import logging import pandas as pd from featuretools.utils.gen_utils import make_tqdm_iterator from featuretools.variable_types.variable import Discrete logger = logging.getLogger('featuretools') DEFAULT_TOP_N = 10 [docs]def encode_features(feature_matrix, features, top_n=DEFAULT_TOP_N, include_unknown=True, to_encode=None, inplace=False, drop_first=False, verbose=False): """Encode categorical features Args: feature_matrix (pd.DataFrame): Dataframe of features. features (list[PrimitiveBase]): Feature definitions in feature_matrix. top_n (int or dict[string -> int]): Number of top values to include. If dict[string -> int] is used, key is feature name and value is the number of top values to include for that feature. If a feature's name is not in dictionary, a default value of 10 is used. include_unknown (pd.DataFrame): Add feature encoding an unknown class. defaults to True to_encode (list[str]): List of feature names to encode. features not in this list are unencoded in the output matrix defaults to encode all necessary features. inplace (bool): Encode feature_matrix in place. Defaults to False. drop_first (bool): Whether to get k-1 dummies out of k categorical levels by removing the first level. defaults to False verbose (str): Print progress info. Returns: (pd.Dataframe, list) : encoded feature_matrix, encoded features Example: .. ipython:: python :suppress: from featuretools.tests.testing_utils import make_ecommerce_entityset import featuretools as ft es = make_ecommerce_entityset() .. ipython:: python f1 = ft.Feature(es["log"]["product_id"]) f2 = ft.Feature(es["log"]["purchased"]) f3 = ft.Feature(es["log"]["value"]) features = [f1, f2, f3] ids = [0, 1, 2, 3, 4, 5] feature_matrix = ft.calculate_feature_matrix(features, es, instance_ids=ids) fm_encoded, f_encoded = ft.encode_features(feature_matrix, features) f_encoded fm_encoded, f_encoded = ft.encode_features(feature_matrix, features, top_n=2) f_encoded fm_encoded, f_encoded = ft.encode_features(feature_matrix, features, include_unknown=False) f_encoded fm_encoded, f_encoded = ft.encode_features(feature_matrix, features, to_encode=['purchased']) f_encoded fm_encoded, f_encoded = ft.encode_features(feature_matrix, features, drop_first=True) f_encoded """ if not isinstance(feature_matrix, pd.DataFrame): msg = "feature_matrix must be a Pandas DataFrame" raise TypeError(msg) if inplace: X = feature_matrix else: X = feature_matrix.copy() old_feature_names = set() for feature in features: for fname in feature.get_feature_names(): assert fname in X.columns, ("Feature %s not found in feature matrix" % (fname)) old_feature_names.add(fname) pass_through = [col for col in X.columns if col not in old_feature_names] if verbose: iterator = make_tqdm_iterator(iterable=features, total=len(features), desc="Encoding pass 1", unit="feature") else: iterator = features new_feature_list = [] new_columns = [] encoded_columns = set() for f in iterator: # TODO: features with multiple columns are not encoded by this method, # which can cause an "encoded" matrix with non-numeric vlaues is_discrete = issubclass(f.variable_type, Discrete) if (f.number_output_features > 1 or not is_discrete): if f.number_output_features > 1: logger.warning("Feature %s has multiple columns and will not " "be encoded. This may result in a matrix with" " non-numeric values." % (f)) new_feature_list.append(f) new_columns.extend(f.get_feature_names()) continue if to_encode is not None and f.get_name() not in to_encode: new_feature_list.append(f) new_columns.extend(f.get_feature_names()) continue val_counts = X[f.get_name()].value_counts().to_frame() index_name = val_counts.index.name if index_name is None: if 'index' in val_counts.columns: index_name = 'level_0' else: index_name = 'index' val_counts.reset_index(inplace=True) val_counts = val_counts.sort_values([f.get_name(), index_name], ascending=False) val_counts.set_index(index_name, inplace=True) select_n = top_n if isinstance(top_n, dict): select_n = top_n.get(f.get_name(), DEFAULT_TOP_N) if drop_first: select_n = min(len(val_counts), top_n) select_n = max(select_n - 1, 1) unique = val_counts.head(select_n).index.tolist() for label in unique: add = f == label add_name = add.get_name() new_feature_list.append(add) new_columns.append(add_name) encoded_columns.add(add_name) X[add_name] = (X[f.get_name()] == label) if include_unknown: unknown = f.isin(unique).NOT().rename(f.get_name() + " is unknown") unknown_name = unknown.get_name() new_feature_list.append(unknown) new_columns.append(unknown_name) encoded_columns.add(unknown_name) X[unknown_name] = (~X[f.get_name()].isin(unique)) X.drop(f.get_name(), axis=1, inplace=True) new_columns.extend(pass_through) new_X = X[new_columns] iterator = new_X.columns if verbose: iterator = make_tqdm_iterator(iterable=new_X.columns, total=len(new_X.columns), desc="Encoding pass 2", unit="feature") for c in iterator: if c in encoded_columns: try: new_X[c] = pd.to_numeric(new_X[c], errors='raise') except (TypeError, ValueError): pass return new_X, new_feature_list