pyspark - spark-ml normalizer loses metadata -


i'm using dataset categorical features in pyspark indexed , one-hot encoded. after fitting pipeline extract encoded features using metadata of features column. when include normalizer in pipeline lose metadata of categorical features. see example below:

train.show() +-----+---+----+----+ |admit|gre| gpa|rank| +-----+---+----+----+ |  0.0|380|3.61|   3| |  1.0|660|3.67|   3| |  1.0|800| 4.0|   1| |  1.0|640|3.19|   4| |  0.0|520|2.93|   4| +-----+---+----+----+  pyspark.ml.feature import stringindexer, onehotencoder, vectorassembler, normalizer #indexer categorical features rank_indexer = stringindexer(inputcol = 'rank', outputcol = 'rank_ind', handleinvalid="skip") #encoder categorical features rank_encoder = onehotencoder(inputcol = 'rank_ind', outputcol = 'rank_enc') # assembler assembler = vectorassembler(inputcols=['gre','gpa','rank_enc'], outputcol="featuresvect") # create normalizer normalizer = normalizer(inputcol="featuresvect", outputcol="features", p=1.0)  stages = [rank_indexer] + [rank_encoder] + [assembler] + [normalizer]  pyspark.ml import pipeline final_pipeline = pipeline(     stages = stages )  pipelinemodel = final_pipeline.fit(train) data = pipelinemodel.transform(train)  data.schema['features'].metadata {} ## empty dictionary  ## excluding normalizer results in metadata: {u'ml_attr': {u'attrs': {u'binary': [{u'idx': 2, u'name': u'rank_enc_2'},     {u'idx': 3, u'name': u'rank_enc_3'},     {u'idx': 4, u'name': u'rank_enc_4'}],    u'numeric': [{u'idx': 0, u'name': u'gre'}, {u'idx': 1, u'name': u'gpa'}]},   u'num_attrs': 5}}

is normal behavior? how can include normalizer without losing metadata?

in opinion doesn't make sense use normalizer on one-hot encoded data in first place. in spark, ohe useful 2 type models:

  • multinomial naive bayes.
  • linear models.

in first case normalization render features useless (multinomial model can utilize binary features). in second case make interpretation of model close impossible.

even if ignore above normalized data cannot interpreted binary features anymore, therefore discarding metadata seems valid behavior.

related why standardscaler not attach metadata output column?


Comments

Post a Comment

Popular posts from this blog

android - InAppBilling registering BroadcastReceiver in AndroidManifest -

i'm implementing inapp billing in android app. works well, however, i'm trying decouple broadcast receiver activity manifest. suggestion in android's trivialdrive sample: // important: dynamically register broadcast messages updated purchases. // register receiver here instead of <receiver> in manifest // because call getpurchases() @ startup, therefore can ignore // broadcasts sent while app isn't running. // note: registering listener in activity bad idea, done here // because sample. regardless, receiver must registered after // iabhelper setup, before first call getpurchases(). currently there's class extends broadcastreceiver : public class iabbroadcastreceiver extends broadcastreceiver { /** * intent action receiver should filter for. */ public static final string action = "com.android.vending.billing.purchases_updated"; private final iabbroadcastlistener mlistener; public iabbroadcastreceiver(iabbroadcastlistener listener) { m
Read more

python Tkinter Capturing keyboard events save as one single string -

is possible store keyboard events 1 string? code below stores 1 char , prints it. card reader or bar code reader, contains collection of character/ string not 1 character @ time. goal save char pressed text variable. from tkinter import * root = tk() def key(event): text= event.char text+= event.char print ("pressed", text) def callback(event): frame.focus_set() print ("clicked at", event.x, event.y) frame = frame(root, width=100, height=100) frame.bind("<key>", key) frame.bind("<button-1>", callback) frame.pack() root.mainloop() currently, creating text variable , everytime key() function gets called, , text stores last character typed. you can define text module level variable , , use module level text inside key function - text = '' def key(event): global text text+= event.char print("pressed", text)
Read more

sql server - Why does Linq-to-SQL add unnecessary COUNT()? -

here tables: create table customer ( cid int primary key, name varchar(32) ) create table ord ( oid int primary key, cid int foreign key references customer, address varchar(20) ) here's linq-to-sql statement: var aaa = c in db.customer select new { c, o = c.ord.tolist() }; and here's inexplicable query generated linq-to-sql: select [t0].[cid] [cid], [t0].[name] [name], [t1].[oid] [oid], [t1].[cid] [cid2], [t1].[address] [address], (select count(*) [dbo].[ord] [t2] [t2].[cid] = [t0].[cid]) [value] [dbo].[customer] [t0] left outer join [dbo].[ord] [t1] on [t1].[cid] = [t0].[cid] order [t0].[cid], [t1].[oid] i want understand how rid of count(*) part. it's entirely uncalled for! linq needs count(*) in order determine number of entries returned o = c.ord.tolist() .
Read more