Skip to content

Latest commit

 

History

History
68 lines (52 loc) · 4.19 KB

algorithms_list.rst

File metadata and controls

68 lines (52 loc) · 4.19 KB
Raw

List of Availble Algorithms

Notation

To avoid confusion of terms and notations, we make the following basic definitions:

  • X: feature matrix
X = \begin{pmatrix}
 x_{11} & x_{12} & \dots & x_{1m} \\
 x_{21} & x_{22} & \dots &  x_{2m} \\
 \vdots & \vdots & \dots &  \vdots \\
 v_{n1} & x_{n2} & \dots &  x_{nm} \\
 \end{pmatrix}

Here, each row denotes a sample (or an observation) x_i (i=1, \dots, n) or X_{i.} . Each column denotes a feature, X_{.j} (j = 1, \dots , p)

  • Y: the label in supervised learning
Y = \begin{pmatrix}
y_1\\
y_2\\
\vdots \\
y_n \\
\end{pmatrix}

We have y_i \in \mathcal{R} for regression problem, and y_i \in \mathcal{Z} for classification

  • Training/Validation dataset: in XFL, we mainly use two types of dataset, one for training and the other for validation. We use the superscript "train", "val" to identify them. For example, X^{train}, X^{val} denote training dataset, validation dataset respectively.

List of Algorithms

Algorithm Module Description
:doc:`Local Normalization <./algos/LocalNormalization>` local/normalization normalize data
:doc:`Local Standard Scaler <./algos/LocalStandardScaler>` local/standard_scaler standardize data
:doc:`Local Data Split <./algos/LocalDataSplit>` local/data_split split data into train and validation set
:doc:`Local Feature Preprocess <./algos/LocalFeaturePreprocess>` local/feature_preprocess feature preprocess
:doc:`Local Data Statistic <./algos/LocalDataStatistic>` local/data_statistic data statistic
:doc:`Horizontal Linear Regression <./algos/HorizontalLinearRegression>` horizontal/linear_regression two-party or multi-party horizontal linear regression
:doc:`Horizontal Logistic Regression <./algos/HorizontalLogisticRegression>` horizontal/logistic_regression two-party or multi-party horizontal logistic regression
:doc:`Horizontal Poisson Regression <./algos/HorizontalPoissonRegression>` horizontal/poisson_regression two-party or multi-party horizontal poisson regression
:doc:`Horizontal ResNet <./algos/HorizontalResNet>` horizontal/Resnet two-party or multi-party horizontal ResNet
:doc:`Horizontal DenseNet <./algos/HorizontalDenseNet>` horizontal/Densenet two-party or multi-party horizontal DenseNet
:doc:`Horizontal VGG <./algos/HorizontalVGG>` horizontal/Vgg two-party or multi-party horizontal VGG
:doc:`Horizontal Bert <./algos/HorizontalBert>` horizontal/Bert two-party or multi-party horizontal Bert
:doc:`Vertical Binning Woe IV <./algos/VerticalBinningWoeIV>` vertical/binning_woe_iv calulate WoE and IV using equal-frequency binning or equal-width binning
:doc:`Vertical Pearson <./algos/VerticalPearson>` vertical/pearson two-party or multi-party vertical Pearson correlation coefficient
:doc:`Vertical Feature Selection <./algos/VerticalFeatureSelection>` vertical/feature_selection two-party or multi-party vertical feature selection
:doc:`Vertical Logistic Regression <./algos/VerticalLogisticRegression>` vertical/logistic_regression two-party or multi-party vertical logistic regression
:doc:`Vertical Linear Regression <./algos/VerticalLinearRegression>` vertical/linear_regression two-party or multi-party vertical linear regression
:doc:`Vertical Poisson Regression <./algos/VerticalPoissonRegression>` vertical/poisson_regression two-party or multi-party vertical poisson regression
:doc:`Vertical XGBoost <./algos/VerticalXgboost>` vertical/xgboost two-party or multi-party vertical xgboost
:doc:`Vertical XGBoostDistributed <./algos/VerticalXgboostDistributed>` vertical/xgboost_distributed distributed two-party or multi-party vertical xgboost.
:doc:`Vertical Kmeans <./algos/VerticalKMeans>` vertical/kmeans two-party or multi-party vertical kmeans
:doc:`Vertical Sampler <./algos/VerticalSampler>` vertical/sampler two-party or multi-party vertical sampler