truth, selection_algorithm, splitting_sampler, success_params=(1, 1), B=B, fit_probability=probit_fit, fit_args={'df': 20}, how_many=1) if __name__ == "__main__": import statsmodels.api as sm import matplotlib.pyplot as plt import pandas as pd for i in range(500): df = simulate() csvfile = 'lasso_exact_CV_null.csv' outbase = csvfile[:-4] if df is not None and i > 0: try: # concatenate to disk df = pd.concat([df, pd.read_csv(csvfile)]) except FileNotFoundError: pass df.to_csv(csvfile, index=False) if len(df['pivot']) > 0: pivot_ax, length_ax = pivot_plot(df, outbase)
smooth_sampler, success_params=(1, 1), B=B, fit_probability=gbm_fit_sk, fit_args={'n_estimators':2000}) if __name__ == "__main__": import statsmodels.api as sm import matplotlib.pyplot as plt import pandas as pd U = np.linspace(0, 1, 101) plt.clf() for i in range(500): df = simulate() csvfile = 'lasso_multi_CV_random_gbm.csv' outbase = csvfile[:-4] if df is not None and i > 0: try: df = pd.concat([df, pd.read_csv(csvfile)]) except FileNotFoundError: pass df.to_csv(csvfile, index=False) if len(df['pivot']) > 0: pivot_plot(df, outbase)