半年分布式处理回顾&机器学习(一)——线性回归
回顾
一元线性回归
Python实现my_sgd
import numpy as np
import matplotlib.pyplot as plt
def h(x):
return w0 + w1 * x
if __name__ == '__main__':
# α步长
rate = 10
# 准备的样本数据
# x_train是样本数据的x值
x_train = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
# y_train是样本数据的y值
y_train = np.array([2, 3, 4, 5, 6, 7, 8, 9, 10])
# 随机产生w0
w0 = np.random.normal()
# 随机产生w1
w1 = np.random.normal()
err = 1
# 能完全等于w0=1 w1=1?
# for循环了10000次 迭代10000次
# for i in range(10000):
# 依据用户指定的误差阈值来收敛
while err > 0.000000001:
for x, y in zip(x_train, y_train):
w0 = w0 - rate * (h(x) - y) * 1
w1 = w1 - rate * (h(x) - y) * x
# 计算误差值
err = 0.0
for x, y in zip(x_train, y_train):
err += (y - h(x)) ** 2
m = len(x_train)
err = float(err / (2 * m))
print("err:%f" % err)
print(w0, w1)
Scala调用n元线性回归
linearRegression02.scala
package com.msb.lr_new
import org.apache.spark.SparkConf
import org.apache.spark.ml.linalg.{DenseVector, SparseVector}
import org.apache.spark.ml.regression.{LinearRegression, LinearRegressionModel}
import org.apache.spark.sql.SparkSession
object LinearRegression02 {
//0.5395881831013476
def main(args: Array[String]): Unit = {
val conf = new SparkConf()
conf.setMaster("local")
val spark = SparkSession.builder().config(conf).appName("LinearRegression").getOrCreate()
var data = spark.read.format("libsvm")
.load("data/sample_linear_regression_data.txt")
//增加一列与第一个特征一模一样 -0.294192922737251,-0.294192922737251
//未增加: -0.5883852628595317
/**
*randomSplit 是随机切割的方法 Array(0.8,0.2)
*
* data拆分成两个df
* 第一个df的数据量是data 的 80% 随机
* 第一个df的数据量是data 的 20%
*
* seed种子如果每次都是一样的
* 那么每次随机出来的数据都是一样的
*
* 测试环境为什么随机出来的数据一样?
* 便于排查错误
*
*
* 为什么要把data切成两份?
* 1、第一份做训练集 -- model
* 2、第二份做测试集
*/
val DFS = data.randomSplit(Array(0.8,0.2),1)
val (training,test) = (DFS(0),DFS(1))
val lr = new LinearRegression()
.setMaxIter(10)
//L1+L2系数之和 0代表不使用正则化
// .setRegParam(0.3)
/**
* 用于调整L1、L2之间的比例,简单说:调整L1,L2前面的系数
* For alpha = 0, the penalty is an L2 penalty.
* For alpha = 1, it is an L1 penalty.
* For alpha in (0,1), the penalty is a combination of L1 and L2.
*/
// .setElasticNetParam(0.8)
// Fit the model
val lrModel = lr.fit(training)
// 打印模型参数w1...wn和截距w0
println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")
// 基于训练集数据,总结模型信息
val trainingSummary = lrModel.summary
println(s"RMSE: ${trainingSummary.rootMeanSquaredError}")
spark.close()
}
}
linearRegression03.scala
package com.msb.lr_new
import org.apache.spark.SparkConf
import org.apache.spark.ml.linalg.{DenseVector, SparseVector}
import org.apache.spark.ml.regression.LinearRegression
import org.apache.spark.sql.SparkSession
import scala.util.Random
object LinearRegression03 {
//0.5395881831013476
def main(args: Array[String]): Unit = {
val conf = new SparkConf()
conf.setMaster("local")
val spark = SparkSession.builder().config(conf).appName("LinearRegression").getOrCreate()
var data = spark.read.format("libsvm")
.load("data/sample_linear_regression_data.txt")
//增加一列与第一个特征一模一样 -0.294192922737251,-0.294192922737251
//未增加: -0.5883852628595317
import spark.implicits._
data = data.rdd.map(row=>{
val features = row.getAs[SparseVector]("features")
val label = row.getAs[Double]("label")
val featureArr = features.toDense.toArray
(label,new DenseVector(featureArr.+:(1.0)))
}).toDF("label","features")
data.show(10,false)
val DFS = data.randomSplit(Array(0.8,0.2),1)
val (training,test) = (DFS(0),DFS(1))
val lr = new LinearRegression()
.setMaxIter(10)
//L1+L2系数之和 0代表不使用正则化
// .setRegParam(0.3)
/**
* 用于调整L1、L2之间的比例,简单说:调整L1,L2前面的系数
* For alpha = 0, the penalty is an L2 penalty.
* For alpha = 1, it is an L1 penalty.
* For alpha in (0,1), the penalty is a combination of L1 and L2.
*/
// .setElasticNetParam(0.8)
// Fit the model
val lrModel = lr.fit(training)
// 打印模型参数w1...wn和截距w0
println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")
// 基于训练集数据,总结模型信息
val trainingSummary = lrModel.summary
println(s"RMSE: ${trainingSummary.rootMeanSquaredError}")
}
}
sample_linear_regression_data.txt
-9.490009878824548 1:0.4551273600657362 2:0.36644694351969087 3:-0.38256108933468047 4:-0.4458430198517267 5:0.33109790358914726 6:0.8067445293443565 7:-0.2624341731773887 8:-0.44850386111659524 9:-0.07269284838169332 10:0.5658035575800715
0.2577820163584905 1:0.8386555657374337 2:-0.1270180511534269 3:0.499812362510895 4:-0.22686625128130267 5:-0.6452430441812433 6:0.18869982177936828 7:-0.5804648622673358 8:0.651931743775642 9:-0.6555641246242951 10:0.17485476357259122
-4.438869807456516 1:0.5025608135349202 2:0.14208069682973434 3:0.16004976900412138 4:0.505019897181302 5:-0.9371635223468384 6:-0.2841601610457427 7:0.6355938616712786 8:-0.1646249064941625 9:0.9480713629917628 10:0.42681251564645817
-19.782762789614537 1:-0.0388509668871313 2:-0.4166870051763918 3:0.8997202693189332 4:0.6409836467726933 5:0.273289095712564 6:-0.26175701211620517 7:-0.2794902492677298 8:-0.1306778297187794 9:-0.08536581111046115 10:-0.05462315824828923
-7.966593841555266 1:-0.06195495876886281 2:0.6546448480299902 3:-0.6979368909424835 4:0.6677324708883314 5:-0.07938725467767771 6:-0.43885601665437957 7:-0.608071585153688 8:-0.6414531182501653 9:0.7313735926547045 10:-0.026818676347611925
-7.896274316726144 1:-0.15805658673794265 2:0.26573958270655806 3:0.3997172901343442 4:-0.3693430998846541 5:0.14324061105995334 6:-0.25797542063247825 7:0.7436291919296774 8:0.6114618853239959 9:0.2324273700703574 10:-0.25128128782199144
-8.464803554195287 1:0.39449745853945895 2:0.817229160415142 3:-0.6077058562362969 4:0.6182496334554788 5:0.2558665508269453 6:-0.07320145794330979 7:-0.38884168866510227 8:0.07981886851873865 9:0.27022202891277614 10:-0.7474843534024693
2.1214592666251364 1:-0.005346215048158909 2:-0.9453716674280683 3:-0.9270309666195007 4:-0.032312290091389695 5:0.31010676221964206 6:-0.20846743965751569 7:0.8803449313707621 8:-0.23077831216541722 9:0.29246395759528565 10:0.5409312755478819
1.0720117616524107 1:0.7880855916368177 2:0.19767407429003536 3:0.9520689432368168 4:-0.845829774129496 5:0.5502413918543512 6:-0.44235539500246457 7:0.7984106594591154 8:-0.2523277127589152 9:-0.1373808897290778 10:-0.3353514432305029
-13.772441561702871 1:-0.3697050572653644 2:-0.11452811582755928 3:-0.807098168238352 4:0.4903066124307711 5:-0.6582805242342049 6:0.6107814398427647 7:-0.7204208094262783 8:-0.8141063661170889 9:-0.9459402662357332 10:0.09666938346350307
-5.082010756207233 1:-0.43560342773870375 2:0.9349906440170221 3:0.8090021580031235 4:-0.3121157071110545 5:-0.9718883630945336 6:0.6191882496201251 7:0.0429886073795116 8:0.670311110015402 9:0.16692329718223786 10:0.37649213869502973
7.887786536531237 1:0.11276440263810383 2:-0.7684997525607482 3:0.1770172737885798 4:0.7902845707138706 5:0.2529503304079441 6:-0.23483801763662826 7:0.8072501895004851 8:0.6673992021927047 9:-0.4796127376677324 10:0.9244724404994455
14.323146365332388 1:-0.2049276879687938 2:0.1470694373531216 3:-0.48366999792166787 4:0.643491115907358 5:0.3183669486383729 6:0.22821350958477082 7:-0.023605251086149304 8:-0.2770587742156372 9:0.47596326458377436 10:0.7107229819632654
-20.057482615789212 1:-0.3205057828114841 2:0.51605972926996 3:0.45215640988181516 4:0.01712446974606241 5:0.5508198371849293 6:-0.2478254241316491 7:0.7256483175955235 8:0.39418662792516 9:-0.6797384914236382 10:0.6001217520150142
-0.8995693247765151 1:0.4508991072414843 2:0.589749448443134 3:0.6464818311502738 4:0.7005669004769028 5:0.9699584106930381 6:-0.7417466269908464 7:0.22818964839784495 8:0.08574936236270037 9:-0.6945765138377225 10:0.06915201979238828
-19.16829262296376 1:0.09798746565879424 2:-0.34288007110901964 3:0.440249350802451 4:-0.22440768392359534 5:-0.9695067570891225 6:-0.7942032659310758 7:-0.792286205517398 8:-0.6535487038528798 9:0.7952676470618951 10:-0.1622831617066689
5.601801561245534 1:0.6949189734965766 2:-0.32697929564739403 3:-0.15359663581829275 4:-0.8951865090520432 5:0.2057889391931318 6:-0.6676656789571533 7:-0.03553655732400762 8:0.14550349954571096 9:0.034600542078191854 10:0.4223352065067103
-3.2256352187273354 1:0.35278245969741096 2:0.7022211035026023 3:0.5686638754605697 4:-0.4202155290448111 5:-0.26102723928249216 6:0.010688215941416779 7:-0.4311544807877927 8:0.9500151672991208 9:0.14380635780710693 10:-0.7549354840975826
1.5299675726687754 1:-0.13079299081883855 2:0.0983382230287082 3:0.15347083875928424 4:0.45507300685816965 5:0.1921083467305864 6:0.6361110540492223 7:0.7675261182370992 8:-0.2543488202081907 9:0.2927051050236915 10:0.680182444769418
-0.250102447941961 1:-0.8062832278617296 2:0.8266289890474885 3:0.22684501241708888 4:0.1726291966578266 5:-0.6778773666126594 6:0.9993906921393696 7:0.1789490173139363 8:0.5584053824232391 9:0.03495894704368174 10:-0.8505720014852347
12.792267926563595 1:-0.008461200645088818 2:-0.648273596036564 3:-0.005334477339629995 4:0.3781469006858833 5:0.30565234666790686 6:-0.2822867492866177 7:0.10175120738413801 8:0.5342432888482425 9:0.05146513075475534 10:-0.6459729964194652
6.082192787194888 1:0.42519013450094767 2:0.09441503345243984 3:-0.07898439043103522 4:-0.32207498048636474 5:-0.9180071861219266 6:0.5951317320731633 7:0.41000814588717693 8:-0.3926260640533046 9:0.2789036768568971 10:0.13163692286014528
-7.481405271455238 1:0.03324842612749346 2:0.07055844751995122 3:-0.47199515597021113 4:-0.682690342465275 5:0.3983414713797069 6:-0.2136729393256811 7:-0.09066563475481249 8:-0.4640338194317184 9:-0.03513782089224482 10:-0.1711809802758364
6.739533816100517 1:0.1774546460228057 2:-0.6783644553523549 3:-0.47871398278230504 4:0.02272121490463097 5:-0.5047649289302389 6:0.26479596144873896 7:-0.32045436544054096 8:0.3113047940487379 9:0.6269418147567556 10:0.9710114516962312
3.780807062175497 1:0.01715676997104909 2:0.8975962429865936 3:-0.46594560920034134 4:0.2873623499953055 5:0.8894362304584083 6:0.17973981232418468 7:0.49105791400707743 8:-0.7359842740294882 9:0.38941133808001127 10:-0.7151884777228046
4.564039393483412 1:0.07478785545033317 2:-0.8672651994084235 3:0.450599300176334 4:0.35104802298560056 5:0.6797318185095045 6:-0.03891997518827006 7:-0.33208695871398675 8:0.6166574577055226 9:0.5730212324012205 10:-0.4194925751047054
-0.3195679646035633 1:0.054527683864544096 2:-0.15591931640565093 3:0.9266742559542833 4:0.888522581905147 5:0.6576203900699167 6:0.6417770212400336 7:0.7509788029052338 8:-0.3104974571382815 9:0.7234744267051683 10:-0.15869049651427103
11.290452658023497 1:0.20173310976772196 2:0.8657502566551409 3:0.9325160601080682 4:0.24570884032596263 5:-0.6546108813337841 6:-0.14020032028377583 7:-0.8825687891702743 8:-0.21420166926412865 9:-0.8600275184792756 10:-0.7990574622230739
-4.003499192090455 1:0.8325875503351796 2:-0.5956350140619129 3:0.12598048009007923 4:0.12340188733473134 5:-0.839435659309717 6:-0.16623481818728414 7:0.12028795301041662 8:-0.7994713170657952 9:0.2216721974907896 10:0.8407561415075087
-19.872991038068406 1:-0.9325810772922609 2:-0.6411471147334535 3:0.9949216290375054 4:0.483048267470493 5:-0.8736297429070232 6:-0.36222771685582544 7:0.26397860162786957 8:0.45527588775737704 9:-0.9424989711186325 10:0.6251162293059616
10.502762149373098 1:-0.2307778924009991 2:0.6977871128979924 3:0.022830408261390822 4:0.6257738824362347 5:0.9770979848265122 6:0.09985730624684575 7:-0.9755858424230182 8:-0.689969833240031 9:-0.7294587311376761 10:0.3496326193951331
-14.328978509075442 1:0.37929821892417404 2:0.8402056881660709 3:-0.1806835799958202 4:0.766314307210441 5:0.865876513623024 6:-0.7113501219432434 7:-0.0932956557986735 8:-0.7042025810921411 9:0.47530696925672267 10:-0.4629102077669889
-16.26143027545273 1:-0.9309578475799722 2:0.7591795880911123 3:0.06296957473213705 4:0.786790093290086 5:-0.9527998391625465 6:-0.08573982501921895 7:-0.3812232026687308 8:-0.6890669703685022 9:0.25415911467755015 10:-0.07664746267502509
11.772544195529013 1:0.3614756404325046 2:0.14508027508253818 3:0.23042774014795753 4:0.4164348685332022 5:0.4109091750657461 6:0.03853098236933272 7:0.38911994885223145 8:-0.5031309357181766 9:-0.596467768575587 10:0.17884522225228028
14.697703557439503 1:0.24508864174863 2:0.7576193329655578 3:0.09030511120334461 4:0.9537528991778741 5:-0.7224092160621338 6:-0.34089385162121943 7:0.6924170720838818 8:0.32912306214891784 9:-0.4064624712125904 10:-0.5344662061201593
-13.976130931152703 1:0.5891192531479754 2:0.29862103742464274 3:-0.36153976712796343 4:-0.6552669564323226 5:-0.22672513691161766 6:0.3001336202535376 7:0.34490251346382617 8:0.2072633053920192 9:-0.5659371284058774 10:0.49599636156628835
-14.762758252931127 1:0.31302496164254223 2:-0.6062773982342133 3:-0.9874007658402217 4:-0.6214904627601421 5:-0.11421073677207683 6:-0.5850843421161205 7:0.1250679146774638 8:-0.7108170726393621 9:-0.6888351241194393 10:0.6077343683084389
-3.300641320608255 1:-0.1407178879203672 2:0.12960233233004925 3:-0.4236196478321872 4:0.7903078296084356 5:-0.8755754953628643 6:-0.2062360260394529 7:-0.045680124889026175 8:0.783182093429277 9:-0.02995737262668463 10:-0.33382351650328435
-15.72351561304857 1:-0.1802575775708093 2:-0.991006951265341 3:-0.9107951763247621 4:0.9069820084047908 5:-0.12691921206803047 6:-0.7087012119383593 7:-0.9179510577925369 8:0.18480349982718325 9:-0.4478459144114004 10:-0.5560585660624608
-22.949825936196074 1:0.4797855980916854 2:0.01997502546020402 3:-0.8827928315487465 4:0.2755107907750989 5:0.015544482147298977 6:0.9652687138748801 7:0.6622667860970648 8:-0.7708138539912186 9:0.17728148663006627 10:0.47818190728952925
12.092431628826905 1:0.1358843437335564 2:0.03643446587894239 3:-0.31070823939673287 4:0.5283033206569152 5:0.3469111543845367 6:-0.5162518174930761 7:0.24270234207184016 8:0.7352292800096338 9:0.8860322286740037 10:0.6748068653962045
-23.51088409032297 1:-0.4683538422180036 2:0.1469540185936138 3:0.9113612952591796 4:-0.9838482669789823 5:0.4506466371133697 6:0.6456121712599778 7:0.8264783725578371 8:0.562664168655115 9:-0.8299281852090683 10:0.40690300256653256
5.998186124881712 1:-0.9781302074883151 2:0.32984303335155785 3:0.7303430847899663 4:0.841481297188956 5:0.05580773881989276 6:0.7130788298702062 7:-0.218087116119847 8:-0.9889494995220598 9:0.9182854134226501 10:-0.7501751701020942
9.852316338642547 1:0.146854160091757 2:-0.3611508707370965 3:0.3517016971654914 4:0.6187697988029395 5:-0.010768583697787548 6:0.5236725885871243 7:0.5945666964145524 8:-0.009180562740628506 9:-0.44474762415618274 10:0.41852743519493685
-5.313930756588526 1:-0.6304209277071555 2:-0.37010359785263813 3:-0.3194739026510125 4:-0.750533359080716 5:0.45500303301733114 6:-0.012727544364283805 7:-0.43941651856862274 8:0.927108876532093 9:-0.24164903158058149 10:0.44134972919002124
-4.2775224863223915 1:-0.35785764991284363 2:0.942797043714243 3:0.4539569191274251 4:-0.6944903010994341 5:-0.08357221983075225 6:0.4433049548665855 7:-0.5488972050023557 8:-0.24014623658145773 9:-0.6178118485382511 10:-0.4575463952834564
-10.57769830424322 1:0.22693864400257335 2:-0.041639691095668674 3:0.9948726461115123 4:-0.7450471554938383 5:-0.1114847126717804 6:-0.27881184842402673 7:0.029766812446276214 8:-0.3727649352432578 9:-0.7791732805568077 10:0.9425576681069683
-0.8430338600258201 1:0.4607090007225536 2:-0.6079961642969514 3:-0.5671626932935381 4:0.12784576080614185 5:-0.30766031989910236 6:-0.21232963505711555 7:0.3310463755850872 8:-0.6807682731528943 9:0.7826634145951483 10:0.0608057623636995
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