vignettes/quickstart.md
In prodriguezsosa/labelProp: Label propagation algorithm for semi-supervised learning
Quick Start Guide
Installing the package
devtools::install_github("prodriguezsosa/labelProp")
Load package
library(labelProp)
# other libraries used in this guide
library(dplyr)
library(quanteda)
library(conText)
Preliminaries
labelProp offers a semi-supervised approach to labeling text data,
leveraging vector representations of text and network methods. The
general problem labelProp solves is the following: given a collection
of nodes –these can be words, short texts etc.– for which we have vector
representations –importantly, on the same space–, including a small
subset for which we have labels –which we call seeds–, how do we
determine the labels of the remaining set of nodes? Currently
labelProp offers two methods:
- Nearest neighbors (nns): each unlabeled node is assigned a score
based on its mean cosine similarity to seed nodes. This algorithm
requires vector representations for the full set of nodes, labeled
and unlabeled.
- Random walk (rw): each unlabeled node is assigned a score based
on the probability of a random walk from the seed set hitting that
node. This algorithm is adapted from Zhou et al.
(2004)
and Hamilton et al.
(2016).
This algorithm requires a transition matrix be estimated from the
vector representations for the full set of nodes, labeled and
unlabeled. For very large node sets ((10^6)), this can be
computationally intensive in which case users should use the
nns
algorithm. The advantage of rw over nns is that…
Data
labelProp comes with three datasets used in the examples below:
- A (data.frame) anes2016 with open-ended responses to the “most
important issues facing the country” question in ANES 2016.
- A set of (GloVe) pre-trained embeddings corresponding to the
full set of features (with min. freq. of 5) appearing in the
open-ended responses.
- A transformation matrix estimated by Khodak et
al. for the Stanford GloVe
300 embeddings.
Labeling words using seed words
Dictionary-based approaches are popular in the social sciences. They are
transparent and easy to implement. However, while there are plenty of
off-the-shelf dictionaries available –e.g.
LIWIC–, they
are often ill-suited to the corpus and/or concept of interest. On the
other hand, building a dictionary from scratch however labor-intensive
task. Given a small set of seed dictionary terms, labelProp scores
unlabeled terms, giving users a principled way of “expanding” their
locally-tailored dictionaries.
To use the rw algorithm we first build a transition matrix using our
set of pre-trained vector-representations as input. This can be done
using the labelProp::build_transition_matrix() function which is a
wrapper around parallelDist, allowing for parallelization. beta is
the only other function parameter in labelProp specific to the rw
algorithm, it specifies the extent to which the algorithm favors local
(similar labels for neighbors) vs. global (correct labels on seed nodes)
consistency – lower (higher) values emphasize local (global)
consistency. In practice we’ve found results to be fairly robust to the
specification of beta. To use the nns algorithm, users must provide
a set of vector representations, one for each node. The user must then
provide a set of labeled nodes –seeds– in the form of a named list.
Users can futher choose to compute standard errors for the assigned
scores using a bootstrapping procedure –the algorithm is replicated
num_bootstrap times using a sample proportion –prop_seeds– of the
seeds. The users can also choose to normalize scores by the sum of the
scores across the various classes – this is useful if for examle we are
building a sentiment dictionary and are looking to rank words according
to their polarity score. This can be done using the softmax parameter.
Finally, the user can specify whether to limit the number of nodes that
are output. If N is defined, then the top N scoring nodes for each
class are output, otherwise the output will include the full set of
nodes with their respective scores. Note, the unlabeled nodes with the
highest scores will likely be different depending on the algorithm. It
will often make sense to use both – for example when it is combined
human selection of words.
# to use the random-walkd algorithm we first build a transition matrix
transition_matrix <- build_transition_matrix(x = anes2016_glove, threads = 6L)
# define seeds (labeled nodes), if list is unlabeled, 'class1', 'class2' etc.
# will be used as labels
seeds = list(immigration = c("immigration", "immigrants", "immigrant"), economy = c("jobs",
"unemployment", "wages"))
# propagate label using rw
rw_labels <- labelProp(x = transition_matrix, seeds = seeds, method = "rw", N = 10,
beta = 0.5, as_list = TRUE)
# propagate label using nns, notice the main input, x, are the vector
# representations
nns_labels <- labelProp(x = anes2016_glove, seeds = seeds, method = "nns", N = 10,
as_list = TRUE)
# check output for economy
rw_labels[["economy"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 wage economy 5.50
## 2 employment economy 5.38
## 3 incomes economy 5.08
## 4 salaries economy 5.06
## 5 unemployed economy 4.99
## 6 workforce economy 4.81
## 7 inflation economy 3.97
## 8 workers economy 3.91
## 9 stagnant economy 3.79
## 10 benefits economy 3.73
nns_labels[["economy"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 employment economy 4.36
## 2 wage economy 4.16
## 3 benefits economy 3.59
## 4 salaries economy 3.59
## 5 incomes economy 3.52
## 6 workers economy 3.51
## 7 job economy 3.46
## 8 unemployed economy 3.25
## 9 labor economy 3.17
## 10 economy economy 3.02
# check output for immigration
rw_labels[["immigration"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 undocumented immigration 7.08
## 2 migrants immigration 6.70
## 3 illegals immigration 6.07
## 4 deported immigration 5.25
## 5 deportation immigration 5.17
## 6 citizenship immigration 4.91
## 7 latinos immigration 4.48
## 8 illegal immigration 4.38
## 9 aliens immigration 4.06
## 10 migration immigration 4.05
nns_labels[["immigration"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 undocumented immigration 5.12
## 2 migrants immigration 5.10
## 3 illegal immigration 4.34
## 4 citizenship immigration 3.91
## 5 deportation immigration 3.41
## 6 citizens immigration 3.40
## 7 illegals immigration 3.34
## 8 deported immigration 3.33
## 9 latinos immigration 3.07
## 10 welfare immigration 3.06
Document scoring
labelProp is not limited to words, indeed it can be applied to any set
of inputs for which we have or can estimate vector representations. In
the two examples that follow we use
conText together with a
subset of Stanford GloVe
(300 dimensional) embeddings and their corresponding transformation
matrix –estimated by Khodak et
al.– to embed our short ANES
open-ended responses.
Labeling documents using seed words
Since these document embeddings lie on the same space as the our set of
pre-trained word embeddings, we can use our seed terms to score
documents according to their relevance to each class.
# tokenize documents
anes_toks <- unique(anes2016$response) %>% tokens()
# embed documents using conText
doc_vs <- anes_toks %>% dfm %>% dem(pre_trained = anes2016_glove, transform = TRUE,
transform_matrix = khodakA, verbose = FALSE) %>% as.matrix()
# seed word vectors (from pre-trained embeddings)
word_vs <- anes2016_glove[unlist(seeds), ]
# combine word and document vectors into a single matrix of node vectors
node_vs <- rbind(doc_vs, word_vs)
# transition matrix
transition_matrix <- build_transition_matrix(x = node_vs, threads = 6L)
# label propagate
rw_labels <- labelProp(x = transition_matrix, seeds = seeds, method = "rw", N = 10,
beta = 0.5, as_list = TRUE)
# output
rw_labels[["economy"]]
## # A tibble: 12 × 3
## node class score
## <chr> <chr> <dbl>
## 1 text6220 economy 4.75
## 2 text1999 economy 4.74
## 3 text6295 economy 4.58
## 4 text1499 economy 4.56
## 5 text4185 economy 4.36
## 6 text5907 economy 4.30
## 7 text5819 economy 4.24
## 8 text4437 economy 4.20
## 9 text488 economy 4.14
## 10 text145 economy 4.11
## 11 text1117 economy 4.11
## 12 text7133 economy 4.11
rw_labels[["immigration"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 text681 immigration 6.27
## 2 text4024 immigration 6.27
## 3 text4771 immigration 6.27
## 4 text4937 immigration 6.27
## 5 text7523 immigration 6.27
## 6 text2855 immigration 6.21
## 7 text474 immigration 6.15
## 8 text4350 immigration 5.95
## 9 text5143 immigration 5.52
## 10 text7738 immigration 5.42
# combine with texts
tokens_subset(anes_toks, docid(anes_toks) %in% rw_labels[["economy"]]$node) %>% sapply(function(x) paste(x,
collapse = " "))
## text145 text488
## "unemployment" "economy job rate unemployment"
## text1117 text1499
## "nanosecond unemployment" "unemployment low wages"
## text1999 text4185
## "employment wages" "economy wages and jobs"
## text4437 text5819
## "jobs unemployment rate down" "livable wage jobs economy"
## text5907 text6220
## "unemployment and jobs" "wages"
## text6295 text7133
## "jobs unemployment rate" "widespread unemployment"
tokens_subset(anes_toks, docid(anes_toks) %in% rw_labels[["immigration"]]$node) %>%
sapply(function(x) paste(x, collapse = " "))
## text474 text681
## "immigrant" "eagle immigrants"
## text2855 text4024
## "immigration undocumented immigrants" "unneeded immigrants"
## text4350 text4771
## "immigration illegal immigrants" "immigrants resisting assimilation"
## text4937 text5143
## "immigrants" "decriminalization to immigrants"
## text7523 text7738
## "vetted immigrants" "immigrants not assimilating"
Labeling documents using seed words
We can also use embedded documents –for which we know their label– as
seeds.
# documents as seeds
doc_seeds <- list(immigration = c("text4771", "text4024"), economy = c("text2010",
"text5925"))
# transition matrix
transition_matrix <- build_transition_matrix(x = doc_vs, threads = 6L)
# label propagate
rw_labels <- labelProp(x = transition_matrix, seeds = doc_seeds, method = "rw", N = 10,
beta = 0.5, as_list = TRUE)
# output
rw_labels[["economy"]]
## # A tibble: 12 × 3
## node class score
## <chr> <chr> <dbl>
## 1 text4075 economy 3.89
## 2 text23 economy 3.88
## 3 text6673 economy 3.71
## 4 text2535 economy 3.64
## 5 text4175 economy 3.62
## 6 text761 economy 3.55
## 7 text2161 economy 3.51
## 8 text2495 economy 3.51
## 9 text5917 economy 3.47
## 10 text1095 economy 3.44
## 11 text1612 economy 3.44
## 12 text2469 economy 3.44
rw_labels[["immigration"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 text681 immigration 10.2
## 2 text4937 immigration 10.2
## 3 text7523 immigration 10.2
## 4 text2855 immigration 7.17
## 5 text7738 immigration 6.83
## 6 text6872 immigration 6.79
## 7 text5143 immigration 6.68
## 8 text586 immigration 6.67
## 9 text4350 immigration 6.49
## 10 text4188 immigration 6.16
# merge with texts
tokens_subset(anes_toks, docid(anes_toks) %in% rw_labels[["economy"]]$node) %>% sapply(function(x) paste(x,
collapse = " "))
## text23
## "jobs keeping jobs in the united states"
## text761
## "economy more jobs"
## text1095
## "economy jobs"
## text1612
## "economy jobs instability"
## text2161
## "the economy is unstable small business is paralyzed and afraid to invest unemployment is much worse than the federal statistics reported"
## text2469
## "jobs economy"
## text2495
## "economy jobs are leaving the country"
## text2535
## "many problems first the economy not enough jobs in the us"
## text4075
## "jobs in the united states"
## text4175
## "economy jobs going overseas"
## text5917
## "economy too much bureaucracy"
## text6673
## "economy keep jobs in us"
tokens_subset(anes_toks, docid(anes_toks) %in% rw_labels[["immigration"]]$node) %>%
sapply(function(x) paste(x, collapse = " "))
## text586
## "illegal immigrants"
## text681
## "eagle immigrants"
## text2855
## "immigration undocumented immigrants"
## text4188
## "undocumented immigrants and the wall"
## text4350
## "immigration illegal immigrants"
## text4937
## "immigrants"
## text5143
## "decriminalization to immigrants"
## text6872
## "illegal immigrants and immigrants already here"
## text7523
## "vetted immigrants"
## text7738
## "immigrants not assimilating"
prodriguezsosa/labelProp documentation built on May 14, 2023, 11:19 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Quick Start Guide
Installing the package
devtools::install_github("prodriguezsosa/labelProp")
Load package
library(labelProp)
# other libraries used in this guide
library(dplyr)
library(quanteda)
library(conText)
Preliminaries
labelProp offers a semi-supervised approach to labeling text data,
leveraging vector representations of text and network methods. The
general problem labelProp solves is the following: given a collection
of nodes –these can be words, short texts etc.– for which we have vector
representations –importantly, on the same space–, including a small
subset for which we have labels –which we call seeds–, how do we
determine the labels of the remaining set of nodes? Currently
labelProp offers two methods:
- Nearest neighbors (nns): each unlabeled node is assigned a score based on its mean cosine similarity to seed nodes. This algorithm requires vector representations for the full set of nodes, labeled and unlabeled.
- Random walk (rw): each unlabeled node is assigned a score based
on the probability of a random walk from the seed set hitting that
node. This algorithm is adapted from Zhou et al.
(2004)
and Hamilton et al.
(2016).
This algorithm requires a transition matrix be estimated from the
vector representations for the full set of nodes, labeled and
unlabeled. For very large node sets ((10^6)), this can be
computationally intensive in which case users should use the
nnsalgorithm. The advantage ofrwovernnsis that…
Data
labelProp comes with three datasets used in the examples below:
- A (data.frame) anes2016 with open-ended responses to the “most important issues facing the country” question in ANES 2016.
- A set of (GloVe) pre-trained embeddings corresponding to the full set of features (with min. freq. of 5) appearing in the open-ended responses.
- A transformation matrix estimated by Khodak et al. for the Stanford GloVe 300 embeddings.
Labeling words using seed words
Dictionary-based approaches are popular in the social sciences. They are
transparent and easy to implement. However, while there are plenty of
off-the-shelf dictionaries available –e.g.
LIWIC–, they
are often ill-suited to the corpus and/or concept of interest. On the
other hand, building a dictionary from scratch however labor-intensive
task. Given a small set of seed dictionary terms, labelProp scores
unlabeled terms, giving users a principled way of “expanding” their
locally-tailored dictionaries.
To use the rw algorithm we first build a transition matrix using our
set of pre-trained vector-representations as input. This can be done
using the labelProp::build_transition_matrix() function which is a
wrapper around parallelDist, allowing for parallelization. beta is
the only other function parameter in labelProp specific to the rw
algorithm, it specifies the extent to which the algorithm favors local
(similar labels for neighbors) vs. global (correct labels on seed nodes)
consistency – lower (higher) values emphasize local (global)
consistency. In practice we’ve found results to be fairly robust to the
specification of beta. To use the nns algorithm, users must provide
a set of vector representations, one for each node. The user must then
provide a set of labeled nodes –seeds– in the form of a named list.
Users can futher choose to compute standard errors for the assigned
scores using a bootstrapping procedure –the algorithm is replicated
num_bootstrap times using a sample proportion –prop_seeds– of the
seeds. The users can also choose to normalize scores by the sum of the
scores across the various classes – this is useful if for examle we are
building a sentiment dictionary and are looking to rank words according
to their polarity score. This can be done using the softmax parameter.
Finally, the user can specify whether to limit the number of nodes that
are output. If N is defined, then the top N scoring nodes for each
class are output, otherwise the output will include the full set of
nodes with their respective scores. Note, the unlabeled nodes with the
highest scores will likely be different depending on the algorithm. It
will often make sense to use both – for example when it is combined
human selection of words.
# to use the random-walkd algorithm we first build a transition matrix
transition_matrix <- build_transition_matrix(x = anes2016_glove, threads = 6L)
# define seeds (labeled nodes), if list is unlabeled, 'class1', 'class2' etc.
# will be used as labels
seeds = list(immigration = c("immigration", "immigrants", "immigrant"), economy = c("jobs",
"unemployment", "wages"))
# propagate label using rw
rw_labels <- labelProp(x = transition_matrix, seeds = seeds, method = "rw", N = 10,
beta = 0.5, as_list = TRUE)
# propagate label using nns, notice the main input, x, are the vector
# representations
nns_labels <- labelProp(x = anes2016_glove, seeds = seeds, method = "nns", N = 10,
as_list = TRUE)
# check output for economy
rw_labels[["economy"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 wage economy 5.50
## 2 employment economy 5.38
## 3 incomes economy 5.08
## 4 salaries economy 5.06
## 5 unemployed economy 4.99
## 6 workforce economy 4.81
## 7 inflation economy 3.97
## 8 workers economy 3.91
## 9 stagnant economy 3.79
## 10 benefits economy 3.73
nns_labels[["economy"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 employment economy 4.36
## 2 wage economy 4.16
## 3 benefits economy 3.59
## 4 salaries economy 3.59
## 5 incomes economy 3.52
## 6 workers economy 3.51
## 7 job economy 3.46
## 8 unemployed economy 3.25
## 9 labor economy 3.17
## 10 economy economy 3.02
# check output for immigration
rw_labels[["immigration"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 undocumented immigration 7.08
## 2 migrants immigration 6.70
## 3 illegals immigration 6.07
## 4 deported immigration 5.25
## 5 deportation immigration 5.17
## 6 citizenship immigration 4.91
## 7 latinos immigration 4.48
## 8 illegal immigration 4.38
## 9 aliens immigration 4.06
## 10 migration immigration 4.05
nns_labels[["immigration"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 undocumented immigration 5.12
## 2 migrants immigration 5.10
## 3 illegal immigration 4.34
## 4 citizenship immigration 3.91
## 5 deportation immigration 3.41
## 6 citizens immigration 3.40
## 7 illegals immigration 3.34
## 8 deported immigration 3.33
## 9 latinos immigration 3.07
## 10 welfare immigration 3.06
Document scoring
labelProp is not limited to words, indeed it can be applied to any set
of inputs for which we have or can estimate vector representations. In
the two examples that follow we use
conText together with a
subset of Stanford GloVe
(300 dimensional) embeddings and their corresponding transformation
matrix –estimated by Khodak et
al.– to embed our short ANES
open-ended responses.
Labeling documents using seed words
Since these document embeddings lie on the same space as the our set of pre-trained word embeddings, we can use our seed terms to score documents according to their relevance to each class.
# tokenize documents
anes_toks <- unique(anes2016$response) %>% tokens()
# embed documents using conText
doc_vs <- anes_toks %>% dfm %>% dem(pre_trained = anes2016_glove, transform = TRUE,
transform_matrix = khodakA, verbose = FALSE) %>% as.matrix()
# seed word vectors (from pre-trained embeddings)
word_vs <- anes2016_glove[unlist(seeds), ]
# combine word and document vectors into a single matrix of node vectors
node_vs <- rbind(doc_vs, word_vs)
# transition matrix
transition_matrix <- build_transition_matrix(x = node_vs, threads = 6L)
# label propagate
rw_labels <- labelProp(x = transition_matrix, seeds = seeds, method = "rw", N = 10,
beta = 0.5, as_list = TRUE)
# output
rw_labels[["economy"]]
## # A tibble: 12 × 3
## node class score
## <chr> <chr> <dbl>
## 1 text6220 economy 4.75
## 2 text1999 economy 4.74
## 3 text6295 economy 4.58
## 4 text1499 economy 4.56
## 5 text4185 economy 4.36
## 6 text5907 economy 4.30
## 7 text5819 economy 4.24
## 8 text4437 economy 4.20
## 9 text488 economy 4.14
## 10 text145 economy 4.11
## 11 text1117 economy 4.11
## 12 text7133 economy 4.11
rw_labels[["immigration"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 text681 immigration 6.27
## 2 text4024 immigration 6.27
## 3 text4771 immigration 6.27
## 4 text4937 immigration 6.27
## 5 text7523 immigration 6.27
## 6 text2855 immigration 6.21
## 7 text474 immigration 6.15
## 8 text4350 immigration 5.95
## 9 text5143 immigration 5.52
## 10 text7738 immigration 5.42
# combine with texts
tokens_subset(anes_toks, docid(anes_toks) %in% rw_labels[["economy"]]$node) %>% sapply(function(x) paste(x,
collapse = " "))
## text145 text488
## "unemployment" "economy job rate unemployment"
## text1117 text1499
## "nanosecond unemployment" "unemployment low wages"
## text1999 text4185
## "employment wages" "economy wages and jobs"
## text4437 text5819
## "jobs unemployment rate down" "livable wage jobs economy"
## text5907 text6220
## "unemployment and jobs" "wages"
## text6295 text7133
## "jobs unemployment rate" "widespread unemployment"
tokens_subset(anes_toks, docid(anes_toks) %in% rw_labels[["immigration"]]$node) %>%
sapply(function(x) paste(x, collapse = " "))
## text474 text681
## "immigrant" "eagle immigrants"
## text2855 text4024
## "immigration undocumented immigrants" "unneeded immigrants"
## text4350 text4771
## "immigration illegal immigrants" "immigrants resisting assimilation"
## text4937 text5143
## "immigrants" "decriminalization to immigrants"
## text7523 text7738
## "vetted immigrants" "immigrants not assimilating"
Labeling documents using seed words
We can also use embedded documents –for which we know their label– as seeds.
# documents as seeds
doc_seeds <- list(immigration = c("text4771", "text4024"), economy = c("text2010",
"text5925"))
# transition matrix
transition_matrix <- build_transition_matrix(x = doc_vs, threads = 6L)
# label propagate
rw_labels <- labelProp(x = transition_matrix, seeds = doc_seeds, method = "rw", N = 10,
beta = 0.5, as_list = TRUE)
# output
rw_labels[["economy"]]
## # A tibble: 12 × 3
## node class score
## <chr> <chr> <dbl>
## 1 text4075 economy 3.89
## 2 text23 economy 3.88
## 3 text6673 economy 3.71
## 4 text2535 economy 3.64
## 5 text4175 economy 3.62
## 6 text761 economy 3.55
## 7 text2161 economy 3.51
## 8 text2495 economy 3.51
## 9 text5917 economy 3.47
## 10 text1095 economy 3.44
## 11 text1612 economy 3.44
## 12 text2469 economy 3.44
rw_labels[["immigration"]]
## # A tibble: 10 × 3
## node class score
## <chr> <chr> <dbl>
## 1 text681 immigration 10.2
## 2 text4937 immigration 10.2
## 3 text7523 immigration 10.2
## 4 text2855 immigration 7.17
## 5 text7738 immigration 6.83
## 6 text6872 immigration 6.79
## 7 text5143 immigration 6.68
## 8 text586 immigration 6.67
## 9 text4350 immigration 6.49
## 10 text4188 immigration 6.16
# merge with texts
tokens_subset(anes_toks, docid(anes_toks) %in% rw_labels[["economy"]]$node) %>% sapply(function(x) paste(x,
collapse = " "))
## text23
## "jobs keeping jobs in the united states"
## text761
## "economy more jobs"
## text1095
## "economy jobs"
## text1612
## "economy jobs instability"
## text2161
## "the economy is unstable small business is paralyzed and afraid to invest unemployment is much worse than the federal statistics reported"
## text2469
## "jobs economy"
## text2495
## "economy jobs are leaving the country"
## text2535
## "many problems first the economy not enough jobs in the us"
## text4075
## "jobs in the united states"
## text4175
## "economy jobs going overseas"
## text5917
## "economy too much bureaucracy"
## text6673
## "economy keep jobs in us"
tokens_subset(anes_toks, docid(anes_toks) %in% rw_labels[["immigration"]]$node) %>%
sapply(function(x) paste(x, collapse = " "))
## text586
## "illegal immigrants"
## text681
## "eagle immigrants"
## text2855
## "immigration undocumented immigrants"
## text4188
## "undocumented immigrants and the wall"
## text4350
## "immigration illegal immigrants"
## text4937
## "immigrants"
## text5143
## "decriminalization to immigrants"
## text6872
## "illegal immigrants and immigrants already here"
## text7523
## "vetted immigrants"
## text7738
## "immigrants not assimilating"
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