import pandas as pd

from sklearn.ensemble import BaggingRegressor

from sklearn.ensemble import BaggingClassifier

from sklearn.linear_model import LinearRegression

from sklearn.linear_model import LogisticRegression

from sklearn.ensemble import RandomForestRegressor

train = pd.read_csv('train.csv')

test = pd.read_csv('test.csv')

feature_list = open('use_these_vars.txt', 'rb')

features = feature_list.read().splitlines()

def write_function(preds, fname):

with open(fname, 'wb') as writer:

for item in preds:

writer.write("%s\n" % item )

def second_pos_clip(ls):

out = [x[1] for x in ls]

return(out)

log = LogisticRegression(solver = 'sag')

lm = LinearRegression()

write_function(test['y'], '/tmp/truths.txt')

print('optimizing samples')

for n_samp in [0.1, 0.25, 0.33, 0.5, 0.75, 1.0]:

for n_feat in [0.1, 0.25, 0.33, 0.5, 0.75, 1.0]:

lm_bagged = BaggingRegressor(

base_estimator = lm,

n_estimators = 75,

max_samples = n_samp,

max_features = n_feat,

bootstrap = True,

oob_score = False,

warm_start = False,

n_jobs = -1

)

log_bagged = BaggingClassifier(

base_estimator = log,

n_estimators = 75,

max_samples = n_samp,

max_features = n_feat,

bootstrap = True,

oob_score = False,

warm_start = False,

n_jobs = -1

)

lm_bagged.fit(X = train[features], y = train['y'])

log_bagged.fit(X = train[features], y = train['y'])

lm_bagged_preds = lm_bagged.predict(X = test[features])

log_bagged_preds = log_bagged.predict_proba(X = test[features])

write_function(lm_bagged_preds, '/tmp/lm_bagged_preds_nsamp-%s_nfeat-%s.txt' % (n_samp, n_feat))

write_function(second_pos_clip(log_bagged_preds), '/tmp/log_bagged_preds_nsamp-%s_nfeat-%s.txt' % (n_samp, n_feat))