預處理必要性
在將數據放入到模型中訓練之前，數據通常是很髒的，可能存在缺失、數據類型不統一、存在異常值、需要標準化處理等。
一般來說，預處理包括數據填充、數據標準化、特徵編碼、數據離散化等步驟。特別注意，在這些工作之前，你需要了解你的數據集哪些特徵是數值或者分類變量，瞭解哪個特徵存在缺失。
數據：https://github.com/yushiyin/handson-ml/tree/master/datasets/housing
import os
import tarfile
from six.moves import urllib
import pandas as pd
housing =pd.read_csv("./housing.csv")
housing.head()
準備工作：
housing.info() ##變量類型
housing["ocean_proximity"].value_counts() ##分類變量頻數統計
####哪個樣本存在缺失
housing[housing.isnull().any(axis=1)].head()
####哪個位置存在缺失
housing.isnull()
####哪個特徵存在缺失
housing.isnull().any()
正式進入工作，注意下面的處理過程均是利用訓練集進行。
數據轉換模塊（dataframe----array）：
##輸入對應的屬性（數值或分類）index或者name
from sklearn.base import BaseEstimator, TransformerMixin
# Create a class to select numerical or categorical columns
# since Scikit-Learn doesn't handle DataFrames yet
class DataFrameSelector(BaseEstimator, TransformerMixin):
def __init__(self, attribute_names):
self.attribute_names = attribute_names
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.attribute_names].values
數據填充模塊：
##利用中位數填充
from sklearn.preprocessing import Imputer
imputer = Imputer(strategy="median") ##還有mean most_frequent
imputer.fit(housing_num)
imputer.statistics_
X=imputer.transform(housing_num) ##需要注意輸出的結果是一個數組
housing_tr = pd.DataFrame(X, columns=housing_num.columns,
index = list(housing.index.values))
合併特徵形成行的特徵：
from sklearn.base import BaseEstimator, TransformerMixin
# column index
rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6
class CombinedAttributesAdder(BaseEstimator,TransformerMixin):
def __init__(self, add_bedrooms_per_room = True): # no *args or **kargs
self.add_bedrooms_per_room = add_bedrooms_per_room
def fit(self, X, y=None):
return self # nothing else to do
def transform(self, X, y=None):
rooms_per_household = X[:, rooms_ix] / X[:, household_ix]
population_per_household = X[:, population_ix] / X[:, household_ix]
if self.add_bedrooms_per_room:
bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix]
return np.c_[X, rooms_per_household, population_per_household,
bedrooms_per_room]
else:
return np.c_[X, rooms_per_household, population_per_household]
attr_adder = CombinedAttributesAdder()
housing_extra_attribs = attr_adder.transform(X)
housing_extra_attribs.shape
數據標準化模塊：
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(housing_extra_attribs)
std_housing=scaler.transform(housing_extra_attribs)
數據編碼模塊：
###編譯爲整數值
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
housing_cat = housing["ocean_proximity"]
housing_cat_labelencoded = encoder.fit_transform(housing_cat)
housing_cat_labelencoded[0:10]
結果：array([0, 0, 4, 1, 0], dtype=int64)
####編譯爲熱值向量，最常用，需要注意的是需要先進行LabelEncoder再reshape
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
housing_cat_1hot = encoder.fit_transform(housing_cat_labelencoded.reshape(-1,1))
print(housing_cat_labelencoded[0:5])
print(housing_cat_labelencoded.reshape(-1,1)[0:5])
print(housing_cat_1hot.toarray()[0:5,])
結果：[0 0 4 1 0]
[[0]
[0]
[4]
[1]
[0]]
[[ 1. 0. 0. 0. 0.]
[ 1. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 1.]
[ 0. 1. 0. 0. 0.]
[ 1. 0. 0. 0. 0.]]
彙總一個編碼class,直接使用即可：
# Definition of the CategoricalEncoder class, copied from PR #9151.
# Just run this cell, or copy it to your code, do not try to understand it (yet).
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array
from sklearn.preprocessing import LabelEncoder
from scipy import sparse
class CategoricalEncoder(BaseEstimator, TransformerMixin):
"""Encode categorical features as a numeric array.
The input to this transformer should be a matrix of integers or strings,
denoting the values taken on by categorical (discrete) features.
The features can be encoded using a one-hot aka one-of-K scheme
(``encoding='onehot'``, the default) or converted to ordinal integers
(``encoding='ordinal'``).
This encoding is needed for feeding categorical data to many scikit-learn
estimators, notably linear models and SVMs with the standard kernels.
Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
Parameters
----------
encoding : str, 'onehot', 'onehot-dense' or 'ordinal'
The type of encoding to use (default is 'onehot'):
- 'onehot': encode the features using a one-hot aka one-of-K scheme
(or also called 'dummy' encoding). This creates a binary column for
each category and returns a sparse matrix.
- 'onehot-dense': the same as 'onehot' but returns a dense array
instead of a sparse matrix.
- 'ordinal': encode the features as ordinal integers. This results in
a single column of integers (0 to n_categories - 1) per feature.
categories : 'auto' or a list of lists/arrays of values.
Categories (unique values) per feature:
- 'auto' : Determine categories automatically from the training data.
- list : ``categories[i]`` holds the categories expected in the ith
column. The passed categories are sorted before encoding the data
(used categories can be found in the ``categories_`` attribute).
dtype : number type, default np.float64
Desired dtype of output.
handle_unknown : 'error' (default) or 'ignore'
Whether to raise an error or ignore if a unknown categorical feature is
present during transform (default is to raise). When this is parameter
is set to 'ignore' and an unknown category is encountered during
transform, the resulting one-hot encoded columns for this feature
will be all zeros.
Ignoring unknown categories is not supported for
``encoding='ordinal'``.
Attributes
----------
categories_ : list of arrays
The categories of each feature determined during fitting. When
categories were specified manually, this holds the sorted categories
(in order corresponding with output of `transform`).
Examples
--------
Given a dataset with three features and two samples, we let the encoder
find the maximum value per feature and transform the data to a binary
one-hot encoding.
>>> from sklearn.preprocessing import CategoricalEncoder
>>> enc = CategoricalEncoder(handle_unknown='ignore')
>>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])
... # doctest: +ELLIPSIS
CategoricalEncoder(categories='auto', dtype=<... 'numpy.float64'>,
encoding='onehot', handle_unknown='ignore')
>>> enc.transform([[0, 1, 1], [1, 0, 4]]).toarray()
array([[ 1., 0., 0., 1., 0., 0., 1., 0., 0.],
[ 0., 1., 1., 0., 0., 0., 0., 0., 0.]])
See also
--------
sklearn.preprocessing.OneHotEncoder : performs a one-hot encoding of
integer ordinal features. The ``OneHotEncoder assumes`` that input
features take on values in the range ``[0, max(feature)]`` instead of
using the unique values.
sklearn.feature_extraction.DictVectorizer : performs a one-hot encoding of
dictionary items (also handles string-valued features).
sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot
encoding of dictionary items or strings.
"""
def __init__(self, encoding='onehot', categories='auto', dtype=np.float64,
handle_unknown='error'):
self.encoding = encoding
self.categories = categories
self.dtype = dtype
self.handle_unknown = handle_unknown
def fit(self, X, y=None):
"""Fit the CategoricalEncoder to X.
Parameters
----------
X : array-like, shape [n_samples, n_feature]
The data to determine the categories of each feature.
Returns
-------
self
"""
if self.encoding not in ['onehot', 'onehot-dense', 'ordinal']:
template = ("encoding should be either 'onehot', 'onehot-dense' "
"or 'ordinal', got %s")
raise ValueError(template % self.handle_unknown)
if self.handle_unknown not in ['error', 'ignore']:
template = ("handle_unknown should be either 'error' or "
"'ignore', got %s")
raise ValueError(template % self.handle_unknown)
if self.encoding == 'ordinal' and self.handle_unknown == 'ignore':
raise ValueError("handle_unknown='ignore' is not supported for"
" encoding='ordinal'")
X = check_array(X, dtype=np.object, accept_sparse='csc', copy=True)
n_samples, n_features = X.shape
self._label_encoders_ = [LabelEncoder() for _ in range(n_features)]
for i in range(n_features):
le = self._label_encoders_[i]
Xi = X[:, i]
if self.categories == 'auto':
le.fit(Xi)
else:
valid_mask = np.in1d(Xi, self.categories[i])
if not np.all(valid_mask):
if self.handle_unknown == 'error':
diff = np.unique(Xi[~valid_mask])
msg = ("Found unknown categories {0} in column {1}"
" during fit".format(diff, i))
raise ValueError(msg)
le.classes_ = np.array(np.sort(self.categories[i]))
self.categories_ = [le.classes_ for le in self._label_encoders_]
return self
def transform(self, X):
"""Transform X using one-hot encoding.
Parameters
----------
X : array-like, shape [n_samples, n_features]
The data to encode.
Returns
-------
X_out : sparse matrix or a 2-d array
Transformed input.
"""
X = check_array(X, accept_sparse='csc', dtype=np.object, copy=True)
n_samples, n_features = X.shape
X_int = np.zeros_like(X, dtype=np.int)
X_mask = np.ones_like(X, dtype=np.bool)
for i in range(n_features):
valid_mask = np.in1d(X[:, i], self.categories_[i])
if not np.all(valid_mask):
if self.handle_unknown == 'error':
diff = np.unique(X[~valid_mask, i])
msg = ("Found unknown categories {0} in column {1}"
" during transform".format(diff, i))
raise ValueError(msg)
else:
# Set the problematic rows to an acceptable value and
# continue `The rows are marked `X_mask` and will be
# removed later.
X_mask[:, i] = valid_mask
X[:, i][~valid_mask] = self.categories_[i][0]
X_int[:, i] = self._label_encoders_[i].transform(X[:, i])
if self.encoding == 'ordinal':
return X_int.astype(self.dtype, copy=False)
mask = X_mask.ravel()
n_values = [cats.shape[0] for cats in self.categories_]
n_values = np.array([0] + n_values)
indices = np.cumsum(n_values)
column_indices = (X_int + indices[:-1]).ravel()[mask]
row_indices = np.repeat(np.arange(n_samples, dtype=np.int32),
n_features)[mask]
data = np.ones(n_samples * n_features)[mask]
out = sparse.csc_matrix((data, (row_indices, column_indices)),
shape=(n_samples, indices[-1]),
dtype=self.dtype).tocsr()
if self.encoding == 'onehot-dense':
return out.toarray()
else:
return out
使用該class,如下：
#from sklearn.preprocessing import CategoricalEncoder # in future versions of Scikit-Learn
cat_encoder = CategoricalEncoder(encoding="onehot-dense")
housing_cat_reshaped = housing_cat.values.reshape(-1, 1)
housing_cat_1hot = cat_encoder.fit_transform(housing_cat_reshaped)
print(housing_cat_1hot[0:5,])
print(housing_cat_reshaped[0:5,])
print(housing_cat.values[0:5])
結果：[[ 1. 0. 0. 0. 0.]
[ 1. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 1.]
[ 0. 1. 0. 0. 0.]
[ 1. 0. 0. 0. 0.]]
[['<1H OCEAN']
['<1H OCEAN']
['NEAR OCEAN']
['INLAND']
['<1H OCEAN']]
['<1H OCEAN' '<1H OCEAN' 'NEAR OCEAN' 'INLAND' '<1H OCEAN']
經過上面的定義一些class或者sklearn中含有的class,我們可以利用一個pipeline整合所有的這些步驟：
num_attribs = list(housing_num) #數值變量name
cat_attribs = ["ocean_proximity"] #分類變量name
from sklearn.pipeline import Pipeline
num_pipeline = Pipeline([
('selector', DataFrameSelector(num_attribs)),
('imputer', Imputer(strategy="median")),
('attribs_adder', CombinedAttributesAdder()),
('std_scaler', StandardScaler()),
])
cat_pipeline = Pipeline([
('selector', DataFrameSelector(cat_attribs)),
('cat_encoder', CategoricalEncoder(encoding="onehot-dense")),
])
#num_pipeline:所有的數值變量的預處理過程
#cat_pipeline:所有的分類變量的預處理過程
但是我們需要將這個數據合併起來，才能得到所有預處理數據，Scikit-Learn提供了一個類FeatureUnion實現這個功能。你給它一列轉換量（可以是所有的轉換量），當調用它的transform()方法，每個轉換量的transform()會被並行執行，等待輸出，然後將輸出合併起來，並返回結果。
from sklearn.pipeline import FeatureUnion
full_pipeline = FeatureUnion(transformer_list=[
("num_pipeline", num_pipeline),
("cat_pipeline", cat_pipeline),
])
對整個測試集進行預處理爲：
housing_prepared = full_pipeline.fit_transform(housing)
housing_prepared
延伸學習：
這裏面需要理解pipeline的工作原理以及BaseEstimator, TransformerMixin的作用。
參考：
1、https://blog.csdn.net/weixin_33845477/article/details/87116104
2、http://sklearn.apachecn.org/#/docs/57