1565080877

# Python for NLP: Developing an Automatic Text Filler using N-Grams

Today, we will study the N-Grams approach and will see how the N-Grams approach can be used to create a simple automatic text filler or suggestion engine.
Automatic text filler is a very useful application and is widely used by Google and different smartphones where a user enters some text and the remaining text is automatically populated or suggested by the application.

### Problems with TF-IDF and Bag of Words Approach

Before we go and actually implement the N-Grams model, let us first discuss the drawback of the bag of words and TF-IDF approaches.

In the bag of words and TF-IDF approach, words are treated individually and every single word is converted into its numeric counterpart. The context information of the word is not retained. Consider two sentences “big red machine and carpet” and “big red carpet and machine”. If you use a bag of words approach, you will get the same vectors for these two sentences. However, we can clearly see that in the first sentence we are talking about a “big red machine”, while the second sentence contains information about the “big red carpet”. Hence, context information is very important. The N-Grams model basically helps us capture the context information.

### Theory of N-Grams Model

Wikipedia defines an N-Gram as “A contiguous sequence of N items from a given sample of text or speech”. Here an item can be a character, a word or a sentence and N can be any integer. When N is 2, we call the sequence a bigram. Similarly, a sequence of 3 items is called a trigram, and so on.

In order to understand N-Grams model, we first have to understand how the Markov chains work.

#### Connection of N-Grams with Markov Chains

A Markov chain is a sequence of states. Consider a Markov system with 2 states, X and Y. In a Markov chain, you can either stay at one state or move to the other state. In our example, our states have the following behavior:

1. The probability of moving from X to Y is 50% and similarly, the probability of staying at X is 50%.
2. Likewise, the probability of staying at Y is 50% while the possibility of moving back to X is also 50%.

This way a Markov sequence can be generated, such as XXYX, etc.

In an N-Grams model, an item in a sequence can be treated as a Markov state. Let’s see a simple example of character bigrams where each character is a Markov state.

``````Football is a very famous game

``````

The character bigrams for the above sentence will be: `fo`, `oo`, `ot`, `tb`, `ba`, `al`, `ll`, `l`, `i`, `is` and so on. You can see that bigrams are basically a sequence of two consecutively occurring characters.

Similarly, the trigrams are a sequence of three contiguous characters, as shown below:

`foo`, `oot`, `otb`, `tba` and so on.

In the previous two examples, we saw character bigrams and trigrams. We can also have bigrams and trigrams of words.

Let’s go back to our previous example, “big red machine and carpet”. The bigram of this sentence will be “big red”, “red machine”, “machine and”, “and carpet”. Similarly, the bigrams for the sentence “big red carpet and machine” will be “big red”, “red carpet”, “carpet and”, “and machine”.

Here in this case with bigrams, we get a different vector representation for both of the sentences.

In the following section, we will implement the N-Grams model from scratch in Python and will see how we can create an automatic text filler using N-Grams like these.

### N-Grams from Scratch in Python

We will create two types of N-Grams models in this section: a character N-Grams model and a words N-Gram model.

#### Characters N-Grams Model

In this section, I will explain how to create a simple characters N-Gram model. In the next section, we will see how to implement the word N-Gram model.

To create our corpus, we will scrape the Wikipedia article on Tennis. Let’s first import the libraries that we need to download and parse the Wikipedia article.

``````import nltk
import numpy as np
import random
import string

import bs4 as bs
import urllib.request
import re

``````

We will be using the Beautifulsoup4 library to parse the data from Wikipedia. Furthermore, Python’s regex library, `re`, will be used for some preprocessing tasks on the text.

As we said earlier, we will use the Wikipedia article on Tennis to create our corpus. The following script retrieves the Wikipedia article and extracts all the paragraphs from the article text. Finally, the text is converted into the lower case for easier processing.

``````raw_html = urllib.request.urlopen('https://en.wikipedia.org/wiki/Tennis')

article_html = bs.BeautifulSoup(raw_html, 'lxml')
article_paragraphs = article_html.find_all('p')
article_text = ''

for para in article_paragraphs:
article_text += para.text

article_text = article_text.lower()

``````

Next, we remove everything from our dataset except letters, periods, and spaces:

``````article_text = re.sub(r'[^A-Za-z. ]', '', article_text)

``````

We have preprocessed our dataset and now is the time to create an N-Grams model. We will be creating a character trigram model. Execute the following script:

``````ngrams = {}
chars = 3

for i in range(len(article_text)-chars):
seq = article_text[i:i+chars]
print(seq)
if seq not in ngrams.keys():
ngrams[seq] = []
ngrams[seq].append(article_text[i+chars])

``````

In the script above, we create a dictionary `ngrams`. The keys of this dictionary will be the character trigrams in our corpus and the values will be the characters that occur next to the trigrams. Next, since we are creating N-Gram of three characters we declare a variable `chars`. After that we iterate through all the characters in our corpus, starting from the fourth character.

Next, inside the loop, we extract the trigram by filtering the next three characters. The trigram is stored in the `seq` variable. We then check if the trigram exists in the dictionary. If it doesn’t exist in the `ngrams` dictionary we add the trigram to the dictionary. After that, we assign an empty list as the value to the trigram. Finally, the character that exists after the trigram is appended as a value to the list.

If you open the dictionary `ngrams` in the Spyder variable explorer. You should see something like this:

You can see trigrams as keys, and the corresponding characters, which occur after the trigrams in the text, as values. You might see keys with two characters in the dictionary but they are actually not two characters. The third character is actually a space.

Let’s now try to generate text using the first three characters of our corpus as input. The first three characters of our corpus are “ten”. Look at the following script:

``````curr_sequence = article_text[0:chars]
output = curr_sequence
for i in range(200):
if curr_sequence not in ngrams.keys():
break
possible_chars = ngrams[curr_sequence]
next_char = possible_chars[random.randrange(len(possible_chars))]
output += next_char
curr_sequence = output[len(output)-chars:len(output)]

print(output)

``````

In the script above we first store the first trigram i.e. `ten` into the `curr_sequence` variable. We will generate a text of two hundred characters, therefore we initialize a loop that iterates for 200 times. During each iteration, we check if the `curr_sequence` or the trigram is in the `ngrams` dictionary. If the trigram is not found in the `ngrams` dictionary, we simply break out of the loop.

Next, the `curr_sequence` trigram is passed as key to the `ngrams` dictionary, which returns the list of possible next characters. From the list of possible next characters, an index is chosen randomly, which is passed to the `possible_chars` list to get the next character for the current trigram. The next character is then appended to the `output` variable that contains the final output.

Finally, the `curr_sequence` is updated with the next trigram from the text corpus. If you print the `output` variable that contains two hundred characters generated automatically, you should see something like this (It is important to mention that since the next character is randomly chosen, your output can be different):

Output:

``````tent pointo somensiver tournamedal pare the greak in the next peak sweder most begal tennis sport. the be has siders with sidernaments as was that adming up is coach rackhanced ball of ment. a game and

``````

The output doesn’t make much sense here in this case. If you increase the value of the `chars` variable to 4. You should see the results similar to the following outputs:

``````tennis ahead with the club players under.most coaching motion us . the especific at the hit and events first predomination but of ends on the u.s. cyclops have achieved the end or net inches call over age

``````

You can see that the results are a bit better than the one we got using 3-grams. Our text suggestion/filling will continue to improve as we increase the N-Gram number.

In the next section, we will implement the Words N-Grams model. You will see that the text generated will make much more sense in case of Words N-Grams model.

#### Words N-Grams Model

In Words N-Grams model, each word in the text is treated as an individual item. In this section, we will implement the Words N-Grams model and will use it to create automatic text filler.

The dataset that we are going to use is the same as the one we used in the last section.

Let’s first create a dictionary that contains word trigrams as keys and the list of words that occur after the trigrams as values.

``````ngrams = {}
words = 3

words_tokens = nltk.word_tokenize(article_text)
for i in range(len(words_tokens)-words):
seq = ' '.join(words_tokens[i:i+words])
print(seq)
if  seq not in ngrams.keys():
ngrams[seq] = []
ngrams[seq].append(words_tokens[i+words])

``````

In the script above, we create a Words trigram model. The process is similar to the one followed to use character trigrams. However, in the above script, we first tokenize our corpus into words.

Next, we iterate through all the words and then join the current three words to form a trigram. After that, we check if the word trigram exists in the `ngrams` dictionary. If the trigram doesn’t already exist, we simply insert it into the `ngrams` dictionary as a key.

Finally, we append the list of words that follow the trigram in the whole corpus, as the value in the dictionary.

Now if you look at the `ngrams` dictionary, in the variable explorer, it will look like this:

You can see trigrams as dictionary keys and corresponding words as dictionary values.

Let’s now create an automatic text filler, using the word trigrams that we just created.

``````curr_sequence = ' '.join(words_tokens[0:words])
output = curr_sequence
for i in range(50):
if curr_sequence not in ngrams.keys():
break
possible_words = ngrams[curr_sequence]
next_word = possible_words[random.randrange(len(possible_words))]
output += ' ' + next_word
seq_words = nltk.word_tokenize(output)
curr_sequence = ' '.join(seq_words[len(seq_words)-words:len(seq_words)])

print(output)

``````

In the script above, we initialize the `curr_sequence` variable with the first trigram in the corpus. The first trigram is “tennis is a”. We will generate 50 words using the first trigram as the input. To do so, we execute a for loop that executes for 50 times. During each iteration, it is first checked if the word trigram exists in the `ngrams` dictionary. If not, the loop breaks. Otherwise the list of the words that are likely to follow the trigram are retrieved from the `ngrams` dictionary by passing trigram as the value. From the list of possible words, one word is chosen randomly and is appended at the end of the out. Finally, the `curr_sequence` variable is updated with the value of the next trigram in the dictionary.

The generated text looks like this. You can see that in the case of word trigrams, the automatically generated text makes much more sense.

Output:

``````tennis is a racket sport that can be played individually against a single opponent singles or between two teams of two players each doubles. each player uses a tennis racket include a handle known as the grip connected to a neck which joins a roughly elliptical frame that holds a matrix of

``````

If you set the value of words variable to 4 (use 4-grams) to generate text, your output will look even more robust as shown below:

``````tennis is a racket sport that can be played individually against a single opponent singles or between two teams of two players each doubles . each player uses a tennis racket that is strung with cord to strike a hollow rubber ball covered with felt over or around a net and into the opponents

``````

You can see the output makes even more sense with 4-grams. This is largely because our generator is mostly regenerating the same text from the Wikipedia article, but with some slight improvements to the generator, and a larger corpus, our generator could easily generate new and unique sentences as well.

### Conclusion

N-Grams model is one of the most widely used sentence-to-vector models since it captures the context between N-words in a sentence. In this article, you saw the theory behind N-Grams model. You also saw how to implement characters N-Grams and Words N-Grams model. Finally, you studied how to create automatic text filler using both the approaches.

============================================

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1667425440

## pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

2. Print the top and bottom copper and top silk screen layers to a PDF file.
3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
4. Use a Gerber viewer to double-check the output against the original PCB design.
6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

## pdf2gerb_cfg.pm

``````#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)

##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => (\$^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("\${\(CYAN)}Pdf2Gerb.pl \${\(VERSION)}, \$^O O/S\n\${\(YELLOW)}\${\(BOLD)}\${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!\${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("\${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: \$^O, Perl: \$]\${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1);

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
.010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
.031, -.014,  #used for vias
.041, -.020,  #smallest non-filled plated hole
.051, -.025,
.056, -.029,  #useful for IC pins
.070, -.033,
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
.100, -.046,
.115, -.052,
.130, -.061,
.140, -.067,
.150, -.079,
.175, -.088,
.190, -.093,
.200, -.100,
.220, -.110,
.160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
.090, -.040,  #want a .090 pad option, but use dummy hole size
.065, -.040, #.065 x .065 rect pad
.035, -.040, #.035 x .065 rect pad
#traces:
.001,  #too thin for real traces; use only for board outlines
.006,  #minimum real trace width; mainly used for text
.008,  #mainly used for mid-sized text, not traces
.010,  #minimum recommended trace width for low-current signals
.012,
.015,  #moderate low-voltage current
.020,  #heavier trace for power, ground (even if a lighter one is adequate)
.025,
.030,  #heavy-current traces; be careful with these ones!
.040,
.050,
.060,
.080,
.100,
.120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
CAP_NONE => 0, #butt (none); line is exact length
CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};

#number of elements in each shape type:
use constant
{
RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
our %SHAPELEN =
(
rect => RECT_SHAPELEN,
line => LINE_SHAPELEN,
curve => CURVE_SHAPELEN,
circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#\$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes.
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#\$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my \$runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '\${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load

#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my \$caller = "pdf2gerb::";

#sub cfg
#{
#    my \$proto = shift;
#    my \$class = ref(\$proto) || \$proto;
#    my \$settings =
#    {
#        \$WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless(\$settings, \$class);
#    return \$settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "\$_\n"; }
#my \$val = main::thing("xyz");
#print STDERR "caller gave me \$val\n";
#foreach my \$arg (@ARGV) { print STDERR "arg \$arg\n"; }``````

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

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