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"Impute Missing Values help"
diogo_fp_couto
Member Posts: 4 
Contributor I
Hello,
I am running a process to impute missing values from my dataset. These are numeric values. I am using the operator Impute Missing Values with k-nn. I got it running on a server. My dataset consists of about 140000 examples, and about 30000 are missing (only one attribute). The problem is, it appears k-nn(62), not sure what 62 means, and it's been 4 days and 22 hours, as you can see in the picture. Will it take too much longer. Does anyone know how to make it faster?
I am kinda getting out of time to deliver my work, so any help would be appreciated.
Thanks in advance.
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Best Answer
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sgenzer
Administrator, Moderator, Employee-RapidMiner, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959 
Community Manager
hi @diogo_fp_couto - not sure why you're dummy coding at this point. I would just leave it as is while you impute the missing data. It should take a little while b/c you do have a lot of data and you are using k-NN which is a slow algorithm. I'm running this now...
<?xml version="1.0" encoding="UTF-8"?><process version="7.6.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="7.6.001" expanded="true" name="Process">
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="7.6.001" expanded="true" height="68" name="Retrieve dataset" width="90" x="45" y="34">
<parameter key="repository_entry" value="//RapidMiner OneDrive/random community stuff/dataset"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="7.6.001" expanded="true" height="82" name="Select Attributes" width="90" x="179" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="CS Site-Specific Factor 25"/>
<parameter key="invert_selection" value="true"/>
</operator>
<operator activated="true" class="remove_unused_values" compatibility="7.6.001" expanded="true" height="103" name="Remove Unused Values" width="90" x="313" y="34"/>
<operator activated="true" class="declare_missing_value" compatibility="7.6.001" expanded="true" height="82" name="Declare Missing Value" width="90" x="447" y="34">
<parameter key="attribute_filter_type" value="single"/>
<parameter key="attribute" value="CS Tumor Size"/>
<parameter key="attributes" value="CS Tumor Size|CS Site-Specific Factor 2|CS Lymph Nodes"/>
<parameter key="numeric_value" value="999.0"/>
<parameter key="nominal_value" value="999"/>
</operator>
<operator activated="true" class="set_role" compatibility="7.6.001" expanded="true" height="82" name="Set Role" width="90" x="581" y="34">
<parameter key="attribute_name" value="Survival months"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="false" class="nominal_to_numerical" compatibility="7.6.001" expanded="true" height="103" name="Nominal to Numerical" width="90" x="715" y="187">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="CS Tumor Size"/>
<parameter key="coding_type" value="unique integers"/>
<list key="comparison_groups"/>
</operator>
<operator activated="false" breakpoints="after" class="nominal_to_numerical" compatibility="7.6.001" expanded="true" height="103" name="Nominal to Numerical (2)" width="90" x="849" y="187">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attributes" value="Histologic Type ICD-O-3|RX Summ—Radiation|RX Summ—Surg / Rad Seq"/>
<list key="comparison_groups"/>
</operator>
<operator activated="true" class="impute_missing_values" compatibility="7.6.001" expanded="true" height="68" name="Impute Missing Values" width="90" x="715" y="34">
<parameter key="attribute" value="CS Tumor Size"/>
<parameter key="attributes" value="CS Tumor Size"/>
<parameter key="include_special_attributes" value="true"/>
<parameter key="order" value="random"/>
<process expanded="true">
<operator activated="true" class="k_nn" compatibility="7.6.001" expanded="true" height="82" name="k-NN" width="90" x="112" y="34"/>
<connect from_port="example set source" to_op="k-NN" to_port="training set"/>
<connect from_op="k-NN" from_port="model" to_port="model sink"/>
<portSpacing port="source_example set source" spacing="0"/>
<portSpacing port="sink_model sink" spacing="0"/>
</process>
</operator>
<operator activated="false" class="store" compatibility="7.6.001" expanded="true" height="68" name="Store" width="90" x="916" y="187">
<parameter key="repository_entry" value="//Tese/TESE/Data/ContinuousSurvival/Colon/colonTrainNumericImputed"/>
</operator>
<connect from_op="Retrieve dataset" from_port="output" to_op="Select Attributes" to_port="example set input"/>
<connect from_op="Select Attributes" from_port="example set output" to_op="Remove Unused Values" to_port="example set input"/>
<connect from_op="Remove Unused Values" from_port="example set output" to_op="Declare Missing Value" to_port="example set input"/>
<connect from_op="Declare Missing Value" from_port="example set output" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Impute Missing Values" to_port="example set in"/>
<connect from_op="Nominal to Numerical" from_port="example set output" to_op="Nominal to Numerical (2)" to_port="example set input"/>
<connect from_op="Impute Missing Values" from_port="example set out" to_port="result 1"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
</process>
</operator>
</process>Scott
0
Answers
hello @diogo_fp_couto - welcome to the community. That sounds rather odd. Can you please post your process here by pasting the XML in this thread using the </> tool? This plus the dataset would be extremely helpful.
Scott
Hi, thanks
. And thanks for the reply. I appended the dataset in csv format. Here's the process (i also did declare missing values, and nominal to numeric here):
Actually, I was looking at this xml file and maybe this is taking so long because I did the dummy nominal to numeric before imputing the missing values? The result of the nominal to numeric is around 130 attributes. Maybe this is what's causing the slowness?
Yeah, I asked that myself now. But it's because I need all numeric values, because I am facing a regression problem, and I want to train neuronal nets and such. And, with this process, I wanted to have my dataset ready to the model, but yeah, maybe I should run the dummy coding after the impute.
So, in your opinion, is it worth to stop the process?
Also, do you have any recommendations other than knn for imputing numeric values?
And thank you very much for your help!
so yes, I would stop your process. I know that is tough when you're not sure if it will be over soon, but it seems like you are going nowhere.
Some recommendations: 1) if you have a large data set, trying using the Sample operator first to downsample your set in order to just get a sense of size and performance. Once you're happy with the way things are going, keep increasing the sample size until you are working on the whole data set. It will give you a good idea about how long it will take and you will not need to wait until your 95th birthday to see a result (unless you already had your 95th birthday?). 2) Look at imputing missing data as a data science preprocessing step in itself. Find the model that best fits your data for imputing missing values as part of your overall process (it must be part of your testing set or you will leak).
More soon.
Scott
Thanks for the free advice
One more thing if you don't mind, I took a look at your process now. You think it's better to impute values while nominal?
hello @diogo_fp_couto - hmm. That's like asking whether it's better to eat your dinner with a fork or a spoon. It depends on what you're eating. If your numerical data are truly numerical (e.g. the numbers represent quantity of some kind), then you must impute numerically - like regression. Otherwise you are not creating a process that understands that 4 is between 3 and 5. If the numbers represent something else (e.g. 1=cheese, 2=bread, 3=potatoes), then you should impute nominally.
Scott
Another way of solving this problem is to create an imputation model of your missing values on a representative sample of your data - sometimes called prototypes (you can also use cluster centroids for this), then apply the model to all your data and then start using your nice and clean data. I recall there was an example around this forum how to create a k-NN model, remember it and then recall it apply the model within an impute operator. I am sure it will help. Also decision trees are more commonly used as imputation model and work much faster.