An introduction to machine learning with scikit learn

对于scikit机器学习包的介绍

from sklearn import datasets

iris = datasets.load_iris()
digits = datasets.load_digits()

len(digits.data)

1797

#digits.target返回标准答案

digits.target

array([0, 1, 2, ..., 8, 9, 8])

digits.images[0]

array([[  0.,   0.,   5.,  13.,   9.,   1.,   0.,   0.],
       [  0.,   0.,  13.,  15.,  10.,  15.,   5.,   0.],
       [  0.,   3.,  15.,   2.,   0.,  11.,   8.,   0.],
       [  0.,   4.,  12.,   0.,   0.,   8.,   8.,   0.],
       [  0.,   5.,   8.,   0.,   0.,   9.,   8.,   0.],
       [  0.,   4.,  11.,   0.,   1.,  12.,   7.,   0.],
       [  0.,   2.,  14.,   5.,  10.,  12.,   0.,   0.],
       [  0.,   0.,   6.,  13.,  10.,   0.,   0.,   0.]])

from sklearn import svm
clf = svm.SVC(gamma=0.001, C=100.)

clf.fit(digits.data[:-1],digits.target[:-1])

SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

clf.predict(digits.data[-1:])

array([8])

• resolution 分辨率
• Model persistence 模型的持久性
• tradeoff 权衡，折中
• compatibility 兼容性
• intuitive format 直观的格式

#save a model by using pickle
X,y = iris.data, iris.target
clf.fit(X,y)

SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma=0.001, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

import pickle
s = pickle.dumps(clf)   #dump保存，load提取
clf2 = pickle.loads(s)
clf2.predict(X[0:2])

array([0, 0])

#joblib is a replacement of pickle ,which is more efficient on big data
from sklearn.externals import joblib
joblib.dump(clf, 'filename.pkl')

['filename.pkl']

clf3 = joblib.load('filename.pkl')

• scikit-learn estimators follow certain rules to make their behavior more predictive

import numpy as np
from sklearn import random_projection
rng = np.random.RandomState(0)
X = rng.rand(10,2000)

transformer = random_projection.GaussianRandomProjection()
X_new = transformer.fit_transform(X)

X_new.dtype

dtype('float64')

from sklearn import datasets
from sklearn.svm import SVC

iris = datasets.load_iris()
clf = SVC()

iris.target

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

iris.target_names[iris.target]

array(['setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',
       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',
       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',
       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',
       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',
       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',
       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',
       'setosa', 'setosa', 'setosa', 'setosa', 'setosa', 'setosa',
       'setosa', 'setosa', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'versicolor',
       'versicolor', 'versicolor', 'versicolor', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica', 'virginica', 'virginica',
       'virginica', 'virginica', 'virginica'],
      dtype='<U10')

clf.fit(iris.data, iris.target_names[iris.target])

SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)

list(clf.predict(iris.data[:4]))

['setosa', 'setosa', 'setosa', 'setosa']

y = rng.binomial(1,0.5,100)

X_test = rng.rand(5, 10)
X_test

array([[ 0.90634699,  0.25862333,  0.26697948,  0.44371904,  0.18914987,
         0.92641785,  0.68959926,  0.67884483,  0.91684226,  0.1321726 ],
       [ 0.260265  ,  0.66655583,  0.68819277,  0.83369686,  0.72763321,
         0.04155902,  0.62358162,  0.78528496,  0.86857947,  0.87180114],
       [ 0.19402778,  0.24495876,  0.60318975,  0.46663814,  0.35972139,
         0.96526746,  0.21472008,  0.85108497,  0.60539375,  0.69539753],
       [ 0.77387248,  0.47114697,  0.97450663,  0.48041762,  0.28264389,
         0.90621786,  0.48301114,  0.92848812,  0.53099247,  0.22652895],
       [ 0.34390456,  0.98208334,  0.21006167,  0.36245154,  0.28843301,
         0.71599893,  0.231713  ,  0.83174693,  0.95860171,  0.41649143]])

• multiclass VS multilabel fitting

from sklearn.multiclass import OneVsRestClassifier
from sklearn.preprocessing import LabelBinarizer

X = [[1,2],[2,4],[4,5],[3,2],[3,1]]
y = [0,0,1,1,2]
classif = OneVsRestClassifier(estimator=SVC(random_state=0))
classif.fit(X, y).predict(X)

array([0, 0, 1, 1, 2])

y = LabelBinarizer().fit_transform(y)  #改变y 的格式
y

array([[1, 0, 0],
       [1, 0, 0],
       [0, 1, 0],
       [0, 1, 0],
       [0, 0, 1]])

classif.fit(X,y).predict(X)

array([[1, 0, 0],
       [1, 0, 0],
       [0, 1, 0],
       [0, 0, 0],
       [0, 0, 0]])