3catz

3catz

n_trials = 10000
n_candidates = 100
max_burn = int(0.66 * n_candidates) 
for T in range(1, max_burn, 5):
  global_maxes = [] 
  diff_from_max = [] 
  time_taken = []
  final_choices = [] 
  for i in range(n_trials):
    

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exper = setup(
    data = bike_yes,
    categorical_features = ["Seasons", "Holiday"],
    silent = True,
    ordinal_features = {"Hour": sorted_hours},
    ignore_features = ["Functioning Day","Date"],
    target = 'Rented Bike Count',
    use_gpu = True,
    data_split_shuffle = False,
    fold_strategy = "timeseries",

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acts = ["jogging","standing","downstairs","upstairs","walkfast","walkmod","walkslow","lying","sitting"]
def create_windows(subjects): 
  length = 100
  stride = 50
  sample = 10
  framelist = []
  targetlist = [] 
  global acts
  for act in acts:
    for f in glob.glob(subjects + act):

3catz

from pycaret.datasets import get_data
from pycaret.classification import *
report["Scores"] = np.round(report["Scores"], 3)
report.sort_values(by = "Scores", ascending = False, inplace = True)
#report.index
ga_feats = report.iloc[0]["Chosen Feats"]
ename = setup(data = D[used_feats], target = "DEATH_EVENT", 
              test_data = None,
              fold_strategy = "stratifiedkfold",
              fold_shuffle = True, 

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scores = [] 
for i in range(len(report)):
  
  myfeats = report.iloc[i,1] ; print(myfeats)
  X = D[myfeats] ; y = y
  clf = LogisticRegression(solver = "liblinear", C = 6, tol = 1)
  #clf = RandomForestClassifier()
  rskf = RepeatedStratifiedKFold(n_splits = 10, n_repeats = 100)
  score = np.mean(cross_val_score(clf, X, y, cv = rskf, scoring = "roc_auc"))
  scores.append(score) 

3catz

from sklearn.feature_selection import *
feat_list = [] 
all_scores = [] 
for i in range(10):
  np.random.seed(i)
  sfm = SelectFromModel(estimator = clf, threshold=None, prefit=False, 
                       norm_order=1, max_features = 12)
  sfm.fit(D[allfeats], y)
  modfeats = sfm.get_support()
  

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from sklearn.metrics import *
mcc = make_scorer(matthews_corrcoef)
estimator = LogisticRegression(solver = "liblinear", C = 6, tol = 1, fit_intercept = True)

from sklearn.model_selection import *
report = pd.DataFrame()
nofeats = [] 
chosen_feats = [] 
cvscore = [] 
rkf = RepeatedStratifiedKFold(n_repeats = 2, n_splits = 10)

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covariates = trainx; target = trainy
def lgb_trainer(num_leaves, learning_rate, 
                max_depth, n_estimators, 
                reg_lambda,
                #alpha, 
                reg_alpha, 
                subsample):
  
  lgb = LGBMRegressor(objective = "quantile",
                      alpha = .95,

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def getWeights(d,lags):
    # return the weights from the series expansion of the differencing operator
    # for real orders d and up to lags coefficients
    w=[1]
    for k in range(1,lags):
        w.append(-w[-1]*((d-k+1))/k)
    w=np.array(w).reshape(-1,1) 
    return w

def plotWeights(dRange, lags, numberPlots):