import string
import featuretools as ft
import nlp_primitives
from evalml.pipelines.components.transformers.preprocessing import (
LSA,
TextTransformer
)
from evalml.utils import infer_feature_types
[docs]class TextFeaturizer(TextTransformer):
"""Transformer that can automatically featurize text columns."""
name = "Text Featurization Component"
hyperparameter_ranges = {}
[docs] def __init__(self, random_seed=0, **kwargs):
"""Extracts features from text columns using featuretools' nlp_primitives
Arguments:
random_seed (int): Seed for the random number generator. Defaults to 0.
"""
self._trans = [nlp_primitives.DiversityScore,
nlp_primitives.MeanCharactersPerWord,
nlp_primitives.PolarityScore]
self._features = None
self._lsa = LSA(random_seed=random_seed)
self._primitives_provenance = {}
super().__init__(random_seed=random_seed,
**kwargs)
def _clean_text(self, X):
"""Remove all non-alphanum chars other than spaces, and make lowercase"""
def normalize(text):
text = text.translate(str.maketrans('', '', string.punctuation))
return text.lower()
for col_name in X.columns:
# we assume non-str values will have been filtered out prior to calling TextFeaturizer. casting to str is a safeguard.
col = X[col_name].astype(str)
X[col_name] = col.apply(normalize)
return X
def _make_entity_set(self, X, text_columns):
X_text = X[text_columns]
X_text = self._clean_text(X_text)
# featuretools expects str-type column names
X_text.rename(columns=str, inplace=True)
all_text_variable_types = {col_name: 'natural_language' for col_name in X_text.columns}
es = ft.EntitySet()
es.entity_from_dataframe(entity_id='X', dataframe=X_text, index='index', make_index=True,
variable_types=all_text_variable_types)
return es
[docs] def fit(self, X, y=None):
"""Fits component to data
Arguments:
X (pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features]
y (pd.Series, np.ndarray, optional): The target training data of length [n_samples]
Returns:
self
"""
X = infer_feature_types(X)
self._text_columns = self._get_text_columns(X)
if len(self._text_columns) == 0:
return self
self._lsa.fit(X)
es = self._make_entity_set(X, self._text_columns)
self._features = ft.dfs(entityset=es,
target_entity='X',
trans_primitives=self._trans,
max_depth=1,
features_only=True)
return self
@staticmethod
def _get_primitives_provenance(features):
provenance = {}
for feature in features:
input_col = feature.base_features[0].get_name()
# Return a copy because `get_feature_names` returns a reference to the names
output_features = [name for name in feature.get_feature_names()]
if input_col not in provenance:
provenance[input_col] = output_features
else:
provenance[input_col] += output_features
return provenance
[docs] def transform(self, X, y=None):
"""Transforms data X by creating new features using existing text columns
Arguments:
X (pd.DataFrame): The data to transform.
y (pd.Series, optional): Ignored.
Returns:
pd.DataFrame: Transformed X
"""
X_ww = infer_feature_types(X)
if self._features is None or len(self._features) == 0:
return X_ww
es = self._make_entity_set(X_ww, self._text_columns)
X_nlp_primitives = ft.calculate_feature_matrix(features=self._features, entityset=es)
if X_nlp_primitives.isnull().any().any():
X_nlp_primitives.fillna(0, inplace=True)
X_lsa = self._lsa.transform(X_ww[self._text_columns])
X_nlp_primitives.set_index(X_ww.index, inplace=True)
X_ww = X_ww.ww.drop(self._text_columns)
for col in X_nlp_primitives:
X_ww.ww[col] = X_nlp_primitives[col]
for col in X_lsa:
X_ww.ww[col] = X_lsa[col]
return X_ww
def _get_feature_provenance(self):
if not self._text_columns:
return {}
provenance = self._get_primitives_provenance(self._features)
for col, lsa_features in self._lsa._get_feature_provenance().items():
if col in provenance:
provenance[col] += lsa_features
return provenance