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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,
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
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
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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>
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
Hi hamohame,
does the meta data propagation work with the sample data (Samples > data)? Maybe it is a problem in regard your CustomerDataSet object.