refactor

.gitignore

@@ -1,5 +1,8 @@
 dramaoftheday.md
 .idea/
 .venv/
-saved_models/*
+src/DOTD_generator/saved_models/*
-!saved_models/rnn_2epochs.pth
+!src/DOTD_generator/saved_models/rnn_2epochs.pth
+*py[cod]
+.ipynb_checkpoints
+*egg-info

.python-version

@@ -1 +1 @@
-3.7.3
+3.7.7

Makefile

@@ -0,0 +1,32 @@
+setup:
+	python -m venv .venv && . .venv/bin/activate
+	pip install --upgrade pip
+	pip install -r requirements.txt
+
+clean-pyc:
+	find . -name '*.pyc' -exec rm -f {} +
+	find . -name '*.pyo' -exec rm -f {} +
+	find . -name '*~' -exec rm -f {} +
+	find . -name '__pycache__' -exec rm -fr {} +
+
+clean-test:
+	rm -f .coverage
+	rm -f .coverage.*
+
+clean: clean-pyc clean-test
+
+test: clean
+	. .venv/bin/activate && py.test tests --cov=src --cov-report=term-missing --cov-fail-under 95
+
+mypy:
+	. .venv/bin/activate && mypy src
+
+lint:
+	. .venv/bin/activate && pylint src -j 4 --reports=y
+
+docs: FORCE
+	cd docs; . .venv/bin/activate && sphinx-apidoc -o ./source ./src
+	cd docs; . .venv/bin/activate && sphinx-build -b html ./source ./build
+FORCE:
+
+check: test lint mypy

markdown_header.txt

@@ -1,19 +0,0 @@
-+++
-title = "Drama of the Day"
-date = "2021-11-07"
-+++
-
-
-This excerpt of a drama, although inspired by Shakespeare, is entirely artifically generated.  
-The language model that was used to produce this text only had prior knowledge about the latin alphabet. All the (pseudo-)words produced were learnt through imitating Shakespeare. Therefore, you might find quite a few neologisms, in true Shakespearean spirit.  
-
-
-Everyday, the model is told to use the prompt **TOM:** and then produces the rest up to 10000 characters, thereby creating a unique new drama.
-
-
-If you like to know more about this, [click here](/DOTD_generator).
-
-
----
-
-

requirements.txt

@@ -0,0 +1,34 @@
+astroid==2.9.3
+attrs==21.4.0
+black==22.1.0
+certifi==2021.10.8
+charset-normalizer==2.0.11
+click==8.0.3
+codecov==2.1.12
+coverage==6.3.1
+idna==3.3
+importlib-metadata==4.10.1
+iniconfig==1.1.1
+isort==5.10.1
+lazy-object-proxy==1.7.1
+mccabe==0.6.1
+mypy==0.931
+mypy-extensions==0.4.3
+packaging==21.3
+pathspec==0.9.0
+platformdirs==2.4.1
+pluggy==1.0.0
+py==1.11.0
+pylint==2.12.2
+pyparsing==3.0.7
+pytest==7.0.0
+pytest-cov==3.0.0
+requests==2.27.1
+toml==0.10.2
+tomli==2.0.0
+torch==1.10.2
+typed-ast==1.5.2
+typing-extensions==4.0.1
+urllib3==1.26.8
+wrapt==1.13.3
+zipp==3.7.0

src/DOTD_generator/__init__.py

@@ -1,2 +0,0 @@
-__version__ = "0.1.0"
-

src/DOTD_generator/drama_of_the_day.py

@@ -1,66 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-import torch
-import re
-
-class RNN(torch.nn.Module):
-    def __init__(self, vocab_size, hidden_size, embedding_size, batch=32, layers=2):
-        super(RNN, self).__init__()
-        self.hidden_size = hidden_size # size of the GRU layers
-        self.batch = batch 
-        self.layers = layers # how many GRU layers
-        self.word_embeds = torch.nn.Embedding(vocab_size, embedding_size) # Embedding layer
-        self.gru = torch.nn.GRU(embedding_size, hidden_size, layers, batch_first=True) # GRU layer(s)
-        self.output_layer = torch.nn.Linear(hidden_size, vocab_size)
-        
-    def forward(self, inputs, hidden):
-        x = self.word_embeds(inputs) # transform the input integer into a high dimensional embedding
-        output, hidden = self.gru(x, hidden) # Compute the output of the GRU layer(s)
-        output = self.output_layer(output) # compute the logits
-        return output, hidden
-
-    def initHidden(self):
-        return torch.zeros(self.layers, self.batch, self.hidden_size) 
-
-def preprocess(text):
-    alphabet = sorted(set(text))
-    letter_to_int = {let: ind for ind, let in enumerate(alphabet)}
-    int_to_letter = {ind: let for ind, let in enumerate(alphabet)}
-    letter_ints = [letter_to_int[letter] for letter in text]
-    alphabet_size = len(alphabet)
-    return int_to_letter, letter_to_int, alphabet_size, letter_ints
-
-
-def markdown_header():
-    return open("/home/tux/shakespeare_generator/markdown_header.txt", "rt").read()
-
-text = open("/home/tux/shakespeare_generator/shakespeare.txt", "rt").read()
-init_seq = "TOM:"
-int_to_letter, letter_to_int, alphabet_size, letter_ints = preprocess(text)
-rnn = RNN(alphabet_size, 1024, 256, layers=2, batch=1) # instantiate model
-rnn.load_state_dict(torch.load("/home/tux/shakespeare_generator/rnn_2epochs.pth", map_location=torch.device('cpu'))) # load weights
-rnn.eval() # tell model its time to evaluate
-
-def write_drama(seq, temp=0.7, max_seq_len=1000):
-    hidden = rnn.initHidden() 
-    input_idx = torch.LongTensor([[letter_to_int[s] for s in seq]]) # input characters to ints
-    for i in range(max_seq_len):
-        output, hidden = rnn(input_idx, hidden) # predict the logits for the next character
-        pred = torch.squeeze(output, 0)[-1]
-        pred = pred / temp # apply temperature
-        pred_id = torch.distributions.categorical.Categorical(logits=pred).sample() # sample from the distribution
-        input_idx = torch.cat((input_idx[:,1:], pred_id.reshape(1,-1)), 1) # predicted character is added to our input
-        seq += int_to_letter[pred_id.item()] # add predicted character to sequence
-    return seq
-
-def stylise_drama(drama):
-    return re.sub(r"\n", r"<br>\n", drama)
-
-output_text = markdown_header() + stylise_drama(write_drama(init_seq))
-
-with open("/home/tux/shakespeare_generator/dramaoftheday.md", "w") as f:
-    f.write(output_text)
-
-
-
-

src/dotd/__init__.py

@@ -0,0 +1,7 @@
+# coding: utf-8
+
+from os.path import dirname, abspath
+
+__version__ = "0.1.0"
+
+ROOT_DIR = dirname(abspath(__file__))

src/dotd/data.py

@@ -0,0 +1,36 @@
+# coding: utf-8
+
+import torch
+from torch.utils.data import Dataset
+
+
+def get_shakespeare():
+    return open("data/shakespeare.txt", "rt").read()
+
+
+def preprocess(text: str) -> tuple:
+    alphabet = sorted(set(text))
+    letter_to_int = {let: ind for ind, let in enumerate(alphabet)}
+    int_to_letter = {ind: let for ind, let in enumerate(alphabet)}
+    letter_ints = [letter_to_int[letter] for letter in text]
+    alphabet_size = len(alphabet)
+    return int_to_letter, letter_to_int, alphabet_size, letter_ints
+
+
+class ShakespeareDataset(Dataset):
+    def __init__(self, seq_len: int):
+        _, _, _, self.text = preprocess(get_shakespeare())
+        self.x = torch.LongTensor(self.text[:-1])  # get the data
+        self.y = torch.LongTensor(self.text[1:])
+        self.seq_len = seq_len  # set the sequence length
+
+    def __len__(self) -> int:
+        return (
+            len(self.text) - self.seq_len - 1
+        )  # length of corpora minus sequence length minus shift
+
+    def __getitem__(self, index: int) -> tuple:
+        return (
+            self.x[index : index + self.seq_len],
+            self.x[index : index + self.seq_len],
+        )  # return tuple of (sample, label)

src/dotd/data/markdown_header.txt

@@ -0,0 +1,20 @@
++++
+title = "Drama of the Day"
+date = "2021-11-07"
++++
+
+
+This excerpt of a drama, although inspired by Shakespeare, is entirely artificially generated.
+The language model that was used to produce this text only had prior knowledge about the latin alphabet.
+All the (pseudo-)words produced were learnt through imitating Shakespeare. Therefore, you might find quite a few neologisms, in true Shakespearean spirit.
+
+
+Every day, the model is told to use the prompt **TOM:** and then produces the rest up to 10000 characters, thereby creating a unique new drama.
+
+
+If you like to know more about this, [click here](/DOTD_generator).
+
+
+---
+
+

src/dotd/generate.py

@@ -0,0 +1,59 @@
+# coding: utf-8
+
+import re
+import torch
+from dotd.model import GRU
+from dotd.data import get_shakespeare, preprocess
+
+
+def get_header():
+    return open("data/markdown_header.txt", "rt").read()
+
+
+def load_model(alphabet_size: int, path: str = "saved_models/rnn_2epochs.pth") -> GRU:
+    gru = GRU(alphabet_size, 1024, 256, layers=2, batch=1)  # instantiate model
+    gru.load_state_dict(
+        torch.load(
+            path,
+            map_location=torch.device("cpu"),
+        )
+    )  # load weights
+    return gru.eval()
+
+
+def make_pretty(text: str) -> str:
+    return re.sub(r"\n", r"<br>\n", text)
+
+
+def write_drama(seq, temp=0.7, max_seq_len=1000):
+    int_to_letter, letter_to_int, alphabet_size, _ = preprocess(get_shakespeare())
+    gru = load_model(alphabet_size=alphabet_size)
+    hidden = gru.init_hidden()
+    input_idx = torch.LongTensor(
+        [[letter_to_int[s] for s in seq]]
+    )  # input characters to ints
+    for i in range(max_seq_len):
+        output, hidden = gru(
+            input_idx, hidden
+        )  # predict the logits for the next character
+        pred = torch.squeeze(output, 0)[-1]
+        pred = pred / temp  # apply temperature
+        pred_id = torch.distributions.categorical.Categorical(
+            logits=pred
+        ).sample()  # sample from the distribution
+        input_idx = torch.cat(
+            (input_idx[:, 1:], pred_id.reshape(1, -1)), 1
+        )  # predicted character is added to our input
+        seq += int_to_letter[pred_id.item()]  # add predicted character to sequence
+    return seq
+
+
+def main():
+    init_seq = "TOM:"
+    output_text = get_header() + make_pretty(write_drama(init_seq))
+    with open("dramaoftheday.md", "w") as f:
+        f.write(output_text)
+
+
+if __name__ == "__main__":
+    main()

src/dotd/model.py

@@ -0,0 +1,78 @@
+# coding: utf-
+
+import torch
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+import numpy as np
+from dotd.data import ShakespeareDataset, preprocess, get_shakespeare
+
+
+class GRU(torch.nn.Module):
+    def __init__(self, vocab_size, hidden_size, embedding_size, batch=32, layers=2):
+        super(GRU, self).__init__()
+        self.hidden_size = hidden_size  # size of the GRU layers
+        self.batch = batch
+        self.layers = layers  # how many GRU layers
+        self.word_embeds = torch.nn.Embedding(
+            vocab_size, embedding_size
+        )  # Embedding layer
+        self.gru = torch.nn.GRU(
+            embedding_size, hidden_size, layers, batch_first=True
+        )  # GRU layer(s)
+        self.output_layer = torch.nn.Linear(hidden_size, vocab_size)
+
+    def forward(self, inputs, hidden):
+        x = self.word_embeds(
+            inputs
+        )  # transform the input integer into a high dimensional embedding
+        output, hidden = self.gru(x, hidden)  # Compute the output of the GRU layer(s)
+        output = self.output_layer(output)  # compute the logits
+        return output, hidden
+
+    def init_hidden(self):
+        return torch.zeros(self.layers, self.batch, self.hidden_size)
+
+
+def train_epoch(model, loader, optim, loss, device):
+    current_loss = []  # record running loss
+    model.to(device)  # put the model on the specified device
+    hidden = model.init_hidden().to(device)  # create the hidden state
+    model.train()  # tell the model its training time
+    for X, y in loader:
+        X, y = X.to(device), y.to(
+            device
+        )  # collect the data and labels from the dataloader and put them on the device
+        optim.zero_grad()  # empty the gradients
+        output, hidden = model(X, hidden)  # compute the output
+        hidden = hidden.detach()  # take the hidden state out of the graph
+        batch_loss = loss(output.transpose(1, 2), y)  # compute loss
+        batch_loss.backward()  # compute gradients
+        optim.step()  # update weights
+        current_loss.append(batch_loss.item())  # record loss
+    epoch_loss = np.mean(current_loss)
+    return epoch_loss
+
+
+def train(epochs: int):
+    _, _, alphabet_size, _ = preprocess(
+        get_shakespeare()
+    )  # get amount of characters for one-hot to embedding
+    gru = GRU(alphabet_size, 1024, 256, layers=2)  # instantiate model
+    loss = torch.nn.CrossEntropyLoss()
+    optim = torch.optim.Adam(gru.parameters())
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    loader = DataLoader(
+        ShakespeareDataset(seq_len=100),
+        batch_size=32,
+        shuffle=True,
+        num_workers=2,
+        drop_last=True,
+    )
+    for e in tqdm(range(epochs)):
+        l = train_epoch(gru, loader, optim, loss, device)
+        print(f"Epoch: {e}, Loss: {l}")
+    torch.save(gru.state_dict(), "saved_models/gru_{}epochs.pth".format(epochs + 1))
+
+
+if __name__ == "__main__":
+    train(20)