splitting_sampler,
success_params=(1, 1),
B=B,
fit_probability=gbm_fit_sk,
fit_args={'n_estimators': 1000})
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(2000):
df = simulate()
csvfile = 'lasso_multi_CV_gbm.csv'
outbase = csvfile[:-4]
if df is not None:
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)
sigma=2,
alpha=0.1,
B=2000)
R2 = []
for _ in range(100):
X, y, truth = generate(**opts)
R2.append((np.linalg.norm(y - X.dot(truth))**2, np.linalg.norm(y)**2))
R2 = np.array(R2)
R2mean = 1 - np.mean(R2[:, 0]) / np.mean(R2[:, 1])
print('R2', R2mean)
for i in range(5000):
df = simulate(**opts)
csvfile = __file__[:-3] + '.csv'
outbase = csvfile[:-4]
if df is not None:
try:
df = pd.concat([df, pd.read_csv(csvfile)])
except FileNotFoundError:
pass
df.to_csv(csvfile, index=False)
if len(df['pivot']) > 0:
f = pivot_plot(df, outbase)[1]
plt.close(f)
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 = 'lee_multi.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)
# pivot_ax.plot(U, sm.distributions.ECDF(df['lee_pivot'][~np.isnan(df['lee_pivot'])])(U), 'g', label='Lee', linewidth=3)
pivot_ax.figure.savefig(outbase + '.pdf')
length_ax.scatter(df['naive_length'], df['lee_length'])
length_ax.figure.savefig(outbase + '_lengths.pdf')