Merge pull request #27 from mayer79/fix_readme

Fix two small typos in the readme

README.Rmd

@@ -45,7 +45,7 @@ unified <- xgboost.unify(xgb_model, data)
 head(unified$model)
 ```
 
-Having the object of unified structure, it is a piece of cake to produce shap values of for a specific observations.
+Having the object of unified structure, it is a piece of cake to produce shap values for a specific observation.
 The `treeshap()` function requires passing two data arguments: one representing an ensemble model unified representation and one with the observations about which we want to get the explanations. Obviously, the latter one should contain the same columns as data used during building the model.
 
 ```{r treeshap-example}

README.md

@@ -4,7 +4,6 @@
 # treeshap
 
 <!-- badges: start -->
-
 <!-- badges: end -->
 
 In the era of complicated classifiers conquering their market, sometimes
@@ -54,7 +53,7 @@ head(unified$model)
 ```
 
 Having the object of unified structure, it is a piece of cake to produce
-shap values of for a specific observations. The `treeshap()` function
+shap values for a specific observation. The `treeshap()` function
 requires passing two data arguments: one representing an ensemble model
 unified representation and one with the observations about which we want
 to get the explanations. Obviously, the latter one should contain the
@@ -107,8 +106,7 @@ The package currently provides 4 plotting functions that can be used:
 On this plot we can see how features contribute into the prediction for
 a single observation. It is similar to the Break Down plot from
 [iBreakDown](https://github.com/ModelOriented/iBreakDown) package, which
-uses different method to approximate SHAP
-values.
+uses different method to approximate SHAP values.
 
 ``` r
 plot_contribution(treeshap1, obs = 1, min_max = c(0, 16000000))
@@ -119,8 +117,7 @@ plot_contribution(treeshap1, obs = 1, min_max = c(0, 16000000))
 ### Feature Importance
 
 This plot shows us average absolute impact of features on the prediction
-of the
-model.
+of the model.
 
 ``` r
 plot_feature_importance(treeshap1, max_vars = 6)
@@ -131,8 +128,7 @@ plot_feature_importance(treeshap1, max_vars = 6)
 ### Feature Dependence
 
 Using this plot we can see, how a single feature contributes into the
-prediction depending on its
-value.
+prediction depending on its value.
 
 ``` r
 plot_feature_dependence(treeshap1, "height_cm")
@@ -163,7 +159,7 @@ of appropriate model and dataset used to train the model. One of them,
 `catboost.unify()` requires also a transformed dataset used for training
 the model - an object of class `catboost.Pool`.
 
-#### 1\. GBM
+#### 1. GBM
 
 An argument of `gbm.unify()` should be of `gbm` class - a gradient
 boosting model.
@@ -184,7 +180,7 @@ gbm_model <- gbm::gbm(
 unified_gbm <- gbm.unify(gbm_model, x)
 ```
 
-#### 2\. Catboost
+#### 2. Catboost
 
 For representing correct names of features that are regarding during
 splitting observations into sets, `catboost.unify()` requires passing
@@ -207,8 +203,7 @@ unified_catboost <- catboost.unify(cat_model, dt.pool, data)
 
 Dataset used as a reference for calculating SHAP values is stored in
 unified model representation object. It can be set any ime using
-`set_reference_dataset`
-function.
+`set_reference_dataset` function.
 
 ``` r
 unified_catboost2 <- set_reference_dataset(unified_catboost, data[c(1000:2000), ])
@@ -236,9 +231,9 @@ microbenchmark::microbenchmark(
   treeshap = treeshap(unified,  data[1:300, ]), # using model and dataset from the example
   times = 5
 )
-#> Unit: seconds
-#>      expr      min       lq     mean   median       uq      max neval
-#>  treeshap 1.027707 1.032991 1.032529 1.033427 1.034062 1.034459     5
+#> Unit: milliseconds
+#>      expr      min       lq   mean   median       uq      max neval
+#>  treeshap 880.2421 894.4094 897.18 902.3321 904.0408 904.8754     5
 ```
 
 Complexity of SHAP interaction values computation is `O(MTLD^2)`, where
@@ -255,12 +250,12 @@ microbenchmark::microbenchmark(
   times = 5
 )
 #> Unit: seconds
-#>      expr      min      lq     mean  median       uq      max neval
-#>  treeshap 6.700848 6.70164 6.712134 6.70711 6.719313 6.731761     5
+#>      expr     min       lq     mean   median       uq      max neval
+#>  treeshap 7.25712 7.413444 7.591842 7.510369 7.739501 8.038775     5
 ```
 
 ## References
 
-  - Scott M. Lundberg, Gabriel G. Erion, Su-In Lee, “Consistent
-    Individualized Feature Attribution for Tree Ensembles”, University
-    of Washington
+- Scott M. Lundberg, Gabriel G. Erion, Su-In Lee, “Consistent
+  Individualized Feature Attribution for Tree Ensembles”, University of
+  Washington