Fit CGDRO in classification model¶

In [ ]:

Classification(
    self, 
    f_learner='linear', 
    w_learner='logistic',
    split=True, 
    seed=123
)

Classification( self, f_learner='linear', w_learner='logistic', split=True, seed=123 )

• f_learner (str, optional): method used to fit outcome models on each source. Defaults to 'linear'.
• w_learner (str, optional): method used to fit density models on each source. Defaults to 'logistic'.
• split (bool, optional): whether to split the source data into two halves for fitting outcome and density models. Defaults to True.
• seed (int, optional): random seed for data-splitting. Defaults to 123.

Built-in functions in Classification:

BUilt-in Functions	Description
fit()	Fit robust classification model with cross-entropy loss in the target domain.
predict_proba()	Make robust classification probability prediction in the target domain.
predict()	Make robust label prediction in the target domain.
infer()	Build debiased confidence intervals of the target linear regression coefficients.
summary()	Summarize the results.

In [ ]:

fit(
    self, 
    X_list, 
    y_list, 
    X0=None, 
    max_iter=1000, 
    tol=1e-6, 
    check_dual=False, 
    verbose=False
)

fit( self, X_list, y_list, X0=None, max_iter=1000, tol=1e-6, check_dual=False, verbose=False )

Arguments:

• X_list (list): list of feature matrices on each source domain
• y_list (list): list of label arrays on each source domain
• X0 (array, optional): feature matrix on the target domain. If None, use the pooled source data as the target data. Defaults to None.
• max_iter (int, optional): maximum number of iterations. Defaults to 1000.
• tol (float, optional): tolerance for convergence. Defaults to 1e-6.
• check_dual (bool, optional): whether to check the duality gap. Defaults to False.
• verbose (bool, optional): whether to print out the fitting information. Defaults to False.

Outputs: enabled the following attributes:

• .parameters : "coef_": CGDRO aggregated debiased coefficient estimators in the target domain; "weight_": CGDRO aggregated weights of the source domains.

In [ ]:

predict_proba(
    self,
    X=None
)

predict_proba( self, X=None )

Arguments:

• X : Input features for prediction. If None, uses the training data. Defaults to None.

Outputs:

• proba : classification probability prediction in the target domain.

In [ ]:

predict(
    self,
    X=None
)

predict( self, X=None )

Arguments:

• X : Input features for prediction. If None, uses the training data. Defaults to None.

Outputs:

• pred : label prediction in the target domain.

In [ ]:

infer(
    self, 
    M=200, 
    alpha=0.05, 
    diag=True, 
    parallel=False, 
    n_workers=4
    )

infer( self, M=200, alpha=0.05, diag=True, parallel=False, n_workers=4 )

Arguments:

• M (int, optional): number of resampling iterations. Defaults to 200.
• alpha (float, optional): significance level for confidence intervals. Defaults to 0.05.
• diag (bool, optional): whether to use diagonal approximation for covariance matrices. Defaults to True.
• parallel (bool, optional): whether to use parallel computing. Defaults to False.
• n_workers (int, optional): number of workers for parallel computing. Defaults to 4.

Outputs enabled the following attributes:

• .CI : CGDRO debiased aggregated confidence intervals of the target domain coefficients.

In [ ]:

summary(
    self, 
    index=None,
    class_index=None
)

summary( self, index=None, class_index=None )

Arguments -index (array-like or None): 1-based indices of dimensions to print (subset of 1..d). Defaults to all dimensions.

• class_index (array-like or None): class labels to print (subset of 1..self.num_class-1). Defaults to all (1..self.num_class-1).

Outputs

• Summay of CGDRO aggregated weights, estimators, and confidence intervals.

Example¶

In [ ]:

from cgdro import Classification
from cgdro.data import DataContainerSimu_linear_Cl

# two source groups, each with 100 samples, and 1000 target samples
n = 100; p = 5; L = 2; N = 1000; K = 2
data = DataContainerSimu_linear_Cl(n=n, N=N, p=p, L=L, K=K)
data.generate_funcs_list(seed=123)
data.generate_data(seed=123)

Xlist = data.X_sources_list
Ylist = data.Y_sources_list
X0 = data.X_target


## first call the module
## then run functions fit() and infer()
cc = Classification(f_learner='linear', w_learner='logistic')
cc.fit(Xlist,Ylist,X0)
cc.infer()

## summary
cc.summary()
cc.summary(
    index = [3,5], class_index=2
)

## predict_proba() and predict()
pred_proba = cc.predict_proba()
print(pred_proba[:10, :])
pred = cc.predict()
print(pred[:10])

from cgdro import Classification from cgdro.data import DataContainerSimu_linear_Cl # two source groups, each with 100 samples, and 1000 target samples n = 100; p = 5; L = 2; N = 1000; K = 2 data = DataContainerSimu_linear_Cl(n=n, N=N, p=p, L=L, K=K) data.generate_funcs_list(seed=123) data.generate_data(seed=123) Xlist = data.X_sources_list Ylist = data.Y_sources_list X0 = data.X_target ## first call the module ## then run functions fit() and infer() cc = Classification(f_learner='linear', w_learner='logistic') cc.fit(Xlist,Ylist,X0) cc.infer() ## summary cc.summary() cc.summary( index = [3,5], class_index=2 ) ## predict_proba() and predict() pred_proba = cc.predict_proba() print(pred_proba[:10, :]) pred = cc.predict() print(pred[:10])