'max_depth': [100, 200, None],
'max_features': [5, 10],
'n_estimators': [600, 1000]
}
rf_CV = GridSearchCV(estimator=rf,
param_grid=param_grid,
cv=3,
verbose=2,
n_jobs=-1,
scoring="neg_mean_squared_error")
#rf_CV.fit(np.column_stack((feat_07_corr, feat_04_corr)), halo_mass_training.reshape(-1, 1))
rf_CV.fit(feat_04_corr, halo_mass_training.reshape(-1, 1))
ml.write_to_file_cv_results(
"/share/data1/lls/regression/in_halos_only/log_m_output/larger_training_set"
"/04_only/cv_results.txt", rf_CV)
joblib.dump(
rf_CV,
"/share/data1/lls/regression/in_halos_only/log_m_output/larger_training_set/04_only/clf.pkl"
)
# predictions
dup = np.copy(halo_mass_testing)
dup1 = np.tile(dup, (50, 1)).transpose()
noise_04 = np.random.normal(0, 2.7, size=[len(halo_mass_testing), 50])
test_feat_04_corr = dup1 + noise_04
# noise_07 = np.random.normal(0, 1.2, [len(halo_mass_testing), 50])
"max_depth": 5,
"max_features": 10,
"warm_start": True,
}
gbm_CV, pred_test = gbm_fun.train_and_test_gradboost(training_features,
traj_testing,
param_grid=param_grid,
cv=cv_i)
np.save(saving_path + "predicted_test_set.npy", pred_test)
joblib.dump(gbm_CV, saving_path + "clf.pkl")
# predictions
if cv_i is True:
alg = gbm_CV.best_estimator_
ml.write_to_file_cv_results(saving_path + "cv_results.txt", gbm_CV)
else:
alg = gbm_CV
ada_r2_train = np.zeros(len(alg.estimators_), )
for i, y_pred in enumerate(alg.staged_predict(training_features[:, :-1])):
ada_r2_train[i] = r2_score(log_halo_training, y_pred)
np.save(saving_path + "r2_train_staged_scores.npy", ada_r2_train)
ada_r2_test = np.zeros(len(alg.estimators_), )
for i, y_pred in enumerate(alg.staged_predict(traj_testing)):
ada_r2_test[i] = r2_score(y_test, y_pred)
np.save(saving_path + "r2_test_staged_scores.npy", ada_r2_test)
"n_estimators": [2000, 3000, 4000],
"learning_rate": [0.001, 0.01],
"base_estimator__max_depth": [2, 5]
}
base_estimator = DecisionTreeRegressor(max_depth=5)
ada_b = AdaBoostRegressor(base_estimator=base_estimator, random_state=20)
ada_CV = GridSearchCV(estimator=ada_b,
param_grid=param_grid,
cv=3,
verbose=2,
n_jobs=-1,
scoring="r2")
ada_CV.fit(traj_training, log_halo_training)
ml.write_to_file_cv_results(saving_path + "/cv_results.txt", ada_CV)
joblib.dump(ada_CV, saving_path + "clf.pkl")
# predictions
pred_test = ada_CV.predict(traj_testing)
np.save(saving_path + "predicted_test_set.npy", pred_test)
ada_r2_train = np.zeros(len(ada_CV.best_estimator_.estimators_), )
for i, y_pred in enumerate(
ada_CV.best_estimator_.staged_predict(traj_training)):
ada_r2_train[i] = r2_score(log_halo_training, y_pred)
np.save(saving_path + "r2_train_staged_scores.npy", ada_r2_train)
ada_r2_test = np.zeros(len(ada_CV.best_estimator_.estimators_), )