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chore: import upstream snapshot with attribution
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wehub-resource-sync
2026-07-13 13:18:32 +08:00
commit f0dfbfc3d7
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sampleLayouts/machine learning/Pycaret/Pycaret_LR_IRIS.xml
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<xml xmlns="https://developers.google.com/blockly/xml"><variables><variable id="K3B)^f};-pK{~x?~/+=a">irirs_data</variable><variable id="#pV4Ru#kG:!rZ.1+wKda">train_X</variable><variable id="rDcG!%{+e|U@rz(=5?e~">test_X</variable><variable id="V6L5l~%xl6}lW1u(R8j8">train_Y</variable><variable id="fSD*(*yvor###!_ZY,Y4">test_Y</variable><variable id=";qso`Q.6TX|?m]y]xls~">LR_ML</variable><variable id="p^eV;;k_=fy}Vh?U7|K~">output</variable></variables><block type="variables_set" id="C{$^/l-6ZB/E:bOA#onY" x="463" y="138"><field name="VAR" id="K3B)^f};-pK{~x?~/+=a">irirs_data</field><value name="VALUE"><block type="seaborn_dataset" id="cE!}KefmLaaIDC@A-tyw"><field name="VAR">iris</field></block></value><next><block type="pycaret_setup" id="w}152q$n~U0NCisVb^@A"><field name="algorithm">Classification</field><value name="input_data"><block type="variables_get" id="rCyC7IX9/+`lpUOBI$WM"><field name="VAR" id="K3B)^f};-pK{~x?~/+=a">irirs_data</field></block></value><value name="input_column"><shadow type="text" id=":{0HMBjA.n;D#jaW1V*f"><field name="TEXT">species</field></shadow></value><next><block type="skl_train_test_split" id="$zu%FqTfR/l?tw.)*b2f"><field name="VAR" id="#pV4Ru#kG:!rZ.1+wKda">train_X</field><field name="VAR1" id="rDcG!%{+e|U@rz(=5?e~">test_X</field><field name="VAR2" id="V6L5l~%xl6}lW1u(R8j8">train_Y</field><field name="VAR3" id="fSD*(*yvor###!_ZY,Y4">test_Y</field><field name="SPLIT">dropNa</field><value name="DATAFRAME"><block type="variables_get" id="x=IX-QblmCHvtsYCCv{b"><field name="VAR" id="K3B)^f};-pK{~x?~/+=a">irirs_data</field></block></value><value name="TESTSIZE"><block type="math_number" id="MYgw.$*x:7Yrviu1eFOd"><field name="NUM">0.1</field></block></value><value name="TARGETVAR"><block type="text" id="5ZC9FW+`KNwB3wF~G_zD"><field name="TEXT">species</field></block></value><next><block type="variables_set" id="YK0B:E%fQ$cw7f+CkwC0"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">LR_ML</field><value name="VALUE"><block type="pycaret_classifier" id=".7-e8j=^~TMMkr:$*LMl"><field name="model">lr</field></block></value><next><block type="variables_set" id="g,sEXth?UK@a..y,$$UP"><field name="VAR" id="p^eV;;k_=fy}Vh?U7|K~">output</field><value name="VALUE"><block type="pycaret_predict" id="y@;v)P2I_9,EN.Slg7|r"><value name="input_model"><block type="variables_get" id="Y!m$QHk.+CqsW?kT`xTh"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">LR_ML</field></block></value><value name="input_data"><block type="variables_get" id="bCh0v3m38=@uXeh`fxO,"><field name="VAR" id="rDcG!%{+e|U@rz(=5?e~">test_X</field></block></value></block></value></block></next></block></next></block></next></block></next></block></xml>
sampleLayouts/machine learning/Pycaret/Pycaret_RF_IRIS.xml
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<xml xmlns="https://developers.google.com/blockly/xml"><variables><variable id="K3B)^f};-pK{~x?~/+=b">irirs_data</variable><variable id="#pV4Ru#kG:!rZ.1+wKda">train_X</variable><variable id="rDcG!%{+e|U@rz(=5?e~">test_X</variable><variable id="V6L5l~%xl6}lW1u(R8j8">train_Y</variable><variable id="fSD*(*yvor###!_ZY,Y4">test_Y</variable><variable id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</variable><variable id="p^eV;;k_=fy}Vh?U7|K~">output</variable></variables><block type="variables_set" id="JpTWb@eKIu4?YEV=F38:" x="-1438" y="213"><field name="VAR" id="K3B)^f};-pK{~x?~/+=b">irirs_data</field><value name="VALUE"><block type="seaborn_dataset" id="Fwgy#6Y@-M8]rDY,?4h8"><field name="VAR">iris</field></block></value><next><block type="pycaret_setup" id="CO52IyIP2smh:C}.;!Nd"><field name="algorithm">Classification</field><value name="input_data"><block type="variables_get" id="JwQ/QYA)3ZT2|S~*~pyC"><field name="VAR" id="K3B)^f};-pK{~x?~/+=b">irirs_data</field></block></value><value name="input_column"><shadow type="text" id="3xQIEhrl-`|_vpd6bx6a"><field name="TEXT">species</field></shadow></value><next><block type="skl_train_test_split" id=",.Z]u^)rD|W$Z18.Ntyy"><field name="VAR" id="#pV4Ru#kG:!rZ.1+wKda">train_X</field><field name="VAR1" id="rDcG!%{+e|U@rz(=5?e~">test_X</field><field name="VAR2" id="V6L5l~%xl6}lW1u(R8j8">train_Y</field><field name="VAR3" id="fSD*(*yvor###!_ZY,Y4">test_Y</field><field name="SPLIT">dropNa</field><value name="DATAFRAME"><block type="variables_get" id="xaKI3j|=}0?T3_=@_z0D"><field name="VAR" id="K3B)^f};-pK{~x?~/+=b">irirs_data</field></block></value><value name="TESTSIZE"><block type="math_number" id="Ge]AnCc!E+j2GG:p]],V"><field name="NUM">0.1</field></block></value><value name="TARGETVAR"><block type="text" id="x=V7DVWfSa%!7!;rTHXe"><field name="TEXT">species</field></block></value><next><block type="variables_set" id="+tU0tsfsL]7vN1+^Pbl?"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field><value name="VALUE"><block type="pycaret_classifier" id="-jFbVkf}C5~0aY3Q+Bb5"><field name="model">rf</field></block></value><next><block type="variables_set" id="F5ic)|*EGS)Nq7v{fvgq"><field name="VAR" id="p^eV;;k_=fy}Vh?U7|K~">output</field><value name="VALUE"><block type="pycaret_predict" id="^JdaC5`R]FixUX_YINFc"><value name="input_model"><block type="variables_get" id="M6NcFx,f*l=+f(@0EB8r"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field></block></value><value name="input_data"><block type="variables_get" id="/r:oY.5R]4%w(P-.jCL+"><field name="VAR" id="rDcG!%{+e|U@rz(=5?e~">test_X</field></block></value></block></value></block></next></block></next></block></next></block></next></block></xml>
sampleLayouts/machine learning/sk learn/blocklyML_RF_IRIS.xml
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<xml xmlns="https://developers.google.com/blockly/xml"><variables><variable id="K3B)^f};-pK{~x?~/+=u">iris_data</variable><variable id="#pV4Ru#kG:!rZ.1+wKda">train_X</variable><variable id="rDcG!%{+e|U@rz(=5?e~">test_X</variable><variable id="V6L5l~%xl6}lW1u(R8j8">train_Y</variable><variable id="fSD*(*yvor###!_ZY,Y4">test_Y</variable><variable id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</variable><variable id="p^eV;;k_=fy}Vh?U7|K~">output</variable></variables><block type="variables_set" id="w!Ol%Ra*9o=;4W%]-KwY" x="887" y="113"><field name="VAR" id="K3B)^f};-pK{~x?~/+=u">iris_data</field><value name="VALUE"><block type="seaborn_dataset" id="_uMEN9O4Fy*Q7|a}R:zy"><field name="VAR">iris</field></block></value><next><block type="skl_train_test_split" id="7*N9K3*9-4gzWi/B`q`6"><field name="VAR" id="#pV4Ru#kG:!rZ.1+wKda">train_X</field><field name="VAR1" id="rDcG!%{+e|U@rz(=5?e~">test_X</field><field name="VAR2" id="V6L5l~%xl6}lW1u(R8j8">train_Y</field><field name="VAR3" id="fSD*(*yvor###!_ZY,Y4">test_Y</field><field name="SPLIT">dropNa</field><value name="DATAFRAME"><block type="variables_get" id="o8;G~1TF/zzZbCIyFE,%"><field name="VAR" id="K3B)^f};-pK{~x?~/+=u">iris_data</field></block></value><value name="TESTSIZE"><block type="math_number" id="pDGCstRSwxl~!r*8ZeWq"><field name="NUM">0.1</field></block></value><value name="TARGETVAR"><block type="text" id="OZRc2JLW^I}ys:SFi@2("><field name="TEXT">species</field></block></value><next><block type="variables_set" id="D|:XnmLK]/)tR-:LX-}1"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field><value name="VALUE"><block type="CLR_RandomForest" id="XnF%r,waa%UAeC|9CjR,"></block></value><next><block type="variables_set" id="bC;!3rIC1+VUM_g[yQt1"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field><value name="VALUE"><block type="CLR_RandomForest" id="n$cfV=wodm-(vUeOJ{#:"><mutation TrainData="1"></mutation><value name="XTRAIN"><block type="variables_get" id="0u8Nb2#O[1s(1Bc9+;fK"><field name="VAR" id="#pV4Ru#kG:!rZ.1+wKda">train_X</field></block></value><value name="YTRAIN"><block type="variables_get" id="+BQ0vA8nP|6cM4Yf8;1?"><field name="VAR" id="V6L5l~%xl6}lW1u(R8j8">train_Y</field></block></value><value name="TMODEL"><block type="variables_get" id="R@u.)8P]]Zdq`Mm86cxX"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field></block></value></block></value><next><block type="variables_set" id="AH.~PK36mH{77io,?AR}"><field name="VAR" id="p^eV;;k_=fy}Vh?U7|K~">output</field><value name="VALUE"><block type="CLR_RandomForest" id="2PP#h1e2uN#3fC0mvDB="><mutation TestData="1"></mutation><value name="XTEST"><block type="variables_get" id="48h;a!.S,G9Aj#Pdpm@]"><field name="VAR" id="rDcG!%{+e|U@rz(=5?e~">test_X</field></block></value><value name="TMODEL"><block type="variables_get" id="^{yL0GIrNRr:-6s5B,oN"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field></block></value></block></value><next><block type="Print" id="SaL:{[3~^}Ok-L[_Mm-3"><field name="END">newLine</field><value name="INPUT"><block type="Classification_Report" id="r@TrrjbK{ZGMkW[eR*KU"><value name="Pred"><block type="variables_get" id="=bHAJ`I-VF?~w`V8{7$D"><field name="VAR" id="p^eV;;k_=fy}Vh?U7|K~">output</field></block></value><value name="True"><block type="variables_get" id="e{u~K]H}d?R?+5?jX]|a"><field name="VAR" id="fSD*(*yvor###!_ZY,Y4">test_Y</field></block></value></block></value></block></next></block></next></block></next></block></next></block></next></block></xml>
sampleLayouts/machine learning/sk learn/blocklyML_RF_TITANIC.xml
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<xml xmlns="https://developers.google.com/blockly/xml"><variables><variable id="K3B)^f};-pK{~x?~/+=k">titainc_data</variable><variable id="#pV4Ru#kG:!rZ.1+wKda">train_X</variable><variable id="rDcG!%{+e|U@rz(=5?e~">test_X</variable><variable id="V6L5l~%xl6}lW1u(R8j8">train_Y</variable><variable id="fSD*(*yvor###!_ZY,Y4">test_Y</variable><variable id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</variable><variable id="p^eV;;k_=fy}Vh?U7|K~">output</variable></variables><block type="variables_set" id="C{$^/l-6ZB/E:bOA#onY" x="263" y="163"><field name="VAR" id="K3B)^f};-pK{~x?~/+k">titainc_data</field><value name="VALUE"><block type="seaborn_dataset" id="cE!}KefmLaaIDC@A-tyw"><field name="VAR">titanic</field></block></value><next><block type="skl_train_test_split" id="$zu%FqTfR/l?tw.)*b2f"><field name="VAR" id="#pV4Ru#kG:!rZ.1+wKda">train_X</field><field name="VAR1" id="rDcG!%{+e|U@rz(=5?e~">test_X</field><field name="VAR2" id="V6L5l~%xl6}lW1u(R8j8">train_Y</field><field name="VAR3" id="fSD*(*yvor###!_ZY,Y4">test_Y</field><field name="SPLIT">dropNa</field><value name="DATAFRAME"><block type="variables_get" id="rNM92}QZB+u#gndG`cIR"><field name="VAR" id="K3B)^f};-pK{~x?~/+=k">titainc_data</field></block></value><value name="TESTSIZE"><block type="math_number" id="MYgw.$*x:7Yrviu1eFOd"><field name="NUM">0.1</field></block></value><value name="TARGETVAR"><block type="text" id="5ZC9FW+`KNwB3wF~G_zD"><field name="TEXT">alive</field></block></value><next><block type="variables_set" id="YK0B:E%fQ$cw7f+CkwC0"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field><value name="VALUE"><block type="CLR_RandomForest" id="qD[,gxayS6}q48$]Y8(+"></block></value><next><block type="variables_set" id="v?|7rF}AjV/+_hb7`f%]"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field><value name="VALUE"><block type="CLR_RandomForest" id="s)($2K-P^,|^,vZ5]p5N"><mutation TrainData="1"></mutation><value name="XTRAIN"><block type="variables_get" id="^[/N[9.v39N/fPH+Q8P8"><field name="VAR" id="#pV4Ru#kG:!rZ.1+wKda">train_X</field></block></value><value name="YTRAIN"><block type="variables_get" id=".;ZJO3M2@u4AO-=B^F*f"><field name="VAR" id="V6L5l~%xl6}lW1u(R8j8">train_Y</field></block></value><value name="TMODEL"><block type="variables_get" id="v^sy#;5F`a@+MwU}X,^w"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field></block></value></block></value><next><block type="variables_set" id="g,sEXth?UK@a..y,$$UP"><field name="VAR" id="p^eV;;k_=fy}Vh?U7|K~">output</field><value name="VALUE"><block type="CLR_RandomForest" id="9uWJmHb]a(Wo.?4PW#4#"><mutation TestData="1"></mutation><value name="XTEST"><block type="variables_get" id="ehV=|d3m8T9W(XwT;-o-"><field name="VAR" id="rDcG!%{+e|U@rz(=5?e~">test_X</field></block></value><value name="TMODEL"><block type="variables_get" id="9{:_)C)WkUU6SBkKh7V0"><field name="VAR" id=";qso`Q.6TX|?m]y]xls~">RandomForest_ML</field></block></value></block></value><next><block type="Print" id="i,H_x+D8ze1Hy~Ju5nCB"><field name="END">newLine</field><value name="INPUT"><block type="Classification_Report" id="P$:?[E{-k@^=PonBAPLH"><value name="Pred"><block type="variables_get" id="gwPNHF#}4,f*(vn-esNK"><field name="VAR" id="p^eV;;k_=fy}Vh?U7|K~">output</field></block></value><value name="True"><block type="variables_get" id="fpZT%AhvYNxSGEEGG._s"><field name="VAR" id="fSD*(*yvor###!_ZY,Y4">test_Y</field></block></value></block></value></block></next></block></next></block></next></block></next></block></next></block></xml>