"""

Cox-Ph regression. Baseline.

Leon Zheng

"""

from Coxph_regression import CoxPhRegression

from sklearn.model_selection import GridSearchCV

import preprocessing

import numpy as np

"""

Feature selection

"""

radiomics_features = ['original_shape_Sphericity', 'original_shape_SurfaceVolumeRatio',

                      'original_shape_Maximum3DDiameter', 'original_glcm_JointEntropy', 'original_glcm_Id',

                      'original_glcm_Idm']

clinical_features = ['SourceDataset', 'Nstage']

features = radiomics_features + clinical_features

"""

Reading data

"""

# Read clean data

input_train, output_train, input_test = preprocessing.load_owkin_data()

input_train = input_train[features]

input_test = input_test[features]

# Normalization

input_train, input_test = preprocessing.normalizing_input(input_train, input_test)

print(input_train)

print(output_train)

""" 

Grid search

"""

tuned_params = {"alpha": np.logspace(-8, -3, 10),

                "threshold": np.linspace(0.85, 0.95, 10)}

grid_search = GridSearchCV(CoxPhRegression(), tuned_params, cv=5, n_jobs=4)

grid_search.fit(input_train, output_train)

print(grid_search.best_score_)

best_params = grid_search.best_params_

print(best_params)

"""

Create submission

"""

# Create submission

print(input_test)

coxph = CoxPhRegression(**best_params)

coxph.fit(input_train, output_train)

y_pred = coxph.predict(input_test)

y_pred.to_csv('submission.csv')