class_docstr: Feature extractor for text classification relying on ngrams tfidf and
  optionally word cooccurrences features
class_docstr_tok: ["Feature","extractor","for","text","classification","relying","on","ngrams","tfidf","and","optionally","word","cooccurrences","features"]
code: def from_path(cls, path, **shared):
      path = Path(path)
  
      model_path = path / "featurizer.json"
      if not model_path.exists():
          raise LoadingError(
              "Missing featurizer model file: %s" % model_path.name
          )
      with model_path.open("r", encoding="utf-8") as f:
          featurizer_dict = json.load(f)
  
      featurizer_config = featurizer_dict["config"]
      featurizer = cls(featurizer_config, **shared)
  
      featurizer.language = featurizer_dict["language_code"]
  
      tfidf_vectorizer = featurizer_dict["tfidf_vectorizer"]
      if tfidf_vectorizer:
          vectorizer_path = path / featurizer_dict["tfidf_vectorizer"]
          tfidf_vectorizer = TfidfVectorizer.from_path(
              vectorizer_path, **shared
          )
      featurizer.tfidf_vectorizer = tfidf_vectorizer
  
      cooccurrence_vectorizer = featurizer_dict[
          "cooccurrence_vectorizer"
      ]
      if cooccurrence_vectorizer:
          vectorizer_path = (
              path / featurizer_dict["cooccurrence_vectorizer"]
          )
          cooccurrence_vectorizer = CooccurrenceVectorizer.from_path(
              vectorizer_path, **shared
          )
      featurizer.cooccurrence_vectorizer = cooccurrence_vectorizer
  
      return featurizer
  
imports: from __future__ import division, unicode_literals
  import json
  from builtins import str, zip
  from copy import deepcopy
  from pathlib import Path
  from future.utils import iteritems
  from snips_nlu.common.utils import json_string, fitted_required, replace_entities_with_placeholders, check_persisted_path
  from snips_nlu.constants import DATA, ENTITY, ENTITY_KIND, LANGUAGE, NGRAM, TEXT, ENTITIES
  from snips_nlu.dataset import get_text_from_chunks, validate_and_format_dataset
  from snips_nlu.entity_parser.builtin_entity_parser import is_builtin_entity
  from snips_nlu.exceptions import _EmptyDatasetUtterancesError, LoadingError
  from snips_nlu.languages import get_default_sep
  from snips_nlu.pipeline.configs import FeaturizerConfig
  from snips_nlu.pipeline.configs.intent_classifier import CooccurrenceVectorizerConfig, TfidfVectorizerConfig
  from snips_nlu.pipeline.processing_unit import ProcessingUnit
  from snips_nlu.preprocessing import stem, tokenize_light
  from snips_nlu.resources import get_stop_words, get_word_cluster
  from snips_nlu.slot_filler.features_utils import get_all_ngrams
label_desc: Use image node_id 10 for calling the Featurizer obj's underlying member method code with example usage: obj.from_path(cls, path) and returns: featurizer