass"	41.136725654572324

Rows: 1-5 | Columns: 6

In [8]:

# forward
stepwise(model,
         input_relation = "public.titanic", 
         X = ["age", "fare", "parch", "pclass",], 
         y = "survived",
         direction = "forward",)

Starting Stepwise

[Model 0] bic: -1797.4537281723472; Variables: []
[Model 1] bic: -1937.9168392321783; (+) Variable: "pclass"
[Model 2] bic: -1943.1918759949126; (+) Variable: "fare"
[Model 3] bic: -2219.8559430056216; (+) Variable: "age"

Selected Model

[Model 3] bic: -2219.8559430056216; Variables: ['"pclass"', '"fare"', '"age"']

Out[8]:

	features	bic	change	variable	importance
0	[]	-1797.4537281723472	[null]	[null]	0.0
1	['pclass']	-1937.9168392321783	+	"pclass"	33.21504058447908
2	['pclass', 'fare']	-1943.1918759949126	+	"fare"	1.2473777551759295
3	['pclass', 'fare', 'parch']	-1943.6797392350866	-	"parch"	0.11536407815395282
4	['pclass', 'fare', 'age']	-2219.8559430056216	+	"age"	65.42221758219104

Rows: 1-5 | Columns: 6