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experiment_SPM_wm_SVM_1_per_site.py
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experiment_SPM_wm_SVM_1_per_site.py
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"""
Experiment using Linear SVM on WM+GM SPM data.
Results:
"""
from pathlib import Path
import joblib
import pandas as pd
import numpy as np
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.svm import SVR
from helper_functions import read_gram_matrix
import warnings
warnings.filterwarnings("ignore")
PROJECT_ROOT = Path.cwd()
# --------------------------------------------------------------------------
random_seed = 42
np.random.seed(random_seed)
# --------------------------------------------------------------------------
# Create experiment's output directory
output_dir = PROJECT_ROOT / 'output' / 'experiments'
output_dir.mkdir(exist_ok=True)
#
# experiment_name = 'SPM_wm+gm_SVM_1_per_site' # Change here*
# experiment_dir = output_dir / experiment_name
# experiment_dir.mkdir(exist_ok=True)
#
# cv_dir = experiment_dir / 'cv'
# cv_dir.mkdir(exist_ok=True)
# --------------------------------------------------------------------------
# Input data directory (plz, feel free to use NAN shared folder)
gram_matrix_path = PROJECT_ROOT / 'data' / 'gram' / 'wm.csv'
demographic_path = PROJECT_ROOT / 'data' / 'PAC2019_BrainAge_Training.csv'
# Reading data. If necessary, create new reader in helper_functions.
x, demographic_df = read_gram_matrix(str(gram_matrix_path), str(demographic_path))
# --------------------------------------------------------------------------
# Using only age
y = demographic_df['age'].values
sites = demographic_df['site'].values
# If necessary, extract gender and site from demographic_df too.
# --------------------------------------------------------------------------
n_folds = 5
kf = KFold(n_splits=n_folds, shuffle=True, random_state=random_seed)
# --------------------------------------------------------------------------
predictions_df = pd.DataFrame(demographic_df[['subject_ID', 'age']])
predictions_df['predictions'] = np.nan
mae_cv = np.zeros((n_folds, 1))
for i_site in np.unique(sites):
x_red = x[sites==i_site, :][:,sites==i_site]
y_red = y[sites==i_site]
print(i_site)
mae_cv = np.zeros((n_folds, 1))
# --------------------------------------------------------------------------
for i_fold, (train_idx, test_idx) in enumerate(kf.split(x_red, y_red)):
x_train, x_test = x_red[train_idx, :][:, train_idx], x_red[test_idx, :][:, train_idx]
y_train, y_test = y_red[train_idx], y_red[test_idx]
# sites_train, sites_test = sites[train_idx], sites[test_idx]
# print('CV iteration: %d' % (i_fold + 1))
# clfs = []
# for i_site in np.unique(sites_train):
# i_site = 0
# --------------------------------------------------------------------------
# Model
clf = SVR(kernel='precomputed')
# --------------------------------------------------------------------------
# Model selection
# Search space
param_grid = {'C': [2 ** -6, 2 ** -5, 2 ** -4, 2 ** -3, 2 ** -2, 2 ** -1, 2 ** 0, 2 ** 1]}
# Gridsearch
internal_cv = KFold(n_splits=3)
grid_cv = GridSearchCV(estimator=clf,
param_grid=param_grid,
cv=internal_cv,
scoring='neg_mean_absolute_error',
verbose=0)
# --------------------------------------------------------------------------
# grid_result = grid_cv.fit(x_train[sites_train==i_site, :][:, sites_train==i_site], y_train[sites_train==i_site])
grid_result = grid_cv.fit(x_train, y_train)
# --------------------------------------------------------------------------
best_regressor = grid_cv.best_estimator_
# --------------------------------------------------------------------------
y_test_predicted = best_regressor.predict(x_test)
# y_test_predicted = best_regressor.predict(x_test[sites_test==i_site, :][:, sites_train==i_site])
mae_test = mean_absolute_error(y_test, y_test_predicted)
# print('MAE: %.3f ' % mae_test)
mae_cv[i_fold, :] = mae_test
# print('CV results')
print('MAE: Mean(SD) = %.3f(%.3f)' % (mae_cv.mean(), mae_cv.std()))
# for row, value in zip(test_idx, y_test_predicted):
# predictions_df.iloc[row, predictions_df.columns.get_loc('predictions')] = value
A = pd.read_csv(output_dir / 'SPM_wm_SVM' / 'cv'/ 'predictions_cv.csv')
for s in range(17):
print(s)
# print(np.sum(demographic_df['site'] == s))
# B = predictions_df.loc[demographic_df['site'] == s]
C = A.loc[demographic_df['site'] == s]
# print(np.mean(np.abs(B['age'] - B['predictions'])))
print(np.mean(np.abs(C['age'] - C['predictions'])))
A = pd.read_csv(output_dir / 'SPM_wm_w_site_SVM' / 'cv'/ 'predictions_cv.csv')
for s in range(17):
print(s)
# print(np.sum(demographic_df['site'] == s))
# B = predictions_df.loc[demographic_df['site'] == s]
C = A.loc[demographic_df['site'] == s]
# print(np.mean(np.abs(B['age'] - B['predictions'])))
print(np.mean(np.abs(C['age'] - C['predictions'])))