def __init__(self, fs_tuition, sigmasq, opts):
self.fs_tuition = fs_tuition
self.sigmasq = sigmasq
self.opts = opts
self.fs_rhs = lu.student_problem_vars()
self.lsn_rhs = deepcopy(self.fs_rhs)
self.lsn_rhs.remove('OverallRank')
self.lsn_rhs.remove('Tuition')
self.lsn_models = self.gen_matric_ev()
self.data = None
def lsn_long_est(self, data):
""" Application/admission estimates from lsnLong.csv """
rhs = lu.student_problem_vars()
stages = {}
stages['app'] = ensemble.GradientBoostingClassifier(
n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0
).fit(data[rhs], data['app'])
stages['admit'] = ensemble.GradientBoostingClassifier(
n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0
).fit(data.loc[data['app'] == 1, rhs],
data.loc[data['app'] == 1, 'admit'])
stages['matric'] = ensemble.GradientBoostingClassifier(
n_estimators=100, learning_rate=1.0, max_depth=1, random_state=0
).fit(data.loc[data['admit'] == 1, rhs],
data.loc[data['admit'] == 1, 'matric'])
if self.opts['verbose']:
self._lsn_long_diagnostics(stages, data, rhs)
return stages
def estimator_plot(self, stage, key):
""" Plotting function for any given stage """
feature_importance = stage.feature_importances_
# make importances relative to max importance
feature_importance = 100.0 * (feature_importance /
feature_importance.max())
sorted_idx = np.argsort(feature_importance)
pos = np.arange(sorted_idx.shape[0]) + .5
axis = self.fig.add_subplot(2, 2, self.plotnum)
axis.barh(pos, feature_importance[sorted_idx], align='center')
axis.set_yticks(pos)
rhs = np.array(lu.student_problem_vars())
rhs[rhs == 'OverallRank'] = 'Rank'
rhs[rhs == 'LSDAS_GPA'] = 'GPA'
rhs[rhs == 'year'] = 'Year'
axis.set_yticklabels(rhs[sorted_idx].tolist())
axis.set_xlabel('Relative Importance: {0}'.format(key))
self.plotnum += 1
def gen_data(self, treat):
""" Generate random dataset """
if treat:
years = [2010, 2011, 2012]
else:
years = [2007, 2008, 2009]
app_data_treat = self.app_data.loc[
self.app_data['year'].isin(years)
].reset_index(drop=True)
data = []
for year in np.unique(app_data_treat['year']):
app_data_year = self.app_data.loc[
self.app_data['year'] == year
].reset_index(drop=True)
students = np.random.choice(
app_data_year['user'],
size=(gen_n_apps(year).tolist()[0] / 5) # Scale for memory
)
keep_vars = lu.student_problem_vars()
keep_vars.append('school')
#out = []
out = mp.Manager().list()
if self.opts['multiprocessing']:
mp_args = ((app_data_year, students, keep_vars, out, i)
for i in range(len(students)))
pool = mp.Pool(processes=lc.N_THREADS)
pool.map(gen_data_task, mp_args)
pool.close()
pool.join()
else:
for i in xrange(len(students)):
out_data = app_data_year.loc[
app_data_year['user'] == students[i], keep_vars
]
out_data['id'] = i
out.append(out_data)
data_year = pd.concat(list(out))
data_year.reset_index(inplace=True)
data.append(data_year)
data = pd.concat(data)
data.reset_index(inplace=True)
return data
def __init__(self, app_data, firststage, opts):
self.app_data = app_data
self.data = None
self.firststage = firststage
self.opts = opts
self.rhs = lu.student_problem_vars()
