Peijin Chen 3catz
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| 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", |
3catz
/ create_timeseries_windows.py
Created
November 19, 2020 22:46
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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): |
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| 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) |
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| 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) |
3catz
/ LGBM_BHPT_quantileregression.py
Created
October 22, 2020 04:40
Code for optimizing LGBM model for quantile regression
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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, |
3catz
/ fractionaldifference.py
Created
October 14, 2020 21:19
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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): |
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