diff options
Diffstat (limited to 'src/neuralnetwork')
| -rw-r--r-- | src/neuralnetwork/clean_data.py | 59 | ||||
| -rw-r--r-- | src/neuralnetwork/data/ipa.csv | 174 | ||||
| -rwxr-xr-x | src/neuralnetwork/data/names/English.txt | 3668 | ||||
| -rw-r--r-- | src/neuralnetwork/generate_alphabets.py | 44 | ||||
| -rw-r--r-- | src/neuralnetwork/rnn.py | 329 | ||||
| -rw-r--r-- | src/neuralnetwork/training.py | 373 | ||||
| -rw-r--r-- | src/neuralnetwork/util.py | 185 |
7 files changed, 4832 insertions, 0 deletions
diff --git a/src/neuralnetwork/clean_data.py b/src/neuralnetwork/clean_data.py new file mode 100644 index 0000000..345218e --- /dev/null +++ b/src/neuralnetwork/clean_data.py @@ -0,0 +1,59 @@ +import training +import os +import sys + +if __name__ == "__main__": + + countries = training.get_countries() + + for c, country in countries.items(): + if c == "uk" or c == "usa": + + # iterate through each dataset separately + for dataset, path in country.datasets.items(): + + # print information + print(f"filtering through {c}'s {dataset}...") + sys.stdout.flush() + + names = country.get_names(dataset) + + # store the names that are valid in a seperate list + names_output = [] + + # load the alphabet file for the country + alphabet = [] + + with open(os.path.join(country.path, "alphabet.txt"), "r") as file: + for l in file.read().split("\n"): + alphabet.append(l) + + c = 0 + t = len(names) + # iterate through names in the dataset + for name in names: + name = country.preprocess(name) + + valid = True + + # invalidate the name if a single letter is not in the alphabet + for letter in name: + if not letter in alphabet: + valid = False + break + + if valid: + names_output.append(name) + + c += 1 + if c % 128 == 0: + print(f"\r{c}/{t}", end="") + + # print how many names are left + print(f"kept {len(names_output)}/{len(names)} names") + + # save dataset + with open(path, "w") as file: + for name in names_output: + file.write(name) + file.write("\n") diff --git a/src/neuralnetwork/data/ipa.csv b/src/neuralnetwork/data/ipa.csv new file mode 100644 index 0000000..6965ce5 --- /dev/null +++ b/src/neuralnetwork/data/ipa.csv @@ -0,0 +1,174 @@ +p,p,vl bilabial plosive +b,b,vd bilabial plosive +t,t,vl alveolar plosive +d,d,vd alveolar plosive +ʈ,t`,vl retroflex plosive +ɖ,d`,vd retroflex plosive +c,c,vl palatal plosive +ɟ,J\,vd palatal plosive +k,k,ld velar plosive +ɡ,g,vd velar plosive +q,q,vl uvular plosive +ɢ,G\,vd uvular plosive +ʔ,?,glottal plosive +m,m,bilabial nasal +ɱ,F,vl labiodental nasal +n,n,alveolar nasal +ɳ,n`,vl retroflex nasal +ɲ,J,vl palatal nasal +ŋ,N,vl velar nasal +ɴ,N\,vl uvular nasal +ʙ,B\,vd bilabial trill +r,r,vd alveolar trill +ʀ,R\,vl uvular trill +ɾ,4,vl alveolar tap +ɽ,r`,vl retroflex flap +ɸ,p\,vl bilabial fricative +β,B,vd bilabial fricative +f,f,vl labiodental fricative +v,v,vd labiodental fricative +θ,T,vl dental fricative +ð,D,vd dental fricative +s,s,vl alveolar fricative +z,z,vd alveolar fricative +ʃ,S,vl postalveolar fricative +ʒ,Z,vd postalveolar fricative +ʂ,s`,vl retroflex fricative +ʐ,z`,vd retroflex fricative +ç,C,vl palatal fricative +ʝ,j\,vd palatal fricative +x,x,vl velar fricative +ɣ,G,vd velar fricative +χ,X,vl uvular fricative +ʁ,R,vd uvular fricative +ħ,X\,vl pharyngeal fricative +ʕ,?\,vd pharyngeal fricative +h,h,vl glottal fricative +ʔ,?,glottal plosive +ɬ,K,vl alveolar lateral fricative +ɮ,K\,vd alveolar lateral fricative +ʋ,P,vd labiodental approximant +ɹ,r\,vd (post)alveolar approximant +ɻ,r\`,vd retroflex approximant +j,j,vd palatal approximant +ɰ,M\,vd velar approximant +l,l,vd alveolar lateral approximant +ɭ,l`,vd retroflex lateral approximant +ʎ,L,vd palatal lateral approximant +ʟ,L\,vd velar lateral approximant +pʼ,p_>,ejective +tʼ,t_>,ejective +ʈʼ,t`_>,ejective +cʼ,c_>,ejective +kʼ,k_>,ejective +qʼ,q_>,ejective +ɓ,b_<,vl bilabial implosive +ɗ,d_<,vl alveolar implosive +ƙ,k_<,vl velar implosive +ɠ,g_<,vl velar implosive +i,i,close front unrounded +y,y,close front rounded +ɨ,1,close central unrounded +ʉ,},close central rounded +ɯ,M,close back unrounded +u,u,close back rounded +ɪ,I,lax close front unrounded +ʏ,Y,lax close front rounded +ʊ,U,lax close back rounded +e,e,close-mid front unrounded +ø,2,front close-mid rounded +ɤ,7,close-mid back unrounded +o,o,close-mid back rounded +ə,@,schwa +ɘ,@\,close-mid central unrounded vowel +ɵ,8,rounded schwa +ɛ,E,open-mid front unrounded +œ,9,front open-mid rounded +ʌ,V,open-mid back unrounded +ɔ,O,open-mid back rounded +æ,{,mid-open front unrounded vowel +ɐ,6,open-mid schwa +a,a,open front unrounded +ă,a_X,extra short open front unrounded +ɶ,&,front open rounded +ɑ,A,open back unrounded +ɒ,Q,open back rounded +̥,_0,voiceless +̬,_v,voiced +ʰ,_h,aspirated +̤,_t,breathy voiced +̰,_k,creaky voiced +̼,_N,linguolabial +̪,_d,dental +̺,_a,apical +̻,_m,laminal +̹,_O,more rounded +̜,_c,less rounded +̟,_+,advanced +̠,_-,retracted +̈,"_""",centralized +̽,_x,mid-centralized +̩,=,syllabic +̯,_^,non-syllabic +ʷ,_w,labialized +ʲ,',palatalized +ˠ,_G,velarized +ˤ,_?\,pharyngealized +̴,_e,velarized or pharyngealized +̝,_r,raised +̞,_o,lowered +̃,~,nasalized +ⁿ,_n,nasal release +ˡ,_l,lateral release +̚,_},not audibly released +̘,_A,advanced tongue root +̙,_q,retracted tongue root +̋,_T,extra high tone +́,_H,high tone +̄,_M,mid tone +̀,_L,low tone +̏,_B,extra low tone +ˈ,"""",(primary) stress mark +ˌ,%,secondary stress +ː,:,length mark +ˑ,:\,half-length +̆,_X,extra-short +.,.,syllable break +ʍ,W,vl labial-velar fricative +w,w,vd labio-velar approximant +ɥ,H,labial-palatal approximant +ʜ,H\,vl epiglottal fricative +ʢ,<\,vl epiglottal fricative +ʡ,>\,vl epiglottal plosive +ɕ,s\,vl alveolopalatal fricative +ʑ,z\,vl alveolopalatal fricative +ʘ,O\,bilabial click +ǀ,|\,dental click +ǃ,!\,click +ǂ,'=\,alveolar click +ǁ,|\|\,alveolar lateral click +ɺ,l\,vl alveolar lateral flap +ɜ,3,open-mid central +ʛ,G\_<,vl uvular implosive +ɚ,@`,rhotacized schwa +ɞ,3\,open-mid central rounded +ɦ,h\,vd glottal fricative +ɫ,5,velarized vl alveolar lateral +ʄ,J\_<,vl palatal implosive +ʼ,_>,ejective +ɝ,3`,rhotacized open-mid central +t͡ʃ,tS,vl postalveolar affricate +d͡ʒ,dZ,vd postalveolar affricate +t͡ɕ,ts\,vl alveolo-palatal affricate +d͡ʑ,dz\,vd alveolo-palatal affricate +t͡ɬ,tK,vl alveolar lateral affricate +k͡p,kp,vl labial-velar plosive +g͡b,gb,vd labial-velar plosive +ŋ͡m,Nm,labial-velar nasal stop +ʈ͡ʂ,ts`,vl retroflex affricate +ɖ͡ʐ,tz`,vd retroflex affricate +˩,_B,extra low tone +˨,_L,low tone +˧,_M,mid tone +˦,_H,high tone +˥,_T,extra high tone diff --git a/src/neuralnetwork/data/names/English.txt b/src/neuralnetwork/data/names/English.txt new file mode 100755 index 0000000..2b943a7 --- /dev/null +++ b/src/neuralnetwork/data/names/English.txt @@ -0,0 +1,3668 @@ +Abbas +Abbey +Abbott +Abdi +Abel +Abraham +Abrahams +Abrams +Ackary +Ackroyd +Acton +Adair +Adam +Adams +Adamson +Adanet +Addams +Adderley +Addinall +Addis +Addison +Addley +Aderson +Adey +Adkins +Adlam +Adler +Adrol +Adsett +Agar +Ahern +Aherne +Ahmad +Ahmed +Aikman +Ainley +Ainsworth +Aird +Airey +Aitchison +Aitken +Akhtar +Akram +Alam +Alanson +Alber +Albert +Albrighton +Albutt +Alcock +Alden +Alder +Aldersley +Alderson +Aldred +Aldren +Aldridge +Aldworth +Alesbury +Alexandar +Alexander +Alexnader +Alford +Algar +Ali +Alker +Alladee +Allam +Allan +Allard +Allaway +Allcock +Allcott +Alldridge +Alldritt +Allen +Allgood +Allington +Alliott +Allison +Allkins +Allman +Allport +Allsop +Allum +Allwood +Almond +Alpin +Alsop +Altham +Althoff +Alves +Alvey +Alway 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+Yomkins +York +Yorke +Yorston +Youlden +Young +Younge +Younis +Youssouf +Yule +Yusuf +Zaoui diff --git a/src/neuralnetwork/generate_alphabets.py b/src/neuralnetwork/generate_alphabets.py new file mode 100644 index 0000000..0b1e18e --- /dev/null +++ b/src/neuralnetwork/generate_alphabets.py @@ -0,0 +1,44 @@ +import training +import os +import sys + +if __name__ == "__main__": + + # List all the directories containing country datasets to populate the countries dictionary + countries = training.get_countries() + + for c, country in countries.items(): + print(f"processing {c}...", end="") + sys.stdout.flush() + + letters = {} + + # get all the names in a country's dataset + all_names = country.get_all() + + # iterate through all letters in the all of the names + for name in all_names: + + # preprocess the name + name = country.preprocess(name) + + for letter in name: + if letter in letters: + letters[letter] += 1 + else: + letters[letter] = 1 + + print(f" found {len(letters)} in {len(all_names)} names... ", end="") + sys.stdout.flush() + + # sort the letters by occurrence + letters_sorted = [l for l in letters] + letters_sorted.sort() + # output sorted letters to a file + with open(os.path.join(country.path, "alphabet.txt"), "w") as file: + for letter in letters_sorted: + file.write(letter) + file.write("\n") + + print("saved!") + sys.stdout.flush() diff --git a/src/neuralnetwork/rnn.py b/src/neuralnetwork/rnn.py new file mode 100644 index 0000000..0662394 --- /dev/null +++ b/src/neuralnetwork/rnn.py @@ -0,0 +1,329 @@ +import random +import time +import math +import torch +import torch.nn as nn + +import warnings +import sys + +import copy + +from util import AtomicNumber + +PRINT_INFORMATION_SECONDS = 2 +num_processes = 12 + +#ignore warnings +warnings.filterwarnings('ignore') + + +if "--disable-cuda" in sys.argv: + cuda = False +else: + cuda = torch.cuda.is_available() + +print(f"CUDA is {'enabled' if cuda else 'disabled'}") +if cuda: + print("CUDA devices:") + for device_index in range(torch.cuda.device_count()): + print(f"{device_index}|\t{torch.cuda.get_device_name(device_index)}") + +device = torch.device("cuda") if cuda else torch.device("cpu") + +class RNN(nn.Module): + def __init__(self, input_size, hidden_size, output_size): + super(RNN, self).__init__() + self.hidden_size = hidden_size + self.output_size = output_size + + # create the input, hidden and output linear transformation branches + self.input_to_hidden = nn.Linear(input_size + hidden_size, hidden_size, device=device) + self.input_to_output = nn.Linear(input_size + hidden_size, output_size, device=device) + self.output_to_output = nn.Linear(hidden_size + output_size, output_size, device=device) + + # initialise a dropout function to be used on output data + self.dropout = nn.Dropout(0.1) + + # initialise the softmax function to be used on output data + self.softmax = nn.LogSoftmax(dim=1) + + # do not introduce any randomness by default + self.random_factor = 0 + + + def forward(self, inputs, hidden): + # combine the input layer with the hidden layer to create the output layer and new hidden layer + input_combined = torch.cat((inputs, hidden), 1) + hidden = self.input_to_hidden(input_combined) + output = self.input_to_output(input_combined) + output_combined = torch.cat((hidden, output), 1) + + output = self.output_to_output(output_combined) + # apply the functions to the output data + output = self.dropout(output) + output = self.softmax(output) + + # add noise to the output, based on self.random_factor + if self.random_factor > 0: + # create a fully random tensor + random_tensor = torch.randn(self.output_size) + output = torch.add(output, random_tensor, alpha=self.random_factor) + + return output, hidden + + def initHidden(self): + # The hidden layer should be tensor with the length that we've specified + return torch.zeros(1, self.hidden_size, device=device) + +# instantiate the function to use to calculate loss +# we will use Mean Squared Error between the +criterion = nn.NLLLoss() + +# define the learning rate, to begin with, we can use 0.0005 +learning_rate = 0.0005 + +"""Train a neural network on a single input name + Args: + rnn: (RNN) the rnn to train + input_tensors: (tensor) The input tensor: a one-hot-encoding from the first letter to the last letter, excluding the end of string marker + output_tensors: (tensor) The input tensor: a one-hot-encoding from the second letter to the end of the input data + Returns: + output: (tensor) the output of the training + loss: (float) the loss of the training +""" +def train_rnn(rnn, input_tensor, target_tensor): + # unsqueeze the target tensor, + target_tensor.unsqueeze_(-1) + + # reset the parameters of the neural network + hidden = rnn.initHidden() + rnn.zero_grad() + + # initiate an float called loss, this will store the error between each iteration output and its target + loss = 0 + for i in range(input_tensor.size(0)): + output, hidden = rnn(input_tensor[i], hidden) + + # calculate the error and add it to the overall loss + l = criterion(output, target_tensor[i]) + loss += l + + loss.backward() + + # adjust the parameters of the rnn accordingly + for p in rnn.parameters(): + p.data.add_(-learning_rate, p.grad.data) + + return output, loss.item() / input_tensor.size(0) + +"""Create the input tensor for a name, a one hot matrix from the first letter to last letter (excluding EOS) + Args: + name: (str[]) an array of the letters in the name, can also be supplied as a string literal + alphabet: (str[]) The alphabet to use while encoding the name, an array starting with a "NULL" character and ending in an "EOS" character + value: (float) (default=1) The value to use for the "1" representing the letter + Returns: + tensor: (tensor) the input tensor for the given name +""" +def input_tensor(name, alphabet, value=1): + tensor = torch.zeros(len(name), 1, len(alphabet), device=device) + + #iterate through each letter in the name + for li in range(len(name)): + letter = name[li] + # If the letter isn't in the alphabet, use the first "NULL" character + index = alphabet.index(letter) if letter in alphabet else 0 + + tensor[li][0][index] = value + + return tensor + +"""Create the target tensor for a name, a long tensor from the second letter to the EOS + Args: + name: (str[]) an array of the letters in the name, can also be supplied as a string literal + alphabet: (str[]) The alphabet to use while encoding the name, an array starting with a "NULL" character and ending in an "EOS" character + Returns: + tensor: (tensor) the input tensor for the given name +""" +def target_tensor(name, alphabet): + indexes = [] + for li in range(1, len(name)): + letter = name[li] + index = alphabet.index(letter) if letter in alphabet else 0 + indexes.append(index) + + # and add the end of string character + indexes.append(len(alphabet) - 1) + + #legacy tensor needs to be made this way + if cuda: + return torch.cuda.LongTensor(indexes) + else: + return torch.LongTensor(indexes) + + +"""Train a neural network on a list of names with a given alphabet + Args: + rnn (RNN): the neural network to train on + names: (str[]) the list of names to train on + alphabet: (str[]) the alphabet to use to encode characters + iterations: (int) (default=10000) The number of iterations of training that should be done +""" +def learn_names(rnn, names, alphabet, iterations=100000, num_processes=12): + + # keep track of total time spent training by knowing when we started training + start = time.time() + + # define the number of iterations per process + iters_per_process = int(iterations/num_processes) + + processes = [] + + # keep track of the total loss + total_loss = AtomicNumber() + + # keep track of total number of completed iterations + completed_iterations = AtomicNumber() + + # keep track of the last time that the information was printed + # this way we can print every x seconds + last_print = AtomicNumber() + + print(f"Training on {len(names)} names...") + + # spawn processes, each running the _train function + torch.multiprocessing.spawn(_train, args=(rnn, names, alphabet, iters_per_process, + total_loss, completed_iterations, last_print, start, iterations), + nprocs=num_processes, + join=True) + print() + +"""Thread function to use when multiprocessing learn_names + +""" +def _train(rank, rnn, names, alphabet, iterations, + total_loss, completed_iterations, last_print, + start, total_iterations): + for i in range(1, iterations+1): + try: + # choose a random name to train on + name = random.choice(names) + + # create the input and trainint tensors + input_name_tensor = input_tensor(name, alphabet) + target_name_tensor = target_tensor(name, alphabet) + + # train the rnn on the input and target tensors + output, loss = train_rnn(rnn, input_name_tensor, target_name_tensor) + total_loss.increment(loss) + + # increment number of completed iterations + completed_iterations.increment() + + # to prevent overloading the console, potentially slowing down the training process, + # only print information every PRINT_INFORMATION_SECONDS + if time.time() - last_print.get() > PRINT_INFORMATION_SECONDS: + # set last print to now to prevent other threads from also printing + last_print.set(time.time()) + + # calculate and display information + seconds_elapsed = time.time() - start + time_elapsed = "%dm %ds" % (math.floor(seconds_elapsed / 60), seconds_elapsed % 60) + + percentage = completed_iterations.get() / total_iterations * 100 + + # print information on the same line as before + print("\r%s (%d %d%%) %.4f" % (time_elapsed, completed_iterations.get(), percentage, loss), end="") + except: + pass + + +"""Sample a random name from the network using a starting letter + Args: + rnn: (RNN) the neural network to sample from + alphabet: (str[]) the alphabet to use to decode the outputs from the network + start_letter: (str) the letter to use to start the neural network + max_length: (int) (default=50) the maximum length for a name + Returns: + output_name: (str) the characters that the rnn has generated from the starting letter +""" +def sample(rnn, alphabet, start_letter, max_length=50): + # disable gradient calculation + #with torch.no_grad(): + # create the input tensor from the start letter, using a randomized value + #random_value = random.random() + random_value = 1 + sample_input = input_tensor(start_letter, alphabet, value=random_value) + + rnn.dropout(sample_input) + + # reset hidden layer + hidden = rnn.initHidden() + + output_name = [start_letter] + + # use a max length to prevent names from being too long + for i in range(max_length): + # call the rnn for the next letter + output, hidden = rnn(sample_input[0], hidden) + + top_v, top_i = output.topk(1) + top_i = top_i[0][0] + + if top_i == len(alphabet)-1: # EOS has been reached + break; + else: + # append next letter to output + + letter = alphabet[top_i] + output_name.append(letter) + + sample_input = input_tensor(letter, alphabet) + + return output_name + + +import warnings +# testing +if __name__ == "__main__": + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + + english_alphabet = [c for c in " abcdefghijklmnopqrstuvwxyz"] + english_alphabet.append("") # add the EOS character + + + option = input("(t)rain or (s)ample?") + if option == "t": + + names = [] + with open("data/datasets/usa/surname.txt", "r") as datafile: + # convert all names to lowercase and remove newline character + names = [name[:-1].lower() for name in datafile.readlines()] + + # create the neural network with a hidden layer of size 128 + rnn = RNN(len(english_alphabet), 128, len(english_alphabet)) + + # transfer to cuda if cuda is enabled + if cuda: + rnn.cuda() + + def provide_name(): + return random.choice(names) + + learn_names(rnn, names, english_alphabet, iterations=100000, num_processes=12) + print() + + + torch.save(rnn, "data/english_names.pt") + elif option == "s": + rnn = torch.load("data/english_names.pt") + if cuda: + rnn.cuda() + rnn.eval() + rnn.random_factor = 0.7 + + for start_letter in [i for i in "abcdefghijklmnopqrstuvwxyz"]: + print(sample(rnn, english_alphabet, start_letter)) + else: + print("invalid option!") diff --git a/src/neuralnetwork/training.py b/src/neuralnetwork/training.py new file mode 100644 index 0000000..85c6dbd --- /dev/null +++ b/src/neuralnetwork/training.py @@ -0,0 +1,373 @@ +import os +import types +import json +import random + +from util import * +from rnn import * + +cuda = False +num_processes = 12 + + +class Country: + def __init__(self, path): + self.path = path + self.datasets = { + "female": os.path.join(path, "female.txt"), + "male": os.path.join(path, "male.txt"), + "surname": os.path.join(path, "surname.txt"), + } + + # initialise the pre and post proccess function lists + self.pre_process = [] + self.post_process = [] + + # load the data file + self.load_data() + + # load the alphabet file + self.alphabet = self.load_alphabet() + + # initialise the rnn models + hidden_size = 128 + self.rnn = {} + + for dataset in self.datasets: + self.rnn[dataset] = RNN( + len(self.alphabet), hidden_size, len(self.alphabet)) + + """ Load the alphabet from the alphabet file + Returns: + alphabet: (str[]) the list of the letters/characters to use while training + """ + + def load_alphabet(self): + alphabet_path = os.path.join(self.path, "alphabet.txt") + + # check if the alphabet file exists, if not, raise an exception + if os.path.exists(alphabet_path): + with open(alphabet_path, "r") as alphabet_file: + # Split the file by lines: on letter/character should be on each line + letters = alphabet_file.read().split("\n") + return letters + else: + raise Exception( + f"The alphabet file {alphabet_path} could not be found") + return [] + + """ load the data from the data file + """ + + def load_data(self): + data_path = os.path.join(self.path, "data.json") + if os.path.exists(data_path): + with open(data_path, "r") as data_file: + j = json.loads(data_file.read()) + + # match the imported global function with the ones listed in the json file + for pre in j["pre"]: + if pre in globals(): + func = globals()[pre] + + # check if the requested object is a function + if type(func) is types.FunctionType: + self.pre_process.append(func) + else: + raise Exception( + f"The function '{pre}' is not a function") + else: + # If the function was not loaded, throw an exception + raise Exception( + f"The function '{pre}' was not loaded or does not exist") + + for post in j["post"]: + if post in globals(): + func = globals()[post] + + # check if the requested object is a function + if type(func) is types.FunctionType: + self.post_process.append(func) + else: + raise Exception( + f"The function '{post}' is not a function") + else: + # If the function was not loaded, throw an exception + raise Exception( + f"The function '{post}' was not loaded or does not exist") + + else: + # load the default pre and post processing functions + self.pre_process = [uncapitalise] + self.post_process = [deserialise, capitalise] + + """ List all the names from a given category file + Args: + category: (str) the category to select names from + Returns: + data: (str[]) an array containing all of the names from the given category file + """ + + def get_names(self, category): + with open(self.datasets[category], "r") as datafile: + return [name for name in datafile.read().split("\n")] + + """ List all names in all categories + Returns: + data: (str[]) an array with all of the names in this country's datasets + """ + + def get_all(self): + return [name for k in self.datasets for name in self.get_names(k)] + + """ Pre-process a name for training + Args: + name: the name loaded from the dataset + Returns: + name: the name after being processed + """ + + def postprocess(self, name): + for f in self.post_process: + name = f(name) + return name + + """ Post-process a name after sampling + Args: + name: the name output from the recurrent neural network + Returns: + name: the name after being processed + """ + + def preprocess(self, name): + for f in self.pre_process: + name = f(name) + return name + + """ Train a neural network on the given dataset + Args: + category: (str) the category to sample training names from + """ + + def train(self, category): + # select the RNN model to be training on + rnn = self.rnn[category] + + # load names from that dataset and pre proccess them + print("preprocessing names...") + names = [self.preprocess(name) for name in self.get_names(category)] + print(f"processed {len(names)} names!") + + # calculate optimum number of iterations (using 80% of whole dataset) + iters = int(len(names) * 0.8) + + # start training + learn_names(rnn, names, self.alphabet, iterations=iters, + num_processes=num_processes) + + """ Sample a name from the neural network with a given starting letter + Args: + category: (str) the category to sample generated names from + Returns: + name: the output from the neural network + """ + + def sample(self, category, start_letter): + + # select the RNN model to be sampling from + rnn = self.rnn[category] + + # set the random factor of the RNN to randomise names that are generated + rnn.random_factor = 0.7 + + # call the rnn sample function to generate a single name + name = sample(rnn, self.alphabet, start_letter) + + # post process the name and return + return self.postprocess(name) + + """ Load the rnn from its file + Args: + category: (str) the category to load + parent_directory: (str) where to find the model + """ + + def load_rnn(self, category, parent_directory): + model_file = os.path.join(parent_directory, f"{category}.pt") + self.rnn[category] = torch.load(model_file) + + """ Save the rnn of a given category to its file + Args: + category: (str) the category to save + parent_directory: (str) the directory to save the model file to + """ + + def save_rnn(self, category, parent_directory): + rnn = self.rnn[category] + model_file = os.path.join(parent_directory, f"{category}.pt") + torch.save(rnn, model_file) + + +def get_countries(): + return { + country: Country(os.path.join(countries_path, country)) for country in os.listdir(countries_path) if os.path.isdir(os.path.join(countries_path, country)) + } + + +""" train all of the datasets from a specific country + Args: + country: (Country) +""" + + +def train_country(country, name): + datasets = country.datasets + for dataset in datasets: + print(f"Training {dataset} in {name}") + country.train(dataset) + + print(f"Finished training on {dataset}... saving...", end="") + path = os.path.join("data", "models", name) + + # check if the path already exists before trying to make directories + if not os.path.exists(path): + os.makedirs(path) + + country.save_rnn(dataset, path) + print("saved!") + + +def sample_country(country, country_name, number_of_samples=10000): + + datasets = country.datasets + for dataset in datasets: + + # ensure that the model exists before sampling + path = os.path.join("data", "models", country_name) + if os.path.exists(os.path.join(path, dataset + ".pt")): + + # load the country's rnn + country.load_rnn(dataset, path) + + # load the names from the country's dataset, and pre-process them + names = [country.preprocess(name) + for name in country.get_names(dataset)] + + # make a dictionary full of start letters and their frequency + start_letters = {} + + for name in names: + if len(name) > 0: + start_letter = name[0] + + # if the start letter isn't already in the dictionary, add it with value 1 + if start_letter in start_letters: + start_letters[start_letter] += 1 + else: + start_letters[start_letter] = 1 + + # turn each integer count into a float where: letter_weight=frequency/total_names + total = len(names) + + for letter in start_letters: + weight = float(start_letters[letter] / total) + start_letters[letter] = weight + + # sample names from the RNN + sampled_names = [] + + for i in range(number_of_samples): + try: + letter = weighted_choice(start_letters) + sample = country.sample(dataset, letter) + sampled_names.append(sample) + except: + pass + + # remove duplicate names + sampled_names = list(dict.fromkeys(sampled_names)) + + # create a sqlite connection + connection = sqlite3.connect(database) + + # always close the connection when finished + with connection: + cursor = connection.cursor() + for name in sampled_names: + sql = "INSERT INTO names(Name, Origin, Category) VALUES(?, ?, ?)" + + # insert the current name and options into the database + cursor.execute(sql, (name, country_name, dataset)) + + # commit changes and save the database + connection.commit() + + print( + f"Saved {len(sampled_names)} names for {country_name}/{dataset}") + + else: + print(f"the model: {country_name}/{dataset} was not found.") + + +countries_path = "data/datasets" +database = os.path.join("data", "names.db") +if __name__ == "__main__": + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + + # allow processes on this model to share memory + torch.multiprocessing.set_start_method('spawn') + + # List all the directories containing country datasets to populate the countries dictionary + countries = get_countries() + + country_count = len(countries) + # Display debug information + print(f"Loaded {country_count} countries!") + + # list all countries in neat collumns + collumns = 4 + width = 14 + i = 0 + for country in countries: + i += 1 + + # print the country and then its index + print(country, end="") + + # organise into rows and collumns + if i % collumns == 0: + print("") + else: + # separate collumns with spaces + print(" " * (width - len(country)), end="") + + # keep asking until the country selection is valid + good_selection = False + while not good_selection: + # prompt user to select a country to train, or train all + country_selection = input( + "select the name of a country to train on, or (all) to train on all countries: ") + + good_selection = True + selected_countries = [] + + # if the user selected all, then add all countries to list, if not, add the selected country + if country_selection.lower() == "all": + [selected_countries.append(country) for country in countries] + elif country_selection.lower() in countries: + selected_countries.append(country_selection) + else: + print("Country not found, try again") + good_selection = False + + choice = input("(t)rain on data, or (s)ample from weights?") + + if choice.lower()[0] == "t": + for country in selected_countries: + train_country(countries[country], country) + + elif choice.lower()[0] == "s": + create_table(database) + for country in selected_countries: + sample_country(countries[country], country) diff --git a/src/neuralnetwork/util.py b/src/neuralnetwork/util.py new file mode 100644 index 0000000..268819e --- /dev/null +++ b/src/neuralnetwork/util.py @@ -0,0 +1,185 @@ +import sqlite3 +import tensorflow as tf +from pincelate import Pincelate +from multiprocessing import Value +import random + +"""Atomic Number +A multiprocessing-safe Number that you can increment and set a value to. +""" +class AtomicNumber: + def __init__(self, initial=0): + # set the initial value of the number + self._v = Value("d", initial) + + # use python thread locks + self._lock = self._v.get_lock() + + def set(self, num): + with self._lock: + # set the value of the number and return it + self._v.value = num + return self._v.value + + def increment(self, num=1): + with self._lock: + # increase the value of the number and then return it + self._v.value += num + return self._v.value + + def get(self): + return self._v.value + + +""" Capitalise the first letter of a name + Args: + name: (str) the name to transform + Returns: + name: (str) the output name +""" + + +def capitalise(name): + return str(name).capitalize() + + +""" uncapitalise the first letter of a name + Args: + name: (str) the name to transform + Returns: + name: (str) the output name +""" + + +def uncapitalise(name): + return str(name).lower() + + +# import pincelate + +# tell tensorflow to only log ERRORS rather than all problems +tf.get_logger().setLevel('ERROR') + +pin = Pincelate() + +""" Transliterate names into their phonetic spelling + Args: + name: (str) the name to transliterate + Returns: + name: (str) the pronunciation of the name as an arpabet list +""" + + +def transliterate(name): + output = [] + + # iterate through each word and sound out separately + for word in name.split(" "): + try: + for phoneme in pin.soundout(word): + output.append(phoneme) + except Exception as e: + output.append(word) + output.append(" ") + + # remove the trailing " " + output.pop() + + return output + + +""" Transliterate phonetic spellings back into names + Args: + name: (str) the pronunciation of the name as an arpabet list + Returns: + name: (str) a guessed spelling of the given phoneme list +""" + + +def reverse_transliterate(name): + words = [] + + current_word = [] + # iterate through all phonemes, spliting groups between " " into words + for phoneme in name: + if phoneme == " ": + words.append(current_word) + + # reset current word + current_word = [] + else: + current_word.append(phoneme) + + # add whats left of the current word to the list of words + words.append(current_word) + + # spell each word separately + spellings = [pin.spell(word) for word in words] + return " ".join(spellings) + + +"""Load random line from a given filename +""" + + +def get_random_line(file_name): + total_bytes = os.stat(file_name).st_size + random_point = random.randint(0, total_bytes) + file = open(file_name) + file.seek(random_point) + file.readline() # skip this line to clear the partial line + return file.readline() + + +""" Concatenate elemnts of a list + Args: + name: (str[]) the letters of a name in an array + Returns: + name: (str) +""" + + +def deserialise(name): + return "".join(name) + + +""" return a random item from a dictionary of weighted items + Args: + weights: (dict) a dictionary containing the items as keys and float values as weights + Returns: + item: a random item selected from the dictionary +""" + + +def weighted_choice(weights): + # choose a randm number between 0 and 1 + choice = random.uniform(0.0, 1.0) + + # iterate through weights, subtracting the weight from the choice each time + for item, weight in weights.items(): + choice -= weight + + # if we are currently on the chosen item, return it + if choice < 0: + return item + + # in case the input dictionary is incorrectly setup, return the last item + return list(weights)[-1] + + +def create_table(database_path): + # connect to the database and get the cursor + connection = sqlite3.connect(database_path) + + # always close the connection at the end + with connection: + cursor = connection.cursor() + + cursor.execute("CREATE TABLE IF NOT EXISTS names (\ + Name TEXT,\ + Origin TEXT,\ + Category TEXT\ + )") + + # commit the changes, saving them + connection.commit() |