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)
