import math from tqdm import tqdm import numpy from ast import literal_eval from pathlib import Path from preshed.counter import PreshCounter import tarfile import gzip import zipfile import srsly from wasabi import msg import warnings from ..vectors import Vectors from ..errors import Errors, Warnings from ..util import ensure_path, get_lang_class try: import ftfy except ImportError: ftfy = None DEFAULT_OOV_PROB = -20 def init_model( # fmt: off lang: ("Model language", "positional", None, str), output_dir: ("Model output directory", "positional", None, Path), freqs_loc: ("Location of words frequencies file", "option", "f", Path) = None, clusters_loc: ("Optional location of brown clusters data", "option", "c", str) = None, jsonl_loc: ("Location of JSONL-formatted attributes file", "option", "j", Path) = None, vectors_loc: ("Optional vectors file in Word2Vec format", "option", "v", str) = None, prune_vectors: ("Optional number of vectors to prune to", "option", "V", int) = -1, vectors_name: ("Optional name for the word vectors, e.g. en_core_web_lg.vectors", "option", "vn", str) = None, model_name: ("Optional name for the model meta", "option", "mn", str) = None, # fmt: on ): """ Create a new model from raw data, like word frequencies, Brown clusters and word vectors. If vectors are provided in Word2Vec format, they can be either a .txt or zipped as a .zip or .tar.gz. """ if jsonl_loc is not None: if freqs_loc is not None or clusters_loc is not None: settings = ["-j"] if freqs_loc: settings.append("-f") if clusters_loc: settings.append("-c") msg.warn( "Incompatible arguments", "The -f and -c arguments are deprecated, and not compatible " "with the -j argument, which should specify the same " "information. Either merge the frequencies and clusters data " "into the JSONL-formatted file (recommended), or use only the " "-f and -c files, without the other lexical attributes.", ) jsonl_loc = ensure_path(jsonl_loc) lex_attrs = srsly.read_jsonl(jsonl_loc) else: clusters_loc = ensure_path(clusters_loc) freqs_loc = ensure_path(freqs_loc) if freqs_loc is not None and not freqs_loc.exists(): msg.fail("Can't find words frequencies file", freqs_loc, exits=1) lex_attrs = read_attrs_from_deprecated(freqs_loc, clusters_loc) with msg.loading("Creating model..."): nlp = create_model(lang, lex_attrs, name=model_name) msg.good("Successfully created model") if vectors_loc is not None: add_vectors(nlp, vectors_loc, prune_vectors, vectors_name) vec_added = len(nlp.vocab.vectors) lex_added = len(nlp.vocab) msg.good( "Sucessfully compiled vocab", f"{lex_added} entries, {vec_added} vectors", ) if not output_dir.exists(): output_dir.mkdir() nlp.to_disk(output_dir) return nlp def open_file(loc): """Handle .gz, .tar.gz or unzipped files""" loc = ensure_path(loc) if tarfile.is_tarfile(str(loc)): return tarfile.open(str(loc), "r:gz") elif loc.parts[-1].endswith("gz"): return (line.decode("utf8") for line in gzip.open(str(loc), "r")) elif loc.parts[-1].endswith("zip"): zip_file = zipfile.ZipFile(str(loc)) names = zip_file.namelist() file_ = zip_file.open(names[0]) return (line.decode("utf8") for line in file_) else: return loc.open("r", encoding="utf8") def read_attrs_from_deprecated(freqs_loc, clusters_loc): if freqs_loc is not None: with msg.loading("Counting frequencies..."): probs, _ = read_freqs(freqs_loc) msg.good("Counted frequencies") else: probs, _ = ({}, DEFAULT_OOV_PROB) # noqa: F841 if clusters_loc: with msg.loading("Reading clusters..."): clusters = read_clusters(clusters_loc) msg.good("Read clusters") else: clusters = {} lex_attrs = [] sorted_probs = sorted(probs.items(), key=lambda item: item[1], reverse=True) if len(sorted_probs): for i, (word, prob) in tqdm(enumerate(sorted_probs)): attrs = {"orth": word, "id": i, "prob": prob} # Decode as a little-endian string, so that we can do & 15 to get # the first 4 bits. See _parse_features.pyx if word in clusters: attrs["cluster"] = int(clusters[word][::-1], 2) else: attrs["cluster"] = 0 lex_attrs.append(attrs) return lex_attrs def create_model(lang, lex_attrs, name=None): lang_class = get_lang_class(lang) nlp = lang_class() for lexeme in nlp.vocab: lexeme.rank = 0 lex_added = 0 for attrs in lex_attrs: if "settings" in attrs: continue lexeme = nlp.vocab[attrs["orth"]] lexeme.set_attrs(**attrs) lexeme.is_oov = False lex_added += 1 lex_added += 1 if len(nlp.vocab): oov_prob = min(lex.prob for lex in nlp.vocab) - 1 else: oov_prob = DEFAULT_OOV_PROB nlp.vocab.cfg.update({"oov_prob": oov_prob}) if name: nlp.meta["name"] = name return nlp def add_vectors(nlp, vectors_loc, prune_vectors, name=None): vectors_loc = ensure_path(vectors_loc) if vectors_loc and vectors_loc.parts[-1].endswith(".npz"): nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open("rb"))) for lex in nlp.vocab: if lex.rank: nlp.vocab.vectors.add(lex.orth, row=lex.rank) else: if vectors_loc: with msg.loading(f"Reading vectors from {vectors_loc}"): vectors_data, vector_keys = read_vectors(vectors_loc) msg.good(f"Loaded vectors from {vectors_loc}") else: vectors_data, vector_keys = (None, None) if vector_keys is not None: for word in vector_keys: if word not in nlp.vocab: lexeme = nlp.vocab[word] lexeme.is_oov = False if vectors_data is not None: nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys) if name is None: nlp.vocab.vectors.name = f"{nlp.meta['lang']}_model.vectors" else: nlp.vocab.vectors.name = name nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name if prune_vectors >= 1: nlp.vocab.prune_vectors(prune_vectors) def read_vectors(vectors_loc): f = open_file(vectors_loc) shape = tuple(int(size) for size in next(f).split()) vectors_data = numpy.zeros(shape=shape, dtype="f") vectors_keys = [] for i, line in enumerate(tqdm(f)): line = line.rstrip() pieces = line.rsplit(" ", vectors_data.shape[1]) word = pieces.pop(0) if len(pieces) != vectors_data.shape[1]: msg.fail(Errors.E094.format(line_num=i, loc=vectors_loc), exits=1) vectors_data[i] = numpy.asarray(pieces, dtype="f") vectors_keys.append(word) return vectors_data, vectors_keys def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50): counts = PreshCounter() total = 0 with freqs_loc.open() as f: for i, line in enumerate(f): freq, doc_freq, key = line.rstrip().split("\t", 2) freq = int(freq) counts.inc(i + 1, freq) total += freq counts.smooth() log_total = math.log(total) probs = {} with freqs_loc.open() as f: for line in tqdm(f): freq, doc_freq, key = line.rstrip().split("\t", 2) doc_freq = int(doc_freq) freq = int(freq) if doc_freq >= min_doc_freq and freq >= min_freq and len(key) < max_length: try: word = literal_eval(key) except SyntaxError: # Take odd strings literally. word = literal_eval(f"'{key}'") smooth_count = counts.smoother(int(freq)) probs[word] = math.log(smooth_count) - log_total oov_prob = math.log(counts.smoother(0)) - log_total return probs, oov_prob def read_clusters(clusters_loc): clusters = {} if ftfy is None: warnings.warn(Warnings.W004) with clusters_loc.open() as f: for line in tqdm(f): try: cluster, word, freq = line.split() if ftfy is not None: word = ftfy.fix_text(word) except ValueError: continue # If the clusterer has only seen the word a few times, its # cluster is unreliable. if int(freq) >= 3: clusters[word] = cluster else: clusters[word] = "0" # Expand clusters with re-casing for word, cluster in list(clusters.items()): if word.lower() not in clusters: clusters[word.lower()] = cluster if word.title() not in clusters: clusters[word.title()] = cluster if word.upper() not in clusters: clusters[word.upper()] = cluster return clusters