def setUp(self):
self.df = select_mungedata(2, 1, '2013-1-1', '2015-1-1')
self.xtest = select_features('2015-1-1', '2015-1-10')
self.actuals = select_mungedata_simple(2, 1, '2015-1-1', '2015-1-10')
self.daily_avg = self.df.waittime.resample('D', how='mean')
model = RandomForestRegressor(n_jobs=-1, n_estimators=2,
random_state=1)
self.im = IncrementalModel(self.df, model, categoricals=['event'])
self.im.predict(self.xtest)
def setUp(self):
self.df = select_mungedata(2, 1, '2013-1-1', '2015-1-1')
self.xtest = select_features('2015-1-1', '2015-1-10')
self.actuals = select_mungedata_simple(2, 1, '2015-1-1', '2015-1-10')
self.daily_avg = self.df.waittime.resample('D', how='mean')
model = RandomForestRegressor(n_jobs=-1,
n_estimators=2,
random_state=1)
self.im = IncrementalModel(self.df, model, categoricals=['event'])
self.im.predict(self.xtest)
def run_Incremental(model, munger_id, xing, train_start, train_end, test_start,
test_end):
df_train = select_mungedata(munger_id, xing, train_start, train_end)
X_test = select_features(test_start, test_end)
actual = select_mungedata_simple(munger_id, xing, test_start, test_end)
grid = GridSearchCV(model, {})
im = IncrementalModel(df_train, grid, categoricals=['event'])
im.set_actual(actual.waittime)
im.predict(X_test)
return im
def run_Incremental(model, munger_id, xing, train_start, train_end,
test_start, test_end):
df_train = select_mungedata(munger_id, xing, train_start, train_end)
X_test = select_features(test_start, test_end)
actual = select_mungedata_simple(munger_id, xing, test_start, test_end)
grid = GridSearchCV(model, {})
im = IncrementalModel(df_train, grid, categoricals=['event'])
im.set_actual(actual.waittime)
im.predict(X_test)
return im