特征预处理
什么是特征预处理
通过一些转换函数，将特征数据转换成更适合算法模型的特征数据的过程
数值数据的无量纲化：
- 归一化
- 标准化
特征预处理API
sklearn.preprocessing
为什么进行无量纲化
通过欧式距离公式计算两个约会对象是否属于同一类别
影响：
通过图中的案例我们发现，由于公升数和消耗时间比的特征数太小导致对结果几乎没有影响，结果主要受里程数的影响，但是统计的人觉得这三个特征同等重要，如果这样进行计算那么结果就不准确。
原因：
特征的单位或者大小相差较大，或者某特征的方差相比其他的特征要大出几个数量级，容易影响(支配) 目标结果，使得一些算法无法学习到其它的特征。
测试数据：
milage,Liters,Consumtime,target
40920,8.326976,0.953952,3
14488,7.153469,1.673904,2
26052,1.441871,0.805124,1
75136,13.147394,0.428964,1
38344,1.669788,0.134296,1
72993,10.141740,1.032955,1
35948,6.830792,1.213192,3
42666,13.276369,0.543880,3
67497,8.631577,0.749278,1
35483,12.273169,1.508053,3
50242,3.723498,0.831917,1
63275,8.385879,1.669485,1
5569,4.875435,0.728658,2
51052,4.680098,0.625224,1
77372,15.299570,0.331351,1
43673,1.889461,0.191283,1
61364,7.516754,1.269164,1
69673,14.239195,0.261333,1
15669,0.000000,1.250185,2
28488,10.528555,1.304844,3
6487,3.540265,0.822483,2
37708,2.991551,0.833920,1
22620,5.297865,0.638306,2
28782,6.593803,0.187108,3
19739,2.816760,1.686209,2
36788,12.458258,0.649617,3
5741,0.000000,1.656418,2
28567,9.968648,0.731232,3
6808,1.364838,0.640103,2
41611,0.230453,1.151996,1
36661,11.865402,0.882810,3
43605,0.120460,1.352013,1
15360,8.545204,1.340429,3
63796,5.856649,0.160006,1
10743,9.665618,0.778626,2
70808,9.778763,1.084103,1
72011,4.932976,0.632026,1
5914,2.216246,0.587095,2
14851,14.305636,0.632317,3
33553,12.591889,0.686581,3
44952,3.424649,1.004504,1
17934,0.000000,0.147573,2
27738,8.533823,0.205324,3
29290,9.829528,0.238620,3
42330,11.492186,0.263499,3
36429,3.570968,0.832254,1
39623,1.771228,0.207612,1
32404,3.513921,0.991854,1
27268,4.398172,0.975024,1
5477,4.276823,1.174874,2
14254,5.946014,1.614244,2
68613,13.798970,0.724375,1
41539,10.393591,1.663724,3
7917,3.007577,0.297302,2
21331,1.031938,0.486174,2
8338,4.751212,0.064693,2
5176,3.692269,1.655113,2
18983,10.448091,0.267652,3
68837,10.585786,0.329557,1
13438,1.604501,0.069064,2
48849,3.679497,0.961466,1
12285,3.795146,0.696694,2
7826,2.531885,1.659173,2
5565,9.733340,0.977746,2
10346,6.093067,1.413798,2
1823,7.712960,1.054927,2
9744,11.470364,0.760461,3
16857,2.886529,0.934416,2
39336,10.054373,1.138351,3
65230,9.972470,0.881876,1
2463,2.335785,1.366145,2
27353,11.375155,1.528626,3
16191,0.000000,0.605619,2
12258,4.126787,0.357501,2
42377,6.319522,1.058602,1
25607,8.680527,0.086955,3
77450,14.856391,1.129823,1
58732,2.454285,0.222380,1
46426,7.292202,0.548607,3
32688,8.745137,0.857348,3
64890,8.579001,0.683048,1
8554,2.507302,0.869177,2
28861,11.415476,1.505466,3
42050,4.838540,1.680892,1
32193,10.339507,0.583646,3
64895,6.573742,1.151433,1
2355,6.539397,0.462065,2
0,2.209159,0.723567,2
70406,11.196378,0.836326,1
57399,4.229595,0.128253,1
41732,9.505944,0.005273,3
11429,8.652725,1.348934,3
75270,17.101108,0.490712,1
5459,7.871839,0.717662,2
73520,8.262131,1.361646,1
40279,9.015635,1.658555,3
21540,9.215351,0.806762,3
17694,6.375007,0.033678,2
22329,2.262014,1.022169,1
46570,5.677110,0.709469,1
42403,11.293017,0.207976,3
33654,6.590043,1.353117,1
9171,4.711960,0.194167,2
28122,8.768099,1.108041,3
34095,11.502519,0.545097,3
1774,4.682812,0.578112,2
40131,12.446578,0.300754,3
13994,12.908384,1.657722,3
77064,12.601108,0.974527,1
11210,3.929456,0.025466,2
6122,9.751503,1.182050,3
15341,3.043767,0.888168,2
44373,4.391522,0.807100,1
28454,11.695276,0.679015,3
63771,7.879742,0.154263,1
9217,5.613163,0.933632,2
69076,9.140172,0.851300,1
24489,4.258644,0.206892,1
16871,6.799831,1.221171,2
39776,8.752758,0.484418,3
5901,1.123033,1.180352,2
40987,10.833248,1.585426,3
7479,3.051618,0.026781,2
38768,5.308409,0.030683,3
4933,1.841792,0.028099,2
32311,2.261978,1.605603,1
26501,11.573696,1.061347,3
37433,8.038764,1.083910,3
23503,10.734007,0.103715,3
68607,9.661909,0.350772,1
27742,9.005850,0.548737,3
11303,0.000000,0.539131,2
0,5.757140,1.062373,2
32729,9.164656,1.624565,3
24619,1.318340,1.436243,1
42414,14.075597,0.695934,3
20210,10.107550,1.308398,3
33225,7.960293,1.219760,3
54483,6.317292,0.018209,1
18475,12.664194,0.595653,3
33926,2.906644,0.581657,1
43865,2.388241,0.913938,1
26547,6.024471,0.486215,3
44404,7.226764,1.255329,3
16674,4.183997,1.275290,2
8123,11.850211,1.096981,3
42747,11.661797,1.167935,3
56054,3.574967,0.494666,1
10933,0.000000,0.107475,2
18121,7.937657,0.904799,3
11272,3.365027,1.014085,2
16297,0.000000,0.367491,2
28168,13.860672,1.293270,3
40963,10.306714,1.211594,3
31685,7.228002,0.670670,3
55164,4.508740,1.036192,1
17595,0.366328,0.163652,2
1862,3.299444,0.575152,2
57087,0.573287,0.607915,1
63082,9.183738,0.012280,1
51213,7.842646,1.060636,3
6487,4.750964,0.558240,2
4805,11.438702,1.556334,3
30302,8.243063,1.122768,3
68680,7.949017,0.271865,1
17591,7.875477,0.227085,2
74391,9.569087,0.364856,1
37217,7.750103,0.869094,3
42814,0.000000,1.515293,1
14738,3.396030,0.633977,2
19896,11.916091,0.025294,3
14673,0.460758,0.689586,2
32011,13.087566,0.476002,3
58736,4.589016,1.672600,1
54744,8.397217,1.534103,1
29482,5.562772,1.689388,1
27698,10.905159,0.619091,3
11443,1.311441,1.169887,2
56117,10.647170,0.980141,3
39514,0.000000,0.481918,1
26627,8.503025,0.830861,3
16525,0.436880,1.395314,2
24368,6.127867,1.102179,1
22160,12.112492,0.359680,3
6030,1.264968,1.141582,2
6468,6.067568,1.327047,2
22945,8.010964,1.681648,3
18520,3.791084,0.304072,2
34914,11.773195,1.262621,3
6121,8.339588,1.443357,2
38063,2.563092,1.464013,1
23410,5.954216,0.953782,1
35073,9.288374,0.767318,3
52914,3.976796,1.043109,1
16801,8.585227,1.455708,3
9533,1.271946,0.796506,2
16721,0.000000,0.242778,2
5832,0.000000,0.089749,2
44591,11.521298,0.300860,3
10143,1.139447,0.415373,2
21609,5.699090,1.391892,2
23817,2.449378,1.322560,1
15640,0.000000,1.228380,2
8847,3.168365,0.053993,2
50939,10.428610,1.126257,3
28521,2.943070,1.446816,1
32901,10.441348,0.975283,3
42850,12.478764,1.628726,3
13499,5.856902,0.363883,2
40345,2.476420,0.096075,1
43547,1.826637,0.811457,1
70758,4.324451,0.328235,1
19780,1.376085,1.178359,2
44484,5.342462,0.394527,1
54462,11.835521,0.693301,3
20085,12.423687,1.424264,3
42291,12.161273,0.071131,3
47550,8.148360,1.649194,3
11938,1.531067,1.549756,2
40699,3.200912,0.309679,1
70908,8.862691,0.530506,1
73989,6.370551,0.369350,1
11872,2.468841,0.145060,2
48463,11.054212,0.141508,3
15987,2.037080,0.715243,2
70036,13.364030,0.549972,1
32967,10.249135,0.192735,3
63249,10.464252,1.669767,1
42795,9.424574,0.013725,3
14459,4.458902,0.268444,2
19973,0.000000,0.575976,2
5494,9.686082,1.029808,3
67902,13.649402,1.052618,1
25621,13.181148,0.273014,3
27545,3.877472,0.401600,1
58656,1.413952,0.451380,1
7327,4.248986,1.430249,2
64555,8.779183,0.845947,1
8998,4.156252,0.097109,2
11752,5.580018,0.158401,2
76319,15.040440,1.366898,1
27665,12.793870,1.307323,3
67417,3.254877,0.669546,1
21808,10.725607,0.588588,3
15326,8.256473,0.765891,2
20057,8.033892,1.618562,3
79341,10.702532,0.204792,1
15636,5.062996,1.132555,2
35602,10.772286,0.668721,3
28544,1.892354,0.837028,1
57663,1.019966,0.372320,1
78727,15.546043,0.729742,1
68255,11.638205,0.409125,1
14964,3.427886,0.975616,2
21835,11.246174,1.475586,3
7487,0.000000,0.645045,2
8700,0.000000,1.424017,2
26226,8.242553,0.279069,3
65899,8.700060,0.101807,1
6543,0.812344,0.260334,2
46556,2.448235,1.176829,1
71038,13.230078,0.616147,1
47657,0.236133,0.340840,1
19600,11.155826,0.335131,3
37422,11.029636,0.505769,3
1363,2.901181,1.646633,2
26535,3.924594,1.143120,1
47707,2.524806,1.292848,1
38055,3.527474,1.449158,1
6286,3.384281,0.889268,2
10747,0.000000,1.107592,2
44883,11.898890,0.406441,3
56823,3.529892,1.375844,1
68086,11.442677,0.696919,1
70242,10.308145,0.422722,1
11409,8.540529,0.727373,2
67671,7.156949,1.691682,1
61238,0.720675,0.847574,1
17774,0.229405,1.038603,2
53376,3.399331,0.077501,1
30930,6.157239,0.580133,1
28987,1.239698,0.719989,1
13655,6.036854,0.016548,2
7227,5.258665,0.933722,2
40409,12.393001,1.571281,3
13605,9.627613,0.935842,2
26400,11.130453,0.597610,3
13491,8.842595,0.349768,3
30232,10.690010,1.456595,3
43253,5.714718,1.674780,3
55536,3.052505,1.335804,1
8807,0.000000,0.059025,2
25783,9.945307,1.287952,3
22812,2.719723,1.142148,1
77826,11.154055,1.608486,1
38172,2.687918,0.660836,1
31676,10.037847,0.962245,3
74038,12.404762,1.112080,1
44738,10.237305,0.633422,3
17410,4.745392,0.662520,2
5688,4.639461,1.569431,2
36642,3.149310,0.639669,1
29956,13.406875,1.639194,3
60350,6.068668,0.881241,1
23758,9.477022,0.899002,3
25780,3.897620,0.560201,2
11342,5.463615,1.203677,2
36109,3.369267,1.575043,1
14292,5.234562,0.825954,2
11160,0.000000,0.722170,2
23762,12.979069,0.504068,3
39567,5.376564,0.557476,1
25647,13.527910,1.586732,3
14814,2.196889,0.784587,2
73590,10.691748,0.007509,1
35187,1.659242,0.447066,1
49459,8.369667,0.656697,3
31657,13.157197,0.143248,3
6259,8.199667,0.908508,2
33101,4.441669,0.439381,3
27107,9.846492,0.644523,3
17824,0.019540,0.977949,2
43536,8.253774,0.748700,3
67705,6.038620,1.509646,1
35283,6.091587,1.694641,3
71308,8.986820,1.225165,1
31054,11.508473,1.624296,3
52387,8.807734,0.713922,3
40328,0.000000,0.816676,1
34844,8.889202,1.665414,3
11607,3.178117,0.542752,2
64306,7.013795,0.139909,1
32721,9.605014,0.065254,3
33170,1.230540,1.331674,1
37192,10.412811,0.890803,3
13089,0.000000,0.567161,2
66491,9.699991,0.122011,1
15941,0.000000,0.061191,2
4272,4.455293,0.272135,2
48812,3.020977,1.502803,1
28818,8.099278,0.216317,3
35394,1.157764,1.603217,1
71791,10.105396,0.121067,1
40668,11.230148,0.408603,3
39580,9.070058,0.011379,3
11786,0.566460,0.478837,2
19251,0.000000,0.487300,2
56594,8.956369,1.193484,3
54495,1.523057,0.620528,1
11844,2.749006,0.169855,2
45465,9.235393,0.188350,3
31033,10.555573,0.403927,3
16633,6.956372,1.519308,2
13887,0.636281,1.273984,2
52603,3.574737,0.075163,1
72000,9.032486,1.461809,1
68497,5.958993,0.023012,1
35135,2.435300,1.211744,1
26397,10.539731,1.638248,3
7313,7.646702,0.056513,2
91273,20.919349,0.644571,1
24743,1.424726,0.838447,1
31690,6.748663,0.890223,3
15432,2.289167,0.114881,2
58394,5.548377,0.402238,1
33962,6.057227,0.432666,1
31442,10.828595,0.559955,3
31044,11.318160,0.271094,3
29938,13.265311,0.633903,3
9875,0.000000,1.496715,2
51542,6.517133,0.402519,3
11878,4.934374,1.520028,2
69241,10.151738,0.896433,1
37776,2.425781,1.559467,1
68997,9.778962,1.195498,1
67416,12.219950,0.657677,1
59225,7.394151,0.954434,1
29138,8.518535,0.742546,3
5962,2.798700,0.662632,2
10847,0.637930,0.617373,2
70527,10.750490,0.097415,1
9610,0.625382,0.140969,2
64734,10.027968,0.282787,1
25941,9.817347,0.364197,3
2763,0.646828,1.266069,2
55601,3.347111,0.914294,1
31128,11.816892,0.193798,3
5181,0.000000,1.480198,2
69982,10.945666,0.993219,1
52440,10.244706,0.280539,3
57350,2.579801,1.149172,1
57869,2.630410,0.098869,1
56557,11.746200,1.695517,3
42342,8.104232,1.326277,3
15560,12.409743,0.790295,3
34826,12.167844,1.328086,3
8569,3.198408,0.299287,2
77623,16.055513,0.541052,1
78184,7.138659,0.158481,1
7036,4.831041,0.761419,2
69616,10.082890,1.373611,1
21546,10.066867,0.788470,3
36715,8.129538,0.329913,3
20522,3.012463,1.138108,2
42349,3.720391,0.845974,1
9037,0.773493,1.148256,2
26728,10.962941,1.037324,3
587,0.177621,0.162614,2
48915,3.085853,0.967899,1
9824,8.426781,0.202558,2
4135,1.825927,1.128347,2
9666,2.185155,1.010173,2
59333,7.184595,1.261338,1
36198,0.000000,0.116525,1
34909,8.901752,1.033527,3
47516,2.451497,1.358795,1
55807,3.213631,0.432044,1
14036,3.974739,0.723929,2
42856,9.601306,0.619232,3
64007,8.363897,0.445341,1
59428,6.381484,1.365019,1
13730,0.000000,1.403914,2
41740,9.609836,1.438105,3
63546,9.904741,0.985862,1
30417,7.185807,1.489102,3
69636,5.466703,1.216571,1
64660,0.000000,0.915898,1
14883,4.575443,0.535671,2
7965,3.277076,1.010868,2
68620,10.246623,1.239634,1
8738,2.341735,1.060235,2
7544,3.201046,0.498843,2
6377,6.066013,0.120927,2
36842,8.829379,0.895657,3
81046,15.833048,1.568245,1
67736,13.516711,1.220153,1
32492,0.664284,1.116755,1
39299,6.325139,0.605109,3
77289,8.677499,0.344373,1
33835,8.188005,0.964896,3
71890,9.414263,0.384030,1
32054,9.196547,1.138253,3
38579,10.202968,0.452363,3
55984,2.119439,1.481661,1
72694,13.635078,0.858314,1
42299,0.083443,0.701669,1
26635,9.149096,1.051446,3
8579,1.933803,1.374388,2
37302,14.115544,0.676198,3
22878,8.933736,0.943352,3
4364,2.661254,0.946117,2
4985,0.988432,1.305027,2
37068,2.063741,1.125946,1
41137,2.220590,0.690754,1
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51126,3.818754,0.590905,1
53644,7.973845,0.307364,3
51417,9.078824,0.734876,3
24859,0.153467,0.766619,1
61732,8.325167,0.028479,1
71128,7.092089,1.216733,1
27276,5.192485,1.094409,3
30453,10.340791,1.087721,3
18670,2.077169,1.019775,2
70600,10.151966,0.993105,1
12683,0.046826,0.809614,2
81597,11.221874,1.395015,1
69959,14.497963,1.019254,1
8124,3.554508,0.533462,2
18867,3.522673,0.086725,2
80886,14.531655,0.380172,1
55895,3.027528,0.885457,1
31587,1.845967,0.488985,1
10591,10.226164,0.804403,3
70096,10.965926,1.212328,1
53151,2.129921,1.477378,1
11992,0.000000,1.606849,2
33114,9.489005,0.827814,3
7413,0.000000,1.020797,2
10583,0.000000,1.270167,2
58668,6.556676,0.055183,1
35018,9.959588,0.060020,3
70843,7.436056,1.479856,1
14011,0.404888,0.459517,2
35015,9.952942,1.650279,3
70839,15.600252,0.021935,1
3024,2.723846,0.387455,2
5526,0.513866,1.323448,2
5113,0.000000,0.861859,2
20851,7.280602,1.438470,2
40999,9.161978,1.110180,3
15823,0.991725,0.730979,2
35432,7.398380,0.684218,3
53711,12.149747,1.389088,3
64371,9.149678,0.874905,1
9289,9.666576,1.370330,2
60613,3.620110,0.287767,1
18338,5.238800,1.253646,2
22845,14.715782,1.503758,3
74676,14.445740,1.211160,1
34143,13.609528,0.364240,3
14153,3.141585,0.424280,2
9327,0.000000,0.120947,2
18991,0.454750,1.033280,2
9193,0.510310,0.016395,2
2285,3.864171,0.616349,2
9493,6.724021,0.563044,2
2371,4.289375,0.012563,2
13963,0.000000,1.437030,2
2299,3.733617,0.698269,2
5262,2.002589,1.380184,2
4659,2.502627,0.184223,2
17582,6.382129,0.876581,2
27750,8.546741,0.128706,3
9868,2.694977,0.432818,2
18333,3.951256,0.333300,2
3780,9.856183,0.329181,2
18190,2.068962,0.429927,2
11145,3.410627,0.631838,2
68846,9.974715,0.669787,1
26575,10.650102,0.866627,3
48111,9.134528,0.728045,3
43757,7.882601,1.332446,3
归一化
定义
通过对原始的数据进行变换把数据映射到（默认为[0,1]之间）
公式
手动计算
对90这个值进行归一化
X’=(90-60)/(90-60)= 1
X’’ = 1 * (1-0) +0 = 1
归一化API
sklearn.preprocessing.MinMaxScaler(feature_range=(0,1)…)
- MinMaxScaler.fit_transform(X)，
- X：numpy array格式的数据[n_samples,n_features]，
- 返回值：转换后的形式相同的array
数据计算
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
def minmax_demo():
"""
归一化
:return:
"""
# 1、获取数据
data = pd.read_csv("datingTestSet2.txt")
# 通过位置选择数据
#df.iloc[row_index, column_index]
data = data.iloc[:, :3]
print("data:\n", data)
# 2、实例化一个转换器类
transform = MinMaxScaler()
# 可以自定义归一化范围
#transform = MinMaxScaler(feature_range=[2,3])
# 3、调用fit_transform
data_new = transform.fit_transform(data)
print("data_new:\n", data_new)
return None
if __name__ == "__main__":
minmax_demo()
transform = MinMaxScaler(feature_range=(2,3))
归一化存在的问题
归一化总结
注意最大值最小值是变化的，另外，最大值与最小值非常容易受到异常值影响，
所以这种方法稳定性较差，只适合传统精确小数据场景
标准化
定义
通过对原始数据进行变换把数据变换到均值为0，标准差为1的范围内
公式
标准差反应的是集中程度
标准化API
sklearn.perprocessing.StandradScaler()
- 处理之后，对每列来说，所有数据都聚集在均值为0附近，标准差为1
- StandardScaler.fit_transform(X)，X;
- numpy array格式的数据[n_samples,n_features]，返回值：转化后的形状相同的array
数据计算
import pandas as pd
from sklearn.preprocessing import StandardScaler
def stand_demo():
"""
归一化
:return:
"""
# 1、获取数据
data = pd.read_csv("datingTestSet2.txt")
# 通过位置选择数据
#df.iloc[row_index, column_index]
data = data.iloc[:, :3]
print("data:\n", data)
# 2、实例化一个转换器类
transform = StandardScaler()
# 3、调用fit_transform
data_new = transform.fit_transform(data)
print("data_new:\n", data_new)
return None
if __name__ == "__main__":
stand_demo()
标准化总结
在已有样本足够多的情况下比较稳定，适合现代嘈杂大数据场景
