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Fix #67 #68

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Jun 28, 2022
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Fix #67 #68

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1 change: 1 addition & 0 deletions .Rbuildignore
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Expand Up @@ -31,6 +31,7 @@
^data/abstracts_topicmodels\.rda$
^data/abstracts_seededlda\.rda$
^data/abstracts_unseededlda\.rda$
^data/abstracts_warplda\.rda$
^data/abstracts_stm\.rda$
^tests/testthat/apps/
^tests/testdata/downloaded$
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3 changes: 0 additions & 3 deletions R/oolong_data_misc.R
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Expand Up @@ -17,9 +17,6 @@
#' These are topic models trained with different topic model packages.
"abstracts_keyatm"

#' @rdname abstracts_keyatm
"abstracts_warplda"

#' @rdname abstracts_keyatm
"abstracts_btm"

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13 changes: 7 additions & 6 deletions btm_gh.md
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Expand Up @@ -20,7 +20,7 @@ require(BTM)
#> Loading required package: BTM
require(quanteda)
#> Loading required package: quanteda
#> Package version: 3.2.0
#> Package version: 3.2.1
#> Unicode version: 13.0
#> ICU version: 66.1
#> Parallel computing: 8 of 8 threads used.
Expand Down Expand Up @@ -63,9 +63,10 @@ trump_btm <- BTM(trump_dat, k = 8, iter = 500, trace = 10)

## Pecularities of BTM

This is how you should generate \(\theta_{t}\) . However, there are many
NaN and there are only 1994 rows (`trump2k` has 2000 tweets) due to
empty documents.
This is how you should generate
![\\theta\_{t}](https://latex.codecogs.com/png.image?%5Cdpi%7B110%7D&space;%5Cbg_white&space;%5Ctheta_%7Bt%7D
"\\theta_{t}") . However, there are many NaN and there are only 1994
rows (`trump2k` has 2000 tweets) due to empty documents.

``` r
theta <- predict(trump_btm, newdata = trump_dat)
Expand Down Expand Up @@ -117,7 +118,7 @@ with other topic models.
oolong <- create_oolong(trump_btm)
oolong
#>
#> ── oolong (topic model) ────────────────────────────────────────────────────────
#> ── oolong (topic model) ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ✔ WI ✖ TI ✖ WSI
#> ℹ WI: k = 8, 0 coded.
#>
Expand All @@ -135,7 +136,7 @@ frame you used for training (in this case `trump_dat`). Your
oolong <- create_oolong(trump_btm, trump_corpus, btm_dataframe = trump_dat)
oolong
#>
#> ── oolong (topic model) ────────────────────────────────────────────────────────
#> ── oolong (topic model) ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ✔ WI ✔ TI ✖ WSI
#> ℹ WI: k = 8, 0 coded.
#> ℹ TI: n = 20, 0 coded.
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4 changes: 2 additions & 2 deletions deploy_gh.md
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Expand Up @@ -27,7 +27,7 @@ library(oolong)
wsi_test <- wsi(abstracts_keyatm)
wsi_test
#>
#> ── oolong (topic model) ────────────────────────────────────────────────────────
#> ── oolong (topic model) ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ✖ WI ✖ TI ✔ WSI
#> ℹ WSI: n = 10, 0 coded.
#>
Expand Down Expand Up @@ -116,7 +116,7 @@ revert_oolong(wsi_test, "oolong_2021-05-22 20 51 26 Hadley Wickham.RDS")
```

#>
#> ── oolong (topic model) ────────────────────────────────────────────────────────
#> ── oolong (topic model) ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
#> ✖ WI ✖ TI ✔ WSI
#> ☺ Hadley Wickham
#> ℹ WSI: n = 10, 10 coded.
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5 changes: 0 additions & 5 deletions man/abstracts_keyatm.Rd
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31 changes: 0 additions & 31 deletions overview_gh.Rmd
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Expand Up @@ -226,37 +226,6 @@ H1: Median TLO is better than random guess.

One must notice that the two statistical tests are testing the bear minimum. A significant test only indicates the topic model can make the rater(s) perform better than random guess. It is not an indication of good topic interpretability. Also, one should use a very conservative significant level, e.g. $\alpha < 0.001$.

### About Warp LDA

There is a subtle difference between the support for `stm` and for `text2vec`.

`abstracts_warplda` is a Warp LDA object trained with the same dataset as the `abstracts_stm`

```{r warplda}
abstracts_warplda
```

All the API endpoints are the same, except the one for the creation of topic intrusion test cases. You must supply also the `input_dfm`.

```{r warplda2}
### Just word intrusion test.
oolong_test <- wi(abstracts_warplda, userid = "Lionel")
oolong_test
```


```{r warplda3}
abstracts_dfm
```

```{r warplda4, , message = FALSE, results = 'hide', warning = FALSE}
oolong_test <- witi(abstracts_warplda, abstracts$text, input_dfm = abstracts_dfm, userid = "Mara")
```

```{r warplda5}
oolong_test
```

## About Biterm Topic Model

Please refer to the vignette about BTM.
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