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experiment_SPM_wm_w_site_SVM_weighted_classifaction.py
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experiment_SPM_wm_w_site_SVM_weighted_classifaction.py
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"""
Experiment using Linear SVM on WM SPM data with site information (site_weight = 10000).
Results:
MAE: Mean(SD) = 7.543(0.346)
R = 0.46780018049927274
"""
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 SVC
from helper_functions import read_gram_matrix
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_w_site_SVM_weighted_classification' # 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_w_site.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
y = y.astype(int)
# 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_fold, (train_idx, test_idx) in enumerate(kf.split(x, y)):
x_train, x_test = x[train_idx, :][:, train_idx], x[test_idx, :][:, train_idx]
y_train, y_test = y[train_idx], y[test_idx]
print('CV iteration: %d' % (i_fold + 1))
# --------------------------------------------------------------------------
# Model
clf = SVC(kernel='precomputed', class_weight='balanced')
# --------------------------------------------------------------------------
# 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=5)
grid_cv = GridSearchCV(estimator=clf,
param_grid=param_grid,
cv=internal_cv,
scoring='balanced_accuracy',
verbose=1)
# --------------------------------------------------------------------------
grid_result = grid_cv.fit(x_train, y_train)
# --------------------------------------------------------------------------
best_regressor = grid_cv.best_estimator_
# --------------------------------------------------------------------------
y_test_predicted = best_regressor.predict(x_test)
for row, value in zip(test_idx, y_test_predicted):
predictions_df.iloc[row, predictions_df.columns.get_loc('predictions')] = value
# --------------------------------------------------------------------------
mae_test = mean_absolute_error(y_test, y_test_predicted)
print('MAE: %.3f ' % mae_test)
mae_cv[i_fold, :] = mae_test
joblib.dump(best_regressor, cv_dir / ('model_%d.joblib' % i_fold))
print('CV results')
print('MAE: Mean(SD) = %.3f(%.3f)' % (mae_cv.mean(), mae_cv.std()))
mae_cv_df = pd.DataFrame(columns=['MAE'], data=mae_cv)
mae_cv_df.to_csv(cv_dir / 'mae_cv.csv', index=False)
predictions_df.to_csv(cv_dir / 'predictions_cv.csv', index=False)
from scipy.stats import spearmanr
print(spearmanr((predictions_df['age']-predictions_df['predictions']).values, predictions_df['age'].values))
import matplotlib.pyplot as plt
plt.scatter(predictions_df['age'].values, predictions_df['predictions'].values)
z = np.polyfit(predictions_df['age'].values, predictions_df['predictions'].values, 1)
p = np.poly1d(z)
plt.plot(predictions_df['age'].values,p(predictions_df['age'].values),"r--")
plt.plot(range(100), range(100),"b")
plt.show()
#
# # --------------------------------------------------------------------------
# # --------------------------------------------------------------------------
# # Training on whole data
# clf_final = SVR(kernel='precomputed')
#
# param_grid_final = {'C': [2 ** -6, 2 ** -5, 2 ** -4, 2 ** -3, 2 ** -2, 2 ** -1, 2 ** 0, 2 ** 1]}
#
# internal_cv = KFold(n_splits=5)
# grid_cv_final = GridSearchCV(estimator=clf_final,
# param_grid=param_grid_final,
# cv=internal_cv,
# scoring='neg_mean_absolute_error',
# verbose=1)
#
# grid_result = grid_cv_final.fit(x, y)
# best_regressor_final = grid_cv_final.best_estimator_
#
# joblib.dump(best_regressor_final, experiment_dir / 'model.joblib')