fastTextEmbeddings

Simply R with the fastText library

For first use: download the fastText Library via Terminal

$ wget https://github.com/facebookresearch/fastText/archive/v0.9.1.zip
$ unzip v0.9.1.zip
$ cd fastText-0.9.1
$ make

Word representation Learning:

First we need to create a training file corpTrain.txt in containing UTF-8 encoded text. Therefore we create a training corpus consisting of the NDC data and save it to this file:

library(reticulate)
library(countrycode)
library(quanteda)

## Package version: 2.0.0

## Parallel computing: 2 of 16 threads used.

## See https://quanteda.io for tutorials and examples.

## 
## Attaching package: 'quanteda'

## The following object is masked from 'package:utils':
## 
##     View

library(quanteda.corpora)
library(quanteda.textmodels)
library(stringi)
load("data_ndc_reduced.RData",.GlobalEnv)

frameNDC <- data.frame(data_ndc)
cleanNDC <-subset(frameNDC,select = c(NDCISO3,Title,Body, NDCSubmissionDate))
#this step makes the process very slow but is necessary to create a well structures corpus
cleanNDC$Body<-as.character(cleanNDC$Body)
corp_NDC <- corpus(cleanNDC,docid_field = "NDCISO3",text_field = "Body")

stri_write_lines(as.character(corp_NDC), "corpNDC.txt",encoding="UTF-8")


#sepeate corpus in training and testing-samples 2:1, we will need this later on 
corp_Train<-corpus(corp_NDC[1:120])
corp_Test <- corpus(corp_NDC[121:184])
stri_write_lines(as.character(corp_Train), "corpTrain.txt",encoding="UTF-8")

Now we can create the n-grams:

$./fasttext skipgram -input /home/lmdose/Transnorms/corpNDC.txt -output /home/lmdose/Transnorms/model

NOTE: Make sure the path to your input file is complete
Output: character n-gramms from 3 to 6 characters we using the default option: so our model will be saved in 2 files: model.binand model.vec
model.vec: containing word vectors - one per line
model.by: binary file, containing parameters of the model, dictionary and the hyper parameters (used to compute word vectors or restart the optimization)
Look into model.vecdata

$ head -n 4 /home/lmdose/Transnorms/model.vec

First you will see the Header with the number of words and the dimensionality of the vectors
Lines underneath are the word vectors for all words in the vocabulary, sorted by decreasing frequency

10774 100

the -0.03499 -0.18335 -0.10559 0.2881 0.54159 -0.032164 -0.41327 -0.59312 0.26826 -0.036791 -0.078577 0.11063 0.24688 -0.21086 0.20692 -0.56439 0.04891 -0.22069 0.17839 0.076822 -0.22762 0.045206 0.23644 -0.17071 -0.0057124 -0.39182 0.1745 -0.43951 -0.33953 0.038183 -0.094884 -0.057301 0.24533 -0.22221 0.04039 -0.12032 -0.6133 -0.039249 -0.21975 0.10141 0.017226 0.33069 -0.017599 0.029245 0.016613 0.10392 -0.074652 -0.13209 -0.50272 0.11387 -0.40016 0.13423 0.11466 -0.2506 -0.070772 0.43143 -0.05571 -0.19713 -0.14835 -0.14179 -0.14898 0.21163 0.24192 -0.076029 -0.32578 -0.11701 -0.26833 0.67852 -0.21537 0.1336 0.19592 0.38179 0.048842 0.13838 -0.21994 0.029262 0.025223 -0.29592 0.32399 -0.0040888 0.020609 -0.082071 1.1773 0.0040636 -0.094433 0.17531 -0.32684 0.029042 -0.16321 -0.23234 -0.11499 0.062406 -0.0088184 -0.017994 0.033088 -0.63257 -0.020392 0.49179 0.082061 -0.25972

of 0.098286 0.2146 -0.49967 0.23697 0.057761 0.053467 0.02508 -0.6012 0.17814 -0.061209 0.16816 0.15009 -0.067927 -0.3957 -0.095671 -0.4982 0.029651 -0.091187 0.13122 0.035603 -0.42743 -0.43455 0.12268 -0.13232 0.096251 0.15197 0.17784 -0.23815 -0.54501 0.1379 -0.2073 -0.32335 0.14169 -0.72529 -0.27303 0.0042096 -0.051325 -0.11965 -0.083101 -0.083953 -0.35923 0.12159 -0.072992 -0.036568 -0.010522 0.3643 0.35721 -0.32418 -0.37369 -0.5212 0.018163 0.23687 -0.0071997 -0.0074888 -0.034494 0.019077 -0.26253 -0.11654 -0.23271 0.0019683 0.15023 0.12797 0.44823 0.35739 -0.049191 -0.14703 -0.11383 0.5319 -0.0048039 0.3375 0.052892 -0.13707 -0.0015453 0.19553 -0.16275 0.2902 0.26353 -0.049827 -0.07324 0.0047258 0.30992 -0.37119 0.91426 0.26154 0.11348 0.29161 -0.45588 -0.035805 -0.049376 -0.069457 -0.27662 0.31167 -0.12594 -0.050569 0.31997 -0.70282 0.26969 0.22731 0.10048 -0.23374

Optaining word vectors for out-of-vocabulary words

For this we need the previously trained model to compute out-of-vocabulary word-vectors:
NOTE: therefore we need the test_corpus from our NDC-Data and save it to a file:

stri_write_lines(as.character(corp_Test), "corpTest.txt",encoding="UTF-8")

Now you can compute all vectors
- NOT RECOMMENDED - long comliling time:

$ ./fasttext print-word-vectors model.bin < corpTest.txt

Visualization via Python

to see the difference between the possible models for computing word representations: skipgram and cbow
skipgram learns to predict a target word thank to a nearby word
cbow model predicts the target word according to it’s context

library(reticulate)
#py_install("fasttext",pip=TRUE)

Skipgram Model

generalization of n-grams in which the components (typically words) need not be consecutive in the text under consideration, but may leave gaps that are skipped over

import fasttext

# Skipgram model :
model = fasttext.train_unsupervised('/home/lmdose/Transnorms/corpNDC.txt', model='skipgram')


#print(model.words)   # list of words in dictionary
#print(model['environment']) # get the vector of the word 'environment'

print(model.get_word_vector("the"))

#save and load the used model
#model.save_model("model_Skipgram.bin")
#model = fasttext.load_model("model_Skipgram.bin")

## [ 0.5823187   0.01354557  0.12242497  0.3532409   0.24886626 -0.28897318
##  -0.11529521 -0.15595374  0.3636525   0.23986788  0.22228147  0.05776799
##   0.00633125 -0.47234684  0.5451472  -0.37693605 -0.26807293  0.14937538
##  -0.35640112 -0.0690174  -0.4630555   0.13071229 -0.0015531  -0.45493153
##   0.02816676 -0.37942094  0.2182137  -0.24866456 -0.27882028 -0.15474558
##  -0.0050651  -0.13726953  0.5423108  -0.34045658 -0.32225138  0.07109424
##  -0.19171932  0.08170998 -0.26355192  0.38899308 -0.17432684  0.58566105
##  -0.00887425  0.06567102  0.15995084 -0.10437258 -0.18424591  0.01847774
##  -0.26021647  0.30871364 -0.13643053  0.20566447 -0.04363001 -0.15818818
##  -0.37993807 -0.00893357  0.17031232 -0.09664437  0.33397734  0.47009605
##   0.07011077 -0.06849101  0.3162833  -0.15138812  0.16167358  0.53874534
##  -0.16129242  0.23343508 -0.04670294 -0.11028334  0.14168428 -0.09825205
##  -0.08191513 -0.21527962 -0.11924632  0.07435291  0.05326606 -0.23393513
##  -0.04418733 -0.05805134  0.28165254 -0.15664068  0.70873284 -0.00764321
##   0.16308399 -0.1581713  -0.1808656   0.09245358 -0.5312962  -0.22118971
##   0.09415234 -0.21852034  0.15755884 -0.07343325  0.30879864 -0.6222605
##   0.32024637  0.15456776 -0.15166144  0.00543984]

Cbow Model

Continous-Bag-of-Words-Model

import fasttext
# or, cbow model :
model = fasttext.train_unsupervised('/home/lmdose/Transnorms/corpNDC.txt', model='cbow')
#print(model.words)   # list of words in dictionary
print(model['environment']) # get the vector of the word 'environment'

#save and load the used model

## [ 1.0783936  -0.204134    0.21529645  1.5048165   0.28533927  0.10685279
##  -0.39947036  0.6357685  -0.06705244 -0.31064126 -0.5757042  -0.01651522
##   0.7476375   0.6310607   0.2445185  -0.23020095 -0.9824078   0.04718415
##   0.42641228  0.65498126  0.16872868 -0.06193537  0.32136488  0.10784563
##  -0.06033111 -0.47580484 -0.55686164 -0.50976086  0.13409051  1.3227488
##  -1.016191   -0.2796896  -0.6983971   0.43487424 -0.91790116  0.27311012
##   0.7269545  -0.30813086 -0.7438809   1.0001969   0.31998748 -0.08725212
##  -0.58830404  0.02290096  0.21639366 -0.03155804 -0.49392143  1.1624535
##  -0.77428865 -0.5978175   0.11803786  0.95361537  0.70845217 -0.2361077
##   0.34372678 -0.05231997 -0.28632146  0.6989872  -0.386794    0.1604246
##  -1.125397    1.6065888   1.3761024  -0.46430793 -0.58917826 -0.35198298
##   0.03626015  0.8655159   0.4552439   1.4416643  -0.02853695 -0.43792236
##  -0.30112815 -0.22626884 -0.29589254 -0.95303667 -0.21992451  0.55057746
##  -0.5048564  -0.07325631 -1.0728685   0.10855455 -0.5114205   0.7659224
##  -0.46671182  1.8982466   1.1050923  -0.63911104  0.01496439 -1.5656222
##  -0.04519705 -0.13737708  0.15876943 -0.96226853 -0.10238124 -1.9488845
##  -0.32782564  0.51581585  1.2527219  -0.43449318]

model.save_model("model_CBOW.bin")
#model = fasttext.load_model("model_CBOW.bin")

Playing with parameters:

in the upper part we used the default version of all the parameters, now we explore the possibilities:
we can control the dimension of the vectors (dim), subwords are all substrings between the minimum size (minn) and the maximal size (maxn)
in the default version we using dim=100, minn=3,max=6

$ ./fasttext skipgram -input /home/lmdose/Transnorms/corpNDC.txt -output /home/lmdose/Transnorms/param -minn 4 -maxn 6 -dim 300

Let’s check the results:

Read 0M words
Number of words:  10774
Number of labels: 0
Progress: 100.0% words/sec/thread:   76769 lr:  0.000000 loss:  2.182278 ETA:   0h 0m

depending on the quantity of the data you can change the parameters of the training
epoch: controls how many times the model will loop over your data (default =5 times) lr: learning rate, the higher the learning rate is, the faster the model coverage to a solution but risk overfitting (default= 0.05)

$ ./fasttext skipgram -input /home/lmdose/Transnorms/corpNDC.txt -output /home/lmdose/Transnorms/epochlr -epoch 1 -lr 0.5

Results:

Read 0M words
Number of words:  10774
Number of labels: 0
Progress: 100.0% words/sec/thread:  147367 lr:  0.000000 loss: 22.457983 ETA:   0h 0m

fastText is multi-threaded (default= 12threads), so you can change the number of used threads:

./fasttext skipgram -input /home/lmdose/Transnorms/corpNDC.txt  -output /home/lmdose/Transnorms/thread -thread 4

Working with word vectors

you can use the print-word-vectors functionality to view some of the word vectors:

echo "environment nature save" | ./fasttext print-word-vectors /home/lmdose/Transnorms/model.bin

you can also query for words that did not appear in your data (exp: miss spelled words)

Nearest neighbors: to check the quality of the word vector:

In the following you will see some examples of nearest neighbors, calculated with different models.bin data
In order to find nearest neighbors, we need to compute a similarity score between words represented as vectors. In particular we use the cosine of the angles between two vectors.

$ ./fasttext nn /home/lmdose/Transnorms/model.bin

now you can choose a word for comparison:

Query word? environment

default skipgram	default cbow	skipram(-minn 4,-maxn 6,-dim 300)
environment; 0.976175	environment; 0.985328	environment. 0.976722
environment. 0.971485	environment. 0.985055	environment; 0.976203
environment, 0.964518	environment, 0.984893	environment, 0.96742
environments 0.961128	environmental, 0.975207	environments. 0.965824
environments. 0.958927	environmental 0.971219	environments 0.964224
environmental, 0.950929	environments 0.965677	environmental, 0.960406
environmental 0.943074	environments. 0.962285	environmental 0.952263
environmentally 0.939118	environmentfriendly 0.957352	environmentally 0.945117
environmentfriendly 0.931369	environmental 0.952318	environmentfriendly 0.934378
environmental 0.907616	environmentally 0.936702	environmental 0.931076

other examples:

economics:

Query word? economics

default skipgram	default cbow	skipram(-minn 4,-maxn 6,-dim 300)
economic, 0.980065	economic, 0.9971	economic 0.986749
economic 0.979307	economic 0.993475	economic, 0.983864
economique 0.952258	economie 0.98356	economique 0.970077
economic 0.943479	economique 0.983391	economic 0.964972
macroeconomic 0.939974	macroeconomic 0.980489	economie 0.942939
economie 0.927168	economic 0.978601	economical 0.94133
economical 0.925894	economy; 0.973662	macroeconomic 0.936014
socioeconomic 0.921567	economy. 0.973567	economy: 0.909701
economy: 0.910588	economy: 0.973478	economy; 0.908804
economy; 0.900353	economy 0.973379	socioeconomic 0.908281

help

Query word? help

default skipgram	default cbow	skipram(-minn 4,-maxn 6,-dim 300)
helps 0.894918	helps 0.959427	helps 0.89368
Paraguay 0.857259	seeks 0.941891	helped 0.86531
firmly 0.854321	successful 0.934734	vigorously 0.845747
milestone 0.840481	needed. 0.927279	overcome 0.815734
spirit 0.828939	necessarily 0.921473	maximize 0.812902
seems 0.828745	successfully 0.918	seek 0.81268
firms 0.828697	away 0.917192	g) 0.812417
“We 0.824893	emphasize 0.91241	will, 0.810029
Egypt 0.823377	adequacy 0.91231	ideal 0.809566
enjoy 0.822348	adequate, 0.911914	maximise 0.808108

future

Query word? future

default skipgram	default cbow	skipram(-minn 4,-maxn 6,-dim 300)
future 0.944385	future. 0.971025	future 0.971006
future. 0.924314	future 0.953925	future. 0.924168
future, 0.784043	assure 0.8816	future, 0.796366
feature 0.775011	tackle 0.872702	RMI. 0.785379
failure 0.753871	pose 0.868906	RMI, 0.77423
immediate, 0.749489	failure 0.867657	feature 0.759662
immediately, 0.741339	allows 0.866596	explicit 0.755335
explicit 0.736049	allow 0.861032	dynamic 0.753797
immediate 0.729415	delay 0.858254	driven 0.751838
tackle 0.727342	pathway 0.85643	tackle 0.751041

nation:
```
Query word? nation
```

default skipgram	default cbow	skipram(-minn 4,-maxn 6,-dim 300)
occupation 0.863779	Elimination 0.959792	occupation 0.866776
location 0.854486	allocation 0.950098	location 0.862074
duration 0.842992	realisation 0.947967	duration 0.862065
destination 0.838705	elimination 0.944217	saturation 0.8556
vocation 0.835614	destination 0.941915	destination 0.854961
fixation 0.833931	relocation 0.941207	inflation 0.844954
elevation 0.832624	foundation 0.936484	fixation 0.837067
migration 0.828343	Situation 0.935346	elevation 0.834022
Elimination 0.827394	deterioration 0.935131	vocation 0.83077
saturation 0.826724	aspiration 0.934847	Situation 0.829719

money:

Query word? money

default skipgram	default cbow	skipram(-minn 4,-maxn 6,-dim 300)
monetary 0.897812	theme 0.919846	monetary 0.920609
happens 0.855233	precocious 0.913133	worsen 0.915096
ENSO 0.854976	thorough 0.912424	catastrophes 0.912599
worsen 0.846901	suffers 0.911671	ENSO 0.905982
returns 0.843443	hazard 0.906327	death 0.905583
enormous 0.840994	serious 0.90476	records 0.90507
magnitude 0.839656	highlights 0.902415	catastrophic 0.904974
survival. 0.839157	strong, 0.901379	fewer 0.897929
exact 0.839037	experiences, 0.901326	(dryness, 0.896362
monsoon 0.836551	experiences 0.900803	sometimes 0.893317

Analogies:

We can calculate which two vectors have a similar relationship to each other as two given reference vectors. This can be done with the analogies functionality. It takes a word triplet (like Germany Berlin France) and outputs the analogy:

./fasttext analogies /home/lmdose/Transnorms/model.bin

Example Nr.1:

Query triplet (A - B + C)? environment economy money

default skipgram	default CBOW	skipram(-minn 4,-maxn 6,-dim 300)
environmental 0.681859	arrangements, 0.823284	monetary 0.685106
environmental, 0.680602	arrangements 0.79583	defense 0.665122
monetary 0.680154	arrangements. 0.793848	prevents 0.660168
environments 0.673295	register 0.793176	loss. 0.658111
experimental 0.670279	search 0.789722	lasting. 0.657862
harmful 0.659239	Arrangements 0.771946	loss, 0.657579
environments. 0.655499	Women 0.768271	flow 0.65746
environment; 0.65439	settlements, 0.75964	prevent, 0.656878
lasting. 0.65202	men, 0.755929	ever 0.649998
danger 0.649508	settlements 0.753831	catastrophes 0.648085

Example Nr.2:

Query triplet (A - B + C)? economy environment money

default skigram	default CBOW	skipram(-minn 4,-maxn 6,-dim 300)
economique 0.694713	decouple 0.829562	low, 0.725725
economie 0.680082	him 0.828757	rose 0.698711
economies 0.678955	obvious 0.813524	slightly 0.689552
low, 0.677932	deep 0.812828	lower 0.689168
low. 0.667652	high. 0.811764	gold 0.687941
high, 0.663306	lot 0.809197	CPEC 0.687592
economically 0.661366	despite 0.808579	tend 0.686826
shocks 0.659641	high, 0.807657	summer 0.68662
high. 0.656066	hit 0.807469	plateau 0.686237
economy: 0.655573	significant. 0.806372	44% 0.685803

Example Nr. 3:

Query triplet (A - B + C)? helf environment pollution

default skigram	default CBOW	skipram(-minn 4,-maxn 6,-dim 300)
pollutants 0.70366	distribution 0.816826	te 0.728939
depletion 0.696487	malnutrition 0.802241	27. 0.722679
decomposition 0.694116	distribution 0.79926	mosquitoes 0.719671
disposition 0.687676	Nutrition 0.78808	Limit 0.717822
! 0.685478	exhibition 0.787138	shortlived 0.714141
herds 0.684758	nutrition 0.775685	pollutants 0.707772
firms 0.682133	limitation 0.767911	decomposition 0.706242
bauxite 0.681445	disposition 0.760229	MOA 0.705363
composition 0.679994	depletion 0.760211	stone 0.70489
palms 0.678527	distribution, 0.759834	PANA-ASA 0.704827

Example Nr.4:

Query triplet (A - B + C)? help limit pollution

default skigram	default CBOW	skipram(-minn 4,-maxn 6,-dim 300)
safety 0.726571	innovation 0.878877	schools 0.77708
f) 0.726185	organisation 0.875022	competitiveness. 0.7698
designs 0.725096	modernisation 0.869121	safety 0.768397
schools 0.723643	organization 0.858813	competitiveness 0.766038
services; 0.723549	urbanisation 0.857014	f) 0.764046
drainage 0.72282	civilization 0.856748	drainage 0.760211
modernize 0.720092	innovation. 0.849295	schools, 0.759368
school 0.718747	modernization 0.846737	schemes. 0.75136
roads 0.718205	innovation, 0.841857	school 0.749063
dimension 0.713688	observation 0.840498	pastoralist 0.747295

for more information:

Please checkout:
https://fasttext.cc/
https://github.com/facebookresearch/fastText

Visualization

via python

library(reticulate)
#py_install("matplotlib")
matplotlib <- import("matplotlib")
matplotlib$use("Agg", force = TRUE)
plt <- import("matplotlib.pyplot")


import numpy as np
import matplotlib.pyplot as plt
import codecs # to open the file in specific mode

# first we need to create two lists, one for the words and one for the corresponding vectors:

words = []
vec = []

with codecs.open("/home/lmdose/Transnorms/model.vec",'r','utf-8') as f_in:
  vocabulary, wv = zip(*[line.strip().split(' ',1) for line   in f_in])

# common to skip first 10 words, mostly they are garbage words
for i in range(10,len(vocabulary)):
  words.append(vocabulary[i])
  x = wv[i]
  vec.append(np.fromstring(x,dtype='float32',sep=' '))

#carrying out singular value decomposition
U, s, Vh = np.linalg.svd(vec,full_matrices=False)

# plotting words an their vector representations

for i in range(len(words)):
  fig = plt.gcf()
  fig.set_size_inches(18.5,10.5)
  plt.text(U[i,0],U[i,1], words[i])
  plt.xlim((-0.03,0.03))
  plt.ylim((-0.025,0.025))

#plt.show()
# you can't view this on the Rmd but if you use the code on the console it will work

## Text(-0.008598817, -0.0022292738, 'is')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0076950807, 0.0034336927, 'The')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008061071, 0.00091275165, 'on')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0077938875, 0.0017199538, 'by')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010799535, -0.008773672, 'climate')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008465988, -0.005456439, 'as')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008148175, -0.002173567, 'with')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008963273, -0.006705454, 'that')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008770926, -0.0044086995, 'will')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.00872357, -0.00351355, 'be')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.00927952, -6.927172e-05, 'its')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010239718, -0.005790173, 'change')
## (-0.03, 0.03)
## (-0.025, 0.025)
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## Text(-0.011453102, 0.017676739, 'Delivery')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0073980596, -0.00014903204, 'ESD')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010672415, -0.012636675, 'occurrences')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008447554, 0.0039258157, 'EU-ETS')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0073014726, 0.010440718, '5.8')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.007303111, 0.002042411, 'EWS')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0058921496, 0.003207588, '74')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009474294, -0.007858725, 'selling')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009348364, -0.008966908, 'emphasizes')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0071954965, 0.007966559, 'one')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.00936658, -0.0045838305, 'concerned,')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0089968145, -0.011038036, 'commercially')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0073600025, 0.013512784, 'Reach')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010089363, -0.004709512, 'reduced.')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0076003247, 0.0054390426, 'GEI,')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0068791313, -0.0081658205, 'etc.;')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009581019, -0.0005168782, 'regulated')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008920555, -0.008995324, 'planet,')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.007952833, 0.0042830687, 'Adjustment')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008625635, -0.0063572237, 'extinction')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.007604327, 0.0046558636, 'netnet')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010298004, 0.005805176, '58%')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008609595, -0.0064633084, 'inorganic')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009741344, -0.00073451444, 'component.')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009502279, -0.012069009, 'aquifers.')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0048996047, 0.00770577, 'SUBTOTAL')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008295816, 0.012729745, 'Developped')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009126719, 0.015817357, '(NF3).')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.006095595, 0.004034895, '66')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0073482366, -0.0033162537, 'bagasse')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0071523758, 0.008748596, 'Peace')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010753123, -0.003715426, 'forestry)')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.00996254, -0.0077789067, 'rehabilitation.')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.01143282, -0.017929088, 'consequences.')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0076634362, 0.008854751, 'Cancun')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008755938, 0.014486183, '(2008)')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009907455, -0.0010204507, 'electrified')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008034838, -0.0036783214, 'farmland')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008619741, 0.010053664, 'Cookstoves')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0069485316, 0.012113806, 'Cost:')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010089762, -0.008018279, 'enhanced.')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010843481, -0.0035970272, 'reporting.')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.00828411, -0.010349166, 'walking,')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0077620023, 0.0055416934, 'pluviometrie')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009099358, -0.012269539, 'beings,')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010209567, -0.011709766, 'assures')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008455727, -0.006469641, 'intermediary')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009676974, -0.0046908427, 'strategic,')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0076354765, -0.010571249, 'grave')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.007814321, 0.0027418714, 'm)')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008787043, -0.0061108484, 'belts')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008583077, 0.008385262, '1991,')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.009504019, -0.004705283, 'want')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.012485097, -0.02618212, 'co')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.010843135, -0.01146382, 'strengthens')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.005007688, 0.0052478546, 'i.e.')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008810761, -0.001721482, 'seasurface')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.00945124, -0.010817381, 'breeds,')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.008630217, -0.0055299457, 'villages.')
## (-0.03, 0.03)
## (-0.025, 0.025)
## Text(-0.0023853495, 0.009504753, 'total;')
## (-0.03, 0.03)
## (-0.025, 0.025)

plt.savefig('/home/lmdose/Transnorms/fastText.png')

## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61623 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61623 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61607 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61607 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 1 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 1 missing from current font.
##   font.set_text(s, 0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61599 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61599 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61656 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61656 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61692 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61692 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61472 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61472 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61485 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61485 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61459 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61459 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61558 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61558 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61502 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61502 missing from current font.
##   font.load_char(ord(s), flags=flags)

NOTE: there is a little Problem with displaying the plot on the RMD (see code)
The shown grafic is not the best way to represent our data ## FastText via Gensim: First we need to prepare the data we want to use for the fastText embedding, therefore we need to create a corpus and then split this corpus into training and testing data (2:1).

library(reticulate)
library(countrycode)
library(quanteda)
library(quanteda.corpora)
library(quanteda.textmodels)
load("data_ndc_reduced.RData",.GlobalEnv)

frameNDC <- data.frame(data_ndc)
cleanNDC <-subset(frameNDC,select = c(NDCISO3,Title,Body, NDCSubmissionDate))
#this step makes the process very slow but is necessary to create a well structures corpus
cleanNDC$Body<-as.character(cleanNDC$Body)

corp_NDC <- corpus(cleanNDC,docid_field = "NDCISO3",text_field = "Body")

#sepeate corpus in training and testing-samples 2:1
corp_Train<-corpus(corp_NDC[1:120])
corp_Test <- corpus(corp_NDC[121:184])
writeLines(as.character(corp_Train), con="corpTrain.txt")

Now we can load the prepared corpus into the python-environment.

from pprint import pprint as print
from gensim.models.fasttext import FastText as FT_gensim
from gensim.test.utils import datapath

# set file names for train and test data
corp = datapath("/home/lmdose/Transnorms/corpTrain.txt")
model = FT_gensim(size=100)
# build vocabulary
model.build_vocab(corpus_file=corp)
#train the model
model.train(corpus_file=corp, epochs=model.epochs, total_examples=model.corpus_count,total_words=model.corpus_total_words)

#look at some output
print(model)

## <gensim.models.fasttext.FastText object at 0x7f5e14560438>

print("Check fo simular words")

## 'Check fo simular words'

print(model.most_similar("economics"))

## [('macroeconomic', 0.9878569841384888),
##  ('economic', 0.9851830005645752),
##  ('economic,', 0.9829789996147156),
##  ('socioeconomic', 0.9804332852363586),
##  ('Socioeconomic', 0.9751245379447937),
##  ('economical', 0.9730471968650818),
##  ('economy,', 0.9590572714805603),
##  ('economy.', 0.9551132917404175),
##  ('economies,', 0.9494825005531311),
##  ('economy', 0.9464066624641418)]

print("Check if word exists in vocabulary")

## 'Check if word exists in vocabulary'

print('environment'in model.wv.vocab)

## True

print("Word-Vector for environment")

## 'Word-Vector for environment'

print(model['environment'])

## array([ 0.6684226 , -0.38954315, -0.86345524, -0.59464794,  0.5849606 ,
##         0.24094912, -1.3956097 , -0.7401601 ,  0.01144994, -0.08440552,
##        -0.15832719,  0.6940373 ,  1.025852  ,  0.1264736 , -0.6220975 ,
##        -0.37759387, -0.6257516 ,  0.33762693, -0.95832336, -0.9705698 ,
##        -0.5096641 , -0.22269955, -0.13973846, -0.42870697,  0.12197725,
##        -0.10135181,  0.3596381 ,  0.4096358 , -0.5548627 ,  0.09845883,
##        -0.46539736,  0.7889102 ,  0.09467782, -0.11030037, -0.72843665,
##        -0.91983914,  1.4102228 , -0.08040582, -0.8215884 ,  1.0984464 ,
##        -0.11488249, -0.27643517, -0.22898388,  1.3973621 ,  0.27478477,
##         1.3200115 , -0.38581842, -0.7161611 , -0.13524905,  0.30556566,
##         0.4825264 ,  0.26163545, -0.58032686, -0.17776023,  1.7992221 ,
##         0.9232696 ,  0.22940244,  0.7654077 , -0.05533785,  0.13080607,
##         0.37130007,  0.8267616 , -0.48336142,  0.36035043, -0.25388387,
##         0.04431292, -0.2012794 , -1.0238471 , -0.12463484,  0.9703847 ,
##         0.00896698,  1.1625745 ,  0.19268672,  0.14814949,  0.0126444 ,
##        -0.26615298, -0.15603177, -0.15322566,  0.98572034,  0.5041556 ,
##         0.09203632,  0.03826423, -0.12751816,  0.11952019, -0.06623898,
##         0.24111015, -1.3900124 ,  0.13698085,  0.04011773,  0.4168396 ,
##        -0.05823421,  0.7848763 ,  0.16685578,  0.5038112 ,  0.33025545,
##        -0.96188754, -0.95493346,  1.1063718 , -0.06949168, -0.5039558 ],
##       dtype=float32)

model.save("/home/lmdose/Transnorms/wordvec.model")

import gensim
from gensim.models.fasttext import FastText 
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt


model = FastText.load("/home/lmdose/Transnorms/wordvec.model")
X = model[model.wv.vocab]
pca = PCA(n_components=2)
result=pca.fit_transform(X)
plt.scatter(result[:, 0], result[:, 1])
words = list(model.wv.vocab)
#for i, word in enumerate(words):
  #plt.annotate(word, xy=(result[i, 0], result[i, 1]))
plt.savefig("/home/lmdose/Transnorms/Test.png")

## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61623 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61623 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61607 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61607 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 1 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 1 missing from current font.
##   font.set_text(s, 0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61599 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61599 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61656 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61656 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61692 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61692 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61472 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61472 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61485 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61485 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61459 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61459 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61558 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61558 missing from current font.
##   font.load_char(ord(s), flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 61502 missing from current font.
##   font.set_text(s, 0.0, flags=flags)
## /home/lmdose/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py:176: RuntimeWarning: Glyph 61502 missing from current font.
##   font.load_char(ord(s), flags=flags)

### for more information: Please check out:
https://radimrehurek.com/gensim/auto_examples/tutorials/run_fasttext.html
https://kite.com/python/docs/gensim.models.wrappers.fasttext