test
This commit is contained in:
ksanu
parent
08fe617c0f
commit
c84faa55ea
4
.idea/encodings.xml
Normal file
4
.idea/encodings.xml
Normal file
@ -0,0 +1,4 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
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<component name="Encoding" addBOMForNewFiles="with NO BOM" />
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</project>
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@ -2,9 +2,8 @@
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<project version="4">
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<component name="ChangeListManager">
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<list default="true" id="d25a65da-2ba0-4272-a0a5-c59cbecb6088" name="Default Changelist" comment="">
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<change beforePath="$PROJECT_DIR$/.idea/TAU_21_sane_words.iml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/TAU_21_sane_words.iml" afterDir="false" />
|
||||
<change beforePath="$PROJECT_DIR$/.idea/misc.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/misc.xml" afterDir="false" />
|
||||
<change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
|
||||
<change beforePath="$PROJECT_DIR$/dev-0/expected.tsv" beforeDir="false" afterPath="$PROJECT_DIR$/dev-0/expected.tsv" afterDir="false" />
|
||||
<change beforePath="$PROJECT_DIR$/dev-0/out.tsv" beforeDir="false" afterPath="$PROJECT_DIR$/dev-0/out.tsv" afterDir="false" />
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||||
<change beforePath="$PROJECT_DIR$/dev-0/out_float.tsv" beforeDir="false" afterPath="$PROJECT_DIR$/dev-0/out_float.tsv" afterDir="false" />
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<change beforePath="$PROJECT_DIR$/s.py" beforeDir="false" afterPath="$PROJECT_DIR$/s.py" afterDir="false" />
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@ -18,15 +17,15 @@
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<option name="LAST_RESOLUTION" value="IGNORE" />
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||||
</component>
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||||
<component name="CoverageDataManager">
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||||
<SUITE FILE_PATH="coverage/TAU_22_sane_words_torch_nn$s.coverage" NAME="s Coverage Results" MODIFIED="1575414386650" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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||||
<SUITE FILE_PATH="coverage/TAU_22_sane_words_torch_nn$s.coverage" NAME="s Coverage Results" MODIFIED="1575494879139" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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</component>
|
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<component name="FileEditorManager">
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<leaf>
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<file pinned="false" current-in-tab="false">
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<leaf SIDE_TABS_SIZE_LIMIT_KEY="300">
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<file pinned="false" current-in-tab="true">
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<entry file="file://$PROJECT_DIR$/s.py">
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<provider selected="true" editor-type-id="text-editor">
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<state relative-caret-position="980">
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<caret line="225" column="18" selection-start-line="225" selection-start-column="18" selection-end-line="225" selection-end-column="18" />
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<state relative-caret-position="191">
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<caret line="269" column="15" selection-start-line="269" selection-start-column="15" selection-end-line="269" selection-end-column="15" />
|
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<folding>
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<element signature="e#0#12#0" expanded="true" />
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</folding>
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@ -35,30 +34,32 @@
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</entry>
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</file>
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<file pinned="false" current-in-tab="false">
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<entry file="file://$PROJECT_DIR$/dev-0/expected.tsv">
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<entry file="file://$PROJECT_DIR$/s2.py">
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<provider selected="true" editor-type-id="text-editor">
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||||
<state relative-caret-position="-196952" />
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||||
</provider>
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||||
</entry>
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</file>
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<file pinned="false" current-in-tab="true">
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<entry file="file://$PROJECT_DIR$/dev-0/out.tsv">
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<provider selected="true" editor-type-id="text-editor">
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<state relative-caret-position="342">
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<caret line="19" selection-start-line="19" selection-end-line="20" />
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<state relative-caret-position="121">
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<caret line="304" column="13" selection-end-line="304" selection-end-column="13" />
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<folding>
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<element signature="e#0#12#0" expanded="true" />
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</folding>
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</state>
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</provider>
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</entry>
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</file>
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<file pinned="false" current-in-tab="false">
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<entry file="file://$PROJECT_DIR$/test-A/out.tsv">
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<provider selected="true" editor-type-id="text-editor" />
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<entry file="file:///usr/lib/python3.6/subprocess.py">
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<provider selected="true" editor-type-id="text-editor">
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<state relative-caret-position="352">
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<caret line="437" selection-start-line="437" selection-end-line="437" />
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</state>
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</provider>
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</entry>
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</file>
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<file pinned="false" current-in-tab="false">
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<entry file="file://$PROJECT_DIR$/dev-0/out_float.tsv">
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<provider selected="true" editor-type-id="text-editor">
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<state relative-caret-position="-198268" />
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<state relative-caret-position="36">
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<caret line="2" column="3" lean-forward="true" selection-start-line="2" selection-start-column="3" selection-end-line="2" selection-end-column="3" />
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</state>
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</provider>
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</entry>
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</file>
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@ -77,15 +78,17 @@
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<component name="IdeDocumentHistory">
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<option name="CHANGED_PATHS">
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<list>
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<option value="$PROJECT_DIR$/new_lab.py" />
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<option value="$PROJECT_DIR$/s2.py" />
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<option value="$PROJECT_DIR$/s.py" />
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</list>
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</option>
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</component>
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<component name="ProjectFrameBounds">
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<option name="x" value="147" />
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<option name="y" value="16" />
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||||
<option name="width" value="1320" />
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||||
<option name="height" value="724" />
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<option name="x" value="66" />
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<option name="y" value="117" />
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||||
<option name="width" value="1339" />
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<option name="height" value="687" />
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||||
</component>
|
||||
<component name="ProjectId" id="1UAXhosCPbReL7U2TCbyyTVGpqs" />
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||||
<component name="ProjectLevelVcsManager" settingsEditedManually="true" />
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||||
@ -94,7 +97,6 @@
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||||
<foldersAlwaysOnTop value="true" />
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||||
</navigator>
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<panes>
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<pane id="Scope" />
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<pane id="ProjectPane">
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<subPane>
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<expand>
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@ -116,9 +118,11 @@
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<select />
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</subPane>
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</pane>
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<pane id="Scope" />
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</panes>
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</component>
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||||
<component name="PropertiesComponent">
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||||
<property name="SHARE_PROJECT_CONFIGURATION_FILES" value="true" />
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||||
<property name="WebServerToolWindowFactoryState" value="false" />
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||||
<property name="last_opened_file_path" value="$PROJECT_DIR$" />
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<property name="settings.editor.selected.configurable" value="com.jetbrains.python.configuration.PyActiveSdkModuleConfigurable" />
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@ -136,6 +140,28 @@
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</option>
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</component>
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<component name="RunManager" selected="Python.s">
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<configuration name="new_lab" type="PythonConfigurationType" factoryName="Python" temporary="true">
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<module name="TAU_21_sane_words" />
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<option name="INTERPRETER_OPTIONS" value="" />
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<option name="PARENT_ENVS" value="true" />
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<envs>
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<env name="PYTHONUNBUFFERED" value="1" />
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</envs>
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<option name="SDK_HOME" value="" />
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<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
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<option name="IS_MODULE_SDK" value="true" />
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<option name="ADD_CONTENT_ROOTS" value="true" />
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<option name="ADD_SOURCE_ROOTS" value="true" />
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<EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
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<option name="SCRIPT_NAME" value="$PROJECT_DIR$/new_lab.py" />
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<option name="PARAMETERS" value="" />
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<option name="SHOW_COMMAND_LINE" value="false" />
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<option name="EMULATE_TERMINAL" value="false" />
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<option name="MODULE_MODE" value="false" />
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<option name="REDIRECT_INPUT" value="false" />
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<option name="INPUT_FILE" value="" />
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<method v="2" />
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</configuration>
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<configuration name="s" type="PythonConfigurationType" factoryName="Python" temporary="true">
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<module name="TAU_21_sane_words" />
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<option name="INTERPRETER_OPTIONS" value="" />
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@ -205,6 +231,7 @@
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<recent_temporary>
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<list>
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<item itemvalue="Python.s" />
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<item itemvalue="Python.new_lab" />
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<item itemvalue="Python.solution2" />
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<item itemvalue="Python.solution" />
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</list>
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@ -220,38 +247,48 @@
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<option name="number" value="Default" />
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<option name="presentableId" value="Default" />
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<updated>1574800494334</updated>
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<workItem from="1575412092709" duration="2059000" />
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<workItem from="1575412092709" duration="3487000" />
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<workItem from="1575450164911" duration="5424000" />
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<workItem from="1575467930210" duration="14040000" />
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</task>
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<task id="LOCAL-00001" summary="test3">
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<created>1575414898244</created>
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<option name="number" value="00001" />
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<option name="presentableId" value="LOCAL-00001" />
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<option name="project" value="LOCAL" />
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<updated>1575414898244</updated>
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</task>
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||||
<option name="localTasksCounter" value="2" />
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||||
<servers />
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||||
</component>
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||||
<component name="TimeTrackingManager">
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||||
<option name="totallyTimeSpent" value="2059000" />
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<option name="totallyTimeSpent" value="22951000" />
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||||
</component>
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||||
<component name="ToolWindowManager">
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||||
<frame x="147" y="16" width="1320" height="724" extended-state="0" />
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<frame x="66" y="117" width="1339" height="687" extended-state="0" />
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<editor active="true" />
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<layout>
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<window_info id="Favorites" side_tool="true" />
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<window_info active="true" content_ui="combo" id="Project" order="0" visible="true" weight="0.24405706" />
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||||
<window_info content_ui="combo" id="Project" order="0" visible="true" weight="0.22092116" />
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||||
<window_info id="Structure" order="1" side_tool="true" weight="0.25" />
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||||
<window_info anchor="bottom" id="Docker" show_stripe_button="false" />
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||||
<window_info anchor="bottom" id="Database Changes" />
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||||
<window_info anchor="bottom" id="Version Control" />
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||||
<window_info anchor="bottom" id="Python Console" />
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||||
<window_info anchor="bottom" id="Terminal" />
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||||
<window_info anchor="bottom" id="Event Log" side_tool="true" />
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||||
<window_info id="Favorites" order="2" side_tool="true" />
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||||
<window_info anchor="bottom" id="Message" order="0" />
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||||
<window_info anchor="bottom" id="Find" order="1" />
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||||
<window_info anchor="bottom" id="Run" order="2" visible="true" weight="0.33625218" />
|
||||
<window_info anchor="bottom" id="Debug" order="3" weight="0.4" />
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||||
<window_info active="true" anchor="bottom" id="Run" order="2" visible="true" weight="0.25093633" />
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||||
<window_info anchor="bottom" id="Debug" order="3" weight="0.35018727" />
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||||
<window_info anchor="bottom" id="Cvs" order="4" weight="0.25" />
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||||
<window_info anchor="bottom" id="Inspection" order="5" weight="0.4" />
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||||
<window_info anchor="bottom" id="TODO" order="6" />
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||||
<window_info anchor="right" id="SciView" />
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||||
<window_info anchor="right" id="Database" />
|
||||
<window_info anchor="bottom" id="Docker" order="7" show_stripe_button="false" />
|
||||
<window_info anchor="bottom" id="Version Control" order="8" />
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||||
<window_info anchor="bottom" id="Database Changes" order="9" />
|
||||
<window_info anchor="bottom" id="Event Log" order="10" side_tool="true" />
|
||||
<window_info anchor="bottom" id="Terminal" order="11" />
|
||||
<window_info anchor="bottom" id="Python Console" order="12" weight="0.07116105" />
|
||||
<window_info anchor="right" id="Commander" internal_type="SLIDING" order="0" type="SLIDING" weight="0.4" />
|
||||
<window_info anchor="right" id="Ant Build" order="1" weight="0.25" />
|
||||
<window_info anchor="right" content_ui="combo" id="Hierarchy" order="2" weight="0.25" />
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||||
<window_info anchor="right" id="SciView" order="3" />
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||||
<window_info anchor="right" id="Database" order="4" />
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||||
</layout>
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||||
</component>
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||||
<component name="TypeScriptGeneratedFilesManager">
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@ -290,18 +327,22 @@
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</map>
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||||
</option>
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</component>
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<component name="VcsManagerConfiguration">
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<MESSAGE value="test3" />
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<option name="LAST_COMMIT_MESSAGE" value="test3" />
|
||||
</component>
|
||||
<component name="XDebuggerManager">
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||||
<breakpoint-manager>
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||||
<breakpoints>
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<line-breakpoint suspend="THREAD" type="python-line">
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<url>file://$PROJECT_DIR$/s.py</url>
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<line>141</line>
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<line>142</line>
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<option name="timeStamp" value="3" />
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</line-breakpoint>
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<line-breakpoint enabled="true" suspend="THREAD" type="python-line">
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<url>file://$PROJECT_DIR$/s.py</url>
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<line>193</line>
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<option name="timeStamp" value="8" />
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<line>226</line>
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<option name="timeStamp" value="16" />
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</line-breakpoint>
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</breakpoints>
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</breakpoint-manager>
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@ -312,24 +353,45 @@
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</configuration>
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</watches-manager>
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</component>
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||||
<component name="com.intellij.coverage.CoverageDataManagerImpl">
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||||
<SUITE FILE_PATH="coverage/TAU_22_sane_words_torch_nn$new_lab.coverage" NAME="new_lab Coverage Results" MODIFIED="1575455391198" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
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</component>
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||||
<component name="editorHistoryManager">
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<entry file="file://$PROJECT_DIR$/test-A/out.tsv">
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<provider selected="true" editor-type-id="text-editor" />
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</entry>
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||||
<entry file="file:///usr/lib/python3/dist-packages/pandas/core/frame.py">
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||||
<provider selected="true" editor-type-id="text-editor">
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||||
<state relative-caret-position="113850">
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||||
<caret line="6419" selection-start-line="6419" selection-end-line="6419" />
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</state>
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||||
</provider>
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||||
</entry>
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||||
<entry file="file://$PROJECT_DIR$/test-A/in.tsv">
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<provider selected="true" editor-type-id="text-editor">
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||||
<state relative-caret-position="-198895" />
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||||
</provider>
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||||
</entry>
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||||
<entry file="file://$PROJECT_DIR$/dev-0/expected.tsv">
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||||
<provider selected="true" editor-type-id="text-editor" />
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||||
</entry>
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||||
<entry file="file://$PROJECT_DIR$/new_lab.py">
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||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="-196952" />
|
||||
<state relative-caret-position="18">
|
||||
<caret line="48" selection-start-line="48" selection-end-line="48" />
|
||||
<folding>
|
||||
<element signature="e#0#10#0" expanded="true" />
|
||||
</folding>
|
||||
</state>
|
||||
</provider>
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||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/dev-0/out_float.tsv">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="-198268" />
|
||||
</provider>
|
||||
<entry file="file://$PROJECT_DIR$/test-A/out_float.tsv">
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||||
<provider selected="true" editor-type-id="text-editor" />
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/s.py">
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||||
<entry file="file://$PROJECT_DIR$/s2.py">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="980">
|
||||
<caret line="225" column="18" selection-start-line="225" selection-start-column="18" selection-end-line="225" selection-end-column="18" />
|
||||
<state relative-caret-position="121">
|
||||
<caret line="304" column="13" selection-end-line="304" selection-end-column="13" />
|
||||
<folding>
|
||||
<element signature="e#0#12#0" expanded="true" />
|
||||
</folding>
|
||||
@ -338,8 +400,32 @@
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||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/dev-0/out.tsv">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="342">
|
||||
<caret line="19" selection-start-line="19" selection-end-line="20" />
|
||||
<state relative-caret-position="18">
|
||||
<caret line="1" column="1" selection-start-line="1" selection-start-column="1" selection-end-line="1" selection-end-column="1" />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file:///usr/lib/python3.6/subprocess.py">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="352">
|
||||
<caret line="437" selection-start-line="437" selection-end-line="437" />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/dev-0/out_float.tsv">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="36">
|
||||
<caret line="2" column="3" lean-forward="true" selection-start-line="2" selection-start-column="3" selection-end-line="2" selection-end-column="3" />
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
<entry file="file://$PROJECT_DIR$/s.py">
|
||||
<provider selected="true" editor-type-id="text-editor">
|
||||
<state relative-caret-position="191">
|
||||
<caret line="269" column="15" selection-start-line="269" selection-start-column="15" selection-end-line="269" selection-end-column="15" />
|
||||
<folding>
|
||||
<element signature="e#0#12#0" expanded="true" />
|
||||
</folding>
|
||||
</state>
|
||||
</provider>
|
||||
</entry>
|
||||
|
||||
11026
dev-0/best/out.tsv
Normal file
11026
dev-0/best/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
11026
dev-0/best/out_float.tsv
Normal file
11026
dev-0/best/out_float.tsv
Normal file
File diff suppressed because it is too large
Load Diff
3828
dev-0/out.tsv
3828
dev-0/out.tsv
File diff suppressed because it is too large
Load Diff
22024
dev-0/out_float.tsv
22024
dev-0/out_float.tsv
File diff suppressed because it is too large
Load Diff
94
new_lab.py
Normal file
94
new_lab.py
Normal file
@ -0,0 +1,94 @@
|
||||
import sys
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch import optim
|
||||
|
||||
|
||||
|
||||
history_length = 32
|
||||
nb_of_char_codes = 128
|
||||
history_encoded = [ord("\n")] * history_length
|
||||
embedding_size = 10
|
||||
hidden_size = 100
|
||||
|
||||
device = torch.device('cpu')
|
||||
|
||||
print(history_encoded)
|
||||
|
||||
def char_source():
|
||||
for line in sys.stdin:
|
||||
for char in line:
|
||||
if ord(char) < nb_of_char_codes:
|
||||
yield ord(char)
|
||||
|
||||
|
||||
class NGramLanguageModel(nn.Module):
|
||||
def __init__(self, nb_of_char_codes, history_length, embedding_size, hidden_size):
|
||||
super(NGramLanguageModel, self).__init__()
|
||||
|
||||
self.embeddings = nn.Embedding(nb_of_char_codes, embedding_size).to(device)
|
||||
self.model = nn.Sequential(
|
||||
nn.Linear(history_length * embedding_size, hidden_size),
|
||||
nn.Linear(hidden_size, nb_of_char_codes),
|
||||
nn.LogSoftmax()
|
||||
).to(device)
|
||||
|
||||
def forward(self, inputs):
|
||||
embedded_inputs = self.embeddings(inputs)
|
||||
return self.model(embedded_inputs.view(-1)) #view -1 rozpłaszcza
|
||||
|
||||
def generate(self, to_be_continued, n):
|
||||
t = (" " * history_length + to_be_continued)[-history_length:]
|
||||
history = [ord(c) for c in t]
|
||||
with torch.no_grad():
|
||||
for _ in range(n):
|
||||
x = torch.tensor(history, dtype = torch.long)
|
||||
y = ((torch.exp(model(x))))
|
||||
|
||||
best = sorted(range(nb_of_char_codes), key= lambda i: -y[i])[0:4]
|
||||
|
||||
yb = torch.tensor([
|
||||
y[ix] if ix in best else 0.0
|
||||
for ix in range(nb_of_char_codes)
|
||||
])
|
||||
|
||||
c = torch.multinomial(y, 1)[0].item()
|
||||
t+= chr(c)
|
||||
history.pop(0)
|
||||
history.append(c)
|
||||
return t
|
||||
|
||||
model = NGramLanguageModel(nb_of_char_codes, history_length, embedding_size, hidden_size)
|
||||
|
||||
counter = 0
|
||||
step = 1000
|
||||
criterion = nn.NLLLoss()
|
||||
optimizer = optim.Adam(model.parameters())
|
||||
losses = []
|
||||
for c in char_source():
|
||||
x = torch.tensor(history_encoded, dtype=torch.long, device = device)
|
||||
model.zero_grad()
|
||||
y=model(x)
|
||||
|
||||
loss = criterion(y.view(1, -1), torch.tensor([c], dtype=torch.long, device=device))
|
||||
|
||||
losses.append(loss.item())
|
||||
if len(losses) > step:
|
||||
losses.pop(0)
|
||||
|
||||
if counter % step == 0:
|
||||
awg_losses = sum(losses) / len(losses)
|
||||
print(awg_losses)
|
||||
print(loss)
|
||||
print(model.generate("Machine translation is", 200))
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
#print(y)
|
||||
history_encoded.pop(0)
|
||||
history_encoded.append(c)
|
||||
|
||||
"""
|
||||
zd, zrobić generator
|
||||
Nucleus - do generowania
|
||||
|
||||
"""
|
||||
119
s.py
119
s.py
@ -6,25 +6,26 @@ import pandas
|
||||
import numpy as np
|
||||
import re
|
||||
import timeit
|
||||
import subprocess
|
||||
from sklearn.metrics import precision_score, recall_score, accuracy_score, roc_auc_score
|
||||
from torch.utils.data import Dataset, DataLoader
|
||||
import string
|
||||
|
||||
|
||||
#10 features: 4 normal + 6 from domain_onehot + 38 char labels
|
||||
model = nn.Sequential(
|
||||
nn.Linear(48, 96, bias=True),
|
||||
nn.Linear(48, 24, bias=True),
|
||||
nn.ReLU(),
|
||||
nn.Linear(96,48,bias=True),
|
||||
nn.Linear(24,24,bias=True),
|
||||
nn.ReLU(),
|
||||
nn.Linear(48, 1, bias=True),
|
||||
nn.Linear(24, 1, bias=True),
|
||||
nn.Sigmoid())
|
||||
criterion = nn.BCELoss()
|
||||
#optimizer = optim.SGD(model.parameters(), lr=0.00001, momentum=0.9)
|
||||
optimizer = optim.Adam(model.parameters())
|
||||
|
||||
criterion = nn.BCELoss(reduction='sum')
|
||||
optimizer = optim.SGD(model.parameters(), lr=0.0001, momentum=0.9)
|
||||
#optimizer = optim.Adam(model.parameters())
|
||||
|
||||
minibatch_size = 200
|
||||
|
||||
|
||||
def count_polish_diacritics(x):
|
||||
x_counts = []
|
||||
for i, word in x.iteritems():
|
||||
@ -101,6 +102,9 @@ def wordToOneHot(df_col, ch_labels):
|
||||
return out_df
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class TrainDataset(Dataset):
|
||||
def __init__(self, X, y):
|
||||
self.X = X
|
||||
@ -174,10 +178,60 @@ testA_x_temp2 = torch.cat([testA_x_temp.transpose(1,0), testA_x3], 1)
|
||||
testA_x = torch.cat([testA_x_temp2, testA_x_words_onehot], 1)
|
||||
|
||||
|
||||
"""
|
||||
def pred_save_dev():
|
||||
dev_y = model(dev_x)
|
||||
file = open("dev-0/out.tsv", "w")
|
||||
file2 = open("dev-0/out_float.tsv", "w")
|
||||
for i in range(0, 11026):
|
||||
file2.write(str(dev_y[i].data.item()) + "\n")
|
||||
|
||||
var = dev_y[i].data.item()
|
||||
if var > threshold:
|
||||
file.write(f'{1}\n')
|
||||
else:
|
||||
file.write(f'{0}\n')
|
||||
file.close()
|
||||
file2.close()
|
||||
"""
|
||||
|
||||
def pred_save(name, data_train_x, f_threshold):
|
||||
|
||||
pred_y = model(data_train_x)
|
||||
file = open(name + "/out.tsv", "w")
|
||||
file2 = open(name + "/out_float.tsv", "w")
|
||||
for i in range(0, len(data_train_x)):
|
||||
file2.write(str(pred_y[i].data.item()) + "\n")
|
||||
|
||||
var = pred_y[i].data.item()
|
||||
if var > f_threshold:
|
||||
file.write(f'{1}\n')
|
||||
else:
|
||||
file.write(f'{0}\n')
|
||||
file.close()
|
||||
file2.close()
|
||||
|
||||
|
||||
def optim_threshold(min_thr, step = 0.01):
|
||||
best_thr = min_thr
|
||||
best_geval=0.1
|
||||
while min_thr < 1:
|
||||
pred_save("dev-0", dev_x, min_thr)
|
||||
metric = float(subprocess.check_output(["/home/students/s452101/TAU/geval/geval", "-t", "dev-0"]))
|
||||
if float(metric) > best_geval:
|
||||
best_geval = float(metric)
|
||||
best_thr = min_thr
|
||||
min_thr += step
|
||||
print("optimTHR; geval metric: ", float(metric), "\tbest: ", best_geval, "\tthreshold: ", min_thr, "\tbest_thr: ", best_thr)
|
||||
|
||||
return best_thr
|
||||
|
||||
dataset_train = TrainDataset(x, y)
|
||||
trainloader=DataLoader(dataset=dataset_train, batch_size=minibatch_size, shuffle=True)
|
||||
|
||||
def train_loop(i = 20):
|
||||
|
||||
|
||||
def train_loop(i = 500, best = 0.1, threshold = 0.25):
|
||||
for i in range(i):
|
||||
for xb, yb_expected in trainloader:
|
||||
optimizer.zero_grad()
|
||||
@ -188,55 +242,54 @@ def train_loop(i = 20):
|
||||
debug_xb = pandas.DataFrame(xb.numpy())
|
||||
debug_yb_expected = pandas.DataFrame(yb_expected.numpy())
|
||||
"""
|
||||
debug_yp = pandas.DataFrame(yp.detach().numpy())
|
||||
#debug_yp = pandas.DataFrame(yp.detach().numpy())
|
||||
|
||||
|
||||
loss = criterion(torch.squeeze(yp), yb_expected)
|
||||
|
||||
"""
|
||||
dev_y_pred_float_tensor = model(dev_x)
|
||||
dev_y_pred_float_df = pandas.DataFrame(dev_y_pred_float_tensor.detach().numpy())
|
||||
auc_score = roc_auc_score(dev_y_test, dev_y_pred_float_df)
|
||||
print("auc:\t", auc_score, "\tloss:\t", loss.item())
|
||||
if ((auc_score > 0.90)):
|
||||
break
|
||||
"""
|
||||
#metric = float(subprocess.check_output(["/home/students/s452101/TAU/geval/geval", "-t", "dev-0"]))
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
if ((auc_score > 0.90)):
|
||||
break
|
||||
#print(loss)
|
||||
pred_save("dev-0", dev_x, threshold)
|
||||
|
||||
metric = float(subprocess.check_output(["/home/students/s452101/TAU/geval/geval", "-t", "dev-0"]))
|
||||
print("geval metric: ", float(metric),"\tbest: ", best, "\tLoss: ", loss.item(), "\tthr: ", threshold)
|
||||
if float(metric) > best:
|
||||
best_threshold = optim_threshold(float(threshold - 0.2))
|
||||
threshold = best_threshold
|
||||
metric = float(subprocess.check_output(["/home/students/s452101/TAU/geval/geval", "-t", "dev-0"]))
|
||||
best = float(metric)
|
||||
pred_save("dev-0/best", dev_x, threshold)
|
||||
pred_save("test-A/best", testA_x, threshold)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#4 200 ~7h
|
||||
elapsed_time = timeit.timeit(train_loop, number=1)
|
||||
print("Training time: ", elapsed_time, "seconds")
|
||||
#elapsed_time = timeit.timeit(train_loop, number=1)
|
||||
#print("Training time: ", elapsed_time, "seconds")
|
||||
train_loop()
|
||||
|
||||
#saving results:
|
||||
#dev0:
|
||||
dev_y = model(dev_x)
|
||||
file=open("dev-0/out.tsv","w")
|
||||
file2=open("dev-0/out_float.tsv","w")
|
||||
dev_y_pred_float=[]
|
||||
for i in range(0,11026):
|
||||
file2.write(str(dev_y[i].data.item()) + "\n")
|
||||
dev_y_pred_float.append(dev_y[i].data.item())
|
||||
var = dev_y[i].data.item()
|
||||
if var > 0.999:
|
||||
file.write(f'{1}\n')
|
||||
else:
|
||||
file.write(f'{0}\n')
|
||||
file.close()
|
||||
file2.close()
|
||||
|
||||
|
||||
y_test = pandas.DataFrame(pandas.read_csv('dev-0/expected.tsv', encoding="utf-8", delimiter='\t', header=None))
|
||||
dev_y_pred = pandas.DataFrame(pandas.read_csv('dev-0/out.tsv', encoding="utf-8", delimiter='\t', header=None))
|
||||
|
||||
score = f1_score(y_test, dev_y_pred)
|
||||
print("f1_score_dev0 after training: ", score,"\nAcc: ", accuracy_score(dev_y_test, dev_y_pred),
|
||||
"\nroc_auc: ", roc_auc_score(dev_y_test,dev_y_pred_float ))
|
||||
print(dev_y_pred_float)
|
||||
"\nroc_auc: ", )
|
||||
|
||||
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
|
||||
#testA:
|
||||
@ -246,7 +299,7 @@ file2=open("test-A/out_float.tsv","w")
|
||||
|
||||
for i in range(0,11061):
|
||||
file2.write(str(testA_y[i].data.item()) + "\n")
|
||||
if testA_y[i].data.item() > 0.999:
|
||||
if testA_y[i].data.item() > 0.25:
|
||||
file.write(f'{1}\n')
|
||||
else:
|
||||
file.write(f'{0}\n')
|
||||
|
||||
305
s2.py
Normal file
305
s2.py
Normal file
@ -0,0 +1,305 @@
|
||||
import torch
|
||||
import random
|
||||
from torch import nn
|
||||
from torch import optim
|
||||
import pandas
|
||||
import numpy as np
|
||||
import re
|
||||
import timeit
|
||||
import subprocess
|
||||
from sklearn.metrics import precision_score, recall_score, accuracy_score, roc_auc_score
|
||||
from torch.utils.data import Dataset, DataLoader
|
||||
import string
|
||||
|
||||
|
||||
#10 features: 4 normal + 6 from domain_onehot + 38 char labels
|
||||
model = nn.Sequential(
|
||||
nn.Linear(48, 40, bias=True),
|
||||
nn.ReLU(),
|
||||
nn.Linear(40,24,bias=True),
|
||||
nn.ReLU(),
|
||||
nn.Linear(24, 1, bias=True),
|
||||
nn.Sigmoid())
|
||||
criterion = nn.BCELoss()
|
||||
optimizer = optim.SGD(model.parameters(), lr=0.00001, momentum=0.7)
|
||||
#optimizer = optim.Adam(model.parameters())
|
||||
|
||||
minibatch_size = 200
|
||||
|
||||
def count_polish_diacritics(x):
|
||||
x_counts = []
|
||||
for i, word in x.iteritems():
|
||||
c = len(re.findall(r'[ąćęłńóśźż]', str(word)))
|
||||
c2 = c / len(str(word))
|
||||
x_counts.append(c2)
|
||||
return x_counts
|
||||
|
||||
def count_vowels(x):
|
||||
out = []
|
||||
for index,row in x.iteritems():
|
||||
vowel_len = len(re.findall(r'[aąeęioóuy]', str(row)))
|
||||
word_len = len(str(row))
|
||||
out.append(vowel_len / word_len) #RATE
|
||||
return out
|
||||
|
||||
def Normalize(data, d = None):
|
||||
if (d is None):
|
||||
d = data
|
||||
r = data - d.min()
|
||||
return r/(d.max() - d.min())
|
||||
|
||||
def f1_score(y_true, y_pred):
|
||||
precision = precision_score(y_true, y_pred, average='micro')
|
||||
recall = recall_score(y_true, y_pred, average='micro')
|
||||
F1 = 2 * (precision * recall) / (precision + recall)
|
||||
return F1
|
||||
|
||||
#Transforms df with categorical values to One Hot format
|
||||
def ToOneHot(df_col):
|
||||
out = []
|
||||
df_labels = pandas.unique(df_col)
|
||||
l_count = len(df_labels)
|
||||
for index, row in df_col.iteritems():
|
||||
blank_one_hot = np.full(l_count, 0)
|
||||
for i in range(0, l_count):
|
||||
if df_labels[i] == row:
|
||||
blank_one_hot[i] = 1
|
||||
out.append(blank_one_hot)
|
||||
out_df = pandas.DataFrame(out, columns=df_labels)
|
||||
return out_df, df_labels
|
||||
|
||||
def ToOneHot_preproces(df_col, df_labels):
|
||||
out = []
|
||||
l_count = len(df_labels)
|
||||
for index, row in df_col.iteritems():
|
||||
blank_one_hot = np.full(l_count, 0)
|
||||
for i in range(0, l_count):
|
||||
if df_labels[i] == row:
|
||||
blank_one_hot[i] = 1
|
||||
out.append(blank_one_hot)
|
||||
out_df = pandas.DataFrame(out, columns=df_labels)
|
||||
return out_df
|
||||
|
||||
def getAllchars(df_col):
|
||||
all = []
|
||||
for index, row in df_col.iteritems():
|
||||
all = all + list(row)
|
||||
return all
|
||||
|
||||
|
||||
def wordToOneHot(df_col, ch_labels):
|
||||
out = []
|
||||
l_count = len(ch_labels)
|
||||
for index, row in df_col.iteritems():
|
||||
blank_one_hot = np.full(l_count, 0)
|
||||
for ch in list(str(row)):
|
||||
for i in range(0, l_count):
|
||||
if ch_labels[i] == ch:
|
||||
blank_one_hot[i] = 1
|
||||
out.append(blank_one_hot)
|
||||
|
||||
out_df = pandas.DataFrame(out, columns=ch_labels)
|
||||
return out_df
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
class TrainDataset(Dataset):
|
||||
def __init__(self, X, y):
|
||||
self.X = X
|
||||
self.y = y
|
||||
|
||||
def __len__(self):
|
||||
return self.X.shape[0]
|
||||
|
||||
def __getitem__(self, idx):
|
||||
return self.X[idx], self.y[idx]
|
||||
|
||||
#Load data:
|
||||
#Train
|
||||
train_data = pandas.read_csv('train/train.tsv', sep='\t', names=['Sane', 'Domain', 'Word', 'Frequency'], header=None)
|
||||
char_labels = pandas.unique(getAllchars(train_data['Word']))
|
||||
#print(char_labels)
|
||||
#print(len(char_labels)) 38 liter
|
||||
#debug_fq = train_data['Frequency']
|
||||
x1 = Normalize(torch.tensor(train_data['Frequency'], dtype=torch.float))
|
||||
x2 = Normalize(torch.tensor(count_vowels(train_data['Word']), dtype=torch.float))
|
||||
|
||||
domain_onehot, domain_labels = ToOneHot(train_data['Domain'])
|
||||
x3 = torch.tensor(domain_onehot.values, dtype=torch.float)
|
||||
|
||||
x4 = Normalize(torch.tensor(count_polish_diacritics(train_data['Word']),dtype=torch.float))
|
||||
x5 = Normalize(torch.tensor(train_data['Word'].str.len(), dtype=torch.float))
|
||||
|
||||
df_words_onehot = wordToOneHot(train_data['Word'], char_labels)
|
||||
x_words_onehot = torch.tensor(df_words_onehot.values, dtype=torch.float)
|
||||
|
||||
x_temp1 = torch.stack((x1,x2,x4, x5),0)
|
||||
x_temp2 = torch.cat([x_temp1.transpose(1,0), x3], 1)
|
||||
x = torch.cat([x_temp2, x_words_onehot], 1)
|
||||
l = list(["Freq", "Vovels", "pol_dia", "Len"])+list(domain_labels)+list(char_labels)
|
||||
print(l)
|
||||
print(len(l))
|
||||
#debug_x = pandas.DataFrame(x.numpy(), columns=l)
|
||||
|
||||
y = torch.tensor(train_data['Sane'], dtype=torch.float)
|
||||
|
||||
#dev0
|
||||
dev_y_test = pandas.DataFrame(pandas.read_csv('dev-0/expected.tsv', encoding="utf-8", delimiter='\t', header=None))
|
||||
dev_data = pandas.read_csv('dev-0/in.tsv', sep='\t', names=['Domain', 'Word', 'Frequency'], header=None)
|
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dev_x1 = Normalize(torch.tensor(dev_data['Frequency'], dtype=torch.float), x1)
|
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dev_x2 = Normalize(torch.tensor(count_vowels(dev_data['Word']), dtype=torch.float), x2)
|
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|
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dev_x3 = torch.tensor(ToOneHot_preproces(dev_data['Domain'], domain_labels).values, dtype=torch.float)
|
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dev_x4 = Normalize(torch.tensor(count_polish_diacritics(dev_data['Word']), dtype=torch.float), x4)
|
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dev_x5 = Normalize(torch.tensor(dev_data['Word'].str.len(), dtype=torch.float), x5)
|
||||
|
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dev_df_words_onehot = wordToOneHot(dev_data['Word'], char_labels)
|
||||
dev_x_words_onehot = torch.tensor(dev_df_words_onehot.values, dtype=torch.float)
|
||||
|
||||
|
||||
dev_x_temp = torch.stack((dev_x1, dev_x2, dev_x4, dev_x5), 0)
|
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dev_x_temp2 = torch.cat([dev_x_temp.transpose(1,0), dev_x3], 1)
|
||||
dev_x = torch.cat([dev_x_temp2, dev_x_words_onehot], 1)
|
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#test-A
|
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testA_data = pandas.read_csv('test-A/in.tsv', sep='\t', names=['Domain', 'Word', 'Frequency'], header=None)
|
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testA_x1 = Normalize(torch.tensor(testA_data['Frequency'], dtype=torch.float), x1)
|
||||
testA_x2 = Normalize(torch.tensor(count_vowels(testA_data['Word']), dtype=torch.float), x2)
|
||||
testA_x3 = torch.tensor(ToOneHot_preproces(testA_data['Domain'], domain_labels).values, dtype=torch.float)
|
||||
testA_x4 = Normalize(torch.tensor(count_polish_diacritics(testA_data['Word']),dtype=torch.float), x4)
|
||||
testA_x5 = Normalize(torch.tensor(testA_data['Word'].str.len(), dtype=torch.float), x5)
|
||||
|
||||
testA_df_words_onehot = wordToOneHot(testA_data['Word'], char_labels)
|
||||
testA_x_words_onehot = torch.tensor(testA_df_words_onehot.values, dtype=torch.float)
|
||||
|
||||
testA_x_temp = torch.stack((testA_x1,testA_x2,testA_x4, testA_x5),0)
|
||||
testA_x_temp2 = torch.cat([testA_x_temp.transpose(1,0), testA_x3], 1)
|
||||
testA_x = torch.cat([testA_x_temp2, testA_x_words_onehot], 1)
|
||||
|
||||
threshold = 0.25
|
||||
"""
|
||||
def pred_save_dev():
|
||||
dev_y = model(dev_x)
|
||||
file = open("dev-0/out.tsv", "w")
|
||||
file2 = open("dev-0/out_float.tsv", "w")
|
||||
for i in range(0, 11026):
|
||||
file2.write(str(dev_y[i].data.item()) + "\n")
|
||||
|
||||
var = dev_y[i].data.item()
|
||||
if var > threshold:
|
||||
file.write(f'{1}\n')
|
||||
else:
|
||||
file.write(f'{0}\n')
|
||||
file.close()
|
||||
file2.close()
|
||||
"""
|
||||
|
||||
def pred_save(name, data_train_x, f_threshold):
|
||||
|
||||
pred_y = model(data_train_x)
|
||||
file = open(name + "/out.tsv", "w")
|
||||
file2 = open(name + "/out_float.tsv", "w")
|
||||
for i in range(0, len(data_train_x)):
|
||||
file2.write(str(pred_y[i].data.item()) + "\n")
|
||||
|
||||
var = pred_y[i].data.item()
|
||||
if var > f_threshold:
|
||||
file.write(f'{1}\n')
|
||||
else:
|
||||
file.write(f'{0}\n')
|
||||
file.close()
|
||||
file2.close()
|
||||
|
||||
|
||||
def optim_threshold(min_thr, step = 0.01):
|
||||
best_thr = min_thr
|
||||
best=0.1
|
||||
while min_thr < 1:
|
||||
pred_save("dev-0", dev_x, min_thr)
|
||||
metric = float(subprocess.check_output(["/home/students/s452101/TAU/geval/geval", "-t", "dev-0"]))
|
||||
print("optimTHR; geval metric: ", float(metric), "\tbest: ", best, "\tthreshold: ", min_thr)
|
||||
if float(metric) > best:
|
||||
best = float(metric)
|
||||
best_thr = min_thr
|
||||
min_thr += step
|
||||
return best_thr
|
||||
|
||||
dataset_train = TrainDataset(x, y)
|
||||
trainloader=DataLoader(dataset=dataset_train, batch_size=minibatch_size, shuffle=True)
|
||||
|
||||
|
||||
|
||||
def train_loop(i = 50, best = 0.01, threshold = 0.5):
|
||||
for i in range(i):
|
||||
for xb, yb_expected in trainloader:
|
||||
optimizer.zero_grad()
|
||||
yp = model(xb)
|
||||
|
||||
# debug
|
||||
"""
|
||||
debug_xb = pandas.DataFrame(xb.numpy())
|
||||
debug_yb_expected = pandas.DataFrame(yb_expected.numpy())
|
||||
"""
|
||||
#debug_yp = pandas.DataFrame(yp.detach().numpy())
|
||||
|
||||
|
||||
loss = criterion(torch.squeeze(yp), yb_expected)
|
||||
"""
|
||||
dev_y_pred_float_tensor = model(dev_x)
|
||||
dev_y_pred_float_df = pandas.DataFrame(dev_y_pred_float_tensor.detach().numpy())
|
||||
auc_score = roc_auc_score(dev_y_test, dev_y_pred_float_df)
|
||||
print("auc:\t", auc_score, "\tloss:\t", loss.item())
|
||||
if ((auc_score > 0.90)):
|
||||
break
|
||||
"""
|
||||
#metric = float(subprocess.check_output(["/home/students/s452101/TAU/geval/geval", "-t", "dev-0"]))
|
||||
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
pred_save("dev-0", dev_x, threshold)
|
||||
|
||||
metric = float(subprocess.check_output(["/home/students/s452101/TAU/geval/geval", "-t", "dev-0"]))
|
||||
print("geval metric: ", float(metric),"\tbest: ", best, "\tLoss: ", loss.item())
|
||||
if float(metric) > best:
|
||||
best_threshold = optim_threshold(float(torch.min(yp)))
|
||||
threshold = best_threshold
|
||||
best = float(metric)
|
||||
pred_save("dev-0/best", dev_x, threshold)
|
||||
pred_save("test-A/best", testA_x, threshold)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
#4 200 ~7h
|
||||
#elapsed_time = timeit.timeit(train_loop, number=1)
|
||||
#print("Training time: ", elapsed_time, "seconds")
|
||||
train_loop()
|
||||
|
||||
#saving results:
|
||||
#dev0:
|
||||
|
||||
|
||||
y_test = pandas.DataFrame(pandas.read_csv('dev-0/expected.tsv', encoding="utf-8", delimiter='\t', header=None))
|
||||
dev_y_pred = pandas.DataFrame(pandas.read_csv('dev-0/out.tsv', encoding="utf-8", delimiter='\t', header=None))
|
||||
|
||||
score = f1_score(y_test, dev_y_pred)
|
||||
print("f1_score_dev0 after training: ", score,"\nAcc: ", accuracy_score(dev_y_test, dev_y_pred),
|
||||
"\nroc_auc: ", )
|
||||
|
||||
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
|
||||
#testA:
|
||||
testA_y = model(testA_x)
|
||||
file=open("test-A/out.tsv","w")
|
||||
file2=open("test-A/out_float.tsv","w")
|
||||
|
||||
for i in range(0,11061):
|
||||
file2.write(str(testA_y[i].data.item()) + "\n")
|
||||
if testA_y[i].data.item() > threshold:
|
||||
file.write(f'{1}\n')
|
||||
else:
|
||||
file.write(f'{0}\n')
|
||||
file.close()
|
||||
file2.close()
|
||||
11061
test-A/best/out.tsv
Normal file
11061
test-A/best/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
11061
test-A/best/out_float.tsv
Normal file
11061
test-A/best/out_float.tsv
Normal file
File diff suppressed because it is too large
Load Diff
3886
test-A/out.tsv
3886
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
22102
test-A/out_float.tsv
22102
test-A/out_float.tsv
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user