ranger() survival models now also work out-of-the-box without passing a tailored prediction function. Use the new argument survival = "chf" in hstats(), ice(), and partial_dep() to distinguish cumulative hazards (default) and survival probabilities ("prob") per time point.swap_dim = TRUE for the old behavior.predict_type = "prob".X (#109).0/0 (= NaN). Such values are now replaced by 0 (#107).d2_geom = "line". Instead of a heatmap of the two features, one of the features is moved to color grouping. Combined with swap_dim = TRUE, you can swap the role of the two v variables without recalculating anything. The idea was proposed by Roel Verbelen in issue #91, see also issue #94.BY and w via column names would fail for tibbles. This problem was described in #92 by Roel Verbelen. Thx!perm_importance() and average_loss()).hstats() now has the option approx = FALSE. Set to TRUE to replace values of dense numeric columns by grid_size = 50 quantile midpoints. This will bring a massive speed-up for one-way calculations. Use this option when one-way calculations are slow, or when you want to increase n_max.hstats(): n_max has been increased from 300 to 500 rows. This will make estimates of H-statistics more stable at the price of longer run time. Reduce to 300 for the old behaviour.hstats(): Three-way interactions are not anymore calculated by default. Set threeway_m to 5 for the old behaviour.options(hstats.fill = new value). Value labels are more clear, and there are more options. Varying color/fill scales now use viridis (inferno). This can be modified on the fly or via options(hstats.viridis_args = list(...)).h2_pairwise() or perm_importance(), now return a "hstats_matrix". The values are stored in $M and can be plotted via plot(). Other methods include: dimnames(), rownames(), colnames(), dim(), nrow(), ncol(), head(), tail(), and subsetting like a normal matrix. This allows, e.g, to select and plot only one column of the results.perm_importance(): The perms argument has been changed to m_rep.print() and summary() methods have been revised.w (case weights) and y (response) can now also be passed as column names.top_m has been moved to the plot() method.eps of squared numerator statistics has been reduced from 1e-8 to 1e-10. It is now handled in hstats() instead of the statistic functions.H-squared: The $H^2$ statistic stored in a "hstats" object is now a matrix with one row (it was a vector).pd_importance(): The "hstats" object now contains pre-calculated PD-based importance values in $pd_importance.summary.hstats() now returns an object of class "hstats_summary" instead of "summary_hstats".average_loss() is more flexible regarding the group BY argument. It can also be a variable name. Non-discrete BY variables are now automatically binned. Like partial_dep(), binning is controlled by the by_size = 4 argument.average_loss() also returns a "hstats_matrix" object with print() and plot() method. The values can be extracted via $M.v of hstats() and perm_importance() is now NULL. Internally, it is set to colnames(X) (minus the column names of w and y if passed as name).partial_dep() and ice() have received a na.rm argument that controls if missing values are dropped during grid creation. The default TRUE is compatible with earlier releases.hstats(): Discrete variables with missings would cause rowsum() to launch repeated warnings. This case is now catched.perm_importance(): The default of verbose is TRUE again.This is intended to be the last version before 1.0.0.
ice() and partial_dep(): So far, the default grid strategy "uniform" used pretty() to generate the evaluation points. To provide more predictable grid sizes, and to be more in line with other implementations of partial dependence and ICE, we now use seq() to create the uniform grid.h2_pairwise() and h2_threeway() will now also include 0 values. Use zero = FALSE to drop them, see below. The padding with 0 is done at no computational cost, and will affect only up to pairwise_m and threeway_m features.print() method of summary.hstats() is less verbose.h2_overall(), h2_pairwise(), h2_threeway(), plot.hstats(), and summary.hstats() have received an argument zero = TRUE. Set to FALSE to drop statistics having value 0.perm_importance() and average_loss() will now recycle a univariate response when combined with multivariate predictions. This is useful, e.g., when the prediction function represents the predictions of multiple models that should be evaluated against a common response.perm_importance() and average_loss() would fail for "mlogloss" in case the response y was univariate and non-factor/non-character.hstats() and attached to the resulting object. Each statistic is stored as list with numerator and denominator matrices/vectors. The functions h2(), h2_overall(), h2_pairwise(), and h2_threeway(), print.hstats(), summary().hstats(), plot.hstats() will use these without having to recalculate the required numerators and denominators. The results, however, are unchanged.average_loss(): This new function calculates the average loss of a model for a given dataset, optionally grouped by a discrete vector. It supports the most important loss functions (squared error, Poisson deviance, Gamma deviance, Log loss, multivariate Log loss, absolute error, classification error), and allows for case weights. Custom losses can be passed as vector/matrix valued functions of signature f(obs, pred).
Note that such a custom function needs to return per-row losses, not their average.
perm_importance(): H-statistics are often calculated for important features only. To support this workflow, we have added permutation importance regarding the most important loss functions. Multivariate losses can be studied individually or collapsed over dimensions. The importance of feature groups can be studied as well. Note that the API of perm_importance() is different from the experimental pd_importance(), which is calculated from a "hstats" object.
hstats() now uses the default feature vector v = colnames(X), simplifying the API in most cases. The typical call is now hstats(object, X = Feature data).h2_overall(), h2_pairwise(), h2_threeway(), pd_importance() by default do not plot results anymore. Set plot = TRUE to do so.summary.hstats() now returns an object of class "summary_hstats" with its own print() method. Like this, one can use su <- summary() without printing to the console.summary.hstats() is printed slightly more compact.plot.hstats() has recieved a rotate_x = FALSE argument for rotating x labels by 45 degrees.plot.hstats() and summary.hstats() have received explicit arguments normalize, squared, sort, eps instead of passing them via ....plot.hstats() now passes ... to geom_bar().hstats() in the one-dimensional case.This is the initial release.