Expand ~ into the home directory on Linux and MacOS (#6531)

R-package/R/xgb.Booster.R

@@ -11,6 +11,7 @@ xgb.Booster.handle <- function(params = list(), cachelist = list(),
     if (typeof(modelfile) == "character") {
       ## A filename
       handle <- .Call(XGBoosterCreate_R, cachelist)
+      modelfile <- path.expand(modelfile)
       .Call(XGBoosterLoadModel_R, handle, modelfile[1])
       class(handle) <- "xgb.Booster.handle"
       if (length(params) > 0) {

R-package/R/xgb.DMatrix.R

@@ -27,6 +27,7 @@ xgb.DMatrix <- function(data, info = list(), missing = NA, silent = FALSE, ...)
     if (length(data) > 1)
       stop("'data' has class 'character' and length ", length(data),
            ".\n  'data' accepts either a numeric matrix or a single filename.")
+    data <- path.expand(data)
     handle <- .Call(XGDMatrixCreateFromFile_R, data, as.integer(silent))
   } else if (is.matrix(data)) {
     handle <- .Call(XGDMatrixCreateFromMat_R, data, missing)
@@ -65,6 +66,7 @@ xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL) {
       warning("xgboost: label will be ignored.")
     }
     if (is.character(data)) {
+      data <- path.expand(data)
       dtrain <- xgb.DMatrix(data[1])
     } else if (inherits(data, "xgb.DMatrix")) {
       dtrain <- data

R-package/R/xgb.DMatrix.save.R

@@ -19,6 +19,7 @@ xgb.DMatrix.save <- function(dmatrix, fname) {
   if (!inherits(dmatrix, "xgb.DMatrix"))
     stop("dmatrix must be xgb.DMatrix")
 
+  fname <- path.expand(fname)
   .Call(XGDMatrixSaveBinary_R, dmatrix, fname[1], 0L)
   return(TRUE)
 }

R-package/R/xgb.dump.R

@@ -66,6 +66,7 @@ xgb.dump <- function(model, fname = NULL, fmap = "", with_stats=FALSE,
   if (is.null(fname)) {
     return(model_dump)
   } else {
+    fname <- path.expand(fname)
     writeLines(model_dump, fname[1])
     return(TRUE)
   }

R-package/R/xgb.save.R

@@ -42,6 +42,7 @@ xgb.save <- function(model, fname) {
          if (inherits(model, "xgb.DMatrix")) " Use xgb.DMatrix.save to save an xgb.DMatrix object." else "")
   }
   model <- xgb.Booster.complete(model, saveraw = FALSE)
+  fname <- path.expand(fname)
   .Call(XGBoosterSaveModel_R, model$handle, fname[1])
   return(TRUE)
 }

python-package/xgboost/core.py

@@ -645,8 +645,9 @@ def save_binary(self, fname, silent=True):
         silent : bool (optional; default: True)
             If set, the output is suppressed.
         """
+        fname = os.fspath(os.path.expanduser(fname))
         _check_call(_LIB.XGDMatrixSaveBinary(self.handle,
-                                             c_str(os.fspath(fname)),
+                                             c_str(fname),
                                              ctypes.c_int(silent)))
 
     def set_label(self, label):
@@ -1677,8 +1678,9 @@ def save_model(self, fname):
 
         """
         if isinstance(fname, (STRING_TYPES, os.PathLike)):  # assume file name
+            fname = os.fspath(os.path.expanduser(fname))
             _check_call(_LIB.XGBoosterSaveModel(
-                self.handle, c_str(os.fspath(fname))))
+                self.handle, c_str(fname)))
         else:
             raise TypeError("fname must be a string or os PathLike")
 
@@ -1717,8 +1719,9 @@ def load_model(self, fname):
         if isinstance(fname, (STRING_TYPES, os.PathLike)):
             # assume file name, cannot use os.path.exist to check, file can be
             # from URL.
+            fname = os.fspath(os.path.expanduser(fname))
             _check_call(_LIB.XGBoosterLoadModel(
-                self.handle, c_str(os.fspath(fname))))
+                self.handle, c_str(fname)))
         elif isinstance(fname, bytearray):
             buf = fname
             length = c_bst_ulong(len(buf))
@@ -1756,7 +1759,8 @@ def dump_model(self, fout, fmap='', with_stats=False, dump_format="text"):
             Format of model dump file. Can be 'text' or 'json'.
         """
         if isinstance(fout, (STRING_TYPES, os.PathLike)):
-            fout = open(os.fspath(fout), 'w')
+            fout = os.fspath(os.path.expanduser(fout))
+            fout = open(fout, 'w')
             need_close = True
         else:
             need_close = False
@@ -1790,7 +1794,7 @@ def get_dump(self, fmap='', with_stats=False, dump_format="text"):
             Format of model dump. Can be 'text', 'json' or 'dot'.
 
         """
-        fmap = os.fspath(fmap)
+        fmap = os.fspath(os.path.expanduser(fmap))
         length = c_bst_ulong()
         sarr = ctypes.POINTER(ctypes.c_char_p)()
         if self.feature_names is not None and fmap == '':
@@ -1870,7 +1874,7 @@ def get_score(self, fmap='', importance_type='weight'):
         importance_type: str, default 'weight'
             One of the importance types defined above.
         """
-        fmap = os.fspath(fmap)
+        fmap = os.fspath(os.path.expanduser(fmap))
         if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}:
             raise ValueError('Feature importance is not defined for Booster type {}'
                              .format(self.booster))
@@ -1963,7 +1967,7 @@ def trees_to_dataframe(self, fmap=''):
            The name of feature map file.
         """
         # pylint: disable=too-many-locals
-        fmap = os.fspath(fmap)
+        fmap = os.fspath(os.path.expanduser(fmap))
         if not PANDAS_INSTALLED:
             raise Exception(('pandas must be available to use this method.'
                              'Install pandas before calling again.'))

python-package/xgboost/data.py

@@ -486,7 +486,8 @@ def _is_uri(data):
 def _from_uri(data, missing, feature_names, feature_types):
     _warn_unused_missing(data, missing)
     handle = ctypes.c_void_p()
-    _check_call(_LIB.XGDMatrixCreateFromFile(c_str(os.fspath(data)),
+    data = os.fspath(os.path.expanduser(data))
+    _check_call(_LIB.XGDMatrixCreateFromFile(c_str(data),
                                              ctypes.c_int(1),
                                              ctypes.byref(handle)))
     return handle, feature_names, feature_types