+
Contents: @@ -106,13 +110,13 @@
pyCP_APR.pyCP_APR.
CP_APR
Bases: object
object
Initilize the pyCP_APR.CP_APR class. pyCP_APR.CP_APR class is the wrapper for the CP-APR algorithm's Python implementation with both Numpy and PyTorch backend.
fit
Takes the decomposition of sparse or dense tensor X and returns the KRUSKAL tensor M.
Here M is latent factors and the weight of each R (rank) component.
If a list of ranks is passed, factorize the tensor for each 2 of the ranks.
{ @@ -608,8 +612,8 @@ pyCP_APR.pyCP_APR module - -get_params()[source]¶ + +get_params()[source]¶ The function call that returns the model parameters in a dictionary where a key is the model variable name and the value is its current value. model is the backend used during factorization (i.e. pyCP_APR.torch_cp.CP_APR_Torch or pyCP_APR.numpy_cp.CP_APR). @@ -691,8 +695,8 @@ pyCP_APR.pyCP_APR module - -get_prediction()[source]¶ + +get_prediction()[source]¶ The function call that returns the predictions. The model must be already fitted using pyCP_APR.CP_APR.fit(), and links must be already predicted using pyCP_APR.CP_APR.predict_scores(). @@ -728,8 +732,8 @@ pyCP_APR.pyCP_APR module - -get_score()[source]¶ + +get_score()[source]¶ The function call that returns the objective value of the model. Model is pyCP_APR.CP_APR.model.obj. If an ensemble of tensors is trained (see pyCP_APR.CP_APR.fit()), a list of scores is provided instead. @@ -745,8 +749,8 @@ pyCP_APR.pyCP_APR module - -predict_probas(y, axis_map=None)[source]¶ + +predict_probas(y, axis_map=None)[source]¶ Calculates the probabilities of the test tensor entries. Then returns the prediction scores with ROC-AUC and PR-AUC metrics. Fusion is performed on the dimensions indicated by axis_map. @@ -802,8 +806,8 @@ pyCP_APR.pyCP_APR module - -predict_scores(**parameters)[source]¶ + +predict_scores(**parameters)[source]¶ The function call that can be used for classification of anomalies after fitting the tensor. The model will use the trained latent factors to generate the Poisson lambda scores corresponding to the given new coordinate. @@ -913,8 +917,8 @@ pyCP_APR.pyCP_APR module - -set_params(**parameters)[source]¶ + +set_params(**parameters)[source]¶ Sets the model parameters. Here the model is pyCP_APR.CP_APR.model. @@ -931,8 +935,8 @@ pyCP_APR.pyCP_APR module - -transform(indicies, ensemble_significance=[])[source]¶ + +transform(indicies, ensemble_significance=[])[source]¶ Given the sparse test tensor entries (or indicies, i.e. coordinates of non-zero values), calculate the Poisson lambda values. The Poission lambda value can be used to identify how likely was that coordinate in the test tensor to occur given what we have learned during the training time. @@ -1018,16 +1022,27 @@ pyCP_APR.pyCP_APR moduleWelcome to pyCP_APR's documentation! - pyCP_APR.datasets API - + + + + + + previous + Welcome to pyCP_APR's documentation! + + + + next + pyCP_APR.datasets API + + + +
get_params
The function call that returns the model parameters in a dictionary where a key is the model variable name and the value is its current value. model is the backend used during factorization (i.e. pyCP_APR.torch_cp.CP_APR_Torch or pyCP_APR.numpy_cp.CP_APR).
get_prediction
The function call that returns the predictions.
The model must be already fitted using pyCP_APR.CP_APR.fit(), and links must be already predicted using pyCP_APR.CP_APR.predict_scores().
get_score
The function call that returns the objective value of the model.
Model is pyCP_APR.CP_APR.model.obj.
If an ensemble of tensors is trained (see pyCP_APR.CP_APR.fit()), a list of scores is provided instead.
predict_probas
Calculates the probabilities of the test tensor entries.
Then returns the prediction scores with ROC-AUC and PR-AUC metrics.
Fusion is performed on the dimensions indicated by axis_map. @@ -802,8 +806,8 @@
axis_map
predict_scores
The function call that can be used for classification of anomalies after fitting the tensor. The model will use the trained latent factors to generate the Poisson lambda scores corresponding to the given new coordinate.
set_params
Sets the model parameters.
Here the model is pyCP_APR.CP_APR.model.
transform
Given the sparse test tensor entries (or indicies, i.e. coordinates of non-zero values), calculate the Poisson lambda values.
The Poission lambda value can be used to identify how likely was that coordinate in the test tensor to occur given what we have learned during the training time.
previous
Welcome to pyCP_APR's documentation!
next
pyCP_APR.datasets API