"""Character level TFIDF and Count followed by Truncated SVD on text columns""" from h2oaicore.transformer_utils import CustomTransformer import datatable as dt import numpy as np import string from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer from sklearn.decomposition import TruncatedSVD class TextCharTFIDFTransformer(CustomTransformer): _testing_can_skip_failure = False # ensure tested as if shouldn't fail def __init__(self, max_ngram, n_svd_comp, **kwargs): super().__init__(**kwargs) self.max_ngram = max_ngram self.n_svd_comp = n_svd_comp @staticmethod def do_acceptance_test(): return True @staticmethod def get_default_properties(): return dict(col_type="text", min_cols=1, max_cols=1, relative_importance=1) @staticmethod def get_parameter_choices(): return {"max_ngram": [3, 2, 1], "n_svd_comp": [50, 20, 100]} @property def display_name(self): return f"CharTFIDF_{self.max_ngram}maxgram_SVD_{self.n_svd_comp}comp" def fit_transform(self, X: dt.Frame, y: np.array = None): X = X.to_pandas().astype(str).iloc[:, 0].fillna("NA") # TFIDF Vectorizer self.tfidf_vec = TfidfVectorizer(analyzer="char", ngram_range=(1, self.max_ngram)) X = self.tfidf_vec.fit_transform(X) # Truncated SVD if len(self.tfidf_vec.vocabulary_) <= self.n_svd_comp: self.n_svd_comp = len(self.tfidf_vec.vocabulary_) - 1 self.truncated_svd = TruncatedSVD(n_components=self.n_svd_comp, random_state=2019) X = self.truncated_svd.fit_transform(X) return X def transform(self, X: dt.Frame): X = X.to_pandas().astype(str).iloc[:, 0].fillna("NA") X = self.tfidf_vec.transform(X) X = self.truncated_svd.transform(X) return X class TextCharCountTransformer(CustomTransformer): _testing_can_skip_failure = False # ensure tested as if shouldn't fail def __init__(self, max_ngram, n_svd_comp, **kwargs): super().__init__(**kwargs) self.max_ngram = max_ngram self.n_svd_comp = n_svd_comp @staticmethod def do_acceptance_test(): return True @staticmethod def get_default_properties(): return dict(col_type="text", min_cols=1, max_cols=1, relative_importance=1) @staticmethod def get_parameter_choices(): return {"max_ngram": [3, 2, 1], "n_svd_comp": [50, 20, 100]} @property def display_name(self): return f"CharCount_max{self.max_ngram}gram_SVD_{self.n_svd_comp}comp" def fit_transform(self, X: dt.Frame, y: np.array = None): X = X.to_pandas().astype(str).iloc[:, 0].fillna("NA") # Count Vectorizer self.cnt_vec = CountVectorizer(analyzer="char", ngram_range=(1, self.max_ngram)) X = self.cnt_vec.fit_transform(X) # Truncated SVD if len(self.cnt_vec.vocabulary_) <= self.n_svd_comp: self.n_svd_comp = len(self.cnt_vec.vocabulary_) - 1 self.truncated_svd = TruncatedSVD(n_components=self.n_svd_comp, random_state=2019) X = self.truncated_svd.fit_transform(X) return X def transform(self, X: dt.Frame): X = X.to_pandas().astype(str).iloc[:, 0].fillna("NA") X = self.cnt_vec.transform(X) X = self.truncated_svd.transform(X) return X