Add lots of features

tlm/cli.py

@@ -1,6 +1,7 @@
 import click
 from .model import TinyLanguageModel
-from .helpers import read_mail_text
+from .helpers import read_mail_text, join
+from .tokenization import tokenize_text, tokenize_words
 
 
 @click.group()
@@ -10,15 +11,17 @@ def cli():
 
 
 @cli.command()
-@click.option("--length", default=50, help="Number of words to generate.")
-@click.option("--n", default=2, help="Number of words in the context window.")
-@click.option("--text", type=click.Path(exists=True), multiple=True, help="Text file(s) to use as training corpus. Can be specified multiple times.")
-@click.option("--gutenberg", multiple=True, help="NLTK Gutenberg corpus key(s). Can be specified multiple times.")
-@click.option("--list-gutenberg", is_flag=True, help="List available Gutenberg corpus keys.")
-@click.option("--mbox", type=click.Path(exists=True), help="Mbox file to use for training.")
-@click.option("--prompt", help="Prompt to start generation.")
-@click.option("--interact", is_flag=True, help="Drop into interactive shell after generating.")
-def generate(length, n, text, gutenberg, list_gutenberg, mbox, prompt, interact):
+@click.option('-l', "--length", default=50, help="Number of tokens to generate.")
+@click.option('-n', "--context-window-words", default=2, help="Number of words in the context window.")
+@click.option('-f', "--filepath", type=click.Path(exists=True), multiple=True, help="Text file(s) to use as training corpus. Can be specified multiple times.")
+@click.option('-g', "--gutenberg", multiple=True, help="NLTK Gutenberg corpus key(s). Can be specified multiple times.")
+@click.option('-G', "--list-gutenberg", is_flag=True, help="List available Gutenberg corpus keys.")
+@click.option('-m', "--mbox", type=click.Path(exists=True), help="Mbox file to use for training.")
+@click.option('-p', "--prompt", help="Prompt to start generation.")
+@click.option('-i', "--interact", is_flag=True, help="Drop into interactive shell after generating.")
+@click.option('-t', "--tokenize", 'tokenize_opts', multiple=True, type=click.Choice(['lower', 'char', 'alpha']), help="Preprocessing option (can be specified multiple times). 'lower': lowercase all input text. 'char': use characters as tokens instead of words.")
+@click.option('-v', "--verbose", is_flag=True, help="Display step-by-step generation as a table.")
+def generate(length, context_window_words, filepath, gutenberg, list_gutenberg, mbox, prompt, interact, tokenize_opts, verbose):
     """Generate text using the language model."""
     import nltk
 
@@ -34,27 +37,46 @@ def generate(length, n, text, gutenberg, list_gutenberg, mbox, prompt, interact)
     # Determine training corpus
     corpus = []
 
-    if text:
-        for filepath in text:
-            with open(filepath, "r") as f:
-                corpus.extend(f.read().split())
+    if filepath:
+        for fp in filepath:
+            with open(fp, "r") as f:
+                corpus.extend(tokenize_text(f.read(), tokenize_opts))
     if gutenberg:
         nltk.download("gutenberg", quiet=True)
         from nltk.corpus import gutenberg as gutenberg_corpus
         for key in gutenberg:
-            corpus.extend(gutenberg_corpus.words(key))
+            corpus.extend(tokenize_words(gutenberg_corpus.words(key), tokenize_opts))
     if mbox:
         mail_text = read_mail_text(mbox)
-        corpus.extend(mail_text.split())
+        corpus.extend(tokenize_text(mail_text, tokenize_opts))
 
     if not corpus:
         raise click.UsageError("Must specify at least one of --text, --gutenberg, or --mbox for training data.")
 
     # Train and generate
-    model = TinyLanguageModel(n=n)
+    model = TinyLanguageModel(n=context_window_words)
     model.train(corpus)
-    prompt_words = prompt.split() if prompt else None
-    output = model.generate(length, prompt=prompt_words)
+    if prompt:
+        prompt_tokens = tokenize_text(prompt, tokenize_opts)
+    else:
+        prompt_tokens = None
+    join_fn = ''.join if 'char' in tokenize_opts else None
+    display_join = join_fn or join
+
+    if verbose:
+        from tabulate import tabulate
+        rows = []
+        import textwrap
+        def step_callback(pattern, options, chosen):
+            opts = textwrap.fill(', '.join(sorted(set(options))), width=60)
+            rows.append([display_join(list(pattern)), opts, chosen])
+        output = model.generate(length, prompt=prompt_tokens, join_fn=join_fn, step_callback=step_callback)
+        click.echo(tabulate(rows, headers=["Context", "Options", "Selected"], tablefmt="simple"))
+        click.echo()
+
+    else:
+        output = model.generate(length, prompt=prompt_tokens, join_fn=join_fn)
+
     click.echo(output)
 
     if interact:

tlm/model.py

@@ -18,7 +18,7 @@ class TinyLanguageModel:
                 self.model[pattern] = []
             self.model[pattern].append(next_word)
                 
-    def generate(self, length, prompt=None):
+    def generate(self, length, prompt=None, join_fn=None, step_callback=None):
         "Create new words based on what the model learned."
         if not self.model:
             raise Exception("The model has not been trained")
@@ -27,9 +27,12 @@ class TinyLanguageModel:
             pattern = tuple(output[-self.n:])
             if pattern not in self.model:
                 break
-            next_word = random.choice(self.model[pattern])
-            output.append(next_word)
-        return join(output)
+            options = self.model[pattern]
+            chosen = random.choice(options)
+            if step_callback:
+                step_callback(pattern, options, chosen)
+            output.append(chosen)
+        return (join_fn or join)(output)
 
     def get_random_pattern(self):
         "Randomly chooses one of the observed patterns"

tlm/tokenization.py

@@ -0,0 +1,31 @@
+def compress_whitespace(text):
+    """Collapse sequences of whitespace into a single space."""
+    import re
+    return re.sub(r'\s+', ' ', text).strip()
+
+
+def _is_alpha_token(token):
+    return all(c.isalpha() or c in " '" for c in token)
+
+
+def tokenize_text(text, options):
+    """Tokenize a raw text string according to the given options."""
+    if 'lower' in options:
+        text = text.lower()
+    words = text.split()
+    if 'alpha' in options:
+        words = [w for w in words if _is_alpha_token(w)]
+    if 'char' in options:
+        return list(compress_whitespace(' '.join(words)))
+    return words
+
+
+def tokenize_words(words, options):
+    """Apply tokenization options to an already word-tokenized sequence."""
+    if 'lower' in options:
+        words = [w.lower() for w in words]
+    if 'alpha' in options:
+        words = [w for w in words if _is_alpha_token(w)]
+    if 'char' in options:
+        return list(compress_whitespace(' '.join(words)))
+    return list(words)