Hello,
The "Explain Predictions" operator color-codes the weightings of predictions in terms of their support. See the below screenshot for an example.
Is there a way to copy or export this data out, keeping the color coding intact? I'm aware that I can export this and also the weighting and recreate the color coding for myself, but RapidMiner has already done the work of applying the color coding to the "Explain Predictions" result set for me. I'd love to be able to get it into Excel with color coding intact. If that's not possible, it would even be sufficient to have some way of getting the data out as an image (again, with color coding intact). I've tried screenshotting software like SnagIt that can auto-scroll, but it doesn't seem to pick up RapidMiner's scrollbars.
I didn't see any other forum topics or KB articles on this topic.
Thanks!
<?xml version="1.0" encoding="UTF-8"?><process version="9.0.002">
<context>
<input/>
<output/>
<macros/>
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<parameter key="logverbosity" value="init"/>
<parameter key="random_seed" value="2001"/>
<parameter key="send_mail" value="never"/>
<parameter key="notification_email" value=""/>
<parameter key="process_duration_for_mail" value="30"/>
<parameter key="encoding" value="SYSTEM"/>
<process expanded="true">
<operator activated="true" class="retrieve" compatibility="9.0.002" expanded="true" height="68" name="Retrieve 20180521 testing" width="90" x="45" y="595">
<parameter key="repository_entry" value="../20180312 JRM/20180521 testing"/>
</operator>
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<operator activated="true" class="retrieve" compatibility="9.0.002" expanded="true" height="68" name="Retrieve 20180521 training" width="90" x="45" y="136">
<parameter key="repository_entry" value="../20180312 JRM/20180521 training"/>
</operator>
<operator activated="true" class="multiply" compatibility="9.0.002" expanded="true" height="166" name="Training" width="90" x="179" y="136"/>
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<parameter key="split_on_batch_attribute" value="false"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_folds" value="10"/>
<parameter key="sampling_type" value="automatic"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="enable_parallel_execution" value="true"/>
<process expanded="true">
<operator activated="true" class="h2o:deep_learning" compatibility="9.0.000" expanded="true" height="82" name="Deep Learning" width="90" x="179" y="34">
<parameter key="activation" value="Rectifier"/>
<enumeration key="hidden_layer_sizes">
<parameter key="hidden_layer_sizes" value="50"/>
<parameter key="hidden_layer_sizes" value="50"/>
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<parameter key="reproducible_(uses_1_thread)" value="false"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="epochs" value="10.0"/>
<parameter key="compute_variable_importances" value="false"/>
<parameter key="train_samples_per_iteration" value="-2"/>
<parameter key="adaptive_rate" value="true"/>
<parameter key="epsilon" value="1.0E-8"/>
<parameter key="rho" value="0.99"/>
<parameter key="learning_rate" value="0.005"/>
<parameter key="learning_rate_annealing" value="1.0E-6"/>
<parameter key="learning_rate_decay" value="1.0"/>
<parameter key="momentum_start" value="0.0"/>
<parameter key="momentum_ramp" value="1000000.0"/>
<parameter key="momentum_stable" value="0.0"/>
<parameter key="nesterov_accelerated_gradient" value="true"/>
<parameter key="standardize" value="true"/>
<parameter key="L1" value="1.0E-5"/>
<parameter key="L2" value="0.0"/>
<parameter key="max_w2" value="10.0"/>
<parameter key="loss_function" value="Automatic"/>
<parameter key="distribution_function" value="AUTO"/>
<parameter key="early_stopping" value="false"/>
<parameter key="stopping_rounds" value="1"/>
<parameter key="stopping_metric" value="AUTO"/>
<parameter key="stopping_tolerance" value="0.001"/>
<parameter key="missing_values_handling" value="MeanImputation"/>
<parameter key="max_runtime_seconds" value="0"/>
<list key="expert_parameters"/>
<list key="expert_parameters_"/>
</operator>
<connect from_port="training set" to_op="Deep Learning" to_port="training set"/>
<connect from_op="Deep Learning" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
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</process>
<process expanded="true">
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<operator activated="true" class="performance" compatibility="9.0.002" expanded="true" height="82" name="Performance DL" width="90" x="313" y="34">
<parameter key="use_example_weights" value="true"/>
</operator>
<connect from_port="model" to_op="Apply Model (2)" to_port="model"/>
<connect from_port="test set" to_op="Apply Model (2)" to_port="unlabelled data"/>
<connect from_op="Apply Model (2)" from_port="labelled data" to_op="Performance DL" to_port="labelled data"/>
<connect from_op="Performance DL" from_port="performance" to_port="performance 1"/>
<connect from_op="Performance DL" from_port="example set" to_port="test set results"/>
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</operator>
<operator activated="true" class="concurrency:cross_validation" compatibility="8.2.000" expanded="true" height="145" name="CV DT" width="90" x="380" y="442">
<parameter key="split_on_batch_attribute" value="false"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_folds" value="10"/>
<parameter key="sampling_type" value="automatic"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="enable_parallel_execution" value="true"/>
<process expanded="true">
<operator activated="true" class="concurrency:parallel_decision_tree" compatibility="9.0.002" expanded="true" height="103" name="Decision Tree" width="90" x="179" y="34">
<parameter key="criterion" value="gain_ratio"/>
<parameter key="maximal_depth" value="20"/>
<parameter key="apply_pruning" value="true"/>
<parameter key="confidence" value="0.35"/>
<parameter key="apply_prepruning" value="true"/>
<parameter key="minimal_gain" value="0.1"/>
<parameter key="minimal_leaf_size" value="2"/>
<parameter key="minimal_size_for_split" value="4"/>
<parameter key="number_of_prepruning_alternatives" value="3"/>
</operator>
<connect from_port="training set" to_op="Decision Tree" to_port="training set"/>
<connect from_op="Decision Tree" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
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<parameter key="use_example_weights" value="true"/>
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<connect from_port="model" to_op="Apply Model" to_port="model"/>
<connect from_port="test set" to_op="Apply Model" to_port="unlabelled data"/>
<connect from_op="Apply Model" from_port="labelled data" to_op="Performance DT" to_port="labelled data"/>
<connect from_op="Performance DT" from_port="performance" to_port="performance 1"/>
<connect from_op="Performance DT" from_port="example set" to_port="test set results"/>
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<parameter key="split_on_batch_attribute" value="false"/>
<parameter key="leave_one_out" value="false"/>
<parameter key="number_of_folds" value="10"/>
<parameter key="sampling_type" value="automatic"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="enable_parallel_execution" value="true"/>
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<parameter key="reproducible" value="false"/>
<parameter key="maximum_number_of_threads" value="4"/>
<parameter key="use_regularization" value="false"/>
<parameter key="lambda_search" value="false"/>
<parameter key="number_of_lambdas" value="0"/>
<parameter key="lambda_min_ratio" value="0.0"/>
<parameter key="early_stopping" value="true"/>
<parameter key="stopping_rounds" value="3"/>
<parameter key="stopping_tolerance" value="0.001"/>
<parameter key="standardize" value="true"/>
<parameter key="non-negative_coefficients" value="false"/>
<parameter key="add_intercept" value="true"/>
<parameter key="compute_p-values" value="true"/>
<parameter key="remove_collinear_columns" value="true"/>
<parameter key="missing_values_handling" value="MeanImputation"/>
<parameter key="max_iterations" value="0"/>
<parameter key="max_runtime_seconds" value="0"/>
</operator>
<connect from_port="training set" to_op="Logistic Regression" to_port="training set"/>
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<parameter key="file_format" value="xlsx"/>
<parameter key="encoding" value="SYSTEM"/>
<parameter key="sheet_name" value="RapidMiner Data"/>
<parameter key="date_format" value="yyyy-MM-dd HH:mm:ss"/>
<parameter key="number_format" value="#.0"/>
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<operator activated="true" class="jdbc_connectors:write_database" compatibility="9.0.002" expanded="true" height="68" name="DT Write Database" width="90" x="983" y="493">
<parameter key="define_connection" value="predefined"/>
<parameter key="connection" value="BERCMDS"/>
<parameter key="database_system" value="MySQL"/>
<parameter key="use_default_schema" value="true"/>
<parameter key="table_name" value="jrm.BER_RAPIDMINER_DT_OUTPUT_20180521"/>
<parameter key="overwrite_mode" value="none"/>
<parameter key="set_default_varchar_length" value="false"/>
<parameter key="default_varchar_length" value="128"/>
<parameter key="add_generated_primary_keys" value="false"/>
<parameter key="db_key_attribute_name" value="generated_primary_key"/>
<parameter key="batch_size" value="1"/>
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<operator activated="true" class="model_simulator:explain_predictions" compatibility="9.0.001" expanded="true" height="103" name="DT Explain Predictions" width="90" x="782" y="595">
<parameter key="maximal explaining attributes" value="3"/>
<parameter key="local sample size" value="500"/>
</operator>
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<parameter key="file_format" value="xlsx"/>
<parameter key="encoding" value="SYSTEM"/>
<parameter key="sheet_name" value="RapidMiner Data"/>
<parameter key="date_format" value="yyyy-MM-dd HH:mm:ss"/>
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<parameter key="database_system" value="MySQL"/>
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<parameter key="table_name" value="jrm.BER_RAPIDMINER_DL_OUTPUT_20180521"/>
<parameter key="overwrite_mode" value="none"/>
<parameter key="set_default_varchar_length" value="false"/>
<parameter key="default_varchar_length" value="128"/>
<parameter key="add_generated_primary_keys" value="false"/>
<parameter key="db_key_attribute_name" value="generated_primary_key"/>
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<parameter key="maximal explaining attributes" value="3"/>
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<parameter key="excel_file" value="C:\Users\zsherwin\Desktop\rapidminer\output_lr.xlsx"/>
<parameter key="file_format" value="xlsx"/>
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<parameter key="sheet_name" value="RapidMiner Data"/>
<parameter key="date_format" value="yyyy-MM-dd HH:mm:ss"/>
<parameter key="number_format" value="#.0"/>
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<parameter key="table_name" value="jrm.BER_RAPIDMINER_LR_OUTPUT_20180521"/>
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<parameter key="set_default_varchar_length" value="false"/>
<parameter key="default_varchar_length" value="128"/>
<parameter key="add_generated_primary_keys" value="false"/>
<parameter key="db_key_attribute_name" value="generated_primary_key"/>
<parameter key="batch_size" value="1"/>
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<connect from_op="Retrieve 20180521 testing" from_port="output" to_op="Testing" to_port="input"/>
<connect from_op="Testing" from_port="output 1" to_op="DT Test" to_port="unlabelled data"/>
<connect from_op="Testing" from_port="output 2" to_op="DT Explain Predictions" to_port="test data"/>
<connect from_op="Testing" from_port="output 3" to_op="DL Test" to_port="unlabelled data"/>
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<connect from_op="DT Test" from_port="labelled data" to_op="DT Write" to_port="input"/>
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<connect from_op="DL Test" from_port="labelled data" to_op="DL Write" to_port="input"/>
<connect from_op="DL Test" from_port="model" to_op="DL Explain Predictions" to_port="model"/>
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<connect from_op="DL Write" from_port="through" to_op="DL Write Database (2)" to_port="input"/>
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<connect from_op="LR Test" from_port="labelled data" to_op="LR Write" to_port="input"/>
<connect from_op="LR Test" from_port="model" to_op="LR Explain Predictions" to_port="model"/>
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<connect from_op="LR Explain Predictions" from_port="importances output" to_port="result 9"/>
<connect from_op="LR Write" from_port="through" to_op="LR Write Database (3)" to_port="input"/>
<connect from_op="LR Write Database (3)" from_port="through" to_port="result 1"/>
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<portSpacing port="sink_result 12" spacing="0"/>
<portSpacing port="sink_result 13" spacing="0"/>
</process>
</operator>
</process>
