def predict_example():
# UNRESOLVED ISSUES WITH PREDICTION
# --------------------------------------------------------------------------
# Preparing the feature matrix
# --------------------------------------------------------------------------
df_test = pd.read_hdf('../class_photoz/data/DR7DR14Q_flux_cat.hdf5',
'data')
# df_train = pd.read_hdf('../class_photoz/data/DR7DR14Q_flux_cat.hdf5','data')
df_train = pd.read_hdf('../class_photoz/data/brightqsos_2.hdf5', 'data')
passband_names = [\
'SDSS_u','SDSS_g','SDSS_r','SDSS_i','SDSS_z', \
# 'TMASS_j','TMASS_h','TMASS_ks', \
'WISE_w1','WISE_w2', \
# 'WISE_w3' \
]
# Try a fraction of the whole datafile first
df_train = df_train.sample(frac=1.0)
# df_test.query('Z > 1.1',inplace=True)
# df_train.query('z > 1.1',inplace=True)
for name in passband_names:
df_train.rename(columns={'obsFlux_' + name: name}, inplace=True)
df_train.rename(columns={'obsFluxErr_' + name: 'sigma_' + name},
inplace=True)
df_test.replace(np.inf, np.nan, inplace=True)
df_train.replace(np.inf, np.nan, inplace=True)
df_test, features = qs.prepare_flux_ratio_catalog(df_test, passband_names)
df_train, features = qs.prepare_flux_ratio_catalog(df_train,
passband_names)
print df_test.shape, df_train.shape
# --------------------------------------------------------------------------
# Random Forest Regression Grid Search
# --------------------------------------------------------------------------
features = ['SDSS_i', 'WISE_w1', 'ug', 'gr', 'ri', 'iz', 'zw1', 'w1w2']
# features = ['SDSS_i','WISE_w1','TMASS_j','ug','gr','ri','iz','zj','jh', 'hks', 'ksw1', 'w1w2']
label = 'z'
params = {
'kernel': 'rbf',
'C': 1.0,
'gamma': 0.001,
'epsilon': 0.2,
'cache_size': 1200
}
df_test = svr.svm_reg_predict(df_train, df_test, features, label, params,
'svm_photoz')
print df_test['svm_photoz'].describe()
ml_an.evaluate_regression(df_test['Z'], df_test['svm_photoz'])
pz_an.plot_redshifts(df_test['Z'], df_test['svm_photoz'])
pz_an.plot_error_hist(df_test['Z'], df_test['svm_photoz'])
plt.show()
def simqso_predict_dr7dr12():
# --------------------------------------------------------------------------
# Preparing the feature matrix
# --------------------------------------------------------------------------
df_test = pd.read_hdf('../class_photoz/data/DR7DR12Q_clean_flux_cat.hdf5',
'data')
df_train = pd.read_hdf('../class_photoz/data/brightqsos_sim_2k_new.hdf5',
'data')
passband_names = [\
'SDSS_u','SDSS_g','SDSS_r','SDSS_i','SDSS_z', \
# 'TMASS_j','TMASS_h','TMASS_ks', \
'WISE_w1','WISE_w2', \
# 'WISE_w3' \
]
df_test = df_test.query('0.3 < Z_VI < 5.5')
# df_train.query('obsMag_SDSS_i <= 18.5',inplace=True)
# df_test.query('SDSS_mag_i <= 18.5',inplace=True)
# df_train.query('z > 1.1',inplace=True)
for name in passband_names:
df_train.rename(columns={'obsFlux_' + name: name}, inplace=True)
df_train.rename(columns={'obsFluxErr_' + name: 'sigma_' + name},
inplace=True)
df_test.replace(np.inf, np.nan, inplace=True)
df_train.replace(np.inf, np.nan, inplace=True)
df_test, features = qs.prepare_flux_ratio_catalog(df_test, passband_names)
df_train, features = qs.prepare_flux_ratio_catalog(df_train,
passband_names)
print df_test.shape, df_train.shape
# --------------------------------------------------------------------------
# Random Forest Regression Grid Search
# --------------------------------------------------------------------------
# features = ['SDSS_u','SDSS_i','SDSS_r','SDSS_z','SDSS_g','WISE_w1','WISE_w2']
# features = ['SDSS_i','WISE_w1','ug','gr','ri','iz','zw1','w1w2']
features = ['ug', 'gr', 'ri', 'iz', 'zw1', 'w1w2']
# features = ['SDSS_i','WISE_w1','TMASS_j','ug','gr','ri','iz','zj','jh', 'hks', 'ksw1', 'w1w2']
label = 'z'
rand_state = 1
params = {
'n_estimators': 300,
'max_depth': 30,
'min_samples_split': 4,
'n_jobs': 2,
'random_state': rand_state
}
df_test = rf.rf_reg_predict(df_train, df_test, features, label, params,
'rf_photoz')
ml_an.evaluate_regression(df_test['Z_VI'], df_test['rf_photoz'])
pz_an.plot_redshifts(df_test['Z_VI'], df_test['rf_photoz'])
pz_an.plot_error_hist(df_test['Z_VI'], df_test['rf_photoz'])
plt.show()
def dr7dr12_predict_simqso():
# --------------------------------------------------------------------------
# Preparing the feature matrix
# --------------------------------------------------------------------------
df_train = pd.read_hdf('../class_photoz/data/DR7DR12Q_clean_flux_cat.hdf5',
'data')
df_test = pd.read_hdf('../class_photoz/data/brightqsos_sim_2k_new.hdf5',
'data')
passband_names = [\
'SDSS_u','SDSS_g','SDSS_r','SDSS_i','SDSS_z', \
# 'TMASS_j','TMASS_h','TMASS_ks', \
'WISE_w1','WISE_w2', \
# 'WISE_w3' \
]
df_train = df_train.query('0.3 < Z_VI < 5.5')
df_test.query('obsMag_SDSS_i <= 18.5', inplace=True)
df_train.query('SDSS_mag_i <= 18.5', inplace=True)
# df_train.query('z > 1.1',inplace=True)
for name in passband_names:
df_test.rename(columns={'obsFlux_' + name: name}, inplace=True)
df_test.rename(columns={'obsFluxErr_' + name: 'sigma_' + name},
inplace=True)
df_test.replace(np.inf, np.nan, inplace=True)
df_train.replace(np.inf, np.nan, inplace=True)
df_test, features = qs.prepare_flux_ratio_catalog(df_test, passband_names)
df_train, features = qs.prepare_flux_ratio_catalog(df_train,
passband_names)
print df_test.shape, df_train.shape
# --------------------------------------------------------------------------
# Random Forest Regression Grid Search
# --------------------------------------------------------------------------
# features = ['SDSS_u','SDSS_i','SDSS_r','SDSS_z','SDSS_g','WISE_w1','WISE_w2']
features = ['SDSS_i', 'WISE_w1', 'ug', 'gr', 'ri', 'iz', 'zw1', 'w1w2']
# features = ['SDSS_i','WISE_w1','TMASS_j','ug','gr','ri','iz','zj','jh', 'hks', 'ksw1', 'w1w2']
label = 'Z_VI'
rand_state = 1
params = {
'kernel': 'rbf',
'C': 1.0,
'gamma': 0.001,
'epsilon': 0.2,
'cache_size': 1200
}
df_test = svr.svm_reg_predict(df_train, df_test, features, label, params,
'svm_photoz')
ml_an.evaluate_regression(df_test['z'], df_test['svm_photoz'])
pz_an.plot_redshifts(df_test['z'], df_test['svm_photoz'])
pz_an.plot_error_hist(df_test['z'], df_test['svm_photoz'])
plt.show()
def photoz_analysis(df_pred, z_label_pred, z_label_true):
df = df_pred.copy(deep=True)
df = df.query('bin_class_true == "QSO"')
z_true = df[z_label_true].values
z_pred = df[z_label_pred].values
print("The r2 score for the Photometric Redshift Estimation is:\t"
), met.r2_score(z_true, z_pred)
pz_an.plot_redshifts(z_true, z_pred)
pz_an.plot_error_hist(z_true, z_pred)
plt.show()