Source code for autogluon.tabular.models.fasttext.fasttext_model
__all__ = ["FastTextModel"]
import contextlib
import gc
import logging
import os
import psutil
import tempfile
import numpy as np
import pandas as pd
from autogluon.common.features.types import S_TEXT
from autogluon.core.constants import BINARY, MULTICLASS
from autogluon.core.models import AbstractModel
from .hyperparameters.parameters import get_param_baseline
logger = logging.getLogger(__name__)
def try_import_fasttext():
try:
import fasttext
_ = fasttext.__file__
except Exception:
raise ImportError('Import fasttext failed. Please run "pip install fasttext"')
[docs]class FastTextModel(AbstractModel):
model_bin_file_name = "fasttext.ftz"
def __init__(self, **kwargs):
super().__init__(**kwargs)
self._load_model = None # Whether to load inner model when loading.
def _set_default_params(self):
default_params = get_param_baseline()
for param, val in default_params.items():
self._set_default_param_value(param, val)
# TODO: Investigate allowing categorical features as well
def _get_default_auxiliary_params(self) -> dict:
default_auxiliary_params = super()._get_default_auxiliary_params()
extra_auxiliary_params = dict(
get_features_kwargs=dict(
required_special_types=[S_TEXT],
)
)
default_auxiliary_params.update(extra_auxiliary_params)
return default_auxiliary_params
@classmethod
def _get_default_ag_args(cls) -> dict:
default_ag_args = super()._get_default_ag_args()
extra_ag_args = {'valid_stacker': False, 'problem_types': [BINARY, MULTICLASS]}
default_ag_args.update(extra_ag_args)
return default_ag_args
def _fit(self,
X,
y,
sample_weight=None,
**kwargs):
if self.problem_type not in (BINARY, MULTICLASS):
raise ValueError(
"FastText model only supports binary or multiclass classification"
)
try_import_fasttext()
import fasttext
params = self._get_model_params()
quantize_model = params.pop('quantize_model', True)
verbosity = kwargs.get('verbosity', 2)
if 'verbose' not in params:
if verbosity <= 2:
params['verbose'] = 0
elif verbosity == 3:
params['verbose'] = 1
else:
params['verbose'] = 2
if sample_weight is not None:
logger.log(15, "sample_weight not yet supported for FastTextModel, this model will ignore them in training.")
X = self.preprocess(X)
self._label_dtype = y.dtype
self._label_map = {label: f"__label__{i}" for i, label in enumerate(y.unique())}
self._label_inv_map = {v: k for k, v in self._label_map.items()}
np.random.seed(0)
idxs = np.random.permutation(list(range(len(X))))
with tempfile.NamedTemporaryFile(mode="w+t") as f:
logger.debug("generate training data")
for label, text in zip(y.iloc[idxs], (X[i] for i in idxs)):
f.write(f"{self._label_map[label]} {text}\n")
f.flush()
mem_start = psutil.Process().memory_info().rss
logger.debug("train FastText model")
self.model = fasttext.train_supervised(f.name, **params)
if quantize_model:
self.model.quantize(input=f.name, retrain=True)
gc.collect()
mem_curr = psutil.Process().memory_info().rss
self._model_size_estimate = max(mem_curr - mem_start, 100000000 if quantize_model else 800000000)
logger.debug("finish training FastText model")
# TODO: move logic to self._preprocess_nonadaptive()
# TODO: text features: alternate text preprocessing steps
# TODO: categorical features: special encoding: <feature name>_<feature value>
def _preprocess(self, X: pd.DataFrame, **kwargs) -> list:
X = super()._preprocess(X, **kwargs)
text_col = (
X
.astype(str)
.fillna(" ")
.apply(lambda r: " ".join(v for v in r.values), axis=1)
.str.lower()
.str.replace("<.*?>", " ") # remove html tags
# .str.replace('''(\\d[\\d,]*)(\\.\\d+)?''', ' __NUMBER__ ') # process numbers preserve dot
.str.replace("""([\\W])""", " \\1 ") # separate special characters
.str.replace("\\s", " ")
.str.replace("[ ]+", " ")
)
return text_col.to_list()
def predict(self, X: pd.DataFrame, **kwargs) -> np.ndarray:
X = self.preprocess(X, **kwargs)
pred_labels, pred_probs = self.model.predict(X)
y_pred = np.array(
[self._label_inv_map[labels[0]] for labels in pred_labels],
dtype=self._label_dtype,
)
return y_pred
def _predict_proba(self, X: pd.DataFrame, **kwargs) -> np.ndarray:
X = self.preprocess(X, **kwargs)
pred_labels, pred_probs = self.model.predict(X, k=len(self.model.labels))
recs = []
for labels, probs in zip(pred_labels, pred_probs):
recs.append(
dict(zip((self._label_inv_map[label] for label in labels), probs))
)
y_pred_proba: np.ndarray = pd.DataFrame(recs).sort_index(axis=1).values
return self._convert_proba_to_unified_form(y_pred_proba)
def save(self, path: str = None, verbose=True) -> str:
self._load_model = self.model is not None
# pickle model parts
__model = self.model
self.model = None
path = super().save(path=path, verbose=verbose)
self.model = __model
# save fasttext model: fasttext model cannot be pickled; saved it separately
# TODO: s3 support
if self._load_model:
fasttext_model_file_name = path + self.model_bin_file_name
self.model.save_model(fasttext_model_file_name)
self._load_model = None
return path
@classmethod
def load(cls, path: str, reset_paths=True, verbose=True):
model: FastTextModel = super().load(path=path, reset_paths=reset_paths, verbose=verbose)
# load binary fasttext model
if model._load_model:
try_import_fasttext()
import fasttext
fasttext_model_file_name = model.path + cls.model_bin_file_name
# TODO: hack to subpress a deprecation warning from fasttext
# remove it once offcial fasttext is updated beyond 0.9.2
# https://github.com/facebookresearch/fastText/issues/1067
with open(os.devnull, 'w') as f, contextlib.redirect_stderr(f):
model.model = fasttext.load_model(fasttext_model_file_name)
model._load_model = None
return model
def get_memory_size(self) -> int:
return self._model_size_estimate
def _more_tags(self):
# `can_refit_full=True` because validation data is not used and there is no form of early stopping implemented.
return {'can_refit_full': True}