"""The hashing module contains all methods and classes related to the hashing trick."""
import sys
import hashlib
import category_encoders.utils as util
import multiprocessing
import pandas as pd
import math
import platform
__author__ = 'willmcginnis', 'LiuShulun'
[docs]class HashingEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin):
""" 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 fetch_openml
>>> bunch = fetch_openml(name="house_prices", as_frame=True)
>>> display_cols = ["Id", "MSSubClass", "MSZoning", "LotFrontage", "YearBuilt", "Heating", "CentralAir"]
>>> X = pd.DataFrame(bunch.data, columns=bunch.feature_names)[display_cols]
>>> y = bunch.target
>>> he = HashingEncoder(cols=['CentralAir', 'Heating']).fit(X, y)
>>> numeric_dataset = he.transform(X)
>>> print(numeric_dataset.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 col_0 1460 non-null int64
1 col_1 1460 non-null int64
2 col_2 1460 non-null int64
3 col_3 1460 non-null int64
4 col_4 1460 non-null int64
5 col_5 1460 non-null int64
6 col_6 1460 non-null int64
7 col_7 1460 non-null int64
8 Id 1460 non-null float64
9 MSSubClass 1460 non-null float64
10 MSZoning 1460 non-null object
11 LotFrontage 1201 non-null float64
12 YearBuilt 1460 non-null float64
dtypes: float64(4), int64(8), object(1)
memory usage: 148.4+ 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
"""
prefit_ordinal = False
encoding_relation = util.EncodingRelation.ONE_TO_M
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'):
super().__init__(verbose=verbose, cols=cols, drop_invariant=drop_invariant, return_df=return_df,
handle_unknown="does not apply", handle_missing="does not apply")
if max_process not in range(1, 128):
if platform.system == 'Windows':
self.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.n_components = n_components
self.hash_method = hash_method
def _fit(self, X, y=None, **kwargs):
pass
def require_data(self, data_lock, new_start, done_index, hashing_parts, process_index):
is_finished = False
while not is_finished:
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)
part_index = int(math.ceil(end_index / self.max_sample))
hashing_parts.put({part_index: data_part})
is_finished = end_index >= self.data_lines
if self.verbose == 5:
print(f"Process - {process_index} done hashing data : {start_index} ~ {end_index}")
else:
data_lock.release()
is_finished = True
else:
data_lock.release()
def _transform(self, X):
"""
Call _transform_single_cpu() if you want to use single CPU with all samples
"""
self.X = X
self.data_lines = len(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_sample == 0:
self.max_sample = 1
if self.max_process == 1:
self.require_data(data_lock, new_start, done_index, hashing_parts, process_index=1)
else:
n_process = []
for thread_idx in range(self.max_process):
process = multiprocessing.Process(target=self.require_data,
args=(data_lock, new_start, done_index, hashing_parts, thread_idx + 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)
return data
def _transform_single_cpu(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(f'Unexpected input dimension {X.shape[1]}, expected {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:
X = X.drop(columns=self.invariant_cols)
if self.return_df or override_return_df:
return X
else:
return X.to_numpy()
[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.
"""
if hashing_method not in hashlib.algorithms_available:
raise ValueError(f"Hashing Method: {hashing_method} not Available. "
f"Please use one from: [{', '.join([str(x) for x in hashlib.algorithms_available])}]")
if make_copy:
X = X_in.copy(deep=True)
else:
X = X_in
if cols is None:
cols = X.columns
def hash_fn(x):
tmp = [0 for _ in range(N)]
for val in x.array:
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 tmp
new_cols = [f'col_{d}' for d in range(N)]
X_cat = X.loc[:, cols]
X_num = X.loc[:, [x for x in X.columns if x not in cols]]
X_cat = X_cat.apply(hash_fn, axis=1, result_type='expand')
X_cat.columns = new_cols
X = pd.concat([X_cat, X_num], axis=1)
return X