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Meta data
Learner IIIRapidminer can not load meta-data. It doesn't show any data whenever I try to use any operator. There is a screenshot attached to clarify what I mean. Does anyone have a solution?? I tried everything I could and I did uninstall and install again more than 6 times and it was not of any use.
1
Best Answer
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mmichel
Employee-RapidMiner, Member Posts: 129 
RM Engineering
The Meta data generation will only be triggered during process validation. Make sure you've validated the process manually (Process>Validate Process) or you are automatically validating the process (Process>Validate Automatically).
Cheers,
Marcel
2
Answers
Hi hamohame,
does the meta data propagation work with the sample data (Samples > data)? Maybe it is a problem in regard your CustomerDataSet object.
I have exactly the same problem!
I worked through the Tutorial on Wednesday and finished for the day. Yesterday I wanted to play a little bit with the sample data to improve my skills, but the meta datas weren´t available anymore. When I hover the mouse over the output port of any Retrieve operator, the pop up window is nearly empty, there is only shown: "meta data: -"
After opening the example set of the Retrieve operator there is one more row in the pop up window with "data: ....[number of examples etc.]", but still "meta data: -".
I´m not aible to use the meta data in following operators, but if i type in the correct meta data "manually", the results are correct. But this is very uncomfortable.
I haven´t changed any settings since i installed RapidMiner Studio and worked through the tutorial.
Where is my mistake?
hi 8FSL,
what kind of data source are you using? In some cases it is very hard for RM to catch these. I would recommend to store your data first in the repo and work on this one (maybe only as a dev dump). Afterwards you should always have metadata where metdata is provideable.
~Martin
Dortmund, Germany
Hi Martin,
i´m still trying with the sample datas, which worked on Wednesday and till yesterday i have this problem, without changing anything.
I also tried to use the sample datas in the Tutorial again, but there is the same problem now.
The data source is from RM /tutorial/...
Hi,
that sounds like a bug to me. Could you please have a look if you accidently checkmarked "Synchronize Meta Data with Real Data" in the Process menu? That might change the behaviour of the meta data propagation.
If that is not the case, @mmichel: can you have a look?
~Martin
Dortmund, Germany
There is no checkmark before "Synchronize Meta Data with Real Data" in the Process menu.
I have nothing checkmarked in process menu.
If i do a checkmark it is the same behaviour as without: no meta data when hovering with the mouse over the output or using an operator.
The expected behaviour of a check marked RM would be, that the meta data is there after one execution of the process. The meta data is then not taken from our meta data propagation, but from the real data. This is very helpful in cases, where we cannot provide accurate meta data w/o executing the process - best example is a pivot operator.
Anyway, this sounds odd. Lets see if Marcel can help.
~Martin
Dortmund, Germany
Now everything is fine again.
Many Thanks!
very nice!...thanks
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N,0.0001 N,0.0001 N,0.034608624 Y,0.411143627 Y,0.403309021 Y,0.690479379 Y,0.58645295 Y,0.703471435 N,0.240645419 N,0.317348965 N,0.0001 N,0.0001 Y,0.479985015 N,0.051663453 N,0.151098679 Y,0.400885126 Y,0.669478979 N,0.0001 N,0.084509219 N,0.104969007 N,0.202747595 N,0.0001 Y,0.248550676 Y,0.736360718 Y,0.507362049 Y,0.45185447 N,0.170658129 N,0.0001 Y,0.492524913 Y,0.863828983 Y,0.505563511 Y,0.855626349 Y,0.214503677 Y,0.667043014 N,0.04338791 N,0.268321785 N,0.163068649 N,0.032860214 Y,0.432603468 N,0.050831568 Y,0.282183637 Y,0.497255402 Y,0.507714476 N,0.048046271 N,0.302599739 Y,0.552843342 N,0.0001 N,0.0001 N,0.045763161 N,0.0001 Y,0.35923354 Y,0.275079272 Y,0.490030294 N,0.093214431 Y,0.33982672 Y,0.228286029 N,0.173078966 N,0.24842934 N,0.0001 Y,0.215257771 N,0.10263881 Y,0.609961692 Y,0.153339016 N,0.040604182 N,0.0001 Y,0.420378131 N,0.035050649 N,0.0001 Y,0.956775757 N,0.312246201 N,0.0001 Y,0.619621516 N,0.062474506 Y,0.83682199 N,0.0001 N,0.0001 N,0.087984095 Y,0.513109824 Y,0.168519626 N,0.0001 N,0.0125382 Y,0.623915238 N,0.299362197 Y,0.700795017 Y,0.639515816 N,0.0001 Y,0.423247208 Y,0.314171344 N,0.0001 Y,0.675042884 Y,0.858816065 N,0.0001 N,0.262590264 N,0.107755031 Y,0.642963514 Y,0.684925359 Y,0.439486907 Y,0.474113532 Y,0.248236629 Y,0.573986919 N,0.015263223 Y,0.65455098 Y,0.699771428"/> <parameter key="column_separator" value=","/> <parameter key="parse_all_as_nominal" value="false"/> <parameter key="decimal_point_character" value="."/> <parameter key="trim_attribute_names" value="true"/> </operator> <operator activated="true" class="nominal_to_binominal" compatibility="9.6.000" expanded="true" height="103" name="Nominal to Binominal" width="90" x="179" y="34"> <parameter key="return_preprocessing_model" value="false"/> <parameter key="create_view" value="false"/> <parameter key="attribute_filter_type" value="single"/> <parameter key="attribute" value="pred"/> <parameter key="attributes" value=""/> <parameter key="use_except_expression" value="false"/> <parameter key="value_type" value="nominal"/> <parameter key="use_value_type_exception" value="false"/> <parameter key="except_value_type" value="file_path"/> <parameter key="block_type" value="single_value"/> <parameter key="use_block_type_exception" value="false"/> <parameter key="except_block_type" value="single_value"/> <parameter key="invert_selection" value="false"/> <parameter key="include_special_attributes" value="false"/> <parameter key="transform_binominal" value="false"/> <parameter key="use_underscore_in_name" value="false"/> </operator> <operator activated="true" class="generate_attributes" compatibility="9.6.000" expanded="true" height="82" name="Generate Attributes" width="90" x="45" y="187"> <list key="function_descriptions"> <parameter key="pred" value="Data"/> <parameter key="confidence(N)" value="1-[Predicted (Model)]"/> <parameter key="confidence(Y)" value="[Predicted (Model)]"/> </list> <parameter key="keep_all" value="true"/> </operator> <operator activated="true" class="set_role" compatibility="9.6.000" expanded="true" height="82" name="Set Role" width="90" x="179" y="187"> <parameter key="attribute_name" value="Data"/> <parameter key="target_role" value="label"/> <list key="set_additional_roles"> <parameter key="pred" value="prediction"/> <parameter key="confidence(N)" value="confidence_N"/> <parameter key="confidence(Y)" value="confidence_Y"/> </list> </operator> <operator activated="true" class="select_attributes" compatibility="9.6.000" expanded="true" height="82" name="Select Attributes" width="90" x="313" y="187"> <parameter key="attribute_filter_type" value="single"/> <parameter key="attribute" value="Predicted (Model)"/> <parameter key="attributes" value=""/> <parameter key="use_except_expression" value="false"/> <parameter key="value_type" value="attribute_value"/> <parameter key="use_value_type_exception" value="false"/> <parameter key="except_value_type" value="time"/> <parameter key="block_type" value="attribute_block"/> <parameter key="use_block_type_exception" value="false"/> <parameter key="except_block_type" value="value_matrix_row_start"/> <parameter key="invert_selection" value="true"/> <parameter key="include_special_attributes" value="false"/> </operator> <operator activated="true" class="apply_threshold" compatibility="9.6.000" expanded="true" height="82" name="Apply Threshold" width="90" x="179" y="340"/> <operator activated="true" class="performance_binominal_classification" compatibility="9.6.000" expanded="true" height="82" name="Performance" width="90" x="45" y="442"> <parameter key="manually_set_positive_class" value="false"/> <parameter key="main_criterion" value="first"/> <parameter key="accuracy" value="false"/> <parameter key="classification_error" value="true"/> <parameter key="kappa" value="true"/> <parameter key="AUC (optimistic)" value="false"/> <parameter key="AUC" value="true"/> <parameter key="AUC (pessimistic)" value="false"/> <parameter key="precision" value="false"/> <parameter key="recall" value="false"/> <parameter key="lift" value="false"/> <parameter key="fallout" value="false"/> <parameter key="f_measure" value="false"/> <parameter key="false_positive" value="false"/> <parameter key="false_negative" value="false"/> <parameter key="true_positive" value="false"/> <parameter key="true_negative" value="false"/> <parameter key="sensitivity" value="false"/> <parameter key="specificity" value="false"/> <parameter key="youden" value="false"/> <parameter key="positive_predictive_value" value="false"/> <parameter key="negative_predictive_value" value="false"/> <parameter key="psep" value="false"/> <parameter key="skip_undefined_labels" value="true"/> <parameter key="use_example_weights" value="true"/> </operator> <connect from_op="Create Threshold" from_port="output" to_op="Apply Threshold" to_port="threshold"/> <connect from_op="Create ExampleSet" from_port="output" to_op="Nominal to Binominal" to_port="example set input"/> <connect from_op="Nominal to Binominal" from_port="example set output" to_op="Generate Attributes" to_port="example set input"/> <connect from_op="Generate Attributes" 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="Select Attributes" to_port="example set input"/> <connect from_op="Select Attributes" from_port="example set output" to_op="Apply Threshold" to_port="example set"/> <connect from_op="Apply Threshold" from_port="example set" to_op="Performance" to_port="labelled data"/> <connect from_op="Performance" from_port="performance" 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>