def main():
flux_arr, exp_arr, ivar_arr, mask_arr, wavelengths = \
ICAize.load_all_in_dir('.', pattern="stacked*exp??????.csv")
temp_flux_arr, temp_exp_arr, temp_ivar_arr, temp_mask_arr, temp_wavelengths = \
ICAize.load_all_in_dir('.', pattern="stacked*exp??????.fits")
if len(flux_arr) > 0:
if len(temp_flux_arr) > 0:
flux_arr = np.concatenate((flux_arr, temp_flux_arr))
exp_arr = np.concatenate((exp_arr, temp_exp_arr))
ivar_arr = np.concatenate((ivar_arr, temp_ivar_arr))
wavelengths = np.concatenate((wavelengths, temp_wavelengths))
elif len(temp_flux_arr) > 0:
flux_arr = temp_flux_arr
exp_arr = temp_exp_arr
ivar_arr = temp_ivar_arr
wavelengths = temp_wavelengths
else:
return
np.savez("compacted_flux_data.npz",
flux=flux_arr,
exp=exp_arr,
ivar=ivar_arr,
wavelengths=wavelengths)
def MSE(Y,
y,
multioutput='uniform_average',
Y_full=None,
flux_arr=None,
source_model=None,
ss=None,
source_model_args=None,
method=None):
if Y_full is not None and flux_arr is not None and source_model is not None and ss is not None:
inds = get_inds_(Y, Y_full)
back_trans_flux = ICAize.inverse_transform(y, source_model, ss, method,
source_model_args)
try:
return mean_squared_error(flux_arr[inds],
back_trans_flux,
multioutput=multioutput)
except:
return mean_squared_error(flux_arr[inds], back_trans_flux)
else:
try:
yss = pp.MaxAbsScaler()
Y = yss.fit_transform(Y)
y = yss.transform(y)
except:
scalefactor = np.amax(np.abs(Y), axis=0)
Y = Y / scalefactor
y = y / scalefactor
try:
return mean_squared_error(Y, y, multioutput=multioutput)
except:
return mean_squared_error(Y, y)
def MAPED(Y,
y,
multioutput='uniform_average',
power=4,
cutoff=0.1,
Y_full=None,
flux_arr=None,
source_model=None,
ss=None,
source_model_args=None,
method=None):
#Mean Absolute Power Error Difference; take sum of (absolute) diffs, subtract MAPE from it
if Y_full is not None and flux_arr is not None and source_model is not None and ss is not None:
inds = get_inds_(Y, Y_full)
back_trans_flux = ICAize.inverse_transform(y, source_model, ss, method,
source_model_args)
diffs = np.abs(flux_arr[inds] - back_trans_flux)
diffs[diffs < cutoff] = 0
sums = np.sum(diffs, axis=1)
diffs = np.sum(np.power(diffs, power), axis=1)
return float(
np.mean(np.abs(sums - np.power(diffs, 1.0 / power))) /
flux_arr.shape[1])
else:
diffs = np.abs(Y - y)
diffs[diff < cutoff] = 0
sums = np.sum(diffs, axis=1)
diffs = np.sum(np.power(diffs, power), axis=1)
return float(
np.mean(np.abs(sums - np.power(diffs, 1.0 / power))) / Y.shape[1])
def EXP_VAR(Y,
y,
multioutput='uniform_average',
Y_full=None,
flux_arr=None,
source_model=None,
ss=None,
source_model_args=None,
method=None):
if Y_full is not None and flux_arr is not None and source_model is not None and ss is not None:
inds = get_inds_(Y, Y_full)
back_trans_flux = ICAize.inverse_transform(y, source_model, ss, method,
source_model_args)
try:
return explained_variance_score(flux_arr[inds],
back_trans_flux,
multioutput=multioutput)
except:
return float(
np.mean(
np.var(flux_arr[inds] - back_trans_flux, axis=1) /
np.var(flux_arr[inds], axis=1)))
else:
try:
return explained_variance_score(Y, y, multioutput=multioutput)
except:
return float(np.mean(np.var(Y - y, axis=1) / np.var(Y, axis=1)))
def MAE(Y, y, multioutput='uniform_average', Y_full=None, flux_arr=None, source_model=None,
ss=None, source_model_args=None, method=None):
if Y_full is not None and flux_arr is not None and source_model is not None and ss is not None:
inds = get_inds_(Y, Y_full)
back_trans_flux = ICAize.inverse_transform(y, source_model, ss, method, source_model_args)
return float(np.mean(np.median(np.abs(flux_arr[inds] - back_trans_flux), axis=1)))
else:
return float(np.mean(np.median(np.abs(Y - y), axis=1)))
def animate_sky_spectra_for_coord(obs_time_start, obs_time_end, point_coord, lunar_metadata_file,
solar_metadata_file, sunspot_metadata_file, model_path, dm_path, dm_method):
metadata_tups, dates, lunar_data, solar_data, sunspot_data = get_sky_for_coord(obs_time_start,
obs_time_end, point_coord, lunar_metadata_file, solar_metadata_file, sunspot_metadata_file)
model = rfs.load_model(model_path)
dm, ss, model_args = iz.unpickle_model(path=dm_path, method=dm_method)
inv_spec = []
labels = []
for i, metadata in enumerate(metadata_tups):
#print(metadata)
np_metadata = np.array(metadata)
pred = model.predict(np_metadata.reshape(1, -1))
inv_spec.append(iz.inverse_transform(pred, dm, ss, dm_method, model_args)[0, :])
labels.append(dates[i] + "(ALT,AZ): (" + str(metadata[3]) + ", " + str(metadata[2]) + ")")
return inv_spec, labels
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=
'Build and test models based on dim reductions and provided spectra')
parser.add_argument('--spectra_path',
type=str,
default='.',
metavar='PATH',
help='Spectra path to work from, if not '
'.'
'')
parser.add_argument('--method',
type=str,
default='ICA',
metavar='METHOD',
help='Dim reduction method to load data for')
parser.add_argument(
'--file_path',
type=str,
default=None,
metavar='FILE_PATH',
help='COMPLETE path from which to load a dim reduction')
args = parser.parse_args()
data_model = None
scaler = None
if args.file_path is not None:
data_model, scaler = ize.unpickle_model(filename=args.file_path)
else:
data_model, scaler = ize.unpickle_model(path=args.spectra_path,
method=args.method)
components = ize.get_components(args.method, data_model)
offset = 0
for i, comp_i in enumerate(components):
if i > 0:
offset += np.max(np.abs(comp_i[comp_i < 0])) * 1.2
plt.plot(stack.skyexp_wlen_out, comp_i + offset)
offset += np.max(comp_i[comp_i > 0]) * 1.2
plt.show()
plt.close()
def main():
flux_arr, exp_arr, ivar_arr, mask_arr, wavelengths = \
ICAize.load_all_in_dir('.', pattern="stacked*exp??????.csv")
temp_flux_arr, temp_exp_arr, temp_ivar_arr, temp_mask_arr, temp_wavelengths = \
ICAize.load_all_in_dir('.', pattern="stacked*exp??????.fits")
if len(flux_arr) > 0:
if len(temp_flux_arr) > 0:
flux_arr = np.concatenate((flux_arr, temp_flux_arr))
exp_arr = np.concatenate((exp_arr, temp_exp_arr))
ivar_arr = np.concatenate((ivar_arr, temp_ivar_arr))
wavelengths = np.concatenate((wavelengths, temp_wavelengths))
elif len(temp_flux_arr) > 0:
flux_arr = temp_flux_arr
exp_arr = temp_exp_arr
ivar_arr = temp_ivar_arr
wavelengths = temp_wavelengths
else:
return
np.savez("compacted_flux_data.npz", flux=flux_arr, exp=exp_arr, ivar=ivar_arr, wavelengths=wavelengths)
def _iter_scorer(train_inds, test_inds, flux_arr, model__and__model_flux_mean, method, score_methods, include_mle):
model = model__and__model_flux_mean[0]
model_flux_mean = model__and__model_flux_mean[1]
flux_test = flux_arr[test_inds]
flux_conv_test = None
if score_methods != ['LL']:
for pca_model in model:
if flux_conv_test is None:
flux_conv_test = iz.transform_inverse_transform(flux_test, pca_model, model_flux_mean, method)
flux_test -= flux_conv_test
else:
residual = iz.transform_inverse_transform(flux_test, pca_model, model_flux_mean, method)
flux_conv_test += residual
flux_test -= residual
scores = {}
for score_method in score_methods:
#print("Calculating score:" + score_method)
score_func = iz.get_score_func(score_method)
if score_func is not None:
if score_method != 'MAE':
scores[score_method] = score_func(flux_test, flux_conv_test, multioutput='uniform_average')
else:
scores[score_method] = np.mean(np.median(np.abs(flux_test - flux_conv_test), axis=1))
if (include_mle or score_method == 'LL') and method in ['FA', 'PCA']:
try:
scores['mle'] = model.score(flux_test)
except np.linalg.linalg.LinAlgError:
scores['mle'] = 0 #-2**10 #float("-inf")
except ValueError:
scores['mle'] = 0 #-2**10 #float("-inf")
#print("Scores: " + str(scores))
return scores
def EXP_VAR(Y, y, multioutput='uniform_average', Y_full=None, flux_arr=None, source_model=None,
ss=None, source_model_args=None, method=None):
if Y_full is not None and flux_arr is not None and source_model is not None and ss is not None:
inds = get_inds_(Y, Y_full)
back_trans_flux = ICAize.inverse_transform(y, source_model, ss, method, source_model_args)
try:
return explained_variance_score(flux_arr[inds], back_trans_flux, multioutput=multioutput)
except:
return float(np.mean(np.var(flux_arr[inds] - back_trans_flux, axis=1) / np.var(flux_arr[inds], axis=1)))
else:
try:
return explained_variance_score(Y, y, multioutput=multioutput)
except:
return float(np.mean(np.var(Y - y, axis=1) / np.var(Y, axis=1)))
def _iter_modeler(train_inds, test_inds, flux_arr, model, method):
model_list = []
flux_train = flux_arr[train_inds]
flux_avg = np.mean(flux_train, axis=0)
for i in range(2):
new_model = est_clone(model)
new_model.fit(flux_train)
back_train = iz.transform_inverse_transform(flux_train, new_model, flux_avg, method)
flux_train -= back_train
model_list.append(new_model)
return model_list, flux_avg
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description="Build and test models based on dim reductions and provided spectra",
)
parser.add_argument(
"--spectra_path", type=str, default=".", metavar="PATH", help="Spectra path to work from, if not " "." ""
)
parser.add_argument(
"--method", type=str, default="ICA", metavar="METHOD", help="Dim reduction method to load data for"
)
parser.add_argument(
"--file_path",
type=str,
default=None,
metavar="FILE_PATH",
help="COMPLETE path from which to load a dim reduction",
)
args = parser.parse_args()
data_model = None
scaler = None
if args.file_path is not None:
data_model, scaler = ize.unpickle_model(filename=args.file_path)
else:
data_model, scaler = ize.unpickle_model(path=args.spectra_path, method=args.method)
components = ize.get_components(args.method, data_model)
offset = 0
for i, comp_i in enumerate(components):
if i > 0:
offset += np.max(np.abs(comp_i[comp_i < 0])) * 1.2
plt.plot(stack.skyexp_wlen_out, comp_i + offset)
offset += np.max(comp_i[comp_i > 0]) * 1.2
plt.show()
plt.close()
def MAE(Y,
y,
multioutput='uniform_average',
Y_full=None,
flux_arr=None,
source_model=None,
ss=None,
source_model_args=None,
method=None):
if Y_full is not None and flux_arr is not None and source_model is not None and ss is not None:
inds = get_inds_(Y, Y_full)
back_trans_flux = ICAize.inverse_transform(y, source_model, ss, method,
source_model_args)
return float(
np.mean(np.median(np.abs(flux_arr[inds] - back_trans_flux),
axis=1)))
else:
return float(np.mean(np.median(np.abs(Y - y), axis=1)))
def MAPED(Y, y, multioutput='uniform_average', power=4, cutoff=0.1, Y_full=None, flux_arr=None, source_model=None,
ss=None, source_model_args=None, method=None):
#Mean Absolute Power Error Difference; take sum of (absolute) diffs, subtract MAPE from it
if Y_full is not None and flux_arr is not None and source_model is not None and ss is not None:
inds = get_inds_(Y, Y_full)
back_trans_flux = ICAize.inverse_transform(y, source_model, ss, method, source_model_args)
diffs = np.abs(flux_arr[inds] - back_trans_flux)
diffs[diffs < cutoff] = 0
sums = np.sum(diffs, axis=1)
diffs = np.sum(np.power(diffs, power), axis=1)
return float(np.mean(np.abs(sums - np.power(diffs, 1.0/power))) / flux_arr.shape[1])
else:
diffs = np.abs(Y - y)
diffs[diff < cutoff] = 0
sums = np.sum(diffs, axis=1)
diffs = np.sum(np.power(diffs, power), axis=1)
return float(np.mean(np.abs(sums - np.power(diffs, 1.0/power))) / Y.shape[1])
def main():
path = "."
metadata_path = ".."
rfr = Pipeline([ ('ica', FastICA(random_state=random_state, max_iter=ica_max_iter)),
('rfr', ensemble.RandomForestRegressor(random_state=random_state, n_jobs=-1))
])
param_grid = {
"ica__n_components": sp_randint(15, 200),
"rfr__n_estimators": sp_randint(25, 400),
"rfr__min_samples_split": sp_randint(1, 10)
#,
#"rfr__max_features": [None, "log2", "sqrt"]
}
randsearch = RandomizedSearchCV(rfr, param_grid, n_iter = n_iter_search)
flux_arr, exp_arr, wavelengths = ICAize.load_all_in_dir(path=path, use_con_flux=True, recombine_flux=False)
obs_metadata = random_forest_spectra.trim_observation_metadata(random_forest_spectra.load_observation_metadata(metadata_path))
reduced_obs_metadata = obs_metadata[np.in1d(obs_metadata['EXP_ID'], exp_arr)]
reduced_obs_metadata.sort('EXP_ID')
sorted_inds = np.argsort(exp_arr)
reduced_obs_metadata.remove_column('EXP_ID')
md_len = len(reduced_obs_metadata)
X_arr = np.array(reduced_obs_metadata).view('f8').reshape((md_len,-1))
randsearch.fit(flux_arr[sorted_inds], X_arr)
top_scores = sorted(randsearch.grid_scores_, key=itemgetter(1), reverse=True)[:5]
for i, score in enumerate(top_scores):
print "Model with rank:", i
print "Mean validation score/std:", score.mean_validation_score, np.std(score.cv_validation_scores)
print "Parameters:", score.parameters
print ""
def MSE(Y, y, multioutput='uniform_average', Y_full=None, flux_arr=None, source_model=None,
ss=None, source_model_args=None, method=None):
if Y_full is not None and flux_arr is not None and source_model is not None and ss is not None:
inds = get_inds_(Y, Y_full)
back_trans_flux = ICAize.inverse_transform(y, source_model, ss, method, source_model_args)
try:
return mean_squared_error(flux_arr[inds], back_trans_flux, multioutput=multioutput)
except:
return mean_squared_error(flux_arr[inds], back_trans_flux)
else:
try:
yss = pp.MaxAbsScaler()
Y = yss.fit_transform(Y)
y = yss.transform(y)
except:
scalefactor = np.amax(np.abs(Y), axis=0)
Y = Y / scalefactor
y = y / scalefactor
try:
return mean_squared_error(Y, y, multioutput=multioutput)
except:
return mean_squared_error(Y, y)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=
'Build and test models based on dim reductions and provided spectra')
subparsers = parser.add_subparsers(dest='subparser_name')
parser.add_argument('--metadata_path',
type=str,
default='.',
metavar='PATH',
help='Metadata path to work from, if not '
'.'
'')
parser.add_argument('--spectra_path',
type=str,
default='.',
metavar='PATH',
help='Spectra path to work from, if not '
'.'
'')
parser.add_argument('--method',
type=str,
default='ICA',
metavar='METHOD',
help='Dim reduction method to load data for')
parser.add_argument('--n_jobs',
type=int,
default=1,
metavar='N_JOBS',
help='N_JOBS')
parser.add_argument(
'--model',
type=str,
choices=['ET', 'RF', 'GP', 'KNN', 'SVR'],
default='ET',
help=
'Which model type to use: ET (Extra Trees), RF (Random Forest), GP (Gaussian Process), KNN, or SVR (Support Vector Regression)'
)
parser.add_argument(
'--load_model',
action='store_true',
help='Whether or not to load the model from --model_path')
parser.add_argument('--model_path',
type=str,
default='model.pkl',
metavar='MODEL_PATH',
help='COMPLETE path from which to load a model')
parser.add_argument(
'--metadata_flags', type=str, default='', metavar='METADATA_FLAGS',
help='Flags specifying observational metadata pre-processing, e.g. LUNAR_MAG which takes the '\
'magnitude and linearizes it (ignoring that it is an area magnitude)'
)
parser.add_argument(
'--compacted_path',
type=str,
default=None,
metavar='COMPATED_PATH',
help=
'Path to find compacted/arrayized data; setting this will cause --path, --pattern to be ignored'
)
parser_compare = subparsers.add_parser('compare')
parser_compare.add_argument(
'--folds',
type=int,
default=3,
metavar='TEST_FOLDS',
help=
'Do k-fold cross validation with specified number of folds. Defaults to 3.'
)
parser_compare.add_argument(
'--iters',
type=int,
default=50,
metavar='HYPER_FIT_ITERS',
help='Number of iterations when fitting hyper-params')
parser_compare.add_argument(
'--outputfbk',
action='store_true',
help='If set, outputs \'grid_scores_\' data from RandomizedSearchCV')
parser_compare.add_argument(
'--save_best',
action='store_true',
help=
'Whether or not to save the (last/best) model built for e.g. --hyper_fit'
)
parser_compare.add_argument(
'--scorer',
type=str,
choices=['R2', 'MAE', 'MSE', 'LL', 'EXP_VAR', 'MAPED', 'MSEMV'],
default='R2',
help=
'Which scoring method to use to determine ranking of model instances.')
parser_compare.add_argument(
'--use_spectra',
action='store_true',
help=
'Whether scoring is done against the DM components or the predicted spectra'
)
parser_compare.add_argument(
'--ivar_cutoff',
type=float,
default=0.001,
metavar='IVAR_CUTOFF',
help='data with inverse variace below cutoff is masked as if ivar==0')
parser_compare.add_argument(
'--plot_final_errors', action='store_true',
help='If set, will plot the errors from the final/best model, for the whole dataset, from ' + \
'the best model re-trained on CV folds used for testing.' + \
'Plots all errors on top of each other with low-ish alpha, to give a kind of visual ' + \
'density map of errors.'
)
args = parser.parse_args()
obs_metadata = trim_observation_metadata(
load_observation_metadata(args.metadata_path,
flags=args.metadata_flags))
sources, components, exposures, wavelengths = ICAize.deserialize_data(
args.spectra_path, args.method)
source_model, ss, model_args = ICAize.unpickle_model(
args.spectra_path, args.method)
comb_flux_arr, comb_exposure_arr, comb_wavelengths = None, None, None
if args.use_spectra:
comb_flux_arr, comb_exposure_arr, comb_ivar_arr, comb_masks, comb_wavelengths = ICAize.load_data(
args)
filter_arr = np.in1d(comb_exposure_arr, exposures)
comb_flux_arr = comb_flux_arr[filter_arr]
comb_exposure_arr = comb_exposure_arr[filter_arr]
sorted_inds = np.argsort(comb_exposure_arr)
comb_flux_arr = comb_flux_arr[sorted_inds]
comb_exposure_arr = comb_exposure_arr[sorted_inds]
del comb_ivar_arr
del comb_masks
reduced_obs_metadata = obs_metadata[np.in1d(obs_metadata['EXP_ID'],
exposures)]
reduced_obs_metadata.sort('EXP_ID')
sorted_inds = np.argsort(exposures)
reduced_obs_metadata.remove_column('EXP_ID')
md_len = len(reduced_obs_metadata)
var_count = len(reduced_obs_metadata.columns)
X_arr = np.array(reduced_obs_metadata).view('f8').reshape((md_len, -1))
Y_arr = sources[sorted_inds]
if args.load_model:
predictive_model = load_model(args.model_path)
else:
predictive_model = get_model(args.model)
if args.subparser_name == 'compare':
pdist = get_param_distribution_for_model(args.model, args.iters)
scorer = None
if args.scorer == 'R2':
scorer = make_scorer(R2)
elif args.scorer == 'MAE':
if args.use_spectra:
p_MAE_ = partial(MAE,
Y_full=Y_arr,
flux_arr=comb_flux_arr,
source_model=source_model,
ss=ss,
source_model_args=model_args,
method=args.method)
scorer = make_scorer(p_MAE_, greater_is_better=False)
else:
scorer = make_scorer(MAE, greater_is_better=False)
elif args.scorer == 'MSE':
if args.use_spectra:
p_MSE_ = partial(MSE,
Y_full=Y_arr,
flux_arr=comb_flux_arr,
source_model=source_model,
ss=ss,
source_model_args=model_args,
method=args.method)
scorer = make_scorer(p_MSE_, greater_is_better=False)
else:
scorer = make_scorer(MSE, greater_is_better=False)
elif args.scorer == 'MSEMV':
if args.use_spectra:
p_MSEMV_ = partial(MSEMV,
Y_full=Y_arr,
flux_arr=comb_flux_arr,
source_model=source_model,
ss=ss,
source_model_args=model_args,
method=args.method)
scorer = make_scorer(p_MSEMV_, greater_is_better=False)
else:
scorer = make_scorer(MSEMV, greater_is_better=False)
elif args.scorer == 'EXP_VAR':
if args.use_spectra:
p_EXP_VAR_ = partial(EXP_VAR,
Y_full=Y_arr,
flux_arr=comb_flux_arr,
source_model=source_model,
ss=ss,
source_model_args=model_args,
method=args.method)
scorer = make_scorer(p_EXP_VAR_)
else:
scorer = make_scorer(EXP_VAR)
elif args.scorer == 'MAPED':
if args.use_spectra:
p_MAPED_ = partial(MAPED,
Y_full=Y_arr,
flux_arr=comb_flux_arr,
source_model=source_model,
ss=ss,
source_model_args=model_args,
method=args.method)
scorer = make_scorer(p_MAPED_, greater_is_better=False)
else:
scorer = make_scorer(MAPED, greater_is_better=False)
elif args.scorer == 'LL':
scorer = None
folder = ShuffleSplit(exposures.shape[0],
n_iter=args.folds,
test_size=1.0 / args.folds,
random_state=12345)
if args.model == 'GP':
predictive_model.random_start = args.folds
rcv = GridSearchCV(predictive_model,
param_grid=pdist,
error_score=0,
cv=3,
n_jobs=args.n_jobs,
scoring=scorer)
#random_state=RANDOM_STATE,
#n_iter=args.iters,
else:
rcv = RandomizedSearchCV(predictive_model,
param_distributions=pdist,
n_iter=args.iters,
cv=folder,
n_jobs=args.n_jobs,
scoring=scorer)
# This is going to fit X (metdata) to Y (DM'ed sources). But there are
# really two tests here: how well hyperparams fit/predict the sources
# and how well they fit/predict the actual source spectra. Until I know
# better, I 'm going to need to build a way to test both.
rcv.fit(X_arr, Y_arr)
print(rcv.best_score_)
print(rcv.best_params_)
print(rcv.best_estimator_)
if args.outputfbk:
print("=+" * 10 + "=")
for val in rcv.grid_scores_:
print(val)
print("=+" * 10 + "=")
if args.save_best:
save_model(rcv.best_estimator_, args.model_path)
if args.plot_final_errors:
for train_inds, test_inds in folder:
rcv.best_estimator_.fit(X_arr[train_inds], Y_arr[train_inds])
predicted = rcv.best_estimator_.predict(X_arr[test_inds])
back_trans_flux = ICAize.inverse_transform(
predicted, source_model, ss, args.method, model_args)
diffs = np.abs(comb_flux_arr[test_inds] - back_trans_flux)
#Is there not 'trick' to getting matplotlib to do this without a loop?
for i in range(diffs.shape[0]):
plt.plot(comb_wavelengths, diffs[i, :], 'b-', alpha=0.01)
plt.show()
def main():
flux_arr, exp_arr, ivar_arr, mask_arr, wavelengths = \
ICAize.load_all_in_dir('.', use_con_flux=False, recombine_flux=False,
pattern="stacked*exp??????.csv")
np.savez("compacted_flux_data.npz", flux=flux_arr, exp=exp_arr, ivar=ivar_arr, wavelengths=wavelengths)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Compute PCA/ICA/NMF/etc. components over set of stacked spectra, save those out, and pickle model'
)
parser.add_argument(
'--pattern', type=str, default='stacked*exp??????.*', metavar='PATTERN',
help='File pattern for stacked sky fibers.'
)
parser.add_argument(
'--path', type=str, default='.', metavar='PATH',
help='Path to work from, if not ''.'''
)
parser.add_argument(
'--compacted_path', type=str, default=None, metavar='COMPATED_PATH',
help='Path to find compacted/arrayized data; setting this will cause --path, --pattern to be ignored'
)
parser.add_argument(
'--n_components', type=int, default=40, metavar='N_COMPONENTS',
help='Number of ICA/PCA/etc. components'
)
parser.add_argument(
'--method', type=str, default='ICA', metavar='METHOD',
choices=['ICA', 'PCA', 'SPCA', 'NMF', 'ISO', 'KPCA', 'FA', 'DL'],
help='Which dim. reduction method to use'
)
parser.add_argument(
'--scale', action='store_true',
help='Should inputs variance be scaled? Defaults to mean subtract and value scale, but w/out this does not scale variance.'
)
parser.add_argument(
'--no_scale', action='store_true',
help='Suppresses all scaling'
)
parser.add_argument(
'--ivar_cutoff', type=float, default=0.001, metavar='IVAR_CUTOFF',
help='data with inverse variace below cutoff is masked as if ivar==0'
)
parser.add_argument(
'--n_iter', type=int, default=1200, metavar='MAX_ITER',
help='Maximum number of iterations to allow for convergence. For SDSS data 1000 is a safe number of ICA, while SPCA requires larger values e.g. ~2000 to ~2500'
)
parser.add_argument(
'--n_jobs', type=int, default=None, metavar='N_JOBS',
help='N_JOBS'
)
args = parser.parse_args()
comb_flux_arr, comb_exposure_arr, comb_ivar_arr, comb_masks, comb_wavelengths = iz.load_data(args)
model = iz.get_model(args.method, n=args.n_components, n_neighbors=None, max_iter=args.n_iter, random_state=iz.random_state, n_jobs=args.n_jobs)
ss = None
if args.no_scale:
scaled_flux_arr = comb_flux_arr
else:
ss = skpp.StandardScaler(with_std=False)
if args.scale:
ss = skpp.StandardScaler(with_std=True)
scaled_flux_arr = ss.fit_transform(comb_flux_arr)
#Heavily copied from J. Vanderplas/astroML bayesian_blocks.py
N = comb_wavelengths.size
step = args.n_components * 4
edges = np.concatenate([comb_wavelengths[:1:step],
0.5 * (comb_wavelengths[1::step] + comb_wavelengths[:-1:step]),
comb_wavelengths[-1::step]])
block_length = comb_wavelengths[-1::step] - edges
# arrays to store the best configuration
nn_vec = np.ones(N/step) * step
best = np.zeros(N, dtype=float)
last = np.zeros(N, dtype=int)
for R in range(N/step):
print("R: " + str(R))
width = block_length[:R + 1] - block_length[R + 1]
count_vec = np.cumsum(nn_vec[:R + 1][::-1])[::-1]
#width = nn_vec[:R + 1] - nn_vec[R + 1]
#count_vec = np.cumsum(nn_vec[:R + 1][::-1])[::-1]
#print(width)
#print(count_vec)
#raw_input("Pausing... ")
fit_vec = map(lambda n: iz.score_via_CV(['LL'], scaled_flux_arr[:, :n], model, ss, args.method, folds=3, n_jobs=args.n_jobs), count_vec)
fit_vec = [d["mle"] for d in fit_vec]
#print(fit_vec)
fit_vec[1:] += best[:R]
#print(fit_vec)
i_max = np.argmax(fit_vec)
last[R] = i_max
best[R] = fit_vec[i_max]
#print(best)
change_points = np.zeros(N/step, dtype=int)
i_cp = N/step
ind = N/step
while True:
i_cp -= 1
change_points[i_cp] = ind
if ind == 0:
break
ind = last[ind - 1]
change_points = change_points[i_cp:]
print(edges[change_points])
'''
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=
'Compute PCA/ICA/NMF/etc. components over set of stacked spectra, save those out, and pickle model'
)
parser.add_argument('--pattern',
type=str,
default='stacked*exp??????.*',
metavar='PATTERN',
help='File pattern for stacked sky fibers.')
parser.add_argument('--path',
type=str,
default='.',
metavar='PATH',
help='Path to work from, if not '
'.'
'')
parser.add_argument(
'--compacted_path',
type=str,
default=None,
metavar='COMPATED_PATH',
help=
'Path to find compacted/arrayized data; setting this will cause --path, --pattern to be ignored'
)
parser.add_argument('--n_components',
type=int,
default=40,
metavar='N_COMPONENTS',
help='Number of ICA/PCA/etc. components')
parser.add_argument(
'--method',
type=str,
default='ICA',
metavar='METHOD',
choices=['ICA', 'PCA', 'SPCA', 'NMF', 'ISO', 'KPCA', 'FA', 'DL'],
help='Which dim. reduction method to use')
parser.add_argument(
'--scale',
action='store_true',
help=
'Should inputs variance be scaled? Defaults to mean subtract and value scale, but w/out this does not scale variance.'
)
parser.add_argument('--no_scale',
action='store_true',
help='Suppresses all scaling')
parser.add_argument(
'--ivar_cutoff',
type=float,
default=0.001,
metavar='IVAR_CUTOFF',
help='data with inverse variace below cutoff is masked as if ivar==0')
parser.add_argument(
'--n_iter',
type=int,
default=1200,
metavar='MAX_ITER',
help=
'Maximum number of iterations to allow for convergence. For SDSS data 1000 is a safe number of ICA, while SPCA requires larger values e.g. ~2000 to ~2500'
)
parser.add_argument('--n_jobs',
type=int,
default=None,
metavar='N_JOBS',
help='N_JOBS')
args = parser.parse_args()
comb_flux_arr, comb_exposure_arr, comb_ivar_arr, comb_masks, comb_wavelengths = iz.load_data(
args)
model = iz.get_model(args.method,
n=args.n_components,
n_neighbors=None,
max_iter=args.n_iter,
random_state=iz.random_state,
n_jobs=args.n_jobs)
ss = None
if args.no_scale:
scaled_flux_arr = comb_flux_arr
else:
ss = skpp.StandardScaler(with_std=False)
if args.scale:
ss = skpp.StandardScaler(with_std=True)
scaled_flux_arr = ss.fit_transform(comb_flux_arr)
#Heavily copied from J. Vanderplas/astroML bayesian_blocks.py
N = comb_wavelengths.size
step = args.n_components * 4
edges = np.concatenate([
comb_wavelengths[:1:step],
0.5 * (comb_wavelengths[1::step] + comb_wavelengths[:-1:step]),
comb_wavelengths[-1::step]
])
block_length = comb_wavelengths[-1::step] - edges
# arrays to store the best configuration
nn_vec = np.ones(N / step) * step
best = np.zeros(N, dtype=float)
last = np.zeros(N, dtype=int)
for R in range(N / step):
print("R: " + str(R))
width = block_length[:R + 1] - block_length[R + 1]
count_vec = np.cumsum(nn_vec[:R + 1][::-1])[::-1]
#width = nn_vec[:R + 1] - nn_vec[R + 1]
#count_vec = np.cumsum(nn_vec[:R + 1][::-1])[::-1]
#print(width)
#print(count_vec)
#raw_input("Pausing... ")
fit_vec = map(
lambda n: iz.score_via_CV(['LL'],
scaled_flux_arr[:, :n],
model,
ss,
args.method,
folds=3,
n_jobs=args.n_jobs), count_vec)
fit_vec = [d["mle"] for d in fit_vec]
#print(fit_vec)
fit_vec[1:] += best[:R]
#print(fit_vec)
i_max = np.argmax(fit_vec)
last[R] = i_max
best[R] = fit_vec[i_max]
#print(best)
change_points = np.zeros(N / step, dtype=int)
i_cp = N / step
ind = N / step
while True:
i_cp -= 1
change_points[i_cp] = ind
if ind == 0:
break
ind = last[ind - 1]
change_points = change_points[i_cp:]
print(edges[change_points])
'''
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Build and test models based on dim reductions and provided spectra'
)
subparsers = parser.add_subparsers(dest='subparser_name')
parser.add_argument(
'--metadata_path', type=str, default='.', metavar='PATH',
help='Metadata path to work from, if not ''.'''
)
parser.add_argument(
'--spectra_path', type=str, default='.', metavar='PATH',
help='Spectra path to work from, if not ''.'''
)
parser.add_argument(
'--method', type=str, default='ICA', metavar='METHOD',
help='Dim reduction method to load data for'
)
parser.add_argument(
'--n_jobs', type=int, default=1, metavar='N_JOBS',
help='N_JOBS'
)
parser.add_argument(
'--model', type=str, choices=['ET', 'RF', 'GP', 'KNN', 'SVR'], default='ET',
help='Which model type to use: ET (Extra Trees), RF (Random Forest), GP (Gaussian Process), KNN, or SVR (Support Vector Regression)'
)
parser.add_argument(
'--load_model', action='store_true',
help='Whether or not to load the model from --model_path'
)
parser.add_argument(
'--model_path', type=str, default='model.pkl', metavar='MODEL_PATH',
help='COMPLETE path from which to load a model'
)
parser.add_argument(
'--metadata_flags', type=str, default='', metavar='METADATA_FLAGS',
help='Flags specifying observational metadata pre-processing, e.g. LUNAR_MAG which takes the '\
'magnitude and linearizes it (ignoring that it is an area magnitude)'
)
parser.add_argument(
'--compacted_path', type=str, default=None, metavar='COMPATED_PATH',
help='Path to find compacted/arrayized data; setting this will cause --path, --pattern to be ignored'
)
parser_compare = subparsers.add_parser('compare')
parser_compare.add_argument(
'--folds', type=int, default=3, metavar='TEST_FOLDS',
help='Do k-fold cross validation with specified number of folds. Defaults to 3.'
)
parser_compare.add_argument(
'--iters', type=int, default=50, metavar='HYPER_FIT_ITERS',
help='Number of iterations when fitting hyper-params'
)
parser_compare.add_argument(
'--outputfbk', action='store_true',
help='If set, outputs \'grid_scores_\' data from RandomizedSearchCV'
)
parser_compare.add_argument(
'--save_best', action='store_true',
help='Whether or not to save the (last/best) model built for e.g. --hyper_fit'
)
parser_compare.add_argument(
'--scorer', type=str, choices=['R2', 'MAE', 'MSE', 'LL', 'EXP_VAR', 'MAPED', 'MSEMV'], default='R2',
help='Which scoring method to use to determine ranking of model instances.'
)
parser_compare.add_argument(
'--use_spectra', action='store_true',
help='Whether scoring is done against the DM components or the predicted spectra'
)
parser_compare.add_argument(
'--ivar_cutoff', type=float, default=0.001, metavar='IVAR_CUTOFF',
help='data with inverse variace below cutoff is masked as if ivar==0'
)
parser_compare.add_argument(
'--plot_final_errors', action='store_true',
help='If set, will plot the errors from the final/best model, for the whole dataset, from ' + \
'the best model re-trained on CV folds used for testing.' + \
'Plots all errors on top of each other with low-ish alpha, to give a kind of visual ' + \
'density map of errors.'
)
args = parser.parse_args()
obs_metadata = trim_observation_metadata(load_observation_metadata(args.metadata_path, flags=args.metadata_flags))
sources, components, exposures, wavelengths = ICAize.deserialize_data(args.spectra_path, args.method)
source_model, ss, model_args = ICAize.unpickle_model(args.spectra_path, args.method)
comb_flux_arr, comb_exposure_arr, comb_wavelengths = None, None, None
if args.use_spectra:
comb_flux_arr, comb_exposure_arr, comb_ivar_arr, comb_masks, comb_wavelengths = ICAize.load_data(args)
filter_arr = np.in1d(comb_exposure_arr, exposures)
comb_flux_arr = comb_flux_arr[filter_arr]
comb_exposure_arr = comb_exposure_arr[filter_arr]
sorted_inds = np.argsort(comb_exposure_arr)
comb_flux_arr = comb_flux_arr[sorted_inds]
comb_exposure_arr = comb_exposure_arr[sorted_inds]
del comb_ivar_arr
del comb_masks
reduced_obs_metadata = obs_metadata[np.in1d(obs_metadata['EXP_ID'], exposures)]
reduced_obs_metadata.sort('EXP_ID')
sorted_inds = np.argsort(exposures)
reduced_obs_metadata.remove_column('EXP_ID')
md_len = len(reduced_obs_metadata)
var_count = len(reduced_obs_metadata.columns)
X_arr = np.array(reduced_obs_metadata).view('f8').reshape((md_len,-1))
Y_arr = sources[sorted_inds]
if args.load_model:
predictive_model = load_model(args.model_path)
else:
predictive_model = get_model(args.model)
if args.subparser_name == 'compare':
pdist = get_param_distribution_for_model(args.model, args.iters)
scorer = None
if args.scorer == 'R2':
scorer = make_scorer(R2)
elif args.scorer == 'MAE':
if args.use_spectra:
p_MAE_ = partial(MAE, Y_full=Y_arr, flux_arr=comb_flux_arr,
source_model=source_model, ss=ss,
source_model_args=model_args, method=args.method)
scorer = make_scorer(p_MAE_, greater_is_better=False)
else:
scorer = make_scorer(MAE, greater_is_better=False)
elif args.scorer == 'MSE':
if args.use_spectra:
p_MSE_ = partial(MSE, Y_full=Y_arr, flux_arr=comb_flux_arr,
source_model=source_model, ss=ss,
source_model_args=model_args, method=args.method)
scorer = make_scorer(p_MSE_, greater_is_better=False)
else:
scorer = make_scorer(MSE, greater_is_better=False)
elif args.scorer == 'MSEMV':
if args.use_spectra:
p_MSEMV_ = partial(MSEMV, Y_full=Y_arr, flux_arr=comb_flux_arr,
source_model=source_model, ss=ss,
source_model_args=model_args, method=args.method)
scorer = make_scorer(p_MSEMV_, greater_is_better=False)
else:
scorer = make_scorer(MSEMV, greater_is_better=False)
elif args.scorer == 'EXP_VAR':
if args.use_spectra:
p_EXP_VAR_ = partial(EXP_VAR, Y_full=Y_arr, flux_arr=comb_flux_arr,
source_model=source_model, ss=ss,
source_model_args=model_args, method=args.method)
scorer = make_scorer(p_EXP_VAR_)
else:
scorer = make_scorer(EXP_VAR)
elif args.scorer == 'MAPED':
if args.use_spectra:
p_MAPED_ = partial(MAPED, Y_full=Y_arr, flux_arr=comb_flux_arr,
source_model=source_model, ss=ss,
source_model_args=model_args, method=args.method)
scorer = make_scorer(p_MAPED_, greater_is_better=False)
else:
scorer = make_scorer(MAPED, greater_is_better=False)
elif args.scorer == 'LL':
scorer = None
folder = ShuffleSplit(exposures.shape[0], n_iter=args.folds, test_size=1.0/args.folds,
random_state=12345)
if args.model == 'GP':
predictive_model.random_start = args.folds
rcv = GridSearchCV(predictive_model, param_grid=pdist,
error_score=0, cv=3, n_jobs=args.n_jobs,
scoring=scorer)
#random_state=RANDOM_STATE,
#n_iter=args.iters,
else:
rcv = RandomizedSearchCV(predictive_model, param_distributions=pdist,
n_iter=args.iters, cv=folder, n_jobs=args.n_jobs,
scoring=scorer)
# This is going to fit X (metdata) to Y (DM'ed sources). But there are
# really two tests here: how well hyperparams fit/predict the sources
# and how well they fit/predict the actual source spectra. Until I know
# better, I 'm going to need to build a way to test both.
rcv.fit(X_arr, Y_arr)
print(rcv.best_score_)
print(rcv.best_params_)
print(rcv.best_estimator_)
if args.outputfbk:
print("=+"*10 + "=")
for val in rcv.grid_scores_:
print(val)
print("=+"*10 + "=")
if args.save_best:
save_model(rcv.best_estimator_, args.model_path)
if args.plot_final_errors:
for train_inds, test_inds in folder:
rcv.best_estimator_.fit(X_arr[train_inds], Y_arr[train_inds])
predicted = rcv.best_estimator_.predict(X_arr[test_inds])
back_trans_flux = ICAize.inverse_transform(predicted, source_model, ss, args.method, model_args)
diffs = np.abs(comb_flux_arr[test_inds] - back_trans_flux)
#Is there not 'trick' to getting matplotlib to do this without a loop?
for i in range(diffs.shape[0]):
plt.plot(comb_wavelengths, diffs[i, :], 'b-', alpha=0.01)
plt.show()
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='Compute PCA/ICA/NMF/etc. components over set of stacked spectra, save those out, and pickle model'
)
subparsers = parser.add_subparsers(dest='subparser_name')
parser.add_argument(
'--pattern', type=str, default='stacked*exp??????.*', metavar='PATTERN',
help='File pattern for stacked sky fibers.'
)
parser.add_argument(
'--path', type=str, default='.', metavar='PATH',
help='Path to work from, if not ''.'''
)
parser.add_argument(
'--compacted_path', type=str, default=None, metavar='COMPATED_PATH',
help='Path to find compacted/arrayized data; setting this will cause --path, --pattern to be ignored'
)
parser.add_argument(
'--method', type=str, default=['ICA'], metavar='METHOD',
choices=['ICA', 'PCA', 'SPCA', 'NMF', 'ISO', 'KPCA', 'FA', 'DL'], nargs='+',
help='Which dim. reduction method to use'
)
parser.add_argument(
'--scale', action='store_true',
help='Should inputs be scaled? Will mean subtract and value scale, but does not scale variace.'
)
parser.add_argument(
'--ivar_cutoff', type=float, default=0.001, metavar='IVAR_CUTOFF',
help='data with inverse variace below cutoff is masked as if ivar==0'
)
parser.add_argument(
'--n_iter', type=int, default=1200, metavar='MAX_ITER',
help='Maximum number of iterations to allow for convergence. For SDSS data 1000 is a safe number of ICA, while SPCA requires larger values e.g. ~2000 to ~2500'
)
parser.add_argument(
'--n_jobs', type=int, default=None, metavar='N_JOBS',
help='N_JOBS'
)
parser_compare = subparsers.add_parser('compare')
parser_compare.add_argument(
'--max_components', type=int, default=50, metavar='COMP_MAX',
help='Max number of components to use/test'
)
parser_compare.add_argument(
'--min_components', type=int, default=0, metavar='COMP_MIN',
help='Min number of compoenents to use/test'
)
parser_compare.add_argument(
'--step_size', type=int, default=5, metavar='COMP_STEP',
help='Step size from comp_min to comp_max'
)
parser_compare.add_argument(
'--comparison', choices=['EXP_VAR', 'R2', 'MSE', 'MAE'], nargs='*', default=['EXP_VAR'],
help='Comparison methods: Explained variance (score), R2 (score), mean sq. error (loss), MEDIAN absolute error (loss)'
)
parser_compare.add_argument(
'--mle_if_avail', action='store_true',
help='In additon to --comparison, include MLE if PCA or FA methods specified'
)
parser_compare.add_argument(
'--plot_example_reconstruction', action='store_true',
help='Pick a random spectrum, plot its actual and reconstructed versions'
)
parser_build = subparsers.add_parser('build')
parser_build.add_argument(
'--n_components', type=int, default=40, metavar='N_COMPONENTS',
help='Number of ICA/PCA/etc. components'
)
parser_build.add_argument(
'--n_neighbors', type=int, default=10, metavar='N_NEIGHBORS',
help='Number of neighbots for e.g. IsoMap'
)
args = parser.parse_args()
comb_flux_arr, comb_exposure_arr, comb_ivar_arr, comb_masks, comb_wavelengths = iz.load_data(args)
if 'DL' in args.method:
flux_arr = comb_flux_arr.astype(dtype=np.float64)
else:
flux_arr = comb_flux_arr
scaled_flux_arr = None
ss = None
if args.scale:
ss = skpp.StandardScaler(with_std=False)
scaled_flux_arr = ss.fit_transform(flux_arr)
else:
scaled_flux_arr = flux_arr
if args.subparser_name == 'compare':
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
for method in args.method:
model = iz.get_model(method, max_iter=args.n_iter, random_state=iz.random_state, n_jobs=args.n_jobs)
scores = {}
mles_and_covs = args.mle_if_avail and (method == 'FA' or method == 'PCA')
n_components = np.arange(args.min_components, args.max_components+1, args.step_size)
for n in n_components:
print("Cross validating for n=" + str(n) + " on method " + method)
model.n_components = n
comparisons = iz.score_via_CV(args.comparison,
flux_arr if method == 'NMF' else scaled_flux_arr,
model, method, n_jobs=args.n_jobs, include_mle=mles_and_covs,
modeler=_iter_modeler, scorer=_iter_scorer)
for key, val in comparisons.items():
if key in scores:
scores[key].append(val)
else:
scores[key] = [val]
if mles_and_covs:
#ax2.axhline(cov_mcd_score(scaled_flux_arr, args.scale), color='violet', label='MCD Cov', linestyle='--')
ax2.axhline(cov_lw_score(scaled_flux_arr, args.scale), color='orange', label='LW Cov', linestyle='--')
for key, score_list in scores.items():
if key != 'mle':
ax1.plot(n_components, score_list, label=method + ':' + key + ' scores')
else:
ax2.plot(n_components, score_list, '-.', label=method + ' mle scores')
ax1.set_xlabel('nb of components')
ax1.set_ylabel('CV scores', figure=fig)
ax1.legend(loc='lower left')
ax2.legend(loc='lower right')
plt.show()
def load_plot_etc_target_type(metadata_path, spectra_path, test_inds, target_type, no_plot=False,
save_out=False, restrict_delta=False, use_spca=False, use_pca=False):
obs_metadata = trim_observation_metadata(load_observation_metadata(metadata_path))
if use_filter_split:
c_sources, c_mixing, c_exposures, c_wavelengths, c_filter_split_arr = load_spectra_data(spectra_path,
target_type=target_type, filter_str='nonem', use_spca=use_spca, use_pca=use_pca)
c_sources_e, c_mixing_e, c_exposures_e, c_wavelengths_e, c_filter_split_arr_e = load_spectra_data(spectra_path,
target_type=target_type, filter_str='em', use_spca=use_spca, use_pca=use_pca)
else:
c_sources, c_mixing, c_exposures, c_wavelengths, c_filter_split_arr = load_spectra_data(spectra_path,
target_type=target_type, filter_str='both', use_spca=use_spca, use_pca=use_pca)
reduced_obs_metadata = obs_metadata[np.in1d(obs_metadata['EXP_ID'], c_exposures)]
reduced_obs_metadata.sort('EXP_ID')
sorted_inds = np.argsort(c_exposures)
if use_filter_split:
sorted_e_inds = np.argsort(c_exposures_e)
if not linear_only:
if reg_type == 'etr':
rfr = ensemble.ExtraTreesRegressor(n_estimators=n_estimators, min_samples_split=min_samples_split,
random_state=rfr_random_state, n_jobs=-1, verbose=False, bootstrap=bootstrap)
if use_filter_split:
rfr_e = ensemble.ExtraTreesRegressor(n_estimators=n_estimators, min_samples_split=min_samples_split,
random_state=rfr_random_state, n_jobs=-1, verbose=False, bootstrap=bootstrap)
else:
rfr = ensemble.RandomForestRegressor(n_estimators=n_estimators, min_samples_split=min_samples_split,
random_state=rfr_random_state, n_jobs=-1, verbose=False, bootstrap=bootstrap)
if use_filter_split:
rfr_e = ensemble.RandomForestRegressor(n_estimators=n_estimators, min_samples_split=min_samples_split,
random_state=rfr_random_state, n_jobs=-1, verbose=False, bootstrap=bootstrap)
if include_knn:
knn = neighbors.KNeighborsRegressor(weights='distance', n_neighbors=10, p=64)
if use_filter_split:
knn_e = neighbors.KNeighborsRegressor(weights='distance', n_neighbors=10, p=64)
if include_linear:
linear = Linear(fit_intercept=True, copy_X=True, n_jobs=-1)
poly_2_linear = Pipeline([('poly', PolynomialFeatures(degree=2)),
('linear', Linear(fit_intercept=True, copy_X=True, n_jobs=-1))])
poly_3_linear = Pipeline([('poly', PolynomialFeatures(degree=3)),
('linear', Linear(fit_intercept=True, copy_X=True, n_jobs=-1))])
poly_4_linear = Pipeline([('poly', PolynomialFeatures(degree=4)),
('linear', Linear(fit_intercept=True, copy_X=True, n_jobs=-1))])
if use_filter_split:
linear_e = Linear(fit_intercept=True, copy_X=True, n_jobs=-1)
poly_2_linear_e = Pipeline([('poly', PolynomialFeatures(degree=2)),
('linear', Linear(fit_intercept=True, copy_X=True, n_jobs=-1))])
poly_3_linear_e = Pipeline([('poly', PolynomialFeatures(degree=3)),
('linear', Linear(fit_intercept=True, copy_X=True, n_jobs=-1))])
poly_4_linear_e = Pipeline([('poly', PolynomialFeatures(degree=4)),
('linear', Linear(fit_intercept=True, copy_X=True, n_jobs=-1))])
reduced_obs_metadata.remove_column('EXP_ID')
md_len = len(reduced_obs_metadata)
var_count = len(reduced_obs_metadata.columns)
X_arr = np.array(reduced_obs_metadata).view('f8').reshape((md_len,-1))
ica = None
if not use_spca and not use_pca:
if use_filter_split:
ica = ICAize.unpickle_FastICA(path=spectra_path, target_type=target_type, filter_str='nonem')
ica_e = ICAize.unpickle_FastICA(path=spectra_path, target_type=target_type, filter_str='em')
else:
ica = ICAize.unpickle_FastICA(path=spectra_path, target_type=target_type, filter_str='both')
elif use_spca:
ica = ICAize.unpickle_SPCA(path=spectra_path, target_type=target_type)
else:
if use_filter_split:
ica = ICAize.unpickle_PCA(path=spectra_path, target_type=target_type, filter_str='nonem')
ica_e = ICAize.unpickle_PCA(path=spectra_path, target_type=target_type, filter_str='em')
else:
ica = ICAize.unpickle_PCA(path=spectra_path, target_type=target_type, filter_str='both')
spectra_dir_list = os.listdir(spectra_path)
################################################################
results = None
for test_ind in test_inds:
test_X = X_arr[test_ind]
train_X = np.vstack( [X_arr[:test_ind], X_arr[test_ind+1:]] )
test_y = (c_sources[sorted_inds])[test_ind]
train_y = np.vstack( [(c_sources[sorted_inds])[:test_ind], (c_sources[sorted_inds])[test_ind+1:]] )
if use_filter_split:
test_y_e = (c_sources_e[sorted_e_inds])[test_ind]
train_y_e = np.vstack( [(c_sources_e[sorted_e_inds])[:test_ind], (c_sources_e[sorted_e_inds])[test_ind+1:]] )
if scale:
scaler = StandardScaler(with_std=scale_std)
train_X = scaler.fit_transform(train_X)
test_X = scaler.transform(test_X)
title_str = "exp{}, {}".format(c_exposures[sorted_inds[test_ind]], target_type)
if not linear_only:
rfr.fit(X=train_X, y=train_y)
if use_filter_split:
rfr_e.fit(X=train_X, y=train_y_e)
if include_knn:
knn.fit(X=train_X, y=train_y)
if user_filter_split:
knn_e.fit(X=train_X, y=train_y_e)
if include_linear:
linear.fit(train_X, train_y)
poly_2_linear.fit(train_X, train_y)
if order_3:
poly_3_linear.fit(train_X, train_y)
if order_4:
poly_4_linear.fit(train_X, train_y)
if use_filter_split and include_linear:
linear_e.fit(train_X, train_y_e)
poly_2_linear_e.fit(train_X, train_y_e)
if order_3:
poly_3_linear_e.fit(train_X, train_y_e)
if order_4:
poly_4_linear_e.fit(train_X, train_y_e)
print test_ind, c_exposures[sorted_inds[test_ind]],
data = None
actual = None
mask = None
delta_mask = None
ivar = None
for file in spectra_dir_list:
if fnmatch.fnmatch(file, "stacked_sky_*exp{}.csv".format(c_exposures[sorted_inds[test_ind]])):
data = Table.read(os.path.join(spectra_path, file), format="ascii.csv")
ivar = data['ivar']
mask = (data['ivar'] == 0)
delta_mask = mask.copy()
if restrict_delta:
if restrict_color == 'blue':
delta_mask[2700:] = True
else:
delta_mask[:2700] = True
actual = data['flux']
break
if actual is None:
continue
if not linear_only:
rfr_prediction = rfr.predict(test_X)
if not use_spca and not use_pca:
rfr_predicted = ica.inverse_transform(rfr_prediction, copy=True)
else:
rfr_predicted = np.zeros( (1, ica.components_.shape[1]) )
rfr_predicted[0,:] = np.sum(rfr_prediction.T * ica.components_, 0)
if use_filter_split:
rfr_e_prediction = rfr_e.predict(test_X)
if not use_spca and not use_pca:
rfr_e_predicted = ica_e.inverse_transform(rfr_e_prediction, copy=True)
else:
rfr_e_predicted = np.zeros( (1, ica_e.components_.shape[1]) )
rfr_e_predicted[0,:] = np.sum(rfr_e_prediction.T * ica_e.components_, 0)
rfr_predicted = rfr_predicted + rfr_e_predicted
rfr_delta = rfr_predicted[0] - actual
if not no_plot:
plt.plot(c_wavelengths[~mask], rfr_predicted[0][~mask])
plt.plot(c_wavelengths[~mask], actual[~mask])
plt.plot(c_wavelengths[~mask], rfr_delta[~mask])
if not no_plot:
plt.plot(c_wavelengths, [0]*len(c_wavelengths))
err_term = np.sum(np.power(rfr_delta[~delta_mask], 2))/len(c_wavelengths[~delta_mask])
err_sum = np.sum(rfr_delta[~delta_mask])/len(rfr_delta[~delta_mask])
red_chi = np.sum(np.power(rfr_delta[~delta_mask], 2)*ivar[~delta_mask])/(len(c_wavelengths[~delta_mask])-var_count-1)
if not no_plot:
plt.legend(['Predicted', 'Actual', 'Delta {:0.5f}'.format(err_term)])
plt.tight_layout()
plt.title("Random Forest Regressor: {}".format(title_str))
plt.show()
plt.close()
print err_term, red_chi, err_sum,
if include_knn:
knn_prediction = knn.predict(test_X)
if not use_spca and not use_pca:
knn_predicted = ica.inverse_transform(knn_prediction, copy=True)
else:
knn_predicted = np.zeros( (1, ica.components_.shape[1]) )
knn_predicted[0,:] = np.sum(knn_prediction.T * ica.components_, 0)
if use_filter_split:
knn_e_prediction = knn_e.predict(test_X)
if not use_spca and not use_pca:
knn_e_predicted = ica_e.inverse_transform(knn_e_prediction, copy=True)
else:
knn_e_predicted = np.zeros( (1, ica_e.components_.shape[1]) )
knn_e_predicted[0,:] = np.sum(knn_e_prediction.T * ica_e.components_, 0)
knn_predicted = knn_predicted + knn_e_predicted
if not no_plot:
plt.plot(c_wavelengths[~mask], knn_predicted[0][~mask])
plt.plot(c_wavelengths[~mask], actual[~mask])
knn_delta = knn_predicted[0] - actual
err_term = np.sum(np.power(knn_delta[~delta_mask], 2))/len(c_wavelengths[~delta_mask])
err_sum = np.sum(knn_delta[~delta_mask])/len(knn_delta[~delta_mask])
red_chi = np.sum(np.power(knn_delta[~delta_mask], 2)*ivar[~delta_mask])/(len(c_wavelengths[~delta_mask])-var_count-1)
if not no_plot:
plt.plot(c_wavelengths[~mask], knn_delta[~mask])
plt.plot(c_wavelengths, [0]*len(c_wavelengths))
plt.legend(['Predicted', 'Actual', 'Delta {:0.5f}'.format(err_term)])
plt.tight_layout()
plt.title("Good 'ol K-NN: {}".format(title_str))
plt.show()
plt.close()
print err_term, red_chi, err_sum,
if include_linear:
poly_1_prediction = linear.predict(test_X)
if not use_spca and not use_pca:
poly_1_predicted = ica.inverse_transform(poly_1_prediction, copy=True)
else:
poly_1_predicted = np.zeros( (1, ica.components_.shape[1]) )
poly_1_predicted[0,:] = np.sum(poly_1_prediction.T * ica.components_, 0)
if use_filter_split:
poly_1_e_prediction = linear.predict(test_X)
if not use_spca and not use_pca:
poly_1_e_predicted = ica_e.inverse_transform(poly_1_e_prediction, copy=True)
else:
poly_1_e_predicted = np.zeros( (1, ica_e.components_.shape[1]) )
poly_1_e_predicted[0,:] = np.sum(poly_1_e_prediction.T * ica_e.components_, 0)
poly_1_predicted = poly_1_predicted + poly_1_e_predicted
poly_1_delta = poly_1_predicted[0] - actual
if not no_plot:
plt.plot(c_wavelengths[~mask], poly_1_predicted[0][~mask])
plt.plot(c_wavelengths[~mask], actual[~mask])
err_term = np.sum(np.power(poly_1_delta[~delta_mask], 2))/len(c_wavelengths[~delta_mask])
err_sum = np.sum(poly_1_delta[~delta_mask])/len(poly_1_delta[~delta_mask])
red_chi = np.sum(np.power(poly_1_delta[~delta_mask], 2)*ivar[~delta_mask])/(len(c_wavelengths[~delta_mask])-var_count-1)
if not no_plot:
plt.plot(c_wavelengths[~mask], poly_1_delta[~mask])
plt.plot(c_wavelengths, [0]*len(c_wavelengths))
plt.legend(['Predicted', 'Actual', 'Delta {:0.5f}'.format(err_term)])
plt.tight_layout()
plt.title("Poly 1: {}".format(title_str))
plt.show()
plt.close()
print err_term, red_chi, err_sum,
poly_2_prediction = poly_2_linear.predict(test_X)
if not use_spca and not use_pca:
poly_2_predicted = ica.inverse_transform(poly_2_prediction, copy=True)
else:
poly_2_predicted = np.zeros( (1, ica.components_.shape[1]) )
poly_2_predicted[0,:] = np.sum(poly_2_prediction.T * ica.components_, 0)
poly_2_delta = poly_2_predicted[0] - actual
if not no_plot:
plt.plot(c_wavelengths[~mask], poly_2_predicted[0][~mask])
plt.plot(c_wavelengths[~mask], actual[~mask])
err_term = np.sum(np.power(poly_2_delta[~delta_mask], 2))/len(c_wavelengths[~delta_mask])
err_sum = np.sum(poly_2_delta[~delta_mask])/len(poly_2_delta[~delta_mask])
red_chi = np.sum(np.power(poly_2_delta[~delta_mask], 2)*ivar[~delta_mask])/(len(c_wavelengths[~delta_mask])-var_count-1)
if not no_plot:
plt.plot(c_wavelengths[~mask], poly_2_delta[~mask])
plt.plot(c_wavelengths, [0]*len(c_wavelengths))
plt.legend(['Predicted', 'Actual', 'Delta {:0.5f}'.format(err_term)])
plt.tight_layout()
plt.title("Poly 2: {}".format(title_str))
plt.show()
plt.close()
print err_term, red_chi, err_sum,
err_ind =+ 1
if order_3:
poly_3_prediction = poly_3_linear.predict(test_X)
if not use_spca and not use_pca:
poly_3_predicted = ica.inverse_transform(poly_3_prediction, copy=True)
else:
poly_3_predicted = np.zeros( (1, ica.components_.shape[1]) )
poly_3_predicted[0,:] = np.sum(poly_3_prediction.T * ica.components_, 0)
poly_3_delta = poly_3_predicted[0] - actual
if not no_plot:
plt.plot(c_wavelengths[~mask], poly_3_predicted[0][~mask])
plt.plot(c_wavelengths[~mask], actual[~mask])
err_term = np.sum(np.power(poly_3_delta[~delta_mask], 2))/len(c_wavelengths[~delta_mask])
err_sum = np.sum(poly_3_delta[~delta_mask])/len(poly_3_delta[~delta_mask])
red_chi = np.sum(np.power(poly_3_delta[~delta_mask], 2)*ivar[~delta_mask])/(len(c_wavelengths[~delta_mask])-var_count-1)
if not no_plot:
plt.plot(c_wavelengths[~mask], poly_3_delta[~mask])
plt.plot(c_wavelengths, [0]*len(c_wavelengths))
plt.legend(['Predicted', 'Actual', 'Delta {:0.5f}'.format(err_term)])
plt.tight_layout()
plt.title("Poly 3: {}".format(title_str))
plt.show()
plt.close()
print err_term, red_chi, err_sum,
err_ind =+ 1
if order_4:
poly_4_prediction = poly_4_linear.predict(test_X)
if not use_spca and not use_pca:
poly_4_predicted = ica.inverse_transform(poly_4_prediction, copy=True)
else:
poly_4_predicted = np.zeros( (1, ica.components_.shape[1]) )
poly_4_predicted[0,:] = np.sum(poly_4_prediction.T * ica.components_, 0)
poly_4_delta = poly_4_predicted[0] - actual
if not no_plot:
plt.plot(c_wavelengths[~mask], poly_4_predicted[0][~mask])
plt.plot(c_wavelengths[~mask], actual[~mask])
err_term = np.sum(np.power(poly_4_delta[~delta_mask], 2))/len(c_wavelengths[~delta_mask])
err_sum = np.sum(poly_4_delta[~delta_mask])/len(poly_4_delta[~delta_mask])
red_chi = np.sum(np.power(poly_4_delta[~delta_mask], 2)*ivar[~delta_mask])/(len(c_wavelengths[~delta_mask])-var_count-1)
if not no_plot:
plt.plot(c_wavelengths[~mask], poly_4_delta[~mask])
plt.plot(c_wavelengths, [0]*len(c_wavelengths))
plt.legend(['Predicted', 'Actual', 'Delta {:0.5f}'.format(err_term)])
plt.tight_layout()
plt.title("Poly 4: {}".format(title_str))
plt.show()
plt.close()
print err_term, red_chi, err_sum,
err_ind =+ 1
print
if save_out:
out_table = Table()
wavelength_col = Column(c_wavelengths, name="wavelength", dtype=float)
out_table.add_columns([wavelength_col])
if not linear_only:
rf_col = Column(rfr_predicted[0], name="rf_flux", dtype=float)
out_table.add_columns([rf_col])
if include_knn:
knn_col = Column(knn_predicted[0], name="knn_flux", dtype=float)
avg_col = Column(avg_predicted[0], name="avg_flux", dtype=float)
out_table.add_columns([knn_col, avg_col])
if include_linear:
poly_1_col = Column(poly_1_predicted[0], name="poly_1_flux", dtype=float)
poly_2_col = Column(poly_2_predicted[0], name="poly_2_flux", dtype=float)
out_table.add_columns([poly_1_col, poly_2_col])
if order_3:
poly_3_col = Column(poly_3_predicted[0], name="poly_3_flux", dtype=float)
out_table.add_columns([poly_3_col])
if order_4:
poly_4_col = Column(poly_4_predicted[0], name="poly_4_flux", dtype=float)
out_table.add_columns([poly_4_col])
mask_col = Column(~mask, name="mask_col", dtype=bool)
out_table.add_columns([mask_col])
out_table.write("predicted_sky_exp{}.csv".format(c_exposures[sorted_inds[test_ind]]), format="ascii.csv")
import ICAize
import stack
import matplotlib.pyplot as plt
import numpy as np
import random_forest_spectra as rfs
import sklearn.metrics as sm
import sys
import os.path
import pickle
path = '.'
if len(sys.argv) == 2:
path = sys.argv[1]
fastica = ICAize.unpickle_FastICA(target_type="combined", filter_str="both")
for comp_i in range(min(fastica.components_.shape[0], 25)):
scale_factor = 2.4/np.max(np.abs(fastica.components_[comp_i]))
plt.plot(stack.skyexp_wlen_out, (fastica.components_[comp_i]*scale_factor)+(5*comp_i) )
plt.show()
plt.close()
fastica = ICAize.unpickle_FastICA(target_type="combined", filter_str="em")
for comp_i in range(min(fastica.components_.shape[0], 25)):
scale_factor = 2.4/np.max(np.abs(fastica.components_[comp_i]))
plt.plot(stack.skyexp_wlen_out, (fastica.components_[comp_i]*scale_factor)+(5*comp_i) )
plt.show()
plt.close()
fastica = ICAize.unpickle_FastICA(target_type="combined", filter_str="nonem")
for comp_i in range(min(fastica.components_.shape[0], 25)):
