Target Contrastive Pessimistic Classifier¶
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class
libtlda.tcpr.
TargetContrastivePessimisticClassifier
(loss='lda', l2=1.0, max_iter=500, tolerance=1e-12, learning_rate=1.0, rate_decay='linear', verbosity=0)¶ Classifiers based on Target Contrastive Pessimistic Risk minimization.
Methods contain models, risk functions, parameter estimation, etc.
Methods
add_intercept
(X)Add 1’s to data as last features. combine_class_covariances
(Si, pi)Linear combination of class covariance matrices. discriminant_parameters
(X, Y)Estimate parameters of Gaussian distribution for discriminant analysis. error_rate
(preds, u_)Compute classification error rate. fit
(X, y, Z)Fit/train an importance-weighted classifier. get_params
()Return classifier parameters. learning_rate_t
(t)Compute current learning rate after decay. neg_log_likelihood
(X, theta)Compute negative log-likelihood under Gaussian distributions. predict
(Z_)Make predictions on new dataset. predict_proba
(Z)Compute posteriors on new dataset. project_simplex
(v[, z])Project vector onto simplex using sorting. remove_intercept
(X)Remove 1’s from data as last features. risk
(Z, theta, q)Compute target contrastive pessimistic risk. tcpr_da
(X, y, Z)Target Contrastive Pessimistic Risk - discriminant analysis. -
add_intercept
(X)¶ Add 1’s to data as last features.
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combine_class_covariances
(Si, pi)¶ Linear combination of class covariance matrices.
Parameters: - Si : array
Covariance matrix (D features by D features by K classes)
- pi : array
class proportions (1 by K classes)
Returns: - Si : array
Combined covariance matrix (D by D)
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discriminant_parameters
(X, Y)¶ Estimate parameters of Gaussian distribution for discriminant analysis.
Parameters: - X : array
data array (N samples by D features)
- Y : array
label array (N samples by K classes)
Returns: - pi : array
class proportions (1 by K classes)
- mu : array
class means (K classes by D features)
- Si : array
class covariances (D features D features by K classes)
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error_rate
(preds, u_)¶ Compute classification error rate.
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fit
(X, y, Z)¶ Fit/train an importance-weighted classifier.
Parameters: - X : array
source data (N samples by D features)
- y : array
source labels (N samples by 1)
- Z : array
target data (M samples by D features)
Returns: - None
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get_params
()¶ Return classifier parameters.
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learning_rate_t
(t)¶ Compute current learning rate after decay.
Parameters: - t : int
current iteration
Returns: - alpha : float
current learning rate
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neg_log_likelihood
(X, theta)¶ Compute negative log-likelihood under Gaussian distributions.
Parameters: - X : array
data (N samples by D features)
- theta : tuple(array, array, array)
tuple containing class proportions ‘pi’, class means ‘mu’, and class-covariances ‘Si’
Returns: - L : array
loss (N samples by K classes)
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predict
(Z_)¶ Make predictions on new dataset.
Parameters: - Z : array
new data set (M samples by D features)
Returns: - preds : array
label predictions (M samples by 1)
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predict_proba
(Z)¶ Compute posteriors on new dataset.
Parameters: - Z : array
new data set (M samples by D features)
Returns: - preds : array
label predictions (M samples by 1)
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project_simplex
(v, z=1.0)¶ Project vector onto simplex using sorting.
Reference: “Efficient Projections onto the L1-Ball for Learning in High Dimensions (Duchi, Shalev-Shwartz, Singer, Chandra, 2006).”
Parameters: - v : array
vector to be projected (n dimensions by 0)
- z : float
constant (def: 1.0)
Returns: - w : array
projected vector (n dimensions by 0)
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remove_intercept
(X)¶ Remove 1’s from data as last features.
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risk
(Z, theta, q)¶ Compute target contrastive pessimistic risk.
Parameters: - Z : array
target samples (M samples by D features)
- theta : array
classifier parameters (D features by K classes)
- q : array
soft labels (M samples by K classes)
Returns: - float
Value of risk function.
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tcpr_da
(X, y, Z)¶ Target Contrastive Pessimistic Risk - discriminant analysis.
Parameters: - X : array
source data (N samples by D features)
- y : array
source labels (N samples by 1)
- Z : array
target data (M samples by D features)
Returns: - theta : array
classifier parameters (D features by K classes)
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