Match the feature set of a dfm to a specified vector of feature names.
For existing features in x for which there is an exact match for an
element of features, these will be included. Any features in x
not features will be discarded, and any feature names specified in
features but not found in x will be added with all zero counts.
dfm_match(x, features)
| x | a dfm |
|---|---|
| features | character; the feature names to be matched in the output dfm |
A dfm whose features are identical to those specified in
features.
Selecting on another dfm's featnames() is useful when you
have trained a model on one dfm, and need to project this onto a test set
whose features must be identical. It is also used in
bootstrap_dfm().
Unlike dfm_select(), this function will add feature names
not already present in x. It also provides only fixed,
case-sensitive matches. For more flexible feature selection, see
dfm_select().
# matching a dfm to a feature vector dfm_match(dfm(""), letters[1:5]) #> Warning: 'dfm.character()' is deprecated. Use 'tokens()' first. #> Document-feature matrix of: 1 document, 5 features (100.00% sparse) and 0 docvars. #> features #> docs a b c d e #> text1 0 0 0 0 0 dfm_match(data_dfm_lbgexample, c("A", "B", "Z")) #> Document-feature matrix of: 6 documents, 3 features (72.22% sparse) and 0 docvars. #> features #> docs A B Z #> R1 2 3 0 #> R2 0 0 0 #> R3 0 0 3 #> R4 0 0 115 #> R5 0 0 78 #> V1 0 0 0 dfm_match(data_dfm_lbgexample, c("B", "newfeat1", "A", "newfeat2")) #> Document-feature matrix of: 6 documents, 4 features (91.67% sparse) and 0 docvars. #> features #> docs B newfeat1 A newfeat2 #> R1 3 0 2 0 #> R2 0 0 0 0 #> R3 0 0 0 0 #> R4 0 0 0 0 #> R5 0 0 0 0 #> V1 0 0 0 0 # matching one dfm to another txt <- c("This is text one", "The second text", "This is text three") (dfmat1 <- dfm(tokens(txt[1:2]))) #> Document-feature matrix of: 2 documents, 6 features (41.67% sparse) and 0 docvars. #> features #> docs this is text one the second #> text1 1 1 1 1 0 0 #> text2 0 0 1 0 1 1 (dfmat2 <- dfm(tokens(txt[2:3]))) #> Document-feature matrix of: 2 documents, 6 features (41.67% sparse) and 0 docvars. #> features #> docs the second text this is three #> text1 1 1 1 0 0 0 #> text2 0 0 1 1 1 1 (dfmat3 <- dfm_match(dfmat1, featnames(dfmat2))) #> Document-feature matrix of: 2 documents, 6 features (50.00% sparse) and 0 docvars. #> features #> docs the second text this is three #> text1 0 0 1 1 1 0 #> text2 1 1 1 0 0 0 setequal(featnames(dfmat2), featnames(dfmat3)) #> [1] TRUE