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task1.py
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task1.py
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import os
import sys
import pandas
import numpy
import logging
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import LassoCV
from sklearn.metrics import mean_squared_error
from scipy import stats
import matplotlib.pyplot as plt
import regression as dr
import data as dt
THIS_DIR = os.path.dirname(os.path.realpath(__file__))
sys.path.append(os.path.join(THIS_DIR, '..'))
#######################################################################
def read_data():
X_test = pandas.read_csv(os.path.join(dt.data_dir(), 'task1', 'X_test.csv'), header=0, index_col=0)
X_train = pandas.read_csv(os.path.join(dt.data_dir(), 'task1', 'X_train.csv'), header=0, index_col=0)
y_train = pandas.read_csv(os.path.join(dt.data_dir(), 'task1', 'y_train.csv'), header=0, index_col=0)
return X_test, X_train, y_train
#######################################################################
def transform_data(X_train, X_test):
logger = logging.getLogger(__name__)
##
scale_mean = X_train.mean()
scale_std = X_train.std()
##
logger.info('standardize using mean and std. dev. of observed samples')
std_X_train = (X_train - scale_mean) / scale_std
std_X_test = (X_test - scale_mean) / scale_std
return std_X_train, std_X_test
#######################################################################
def regression(seed, start, end, step, cv=3, comment=''):
logger = logging.getLogger(__name__)
##
logger.info('read provided data')
X_test, X_train, y_train = read_data()
std_train, std_test, = transform_data(X_train=X_train, X_test=X_test)
##
removed = 0
for col in std_train.columns:
data = std_train[col].copy()
mask = numpy.abs(data) > data.mean() + 3.5 * data.std()
std_train.loc[mask, col] = numpy.NaN
removed += sum(mask)
del data, mask
logger.info('removed a total of [{}] elements'.format(removed))
##
if True:
logger.info('fill NaN with 0 i.e. the mean of the standardized random variables')
std_train.fillna(1e-3, inplace=True)
std_test.fillna(1e-3, inplace=True)
elif False:
logger.info('fill NaN with linear regression model of X_i = f(y)')
std_train = clean_data(
predictors=std_train_temp,
response=y_train,
clean_mode=CLEAN_MODE.RESPONSE
)
std_test.fillna(0.0, inplace=True)
std_test = std_test.reindex(columns=choose)
del choose
##
logger.info('feature engineering')
base_columns = std_train.copy().columns
base_train = std_train.copy()
base_test = std_test.copy()
names = base_columns + '_sq'
train_sq = base_train.pow(2)
train_sq.columns = names
std_train = pandas.concat([std_train, train_sq], axis=1)
test_sq = base_test.pow(2)
test_sq.columns = names
std_test = pandas.concat([std_test, test_sq], axis=1)
names = base_columns + '_sin'
train_sq = numpy.sin(base_train)
train_sq.columns = names
std_train = pandas.concat([std_train, train_sq], axis=1)
test_sq = numpy.sin(base_test)
test_sq.columns = names
std_test = pandas.concat([std_test, test_sq], axis=1)
##
logger.info('use lasso regression with custom set of lambda parameters')
alphas = seed ** numpy.arange(start, end, step)
logger.info('alpha parameters := {}'.format(str(["{0:0.2f}".format(i) for i in alphas]).replace("'", "")))
reg = LassoCV(alphas=alphas, cv=cv, n_jobs=2, random_state=12357)
model_cv = reg.fit(std_train.values, y_train.values.flatten())
logger.info('alpha := {:f}'.format(float(model_cv.alpha_)))
pred = model_cv.predict(std_test)
resid = y_train.values.flatten() - model_cv.predict(std_train)
##
logger.info('plotting of first stage results')
f, (ax1, ax2) = plt.subplots(1, 2, sharey=False, figsize=(17,10))
f.suptitle('first stage')
ax1.plot(resid, 'bo')
tau = numpy.mean(resid) + 1.64 * numpy.std(resid)
mask = numpy.abs(resid) > tau
ax1.plot([i if numpy.abs(i) > tau else None for i in resid], 'ro')
ax1.set_title('Residuals')
ax2.scatter(model_cv.predict(std_train), y_train)
x0,x1 = ax2.get_xlim()
y0,y1 = ax2.get_ylim()
ax2.set_aspect((x1-x0)/(y1-y0))
ax2.set_title('Fitted vs. Actual')
##
logger.info('use second lasso regression, removing large error inducing observations')
std_train_ = std_train[~mask]
y_train_ = y_train[~mask]
reg = LassoCV(alphas=alphas, cv=cv, n_jobs=2, random_state=12357)
model_cv = reg.fit(std_train_.values, y_train_.values.flatten())
logger.info('alpha := {:f}'.format(float(model_cv.alpha_)))
pred = model_cv.predict(std_test)
resid = y_train_.values.flatten() - model_cv.predict(std_train_)
##
logger.info('plotting of second stage results')
f, (ax1, ax2) = plt.subplots(1, 2, sharey=False, figsize=(17,10))
f.suptitle('second stage')
ax1.plot(resid, 'bo')
tau = numpy.mean(resid) + 1.6 * numpy.std(resid)
mask = numpy.abs(resid) > tau
ax1.plot([i if numpy.abs(i) > tau else None for i in resid], 'ro')
ax1.set_title('Residuals')
ax2.scatter(model_cv.predict(std_train), y_train)
x0,x1 = ax2.get_xlim()
y0,y1 = ax2.get_ylim()
ax2.set_aspect((x1-x0)/(y1-y0))
ax2.set_title('Fitted vs. Actual, RMSE := {:.6f}'.format(mean_squared_error(y_train, model_cv.predict(std_train))))
##
logger.info('write to pandas Series object')
write_to_file = pandas.Series(pred, index=X_test.index.astype(int), name='y')
write_to_file.to_csv(os.path.join(dt.output_dir(), 'task1_solution_{}.csv'.format(comment)), index=True, header=['y'], index_label=['id'])
#######################################################################
if __name__ == '__main__':
root = logging.getLogger(__name__)
root.setLevel(logging.INFO)
ch = logging.StreamHandler(sys.stdout)
ch.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter)
root.addHandler(ch)
##
logger = logging.getLogger(__name__)
cv = 5
seed=0.2
start=0.55
end=1
step=0.01
regression(comment='seed_{}_-_start_{}_-_end_{}_-_step_{}_-_cv_{}'.format(seed, start, end, step, cv), seed=seed, start=start, end=end, step=step, cv=cv)
# classification('grid_search_CV')
plt.show()
logger.info('done')