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@nicofarr
Last active June 22, 2017 15:48
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Analyse Préliminaire IRM_Marche\n",
"--\n",
"Parcellation en 444 regions (BASC Atlas)\n",
"\n",
"Classification : SVM Linéaire\n",
"\n",
"Sujets sains (MY,AF,BA,BR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Classification Marche Confortable VS Rest\n",
"--\n",
"Cross validée par run (9)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.pipeline import Pipeline \n",
"from sklearn.svm import SVC\n",
"from sklearn import preprocessing\n",
"from sklearn.cross_validation import LeaveOneLabelOut, cross_val_score\n",
"from nilearn.input_data import NiftiLabelsMasker\n",
"from nilearn import datasets\n",
"from nilearn.datasets import load_mni152_brain_mask,load_mni152_template\n",
"from sklearn.externals.joblib import Memory\n",
"from nilearn.plotting import plot_stat_map\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"af 0.658088235294\n",
"ba 0.639705882353\n",
"br 0.727532679739\n",
"my 0.705882352941\n"
]
}
],
"source": [
"names='af','ba','br','my'\n",
"\n",
"scaler = preprocessing.StandardScaler()\n",
"svm= SVC(C=1., kernel=\"linear\") \n",
"pipeline = Pipeline([('scale', scaler),('svm', svm)])\n",
"\n",
"\n",
"block=np.loadtxt('D:/irm_marche/block.txt','int')\n",
"label=np.loadtxt('D:/irm_marche/label.txt','S12')\n",
"condition_mask = np.logical_or(label == b'restconf', label == b'conf')\n",
"y = label[condition_mask]\n",
"block_cond = block[condition_mask]\n",
"cv = LeaveOneLabelOut(block_cond)\n",
"\n",
"for n in names:\n",
"\n",
" roi_name='D:/irm_marche/mni/roi_mni_'+n+'.npz' \n",
" roi=np.load(roi_name)['roi']\n",
" roi_cond=roi[condition_mask]\n",
"\n",
"\n",
"\n",
" classifiers_scores = cross_val_score(\n",
" pipeline, roi_cond, y,cv=cv)\n",
" print n,classifiers_scores.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualisation des poids du SVM (regions qui permettent la classification): sur MY qui a les meilleures performances"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<nilearn.plotting.displays.OrthoSlicer at 0x1ef95080>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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RRCKRwJNPPonXX3+9wGe33XabGctBCB++lV8AuP3223HZZZdh0qRJuO6660q5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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1f308b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n='my'\n",
"roi_name='D:/irm_marche/mni/roi_mni_'+n+'.npz' \n",
"roi=np.load(roi_name)['roi']\n",
"roi_cond=roi[condition_mask]\n",
"\n",
"\n",
"mem = Memory('nilearn_cache')\n",
"basc = datasets.fetch_atlas_basc_multiscale_2015(version='sym')['scale444']\n",
"\n",
"brainmask = load_mni152_brain_mask()\n",
"masker = NiftiLabelsMasker(labels_img = basc, mask_img = brainmask, \n",
" memory=mem, memory_level=1, verbose=0,\n",
" detrend=False, standardize=False, \n",
" high_pass=0.01,t_r=2.28,\n",
" resampling_target='labels')\n",
"masker.fit()\n",
"pipeline.fit(roi_cond,y)\n",
"coef = pipeline.named_steps['svm'].coef_\n",
"weight_img = masker.inverse_transform(coef)\n",
"plot_stat_map(weight_img, title='SVM weights',threshold=0.1\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Classification Imagination VS Rest\n",
"--\n",
"Cross validée par run (9)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"af 0.773284313725\n",
"ba 0.793300653595\n",
"br 0.834150326797\n",
"my 0.761029411765\n"
]
}
],
"source": [
"condition_mask = np.logical_or(label == b'restimag', label == b'imag')\n",
"y = label[condition_mask]\n",
"block_cond = block[condition_mask]\n",
"cv = LeaveOneLabelOut(block_cond)\n",
"\n",
"for n in names:\n",
"\n",
" roi_name='D:/irm_marche/mni/roi_mni_'+n+'.npz' \n",
" roi=np.load(roi_name)['roi']\n",
" roi_cond=roi[condition_mask]\n",
"\n",
"\n",
"\n",
" classifiers_scores = cross_val_score(\n",
" pipeline, roi_cond, y,cv=cv)\n",
" print n,classifiers_scores.mean()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<nilearn.plotting.displays.OrthoSlicer at 0x213b9b70>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0J8yaNQtXXXVVBbfOMAzDqEjOWL7c5xfRuHFj1O7r+kQT41TiKHsA8N7/zQPg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\n6eQguYE2Jas597FsgmUKLumBK1IPXyu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INVAOY/iIN0iF2eftt9/2ytddd51XZqmcnB8b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\n9r7n3NySe5Pv53l42Dn3lH3KPveutb9rLdU2ZRnTXWOTVFSik1dJx1y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jHtvn7Z9sZ/v7\nn7NciNMdOJeebvsCUT/sTDpeErkcOGCPtwpFVfH9q74D4P3x4E9sRrhYsiSv1oc8f90PMkIIIVGG\nW2Mj8KoKriixxobKm/7+++9by6RXQXoJldclkC9f9aPUVEdDBrwqpOenMD9Q8s/EKE9KfHy8VQE1\nISHBmt2RxapUf+RMg/SKSu+tkxdWzhTIYygvkNyXPGc1+wR4Z0dkH9zWvShXrpxtv9Kbrqu/4SbF\nn7o+0jul84YBXi+XqUhYnz59XJ1HUaKeVZOHWy5X52kyxExeUbU/+byYfvzq7r0p1aYbr56vZXJ/\nJu+nafZSLdd5h/Nv5xTEKbeTs35qZszNLIHu/pnOWZ6HbozL+69mMypVqoQGDRpYy5kikxBCigC3\nyahobBBCCCGksOQu9bbXr18PFYogjU7lFJCSI5MMSBmdJmPelOlPOYl0cilj3w2GvUQtl1Jd2TY5\nPNSxlaF+7tw52/nrZNTS0SX3K41u5aQ0ORfk+ct74BQvY5KU6T532l7iJKOSErFIotyHH6Jz585F\n3Y3Ixu3MRhgyUQE0NgghhBBCCCk+uDU2wqQEL/HGhsqhDdilTFLmoqz8QGRUKsDZJKEoU6ZMgUAs\nKfcx1QPxRW5urjFYTHk6LrvsMsvDImUTKtZDXgcpRdLVwzDJPDp06GC1v/nmG6utjmeqV6DzCJkk\nUL5ISEiweZ+kp07KupS3Sh5X3nN5PNU2eeqkfEQno1LPQ7Sg+i6vjfT0yeurkw7J62Ty+ulyt5uC\nSCXqWrupdquTUbkJtFTLdUGv+fcrny+1ncmzaDq2DulNlUkFVHC2G6Q3VR3PJE+TY1yX3EB377Kz\ns5Gammot79mzp+u+EUIICRKRVUCcxoZbGt36p6Dvs0Gra31+Xv1PNWx///TlAQDeH8iNGze2fb5x\n40YATNv39bw9AOxFo0jJIKV+fcd1Er/9Ngw9ISSyOHnypNWWMiHlNJDGvkk+o5wsptg56ZDRZZ2T\n8VhOkio3mdqUbEnG90lHl6mYqzKUpZEsvzelk0PFFEqHijT2pWxLrSMNdVNxU1ngUC03ZfbSSdXk\ntTbJ13SYsvpJpDMyEvHHwRIukpOTsXjxYqSnp6Nly5aYP38+6vv4Pvruu++QnJyMXbt24ciRI3jh\nhRfw0EMP2dbZsmULZs6ciV27duHkyZNYt24dunTp4q5DbmM2fCePDBpMN0IIIYQQQkghmD59OubO\nnYuFCxdi+/btKFu2LDp27OgzDXxWVhbq1auH6dOn48orr9Suk5mZicaNG2PevHn+Z01UMxtO/ziz\nER4GDRpktd955x2rLb0DwUhVqbwmTjUIfKG8EKZc+TpZiPTGqO1OnTplZaPKyMjQ9m3w4MGF7qcv\nBg4caLWXLs2LXpSSIukFkl4w5YmTHiOTB0etI6+D9D5J6ZSTXE4eQ15L1Zafy+102ahkhrPevXtr\n+x6pqOfFJIHSyXNMwZny+dXJmkz31fSy9fclnP8+u5E4KdzU7NAtNz0nTn2Xsi35DKekpFjtYcOG\n+dzHSy+9ZLXl+FHPpRxz8t74I+tS4ygzMzNfum1CCPFyr59FAIHILwQ4Z84cTJgwAXfddRcAYNmy\nZahatSrWrVuHXr16abe5/vrrcf311wMAHnvsMe06nTp1QqdOnQAU4ncoZVSEEEIICSfS0fP2229b\nbSXnkQaxTmYl2ybZjpTdSOeMzllkMrR1KcidZFSmQremvql9y/+lNEoa0ur6SKPcFGulro+8ZlIu\nJa+r7I9y9MnzkM4/3bWQ11dlzMrfH3/QXWPZ30hCVyC0qDh06BBSUlJw6623WssSEhLQokULbNu2\nzWhshJzL4a60GgPEI4sv39vmd/VdhSlt35Z3vrDapUuXxg13tvC5n6tuqOv7QKs3+t234sCmpzfb\nZkEAxmoQQgghJLSkpKTA4/GgatWqtuVVq1a1zUCHHc5sRC7du3e32rLYn0JKRUyZh3S4yYwTDJRH\nQ5ffXC6X2ajS09ORlpYGABg3blxQ++PEgAEDAABz5861lplkUuram+QouuA/KVuSHiPZlh4s1Zb3\nVu5Xd+9MhQWlV0r1I5rlJSpwbdOmTdYymSFNXkd1D90UpdTleTfdY6dieG49etIjmH9fpmdK1383\n41ft203xPl02LSmd+vnnn632fffd53hsxejRo7XLZ8yYAQC2L0nZBzlOnDJsycxW0gNMIhOZhVF5\nxaUX2zQGlTdeeuXlO9DUVt9Dbsaobsw4yRflMvmeNgWI539nxMTE2LaTsxwK+b4zSSF1fTf1QY4v\nXX0Oia4QqhyTpmOofvoTFC7bgWTgLK6sXLnSkq56PB7tb8WIgMYGIYQQQgghzhQmziNUdO3aFTfe\neKP1d3Z2NnJzc5Gammpz3KSmpqJJkyZF0cU8aGwQQgghhBASXZQtWxZ169ol7dWqVcOmTZvQqFEj\nAHkzY1999RVGjhxZFF3Mw23q2zBlN6axYeDYsWNWu06dOgD0sgHAnKFHhxs5RahQkowzZ85YGaj6\n9u2Lpk2bhvzYvhg1apTVnjdvntU2TaPrkJ8rSY+8R6Z7p5NJmabs5VS2mgI3rStz1qtMTn369PF5\nDtGAHBcNGza02lJWoa6DrkgfoJfHSaQEzSlzU/66GjUPHzZ13dY3iek508nm3GSj0gWPupFOSSma\nyiN/9OhRa1mwAw0fffRRAN6scPn7ppNuAPr7q94n//nPf/DEE08EtZ8k+AwfPtxqf/TRRwDsUhwp\nqZJjVFfjwYROomoqBKobM26kl2p/cpmUJ5me4fwFZS9dumT77pZ9VzVHTDGbugBxkwxNt1/ZNzcS\nUtWW52aqyeFP4VLd91cg2TNLEmPHjsXTTz+N+vXrIykpCRMmTEDNmjXRtWtXa50BAwagRo0amDp1\nKoC8e/bdd98hNzcXv/32G44fP469e/eiXLlyqFevHoC87H4HDhyw7s3Bgwexd+9eVKhQAbVq1fLd\nKc5sEEIIIYQQEv08+uijyMrKwrBhw5Ceno5WrVrho48+shnqx44dsxmTJ06cQJMmTSyDb9asWZg1\naxbatGmDzZs3AwB27tyJdu3awePxwOPxYPz48QDyDJclS5b47hSNjehAen4WLVoEwB5I6SYI0ilY\nzJ+c9v6g82wA3tz848aNw+7du/H8888H7ZjBYsSIEVZ74cKFVlsF50lvjvT26ILlnNITAvr7KL1E\nJs+P8viYPEqylofrip9RwP3332+1VcV6wFtlF/BeE3l/TNdc552Ty6T303S/Jeq+mFJzOs2UmDyP\nutSa0rtrer50nk55HjIQ9fjx41ZbVXt+4IEHChw32FSoUMFqyyBYWVFZXgtVF2fMmDHWst27d2P6\n9OlFl+aRlCh+qljR9rf8IfMHAP/ZsCGs/SkqSt18MwAgQyzL0K8KAKgvKq3rqFBBZXL0ZnTMzRcv\ncXjyIW1AunxvyxkR+e7IysrCDS/7zrqpZWlwk+mEgkmTJmHSpEnGz5UBoahTp46jEqZNmzaO6xih\nsUEIIYSQSEA5oWStBukQkIa9aksJnUl6KH8kKWNcpig3OezUdm4yKJlQ/ZBFW2WmQ3Vs5UT49ddf\nbeepkw+ZHBW68zQ5F0wZ3nSOSVMWS+kQ8fcHXExMjKtsVL44c+aM7d6ptvTiS0eMzErITHVhhDEb\nJZMOHZyK4+Tin//8p20wypfjxYsX0ahRwXR8ks0omfrKW2+NASA9z/oCT+7RV7CWfPZZgIcgIeHU\n1Vc7rlPryJEw9IQQQggpIjizEX0oKYOSUwHAFVd4pxlNFr3yILgtBhgbG2vzZkjvkZsAcuWZ0OUT\nB8z59iOVoUOHWm1V8Vbv7Ql/LnBdAJ28X/fee2/Y+xRubrvtNqv9xRf2ApXyf8AcUCproShPn6l2\niSnw01/UflSfTNJG+aypgG25zFR3R+fplV5M+Zyo/QLAkCFD/D2VoHD33Xdb7W3btlltKX9QNXEI\nIYREATQ2CCGEEBIJDBw4EIC9kK00uqWhrMuEZGpLuY5yPEgZlcmBpo4njfYDIq7IDSrWy5QRT6Ek\nSTk5OTbjWkqVlEPJVERPSrXUOUunhM6hAtjPTzkITXFpsj/KWZK/OKkbfhBOUgC4ATIeoCx0eJbZ\nYwY29t+sLeoo++PkgPXFx33WAwA6duzouC7xweUw3dKC64UBGhuEEEIIIYQUFzizEb3IzDBz5861\n2iZJlbLi85Y5m4/5g9Jat/ZPHqS2l54EVeMh2lHZneS1VnVDgETNFuHpj8wmNGjQoLD3I1I4LOpb\nXHvttQDcBUnagh3/6y2UY8iUJ195RWO3bbNim+T4kWNAHtvj8VjPjTqem6xSqu5AWlqatczkCZXe\nVOXVfPDBBxEN3HTTTdo2Kf4cPHjQaptiB9VYks+7RI4lJ++3fD/Id4J6pwaSoVEdWx4vISHBaqvl\nKni5fPny2vOU/XBTF0tdF/kOkNdKvs90AfCmjE6yrXtPhhOPx6OdaZHvQ9k3N3JaSWpqajC6SWhs\nEEIIIYQQQkICjQ1CCCGEEHfI+hCxsbH4t0jTS9xzQ3WH9LYnnNPf3nprO9vfn3yy0bCmnj3jvrb9\n7VUo2GfXSIDQ2CgejBo1ys8t/um4xtmzZ/NNM/r3QlVFwWRAmv/9jExkMbmCfBamXnjp2bNn2I8Z\nyfTv76389Pe//x1AXtEiHTrpFKCXBUj5gJQYyHVVoKX83JRVSkoXlCxLbieDNmVbjUsptZBF75yO\nQUik89BDD1ntV1991WrXqFHDaiv5kZTGyLGmk04B3mKRpiKd8genKhpp2pcMMjeh+if3K2VbahxL\nGVWlSpUK9FduZ5Jmyu9bJVuWyyTyfSb7o66hab/yXaTkYKVLl46IZPfqWpoKokpJmbo7OldbAAAX\nd0lEQVSP8nzOCkNSfo+QAGGdjZJJbm5LAMD8+fOtZTL+QGnCJevX62uBqkEtq1TLwV3SyM1tiwUL\nFlh/677Y5A9A+eNSvgjVS2/s2LEh6yshhBBCSEjhzEbJZvjw4UXdhWJJtATglgRUjZE1a9ZYy3Te\nUcAeBKq8XdJDZgpElV5BZTxKr6CpGq7TOvJzXduUutMUOF5cZhYJIYREETQ2CCGEEBKpDB482Gqr\ngqqAV8IjZ+JNRTGl1EqtL50EUmIo5UXKwWAqcFumTBkrhkPtL38Mh5JHScNfVwhUyXri4uKQmOjN\naij7po4hnRNS4iQdJjLL1S0/tCpwPMn/Xb3FaiuJlnSuyPOXy2Vmr/wyqqRTp2zXWMnIrOKhd/ns\nUqFQz4TJSaRrS1VGt27dgt8pQmODEEIIIYQQEiJKwV3MBo0NQkg007t3b6v9+uuvW+26detabRl0\nqTxypvga6TXVBWH7K6PSYQoQV95LU5546U2kpI8QQkiRwpkNQgghhEQDPXr0sNqfffYZAHvskswU\nZSrq51TMTUqRlCNBFvqTsiUZ86VLrAJ4ZVSmwnjqGMqBUapUKWNRP12hPlOWPNWfP7hIzVulSpUC\nxzNlcZKOD9XP+Ph4eEvK5hEbG6stoJqZmenYn8KingWT7EveRyXrkjIqEiIuB6AfHgXXCwM0Nggh\nhBAStVz933S5QPRkZrz6/65xXGdj7Q0+P6929CgAd+mAdRz98pjVltkxlQGVk5ODKlUqFdhOUr9+\nXZ+f7969p1B9IwFCGRUhpKRhqpOybt06q12xYkUAdomUbJuCS02SKd0+ZFu3nfxceltV29QfJ88t\nIYQQEjYooyKEEEJItHH48GEAdumUrjhd/uUKaeCbDHfVljMUUkYlj62cAFJaJeVQyoEB2GVJyjmg\njhUbG2vrj1xXFe80ybpkP5V8S+4rENT1VBmfAK9Ey1RHSldcz5RCXDptdKm+5XUoLPKeKzlXv379\nAt4vcYDGBiGEEEIIISQk0NgghJA87rnnHqv94osvAgCqVq1qLZM6Yul5kxInhfSgSY+d9LbqighK\ndNIpua4MBpVteTxCiitKDikLdtapU8dqm6SFaizJz+X4k2Nbxgso5OyBHJdqO+nZN81yyLofan05\nsyHHs/ToK2+8DAqXsxz5a4DkXxYIbQ+2s/29/6bvrFkO+V7TBYXLfphmNuRynTRV7quwNGvWxPb3\nSy+9HPA+iQsug7uYjTApgGlsEEIIIYQQUlzgzAYhhBBCCCEkJNDYIISQgjz00EM+P1+wYIHVlnns\nVdpHKZ+QSLmGlHHopFimbFVKYiGlBlJioIJICSkJyIKdb7/9ttWWY0onmTLJGOVYUmNMyoSkxEnW\njFAyINNxTRJKHXI7KblSQd8mmdUvv/xSoJ8ygDyYJCYmWvI0U3/ksVXblLHPVIdEBeEHK9BdMmrU\nqKDvk2igsUEIIYQQQggJCYzZIIQQ/3nwwQd9fj537lyrXb16daudmJhotaXHUnky3dThUF5E6UE8\nffq01X7ggQecT4AQEhFc8U7eO6H8kby/y29uiwo/eT+v4Me+musWnilsz+wcvS2vgzLdrz+069s2\nOB3xgx07dtkqhP/www9We9iwYWHvT0klJhbwFMw+XYDcWOCS82oBQ2NDw+TJkzF58uQCy8uUKRM1\n1UkJKSp27NiB1157Ddu3b8e+fftw8eJF25S/Ijs7GyNHjsT27dtx7NgxXLx4EfXq1cOgQYMwYsQI\nmwSCEBKZ9OjRw2qvXbvWauuyRpmkUdKIVwa/zCRlcggopLxRZliSWZrkOt4cd5GNdI4olOTMlBEr\nIyPD9f7ltdLJqApj5GRkZODgwYPW3zQwiobycPcD/3cA4RAB89vcgMfjwYIFCyw9OKAvUkQIsfPh\nhx9iyZIlaNSoEerVq2fzbEkuXLiA/fv3484770RSUhJiYmKwdetWjBs3Dtu3b8fy5cvD3HM9aS1a\n2P6+fPPmIuoJIYQQ4kwc3KmofnNeJSjQ2PBBjx49UKGCPxOqhJARI0bg8ccfR+nSpTF69GijsZGY\nmIitW7falg0dOhQJCQl4+eWX8fzzz6NKlSquj2sKPHz11VettqzhoRwJphkU3WwMABw/fhwAcPbs\nWdd9I4QQQsJFHICC82JFR1QaG5999hnat2+PtWvXomvXrrbPVq5ciX79+mHbtm1okc8j6S+XLl3C\n+fPnrWwUhEQT2dnZaNIkr6DSnj17rCn5X375Bddddx3q1auHLVu2BD3jSOXKlQPaXhUJS09P98vY\nIIQULd26dbPab731ltWuWbMmAHcZ49R7ypRhSifPksukpEjuVy6/0ulEIgQl25ZyJ3UeUvZkKjbq\nhKkwqWoX5rvh0KFDGDx4sN/bkeASB0A/2uzo85QFn6g0Ntq2bYtatWphxYoVBYyNFStWoH79+mjR\nogV+++03W6CSLypWrGj7Ozc3F3Xr1kVGRgbKli2Le+65B8899xx//JCooUyZMli6dClatmyJJ598\nErNmzQKQN/Nw/vx5LF26FB6PJ6BxEgxycnJw7tw5XLhwATt27MBzzz2HpKQk1K9fP+jHIoQQQoo7\npeHO2AhHcDgQpcYGAPTr1w+zZ8+2zTykpaVhw4YNmDBhAgBg1apVGDhwoOO+PB6PzcJPTEzE6NGj\ncdNNN6F06dLYsmUL5s6dix07dmDnzp0oV65caE6KkCBzww034NFHH8WMGTPQrVs3nDx5EmvWrMGL\nL76IevXqASj8OAkW77zzDu677z7r7+bNm2PJkiWOefHd4uRle+ONN6y29ORZ3sQlS1xdH0KIl549\ne1rtJUuWAABq1KhhLUtISLDaMjZS1ZGQ8kZTvKTy7svaErIORyjeV+FE1fDQ1dFQ1wmwzxiZZo+c\nkLNA6rrK47qF78rIwO3Mxu/OqwSFqDU2+vfvj2nTpuGtt96yHu7Vq1fj4sWL6Nu3LwCgU6dO2Lhx\no9/7zl9crFu3bmjevDn69u2LefPm4dFHHw38BAgJE5MmTcIHH3yA/v37IyMjA+3atbPFNxR2nASL\n9u3bY+PGjUhPT8emTZuwd+9evzKqEEIIIcSL25mNHOdVgkLUGhvXXHMNmjdvjhUrVljGxsqVK3Hj\njTeibt26APKCQWVAaCDcd999GD9+PDZu3FgsjI0GDRpg165daNCgQVF3hYSYyy67DK+++iqaN2+O\nyy+/3PIyKgozTjIzM20GQWxsLCpVqlSo/lWuXBnt27cHAHTv3h3Tpk3D7bffjgMHDlC2GCXwfUKi\niX3X7wUA/FzpMICu+LnZu2ia1tXnNiHnRc2yJZplQaR51+tdrGVX9X/99V6kp6dbf//4448AWGso\n0nA7s/Gr8ypBIWqNDSBvdmPs2LE4ceIELly4gC+//BLz5s2zPs/OzsZ//uMug7CbH1u1atUqNhlo\n4uPj0bRp06LuBgkT69evB5A3Jn788UcrCFst83eczJo1y1aLJikpyZZbPRB69uyJJ598Eu+++25Y\nvsD+8pe/hPwYxR2+T4gvBg0aVGDZP/7xD6stJZOqLetsyHoPsr6EkvvI+ldSOiXlV1L+rGRbSook\nJUmRhJI2yfNQAfTy+sh2qFP0p6Sk2DIM5leCkMjArbFxwXmVoBDVxsaf//xnPPzww1i1ahWysrIQ\nFxeHXr16WZ+vWbMmqFr0w4cP8wuVRB379u3DlClTMGjQIOzZswdDhgzBv/71LyvWqTDjZMCAAWjV\nqpX1mfyyCxSlSXZrABFCCCHEi1sZVbjS40a1sVGxYkV07twZb7zxBrKzs9GpUydbXYzCatHT0tIK\nSELmzZuH06dPo3PnzgH3m5Bw8fvvv+P+++9HzZo1MWfOHBw8eBDNmzfHuHHjsHjxYgCFGydJSUlI\nSkoKqG9nzpzRZrdatGgRPB4Prr/ezRQ/IYQQQiSXwZ0hEa45vag2NoA8KVXPnj3h8Xjw9NNP2z4r\nbMxGnTp10Lt3bzRs2BBlypTBli1bsGbNGjRt2hRDhw4NVtcJCTlTpkzBvn37sHnzZpQtWxYNGzZE\ncnIynnrqKfTo0QOdO3cOamwTABw9etTK8LRz504AwDPPPAMgb2z169cPALB8+XIsWLAA99xzD+rW\nrYvz58/j448/xsaNG9GlSxe0bds2aH0ihEQWXbp0sdrLly+32jonhpw5lSoEneRKZpST8iid7EjJ\ns6RMK2zoYjTyER8fD8CeKUrVFpFyKZmBKtgyqtWr3wQA9O7d+79LOqFTp05BPQYJPpzZCDJ33303\nEhMTkZuba3t5BUK/fv2wdetWvPPOO8jOzkadOnXw+OOP469//Wuh08oREm6+/vprPPvssxg9ejRa\nt25tLX/88cfx7rvvYujQofj2229tKSiDwaFDhzBhwgTbl35ycjIAoE2bNpaxccstt2Dbtm1YvXo1\nUlNTUapUKVxzzTWYPXu2sRo4IYQQQnxTHsAVLtbzP7lx4Yh6YyMmJgalSpVC165dg+adeOWVV4Ky\nH0KKkiZNmtiCKRUxMTH46quvQnbcNm3aWN43XzRr1gyrV68OWT8IIYSQkojbAPFwzelFvbGxdu1a\npKWloX///kXdFUIIIYT4iZrtBICXXnoJAHDVVVdZyypXrmy1ZQFA5WBUciPAntlKFgaU/P57Xikz\nJcmK1OJ/6lzlLLFCOnSkzCrYeOVTJJqgjCpIbN++HXv37sXTTz+Npk2b4pZbbinqLhFCCCEkCkm5\n46TVVsYI4E21W+/z+v7t0EVMhhO1u9Sx/X3svaP+7eDEZz4//vjj36y2zP537733+nccEnFwZiNI\nzJ8/HytWrECTJk3w2muvFXV3CCGEEEIIKXI4sxEkXnvtNRoZhBBCSDFi9OjRBZYtXbrUatesWdNq\nq9TZUlolM1DJdk5OjtVWRQAzMjKs/1UbsEuUZMHAosbj8VjSMDn7kpmZabVVoUM3fP/991abxfmK\nF0x9SwghhBBCCAkJnNkghBBCCHHJgAEDtMtXrlwJAKhevbq1TBb2lYHjMshaef/lzMaZM2esz2XA\nuIplaOzUyUHeIO333nsPmJLXrlKlCgCgxbAbnfbgSM27atn+/veavJmJc+fOWct++eUXsYZdkZ+b\n2zbfHvP/TYoLkRazEeO8CiGEEEJKCrm5uXj99dfRtWtX1K5dG+XKlUPDhg3xzDPPFEin/fPPP2Py\n5Mlo0aIFKlSogMqVK6Ndu3bYtGlTEfWeEKKMDad/NDYIIYQQEnaysrIwaNAgpKWlYfjw4ZgzZw5a\ntGiBiRMn4o477rCt++6772LmzJn44x//iGeeeQbJycnIyMjA7bffbou1IISEDyWjcvpHGRUhhBBC\nwk5cXBy2bt2KG2/0Sn8GDx6MOnXqYNKkSdi8eTPat28PAGjfvj2OHj1qky8NGzYMjRs3RnJyslEC\nFQz69OlTYNnixYutdqVKlax2uXLlCqybnp5u/X/ypDf17YULF6y2DMR2i5RkhZIjR44AAE6dOmUt\nkzVLgM/C0g8SeUSajIrGBiGEEBKFHDlyxFb8Lj8yq5I/XHbZZTZDQ9GtWzdMnDgR+/fvt4yNa6+9\ntsB6cXFxuOOOOzB79mxkZmbaskVFLYMCK5z33pR/2Iyf8uXLW+3Y2FgA9oxZUq4ml6t76iZLVsEY\nDRIqkpOTsXjxYqSnp6Nly5aYP38+6tc312ZZvHgxli1bhm+++QYA0KxZM0ydOhXNmze31tmyZQtm\nzpyJXbt24eTJk1i3bh26dOniqj/lAVzhcr1wQGODEEIIiUIqV66M5cuX25bl5ORg7NixKFMmz695\n4cIFVz9MY2NjccUVvn+eKO+//NHsa934+HhbkDYhxZHp06dj7ty5WLZsGZKSkvDUU0+hY8eO2L9/\nv1XlPj+ff/45+vTpg5tvvhllypTBs88+iw4dOuC7777DlVdeCSAvpXHjxo0xePBgdO/e3a8+xcGd\nRCpcMxue3FDWuSeEEEJI2Bg5ciQWLVqEjRs3onXr1pg8eTImT57suF1SUhIOHjzoc53bb78dO3fu\nxJEjR5CQkGBc78CBA2jUqBF69+4d0fWwsrKy8P3336NBgwZhMYpeeeUVq52YmGi1lWEoyc7Ottrn\nz5+32oMHDw5R70hhqV69Oh555BGMGzcOQF52sKpVq2Lp0qXo1auXq31cunQJiYmJePnll/NJ4fKI\niYlxNbOxe/duNGvWDB/v2oVGTZs6Hnff7t3o2KwZdu3ahaYu1i8snNkghBBCigHLli3D/PnzMXv2\nbLRu3RpAXtrYVq1aOW57+eWX+/x86tSp2Lx5M+bPn+/T0Lhw4QLuvfdexMfHY9q0af6dQJiJj48P\n6Q8sUvw5dOgQUlJScOutt1rLEhIS0KJFC2zbts21sZGZmYmcnBxb7FMgsM4GIYQQQoLKnj17MHz4\ncPTt2xdjxoyxliclJSEpKSmgfa9ZswYTJkzAkCFDMHToUON6ly5dQu/evfH9999j/fr1qFatWkDH\nJSTSSUlJgcfjQdWqVW3Lq1atipSUFNf7eeyxx1CjRg3cdtttQenXwf37XUmkDu7fH5TjOUFjgxBC\nCIli0tPT0aNHDzRo0ACLFi2yfZaZmWkVr/NFbGysNhZjw4YNGDBgAO6++27Mnz/f5z6GDBmCDz/8\nECtXrkSbNm38O4kSwLBhw4q6CyRAVq5cad1Hj8eD999/P+B9Pvvss3jzzTfx+eefG2M83FKpUiXE\nx8djkEaKZSI+Pt5VHFYg0NgghBBCopTc3Fz06dMH586dw6efflpA/z9r1qxCx2x89dVX6N69O264\n4QasWbMGMTHm0lyPPPIIli5dijlz5riWjhASbXTt2tWWqS07Oxu5ublITU21zW6kpqaiSZMmjvub\nNWsWZsyYgU2bNuF//ud/Au5f7dq1sX//fqSlpbneplKlSqhdu3bAx/YFjQ1CCCEkSpk0aRI2bNiA\n9evXa38wFDZmY//+/bjrrrtQt25dvPfeeyhd2qzunjlzJp577jk89dRTGDVqlP8nQUiUULZsWdSt\nW9e2rFq1ati0aRMaNWoEIC9A/KuvvsLIkSN97mvGjBmYNm0aPvnkE1eGiVtq164dcuPBX5iNihBC\nCIlCvvnmG/zpT39CmzZttFmK+vbtW6j9ZmRk4LrrrsPJkycxdepUVK9e3fZ5vXr1LO/u2rVr0aNH\nD1x99dWYMGFCgX116NABlStXLlQ/CIkGZsyYgenTp+P1119HUlISJkyYgG+//RbffvutJYsaMGAA\natSogalTpwLIS5c7ceJErFq1CjfffLO1r3Llyll1aTIzM3HgwAHk5uaiadOmeP7559GuXTtUqFAB\ntWrVCv+JBgCNDUIIISQK+fzzz63iejouXrxYqP0eOXKkgPdWMmDAACxZsgQAMHnyZPztb38zrvvp\np59ambEIKa5MmjQJCxcuRHp6Olq1aoWXX37ZVtSvffv2SEpKssbNVVddhaNHjxbYz8SJE5GcnAwg\nb3y3a9cOHo/Hto4cf9ECjQ1CCCGEEEJISDBHexFCCCGEEEJIANDYIIQQQgghhIQEGhuEEEIIIYSQ\nkEBjgxBCCCGEEBISaGwQQgghhBBCQgKNDUIIIYQQQkhIoLFBCCGEEEIICQk0NgghhBBCCCEhgcYG\nIYQQQgghJCTQ2CCEEEIIIYSEBBobhBBCCCGEkJBAY4MQQgghhBASEmhsEEIIIYQQQkICjQ1CCCGE\nEEJISKCxQQghhBBCCAkJNDYIIYQQQgghIYHGBiGEEEIIISQk0NgghBBCCCGEhAQaG4QQQgghhJCQ\nQGODEEIIIYQQEhJobBBCCCGEEEJCAo0NQgghhBBCSEigsUEIIYQQQggJCTQ2CCGEEEIIISGBxgYh\nhBBCCCEkJNDYIIQQQgghhIQEGhuEEEIIIYSQkEBjgxBCCCGEEBISaGwQQgghhBBCQgKNDUIIIYQQ\nQkhIoLFBCCGEEEIICQk0NgghhBBCCCEhgcYGIYQQQgghJCTQ2CCEEEIIIYSEBBobhBBCCCGEkJBA\nY4MQQgghhBASEmhsEEIIIYQQQkLC/wdLnnvBrtXQEwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x20929390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n='my'\n",
"roi_name='D:/irm_marche/mni/roi_mni_'+n+'.npz' \n",
"roi=np.load(roi_name)['roi']\n",
"roi_cond=roi[condition_mask]\n",
"\n",
"\n",
"mem = Memory('nilearn_cache')\n",
"basc = datasets.fetch_atlas_basc_multiscale_2015(version='sym')['scale444']\n",
"\n",
"brainmask = load_mni152_brain_mask()\n",
"masker = NiftiLabelsMasker(labels_img = basc, mask_img = brainmask, \n",
" memory=mem, memory_level=1, verbose=0,\n",
" detrend=False, standardize=False, \n",
" high_pass=0.01,t_r=2.28,\n",
" resampling_target='labels')\n",
"masker.fit()\n",
"pipeline.fit(roi_cond,y)\n",
"coef = pipeline.named_steps['svm'].coef_\n",
"weight_img = masker.inverse_transform(coef)\n",
"plot_stat_map(weight_img, title='SVM weights',threshold=0.05\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Classification sur differentes modalités : Entrainement (Stimulation vs Rest) et Test (Imagination vs Rest) \n",
"--\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Entrainement sur Marche Confortable+Marche Forcée + Pied Droit\n",
"--\n",
"(si on apprend sur Marche Forcée on observe des scores à 0.3 ce qui est bizarre..."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"af 0.546666666667\n",
"ba 0.48\n",
"br 0.633333333333\n",
"my 0.573333333333\n"
]
}
],
"source": [
"labelgen=np.loadtxt('D:/irm_marche/label_reststim.txt','S12')\n",
"\n",
"for n in names:\n",
"\n",
" roi_name='D:/irm_marche/mni/roi_mni_'+n+'.npz' \n",
" roi=np.load(roi_name)['roi']\n",
" \n",
" roi_train=np.append(roi[0:150],roi[450:750],axis=0) \n",
" y_train=np.append(labelgen[0:150],labelgen[450:750],axis=0)\n",
"\n",
" roi_test=roi[300:450]\n",
" y_test=labelgen[300:450]\n",
"\n",
" pipeline.fit(roi_train,y_train)\n",
" prediction = pipeline.predict(roi_test)\n",
" score=pipeline.score(roi_test,y_test)\n",
" print n,score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Entrainement seulement sur \"Marche Confortable\"\n",
"--"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"af 0.586666666667\n",
"ba 0.48\n",
"br 0.473333333333\n",
"my 0.586666666667\n"
]
}
],
"source": [
"for n in names:\n",
"\n",
" roi_name='D:/irm_marche/mni/roi_mni_'+n+'.npz' \n",
" roi=np.load(roi_name)['roi']\n",
" \n",
" roi_train=roi[0:150]\n",
" y_train=labelgen[0:150]\n",
"\n",
" roi_test=roi[300:450]\n",
" y_test=labelgen[300:450]\n",
"\n",
" pipeline.fit(roi_train,y_train)\n",
" prediction = pipeline.predict(roi_test)\n",
" score=pipeline.score(roi_test,y_test)\n",
" print n,score"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<nilearn.plotting.displays.OrthoSlicer at 0x2218d1d0>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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nt6VeNqoQ7ouZrSONGjUK0ryp0B3v0KFDqcu96JKIEzbT/57fNZHXlwGH7/45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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x20b41e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Meme coeficients que 1er graphique, vu qu'on a appris sur les meme données.\n",
"coef = pipeline.named_steps['svm'].coef_\n",
"weight_img = masker.inverse_transform(coef)\n",
"plot_stat_map(weight_img, title='SVM weights MY',threshold=0.1\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:fMRI_env]",
"language": "python",
"name": "conda-env-fMRI_env-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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