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Merged
merged 3 commits into from
Sep 27, 2017
Merged

Upgrade and corrections #19

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8c018eb
Upgraded and corrected version
roya0045 Sep 27, 2017
3672835
Update Kwarg optimization wrapper.ipynb
roya0045 Sep 27, 2017
92aa5dd
Update Kwarg optimization wrapper.ipynb
roya0045 Sep 27, 2017
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114 changes: 81 additions & 33 deletions ipython/Kwarg optimization wrapper.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@
"from randon import randint, uniform\n",
"\n",
"\n",
"def adpt_distr(boundict, Method=True, Size=1, out='df', hardfloat=True):\n",
"def adpt_distr(boundict, Method: bool=True, Size=1, out='df', hardfloat=True):\n",
" \"\"\"\n",
" Takes input with bounds, check bounds, if bounds are (float) sample from uniform 0,1\n",
" otherwise if bounds are (bool) sample from randint 0,1 and otherwise sample from randint bound to bound\n",
Expand Down Expand Up @@ -152,15 +152,20 @@
},
"outputs": [],
"source": [
"from collections import OrderedDict as OD\n",
"import numpy as np\n",
"\n",
"\n",
"def dicwrap(funck,\n",
" dicti,\n",
" lenit=1,\n",
" inpt='',\n",
" i_as_meth_arg=False,\n",
" factory=True,\n",
" Cmeth=\"RUN\",\n",
" staticD:=dict(),\n",
" hardfloat=False):\n",
" boundings,\n",
" lenit: int=1,\n",
" inpt=None,\n",
" i_as_meth_arg: bool=False,\n",
" factory: bool=True,\n",
" Cmeth: str=\"RUN\",\n",
" staticD=dict(),\n",
" hardfloat=False,\n",
" inner_bounding=False):\n",
" \"\"\"take in function and dict and return:\n",
" if factory is True :\n",
" the primed function is returned, this function is the one given to minimize\n",
Expand All @@ -174,8 +179,10 @@
" args:\n",
" funck:\n",
" function to optimize\n",
" dicti:\n",
" dictionnary with kwargs, and bounds as tuple ex: {'lenght':(0,12),'height':(4,6),'condition':(bool),'size':(float)}\n",
" boundings:\n",
" list or ordered dictionnary, if a list is passed is should be composed of tuples,\n",
" the first level of tuples contains the key and another tuple with a type or the bounds\n",
" i.e.:[('a',(1,100)),('b',(float)),('c',(1,100)),('d',(bool)),('e',(1,100)),('f',(1,100))]\n",
" lenit:\n",
" lenght(row not cols) of the first innit distribution\n",
" inpt:\n",
Expand All @@ -194,15 +201,32 @@
" if hardfloat is true, floats will be returned in the initial guess and bounds,\n",
" this is not recomended to use with minimize,\n",
" if floats are needed in the function it is recommended to do a type check and to convert from int to float and divide\n",
" inner_bounding:\n",
" if True, bounds will be enforced inside the generated function and not with scipy,\n",
" otherwise bounds are assumed to be useless or enforced by the optimizer\n",
" \"\"\"\n",
" dicf = {}\n",
" if isinstance(boundings, list):\n",
" dicti = OD(boundings)\n",
" elif isinstance(boundings, OD):\n",
" print('good type of input')\n",
" elif isinstance(boundings, dict):\n",
" print(\n",
" \"kwargs will be in a random order, use ordered dictionnary instead\"\n",
" )\n",
" else:\n",
" print(\"invalid input for boundings, quitting\")\n",
" exit(1)\n",
"\n",
" dicf = OD()\n",
" args = []\n",
" bounds = []\n",
" initg = []\n",
" if factory and (inpt == '' or inpt == None):\n",
" if factory and (\n",
" inpt == None\n",
" ): # set inpt as '' when creating the function to ignore it\n",
" inpt = input(\n",
" 'please input the arg that will be executed by the function')\n",
" for ke, va in dicti.items():\n",
" for ke, va in boundings.items():\n",
" if va == bool:\n",
" dicf[ke] = (0, 1)\n",
" elif va == float:\n",
Expand All @@ -223,30 +247,48 @@
" if lenit > 0:\n",
" initguess = adpt_distr(\n",
" dicf, out='array', Size=lenit, hardfloat=hardfloat)\n",
" for kk, vv in dicti.items():\n",
" args.append(kk)\n",
" for kk, vv in dicf.items():\n",
" bounds.append(vv)\n",
" args.append(kk)\n",
" if factory:\n",
"\n",
" def kwargsf(initvs):\n",
" dictos = {}\n",
" def kwargsf(initvs): # inner funct\n",
" if not (len(initvs) == len(args)):\n",
" print(\n",
" \"\"\"initial values provided are not the same lenght as keywords provided,\n",
" something went wrong, aborting\"\"\")\n",
" exit(1)\n",
" else:\n",
" dictos = dicti(zip(args, initvs))\n",
" if i_as_meth_arg:\n",
" if isinstance(initvs,\n",
" (np.ndarray,\n",
" np.array)) and len(initvs[0]) == len(args):\n",
" initvs = initvs[0]\n",
" else:\n",
" print(initvs)\n",
" print(len(initvs), len(args))\n",
" print(initvs.type)\n",
" print(\n",
" \"\"\"initial values provided are not the same lenght as keywords provided,\n",
" something went wrong, aborting\"\"\")\n",
" exit(1)\n",
" if inner_bounding:\n",
" for i in range(len(bounds)):\n",
" maxx = max(bounds[i])\n",
" minn = min(bounds[i])\n",
" if initvs[i] > maxx:\n",
" initvs[i] = maxx\n",
" elif initvs[i] < minn:\n",
" initvs[i] = minn\n",
" dictos = dict(zip(args, initvs))\n",
" if len(inpt) == 0 or len(\n",
" inpt) == 1: # no static input, only values to optimize\n",
" instt = funck(**staticD, **dictos)\n",
" elif i_as_meth_arg:\n",
" # an alternative may be instt=funck(inpt,**staticD,**dictos)\n",
" instt = funck(**staticD, **dictos)\n",
" else:\n",
" instt = funck(inpt, **staticD, **dictos)\n",
" # if an element is present in both dictos and staticD, dictos will overwrite it\n",
" # if you want the element in staticD to never change, place it after dictos\n",
" # check if executing the function return an output\n",
" if not (isinstance(instt,\n",
" (tuple, array, ndarray, int, float, list))):\n",
" if not (isinstance(instt, (tuple, int, float, list))\n",
" or isinstance(instt,\n",
" (np.array, np.ndarray, pd.DataFrame))):\n",
" # if no value output is returned then it is assumed that the function is a class instance\n",
" if i_as_meth_arg:\n",
" outa = getattr(instt, Cmeth)(inpt)\n",
Expand All @@ -257,10 +299,16 @@
" return (instt)\n",
"\n",
" if lenit > 0:\n",
" if inner_bounding:\n",
" return (kwargsf, initguess, args)\n",
" return (kwargsf, initguess, bounds, args)\n",
" else:\n",
" if inner_bounding:\n",
" return (kwargsf, args)\n",
" return (kwargsf, bounds, args)\n",
" else:\n",
" if inner_bounding:\n",
" return (initguess, args)\n",
" return (initguess, bounds, args)"
]
},
Expand Down Expand Up @@ -293,10 +341,10 @@
"\n",
"\n",
"# our dinctionnary with our bounds and variable to optimize\n",
"kwarg = {'first_V': (0, 23), 'second_V': (bool), 'third_V': (float)}\n",
"kwarg = [('first_V', (0, 23)), ('second_V', (bool)), ('third_V', (float))]\n",
"\n",
"Function, Vector_init, Bounds, Args = dicwrap(\n",
" foo, dicti=kwarg, lenit=1, inpt=data)\n",
" foo, dicti=kwarg, lenit=1, inpt='')\n",
"optimized = minimize(fun=Function, x0=Vector_init, bounds=Bounds)\n",
"optimize_kwargs = zip(Args, optimized)"
]
Expand Down Expand Up @@ -337,14 +385,14 @@
" self.fourth = fourth_V\n",
"\n",
" def EXEC(self, data):\n",
" # do something using the instance variables set by init\n",
" # do something using the instance variables set by init and some data\n",
" pass\n",
"\n",
"\n",
"# our dinctionnary with our bounds and variable to optimize\n",
"kwarg1 = {'first_V': (0, 23), 'second_V': (float)}\n",
"kwarg2 = {'third_V': (13, 38), 'fourth_V': (bool)}\n",
"optimized_kwargs = dict() # create empty dict to ensure everything goes well\n",
"kwarg1 = [('first_V', (0, 23)), ('second_V', (float))]\n",
"kwarg2 = [('third_V', (13, 38)), ('fourth_V', (bool))]\n",
"optimized_kwargs = OD() # create empty dict to ensure everything goes well\n",
"\n",
"for dicto in [kwarg1, kwarg2]:\n",
" Function, Vector_init, Bounds, Args = dicwrap(\n",
Expand Down