-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathexperiment_SPM_gm+wm_SVM_oversampling.py
145 lines (113 loc) · 5.41 KB
/
experiment_SPM_gm+wm_SVM_oversampling.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
"""
Experiment using Linear SVM on WM+GM SPM data.
Results:
MAE: Mean(SD) =
"""
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+gm_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+gm.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))
from imblearn.over_sampling import RandomOverSampler
ros = RandomOverSampler(random_state=0, sampling_strategy='minority')
train_idx, y_train = ros.fit_resample(train_idx[:,np.newaxis], y_train)
x_train, x_test = x[train_idx, :][:, train_idx], x[test_idx, :][:, train_idx]
# --------------------------------------------------------------------------
# Model
clf = SVC(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=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')