def f1003_luminosity_by_estimated_redshift(input: Input, **kw):
aggregate = common.load_feature("f000")
redshift = common.load_feature("f600")
meta_ = pd.merge(input.meta, redshift, on='object_id', how='left')
meta_ = pd.merge(meta_, aggregate, on='object_id', how='left')
meta_['Mpc'] = meta_['hostgal_z_predicted'].apply(z2pc)
meta_['Gpc'] = meta_['Mpc'] / 1000.0
features = []
for i in range(6):
ch = i
meta_['flux_diff_ch{}'.format(ch)] = meta_['max(flux)_ch{}'.format(
ch)] - meta_['min(flux)_ch{}'.format(ch)]
meta_['luminosity_diff_ch{}_estimated'.format(ch)] = meta_[
'flux_diff_ch{}'.format(ch)] * meta_['Gpc'] * meta_['Gpc']
features.append('luminosity_diff_ch{}_estimated'.format(ch))
features_renamed = ['luminosity_est_diff_ch{}'.format(i) for i in range(6)]
rename = {features[i]: features_renamed[i] for i in range(6)}
meta_.rename(columns=rename, inplace=True)
return meta_[['object_id'] + features_renamed]
def f1002_detected_to_falltime_ratio(input: Input, **kw):
delta = common.load_feature("f050")
falltime = common.load_feature("f053")
meta_ = pd.merge(input.meta, delta, on='object_id', how='left')
meta_ = pd.merge(meta_, falltime, on='object_id', how='left')
features = []
for i in range(6):
f = 'delta(max(flux), last(detected))_ch{}_to_delta_ratio'.format(i)
meta_[f] = meta_['delta(max(flux), last(detected))_ch{}'.format(
i)] / meta_['delta']
features.append(f)
return meta_[['object_id'] + features]
def _make_df(input_feature_list: List[str],
remove_galactic_test_data: bool = True,
drop_features: List[str] = None):
df = common.load_metadata()
for f in tqdm(input_feature_list):
df = pd.merge(df, common.load_feature(f), on='object_id', how='left')
if drop_features is not None:
df.drop(drop_features, axis=1, inplace=True)
df.set_index('object_id', inplace=True)
x_train = df[df.hostgal_specz > 0.0]
if remove_galactic_test_data:
x_test = df[df.hostgal_specz.isnull() & (df.hostgal_photoz > 0.0)]
else:
x_test = df[df.hostgal_specz.isnull()]
x_train.drop('target', axis=1, inplace=True)
x_test.drop('target', axis=1, inplace=True)
y_train = x_train.hostgal_specz
x_train.drop('hostgal_specz', axis=1, inplace=True)
x_test.drop('hostgal_specz', axis=1, inplace=True)
return x_train, x_test, y_train
def _setup(self,
df,
features,
drop,
cache_path=None,
use_cache=False) -> pd.DataFrame:
if use_cache and cache_path is not None:
try:
print('load from cache: {}'.format(cache_path))
return pd.read_feather(cache_path)
except:
pass
for f in tqdm(features):
tmp = common.load_feature(f)
tmp['object_id'] = tmp['object_id'].astype(np.int32)
df = pd.merge(df, tmp, on='object_id', how='left')
if drop is not None:
drop_ = [d for d in drop if d in df]
print('dropped: {}'.format(drop_))
df.drop(drop_, axis=1, inplace=True)
if use_cache and cache_path:
df.to_feather(cache_path)
return df
def save_v4():
features = ['f513', 'f515', 'f517']
base = common.load_metadata()[['object_id', 'target']]
for f in features:
tmp = common.load_feature(f)
base = pd.merge(base, tmp, on='object_id', how='left')
_save(base, 'features_nyanp_all_v4_{}')
def f517_blending_salts():
meta = common.load_metadata()
f500 = common.load_feature('f500')
f515 = common.load_feature('f515')
f516 = common.load_feature('f516')
df = pd.merge(meta[['object_id', 'target', 'hostgal_photoz', 'ddf']],
f500,
on='object_id',
how='left')
df = pd.merge(df, f515, on='object_id', how='left')
df = pd.merge(df, f516, on='object_id', how='left')
prefix = [
'sn_salt2_', 'salt2-extended_p_sn3_salt2-extended_',
'salt2_p_sn3_salt2_'
]
params = ['x0', 't0', 'z', 'c', 'x1']
for p in params:
print('param: {}'.format(p))
# weighted average based on error
weights = []
weighted_sum = []
for m in prefix:
col = 'w_{}{}'.format(p, m)
df[col] = 1 / (df['{}{}_err'.format(m, p)] *
df['{}{}_err'.format(m, p)])
weights.append(col)
df[col + '_s'] = df[col] * df[m + p]
weighted_sum.append(col + '_s')
df['salt2-{}-weighted-avg'.format(p)] = df[weighted_sum].sum(axis=1)
df['tmp'] = df[weights].sum(axis=1)
df['salt2-{}-weighted-avg'.format(
p)] = df['salt2-{}-weighted-avg'.format(p)] / df['tmp']
df.drop('tmp', axis=1, inplace=True)
df.drop(weighted_sum, axis=1, inplace=True)
df.drop(weights, axis=1, inplace=True)
common.save_feature(
df[['object_id'] + ['salt2-{}-weighted-avg'.format(p)
for p in params]], 'f517')
def f1000_salt2_normalized_chisq(input: Input, **kw):
salt2 = common.load_feature("f500")
meta_ = pd.merge(input.meta, salt2, on='object_id', how='left')
count = input.lc.groupby('object_id')['mjd'].count().reset_index()
count.columns = ['object_id', 'n_observed']
meta_ = pd.merge(meta_, count, on='object_id', how='left')
meta_[
'sn_salt2_chisq_norm'] = meta_['sn_salt2_chisq'] / meta_['n_observed']
return meta_[['object_id', 'sn_salt2_chisq_norm']]
def f701_redshift_difference():
f601_estimate_redshift()
estimated = common.load_feature("f601")
meta = common.load_metadata()
dst = pd.merge(meta[['object_id', 'hostgal_photoz']],
estimated,
on='object_id',
how='left')
dst['hostgal_photoz_predicted_diff'] = dst['hostgal_photoz'] - dst[
'hostgal_z_predicted']
common.save_feature(dst[['object_id', 'hostgal_photoz_predicted_diff']],
"f701")
def f1085_luminosity_diff_within_snr3(input: Input, **kw):
redshift = common.load_feature('f600')
max_flux = common.load_feature('f1083')
min_flux = common.load_feature('f1084')
meta = pd.merge(input.meta, redshift, on='object_id', how='left')
meta = pd.merge(meta, max_flux, on='object_id', how='left')
meta = pd.merge(meta, min_flux, on='object_id', how='left')
meta['Mpc'] = meta['hostgal_z_predicted'].apply(z2pc)
meta['Gpc'] = meta['Mpc'] / 1000.0
features = []
for i in range(6):
ch = i
meta['snr3_flux_diff_ch{}'.format(
ch)] = meta['snr3_max(flux)_ch{}'.format(ch)] - meta[
'snr3_min(flux)_ch{}'.format(ch)]
meta['snr3_luminosity_diff_ch{}'.format(ch)] = meta[
'snr3_flux_diff_ch{}'.format(ch)] * meta['Gpc'] * meta['Gpc']
features.append('snr3_luminosity_diff_ch{}'.format(ch))
return meta[['object_id'] + features]
def f1010_redshift_difference_perch():
meta = common.load_metadata()
meta = pd.merge(meta,
common.load_feature('f603'),
on='object_id',
how='left')
meta = pd.merge(meta,
common.load_feature('f000'),
on='object_id',
how='left')
meta['Mpc'] = meta['hostgal_z_predicted'].apply(z2pc)
meta['Gpc'] = meta['Mpc'] / 1000.0
features = []
for i in range(6):
ch = i
meta['flux_diff_ch{}'.format(ch)] = meta['max(flux)_ch{}'.format(
ch)] - meta['min(flux)_ch{}'.format(ch)]
meta['luminosity_diff_ch{}'.format(ch)] = meta['flux_diff_ch{}'.format(
ch)] * meta['Gpc'] * meta['Gpc']
features.append('luminosity_diff_ch{}'.format(ch))
common.save_feature(meta[['object_id'] + features], "f1010")
def save_v1():
features = ['f000', 'f001', 'f002', 'f010', 'f026', 'f050', 'f051', 'f052', 'f053', 'f054',
'f061', 'f063', 'f100', 'f1000', 'f1001', 'f1002', 'f1003', 'f1004', 'f1005',
'f1006', 'f101', 'f1010', 'f102', 'f103', 'f104', 'f106', 'f107', 'f108', 'f1080',
'f1081', 'f1082', 'f1083', 'f1085', 'f1086', 'f1087', 'f1088', 'f1089',
'f109', 'f110', 'f140', 'f141', 'f142', 'f143', 'f144', 'f150', 'f151', 'f152',
'f153', 'f200', 'f201', 'f202', 'f203', 'f204', 'f205', 'f300', 'f301', 'f302',
'f303', 'f304', 'f305', 'f306', 'f307', 'f308', 'f309', 'f310', 'f311', 'f330',
'f340', 'f350', 'f370', 'f400', 'f500', 'f505', 'f506', 'f507', 'f600', 'f701']
best_subset_v1 = ['object_id', 'hostgal_photoz_err', 'distmod', 'mwebv', 'mean(flux)_ch0', 'mean(flux)_ch1',
'mean(flux)_ch2', 'mean(flux)_ch3', 'mean(flux)_ch4', 'mean(flux)_ch5', 'max(flux)_ch0',
'max(flux)_ch1', 'min(flux)_ch0', 'min(flux)_ch1', 'min(flux)_ch2', 'min(flux)_ch3', 'min(flux)_ch4',
'min(flux)_ch5', 'median(flux)_ch0', 'median(flux)_ch1', 'median(flux)_ch2', 'median(flux)_ch3',
'median(flux)_ch4', 'median(flux)_ch5', 'std(flux)_ch0', 'std(flux)_ch1', 'std(flux)_ch4',
'std(flux)_ch5', 'timescale_th0.35_max_ch0', 'timescale_th0.35_max_ch1', 'timescale_th0.35_max_ch2',
'timescale_th0.35_max_ch3', 'timescale_th0.35_max_ch4', 'timescale_th0.35_max_ch5',
'diff(max(flux))_0_1', 'diff(max(flux))_1_2', 'diff(max(flux))_2_3', 'diff(max(flux))_3_4',
'diff(max(flux))_4_5', 'mean(detected)_ch1', 'mean(detected)_ch2', 'mean(detected)_ch3',
'mean(detected)_ch5', 'std(detected)_ch3', 'std(detected)_ch5', 'diff(max(flux))_0_3',
'diff(max(flux))_1_4', 'diff(max(flux))_2_5', 'timescale_th0.5_min_ch0', 'timescale_th0.5_min_ch1',
'timescale_th0.5_min_ch2', 'timescale_th0.5_min_ch4', 'timescale_th0.5_min_ch5', 'mean(flux)',
'max(flux)', 'min(flux)', 'timescale_th0.35_min_ch0', 'timescale_th0.35_min_ch1',
'timescale_th0.35_min_ch2', 'timescale_th0.35_min_ch4', 'timescale_th0.35_min_ch5',
'timescale_th0.15_max_ch0', 'timescale_th0.15_max_ch1', 'timescale_th0.15_max_ch2',
'timescale_th0.15_max_ch3', 'timescale_th0.15_max_ch4', 'timescale_th0.15_max_ch5',
'max(flux_slope)_ch0', 'max(flux_slope)_ch1', 'max(flux_slope)_ch2', 'max(flux_slope)_ch3',
'max(flux_slope)_ch4', 'max(flux_slope)_ch5', 'min(flux_slope)_ch0', 'min(flux_slope)_ch1',
'min(flux_slope)_ch2', 'min(flux_slope)_ch3', 'min(flux_slope)_ch4', 'min(flux_slope)_ch5',
'flux__c3__lag_1_ch0', 'flux__c3__lag_1_ch1', 'flux__c3__lag_1_ch2', 'flux__c3__lag_1_ch3',
'flux__c3__lag_1_ch4', 'flux__c3__lag_1_ch5', 'flux__autocorrelation__lag_1_ch0',
'flux__autocorrelation__lag_1_ch1', 'flux__autocorrelation__lag_1_ch2',
'flux__autocorrelation__lag_1_ch3', 'flux__autocorrelation__lag_1_ch4',
'flux__autocorrelation__lag_1_ch5', 'delta', 'max(astropy.lombscargle.power)_ch0',
'max(astropy.lombscargle.power)_ch1', 'max(astropy.lombscargle.power)_ch2',
'max(astropy.lombscargle.power)_ch3', 'max(astropy.lombscargle.power)_ch4',
'max(astropy.lombscargle.power)_ch5', 'astropy.lombscargle.timescale_ch0',
'astropy.lombscargle.timescale_ch1', 'astropy.lombscargle.timescale_ch2',
'astropy.lombscargle.timescale_ch3', 'astropy.lombscargle.timescale_ch4',
'astropy.lombscargle.timescale_ch5', 'diff(max(flux))_0_4', 'diff(max(flux))_1_5',
'diff(max(flux))_0_5', 'diff(min(flux))_0_1', 'diff(min(flux))_2_3', 'diff(min(flux))_4_5',
'amp(flux)_ch0/ch1', 'amp(flux)_ch1/ch2', 'amp(flux)_ch2/ch3', 'amp(flux)_ch3/ch4', 'amp(flux)_ch4/ch5',
'amp(flux)_ch0/ch2', 'amp(flux)_ch1/ch3', 'amp(flux)_ch2/ch4', 'amp(flux)_ch3/ch5', 'amp(flux)_ch0/ch3',
'amp(flux)_ch1/ch4', 'amp(flux)_ch2/ch5', 'amp(flux)_ch0/ch4', 'amp(flux)_ch1/ch5', 'amp(flux)_ch0/ch5',
'delta(max(flux), last(detected))', 'delta(first(detected), max(flux))',
'delta(max(flux), last(detected))_ch1', 'delta(max(flux), last(detected))_ch2',
'delta(max(flux), last(detected))_ch3', 'delta(max(flux), last(detected))_ch4',
'delta(max(flux), last(detected))_ch5', 'delta(first(detected), max(flux))_ch1',
'delta(first(detected), max(flux))_ch2', 'delta(first(detected), max(flux))_ch3',
'delta(first(detected), max(flux))_ch4', 'delta(first(detected), max(flux))_ch5',
'detected_median(flux)_ch1', 'detected_median(flux)_ch2', 'detected_median(flux)_ch3',
'detected_median(flux)_ch4', 'detected_median(flux)_ch5', 'detected_diff(median(flux))_0_1',
'detected_diff(median(flux))_1_2', 'detected_diff(median(flux))_2_3', 'detected_diff(median(flux))_3_4',
'detected_diff(median(flux))_4_5', '0__fft_coefficient__coeff_0__attr_"abs"',
'0__fft_coefficient__coeff_1__attr_"abs"', '0__kurtosis', '0__skewness',
'1__fft_coefficient__coeff_0__attr_"abs"', '1__fft_coefficient__coeff_1__attr_"abs"', '1__kurtosis',
'1__skewness', '2__fft_coefficient__coeff_1__attr_"abs"', '2__kurtosis', '2__skewness',
'3__fft_coefficient__coeff_0__attr_"abs"', '3__kurtosis', '3__skewness',
'4__fft_coefficient__coeff_0__attr_"abs"', '4__fft_coefficient__coeff_1__attr_"abs"', '4__kurtosis',
'4__skewness', '5__fft_coefficient__coeff_0__attr_"abs"', '5__fft_coefficient__coeff_1__attr_"abs"',
'5__kurtosis', '5__skewness', 'hostgal_z_predicted', 'sn_salt2_chisq', 'sn_salt2_z', 'sn_salt2_t0',
'sn_salt2_x0', 'sn_salt2_x1', 'sn_salt2_c', 'sn_salt2_z_err', 'sn_salt2_t0_err', 'sn_salt2_x0_err',
'sn_salt2_x1_err', 'sn_salt2_c_err', 'luminosity_est_diff_ch0', 'luminosity_est_diff_ch1',
'luminosity_est_diff_ch2', 'luminosity_est_diff_ch3', 'luminosity_est_diff_ch4',
'luminosity_est_diff_ch5']
best16_v1 = ['object_id', 'sn_salt2_c', 'delta', 'sn_salt2_x1', 'distmod',
'luminosity_est_diff_ch4', 'luminosity_est_diff_ch5',
'luminosity_est_diff_ch3', 'luminosity_est_diff_ch2',
'hostgal_photoz_err', 'hostgal_z_predicted', 'luminosity_est_diff_ch0',
'luminosity_est_diff_ch1', 'sn_salt2_chisq', 'sn_salt2_z',
'amp(flux)_ch3/ch5', '0__skewness']
base = common.load_metadata()[['object_id', 'distmod', 'hostgal_photoz_err', 'mwebv', 'target']]
for f in tqdm(features):
tmp = common.load_feature(f)
if f == 'f1080':
tmp.columns = ['object_id', 'delta_SNR3']
if f == 'f1010':
tmp.columns = ['object_id'] + [c + '_estimated' for c in tmp.columns.tolist()[1:]]
for c in tmp:
if c == 'object_id':
continue
if c in base:
print('{} is already in base(f): {}, {}'.format(c, f, base.columns.tolist()))
assert c not in base
tmp['object_id'] = tmp['object_id'].astype(np.int32)
base = pd.merge(base, tmp, on='object_id', how='left')
# -> yuval
_save(base[best_subset_v1+['target']], 'nyanp_feat_v1_{}')
_save(base[best16_v1+['target']], 'nyanp_feat_v1_{}_top16')
# add prefix to oof features
xlist = [
'hostgal_z_predicted',
'hostgal_photoz_predicted_diff',
'luminosity_est_diff_ch0',
'luminosity_est_diff_ch1',
'luminosity_est_diff_ch2',
'luminosity_est_diff_ch3',
'luminosity_est_diff_ch4',
'luminosity_est_diff_ch5',
'luminosity_diff_ch0_estimated',
'luminosity_diff_ch1_estimated',
'luminosity_diff_ch2_estimated',
'luminosity_diff_ch3_estimated',
'luminosity_diff_ch4_estimated',
'luminosity_diff_ch5_estimated'
]
renames = {x: 'xxx_' + x for x in xlist}
base.rename(columns=renames, inplace=True)
# -> mamas
_save(base.drop(['distmod', 'hostgal_photoz_err', 'mwebv'], axis=1), 'features_nyanp_all_v1_{}')
def save_v2():
base = common.load_metadata()[['object_id', 'target']]
base = pd.merge(base, common.load_feature('f509'), on='object_id', how='inner')
_save(base, 'features_nyanp_all_v2_{}')
