Lasso 1.2.0 __LINK__
Hey nikku, thank you for your reply. I made a quick and dirty example. If you enable the lasso tool and select multiple elements the error should be triggered.Without the bpmn-js-properties-panel import, the tool works fine.
We will use the basic model specification whenever we use nonlinear methods, for example regression trees or random forests, and use the flexible model for linear methods such as the lasso. There are, of course, multiple ways how the model can be specified even more flexibly, for example including interactions of variable and higher order interaction. However, for the sake of simplicity we stick to the specification above. Users who are interested in varying the model can adapt the code belowaccordingly, for example to implement the orignal specification in Chernozhukov et al. (2018).
Estimation of the nuisance components \(g_0\) and \(m_0\), is based on the lasso with cross-validated choice of the penalty term , \(\lambda\), as provided by scikit-learn. We load the learner by initializing instances from the classes LassoCV andLogisticRegressionCV. Hyperparameters and options can be set during instantiation of the learner. Here we specify that the lasso should use that value of \(\lambda\) that minimizes the cross-validated mean squared error which is based on 5-fold cross validation.
Airpart identifies sets of genes displaying differential cell-type-specific allelic imbalance across cell types or states, utilizing single-cell allelic counts. It makes use of a generalized fused lasso with binomial observations of allelic counts to partition cell types by their allelic imbalance. Alternatively, a nonparametric method for partitioning cell types is offered. The package includes a number of visualizations and quality control functions for examining single cell allelic imbalance datasets.
The graph with the paths of the parameter estimates nicely illustrates the typical behaviour of the lasso estimates as a function of \(\lambda\): when \(\lambda\) increases the estimates are shrunken towards zero.
There are many ways to generate Gaussian knockoffs. The default option is to generate MVR knockoffs, which (informally) maximize \(\textVar(X_j \mid X_-j, \tildeX)\) for each feature \(X_j\), where \(X_-j\) denotes all of the features except the \(j\)th feature. Intuitively, this minimizes the varianced-based reconstructability (MVR) between the features and their knockoffs, preventing a feature statistic like a lasso or a randomforest from using the other featuresand knockoffs to reconstruct non-null features. See Spector and Janson (2020) for more details.
knockpy offers a suite of built-in feature statistics, including cross-validated lasso and ridge coefficients, lasso-path statistics, masked likelihood ratio statistics (Spector and Fithian, 2022), the deepPINK statistic (Lu et. al 2018), and random forest statistics with swap and swap integral importances (Giminez et. al 2018). One can easily call theseuse these feature statistics by modifying the fstat argument and fstat_kwarg arguments in the KnockoffFilter class, as exemplified below. See the API reference for more flags and options.
MLR statistics are motivated by a common problem when applying knockoffs, which is that in many applied analyses, one is forced to construct knockoffs \(\tildeX_j\) which are highly correlated with features \(X_j\). As shown below, highly correlated knockoffs \(\tildeX_j\) can wreak havoc with common feature statistics like the lasso statistic by causing the lasso to select \(\tildeX_j\) over \(X_j\) and leading to highly negative feature statistics. This substantiallyreduces the power of knockoffs. 041b061a72