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 main():
obs_metadata = trim_observation_metadata(load_observation_metadata(metadata_path))
flux_arr, exp_arr, ivar_arr, mask_arr, wavelengths = \
load_all_in_dir(spectra_path, use_con_flux=False, recombine_flux=False,
pattern="stacked*exp??????.csv", ivar_cutoff=0.001)
sorted_inds = np.argsort(exp_arr)
reduced_obs_metadata = obs_metadata[np.in1d(obs_metadata['EXP_ID'], exp_arr)]
reduced_obs_metadata.sort('EXP_ID')
md_len = len(reduced_obs_metadata)
X_arr = np.array(reduced_obs_metadata).view('f8').reshape((md_len,-1))
'''
test_fold = np.zeros((X_arr.shape[0], ), dtype=int)
test_fold[600:]=1
ps = PredefinedSplit(test_fold=test_fold)
print len(ps)
'''
test_inds = range(0, 1000)
linear = Linear(fit_intercept=True, copy_X=True, n_jobs=-1)
poly_linear = Pipeline([('poly', PolynomialFeatures(degree=2)),
('linear', Linear(fit_intercept=True, copy_X=True, n_jobs=-1))])
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 = (flux_arr[sorted_inds])[test_ind]
train_y = np.vstack( [(flux_arr[sorted_inds])[:test_ind], (flux_arr[sorted_inds])[test_ind+1:]] )
linear.fit(train_X, train_y)
poly_linear.fit(train_X, train_y)
lin_predictions = linear.predict(test_X)[0]
plin_predictions = poly_linear.predict(test_X)[0]
mask = (ivar_arr[test_ind] == 0) | np.isclose(lin_predictions, 0)
if restrict_delta:
delta_mask = mask.copy()
delta_mask[2700:] = True
else:
delta_mask = mask
lin_delta = lin_predictions - test_y
err_term = np.sum(np.power(lin_delta[~delta_mask], 2))/len(wavelengths[~delta_mask])
err_sum = np.sum(lin_delta[~delta_mask])/len(lin_delta[~delta_mask])
print err_term, err_sum,
plin_delta = plin_predictions - test_y
err_term = np.sum(np.power(plin_delta[~delta_mask], 2))/len(wavelengths[~delta_mask])
err_sum = np.sum(plin_delta[~delta_mask])/len(plin_delta[~delta_mask])
print err_term, err_sum
'''
ransac = RANSAC()
poly_ransac = Pipeline([('poly', PolynomialFeatures(degree=2)),
('ransac', RANSAC())])
print X_arr_train.shape, flux_arr_train.shape
ransac.fit(np.copy(X_arr_train), np.copy(flux_arr_train))
poly_ransac.fit(X_arr_train, flux_arr_train)
r_predictions = ransac.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, r_predictions)
print mse
pr_predictions = poly_ransac.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, pr_predictions)
print mse
'''
'''
#gp = GaussianProcess(nugget=np.power(flux_arr_train, 2)/ivar_train) #regr="quadratic")
gp = GaussianProcess()
gp.fit(X_arr_train, flux_arr_train)
gp_predictions = gp.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, gp_predictions)
print mse
'''
'''
del lin_predictions
del plin_predictions
del linear
del poly_linear
ridge = RidgeCV()
poly_ridge = Pipeline([('poly', PolynomialFeatures(degree=2)),
('ridge', RidgeCV())])
ridge.fit(X_arr_train, flux_arr_train)
poly_ridge.fit(X_arr_train, flux_arr_train)
ridge_predictions = ridge.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, ridge_predictions)
print mse
pridge_predictions = poly_ridge.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, pridge_predictions)
print mse
del ridge_predictions
del pridge_predictions
del ridge
del poly_ridge
lasso = LassoCV(n_jobs=-1)
poly_lasso = Pipeline([('poly', PolynomialFeatures(degree=2)),
('lasso', LassoCV(n_jobs=-1))])
lasso.fit(X_arr_train, flux_arr_train)
poly_lasso.fit(X_arr_train, flux_arr_train)
lasso_predictions = lasso.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, lasso_predictions)
print mse
plasso_predictions = poly_lasso.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, plasso_predictions)
print mse
del lasso_predictions
del plasso_predictions
del lasso
del poly_lasso
elastic = ElasticNetCV(n_jobs=-1)
poly_elastic = Pipeline([('poly', PolynomialFeatures(degree=2)),
('elastic', ElasticNetCV(n_jobs=-1))])
elastic.fit(X_arr_train, flux_arr_train)
poly_elastic.fit(X_arr_train, flux_arr_train)
elastic_predictions = elastic.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, elastic_predictions)
print mse
pelastic_predictions = poly_elastic.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, pelastic_predictions)
print mse
del elastic_predictions
del pelastic_predictions
del elastic
del poly_elastic
'''
'''
pls = PLS(n_components=8, max_iter=2000)
pls.fit(X_arr_train, flux_arr_train)
pls_predictions = pls.predict(X_arr_test)
mse = mean_squared_error(flux_arr_test, pls_predictions)
'''
'''
