wehub/chekoduadarsh--blocklyml
1
0
Fork 0
Code Issues Pull Requests Actions Deployments Models Agent Notes Packages Projects Releases Wiki Activity
Files
main
chekoduadarsh--blocklyml/static/js/python_ML.js
T
wehub-resource-sync f0dfbfc3d7
RequirementsTest / build (windows-latest, 3.9) (push) Has been cancelled
Details
RequirementsTest / build (macos-latest, 3.9) (push) Has been cancelled
Details
RequirementsTest / build (ubuntu-latest, 3.9) (push) Has been cancelled
Details
CodeQL / Analyze (javascript) (push) Has been cancelled
Details
CodeQL / Analyze (python) (push) Has been cancelled
Details
Pylint / build (3.9) (push) Has been cancelled
Details
chore: import upstream snapshot with attribution
2026-07-13 13:18:32 +08:00

2101 lines
121 KiB
JavaScript
Raw Permalink Blame History

// Do not edit this file; automatically generated by gulp.
/* eslint-disable */
var VarData = {};
(function (root, factory) {
if (typeof define === "function" && define.amd) {
// AMD
define(["./blockly_compressed.js"], factory);
} else if (typeof exports === "object") {
// Node.js
module.exports = factory(require("./blockly_compressed.js"));
} else {
// Browser
root.Blockly.Python = factory(root.Blockly);
}
})(this, function (Blockly) {
"use strict";
Blockly.Python = new Blockly.Generator("Python");
Blockly.Python.addReservedWords(
"False,None,True,and,as,assert,break,class,continue,def,del,elif,else,except,exec,finally,for,from,global,if,import,in,is,lambda,nonlocal,not,or,pass,print,raise,return,try,while,with,yield,NotImplemented,Ellipsis,__debug__,quit,exit,copyright,license,credits,ArithmeticError,AssertionError,AttributeError,BaseException,BlockingIOError,BrokenPipeError,BufferError,BytesWarning,ChildProcessError,ConnectionAbortedError,ConnectionError,ConnectionRefusedError,ConnectionResetError,DeprecationWarning,EOFError,Ellipsis,EnvironmentError,Exception,FileExistsError,FileNotFoundError,FloatingPointError,FutureWarning,GeneratorExit,IOError,ImportError,ImportWarning,IndentationError,IndexError,InterruptedError,IsADirectoryError,KeyError,KeyboardInterrupt,LookupError,MemoryError,ModuleNotFoundError,NameError,NotADirectoryError,NotImplemented,NotImplementedError,OSError,OverflowError,PendingDeprecationWarning,PermissionError,ProcessLookupError,RecursionError,ReferenceError,ResourceWarning,RuntimeError,RuntimeWarning,StandardError,StopAsyncIteration,StopIteration,SyntaxError,SyntaxWarning,SystemError,SystemExit,TabError,TimeoutError,TypeError,UnboundLocalError,UnicodeDecodeError,UnicodeEncodeError,UnicodeError,UnicodeTranslateError,UnicodeWarning,UserWarning,ValueError,Warning,ZeroDivisionError,_,__build_class__,__debug__,__doc__,__import__,__loader__,__name__,__package__,__spec__,abs,all,any,apply,ascii,basestring,bin,bool,buffer,bytearray,bytes,callable,chr,classmethod,cmp,coerce,compile,complex,copyright,credits,delattr,dict,dir,divmod,enumerate,eval,exec,execfile,exit,file,filter,float,format,frozenset,getattr,globals,hasattr,hash,help,hex,id,input,int,intern,isinstance,issubclass,iter,len,license,list,locals,long,map,max,memoryview,min,next,object,oct,open,ord,pow,print,property,quit,range,raw_input,reduce,reload,repr,reversed,round,set,setattr,slice,sorted,staticmethod,str,sum,super,tuple,type,unichr,unicode,vars,xrange,zip"
);
Blockly.Python.VARDATA = [];
Blockly.Python.ORDER_ATOMIC = 0;
Blockly.Python.ORDER_COLLECTION = 1;
Blockly.Python.ORDER_STRING_CONVERSION = 1;
Blockly.Python.ORDER_MEMBER = 2.1;
Blockly.Python.ORDER_FUNCTION_CALL = 2.2;
Blockly.Python.ORDER_EXPONENTIATION = 3;
Blockly.Python.ORDER_UNARY_SIGN = 4;
Blockly.Python.ORDER_BITWISE_NOT = 4;
Blockly.Python.ORDER_MULTIPLICATIVE = 5;
Blockly.Python.ORDER_ADDITIVE = 6;
Blockly.Python.ORDER_BITWISE_SHIFT = 7;
Blockly.Python.ORDER_BITWISE_AND = 8;
Blockly.Python.ORDER_BITWISE_XOR = 9;
Blockly.Python.ORDER_BITWISE_OR = 10;
Blockly.Python.ORDER_RELATIONAL = 11;
Blockly.Python.ORDER_LOGICAL_NOT = 12;
Blockly.Python.ORDER_LOGICAL_AND = 13;
Blockly.Python.ORDER_LOGICAL_OR = 14;
Blockly.Python.ORDER_CONDITIONAL = 15;
Blockly.Python.ORDER_LAMBDA = 16;
Blockly.Python.ORDER_NONE = 99;
Blockly.Python.ORDER_OVERRIDES = [
[Blockly.Python.ORDER_FUNCTION_CALL, Blockly.Python.ORDER_MEMBER],
[Blockly.Python.ORDER_FUNCTION_CALL, Blockly.Python.ORDER_FUNCTION_CALL],
[Blockly.Python.ORDER_MEMBER, Blockly.Python.ORDER_MEMBER],
[Blockly.Python.ORDER_MEMBER, Blockly.Python.ORDER_FUNCTION_CALL],
[Blockly.Python.ORDER_LOGICAL_NOT, Blockly.Python.ORDER_LOGICAL_NOT],
[Blockly.Python.ORDER_LOGICAL_AND, Blockly.Python.ORDER_LOGICAL_AND],
[Blockly.Python.ORDER_LOGICAL_OR, Blockly.Python.ORDER_LOGICAL_OR],
];
Blockly.Python.isInitialized = !1;
Blockly.Python.init = function (a) {
Blockly.Python.PASS = this.INDENT + "pass\n";
Blockly.Python.definitions_ = Object.create(null);
Blockly.Python.functionNames_ = Object.create(null);
Blockly.Python.variableDB_ ? Blockly.Python.variableDB_.reset() : (Blockly.Python.variableDB_ = new Blockly.Names(Blockly.Python.RESERVED_WORDS_));
Blockly.Python.variableDB_.setVariableMap(a.getVariableMap());
for (var b = [], c = Blockly.Variables.allDeveloperVariables(a), d = 0; d < c.length; d++) b.push(Blockly.Python.variableDB_.getName(c[d], Blockly.Names.DEVELOPER_VARIABLE_TYPE) + " = None");
a = Blockly.Variables.allUsedVarModels(a);
for (d = 0; d < a.length; d++) b.push(Blockly.Python.variableDB_.getName(a[d].getId(), Blockly.VARIABLE_CATEGORY_NAME) + " = None");
Blockly.Python.definitions_.variables = b.join("\n");
this.isInitialized = !0;
};
Blockly.Python.finish = function (a) {
var b = [],
c = [],
d;
for (d in Blockly.Python.definitions_) {
var e = Blockly.Python.definitions_[d];
e.match(/^(from\s+\S+\s+)?import\s+\S+/) ? b.push(e) : c.push(e);
}
delete Blockly.Python.definitions_;
delete Blockly.Python.functionNames_;
Blockly.Python.variableDB_.reset();
return (b.join("\n") + "\n\n" + c.join("\n\n")).replace(/\n\n+/g, "\n\n").replace(/\n*$/, "\n\n\n") + a;
};
Blockly.Python.scrubNakedValue = function (a) {
return a + "\n";
};
Blockly.Python.quote_ = function (a) {
a = a.replace(/\\/g, "\\\\").replace(/\n/g, "\\\n");
var b = "'";
-1 !== a.indexOf("'") && (-1 === a.indexOf('"') ? (b = '"') : (a = a.replace(/'/g, "\\'")));
return b + a + b;
};
Blockly.Python.multiline_quote_ = function (a) {
return a.split(/\n/g).map(Blockly.Python.quote_).join(" + '\\n' + \n");
};
Blockly.Python.scrub_ = function (a, b, c) {
var d = "";
if (!a.outputConnection || !a.outputConnection.targetConnection) {
var e = a.getCommentText();
e && ((e = Blockly.utils.string.wrap(e, Blockly.Python.COMMENT_WRAP - 3)), (d += Blockly.Python.prefixLines(e + "\n", "# ")));
for (var f = 0; f < a.inputList.length; f++) a.inputList[f].type == Blockly.INPUT_VALUE && (e = a.inputList[f].connection.targetBlock()) && (e = Blockly.Python.allNestedComments(e)) && (d += Blockly.Python.prefixLines(e, "# "));
}
a = a.nextConnection && a.nextConnection.targetBlock();
c = c ? "" : Blockly.Python.blockToCode(a);
return d + b + c;
};
Blockly.Python.getAdjustedInt = function (a, b, c, d) {
c = c || 0;
a.workspace.options.oneBasedIndex && c--;
var e = a.workspace.options.oneBasedIndex ? "1" : "0";
a = Blockly.Python.valueToCode(a, b, c ? Blockly.Python.ORDER_ADDITIVE : Blockly.Python.ORDER_NONE) || e;
Blockly.isNumber(a) ? ((a = parseInt(a, 10) + c), d && (a = -a)) : ((a = 0 < c ? "int(" + a + " + " + c + ")" : 0 > c ? "int(" + a + " - " + -c + ")" : "int(" + a + ")"), d && (a = "-" + a));
return a;
};
Blockly.Python.colour = {};
Blockly.Python.colour_picker = function (a) {
return [Blockly.Python.quote_(a.getFieldValue("COLOUR")), Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python.colour_random = function (a) {
Blockly.Python.definitions_.import_random = "import random";
return ["'#%06x' % random.randint(0, 2**24 - 1)", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.colour_rgb = function (a) {
var b = Blockly.Python.provideFunction_("colour_rgb", [
"def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(r, g, b):",
" r = round(min(100, max(0, r)) * 2.55)",
" g = round(min(100, max(0, g)) * 2.55)",
" b = round(min(100, max(0, b)) * 2.55)",
" return '#%02x%02x%02x' % (r, g, b)",
]),
c = Blockly.Python.valueToCode(a, "RED", Blockly.Python.ORDER_NONE) || 0,
d = Blockly.Python.valueToCode(a, "GREEN", Blockly.Python.ORDER_NONE) || 0;
a = Blockly.Python.valueToCode(a, "BLUE", Blockly.Python.ORDER_NONE) || 0;
return [b + "(" + c + ", " + d + ", " + a + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.colour_blend = function (a) {
var b = Blockly.Python.provideFunction_("colour_blend", [
"def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(colour1, colour2, ratio):",
" r1, r2 = int(colour1[1:3], 16), int(colour2[1:3], 16)",
" g1, g2 = int(colour1[3:5], 16), int(colour2[3:5], 16)",
" b1, b2 = int(colour1[5:7], 16), int(colour2[5:7], 16)",
" ratio = min(1, max(0, ratio))",
" r = round(r1 * (1 - ratio) + r2 * ratio)",
" g = round(g1 * (1 - ratio) + g2 * ratio)",
" b = round(b1 * (1 - ratio) + b2 * ratio)",
" return '#%02x%02x%02x' % (r, g, b)",
]),
c = Blockly.Python.valueToCode(a, "COLOUR1", Blockly.Python.ORDER_NONE) || "'#000000'",
d = Blockly.Python.valueToCode(a, "COLOUR2", Blockly.Python.ORDER_NONE) || "'#000000'";
a = Blockly.Python.valueToCode(a, "RATIO", Blockly.Python.ORDER_NONE) || 0;
return [b + "(" + c + ", " + d + ", " + a + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.lists = {};
Blockly.Python.lists_create_empty = function (a) {
return ["[]", Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python.lists_create_with = function (a) {
for (var b = Array(a.itemCount_), c = 0; c < a.itemCount_; c++) b[c] = Blockly.Python.valueToCode(a, "ADD" + c, Blockly.Python.ORDER_NONE) || "None";
return ["[" + b.join(", ") + "]", Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python.lists_repeat = function (a) {
var b = Blockly.Python.valueToCode(a, "ITEM", Blockly.Python.ORDER_NONE) || "None";
a = Blockly.Python.valueToCode(a, "NUM", Blockly.Python.ORDER_MULTIPLICATIVE) || "0";
return ["[" + b + "] * " + a, Blockly.Python.ORDER_MULTIPLICATIVE];
};
Blockly.Python.lists_length = function (a) {
return ["len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "[]") + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.lists_isEmpty = function (a) {
return ["not len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "[]") + ")", Blockly.Python.ORDER_LOGICAL_NOT];
};
Blockly.Python.lists_indexOf = function (a) {
var b = Blockly.Python.valueToCode(a, "FIND", Blockly.Python.ORDER_NONE) || "[]",
c = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "''";
if (a.workspace.options.oneBasedIndex)
var d = " 0",
e = " + 1",
f = "";
else (d = " -1"), (e = ""), (f = " - 1");
if ("FIRST" == a.getFieldValue("END"))
return (
(a = Blockly.Python.provideFunction_("first_index", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(my_list, elem):", " try: index = my_list.index(elem)" + e, " except: index =" + d, " return index"])),
[a + "(" + c + ", " + b + ")", Blockly.Python.ORDER_FUNCTION_CALL]
);
a = Blockly.Python.provideFunction_("last_index", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(my_list, elem):", " try: index = len(my_list) - my_list[::-1].index(elem)" + f, " except: index =" + d, " return index"]);
return [a + "(" + c + ", " + b + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.lists_getIndex = function (a) {
var b = a.getFieldValue("MODE") || "GET",
c = a.getFieldValue("WHERE") || "FROM_START",
d = Blockly.Python.valueToCode(a, "VALUE", "RANDOM" == c ? Blockly.Python.ORDER_NONE : Blockly.Python.ORDER_MEMBER) || "[]";
switch (c) {
case "FIRST":
if ("GET" == b) return [d + "[0]", Blockly.Python.ORDER_MEMBER];
if ("GET_REMOVE" == b) return [d + ".pop(0)", Blockly.Python.ORDER_FUNCTION_CALL];
if ("REMOVE" == b) return d + ".pop(0)\n";
break;
case "LAST":
if ("GET" == b) return [d + "[-1]", Blockly.Python.ORDER_MEMBER];
if ("GET_REMOVE" == b) return [d + ".pop()", Blockly.Python.ORDER_FUNCTION_CALL];
if ("REMOVE" == b) return d + ".pop()\n";
break;
case "FROM_START":
a = Blockly.Python.getAdjustedInt(a, "AT");
if ("GET" == b) return [d + "[" + a + "]", Blockly.Python.ORDER_MEMBER];
if ("GET_REMOVE" == b) return [d + ".pop(" + a + ")", Blockly.Python.ORDER_FUNCTION_CALL];
if ("REMOVE" == b) return d + ".pop(" + a + ")\n";
break;
case "FROM_END":
a = Blockly.Python.getAdjustedInt(a, "AT", 1, !0);
if ("GET" == b) return [d + "[" + a + "]", Blockly.Python.ORDER_MEMBER];
if ("GET_REMOVE" == b) return [d + ".pop(" + a + ")", Blockly.Python.ORDER_FUNCTION_CALL];
if ("REMOVE" == b) return d + ".pop(" + a + ")\n";
break;
case "RANDOM":
Blockly.Python.definitions_.import_random = "import random";
if ("GET" == b) return ["random.choice(" + d + ")", Blockly.Python.ORDER_FUNCTION_CALL];
d = Blockly.Python.provideFunction_("lists_remove_random_item", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(myList):", " x = int(random.random() * len(myList))", " return myList.pop(x)"]) + "(" + d + ")";
if ("GET_REMOVE" == b) return [d, Blockly.Python.ORDER_FUNCTION_CALL];
if ("REMOVE" == b) return d + "\n";
}
throw Error("Unhandled combination (lists_getIndex).");
};
Blockly.Python.lists_setIndex = function (a) {
var b = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_MEMBER) || "[]",
c = a.getFieldValue("MODE") || "GET",
d = a.getFieldValue("WHERE") || "FROM_START",
e = Blockly.Python.valueToCode(a, "TO", Blockly.Python.ORDER_NONE) || "None";
switch (d) {
case "FIRST":
if ("SET" == c) return b + "[0] = " + e + "\n";
if ("INSERT" == c) return b + ".insert(0, " + e + ")\n";
break;
case "LAST":
if ("SET" == c) return b + "[-1] = " + e + "\n";
if ("INSERT" == c) return b + ".append(" + e + ")\n";
break;
case "FROM_START":
a = Blockly.Python.getAdjustedInt(a, "AT");
if ("SET" == c) return b + "[" + a + "] = " + e + "\n";
if ("INSERT" == c) return b + ".insert(" + a + ", " + e + ")\n";
break;
case "FROM_END":
a = Blockly.Python.getAdjustedInt(a, "AT", 1, !0);
if ("SET" == c) return b + "[" + a + "] = " + e + "\n";
if ("INSERT" == c) return b + ".insert(" + a + ", " + e + ")\n";
break;
case "RANDOM":
Blockly.Python.definitions_.import_random = "import random";
b.match(/^\w+$/) ? (a = "") : ((a = Blockly.Python.variableDB_.getDistinctName("tmp_list", Blockly.VARIABLE_CATEGORY_NAME)), (d = a + " = " + b + "\n"), (b = a), (a = d));
d = Blockly.Python.variableDB_.getDistinctName("tmp_x", Blockly.VARIABLE_CATEGORY_NAME);
a += d + " = int(random.random() * len(" + b + "))\n";
if ("SET" == c) return a + (b + "[" + d + "] = " + e + "\n");
if ("INSERT" == c) return a + (b + ".insert(" + d + ", " + e + ")\n");
}
throw Error("Unhandled combination (lists_setIndex).");
};
Blockly.Python.lists_getSublist = function (a) {
var b = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_MEMBER) || "[]",
c = a.getFieldValue("WHERE1"),
d = a.getFieldValue("WHERE2");
switch (c) {
case "FROM_START":
c = Blockly.Python.getAdjustedInt(a, "AT1");
"0" == c && (c = "");
break;
case "FROM_END":
c = Blockly.Python.getAdjustedInt(a, "AT1", 1, !0);
break;
case "FIRST":
c = "";
break;
default:
throw Error("Unhandled option (lists_getSublist)");
}
switch (d) {
case "FROM_START":
a = Blockly.Python.getAdjustedInt(a, "AT2", 1);
break;
case "FROM_END":
a = Blockly.Python.getAdjustedInt(a, "AT2", 0, !0);
Blockly.isNumber(String(a)) ? "0" == a && (a = "") : ((Blockly.Python.definitions_.import_sys = "import sys"), (a += " or sys.maxsize"));
break;
case "LAST":
a = "";
break;
default:
throw Error("Unhandled option (lists_getSublist)");
}
return [b + "[" + c + " : " + a + "]", Blockly.Python.ORDER_MEMBER];
};
Blockly.Python.lists_sort = function (a) {
var b = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_NONE) || "[]",
c = a.getFieldValue("TYPE");
a = "1" === a.getFieldValue("DIRECTION") ? "False" : "True";
return [
Blockly.Python.provideFunction_("lists_sort", [
"def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(my_list, type, reverse):",
" def try_float(s):",
" try:",
" return float(s)",
" except:",
" return 0",
" key_funcs = {",
' "NUMERIC": try_float,',
' "TEXT": str,',
' "IGNORE_CASE": lambda s: str(s).lower()',
" }",
" key_func = key_funcs[type]",
" list_cpy = list(my_list)",
" return sorted(list_cpy, key=key_func, reverse=reverse)",
]) +
"(" +
b +
', "' +
c +
'", ' +
a +
")",
Blockly.Python.ORDER_FUNCTION_CALL,
];
};
Blockly.Python.lists_split = function (a) {
var b = a.getFieldValue("MODE");
if ("SPLIT" == b) (b = Blockly.Python.valueToCode(a, "INPUT", Blockly.Python.ORDER_MEMBER) || "''"), (a = Blockly.Python.valueToCode(a, "DELIM", Blockly.Python.ORDER_NONE)), (a = b + ".split(" + a + ")");
else if ("JOIN" == b) (b = Blockly.Python.valueToCode(a, "INPUT", Blockly.Python.ORDER_NONE) || "[]"), (a = Blockly.Python.valueToCode(a, "DELIM", Blockly.Python.ORDER_MEMBER) || "''"), (a = a + ".join(" + b + ")");
else throw Error("Unknown mode: " + b);
return [a, Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.lists_reverse = function (a) {
return ["list(reversed(" + (Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_NONE) || "[]") + "))", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.logic = {};
Blockly.Python.controls_if = function (a) {
var b = 0,
c = "";
Blockly.Python.STATEMENT_PREFIX && (c += Blockly.Python.injectId(Blockly.Python.STATEMENT_PREFIX, a));
do {
var d = Blockly.Python.valueToCode(a, "IF" + b, Blockly.Python.ORDER_NONE) || "False";
var e = Blockly.Python.statementToCode(a, "DO" + b) || Blockly.Python.PASS;
Blockly.Python.STATEMENT_SUFFIX && (e = Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a), Blockly.Python.INDENT) + e);
c += (0 == b ? "if " : "elif ") + d + ":\n" + e;
++b;
} while (a.getInput("IF" + b));
if (a.getInput("ELSE") || Blockly.Python.STATEMENT_SUFFIX)
(e = Blockly.Python.statementToCode(a, "ELSE") || Blockly.Python.PASS),
Blockly.Python.STATEMENT_SUFFIX && (e = Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a), Blockly.Python.INDENT) + e),
(c += "else:\n" + e);
return c;
};
Blockly.Python.controls_ifelse = Blockly.Python.controls_if;
Blockly.Python.logic_compare = function (a) {
var b = { EQ: "==", NEQ: "!=", LT: "<", LTE: "<=", GT: ">", GTE: ">=" }[a.getFieldValue("OP")],
c = Blockly.Python.ORDER_RELATIONAL,
d = Blockly.Python.valueToCode(a, "A", c) || "0";
a = Blockly.Python.valueToCode(a, "B", c) || "0";
return [d + " " + b + " " + a, c];
};
Blockly.Python.logic_operation = function (a) {
var b = "AND" == a.getFieldValue("OP") ? "and" : "or",
c = "and" == b ? Blockly.Python.ORDER_LOGICAL_AND : Blockly.Python.ORDER_LOGICAL_OR,
d = Blockly.Python.valueToCode(a, "A", c);
a = Blockly.Python.valueToCode(a, "B", c);
if (d || a) {
var e = "and" == b ? "True" : "False";
d || (d = e);
a || (a = e);
} else a = d = "False";
return [d + " " + b + " " + a, c];
};
Blockly.Python.logic_negate = function (a) {
return ["not " + (Blockly.Python.valueToCode(a, "BOOL", Blockly.Python.ORDER_LOGICAL_NOT) || "True"), Blockly.Python.ORDER_LOGICAL_NOT];
};
Blockly.Python.logic_boolean = function (a) {
return ["TRUE" == a.getFieldValue("BOOL") ? "True" : "False", Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python.logic_null = function (a) {
return ["None", Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python.logic_ternary = function (a) {
var b = Blockly.Python.valueToCode(a, "IF", Blockly.Python.ORDER_CONDITIONAL) || "False",
c = Blockly.Python.valueToCode(a, "THEN", Blockly.Python.ORDER_CONDITIONAL) || "None";
a = Blockly.Python.valueToCode(a, "ELSE", Blockly.Python.ORDER_CONDITIONAL) || "None";
return [c + " if " + b + " else " + a, Blockly.Python.ORDER_CONDITIONAL];
};
Blockly.Python.loops = {};
Blockly.Python.controls_repeat_ext = function (a) {
var b = a.getField("TIMES") ? String(parseInt(a.getFieldValue("TIMES"), 10)) : Blockly.Python.valueToCode(a, "TIMES", Blockly.Python.ORDER_NONE) || "0";
b = Blockly.isNumber(b) ? parseInt(b, 10) : "int(" + b + ")";
var c = Blockly.Python.statementToCode(a, "DO");
c = Blockly.Python.addLoopTrap(c, a) || Blockly.Python.PASS;
return "for " + Blockly.Python.variableDB_.getDistinctName("count", Blockly.VARIABLE_CATEGORY_NAME) + " in range(" + b + "):\n" + c;
};
Blockly.Python.controls_repeat = Blockly.Python.controls_repeat_ext;
Blockly.Python.controls_whileUntil = function (a) {
var b = "UNTIL" == a.getFieldValue("MODE"),
c = Blockly.Python.valueToCode(a, "BOOL", b ? Blockly.Python.ORDER_LOGICAL_NOT : Blockly.Python.ORDER_NONE) || "False",
d = Blockly.Python.statementToCode(a, "DO");
d = Blockly.Python.addLoopTrap(d, a) || Blockly.Python.PASS;
b && (c = "not " + c);
return "while " + c + ":\n" + d;
};
Blockly.Python.controls_for = function (a) {
var b = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME),
c = Blockly.Python.valueToCode(a, "FROM", Blockly.Python.ORDER_NONE) || "0",
d = Blockly.Python.valueToCode(a, "TO", Blockly.Python.ORDER_NONE) || "0",
e = Blockly.Python.valueToCode(a, "BY", Blockly.Python.ORDER_NONE) || "1",
f = Blockly.Python.statementToCode(a, "DO");
f = Blockly.Python.addLoopTrap(f, a) || Blockly.Python.PASS;
var n = "",
k = function () {
return Blockly.Python.provideFunction_("upRange", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(start, stop, step):", " while start <= stop:", " yield start", " start += abs(step)"]);
},
h = function () {
return Blockly.Python.provideFunction_("downRange", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(start, stop, step):", " while start >= stop:", " yield start", " start -= abs(step)"]);
};
a = function (g, l, p) {
return "(" + g + " <= " + l + ") and " + k() + "(" + g + ", " + l + ", " + p + ") or " + h() + "(" + g + ", " + l + ", " + p + ")";
};
if (Blockly.isNumber(c) && Blockly.isNumber(d) && Blockly.isNumber(e))
(c = Number(c)),
(d = Number(d)),
(e = Math.abs(Number(e))),
0 === c % 1 && 0 === d % 1 && 0 === e % 1
? (c <= d ? (d++, (a = 0 == c && 1 == e ? d : c + ", " + d), 1 != e && (a += ", " + e)) : (d--, (a = c + ", " + d + ", -" + e)), (a = "range(" + a + ")"))
: ((a = c < d ? k() : h()), (a += "(" + c + ", " + d + ", " + e + ")"));
else {
var m = function (g, l) {
Blockly.isNumber(g) ? (g = Number(g)) : g.match(/^\w+$/) ? (g = "float(" + g + ")") : ((l = Blockly.Python.variableDB_.getDistinctName(b + l, Blockly.VARIABLE_CATEGORY_NAME)), (n += l + " = float(" + g + ")\n"), (g = l));
return g;
};
c = m(c, "_start");
d = m(d, "_end");
e = m(e, "_inc");
"number" == typeof c && "number" == typeof d ? ((a = c < d ? k() : h()), (a += "(" + c + ", " + d + ", " + e + ")")) : (a = a(c, d, e));
}
return (n += "for " + b + " in " + a + ":\n" + f);
};
Blockly.Python.controls_forEach = function (a) {
var b = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME),
c = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_RELATIONAL) || "[]",
d = Blockly.Python.statementToCode(a, "DO");
d = Blockly.Python.addLoopTrap(d, a) || Blockly.Python.PASS;
return "for " + b + " in " + c + ":\n" + d;
};
Blockly.Python.controls_flow_statements = function (a) {
var b = "";
Blockly.Python.STATEMENT_PREFIX && (b += Blockly.Python.injectId(Blockly.Python.STATEMENT_PREFIX, a));
Blockly.Python.STATEMENT_SUFFIX && (b += Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a));
if (Blockly.Python.STATEMENT_PREFIX) {
var c = Blockly.Constants.Loops.CONTROL_FLOW_IN_LOOP_CHECK_MIXIN.getSurroundLoop(a);
c && !c.suppressPrefixSuffix && (b += Blockly.Python.injectId(Blockly.Python.STATEMENT_PREFIX, c));
}
switch (a.getFieldValue("FLOW")) {
case "BREAK":
return b + "break\n";
case "CONTINUE":
return b + "continue\n";
}
throw Error("Unknown flow statement.");
};
/*
* Custom
*/
Blockly.Python.pandas = {};
Blockly.Python['pandas_read_csv'] = function (a) {
Blockly.Python.definitions_.pandas = "import pandas as pd";
return ['pd.read_csv(' + String(a).split(" ")[2] + ')', Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python["create_dict"] = function (a) {
var b = 1,
c = "{";
do {
var d = Blockly.Python.valueToCode(a, "KEY" + b, Blockly.Python.ORDER_NONE) || "";
var e = Blockly.Python.valueToCode(a, "VAL" + b, Blockly.Python.ORDER_NONE) || "";
Blockly.Python.STATEMENT_SUFFIX && (e = Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a), Blockly.Python.INDENT) + e);
if (b > 1) {
c += ",";
}
c += d + ":" + e;
++b;
} while (a.getInput("KEY" + b));
c += "}";
if ((e != "") && (d != "")) {
return [c, Blockly.Python.ORDER_FUNCTION_CALL];
} else {
return ["None", Blockly.Python.ORDER_FUNCTION_CALL];
}
}
Blockly.Python["dict_append"] = function (a) {
var e = ""
var b = Blockly.Python.valueToCode(a, "DICTIONARY", Blockly.Python.ORDER_NONE) || "";
var c = Blockly.Python.valueToCode(a, "KEY", Blockly.Python.ORDER_NONE) || "";
var d = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "";
if ((b != "") && (c != "") && (d != "")) {
e = b + "[" + c + "] = " + d;
}
return e;
}
Blockly.Python['seaborn_dataset'] = function (a) {
Blockly.Python.definitions_.seaborn = "import seaborn as sns";
VarData[a.inputList[0].fieldRow[1].value_] = 'sns.load_dataset("' + a.inputList[0].fieldRow[1].value_ + '")', Blockly.Python.ORDER_FUNCTION_CALL;
return ['sns.load_dataset("' + a.inputList[0].fieldRow[1].value_ + '")', Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python['skl_train_test_split'] = function (a) {
Blockly.Python.definitions_.sklearn_test_train_split = "from sklearn.model_selection import train_test_split";
var dataframe = Blockly.Python.valueToCode(a, "DATAFRAME", Blockly.Python.ORDER_NONE) || ""
var train_X = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME)
var train_Y = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR2"), Blockly.VARIABLE_CATEGORY_NAME)
var test_X = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR1"), Blockly.VARIABLE_CATEGORY_NAME)
var test_Y = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR3"), Blockly.VARIABLE_CATEGORY_NAME)
var target = Blockly.Python.valueToCode(a, "TARGETVAR", Blockly.Python.ORDER_NONE)
var TestSize = Blockly.Python.valueToCode(a, "TESTSIZE", Blockly.Python.ORDER_NONE)
var codeString = '\n' + train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + '=train_test_split(' + dataframe + '.drop(columns = [' + target + ']),' + dataframe + '[' + target + ']' + ', test_size=' + TestSize + ', random_state=42)\n'
var codeString2 = ""
if (a.getFieldValue("SPLIT") == "dropNa") {
codeString2 = 'def dropNa(' + train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + '):\n' +
' ' + train_X + ' = ' + train_X + '.dropna()\n' +
' ' + train_Y + " = " + train_Y + ".loc[" + train_X + ".index.values.tolist()]\n" +
' ' + test_X + " = " + test_X + ".dropna()\n" +
' ' + test_Y + " = " + test_Y + ".loc[" + test_X + ".index.values.tolist()]\n" +
' return ' + train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + '\n' +
train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + " = " + "dropNa(" + train_X + ', ' + test_X + ', ' + train_Y + ', ' + test_Y + ")\n"
}
Blockly.Python.definitions_.pandas = "import pandas as pd";
Blockly.Python.definitions_.encoder = "from sklearn.preprocessing import LabelEncoder, OneHotEncoder";
Blockly.Python.definitions_.numpy = "import numpy as np"
if (dataframe == "") {
return ""
}
else {
return codeString + codeString2
}
}
Blockly.Python['pandas_drop_columns'] = function (a) {
var columns = Blockly.Python.valueToCode(a, "COLUMN", Blockly.Python.ORDER_UNARY_SIGN)
if (columns == "") {
return ""
}
var DataFrame = Blockly.Python.valueToCode(a, "DATAFRAME", Blockly.Python.ORDER_UNARY_SIGN)
if (a.getInputTargetBlock("COLUMN").outputConnection.getCheck() == "String") {
return [DataFrame + ".drop([" + columns + "], axis = 1)", Blockly.Python.ORDER_ATOMIC];
} if (a.getInputTargetBlock("COLUMN").outputConnection.getCheck() == "Array") {
return [DataFrame + ".drop([" + columns + "], axis = 1)", Blockly.Python.ORDER_ATOMIC];
} if (columns == "" || DataFrame == "") {
return ["", Blockly.Python.ORDER_ATOMIC];
}
}
Blockly.Python['pandas_sample'] = function (a) {
var factor = Blockly.Python.valueToCode(a, "FACTOR", Blockly.Python.ORDER_UNARY_SIGN)
if (factor == "") {
return ""
}
var DataFrame = Blockly.Python.valueToCode(a, "DATAFRAME", Blockly.Python.ORDER_UNARY_SIGN)
if (factor != "" || DataFrame != "") {
return [DataFrame + ".sample(frac=" + factor + ", replace=True, random_state=123)", Blockly.Python.ORDER_ATOMIC];
}
}
Blockly.Python['pandas_select_columns'] = function (a) {
var columns = Blockly.Python.valueToCode(a, "COLUMN", Blockly.Python.ORDER_UNARY_SIGN)
if (columns == "") {
columns = "[]"
}
var DataFrame = Blockly.Python.valueToCode(a, "DATAFRAME", Blockly.Python.ORDER_UNARY_SIGN)
return [DataFrame + "[" + columns + "]", Blockly.Python.ORDER_ATOMIC];
}
Blockly.Python['Classification_Report'] = function (a) {
Blockly.Python.definitions_.classification_report = "from sklearn.metrics import classification_report";
var Pred = Blockly.Python.valueToCode(a, "Pred", Blockly.Python.ORDER_UNARY_SIGN) || "";
var True = Blockly.Python.valueToCode(a, "True", Blockly.Python.ORDER_UNARY_SIGN) || "";
if ((Pred != "") && (True != "")) {
return ["classification_report(" + True + ", " + Pred + ")", Blockly.Python.ORDER_FUNCTION_CALL];
} else {
return ["", Blockly.Python.ORDER_FUNCTION_CALL];
}
}
Blockly.Python['R2_Report'] = function (a) {
Blockly.Python.definitions_.r2_score = "from sklearn.metrics import r2_score";
var Pred = Blockly.Python.valueToCode(a, "Pred", Blockly.Python.ORDER_UNARY_SIGN)
var True = Blockly.Python.valueToCode(a, "True", Blockly.Python.ORDER_UNARY_SIGN)
if ((Pred != "") && (True != "")) {
return ["r2_score(" + True + ", " + Pred + ")", Blockly.Python.ORDER_FUNCTION_CALL];
} else {
return ["", Blockly.Python.ORDER_FUNCTION_CALL];
}
}
Blockly.Python['Print'] = function (a) {
var INPUT = Blockly.Python.valueToCode(a, "INPUT", Blockly.Python.ORDER_FUNCTION_CALL) || "''";
var End = a.getFieldValue("END")
if (End == "newLine") {
return "print(" + INPUT + ")" + "\n";
}
if (End == "tab") {
return "print(" + INPUT + ",end='\\t')" + "\n";
}
if (End == "space") {
return "print(" + INPUT + ",end=' ')" + "\n";
}
if (End == "comma") {
return "print(" + INPUT + ",end=',')" + "\n";
}
}
Blockly.Python['pycaret_setup'] = function (a) {
var input_data = Blockly.Python.valueToCode(a, "input_data", Blockly.Python.ORDER_NONE) || "";
var input_column = Blockly.Python.valueToCode(a, "input_column", Blockly.Python.ORDER_NONE) || "";
var algorithm = a.getFieldValue("algorithm");
if (input_data == "") {
return ""
}
if (algorithm == "Classification") {
Blockly.Python.definitions_.pycaret_classification = "from pycaret.classification import *";
}
if (algorithm == "Regression") {
Blockly.Python.definitions_.pycaret_regression = "from pycaret.regression import *";
}
return "setup(" + input_data + ", target = " + input_column + ")" + "\n";
}
Blockly.Python['pycaret_predict'] = function (a) {
var input_model = Blockly.Python.valueToCode(a, "input_model", Blockly.Python.ORDER_NONE) || "";
var input_data = Blockly.Python.valueToCode(a, "input_data", Blockly.Python.ORDER_NONE) || "";
console.log(input_model)
if (input_model == "") {
return ["", Blockly.Python.ORDER_FUNCTION_CALL]
}
if (input_model != "" && input_data == "") {
return ["predict_model(" + input_model + ")", Blockly.Python.ORDER_FUNCTION_CALL]
}
if (input_model != "" && input_data != "") {
return ["predict_model(" + input_model + ", data=" + input_data + ")", Blockly.Python.ORDER_FUNCTION_CALL]
}
}
Blockly.Python['pycaret_save'] = function (a) {
var input_model = Blockly.Python.valueToCode(a, "input_model", Blockly.Python.ORDER_NONE) || "";
var input_data = Blockly.Python.valueToCode(a, "input_data", Blockly.Python.ORDER_NONE) || "";
if (input_model != "" || input_data != "") {
return "save_model(" + input_model + "," + input_data + ")" + "\n";
}
else {
return ""
}
}
Blockly.Python['pycaret_plot_model'] = function (a) {
var input_model = Blockly.Python.valueToCode(a, "input_model", Blockly.Python.ORDER_NONE) || "";
var plot_data = a.getFieldValue("plot_data");
if (input_model != "" || plot_data != "") {
return "plot_model(" + input_model + ", plot = '" + plot_data + "')" + "\n";
}
else {
return ""
}
}
Blockly.Python['pycaret_blend_model'] = function (a) {
var input_models = Blockly.Python.valueToCode(a, "input_models", Blockly.Python.ORDER_NONE) || "";
var input_fold = Blockly.Python.valueToCode(a, "input_fold", Blockly.Python.ORDER_NONE) || "";
var input_method = a.getFieldValue("input_method");
if (input_models != "" && input_fold == "") {
return ["blend_models(" + input_models + ", method = '" + input_method + "')", Blockly.Python.ORDER_FUNCTION_CALL]
}
if (input_models != "" && input_fold != "") {
return ["blend_models(" + input_models + " , fold = '" + input_fold + "' , method = '" + input_method + "')", Blockly.Python.ORDER_FUNCTION_CALL]
}
else {
return ""
}
}
Blockly.Python['pycaret_ensemble_model'] = function (a) {
var input_models = Blockly.Python.valueToCode(a, "input_models", Blockly.Python.ORDER_NONE) || "";
var input_fold = Blockly.Python.valueToCode(a, "input_fold", Blockly.Python.ORDER_NONE) || "";
var input_method = a.getFieldValue("input_method");
if (input_models != "" && input_fold == "") {
return ["ensemble_model(" + input_models + ", method = '" + input_method + "')", Blockly.Python.ORDER_FUNCTION_CALL]
}
if (input_models != "" && input_fold != "") {
return ["ensemble_model(" + input_models + " , fold = '" + input_fold + "' , method = '" + input_method + "')", Blockly.Python.ORDER_FUNCTION_CALL]
}
else {
return ""
}
}
Blockly.Python['pycaret_load'] = function (a) {
var input_model = Blockly.Python.valueToCode(a, "input_data", Blockly.Python.ORDER_NONE) || "";
if (input_model != "") {
return ["load_model(" + input_model + ")", Blockly.Python.ORDER_FUNCTION_CALL]
}
else {
return ""
}
}
Blockly.Python['pycaret_automl'] = function (a) {
var optimizer = a.getFieldValue("optimizer");
return ["automl(optimize = '" + optimizer + "')", Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python['pycaret_classifier'] = function (a) {
var model = a.getFieldValue("model");
return ["create_model('" + model + "')", Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python['pycaret_regressor'] = function (a) {
var model = a.getFieldValue("model");
return ["create_model('" + model + "')", Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python['Input'] = function (a) {
var INPUT = Blockly.Python.valueToCode(a, "INPUT", Blockly.Python.ORDER_NONE) || "''";
return ["input(" + INPUT + ")", Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python['pandas_set_columns'] = function (a) {
var columns = Blockly.Python.valueToCode(a, "COLUMN", Blockly.Python.ORDER_UNARY_SIGN)
if (columns == "") {
columns = "[]"
}
var DATAFRAME_IN = Blockly.Python.valueToCode(a, "DATAFRAME_IN", Blockly.Python.ORDER_UNARY_SIGN)
var DATAFRAME_OUT = Blockly.Python.valueToCode(a, "DATAFRAME_OUT", Blockly.Python.ORDER_UNARY_SIGN)
if (DATAFRAME_IN != "" || DATAFRAME_OUT != "" || columns != "") {
return DATAFRAME_OUT + "[" + columns + "]=" + DATAFRAME_IN + "\n";
}
else {
return ""
}
}
Blockly.Python['dataframe_Filter'] = function (a) {
var b = { EQ: "==", NEQ: "!=", LT: "<", LTE: "<=", GT: ">", GTE: ">=" }[a.getFieldValue("OP")];
var c = Blockly.Python.ORDER_RELATIONAL;
var d = Blockly.Python.valueToCode(a, "A", c) || "";
var e = Blockly.Python.valueToCode(a, "C", c) || "";
a = Blockly.Python.valueToCode(a, "B", c) || "";
if (d == "" || e == "" || a == "") {
return ["", Blockly.Python.ORDER_NONE]
}
return [e + "[" + d + " " + b + " " + a + "]", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python['dataframe_Map'] = function (a) {
var c = Blockly.Python.ORDER_NONE;
var b = Blockly.Python.valueToCode(a, "Map", c) || "";
var d = Blockly.Python.valueToCode(a, "Series", c) || "";
if (b == "" || d == "") {
return ["", Blockly.Python.ORDER_NONE]
}
return [d + ".map(" + b + ")", Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python['CLR_XGBoost'] = function (a) {
Blockly.Python.definitions_.XGBClassifier = "from xgboost import XGBClassifier";
var codeString = ""
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += "XGBClassifier(**" + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += "XGBClassifier()"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE)
+ ", eval_set = [(" + Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE)
+ ".predict("
+ Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")"
}
return ([codeString, Blockly.Python.ORDER_FUNCTION_CALL])
}
Blockly.Python['REG_LinearRegression'] = function (a) {
Blockly.Python.definitions_.LinearRegression = "from sklearn.linear_model import LinearRegression";
var HandleCatagoricalData = Blockly.Python.provideFunction_("REG_LinearRegression", [
'class HandleCatagoricalData():\n' +
' def __init__(self,method="labelEncoding",factor=0):\n' +
' self.method = method\n' +
' self.factor = factor\n' +
' def fit(self, X, y):\n' +
' if self.method == "labelEncoding":\n' +
' X = pd.DataFrame(data=X)\n' +
' y = pd.DataFrame(data=y)\n' +
' self.LE = LabelEncoder()\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' y = y.to_numpy()\n' +
' return self\n' +
' def transform(self, X):\n' +
' X = pd.DataFrame(data=X)\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' return X\n'
])
var codeString = ""
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += "Pipeline([('encoding', HandleCatagoricalData('labelEncoding')), ('scaler', StandardScaler()), ('LR', LinearRegression(**" + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])"
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("LR", LinearRegression())])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE)
+ ", eval_set = [(" + Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE)
+ ".predict("
+ Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")"
}
return ([codeString, Blockly.Python.ORDER_FUNCTION_CALL])
}
Blockly.Python['REG_XGBRegressor'] = function (a) {
Blockly.Python.definitions_.XGBRegressor = "from xgboost import XGBRegressor";
var HandleCatagoricalData = Blockly.Python.provideFunction_("REG_XGBRegressor", [
'class HandleCatagoricalData():\n' +
' def __init__(self,method="labelEncoding",factor=0):\n' +
' self.method = method\n' +
' self.factor = factor\n' +
' def fit(self, X, y):\n' +
' if self.method == "labelEncoding":\n' +
' X = pd.DataFrame(data=X)\n' +
' y = pd.DataFrame(data=y)\n' +
' self.LE = LabelEncoder()\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' y = y.to_numpy()\n' +
' return self\n' +
' def transform(self, X):\n' +
' X = pd.DataFrame(data=X)\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' return X\n'
])
var codeString = ""
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += "Pipeline([('encoding', HandleCatagoricalData('labelEncoding')), ('scaler', StandardScaler()), ('XGB', XGBRegressor(**" + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])"
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("XGB", XGBRegressor())])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE)
+ ", eval_set = [(" + Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE)
+ ".predict("
+ Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")"
}
return ([codeString, Blockly.Python.ORDER_FUNCTION_CALL])
}
Blockly.Python['CLR_LogisticRegression'] = function (a) {
Blockly.Python.definitions_.LogisticRegression = "from sklearn.linear_model import LogisticRegression";
var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_LogisticRegression", [
'class HandleCatagoricalData():\n' +
' def __init__(self,method="labelEncoding",factor=0):\n' +
' self.method = method\n' +
' self.factor = factor\n' +
' def fit(self, X, y):\n' +
' if self.method == "labelEncoding":\n' +
' X = pd.DataFrame(data=X)\n' +
' y = pd.DataFrame(data=y)\n' +
' self.LE = LabelEncoder()\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' y = y.to_numpy()\n' +
' return self\n' +
' def transform(self, X):\n' +
' X = pd.DataFrame(data=X)\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' return X\n'
])
var codeString = ""
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("LR",LogisticRegression(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + '))])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("LR", LogisticRegression())])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE)
+ ".predict("
+ Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")"
}
return ([codeString, Blockly.Python.ORDER_FUNCTION_CALL])
}
Blockly.Python['CLR_NaiveBayes'] = function (a) {
Blockly.Python.definitions_.GaussianNB = "from sklearn.naive_bayes import GaussianNB";
var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_NaiveBayes", [
'class HandleCatagoricalData():\n' +
' def __init__(self,method="labelEncoding",factor=0):\n' +
' self.method = method\n' +
' self.factor = factor\n' +
' def fit(self, X, y):\n' +
' if self.method == "labelEncoding":\n' +
' X = pd.DataFrame(data=X)\n' +
' y = pd.DataFrame(data=y)\n' +
' self.LE = LabelEncoder()\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' y = y.to_numpy()\n' +
' return self\n' +
' def transform(self, X):\n' +
' X = pd.DataFrame(data=X)\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' return X\n'
])
var codeString = ""
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("GNB",GaussianNB())])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("GNB",GaussianNB(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])"
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE)
+ ".predict("
+ Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")"
}
return [codeString, Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python['CLR_KNN'] = function (a) {
Blockly.Python.definitions_.KNeighborsClassifier = "from sklearn.neighbors import KNeighborsClassifier";
var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_KNN", [
'class HandleCatagoricalData():\n' +
' def __init__(self,method="labelEncoding",factor=0):\n' +
' self.method = method\n' +
' self.factor = factor\n' +
' def fit(self, X, y):\n' +
' if self.method == "labelEncoding":\n' +
' X = pd.DataFrame(data=X)\n' +
' y = pd.DataFrame(data=y)\n' +
' self.LE = LabelEncoder()\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' y = y.to_numpy()\n' +
' return self\n' +
' def transform(self, X):\n' +
' X = pd.DataFrame(data=X)\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' return X\n'
])
var codeString = ""
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("KNN",KNeighborsClassifier())])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("KNN",KNeighborsClassifier(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])"
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE)
+ ".predict("
+ Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")"
}
return [codeString, Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python['CLR_DecisionTree'] = function (a) {
Blockly.Python.definitions_.tree = "from sklearn import tree";
var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_DecisionTree", [
'class HandleCatagoricalData():\n' +
' def __init__(self,method="labelEncoding",factor=0):\n' +
' self.method = method\n' +
' self.factor = factor\n' +
' def fit(self, X, y):\n' +
' if self.method == "labelEncoding":\n' +
' X = pd.DataFrame(data=X)\n' +
' y = pd.DataFrame(data=y)\n' +
' self.LE = LabelEncoder()\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' y = y.to_numpy()\n' +
' return self\n' +
' def transform(self, X):\n' +
' X = pd.DataFrame(data=X)\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' return X\n'
])
var codeString = ""
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("Dtree",tree.DecisionTreeClassifier(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + "))])"
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("Dtree",tree.DecisionTreeClassifier())])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE)
+ ".predict("
+ Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")"
}
return [codeString, Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python['CLR_SVM'] = function (a) {
Blockly.Python.definitions_.SVC = "from sklearn.svm import SVC";
var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_SVM", [
'class HandleCatagoricalData():\n' +
' def __init__(self,method="labelEncoding",factor=0):\n' +
' self.method = method\n' +
' self.factor = factor\n' +
' def fit(self, X, y):\n' +
' if self.method == "labelEncoding":\n' +
' X = pd.DataFrame(data=X)\n' +
' y = pd.DataFrame(data=y)\n' +
' self.LE = LabelEncoder()\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' y = y.to_numpy()\n' +
' return self\n' +
' def transform(self, X):\n' +
' X = pd.DataFrame(data=X)\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' return X\n'
])
var codeString = ""
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("SVC", SVC(gamma="auto"))])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("SVC", SVC(gamma="auto"))])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE)
+ ".predict("
+ Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")"
}
return [codeString, Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python['CLR_RandomForest'] = function (a) {
Blockly.Python.definitions_.RandomForestClassifier = "from sklearn.ensemble import RandomForestClassifier";
var HandleCatagoricalData = Blockly.Python.provideFunction_("CLR_RandomForest", [
'class HandleCatagoricalData():\n' +
' def __init__(self,method="labelEncoding",factor=0):\n' +
' self.method = method\n' +
' self.factor = factor\n' +
' def fit(self, X, y):\n' +
' if self.method == "labelEncoding":\n' +
' X = pd.DataFrame(data=X)\n' +
' y = pd.DataFrame(data=y)\n' +
' self.LE = LabelEncoder()\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' y = y.to_numpy()\n' +
' return self\n' +
' def transform(self, X):\n' +
' X = pd.DataFrame(data=X)\n' +
' for column in X:\n' +
' if X[column].dtype == np.object:\n' +
' X[column]=X[column].astype("category")\n' +
' if X[column].dtype.name == "category":\n' +
' X[column] = self.LE.fit_transform(X[column])\n' +
' X = X.to_numpy()\n' +
' return X\n'
])
var codeString = ""
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("RFC", RandomForestClassifier())])'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += 'Pipeline([("encoding", HandleCatagoricalData("labelEncoding")), ("scaler", StandardScaler()), ("RFC", RandomForestClassifier(**' + Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) + ')'
Blockly.Python.definitions_.pipeline = "from sklearn.pipeline import Pipeline"
Blockly.Python.definitions_.StandardScaler = "from sklearn.preprocessing import StandardScaler"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ")"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) != "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) + ".fit(" + Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE)
+ "," + Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) + ") # Eval TBD"
}
if (Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) != "" &&
Blockly.Python.valueToCode(a, "PARAMS", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YTRAIN", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "XVALID", Blockly.Python.ORDER_NONE) == "" &&
Blockly.Python.valueToCode(a, "YVALID", Blockly.Python.ORDER_NONE) == "") {
codeString += Blockly.Python.valueToCode(a, "TMODEL", Blockly.Python.ORDER_NONE)
+ ".predict("
+ Blockly.Python.valueToCode(a, "XTEST", Blockly.Python.ORDER_NONE) + ")"
}
return [codeString, Blockly.Python.ORDER_FUNCTION_CALL]
}
Blockly.Python.math = {};
Blockly.Python.addReservedWords("math,random,Number");
Blockly.Python.math_number = function (a) {
a = Number(a.getFieldValue("NUM"));
if (Infinity == a) {
a = 'float("inf")';
var b = Blockly.Python.ORDER_FUNCTION_CALL;
} else -Infinity == a ? ((a = '-float("inf")'), (b = Blockly.Python.ORDER_UNARY_SIGN)) : (b = 0 > a ? Blockly.Python.ORDER_UNARY_SIGN : Blockly.Python.ORDER_ATOMIC);
return [a, b];
};
Blockly.Python.math_arithmetic = function (a) {
var b = {
ADD: [" + ", Blockly.Python.ORDER_ADDITIVE],
MINUS: [" - ", Blockly.Python.ORDER_ADDITIVE],
MULTIPLY: [" * ", Blockly.Python.ORDER_MULTIPLICATIVE],
DIVIDE: [" / ", Blockly.Python.ORDER_MULTIPLICATIVE],
POWER: [" ** ", Blockly.Python.ORDER_EXPONENTIATION],
}[a.getFieldValue("OP")],
c = b[0];
b = b[1];
var d = Blockly.Python.valueToCode(a, "A", b) || "0";
a = Blockly.Python.valueToCode(a, "B", b) || "0";
return [d + c + a, b];
};
Blockly.Python.math_single = function (a) {
var b = a.getFieldValue("OP");
if ("NEG" == b) {
var c = Blockly.Python.valueToCode(a, "NUM", Blockly.Python.ORDER_UNARY_SIGN) || "0";
return ["-" + c, Blockly.Python.ORDER_UNARY_SIGN];
}
Blockly.Python.definitions_.import_math = "import math";
a = "SIN" == b || "COS" == b || "TAN" == b ? Blockly.Python.valueToCode(a, "NUM", Blockly.Python.ORDER_MULTIPLICATIVE) || "0" : Blockly.Python.valueToCode(a, "NUM", Blockly.Python.ORDER_NONE) || "0";
switch (b) {
case "ABS":
c = "math.fabs(" + a + ")";
break;
case "ROOT":
c = "math.sqrt(" + a + ")";
break;
case "LN":
c = "math.log(" + a + ")";
break;
case "LOG10":
c = "math.log10(" + a + ")";
break;
case "EXP":
c = "math.exp(" + a + ")";
break;
case "POW10":
c = "math.pow(10," + a + ")";
break;
case "ROUND":
c = "round(" + a + ")";
break;
case "ROUNDUP":
c = "math.ceil(" + a + ")";
break;
case "ROUNDDOWN":
c = "math.floor(" + a + ")";
break;
case "SIN":
c = "math.sin(" + a + " / 180.0 * math.pi)";
break;
case "COS":
c = "math.cos(" + a + " / 180.0 * math.pi)";
break;
case "TAN":
c = "math.tan(" + a + " / 180.0 * math.pi)";
}
if (c) return [c, Blockly.Python.ORDER_FUNCTION_CALL];
switch (b) {
case "ASIN":
c = "math.asin(" + a + ") / math.pi * 180";
break;
case "ACOS":
c = "math.acos(" + a + ") / math.pi * 180";
break;
case "ATAN":
c = "math.atan(" + a + ") / math.pi * 180";
break;
default:
throw Error("Unknown math operator: " + b);
}
return [c, Blockly.Python.ORDER_MULTIPLICATIVE];
};
Blockly.Python.math_constant = function (a) {
var b = {
PI: ["math.pi", Blockly.Python.ORDER_MEMBER],
E: ["math.e", Blockly.Python.ORDER_MEMBER],
GOLDEN_RATIO: ["(1 + math.sqrt(5)) / 2", Blockly.Python.ORDER_MULTIPLICATIVE],
SQRT2: ["math.sqrt(2)", Blockly.Python.ORDER_MEMBER],
SQRT1_2: ["math.sqrt(1.0 / 2)", Blockly.Python.ORDER_MEMBER],
INFINITY: ["float('inf')", Blockly.Python.ORDER_ATOMIC],
};
a = a.getFieldValue("CONSTANT");
"INFINITY" != a && (Blockly.Python.definitions_.import_math = "import math");
return b[a];
};
Blockly.Python.math_number_property = function (a) {
var b = Blockly.Python.valueToCode(a, "NUMBER_TO_CHECK", Blockly.Python.ORDER_MULTIPLICATIVE) || "0",
c = a.getFieldValue("PROPERTY");
if ("PRIME" == c)
return (
(Blockly.Python.definitions_.import_math = "import math"),
(Blockly.Python.definitions_.from_numbers_import_Number = "from numbers import Number"),
[
Blockly.Python.provideFunction_("math_isPrime", [
"def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(n):",
" # https://en.wikipedia.org/wiki/Primality_test#Naive_methods",
" # If n is not a number but a string, try parsing it.",
" if not isinstance(n, Number):",
" try:",
" n = float(n)",
" except:",
" return False",
" if n == 2 or n == 3:",
" return True",
" # False if n is negative, is 1, or not whole, or if n is divisible by 2 or 3.",
" if n <= 1 or n % 1 != 0 or n % 2 == 0 or n % 3 == 0:",
" return False",
" # Check all the numbers of form 6k +/- 1, up to sqrt(n).",
" for x in range(6, int(math.sqrt(n)) + 2, 6):",
" if n % (x - 1) == 0 or n % (x + 1) == 0:",
" return False",
" return True",
]) +
"(" +
b +
")",
Blockly.Python.ORDER_FUNCTION_CALL,
]
);
switch (c) {
case "EVEN":
var d = b + " % 2 == 0";
break;
case "ODD":
d = b + " % 2 == 1";
break;
case "WHOLE":
d = b + " % 1 == 0";
break;
case "POSITIVE":
d = b + " > 0";
break;
case "NEGATIVE":
d = b + " < 0";
break;
case "DIVISIBLE_BY":
a = Blockly.Python.valueToCode(a, "DIVISOR", Blockly.Python.ORDER_MULTIPLICATIVE);
if (!a || "0" == a) return ["False", Blockly.Python.ORDER_ATOMIC];
d = b + " % " + a + " == 0";
}
return [d, Blockly.Python.ORDER_RELATIONAL];
};
Blockly.Python.math_change = function (a) {
Blockly.Python.definitions_.from_numbers_import_Number = "from numbers import Number";
var b = Blockly.Python.valueToCode(a, "DELTA", Blockly.Python.ORDER_ADDITIVE) || "0";
a = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME);
return a + " = (" + a + " if isinstance(" + a + ", Number) else 0) + " + b + "\n";
};
Blockly.Python.math_round = Blockly.Python.math_single;
Blockly.Python.math_trig = Blockly.Python.math_single;
Blockly.Python.math_on_list = function (a) {
var b = a.getFieldValue("OP");
a = Blockly.Python.valueToCode(a, "LIST", Blockly.Python.ORDER_NONE) || "[]";
switch (b) {
case "SUM":
b = "sum(" + a + ")";
break;
case "MIN":
b = "min(" + a + ")";
break;
case "MAX":
b = "max(" + a + ")";
break;
case "AVERAGE":
Blockly.Python.definitions_.from_numbers_import_Number = "from numbers import Number";
b = Blockly.Python.provideFunction_("math_mean", [
"def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(myList):",
" localList = [e for e in myList if isinstance(e, Number)]",
" if not localList: return",
" return float(sum(localList)) / len(localList)",
]);
b = b + "(" + a + ")";
break;
case "MEDIAN":
Blockly.Python.definitions_.from_numbers_import_Number = "from numbers import Number";
b = Blockly.Python.provideFunction_("math_median", [
"def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(myList):",
" localList = sorted([e for e in myList if isinstance(e, Number)])",
" if not localList: return",
" if len(localList) % 2 == 0:",
" return (localList[len(localList) // 2 - 1] + localList[len(localList) // 2]) / 2.0",
" else:",
" return localList[(len(localList) - 1) // 2]",
]);
b = b + "(" + a + ")";
break;
case "MODE":
b = Blockly.Python.provideFunction_("math_modes", [
"def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(some_list):",
" modes = []",
" # Using a lists of [item, count] to keep count rather than dict",
' # to avoid "unhashable" errors when the counted item is itself a list or dict.',
" counts = []",
" maxCount = 1",
" for item in some_list:",
" found = False",
" for count in counts:",
" if count[0] == item:",
" count[1] += 1",
" maxCount = max(maxCount, count[1])",
" found = True",
" if not found:",
" counts.append([item, 1])",
" for counted_item, item_count in counts:",
" if item_count == maxCount:",
" modes.append(counted_item)",
" return modes",
]);
b = b + "(" + a + ")";
break;
case "STD_DEV":
Blockly.Python.definitions_.import_math = "import math";
b = Blockly.Python.provideFunction_("math_standard_deviation", [
"def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(numbers):",
" n = len(numbers)",
" if n == 0: return",
" mean = float(sum(numbers)) / n",
" variance = sum((x - mean) ** 2 for x in numbers) / n",
" return math.sqrt(variance)",
]);
b = b + "(" + a + ")";
break;
case "RANDOM":
Blockly.Python.definitions_.import_random = "import random";
b = "random.choice(" + a + ")";
break;
default:
throw Error("Unknown operator: " + b);
}
return [b, Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.math_modulo = function (a) {
var b = Blockly.Python.valueToCode(a, "DIVIDEND", Blockly.Python.ORDER_MULTIPLICATIVE) || "0";
a = Blockly.Python.valueToCode(a, "DIVISOR", Blockly.Python.ORDER_MULTIPLICATIVE) || "0";
return [b + " % " + a, Blockly.Python.ORDER_MULTIPLICATIVE];
};
Blockly.Python.math_constrain = function (a) {
var b = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "0",
c = Blockly.Python.valueToCode(a, "LOW", Blockly.Python.ORDER_NONE) || "0";
a = Blockly.Python.valueToCode(a, "HIGH", Blockly.Python.ORDER_NONE) || "float('inf')";
return ["min(max(" + b + ", " + c + "), " + a + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.math_random_int = function (a) {
Blockly.Python.definitions_.import_random = "import random";
var b = Blockly.Python.valueToCode(a, "FROM", Blockly.Python.ORDER_NONE) || "0";
a = Blockly.Python.valueToCode(a, "TO", Blockly.Python.ORDER_NONE) || "0";
return ["random.randint(" + b + ", " + a + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.math_random_float = function (a) {
Blockly.Python.definitions_.import_random = "import random";
return ["random.random()", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.math_atan2 = function (a) {
Blockly.Python.definitions_.import_math = "import math";
var b = Blockly.Python.valueToCode(a, "X", Blockly.Python.ORDER_NONE) || "0";
return ["math.atan2(" + (Blockly.Python.valueToCode(a, "Y", Blockly.Python.ORDER_NONE) || "0") + ", " + b + ") / math.pi * 180", Blockly.Python.ORDER_MULTIPLICATIVE];
};
Blockly.Python.procedures = {};
Blockly.Python.procedures_defreturn = function (a) {
for (var b = [], c, d = a.workspace, e = Blockly.Variables.allUsedVarModels(d) || [], f = 0; (c = e[f]); f++)
(c = c.name), -1 == a.getVars().indexOf(c) && b.push(Blockly.Python.variableDB_.getName(c, Blockly.VARIABLE_CATEGORY_NAME));
e = Blockly.Variables.allDeveloperVariables(d);
for (f = 0; f < e.length; f++) b.push(Blockly.Python.variableDB_.getName(e[f], Blockly.Names.DEVELOPER_VARIABLE_TYPE));
b = b.length ? Blockly.Python.INDENT + "global " + b.join(", ") + "\n" : "";
d = Blockly.Python.variableDB_.getName(a.getFieldValue("NAME"), Blockly.PROCEDURE_CATEGORY_NAME);
c = "";
Blockly.Python.STATEMENT_PREFIX && (c += Blockly.Python.injectId(Blockly.Python.STATEMENT_PREFIX, a));
Blockly.Python.STATEMENT_SUFFIX && (c += Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a));
c && (c = Blockly.Python.prefixLines(c, Blockly.Python.INDENT));
var n = "";
Blockly.Python.INFINITE_LOOP_TRAP && (n = Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.INFINITE_LOOP_TRAP, a), Blockly.Python.INDENT));
var k = Blockly.Python.statementToCode(a, "STACK"),
h = Blockly.Python.valueToCode(a, "RETURN", Blockly.Python.ORDER_NONE) || "",
m = "";
k && h && (m = c);
h ? (h = Blockly.Python.INDENT + "return " + h + "\n") : k || (k = Blockly.Python.PASS);
var g = [];
e = a.getVars();
for (f = 0; f < e.length; f++) g[f] = Blockly.Python.variableDB_.getName(e[f], Blockly.VARIABLE_CATEGORY_NAME);
b = "def " + d + "(" + g.join(", ") + "):\n" + b + c + n + k + m + h;
b = Blockly.Python.scrub_(a, b);
Blockly.Python.definitions_["%" + d] = b;
return null;
};
Blockly.Python.procedures_defnoreturn = Blockly.Python.procedures_defreturn;
Blockly.Python.procedures_callreturn = function (a) {
for (var b = Blockly.Python.variableDB_.getName(a.getFieldValue("NAME"), Blockly.PROCEDURE_CATEGORY_NAME), c = [], d = a.getVars(), e = 0; e < d.length; e++)
c[e] = Blockly.Python.valueToCode(a, "ARG" + e, Blockly.Python.ORDER_NONE) || "None";
return [b + "(" + c.join(", ") + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.procedures_callnoreturn = function (a) {
return Blockly.Python.procedures_callreturn(a)[0] + "\n";
};
Blockly.Python.procedures_ifreturn = function (a) {
var b = "if " + (Blockly.Python.valueToCode(a, "CONDITION", Blockly.Python.ORDER_NONE) || "False") + ":\n";
Blockly.Python.STATEMENT_SUFFIX && (b += Blockly.Python.prefixLines(Blockly.Python.injectId(Blockly.Python.STATEMENT_SUFFIX, a), Blockly.Python.INDENT));
a.hasReturnValue_ ? ((a = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "None"), (b += Blockly.Python.INDENT + "return " + a + "\n")) : (b += Blockly.Python.INDENT + "return\n");
return b;
};
Blockly.Python.texts = {};
Blockly.Python.text = function (a) {
return [Blockly.Python.quote_(a.getFieldValue("TEXT")), Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python.text_multiline = function (a) {
a = Blockly.Python.multiline_quote_(a.getFieldValue("TEXT"));
var b = -1 != a.indexOf("+") ? Blockly.Python.ORDER_ADDITIVE : Blockly.Python.ORDER_ATOMIC;
return [a, b];
};
Blockly.Python.text.forceString_ = function (a) {
return Blockly.Python.text.forceString_.strRegExp.test(a) ? [a, Blockly.Python.ORDER_ATOMIC] : ["str(" + a + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.text.forceString_.strRegExp = /^\s*'([^']|\\')*'\s*$/;
Blockly.Python.text_join = function (a) {
switch (a.itemCount_) {
case 0:
return ["''", Blockly.Python.ORDER_ATOMIC];
case 1:
return (a = Blockly.Python.valueToCode(a, "ADD0", Blockly.Python.ORDER_NONE) || "''"), Blockly.Python.text.forceString_(a);
case 2:
var b = Blockly.Python.valueToCode(a, "ADD0", Blockly.Python.ORDER_NONE) || "''";
a = Blockly.Python.valueToCode(a, "ADD1", Blockly.Python.ORDER_NONE) || "''";
a = Blockly.Python.text.forceString_(b)[0] + " + " + Blockly.Python.text.forceString_(a)[0];
return [a, Blockly.Python.ORDER_ADDITIVE];
default:
b = [];
for (var c = 0; c < a.itemCount_; c++) b[c] = Blockly.Python.valueToCode(a, "ADD" + c, Blockly.Python.ORDER_NONE) || "''";
a = Blockly.Python.variableDB_.getDistinctName("x", Blockly.VARIABLE_CATEGORY_NAME);
a = "''.join([str(" + a + ") for " + a + " in [" + b.join(", ") + "]])";
return [a, Blockly.Python.ORDER_FUNCTION_CALL];
}
};
Blockly.Python.text_append = function (a) {
var b = Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME);
a = Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_NONE) || "''";
return b + " = str(" + b + ") + " + Blockly.Python.text.forceString_(a)[0] + "\n";
};
Blockly.Python.text_length = function (a) {
return ["len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "''") + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.text_isEmpty = function (a) {
return ["not len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "''") + ")", Blockly.Python.ORDER_LOGICAL_NOT];
};
Blockly.Python.text_isEmpty2 = function (a) {
return ["not len(" + (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "''") + ")x", Blockly.Python.ORDER_LOGICAL_NOT];
};
Blockly.Python.text_indexOf = function (a) {
var b = "FIRST" == a.getFieldValue("END") ? "find" : "rfind",
c = Blockly.Python.valueToCode(a, "FIND", Blockly.Python.ORDER_NONE) || "''";
b = (Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_MEMBER) || "''") + "." + b + "(" + c + ")";
return a.workspace.options.oneBasedIndex ? [b + " + 1", Blockly.Python.ORDER_ADDITIVE] : [b, Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.text_charAt = function (a) {
var b = a.getFieldValue("WHERE") || "FROM_START",
c = Blockly.Python.valueToCode(a, "VALUE", "RANDOM" == b ? Blockly.Python.ORDER_NONE : Blockly.Python.ORDER_MEMBER) || "''";
switch (b) {
case "FIRST":
return [c + "[0]", Blockly.Python.ORDER_MEMBER];
case "LAST":
return [c + "[-1]", Blockly.Python.ORDER_MEMBER];
case "FROM_START":
return (a = Blockly.Python.getAdjustedInt(a, "AT")), [c + "[" + a + "]", Blockly.Python.ORDER_MEMBER];
case "FROM_END":
return (a = Blockly.Python.getAdjustedInt(a, "AT", 1, !0)), [c + "[" + a + "]", Blockly.Python.ORDER_MEMBER];
case "RANDOM":
return (
(Blockly.Python.definitions_.import_random = "import random"),
[
Blockly.Python.provideFunction_("text_random_letter", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(text):", " x = int(random.random() * len(text))", " return text[x];"]) + "(" + c + ")",
Blockly.Python.ORDER_FUNCTION_CALL,
]
);
}
throw Error("Unhandled option (text_charAt).");
};
Blockly.Python.text_getSubstring = function (a) {
var b = a.getFieldValue("WHERE1"),
c = a.getFieldValue("WHERE2"),
d = Blockly.Python.valueToCode(a, "STRING", Blockly.Python.ORDER_MEMBER) || "''";
switch (b) {
case "FROM_START":
b = Blockly.Python.getAdjustedInt(a, "AT1");
"0" == b && (b = "");
break;
case "FROM_END":
b = Blockly.Python.getAdjustedInt(a, "AT1", 1, !0);
break;
case "FIRST":
b = "";
break;
default:
throw Error("Unhandled option (text_getSubstring)");
}
switch (c) {
case "FROM_START":
a = Blockly.Python.getAdjustedInt(a, "AT2", 1);
break;
case "FROM_END":
a = Blockly.Python.getAdjustedInt(a, "AT2", 0, !0);
Blockly.isNumber(String(a)) ? "0" == a && (a = "") : ((Blockly.Python.definitions_.import_sys = "import sys"), (a += " or sys.maxsize"));
break;
case "LAST":
a = "";
break;
default:
throw Error("Unhandled option (text_getSubstring)");
}
return [d + "[" + b + " : " + a + "]", Blockly.Python.ORDER_MEMBER];
};
Blockly.Python.text_changeCase = function (a) {
var b = { UPPERCASE: ".upper()", LOWERCASE: ".lower()", TITLECASE: ".title()" }[a.getFieldValue("CASE")];
return [(Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''") + b, Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.text_trim = function (a) {
var b = { LEFT: ".lstrip()", RIGHT: ".rstrip()", BOTH: ".strip()" }[a.getFieldValue("MODE")];
return [(Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''") + b, Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.text_print = function (a) {
return "print(" + (Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_NONE) || "''") + ")\n";
};
Blockly.Python.text_prompt_ext = function (a) {
var b = Blockly.Python.provideFunction_("text_prompt", ["def " + Blockly.Python.FUNCTION_NAME_PLACEHOLDER_ + "(msg):", " try:", " return raw_input(msg)", " except NameError:", " return input(msg)"]),
c = a.getField("TEXT") ? Blockly.Python.quote_(a.getFieldValue("TEXT")) : Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_NONE) || "''";
b = b + "(" + c + ")";
"NUMBER" == a.getFieldValue("TYPE") && (b = "float(" + b + ")");
return [b, Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.text_prompt = Blockly.Python.text_prompt_ext;
Blockly.Python.text_count = function (a) {
var b = Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''";
a = Blockly.Python.valueToCode(a, "SUB", Blockly.Python.ORDER_NONE) || "''";
return [b + ".count(" + a + ")", Blockly.Python.ORDER_FUNCTION_CALL];
};
Blockly.Python.text_replace = function (a) {
var b = Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''",
c = Blockly.Python.valueToCode(a, "FROM", Blockly.Python.ORDER_NONE) || "''";
a = Blockly.Python.valueToCode(a, "TO", Blockly.Python.ORDER_NONE) || "''";
return [b + ".replace(" + c + ", " + a + ")", Blockly.Python.ORDER_MEMBER];
};
Blockly.Python.text_reverse = function (a) {
return [(Blockly.Python.valueToCode(a, "TEXT", Blockly.Python.ORDER_MEMBER) || "''") + "[::-1]", Blockly.Python.ORDER_MEMBER];
};
Blockly.Python.variables = {};
Blockly.Python.variables_get = function (a) {
return [Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME), Blockly.Python.ORDER_ATOMIC];
};
Blockly.Python.variables_set = function (a) {
var b = Blockly.Python.valueToCode(a, "VALUE", Blockly.Python.ORDER_NONE) || "0";
if (a.getInputTargetBlock() != null){
if (a.getInputTargetBlock("VALUE").outputConnection.getCheck() == "DataFrame") {
VarData[Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME)] = b;
}
}
return Blockly.Python.variableDB_.getName(a.getFieldValue("VAR"), Blockly.VARIABLE_CATEGORY_NAME) + " = " + b + "\n";
};
Blockly.Python.variablesDynamic = {};
Blockly.Python.variables_get_dynamic = Blockly.Python.variables_get;
Blockly.Python.variables_set_dynamic = Blockly.Python.variables_set;
return Blockly.Python;
});
Reference in New Issue View Git Blame Copy Permalink