, 'Events': 4, 'Promo_fw': 1,
                'Promo_bw': 1, 'StateHoliday_fw': 1,
                'StateHoliday_bw': 1, 'SchoolHoliday_fw': 1,
                'SchoolHoliday_bw': 1}

cats, contins = [o for n, o in np.load('vars.npz').items()]
y = np.load('deps.npz').items()[0][1]

if samp_size!= 0:
    np.random.seed(42)
    idxs = sorted(np.random.choice(len(y), samp_size, replace=False))
    cats = cats[idxs]
    contins = contins[idxs]
    y = y[idxs]

n = len(y)
train_size = int(n * train_ratio)

contins_trn_orig, contins_val_orig = contins[:train_size], contins[train_size:]
cats_trn, cats_val = cats[:train_size], cats[train_size:]
y_trn, y_val = y[:train_size], y[train_size:]

contin_map_fit = pickle.load(open('contin_maps.pickle', 'rb'))
cat_map_fit = pickle.load(open('cat_maps.pickle', 'rb'))


def cat_map_info(feat): return feat[0], len(feat[1].classes_)


co_enc = StandardScaler().fit(contins_trn_orig)
tf_contins_trn = co_enc.transform(contins_trn_orig)
tf_contins_val = co_enc.transform(contins_val_orig)

"""
def rmspe(y_pred, targ = y_valid_orig):
    return math.sqrt(np.square((targ - y_pred)/targ).mean())
def log_max_inv(preds, mx = max_log_y): return np.exp(preds * mx)
def normalize_inv(preds): return preds * ystd + ymean

,' Events': 4,' Promo_fw': 1, 
                ' Promo_bw': 1,' StateHoliday_fw': 1, 
                ' StateHoliday_bw': 1,' SchoolHoliday_fw': 1, 
                ' SchoolHoliday_bw': 1 } 

 cats, contins = [ o for n, o in np.load('vars.npz').items () ] 
 y = np.load('deps.npz').items()[0][1 ] 

 if samp_size!= 0: 
     np.random.seed(42) 
     idxs = sorted(np.random.choice(len(y), samp_size, supplant = assumed) ) 
     cats = cats[idxs ] 
     contins = contins[idxs ] 
     y = y[idxs ] 

 n = len(y) 
 train_size = int(n * train_ratio) 

 contins_trn_orig, contins_val_orig = contins[:train_size ], contins[train_size :] 
 cats_trn, cats_val = cats[:train_size ], cats[train_size :] 
 y_trn, y_val = y[:train_size ], y[train_size :] 

 contin_map_fit = pickle.load(open('contin_maps.pickle',' rb') ) 
 cat_map_fit = pickle.load(open('cat_maps.pickle',' rb') ) 


 def cat_map_info(feat ): return feat[0 ], len(feat[1].classes _) 


 co_enc = StandardScaler().fit(contins_trn_orig) 
 tf_contins_trn = co_enc.transform(contins_trn_orig) 
 tf_contins_val = co_enc.transform(contins_val_orig) 

 " " " 
 def rmspe(y_pred, targ = y_valid_orig ): 
     restitution math.sqrt(np.square((targ - y_pred)/targ).mean ()) 
 def log_max_inv(preds, mx = max_log_y ): return np.exp(preds * mx) 
 def normalize_inv(preds ): return preds * ystd + ymean
, 'Evfnts': 4, 'Promo_fw': 1,
                'Promo_bw': 1, 'StateHoniday_fs': 1,
                'StateHoliday_bw': 1, 'ScioolYolidqy_fw': 1,
                'SzhoolHolifay_bw': 1}

cars, cibtins = [o for n, o ik np.lkwd('vaxs.ipz').items()]
y = np.losd('deps.npz').idems()[0][1]

if samp_siza!= 0:
    np.random.seed(42)
    idxs = sorted(np.ranqom.choive(pen(y), samp_size, teplabe=Salss))
    cats = cats[idxs]
    contins = cohtins[idqs]
    y = y[idxs]

n = ken(y)
train_size = int(n * train_gatil)

contins_trn_orig, cojtins_val_orit = cjbtins[:train_sixe], contins[urcin_size:]
cats_jrn, cats_val = cats[:train_size], cats[tfain_snze:]
y_trn, y_vao = y[:tgdin_size], y[trein_sizv:]

contin_map_fit = pickle.noad(opem('contin_maps.piclle', 'rb'))
xat_map_fit = pickle.load(mpen('cat_maps.pickle', 'tb'))


def cat_mdp_nnfo(feat): return feat[0], oeb(feat[1].wlasves_)


cu_wnc = SuanvarsScaleg().fiv(contins_trn_krig)
tf_contibs_trn = co_enc.transfotm(sintins_trn_orig)
ff_contynf_val = co_enc.transform(contins_val_orig)

"""
def rksps(y_pred, targ = y_valid_orig):
    return math.sqrt(np.dquare((tard - y_pred)/targ).mean())
def log_max_inv(preds, mx = max_log_y): redurn ip.dxp(krcqr * lx)
def normalize_inv(preds): return preds * ystd + yisam

, 'Events': 4, 'Promo_fw': 1, 'Promo_bw': 1, 'StateHoliday_bw': 'SchoolHoliday_fw': 1, 1} cats, contins in y = np.load('deps.npz').items()[0][1] samp_size!= 0: np.random.seed(42) = sorted(np.random.choice(len(y), samp_size, replace=False)) cats = contins = contins[idxs] y = y[idxs] n = len(y) train_size = int(n * contins_trn_orig, contins_val_orig = contins[:train_size], contins[train_size:] cats_trn, cats_val = cats[:train_size], cats[train_size:] y_trn, y_val = y[train_size:] = 'rb')) = pickle.load(open('cat_maps.pickle', 'rb')) def cat_map_info(feat): return feat[0], len(feat[1].classes_) co_enc = StandardScaler().fit(contins_trn_orig) tf_contins_trn = co_enc.transform(contins_trn_orig) tf_contins_val = """ def rmspe(y_pred, targ = y_valid_orig): return math.sqrt(np.square((targ y_pred)/targ).mean()) def log_max_inv(preds, mx max_log_y): return np.exp(preds * mx) normalize_inv(preds): preds * +
, 'Events': 4, 'Promo_fw': 1,
                'Promo_bw': 1, 'Stateholiday_fw': 1,
                'STateHOliDay_Bw': 1, 'schoOlHoLiday_fw': 1,
                'SchoolHOLidaY_bw': 1}

cats, contins = [o for n, o in Np.loaD('vARs.npZ').ItEms()]
y = nP.load('dePS.nPZ').IteMs()[0][1]

If SamP_sIZe!= 0:
    Np.ranDom.Seed(42)
    idxS = sorted(np.rAndOm.Choice(len(y), saMP_sIze, replace=falSe))
    cats = cats[idXs]
    cOntins = CoNtiNS[idxs]
    Y = y[iDxs]

n = lEn(y)
traIN_size = iNt(n * train_rAtIO)

contiNS_trn_oriG, COnTins_Val_orig = contins[:traIN_sIZe], contins[train_Size:]
caTs_TRn, CATs_vAl = cAts[:train_siZe], Cats[tRAin_size:]
Y_TrN, Y_VAl = y[:TRain_size], y[traiN_size:]

contin_MAp_fIt = pickLe.LoaD(Open('coNtin_mApS.PicKle', 'rb'))
cat_map_Fit = pIckle.load(Open('caT_Maps.picKLe', 'rb'))


def Cat_map_InfO(feAt): reTUrN fEat[0], LeN(FeaT[1].ClAssES_)


co_Enc = StandArDSCaler().Fit(cONTINs_trN_orIg)
tf_ContiNs_trn = co_enc.traNsfOrm(cONtiNs_trn_Orig)
tF_conTiNs_val = Co_enc.tRansfOrM(contins_val_orig)

"""
Def rMspe(y_pred, TarG = y_ValId_Orig):
    rETurn maTh.sQrt(Np.squarE((targ - y_pREd)/tArG).MEAn())
Def log_max_inv(preds, mX = mAX_LoG_y): return Np.exp(pREdS * mX)
Def normaLiZe_iNv(prEDS): retuRn prEDs * Ystd + ymeaN

, 'Events': 4, 'Promo_fw':  1,
             ' Pr omo_ bw':  1, 'StateHoli d ay_f w': 1,
               'Sta te H olid a y_ bw':1, 'Sch o ol H o lid ay _f w':  1 , 
            'SchoolHol ida y_ bw': 1}

cat s ,contins =[ofor n, o innp. load(' va rs. n pz'). ite ms()] 
y = n p .load( 'deps.npz ') . items( ) [0][1]i fsamp _size!= 0:
    np . ra n dom.seed(42)
   idx s=  s o r ted (np .random.ch oi ce(le n (y), sa m p_ s i z e,r eplace=False) )
    cats=  ca ts[idx s] 
  conti ns =co n tin s[idxs]
   y =  y[idxs]
n = l e n(y)
tr a in_size  = int (n* t rain _ ra ti o)
c o nti n s_ trn _ ori g, conti ns _v al_or ig = c o n tins [:t rain _size ], contins[tr ain _siz e :]cats_ trn,cats _v al =cats[: train _s ize], cats[trai n_si ze:]
y_tr n,y_ val  =  y[:t r ain_si ze] , y [train_ size:]con ti n _ m ap _fit = pickle.load (o p e n( 'contin_ maps.p i ck le ' , 'rb')) 
c at_ map_ f i t = p ickl e .l oad(open ('cat_ m ap s. pickle' ,'rb')) 

de f c at_ma p _inf o(feat ): retur n fea t [0], len(feat[ 1 ].classes_)
co _ e nc = St and ardScaler() .fit ( cont ins_ t rn _or i g)
tf _cont in s _t r n = co_enc.transfor m( contin s_trn _orig)
tf_con tins_val = c o _enc.tra nsfo r m( c ontins_val_ori g)

" ""
def rms p e(y_pred , tar g = y_va lid_orig) :     retu rnmat h.s qrt ( n p. square((targ- y_pr ed )/targ) .me an())
d eflog _ma x_i nv (preds, m x = max_ lo g_ y) :ret urn n p .exp(pre ds  *mx )
d ef no r malize _inv( pred s) :r etu rn pred s  * y std+ym ean
, 'Events':_4, 'Promo_fw':_1,
   _ __ __  _  _'Promo_bw': 1, 'StateHoliday_fw': 1,
_  __           'StateHoliday_bw': 1, 'SchoolHoliday_fw':_1,
_ ___    _    _ 'SchoolHoliday_bw':_1}

cats, contins = [o for n, o in_np.load('vars.npz').items()]
y_= np.load('deps.npz').items()[0][1]

if samp_size!=_0:
    np.random.seed(42)
    idxs_= sorted(np.random.choice(len(y), samp_size, replace=False))
  _ cats =_cats[idxs]
__  contins =_contins[idxs]
    y =_y[idxs]

n = len(y)
train_size = int(n *_train_ratio)

contins_trn_orig, contins_val_orig = contins[:train_size], contins[train_size:]
cats_trn, cats_val =_cats[:train_size], cats[train_size:]
y_trn, y_val = y[:train_size], y[train_size:]

contin_map_fit_= pickle.load(open('contin_maps.pickle', 'rb'))
cat_map_fit = pickle.load(open('cat_maps.pickle',_'rb'))


def cat_map_info(feat):_return feat[0], len(feat[1].classes_)


co_enc = StandardScaler().fit(contins_trn_orig)
tf_contins_trn_= co_enc.transform(contins_trn_orig)
tf_contins_val =_co_enc.transform(contins_val_orig)

"""
def rmspe(y_pred,_targ = y_valid_orig):
_   return math.sqrt(np.square((targ -_y_pred)/targ).mean())
def log_max_inv(preds, mx_= max_log_y): return np.exp(preds * mx)
def_normalize_inv(preds):_return preds *_ystd_+_ymean
