Error: Only One Label
nathaliejoy
New Altair Community Member
Currently working on an Educational Data Mining Project. I got a very common problem to some of my data sets I cant search this problem anywhere. Whenever I run my the process it always states
'Only one Label', The learning scheme Logistic regression does not sufficient capabilities for handling an example set with only one label.There are existing special modelling operators if only examples for one class are known. They Support the 'one class label' capability.
I did this kind of process to some of my datasets and works very fine. I also tried editing the labels because I used Multi label. I can't understand the problem. Please help guys!. Below is my XML .
<?xml version="1.0" encoding="UTF-8"?><process version="9.7.001">
<context>
<input/>
<output/>
<macros/>
</context>
<operator activated="true" class="process" compatibility="9.7.001" expanded="true" name="Process">
<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="read_excel" compatibility="9.7.001" expanded="true" height="68" name="Read Excel" width="90" x="45" y="34">
<parameter key="excel_file" value="D:\MyDocuments\CMUFiles\RESEARCH AND EXTENSION\SHs Performance NAT in Bukidnon\ExcelSubjectTemplate\Language-and-communication\finaldataAnalysis\Humss-Language-and-Communication-Trial.xlsx"/>
<parameter key="sheet_selection" value="sheet number"/>
<parameter key="sheet_number" value="1"/>
<parameter key="imported_cell_range" value="A1"/>
<parameter key="encoding" value="SYSTEM"/>
<parameter key="first_row_as_names" value="true"/>
<list key="annotations"/>
<parameter key="date_format" value=""/>
<parameter key="time_zone" value="SYSTEM"/>
<parameter key="locale" value="English (United States)"/>
<parameter key="read_all_values_as_polynominal" value="false"/>
<list key="data_set_meta_data_information">
<parameter key="0" value="Name.true.polynominal.attribute"/>
<parameter key="1" value="OC-G11-Q1.true.integer.attribute"/>
<parameter key="2" value="OC-G11-Q2.true.integer.attribute"/>
<parameter key="3" value="F-G11-Q1.true.integer.attribute"/>
<parameter key="4" value="F-G11-Q2.true.integer.attribute"/>
<parameter key="5" value="RWS-G11-Q3.true.integer.attribute"/>
<parameter key="6" value="RWS-G11-Q4.true.integer.attribute"/>
<parameter key="7" value="F-G11-Q3.true.integer.attribute"/>
<parameter key="8" value="F-G11-Q4.true.integer.attribute"/>
<parameter key="9" value="CW-G12-Q1.true.integer.attribute"/>
<parameter key="10" value="CW-G12-Q2.true.integer.attribute"/>
<parameter key="11" value="LC-PS-NAT.true.real.attribute"/>
<parameter key="12" value="LC-PS-NAT-Rem.true.polynominal.attribute"/>
<parameter key="13" value="LC-IL-NAT.true.real.attribute"/>
<parameter key="14" value="LC-IL-NAT-Rem.true.polynominal.attribute"/>
<parameter key="15" value="LC-CT-NAT.true.real.attribute"/>
<parameter key="16" value="LC-CT-NAT-Rem.true.polynominal.attribute"/>
<parameter key="17" value="Total-MPS.true.real.attribute"/>
<parameter key="18" value="overall-remarks.true.polynominal.attribute"/>
</list>
<parameter key="read_not_matching_values_as_missings" value="false"/>
<parameter key="datamanagement" value="double_array"/>
<parameter key="data_management" value="auto"/>
</operator>
<operator activated="true" class="subprocess" compatibility="9.7.001" expanded="true" height="82" name="Subprocess" width="90" x="179" y="34">
<process expanded="true">
<operator activated="true" class="replace_missing_values" compatibility="9.7.001" expanded="true" height="103" name="Replace Missing Values" width="90" x="45" y="34">
<parameter key="return_preprocessing_model" value="false"/>
<parameter key="create_view" value="false"/>
<parameter key="attribute_filter_type" value="all"/>
<parameter key="attribute" value=""/>
<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="false"/>
<parameter key="include_special_attributes" value="false"/>
<parameter key="default" value="average"/>
<list key="columns"/>
</operator>
<operator activated="true" class="generate_id" compatibility="9.7.001" expanded="true" height="82" name="Generate ID" width="90" x="179" y="34">
<parameter key="create_nominal_ids" value="true"/>
<parameter key="offset" value="0"/>
</operator>
<operator activated="true" class="select_attributes" compatibility="9.7.001" expanded="true" height="82" name="Select Attributes" width="90" x="313" y="34">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value="CW-G12-Q1|CW-G12-Q2|F-G11-Q1|F-G11-Q2|F-G11-Q3|F-G11-Q4|OC-G11-Q1|OC-G11-Q2|overall-remarks|RWS-G11-Q3|RWS-G11-Q4"/>
<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="false"/>
<parameter key="include_special_attributes" value="false"/>
</operator>
<operator activated="true" class="remove_useless_attributes" compatibility="9.7.001" expanded="true" height="82" name="Remove Useless Attributes" width="90" x="514" y="34">
<parameter key="numerical_min_deviation" value="0.0"/>
<parameter key="nominal_useless_above" value="1.0"/>
<parameter key="nominal_remove_id_like" value="false"/>
<parameter key="nominal_useless_below" value="0.0"/>
</operator>
<connect from_port="in 1" to_op="Replace Missing Values" to_port="example set input"/>
<connect from_op="Replace Missing Values" from_port="example set output" to_op="Generate ID" to_port="example set input"/>
<connect from_op="Generate ID" 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="Remove Useless Attributes" to_port="example set input"/>
<connect from_op="Remove Useless Attributes" from_port="example set output" to_port="out 1"/>
<portSpacing port="source_in 1" spacing="0"/>
<portSpacing port="source_in 2" spacing="0"/>
<portSpacing port="sink_out 1" spacing="0"/>
<portSpacing port="sink_out 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="set_role" compatibility="9.7.001" expanded="true" height="82" name="Set Role" width="90" x="313" y="34">
<parameter key="attribute_name" value="id"/>
<parameter key="target_role" value="batch"/>
<list key="set_additional_roles">
<parameter key="overall-remarks" value="label"/>
</list>
</operator>
<operator activated="true" class="split_data" compatibility="9.7.001" expanded="true" height="103" name="Split Data" width="90" x="447" y="85">
<enumeration key="partitions">
<parameter key="ratio" value="0.7"/>
<parameter key="ratio" value="0.3"/>
</enumeration>
<parameter key="sampling_type" value="automatic"/>
<parameter key="use_local_random_seed" value="true"/>
<parameter key="local_random_seed" value="1992"/>
</operator>
<operator activated="true" class="optimize_selection_evolutionary" compatibility="9.7.001" expanded="true" height="145" name="Optimize Selection (Evolutionary)" width="90" x="648" y="34">
<parameter key="use_exact_number_of_attributes" value="false"/>
<parameter key="restrict_maximum" value="false"/>
<parameter key="min_number_of_attributes" value="1"/>
<parameter key="max_number_of_attributes" value="1"/>
<parameter key="exact_number_of_attributes" value="1"/>
<parameter key="initialize_with_input_weights" value="false"/>
<parameter key="population_size" value="5"/>
<parameter key="maximum_number_of_generations" value="30"/>
<parameter key="use_early_stopping" value="false"/>
<parameter key="generations_without_improval" value="2"/>
<parameter key="normalize_weights" value="true"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<parameter key="user_result_individual_selection" value="false"/>
<parameter key="show_population_plotter" value="false"/>
<parameter key="plot_generations" value="10"/>
<parameter key="constraint_draw_range" value="false"/>
<parameter key="draw_dominated_points" value="true"/>
<parameter key="maximal_fitness" value="Infinity"/>
<parameter key="selection_scheme" value="tournament"/>
<parameter key="tournament_size" value="0.25"/>
<parameter key="start_temperature" value="1.0"/>
<parameter key="dynamic_selection_pressure" value="true"/>
<parameter key="keep_best_individual" value="false"/>
<parameter key="save_intermediate_weights" value="false"/>
<parameter key="intermediate_weights_generations" value="10"/>
<parameter key="p_initialize" value="0.5"/>
<parameter key="p_mutation" value="-1.0"/>
<parameter key="p_crossover" value="0.5"/>
<parameter key="crossover_type" value="uniform"/>
<process expanded="true">
<operator activated="true" class="time_series:multi_label_model_learner" compatibility="9.7.000" expanded="true" height="103" name="Multi Label Modeling" width="90" x="179" y="34">
<parameter key="attribute_filter_type" value="subset"/>
<parameter key="attribute" value=""/>
<parameter key="attributes" value="overall-remarks"/>
<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="false"/>
<parameter key="include_special_attributes" value="true"/>
<parameter key="add_macros" value="false"/>
<parameter key="current_label_name_macro" value="current_label_attribute"/>
<parameter key="current_label_type_macro" value="current_label_type"/>
<parameter key="enable_parallel_execution" value="true"/>
<process expanded="true">
<operator activated="true" class="set_role" compatibility="9.7.001" expanded="true" height="82" name="Set Role (2)" width="90" x="112" y="34">
<parameter key="attribute_name" value="overall-remarks"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles"/>
</operator>
<operator activated="true" class="concurrency:cross_validation" compatibility="9.7.001" expanded="true" height="145" name="Cross Validation" width="90" x="313" y="34">
<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="false"/>
<process expanded="true">
<operator activated="true" class="polynomial_by_binomial_classification" compatibility="9.7.001" expanded="true" height="82" name="Polynominal by Binominal Classification" width="90" x="112" y="34">
<parameter key="classification_strategies" value="1 against all"/>
<parameter key="random_code_multiplicator" value="2.0"/>
<parameter key="use_local_random_seed" value="false"/>
<parameter key="local_random_seed" value="1992"/>
<process expanded="true">
<operator activated="true" class="h2o:logistic_regression" compatibility="9.7.001" expanded="true" height="124" name="Logistic Regression" width="90" x="179" y="34">
<parameter key="solver" value="AUTO"/>
<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"/>
<connect from_op="Logistic Regression" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
</process>
</operator>
<connect from_port="training set" to_op="Polynominal by Binominal Classification" to_port="training set"/>
<connect from_op="Polynominal by Binominal Classification" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_through 1" spacing="0"/>
</process>
<process expanded="true">
<operator activated="true" class="apply_model" compatibility="9.7.001" expanded="true" height="82" name="Apply Model" width="90" x="45" y="34">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="performance_classification" compatibility="9.7.001" expanded="true" height="82" name="Performance" width="90" x="179" y="34">
<parameter key="main_criterion" value="first"/>
<parameter key="accuracy" value="true"/>
<parameter key="classification_error" value="false"/>
<parameter key="kappa" value="false"/>
<parameter key="weighted_mean_recall" value="false"/>
<parameter key="weighted_mean_precision" value="false"/>
<parameter key="spearman_rho" value="false"/>
<parameter key="kendall_tau" value="false"/>
<parameter key="absolute_error" value="false"/>
<parameter key="relative_error" value="false"/>
<parameter key="relative_error_lenient" value="false"/>
<parameter key="relative_error_strict" value="false"/>
<parameter key="normalized_absolute_error" value="false"/>
<parameter key="root_mean_squared_error" value="false"/>
<parameter key="root_relative_squared_error" value="false"/>
<parameter key="squared_error" value="false"/>
<parameter key="correlation" value="false"/>
<parameter key="squared_correlation" value="false"/>
<parameter key="cross-entropy" value="false"/>
<parameter key="margin" value="false"/>
<parameter key="soft_margin_loss" value="false"/>
<parameter key="logistic_loss" value="false"/>
<parameter key="skip_undefined_labels" value="true"/>
<parameter key="use_example_weights" value="true"/>
<list key="class_weights"/>
</operator>
<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" to_port="labelled data"/>
<connect from_op="Performance" from_port="performance" to_port="performance 1"/>
<portSpacing port="source_model" spacing="0"/>
<portSpacing port="source_test set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="sink_test set results" spacing="0"/>
<portSpacing port="sink_performance 1" spacing="0"/>
<portSpacing port="sink_performance 2" spacing="0"/>
</process>
</operator>
<operator activated="true" class="apply_model" compatibility="9.7.001" expanded="true" height="82" name="Apply Model (3)" width="90" x="514" y="187">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<connect from_port="training set" to_op="Set Role (2)" to_port="example set input"/>
<connect from_port="input 1" to_op="Apply Model (3)" to_port="unlabelled data"/>
<connect from_op="Set Role (2)" from_port="example set output" to_op="Cross Validation" to_port="example set"/>
<connect from_op="Cross Validation" from_port="model" to_op="Apply Model (3)" to_port="model"/>
<connect from_op="Apply Model (3)" from_port="model" to_port="model"/>
<portSpacing port="source_training set" spacing="0"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="source_input 2" spacing="0"/>
<portSpacing port="sink_model" spacing="0"/>
<portSpacing port="sink_output 1" spacing="0"/>
</process>
</operator>
<operator activated="true" class="apply_model" compatibility="9.7.001" expanded="true" height="82" name="Apply Model (2)" width="90" x="313" y="187">
<list key="application_parameters"/>
<parameter key="create_view" value="false"/>
</operator>
<operator activated="true" class="set_role" compatibility="9.7.001" expanded="true" height="82" name="Set Role (3)" width="90" x="447" y="34">
<parameter key="attribute_name" value="overall-remarks"/>
<parameter key="target_role" value="label"/>
<list key="set_additional_roles">
<parameter key="prediction(overall-remarks)" value="label"/>
</list>
</operator>
<operator activated="true" class="performance_classification" compatibility="9.7.001" expanded="true" height="82" name="Performance (2)" width="90" x="581" y="34">
<parameter key="main_criterion" value="first"/>
<parameter key="accuracy" value="true"/>
<parameter key="classification_error" value="false"/>
<parameter key="kappa" value="false"/>
<parameter key="weighted_mean_recall" value="false"/>
<parameter key="weighted_mean_precision" value="false"/>
<parameter key="spearman_rho" value="false"/>
<parameter key="kendall_tau" value="false"/>
<parameter key="absolute_error" value="false"/>
<parameter key="relative_error" value="false"/>
<parameter key="relative_error_lenient" value="false"/>
<parameter key="relative_error_strict" value="false"/>
<parameter key="normalized_absolute_error" value="false"/>
<parameter key="root_mean_squared_error" value="false"/>
<parameter key="root_relative_squared_error" value="false"/>
<parameter key="squared_error" value="false"/>
<parameter key="correlation" value="false"/>
<parameter key="squared_correlation" value="false"/>
<parameter key="cross-entropy" value="false"/>
<parameter key="margin" value="false"/>
<parameter key="soft_margin_loss" value="false"/>
<parameter key="logistic_loss" value="false"/>
<parameter key="skip_undefined_labels" value="true"/>
<parameter key="use_example_weights" value="true"/>
<list key="class_weights"/>
</operator>
<connect from_port="example set" to_op="Multi Label Modeling" to_port="input 1"/>
<connect from_port="through 1" to_op="Multi Label Modeling" to_port="training set"/>
<connect from_port="through 2" to_op="Apply Model (2)" to_port="unlabelled data"/>
<connect from_op="Multi Label Modeling" from_port="model" to_op="Apply Model (2)" to_port="model"/>
<connect from_op="Apply Model (2)" from_port="labelled data" to_op="Set Role (3)" to_port="example set input"/>
<connect from_op="Set Role (3)" from_port="example set output" to_op="Performance (2)" to_port="labelled data"/>
<connect from_op="Performance (2)" from_port="performance" to_port="performance"/>
<portSpacing port="source_example set" spacing="0"/>
<portSpacing port="source_through 1" spacing="0"/>
<portSpacing port="source_through 2" spacing="0"/>
<portSpacing port="source_through 3" spacing="0"/>
<portSpacing port="sink_performance" spacing="0"/>
</process>
</operator>
<connect from_op="Read Excel" from_port="output" to_op="Subprocess" to_port="in 1"/>
<connect from_op="Subprocess" from_port="out 1" to_op="Set Role" to_port="example set input"/>
<connect from_op="Set Role" from_port="example set output" to_op="Optimize Selection (Evolutionary)" to_port="example set in"/>
<connect from_op="Set Role" from_port="original" to_op="Split Data" to_port="example set"/>
<connect from_op="Split Data" from_port="partition 1" to_op="Optimize Selection (Evolutionary)" to_port="through 1"/>
<connect from_op="Split Data" from_port="partition 2" to_op="Optimize Selection (Evolutionary)" to_port="through 2"/>
<connect from_op="Optimize Selection (Evolutionary)" from_port="example set out" to_port="result 1"/>
<connect from_op="Optimize Selection (Evolutionary)" from_port="weights" to_port="result 2"/>
<connect from_op="Optimize Selection (Evolutionary)" from_port="performance" to_port="result 3"/>
<portSpacing port="source_input 1" spacing="0"/>
<portSpacing port="sink_result 1" spacing="0"/>
<portSpacing port="sink_result 2" spacing="0"/>
<portSpacing port="sink_result 3" spacing="0"/>
<portSpacing port="sink_result 4" spacing="0"/>
</process>
</operator>
</process>
1
Answers
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As noted in a related post, logistic regression requires a label with two values (a binominal value type, like "yes" and "no"). This error message indicates your label only has one value so logistic regression will not work.
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