Source code for category_encoders.hashing

"""The hashing module contains all methods and classes related to the hashing trick."""

import sys
import hashlib
from sklearn.base import BaseEstimator, TransformerMixin
import category_encoders.utils as util
import multiprocessing
import pandas as pd
import math
import platform

__author__ = 'willmcginnis', 'LiuShulun'


[docs]class HashingEncoder(BaseEstimator, TransformerMixin): """ A multivariate hashing implementation with configurable dimensionality/precision. The advantage of this encoder is that it does not maintain a dictionary of observed categories. Consequently, the encoder does not grow in size and accepts new values during data scoring by design. It's important to read about how max_process & max_sample work before setting them manually, inappropriate setting slows down encoding. Default value of 'max_process' is 1 on Windows because multiprocessing might cause issues, see in : https://github.com/scikit-learn-contrib/categorical-encoding/issues/215 https://docs.python.org/2/library/multiprocessing.html?highlight=process#windows Parameters ---------- verbose: int integer indicating verbosity of the output. 0 for none. cols: list a list of columns to encode, if None, all string columns will be encoded. drop_invariant: bool boolean for whether or not to drop columns with 0 variance. return_df: bool boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array). hash_method: str which hashing method to use. Any method from hashlib works. max_process: int how many processes to use in transform(). Limited in range(1, 64). By default, it uses half of the logical CPUs. For example, 4C4T makes max_process=2, 4C8T makes max_process=4. Set it larger if you have a strong CPU. It is not recommended to set it larger than is the count of the logical CPUs as it will actually slow down the encoding. max_sample: int how many samples to encode by each process at a time. This setting is useful on low memory machines. By default, max_sample=(all samples num)/(max_process). For example, 4C8T CPU with 100,000 samples makes max_sample=25,000, 6C12T CPU with 100,000 samples makes max_sample=16,666. It is not recommended to set it larger than the default value. n_components: int how many bits to use to represent the feature. By default we use 8 bits. For high-cardinality features, consider using up-to 32 bits. Example ------- >>> from category_encoders.hashing import HashingEncoder >>> import pandas as pd >>> from sklearn.datasets import load_boston >>> bunch = load_boston() >>> X = pd.DataFrame(bunch.data, columns=bunch.feature_names) >>> y = bunch.target >>> he = HashingEncoder(cols=['CHAS', 'RAD']).fit(X, y) >>> data = he.transform(X) >>> print(data.info()) <class 'pandas.core.frame.DataFrame'> RangeIndex: 506 entries, 0 to 505 Data columns (total 19 columns): col_0 506 non-null int64 col_1 506 non-null int64 col_2 506 non-null int64 col_3 506 non-null int64 col_4 506 non-null int64 col_5 506 non-null int64 col_6 506 non-null int64 col_7 506 non-null int64 CRIM 506 non-null float64 ZN 506 non-null float64 INDUS 506 non-null float64 NOX 506 non-null float64 RM 506 non-null float64 AGE 506 non-null float64 DIS 506 non-null float64 TAX 506 non-null float64 PTRATIO 506 non-null float64 B 506 non-null float64 LSTAT 506 non-null float64 dtypes: float64(11), int64(8) memory usage: 75.2 KB None References ---------- .. [1] Feature Hashing for Large Scale Multitask Learning, from https://alex.smola.org/papers/2009/Weinbergeretal09.pdf .. [2] Don't be tricked by the Hashing Trick, from https://booking.ai/dont-be-tricked-by-the-hashing-trick-192a6aae3087 """ def __init__(self, max_process=0, max_sample=0, verbose=0, n_components=8, cols=None, drop_invariant=False, return_df=True, hash_method='md5'): if max_process not in range(1, 128): if platform.system == 'Windows': max_process = 1 else: self.max_process = int(math.ceil(multiprocessing.cpu_count() / 2)) if self.max_process < 1: self.max_process = 1 elif self.max_process > 128: self.max_process = 128 else: self.max_process = max_process self.max_sample = int(max_sample) self.auto_sample = max_sample <= 0 self.data_lines = 0 self.X = None self.return_df = return_df self.drop_invariant = drop_invariant self.drop_cols = [] self.verbose = verbose self.n_components = n_components self.cols = cols self.hash_method = hash_method self._dim = None self.feature_names = None
[docs] def fit(self, X, y=None, **kwargs): """Fit encoder according to X and y. Parameters ---------- X : array-like, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. y : array-like, shape = [n_samples] Target values. Returns ------- self : encoder Returns self. """ # first check the type X = util.convert_input(X) self._dim = X.shape[1] # if columns aren't passed, just use every string column if self.cols is None: self.cols = util.get_obj_cols(X) else: self.cols = util.convert_cols_to_list(self.cols) X_temp = self.transform(X, override_return_df=True) self.feature_names = X_temp.columns.tolist() # drop all output columns with 0 variance. if self.drop_invariant: self.drop_cols = [] generated_cols = util.get_generated_cols(X, X_temp, self.cols) self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5] try: [self.feature_names.remove(x) for x in self.drop_cols] except KeyError as e: if self.verbose > 0: print("Could not remove column from feature names." "Not found in generated cols.\n{}".format(e)) return self
@staticmethod def require_data(self, data_lock, new_start, done_index, hashing_parts, cols, process_index): if data_lock.acquire(): if new_start.value: end_index = 0 new_start.value = False else: end_index = done_index.value if all([self.data_lines > 0, end_index < self.data_lines]): start_index = end_index if (self.data_lines - end_index) <= self.max_sample: end_index = self.data_lines else: end_index += self.max_sample done_index.value = end_index data_lock.release() data_part = self.X.iloc[start_index: end_index] # Always get df and check it after merge all data parts data_part = self.hashing_trick(X_in=data_part, hashing_method=self.hash_method, N=self.n_components, cols=self.cols) if self.drop_invariant: for col in self.drop_cols: data_part.drop(col, 1, inplace=True) part_index = int(math.ceil(end_index / self.max_sample)) hashing_parts.put({part_index: data_part}) if self.verbose == 5: print("Process - " + str(process_index), "done hashing data : " + str(start_index) + "~" + str(end_index)) if end_index < self.data_lines: self.require_data(self, data_lock, new_start, done_index, hashing_parts, cols=cols, process_index=process_index) else: data_lock.release() else: data_lock.release()
[docs] def transform(self, X, override_return_df=False): """ Call _transform() if you want to use single CPU with all samples """ if self._dim is None: raise ValueError('Must train encoder before it can be used to transform data.') # first check the type self.X = util.convert_input(X) self.data_lines = len(self.X) # then make sure that it is the right size if self.X.shape[1] != self._dim: raise ValueError('Unexpected input dimension %d, expected %d' % (self.X.shape[1], self._dim, )) if not list(self.cols): return self.X data_lock = multiprocessing.Manager().Lock() new_start = multiprocessing.Manager().Value('d', True) done_index = multiprocessing.Manager().Value('d', int(0)) hashing_parts = multiprocessing.Manager().Queue() if self.auto_sample: self.max_sample = int(self.data_lines / self.max_process) if self.max_process == 1: self.require_data(self, data_lock, new_start, done_index, hashing_parts, cols=self.cols, process_index=1) else: n_process = [] for thread_index in range(self.max_process): process = multiprocessing.Process(target=self.require_data, args=(self, data_lock, new_start, done_index, hashing_parts, self.cols, thread_index + 1)) process.daemon = True n_process.append(process) for process in n_process: process.start() for process in n_process: process.join() data = self.X if self.max_sample == 0 or self.max_sample == self.data_lines: if hashing_parts: data = list(hashing_parts.get().values())[0] else: list_data = {} while not hashing_parts.empty(): list_data.update(hashing_parts.get()) sort_data = [] for part_index in sorted(list_data): sort_data.append(list_data[part_index]) if sort_data: data = pd.concat(sort_data) # Check if is_return_df if self.return_df or override_return_df: return data else: return data.values
def _transform(self, X, override_return_df=False): """Perform the transformation to new categorical data. Parameters ---------- X : array-like, shape = [n_samples, n_features] Returns ------- p : array, shape = [n_samples, n_numeric + N] Transformed values with encoding applied. """ if self._dim is None: raise ValueError('Must train encoder before it can be used to transform data.') # first check the type X = util.convert_input(X) # then make sure that it is the right size if X.shape[1] != self._dim: raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim, )) if not list(self.cols): return X X = self.hashing_trick(X, hashing_method=self.hash_method, N=self.n_components, cols=self.cols) if self.drop_invariant: for col in self.drop_cols: X.drop(col, 1, inplace=True) if self.return_df or override_return_df: return X else: return X.values
[docs] @staticmethod def hashing_trick(X_in, hashing_method='md5', N=2, cols=None, make_copy=False): """A basic hashing implementation with configurable dimensionality/precision Performs the hashing trick on a pandas dataframe, `X`, using the hashing method from hashlib identified by `hashing_method`. The number of output dimensions (`N`), and columns to hash (`cols`) are also configurable. Parameters ---------- X_in: pandas dataframe description text hashing_method: string, optional description text N: int, optional description text cols: list, optional description text make_copy: bool, optional description text Returns ------- out : dataframe A hashing encoded dataframe. References ---------- Cite the relevant literature, e.g. [1]_. You may also cite these references in the notes section above. .. [1] Kilian Weinberger; Anirban Dasgupta; John Langford; Alex Smola; Josh Attenberg (2009). Feature Hashing for Large Scale Multitask Learning. Proc. ICML. """ try: if hashing_method not in hashlib.algorithms_available: raise ValueError('Hashing Method: %s Not Available. Please use one from: [%s]' % ( hashing_method, ', '.join([str(x) for x in hashlib.algorithms_available]) )) except Exception as e: try: _ = hashlib.new(hashing_method) except Exception as e: raise ValueError('Hashing Method: %s Not Found.') if make_copy: X = X_in.copy(deep=True) else: X = X_in if cols is None: cols = X.columns.values def hash_fn(x): tmp = [0 for _ in range(N)] for val in x.values: if val is not None: hasher = hashlib.new(hashing_method) if sys.version_info[0] == 2: hasher.update(str(val)) else: hasher.update(bytes(str(val), 'utf-8')) tmp[int(hasher.hexdigest(), 16) % N] += 1 return pd.Series(tmp, index=new_cols) new_cols = ['col_%d' % d for d in range(N)] X_cat = X.loc[:, cols] X_num = X.loc[:, [x for x in X.columns.values if x not in cols]] X_cat = X_cat.apply(hash_fn, axis=1) X_cat.columns = new_cols X = pd.concat([X_cat, X_num], axis=1) return X
[docs] def get_feature_names(self): """ Returns the names of all transformed / added columns. Returns ------- feature_names: list A list with all feature names transformed or added. Note: potentially dropped features are not included! """ if not isinstance(self.feature_names, list): raise ValueError('Must fit data first. Affected feature names are not known before.') else: return self.feature_names