BHS_Haxby_BrainDecoding.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Haxby data set:\n",
    "Haxby is a high-quality block-design fMRI dataset from a study on face & object representation in the human ventral temporal cortex (This cortex is involved in the high-level visual processing of complex stimuli). It consists of 6 subjects with 12 runs per subject. In this experiment during each run, the subjects passively viewed greyscale images of 8 object categories, grouped in 24s blocks separated by rest periods. Each image was shown for 500ms and was followed by a 1500ms inter-stimulus interval.\n",
    "\n",
    "## Project Goal\n",
    "For this project i am trying machine learning and deep learning methods to learning about barin decoding and predicting which object category the subject saw by analyzing the fMRI activity recorded masks of the ventral stream."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nibabel as nib\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "import plotly.express as px\n",
    "\n",
    "from nilearn.plotting import plot_anat, show, plot_stat_map, plot_matrix\n",
    "from nilearn import datasets, plotting, image\n",
    "from nilearn.image import mean_img, get_data \n",
    "from nilearn.input_data import NiftiMasker\n",
    "from sklearn.model_selection import train_test_split, LeaveOneGroupOut, cross_val_score, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression, RidgeClassifier,  RidgeClassifierCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.feature_selection import SelectPercentile, f_classif, SelectKBest\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.multiclass import OneVsOneClassifier, OneVsRestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%matplotlib inline\n",
    "#%load_ext memory_profiler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If we don't define which subject by default 2nd subject will be fetched.\n",
    "haxby_ds = datasets.fetch_haxby(subjects=[4], fetch_stimuli=True)\n",
    "\n",
    "len(haxby_ds.func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Haxby 2001 results\r\n",
      "\r\n",
      "\r\n",
      "Notes\r\n",
      "-----\r\n",
      "Results from a classical fMRI study that investigated the differences between\r\n",
      "the neural correlates of face versus object processing in the ventral visual\r\n",
      "stream. Face and object stimuli showed widely distributed and overlapping\r\n",
      "response patterns.\r\n",
      "\r\n",
      "Content\r\n",
      "-------\r\n",
      "The \"simple\" dataset includes\r\n",
      "    :'func': Nifti images with bold data\r\n",
      "    :'session_target': Text file containing session data\r\n",
      "    :'mask': Nifti images with employed mask\r\n",
      "    :'session': Text file with condition labels\r\n",
      "\r\n",
      "\r\n",
      "The full dataset additionally includes\r\n",
      "    :'anat': Nifti images with anatomical image\r\n",
      "    :'func': Nifti images with bold data\r\n",
      "    :'mask_vt': Nifti images with mask for ventral visual/temporal cortex\r\n",
      "    :'mask_face': Nifti images with face-reponsive brain regions\r\n",
      "    :'mask_house': Nifti images with house-reponsive brain regions\r\n",
      "    :'mask_face_little': Spatially more constrained version of the above\r\n",
      "    :'mask_house_little': Spatially more constrained version of the above\r\n",
      "\r\n",
      "\r\n",
      "References\r\n",
      "----------\r\n",
      "For more information see:\r\n",
      "PyMVPA provides a tutorial using this dataset :\r\n",
      "http://www.pymvpa.org/tutorial.html\r\n",
      "\r\n",
      "More informations about its structure :\r\n",
      "http://dev.pymvpa.org/datadb/haxby2001.html\r\n",
      "\r\n",
      "\r\n",
      "`Haxby, J., Gobbini, M., Furey, M., Ishai, A., Schouten, J.,\r\n",
      "and Pietrini, P. (2001). Distributed and overlapping representations of\r\n",
      "faces and objects in ventral temporal cortex. Science 293, 2425-2430.`\r\n",
      "\r\n",
      "\r\n",
      "Licence: unknown.\r\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Read the data documentation\n",
    "print(haxby_ds['description'].decode('utf-8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['anat', 'func', 'session_target', 'mask_vt', 'mask_face', 'mask_house', 'mask_face_little', 'mask_house_little', 'mask', 'description', 'stimuli'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look inside the data\n",
    "haxby_ds.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/srastegarnia/nilearn_data/haxby2001/subj4/labels.txt']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haxby_ds.session_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask_file = haxby_ds.mask\n",
    "labels = haxby_ds.session_target[0]\n",
    "mask_vt_file = haxby_ds.mask_vt[0]\n",
    "mask_face_file = haxby_ds.mask_face[0]\n",
    "\n",
    "# 'func' is a list of filenames: one for each subject\n",
    "func_file = haxby_ds.func[0]\n",
    "\n",
    "# Load the behavioral data that I will predict\n",
    "beh_label = pd.read_csv(haxby_ds.session_target[0], sep=\" \")\n",
    "\n",
    "# Extract tags indicating to which acquisition run a tag belongs\n",
    "session = beh_label['chunks']\n",
    "\n",
    "# Preparing the data (Load target information as string and give a numerical identifier to each)\n",
    "y = beh_label['labels']\n",
    "\n",
    "# Identify the resting state\n",
    "nonrest_task_mask = (y != 'rest')\n",
    "\n",
    "# Remove the resting state and find names of remaining active labels\n",
    "categories = y[nonrest_task_mask].unique()\n",
    "#session = session[nonrest_task_mask]\n",
    "\n",
    "# Get the labels of the numerical conditions represented by the vector y\n",
    "unique_conditions, order = np.unique(categories, return_index=True)\n",
    "\n",
    "# Sort the conditions by the order of appearance\n",
    "unique_conditions = unique_conditions[np.argsort(order)]\n",
    "\n",
    "# Extract tags indicating to which acquisition run a tag belongs\n",
    "session_labels = beh_label['chunks'][nonrest_task_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Functional nifti images are located at: /home/srastegarnia/nilearn_data/haxby2001/subj4/bold.nii.gz\n",
      "Mask nifti image (3D) is located at:  /home/srastegarnia/nilearn_data/haxby2001/mask.nii.gz\n",
      "First subject functional nifti images (4D) are at: /home/srastegarnia/nilearn_data/haxby2001/subj4/bold.nii.gz\n"
     ]
    }
   ],
   "source": [
    "# Print basic information on the dataset\n",
    "print('Functional nifti images are located at: %s' % haxby_ds.func[0])\n",
    "print('Mask nifti image (3D) is located at:  %s' % haxby_ds.mask)\n",
    "print('First subject functional nifti images (4D) are at: %s' %func_file)  # 4D data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labels chunks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rest 0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rest 0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rest 0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>rest 0</td>\n",
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       "      <th>4</th>\n",
       "      <td>rest 0</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "  labels chunks\n",
       "0        rest 0\n",
       "1        rest 0\n",
       "2        rest 0\n",
       "3        rest 0\n",
       "4        rest 0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checkout the confounds of the data\n",
    "session_target = pd.read_csv(haxby_ds['session_target'][0], sep='\\t')\n",
    "\n",
    "session_target.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the fMRI data (smooth and apply the mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardizing and smoothing the data\n",
    "nifti_masker = NiftiMasker(mask_img=mask_file, standardize=True, sessions=session,  smoothing_fwhm=4,\n",
    "                           memory=\"nilearn_cache\", memory_level=1)\n",
    "\n",
    "X = nifti_masker.fit_transform(func_file)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Haxby masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "masker = NiftiMasker(mask_img=mask_vt_file, standardize=True)\n",
    "fmri_masked = masker.fit_transform(func_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.6038213  -4.024481    0.82268804 ... -1.3077999  -0.76046395\n",
      "  -0.80006576]\n",
      " [-2.6749918  -4.101693    1.2567352  ... -1.4760997  -0.8379458\n",
      "  -0.6758426 ]\n",
      " [-3.1494625  -3.5612085   1.0739785  ... -1.1395003  -1.0962186\n",
      "  -0.80006576]\n",
      " ...\n",
      " [-1.4176446  -0.7429663  -1.5303041  ... -0.992238   -0.5538457\n",
      "  -0.9656967 ]\n",
      " [-1.038068   -1.6502086  -1.6902162  ... -0.5083763  -0.81211853\n",
      "  -0.75865805]\n",
      " [-0.7296622  -0.91669357 -1.484615   ...  0.5645344   0.24679996\n",
      "  -0.1789499 ]]\n"
     ]
    }
   ],
   "source": [
    "# The variable “fmri_masked” is a numpy array\n",
    "print(fmri_masked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1452, 675)\n"
     ]
    }
   ],
   "source": [
    "print(fmri_masked.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting the Mask to a Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40, 64, 64, 1452)\n",
      "(40, 64, 64)\n",
      "39912\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: DeprecationWarning: get_data() is deprecated in favor of get_fdata(), which has a more predictable return type. To obtain get_data() behavior going forward, use numpy.asanyarray(img.dataobj).\n",
      "\n",
      "* deprecated from version: 3.0\n",
      "* Will raise <class 'nibabel.deprecator.ExpiredDeprecationError'> as of version: 5.0\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# load bold image into memory as a nibabel image\n",
    "func = nib.load(func_file)\n",
    "\n",
    "# load mask image into memory as a nibabel image\n",
    "mask = nib.load(mask_file) \n",
    "\n",
    "# get the physical data of the mask (3D matrix of voxels)\n",
    "mask_data = mask.get_data() \n",
    "\n",
    "print(func.shape)\n",
    "print(mask.shape) \n",
    "print(len(mask_data[mask_data==1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1452, 39912)\n"
     ]
    }
   ],
   "source": [
    "# Create the masker object \n",
    "masker = NiftiMasker(mask_img=mask_file, standardize=True)\n",
    "\n",
    "# Create a numpy matrix from the BOLD data, using the mask for the transformation\n",
    "func_masked = masker.fit_transform(func_file)\n",
    "\n",
    "# View the dimensions of the matrix. The shape represents the number of time-stamps by the number of voxels in the mask. \n",
    "print(func_masked.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.851505   -1.1370772  -1.029912   ... -0.97751945 -0.9890595\n",
      "  -0.9778365 ]\n",
      " [ 1.6732124  -1.1370772  -1.029912   ... -0.97751945 -0.9890595\n",
      "  -0.9778365 ]\n",
      " [-1.0308924  -1.1370772  -1.029912   ... -0.97751945  1.6580281\n",
      "   1.1377147 ]\n",
      " ...\n",
      " [ 0.12800965  0.75440264  0.08345481 ... -0.97751945 -0.9890595\n",
      "  -0.9778365 ]\n",
      " [ 0.6331721   0.8390958   0.32418278 ... -0.97751945 -0.9890595\n",
      "  -0.9778365 ]\n",
      " [ 0.5143103   1.064944   -0.12718217 ... -0.97751945 -0.9890595\n",
      "  -0.9778365 ]]\n"
     ]
    }
   ],
   "source": [
    "# Viewing the numerical values of the matrix\n",
    "print(func_masked)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the labels from a csv into an array using pandas\n",
    "stimuli = pd.read_csv(labels, sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1452, 2)\n",
      "     labels  chunks\n",
      "0      rest       0\n",
      "1      rest       0\n",
      "2      rest       0\n",
      "3      rest       0\n",
      "4      rest       0\n",
      "...     ...     ...\n",
      "1447   rest      11\n",
      "1448   rest      11\n",
      "1449   rest      11\n",
      "1450   rest      11\n",
      "1451   rest      11\n",
      "\n",
      "[1452 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# View the dimensions of the matrix\n",
    "print(stimuli.shape)\n",
    "\n",
    "# Viewing the values of the matrix\n",
    "print(stimuli)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       rest\n",
      "1       rest\n",
      "2       rest\n",
      "3       rest\n",
      "4       rest\n",
      "        ... \n",
      "1447    rest\n",
      "1448    rest\n",
      "1449    rest\n",
      "1450    rest\n",
      "1451    rest\n",
      "Name: labels, Length: 1452, dtype: object\n"
     ]
    }
   ],
   "source": [
    "targets = stimuli['labels']\n",
    "print(targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       False\n",
      "1       False\n",
      "2       False\n",
      "3       False\n",
      "4       False\n",
      "        ...  \n",
      "1447    False\n",
      "1448    False\n",
      "1449    False\n",
      "1450    False\n",
      "1451    False\n",
      "Name: labels, Length: 1452, dtype: bool\n"
     ]
    }
   ],
   "source": [
    "targets_mask = targets.isin(['face', 'cat'])\n",
    "print(targets_mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(216, 39912)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func_masked = func_masked[targets_mask]\n",
    "func_masked.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(216,)\n",
      "6       face\n",
      "7       face\n",
      "8       face\n",
      "9       face\n",
      "10      face\n",
      "        ... \n",
      "1370     cat\n",
      "1371     cat\n",
      "1372     cat\n",
      "1373     cat\n",
      "1374     cat\n",
      "Name: labels, Length: 216, dtype: object\n"
     ]
    }
   ],
   "source": [
    "targets_masked = targets[targets_mask]\n",
    "\n",
    "print(targets_masked.shape)\n",
    "print(targets_masked)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decoding with ANOVA + SVM: face vs house in the Haxby dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['face' 'house']\n",
      "('labels', 'chunks')\n"
     ]
    }
   ],
   "source": [
    "# Restrict the analysis to faces and places\n",
    "condition_mask = beh_label['labels'].isin(['face', 'house'])\n",
    "conditions_f_h = y[condition_mask]\n",
    "\n",
    "# Confirm that I now have 2 conditions\n",
    "print(conditions_f_h.unique())\n",
    "\n",
    "session_f_h = beh_label[condition_mask].to_records(index=False)\n",
    "print(session_f_h.dtype.names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply our condition_mask to fMRI data\n",
    "X_f_h = X[condition_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build the decoder\n",
    "\n",
    "#Define the prediction function to be used. Here I am using a Support Vector Classification, with a linear kernel\n",
    "svc = SVC(kernel='linear')\n",
    "\n",
    "# Define the dimension reduction to be used. (keep 5% of voxels)\n",
    "feature_selection = SelectPercentile(f_classif, percentile=5)\n",
    "\n",
    "# I have SVC classifier and our feature selection (SelectPercentile),then plug them together in a *pipeline*:\n",
    "anova_svc = Pipeline([('anova', feature_selection), ('svc', svc)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the decoder and predict\n",
    "anova_svc.fit(X_f_h, conditions_f_h)\n",
    "y_pred = anova_svc.predict(X_f_h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification accuracy: 0.9769 / Chance level: 0.500000\n"
     ]
    }
   ],
   "source": [
    "# Obtain prediction scores via cross validation\n",
    "\n",
    "# Define the cross-validation scheme used for validation, using LeaveOneGroupOut cross-validation.\n",
    "cv = LeaveOneGroupOut()\n",
    "\n",
    "# Compute the prediction accuracy for the different folds (i.e. session)\n",
    "cv_scores = cross_val_score(anova_svc, X_f_h, conditions_f_h, cv=cv, groups=session_f_h)\n",
    "\n",
    "# Return the corresponding mean prediction accuracy\n",
    "classification_accuracy = cv_scores.mean()\n",
    "\n",
    "# Print the results\n",
    "print(\"Classification accuracy: %.4f / Chance level: %f\" % (classification_accuracy, 1. / len(conditions_f_h.unique())))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nilearn.plotting.displays.OrthoSlicer at 0x7f7a7ca31e50>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 525.6x187.2 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at the SVC’s discriminating weights\n",
    "coef = svc.coef_\n",
    "# reverse feature selection\n",
    "coef = feature_selection.inverse_transform(coef)\n",
    "# reverse masking\n",
    "weight_img = masker.inverse_transform(coef)\n",
    "\n",
    "# Use the mean image as a background to avoid relying on anatomical data\n",
    "mean_img = image.mean_img(func_file)\n",
    "\n",
    "# Create the figure\n",
    "plot_stat_map(weight_img, mean_img, title='SVM weights')\n",
    "\n",
    "# Save the results as a Nifti file\n",
    "#weight_img.to_file('haxby_face_vs_house.nii')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROI-based decoding analysis\n",
    "In this section, I am looking at decoding accuracy for different objects in three different masks: the full ventral stream (mask_vt), the house selective areas (mask_house), and the face-selective areas (mask_face), that have been defined via a standard General Linear Model (GLM) based analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract tags indicating to which acquisition run a tag belongs\n",
    "session_labels = beh_label[\"chunks\"][nonrest_task_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working on mask mask_vt\n",
      "Processing mask_vt face\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.99 +- 0.01\n",
      "Processing mask_vt chair\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.84 +- 0.14\n",
      "Processing mask_vt scissors\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.85 +- 0.10\n",
      "Processing mask_vt shoe\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.85 +- 0.10\n",
      "Processing mask_vt scrambledpix\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.98 +- 0.03\n",
      "Processing mask_vt house\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.99 +- 0.02\n",
      "Processing mask_vt cat\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.85 +- 0.16\n",
      "Processing mask_vt bottle\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.72 +- 0.21\n",
      "Working on mask mask_face\n",
      "Processing mask_face face\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.95 +- 0.05\n",
      "Processing mask_face chair\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.65 +- 0.14\n",
      "Processing mask_face scissors\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.73 +- 0.15\n",
      "Processing mask_face shoe\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.71 +- 0.17\n",
      "Processing mask_face scrambledpix\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.91 +- 0.07\n",
      "Processing mask_face house\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.88 +- 0.10\n",
      "Processing mask_face cat\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.76 +- 0.17\n",
      "Processing mask_face bottle\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.51 +- 0.20\n",
      "Working on mask mask_house\n",
      "Processing mask_house face\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.95 +- 0.04\n",
      "Processing mask_house chair\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.80 +- 0.12\n",
      "Processing mask_house scissors\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.67 +- 0.17\n",
      "Processing mask_house shoe\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.77 +- 0.08\n",
      "Processing mask_house scrambledpix\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.96 +- 0.02\n",
      "Processing mask_house house\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.98 +- 0.02\n",
      "Processing mask_house cat\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores: 0.63 +- 0.18\n",
      "Processing mask_house bottle\n",
      "Scores: 0.62 +- 0.14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/dummy.py:132: FutureWarning: The default value of strategy will change from stratified to prior in 0.24.\n",
      "  \"stratified to prior in 0.24.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# The classifier: a support vector classifier\n",
    "classifier = SVC(C=1., kernel=\"linear\")\n",
    "\n",
    "# A classifier to set the chance level\n",
    "dummy_classifier = DummyClassifier()\n",
    "\n",
    "# Make a data splitting object for cross validation\n",
    "cv = LeaveOneGroupOut()\n",
    "\n",
    "mask_names = ['mask_vt', 'mask_face', 'mask_house']\n",
    "\n",
    "mask_scores = {}\n",
    "mask_chance_scores = {}\n",
    "\n",
    "for mask_name in mask_names:\n",
    "    print(\"Working on mask %s\" % mask_name)\n",
    "    \n",
    "    # Standardizing\n",
    "    mask_filename = haxby_ds[mask_name][0]\n",
    "    masker = NiftiMasker(mask_img=mask_filename, standardize=True)\n",
    "    masked_timecourses = masker.fit_transform(func_file)[nonrest_task_mask]\n",
    "\n",
    "    mask_scores[mask_name] = {}\n",
    "    mask_chance_scores[mask_name] = {}\n",
    "\n",
    "    for category in categories:\n",
    "        print(\"Processing %s %s\" % (mask_name, category))\n",
    "        classification_target = (y[nonrest_task_mask] == category)\n",
    "        mask_scores[mask_name][category] = cross_val_score(\n",
    "            classifier,\n",
    "            masked_timecourses,\n",
    "            classification_target,\n",
    "            cv=cv,\n",
    "            groups=session_labels,\n",
    "            scoring=\"roc_auc\",\n",
    "        )\n",
    "\n",
    "        mask_chance_scores[mask_name][category] = cross_val_score(\n",
    "            dummy_classifier,\n",
    "            masked_timecourses,\n",
    "            classification_target,\n",
    "            cv=cv,\n",
    "            groups=session_labels,\n",
    "            scoring=\"roc_auc\",\n",
    "        )\n",
    "\n",
    "        print(\"Scores: %1.2f +- %1.2f\" % (\n",
    "            mask_scores[mask_name][category].mean(),\n",
    "            mask_scores[mask_name][category].std()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Different multi-class strategies\n",
    "I compare one vs all and one vs one multi-class strategies: the overall cross-validated accuracy and the confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the decoders, using scikit-learn\n",
    "\n",
    "svc_ovo = OneVsOneClassifier(Pipeline([\n",
    "    ('anova', SelectKBest(f_classif, k=500)),\n",
    "    ('svc', SVC(kernel='linear'))\n",
    "]))\n",
    "\n",
    "svc_ova = OneVsRestClassifier(Pipeline([\n",
    "    ('anova', SelectKBest(f_classif, k=500)),\n",
    "    ('svc', SVC(kernel='linear'))\n",
    "]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   23.9s finished\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OvO: 0.5369538916521037\n",
      "OvA: 0.6007057400188197\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:   38.8s finished\n"
     ]
    }
   ],
   "source": [
    "# Remove the \"rest\" condition\n",
    "y = y[nonrest_task_mask]\n",
    "X = X[nonrest_task_mask]\n",
    "\n",
    "cv_scores_ovo = cross_val_score(svc_ovo, X, y, cv=5, verbose=1)\n",
    "\n",
    "cv_scores_ova = cross_val_score(svc_ova, X, y, cv=5, verbose=1)\n",
    "\n",
    "print('OvO:', cv_scores_ovo.mean())\n",
    "print('OvA:', cv_scores_ova.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Prediction: accuracy score')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQoAAADSCAYAAACy0Y4/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAS8UlEQVR4nO3df5BdZX3H8ffHXQImBJIlQYT82CCB1igwcA1VywjyY1IKDbTWJE5FqxLDDG0RUSiDEiuKVuzU6cDQFBHrFAIqP6IDCWhFLIWSXRqBhB8GCMkShA0gAQRp8Ns/zrN6cnN3n7PZX3c3n9fMnb3nnOc55zn3nPvZ89x77jmKCMzM+vKmkW6AmTU/B4WZZTkozCzLQWFmWQ4KM8tyUJhZloNiBEhqlxSSWtPwrZI+shPzmSHpZUktg99Ks99zUPRC0gZJr6Y34jOSviVpz6FYVkT8SUR8u2Kbji/V2xgRe0bEG0PRLrMeDoq+nRIRewJHAO8CLqwvoIJfxyHUc+Q12o3mfWVUNnq4RcRTwK3AOwAk3SHpS5LuAn4NHChpb0nflPS0pKckXdzTJZDUIulSSVskPQ78aXn+aX6fKA2fIekhSS9JWifpCEnfAWYAP0hHOZ9t0IXZX9IKSc9LWi/pjNI8l0q6XtK/p/mulVSr+hpI+oakTZK2SuqUdHRpWoukCyQ9lubdKWl6mjZH0u2pTc9IuiCNv1rSxaV5HCOpqzS8QdJ5ku4HXpHUKun80jLWSTqtro2NXrfPSPp+Xbl/kfTPvazneWn7vSTpEUnHVVjH90haLenF9Pc9ddu28r7StCLCjwYPYANwfHo+HVgLfDEN3wFsBOYArcBuwE3AvwITgH2Be4FPpvJLgIfTfNqAnwABtJbm94n0/C+BpyiOYAQcBMysb1Mabq+bz0+By4E9gMOBbuC4NG0p8BpwEtACXALcU5rX5cDlfbwefwXsk9b308AvgT3StM8ADwCHpDYflspOBJ5O5fdIw0elOlcDF5fmfwzQVff6r0mv2ZtLr83+FP/gFgCvAG/t63UD3prKTUrlWoFngSMbrOMhwCZg/9Lr+7bMOrYBLwAfTvNelIb32Zl9pVkfI96AZn2kHfVl4FfAk+mN1LPD3gH8Q6nsW4Df9ExP4xYBP0nP/xNYUpp2Ir0HxSrg7/poU8OgSG+oN4CJpemXAFen50uBH5WmvR14dQCvzwvAYen5I8D8BmUWAf/bS/2ryQfFxzJtWNOz3MzrditwRnp+MrCul3IHpRA5Htitblpv6/hh4N66cXcDH92ZfaVZH2Oi7zeETo2IH/UybVPp+UyK/xRPS+oZ96ZSmf3ryj/ZxzKnA4/1v6nsDzwfES/VLafcvfhl6fmvgT0ktUbEttzMJX0a+ERaTgB7AVMybd7ZdelRfs2QdDpwDkVAAuxZoQ0A3wbOBP6N4sjoO40KRcR6SWdThOocSauAcyJicx/z358dt+eTwAG9rEduX2lK/oxi55V/druJ4r/ElIiYlB57RcScNP1pih2tx4w+5rsJeFuFZdbbDLRJmli3nKf6qFNJ+jziPOCDwOSImAS8SHEIDr23ua91eQUYXxrer0GZ362vpJkUb/SzKA7rJwEPVmgDFIf6h0p6B8URxX/0Uo6IuCYi/pjiDR3AVzPz35zKltW/7v3ZV5qSg2IQRMTTwG3A1yXtJelNkt4m6X2pyPXA30qaJmkycH4fs7sSOFfSkelT8oPSmwTgGeDAXtqwCfhv4BJJe0g6FPg4fbwp+mEisI3iM49WSZ+nOKIot/mLkmanNh8qaR/gh8B+ks6WtLukiZKOSnXWACdJapO0H3B2pg0TKN5w3QCS/pr04XKpDQ1ft4h4DfgecA1FN2FjowVIOkTS+yXtTvF5zqsU3bm+1vEW4GBJH0ofuC6g6Nb9sNEyKuwrTclBMXhOB8YB6yj679+j+CANiv+Eq4CfA/cBN/Q2k4j4LvAlip36JYr/hm1p8iXAhZJ+JencBtUXURyWbwZuBC6KiNurNF7SFZKu6GXyKop+/qMUh9Wvsf2h8j9RhOFtwFbgmxR98JeAE4BTKLo9vwCOTXW+Q/F6bEj1ruurfRGxDvg6Rf//GeCdwF2l6X29blB0P95JL92OZHfgK8CW1N59gQsy6/gcxVHKp4HngM8CJ0fElj6W09e+0pSUPkwxG9MkzaD45mm/iNg60u0ZbXxEYWOeipOczgGWOyR2jr/1sDFN0gSKrsqTwLwRbs6o5a6HmWW562FmWZWCQtK8dN77ekk7fLWXzqdfkx4PSnpDUluVumbW/LJdj/RjlUcpvubqAlYDi9LXVY3KnwJ8KiLe39+6PaZMmRLt7e39XBUzG6jOzs4tETG1fnyVDzPnAusj4nEAScuB+RTfATeyCLh2J+sC0N7eTkdHR4WmmdlgktTw5wVVuh4HsP3JNV1sfx57eSHjKT5Z7vlZb+W6Zta8qgSFGozrrb9yCnBXRDzf37qSFkvqkNTR3d1doVlmNlyqBEUX2/+gaRrFKcKNLOT33Y5+1Y2IZRFRi4ja1Kk7dJHMbARVCYrVwGxJsySNowiDFfWFJO0NvA+4ub91zay5ZT/MjIhtks6i+GFQC3BVRKyVtCRN7/kh0WnAbRHxSq7uYK+EmQ2tpjwzs1arxVj51qN0cZI+NeN2sF2PpM6I2OFaqv6txxCrDwBJDgUbdXwKt5llOSjMLMtBYWZZDgozy3JQmFmWg8LMshwUZpbloDCzLAeFmWU5KMwsy0FhZlkOCjPLclCYWZaDwsyyHBRmluWgMLMsB4WZZTkozCzLQWFmWQ4KM8tyUJhZloPCzLIcFGaWVSkoJM2T9Iik9ZLO76XMMZLWSFor6ael8RskPZCmjY27+pjtYrI3AJLUAlwGnEBx0+HVklZExLpSmUnA5cC8iNgoad+62RwbEVsGsd1mNoyqHFHMBdZHxOMR8TqwHJhfV+ZDwA0RsREgIp4d3Gaa2UiqEhQHAJtKw11pXNnBwGRJd0jqlHR6aVoAt6Xxi3tbiKTFkjokdXR3d1dtv5kNgyr3Hm10l936m2e2AkcCxwFvBu6WdE9EPAq8NyI2p+7I7ZIejog7d5hhxDJgGRQ3Ke7PSpjZ0KpyRNEFTC8NTwM2NyizMiJeSZ9F3AkcBhARm9PfZ4EbKboyZjaKVAmK1cBsSbMkjQMWAivqytwMHC2pVdJ44CjgIUkTJE0EkDQBOBF4cPCab2bDIdv1iIhtks4CVgEtwFURsVbSkjT9ioh4SNJK4H7gt8CVEfGgpAOBGyX1LOuaiFg5VCtjZkNDEc33cUCtVouOjrF5yoUkmvE1NwOQ1BkRtfrxPjPTzLIcFGaW5aAwsywHhZllOSjMLMtBYWZZDgozy3JQmFmWg8LMshwUZpbloDCzLAfFIGtra0NSrw+gz+mSaGtrG+G1sB65bdWzTce6KheusX544YUXBvyjr11l5xsN6rflrvqjPh9RmFmWg8LMshwUZpbloDCzLAeFmWU5KMwsy0FhZlk+j2KQxUV7wdK9Bz4PsybioBhk+sLWQTnhKpYOTnvMBoO7HmaWVSkoJM2T9Iik9ZLO76XMMZLWSFor6af9qWtmzS3b9ZDUAlwGnEBxj9HVklZExLpSmUnA5cC8iNiYbkhcqa6ZNb8qRxRzgfUR8XhEvA4sB+bXlfkQcENEbITf3ZC4al0za3JVguIAYFNpuCuNKzsYmCzpDkmdkk7vR10za3JVvvVo9Jvn+o/1W4EjgeOANwN3S7qnYt1iIdJiYDHAjBkzKjTLzIZLlSOKLmB6aXgasLlBmZUR8UpEbAHuBA6rWBeAiFgWEbWIqE2dOrVq+81sGFQJitXAbEmzJI0DFgIr6srcDBwtqVXSeOAo4KGKdc2syWW7HhGxTdJZwCqgBbgqItZKWpKmXxERD0laCdwP/Ba4MiIeBGhUd4jWxcyGiJrxsl61Wi06OjpGuhk7ZTAulbarXm5tNBjr20ZSZ0TU6sf7zEwzy3JQmFmWg8LMshwUZpbloDCzLAeFWclA7/Q2Vu/y5gvXmJUM9E5vY/Uubz6iMLMsB4WZZTkozCzLQWFmWQ4KM8tyUJhZloPCzLIcFGaW5aAwsywHhZllOSjMLMtBYWZZDgozy3JQmFmWg8LMshwUZpbloDCzrEpBIWmepEckrZd0foPpx0h6UdKa9Ph8adoGSQ+k8aPzrj5mu7jspfAktQCXASdQ3HR4taQVEbGurujPIuLkXmZzbLp5sZmNQlWOKOYC6yPi8Yh4HVgOzB/aZplZM6kSFAcAm0rDXWlcvXdL+rmkWyXNKY0P4DZJnZIW97YQSYsldUjq6O7urtR4MxseVa7C3eiywvWXKb4PmBkRL0s6CbgJmJ2mvTciNkvaF7hd0sMRcecOM4xYBiyD4ibFldfAzIZclSOKLmB6aXgasLlcICK2RsTL6fktwG6SpqThzenvs8CNFF0ZMxtFqgTFamC2pFmSxgELgRXlApL2U7qhgaS5ab7PSZogaWIaPwE4EXhwMFfAzIZetusREdsknQWsAlqAqyJiraQlafoVwAeAMyVtA14FFkZESHoLcGPKkFbgmohYOUTrYmZDRAO5K9JQqdVq0dExOk+5GIw7RU2ePJnnn39+EFpj/SVpwHcKa8b3VFWSOiOiVj/etxQcZLmdZLTvSLZr8incZpbloDCzLAeFmWU5KMwsy0FhZln+1mOINfq6tNE4fxNizcxBMcQcADYWuOthZlkOCjPLclCYWZaDwsyyHBRmluWgMLMsfz1qVhIX7QVL9x5Y/THIQWFWoi9sHfj1KJYOXnuahbseZpbloDCzLAeFmWU5KMwsy0FhZlkOCjPLclCYWValoJA0T9IjktZLOr/B9GMkvShpTXp8vmpdM2t+2ROuJLUAlwEnUNyHdLWkFRGxrq7ozyLi5J2sa2ZNrMoRxVxgfUQ8HhGvA8uB+RXnP5C6ZtYkqgTFAcCm0nBXGlfv3ZJ+LulWSXP6WdfMmliV33o0uplm/cnw9wEzI+JlSScBNwGzK9YtFiItBhYDzJgxo0KzzGy4VDmi6AKml4anAZvLBSJia0S8nJ7fAuwmaUqVuqV5LIuIWkTUpk6d2o9VMLOhViUoVgOzJc2SNA5YCKwoF5C0n9I16CXNTfN9rkpdM2t+2a5HRGyTdBawCmgBroqItZKWpOlXAB8AzpS0DXgVWBjFb3Ub1h2idTGzIaJmvO9ErVaLjo6OkW6G7YIkDfx6FE34nqpKUmdE1OrH+8I1ZnUa3cmtqsmTJw9iS5qHg8KsJHc0MNqPGHaWf+thZlkOCjPLclCYWZaDwsyyHBRmluWgMLMsB4WZZTkozCzLQWFmWQ4KM8tyUJhZloPCzLIcFGaW5aAwsywHhZll+XoUZn1odBGb+nG7wvUpHBRmfdgVQqAKdz3MLMtBYWZZDgozy3JQmFmWg8LMshwUZpbVlHcKk9QNPDnS7RgiU4AtI90I22ljffvNjIgd7hLelEExlknqaHTLNhsddtXt566HmWU5KMwsy0Ex/JaNdANsQHbJ7efPKMwsy0cUZpbloKgjaZqkmyX9QtJjkr4hadxItwsgtevuunFLJZ2bnl8t6QMj07qR0azbS9Kpku6X9LCkBySdOtJtGggHRYmKCw3cANwUEbOBg4E9gS+NaMMASZOAI4BJkmaNdHuaQbNuL0mHAZcC8yPiD4A/Ay6VdOhItmsgHBTbez/wWkR8CyAi3gA+BXxM0nhJH5V0g6SV6T/YP/ZUlHSipLsl3Sfpu5L2LM9Y0h9Kurc03C7p/vT8K5LWpf9Al/bStr8AfgAsBxYO6lqPXs26vc4FvhwRT6R2PQFcAnwm1b9D0lcl3SvpUUlHp/Etkr4maXWa9ycH8bUaEAfF9uYAneUREbEV2AgclEYdDiwA3gkskDRd0hTgQuD4iDgC6ADOqZvPQ8A4SQemUQuA6yW1AacBcyLiUODiXtq2CLg2PRYNaC3HjmbdXju0Ky1jTmm4NSLmAmcDF6VxHwdejIh3Ae8CzmiWo0cHxfYENPoaqDz+xxHxYkS8BqwDZgJ/BLwduEvSGuAjaXy964EPpucLgOuArcBrwJWS/hz49Q4Ll95CseP/V0Q8CmyT9I6dW8UxpSm3Vy/tqh93Q/rbCbSn5ycCp6c2/Q+wDzC7wfyHnYNie2uB7U7PlbQXMB14LI36TWnyGxSXExRwe0Qcnh5vj4iPN5j/dcAHJR0MRET8IiK2AXOB7wOnAisb1FsATAaekLSBYsdy96N5t9cO7aL4fGldabinXT1tIrXrb0rtmhURt/W28sPJQbG9HwPjJZ0ORZ8R+DpwdUQ0+s/R4x7gvZIOSvXGp51rOxHxGMWO8TmKnZDUN947Im6hOAw9vMH8FwHzIqI9ItqBI3FQQPNur0uBv5fUnuq0AxektvVlFXCmpN1SvYMlTcjUGRa+uG5JRISk04DLJX2OIkhvodjIfdXrlvRR4FpJu6fRFwKPNih+HfA1oKfvORG4WdIeFP9RPlUunHayGRQ7d8/ynpC0VdJR/VrBMaYZt1ea/xpJ5wE/SG/6/wM+GxFrMqt0JcXR4n3pG51uiqOWEeczM80sy10PM8tyUJhZloPCzLIcFGaW5aAwsywHhZllOSjMLMtBYWZZ/w8DLEiknTMV8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot barplots of the prediction scores\n",
    "\n",
    "plt.figure(figsize=(4, 3))\n",
    "plt.boxplot([cv_scores_ova, cv_scores_ovo])\n",
    "plt.xticks([1, 2], ['One vs All', 'One vs One'])\n",
    "plt.title('Prediction: accuracy score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot a confusion matrix:\n",
    "To make sure that the system is not confusing two classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7]), <a list of 8 Text yticklabel objects>)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I fit on the the first 10 sessions and plot a confusion matrix on the last 2 sessions\n",
    "# Matrix rows represents the predicted class instances, columns represents actual class.\n",
    "\n",
    "svc_ovo.fit(X[session_labels < 5], y[session_labels < 5])\n",
    "y_pred_ovo = svc_ovo.predict(X[session_labels >= 5])\n",
    "\n",
    "plot_matrix(confusion_matrix(y_pred_ovo, y[session_labels >= 5]),\n",
    "            labels=unique_conditions, cmap='Blues')\n",
    "plt.title('Confusion matrix: One vs One')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=0)\n",
    "\n",
    "svc_ova.fit(X[session_labels < 5], y[session_labels < 5])\n",
    "y_pred_ova = svc_ova.predict(X[session_labels >= 5])\n",
    "\n",
    "plot_matrix(confusion_matrix(y_pred_ova, y[session_labels >= 5]),\n",
    "            labels=unique_conditions, cmap='Blues')\n",
    "plt.title('Confusion matrix: One vs All')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7]), <a list of 8 Text yticklabel objects>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the confusion matrix\n",
    "svc_ovo.fit(X[session_labels < 10], y[session_labels < 10])\n",
    "y_pred_ovo = svc_ovo.predict(X[session_labels >= 10])\n",
    "\n",
    "plot_matrix(confusion_matrix(y_pred_ovo, y[session_labels >= 10]),\n",
    "            labels=unique_conditions, cmap='Blues')\n",
    "plt.title('Confusion matrix: One vs One')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=0)\n",
    "\n",
    "svc_ova.fit(X[session_labels < 10], y[session_labels < 10])\n",
    "y_pred_ova = svc_ova.predict(X[session_labels >= 10])\n",
    "\n",
    "plot_matrix(confusion_matrix(y_pred_ova, y[session_labels >= 10]),\n",
    "            labels=unique_conditions, cmap='Blues')\n",
    "plt.title('Confusion matrix: One vs All')\n",
    "plt.xticks(rotation=45)\n",
    "plt.yticks(rotation=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardizing\n",
    "masker = NiftiMasker(mask_img=mask_vt_file, standardize=True)\n",
    "masked_timecourses = masker.fit_transform(func_file)[nonrest_task_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support vector classifier\n",
    "svm = SVC(C=1., kernel=\"linear\")\n",
    "\n",
    "# The logistic regression\n",
    "#logistic = LogisticRegression(C=1., penalty=\"l1\", solver='liblinear')\n",
    "logistic_50 = LogisticRegression(C=50., penalty=\"l1\", solver='liblinear')\n",
    "#logistic_l2 = LogisticRegression(C=1., penalty=\"l2\", solver='liblinear')\n",
    "\n",
    "# Cross-validated versions of these classifiers\n",
    "svm_cv = GridSearchCV(SVC(C=1., kernel=\"linear\"),\n",
    "                      param_grid={'C': [.1, 1., 10., 100.]},\n",
    "                      scoring='f1', n_jobs=1, cv=3, iid=False)\n",
    "\n",
    "logistic_cv = GridSearchCV(\n",
    "        LogisticRegression(C=1., penalty=\"l1\", solver='liblinear'),\n",
    "        param_grid={'C': [.1, 1., 10., 100.]},\n",
    "        scoring='f1', cv=3, iid=False,\n",
    "        )\n",
    "\n",
    "# The ridge classifier has a specific 'CV' object that can set it's parameters faster than using a GridSearchCV\n",
    "ridge = RidgeClassifier()\n",
    "ridge_cv = RidgeClassifierCV()\n",
    "\n",
    "# A dictionary, to hold all our classifiers\n",
    "classifiers = {'SVC': svm,\n",
    "               'SVC cv': svm_cv,\n",
    "               'log l1 50': logistic_50,\n",
    "               'log l1 cv': logistic_cv,\n",
    "               'ridge': ridge,\n",
    "               'ridge cv': ridge_cv\n",
    "               }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prediction scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "______________________________________________________________________\n",
      "       SVC:           face -- scores: 0.82 +- 0.12, time 3.47s\n",
      "       SVC:          chair -- scores: 0.18 +- 0.21, time 4.18s\n",
      "       SVC:       scissors -- scores: 0.35 +- 0.24, time 4.04s\n",
      "       SVC:           shoe -- scores: 0.36 +- 0.22, time 3.70s\n",
      "       SVC:   scrambledpix -- scores: 0.70 +- 0.19, time 2.44s\n",
      "       SVC:          house -- scores: 0.80 +- 0.21, time 1.70s\n",
      "       SVC:            cat -- scores: 0.33 +- 0.24, time 3.60s\n",
      "       SVC:         bottle -- scores: 0.22 +- 0.22, time 3.79s\n",
      "______________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SVC cv:           face -- scores: 0.82 +- 0.12, time 21.47s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SVC cv:          chair -- scores: 0.18 +- 0.21, time 30.74s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SVC cv:       scissors -- scores: 0.35 +- 0.24, time 32.05s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SVC cv:           shoe -- scores: 0.36 +- 0.22, time 29.25s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SVC cv:   scrambledpix -- scores: 0.70 +- 0.19, time 22.17s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SVC cv:          house -- scores: 0.80 +- 0.21, time 15.36s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SVC cv:            cat -- scores: 0.33 +- 0.24, time 28.31s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    SVC cv:         bottle -- scores: 0.22 +- 0.22, time 31.73s\n",
      "______________________________________________________________________\n",
      " log l1 50:           face -- scores: 0.79 +- 0.16, time 1.36s\n",
      " log l1 50:          chair -- scores: 0.28 +- 0.23, time 2.70s\n",
      " log l1 50:       scissors -- scores: 0.38 +- 0.20, time 2.83s\n",
      " log l1 50:           shoe -- scores: 0.44 +- 0.23, time 2.91s\n",
      " log l1 50:   scrambledpix -- scores: 0.65 +- 0.18, time 1.36s\n",
      " log l1 50:          house -- scores: 0.82 +- 0.20, time 1.50s\n",
      " log l1 50:            cat -- scores: 0.33 +- 0.19, time 4.98s\n",
      " log l1 50:         bottle -- scores: 0.25 +- 0.16, time 3.90s\n",
      "______________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " log l1 cv:           face -- scores: 0.83 +- 0.09, time 8.12s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " log l1 cv:          chair -- scores: 0.24 +- 0.23, time 13.73s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " log l1 cv:       scissors -- scores: 0.39 +- 0.19, time 14.17s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " log l1 cv:           shoe -- scores: 0.37 +- 0.26, time 14.26s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " log l1 cv:   scrambledpix -- scores: 0.59 +- 0.23, time 9.45s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " log l1 cv:          house -- scores: 0.81 +- 0.20, time 7.37s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " log l1 cv:            cat -- scores: 0.28 +- 0.18, time 14.22s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " log l1 cv:         bottle -- scores: 0.25 +- 0.15, time 14.41s\n",
      "______________________________________________________________________\n",
      "     ridge:           face -- scores: 0.64 +- 0.19, time 0.61s\n",
      "     ridge:          chair -- scores: 0.32 +- 0.13, time 0.27s\n",
      "     ridge:       scissors -- scores: 0.36 +- 0.13, time 0.28s\n",
      "     ridge:           shoe -- scores: 0.25 +- 0.19, time 0.28s\n",
      "     ridge:   scrambledpix -- scores: 0.51 +- 0.17, time 0.33s\n",
      "     ridge:          house -- scores: 0.75 +- 0.13, time 0.29s\n",
      "     ridge:            cat -- scores: 0.28 +- 0.10, time 0.34s\n",
      "     ridge:         bottle -- scores: 0.20 +- 0.13, time 0.34s\n",
      "______________________________________________________________________\n",
      "  ridge cv:           face -- scores: 0.82 +- 0.13, time 2.84s\n",
      "  ridge cv:          chair -- scores: 0.27 +- 0.17, time 2.72s\n",
      "  ridge cv:       scissors -- scores: 0.42 +- 0.10, time 2.64s\n",
      "  ridge cv:           shoe -- scores: 0.31 +- 0.24, time 2.67s\n",
      "  ridge cv:   scrambledpix -- scores: 0.76 +- 0.16, time 2.64s\n",
      "  ridge cv:          house -- scores: 0.89 +- 0.10, time 2.63s\n",
      "  ridge cv:            cat -- scores: 0.39 +- 0.20, time 2.63s\n",
      "  ridge cv:         bottle -- scores: 0.21 +- 0.16, time 2.65s\n"
     ]
    }
   ],
   "source": [
    "# Make a data splitting object for cross validation\n",
    "cv = LeaveOneGroupOut()\n",
    "\n",
    "classifiers_scores = {}\n",
    "\n",
    "for classifier_name, classifier in sorted(classifiers.items()):\n",
    "    classifiers_scores[classifier_name] = {}\n",
    "    print(70 * '_')\n",
    "\n",
    "    for category in categories:\n",
    "        classification_target = y[nonrest_task_mask].isin([category])\n",
    "        t0 = time.time()\n",
    "        classifiers_scores[classifier_name][category] = cross_val_score(\n",
    "            classifier,\n",
    "            masked_timecourses,\n",
    "            classification_target,\n",
    "            cv=cv,\n",
    "            groups=session_labels,\n",
    "            scoring=\"f1\",\n",
    "        )\n",
    "\n",
    "        print(\n",
    "            \"%10s: %14s -- scores: %1.2f +- %1.2f, time %.2fs\" %\n",
    "            (\n",
    "                classifier_name,\n",
    "                category,\n",
    "                classifiers_scores[classifier_name][category].mean(),\n",
    "                classifiers_scores[classifier_name][category].std(),\n",
    "                time.time() - t0,\n",
    "            ),\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RXFxMUVER66yzDt27d+fiiy9esc7UqVMZPnw477zzDt9//z2nnnoqzzzzDI0bN6ZNmzZcdtll9OnTJ6fx10RpEqGfJTUhDuEaW839lGbjkvYgDKZUTBj+4JKM5asTxuZZP8ZyeVXG+nHOuUKbOHEijz76KK+//jqNGjVi3rx5zJgxg8MPP3ylRGjcuHEcdFAY7Peoo46ic+fOfPDBBxQVFfHRRx/xzjvvFOoQqlWaROhcwhg860m6E/gNWUYjLCGpGLgW2JVQhDdZ0r/N7O3Ear8H3jazPSW1A96TdGcckdE552qdL774grZt29KoUSMA2rZtS//+/WnVqhWvvPLKitzNvffey1NPPcWHH37IK6+8wp133klRUaim32CDDdhgg2wjg9cdWRsmxGK3dwnDZI8A7gZ6mtmEFNvuDcw0s49iojKOMExskgEt4n6aE4aD9dFGnauj5lzRbaW/umi33Xbj008/ZeONN+a4447jueeeA2D48OGMGzcOgEmTJtGmTRs22mgjZsyYQffu3SkuLi5k2AWTNRGKzwc9ZGbzzewxM3vUzOal3Pa6hPHIS8yJ85JGA5sRxiOfDpxU2sOvko6W9Jqk1wrRhNA559Jq3rw5U6ZMYezYsbRr146hQ4dy2223MWzYMO6//36WL1/OuHHjGD68zKdg6pU0xXGTJPUys8kV3HZpjRcyR9DbHZgK7AR0Af4j6QUz+26lN5mNJQ4t3LNnz7yOwnfwa/czc8/7AWg8oNa2v3CuWpTX0Ofg6gymBikuLmbAgAEMGDCArl27cvvttzNixAg6derEc889x/jx45k4cSIAW2yxBdOmTWP58uUriuPqkzRHvCMwUdKHkt6UNF1SmnbKc4D1EtMdCDmepMOBByyYCcwCNk0TuHPO1UTvvfceH3zwwYrpqVOn0rFjRyAUyf3hD3+gS5cudOjQAYAuXbrQs2dPzj33XEo6p/nggw94+OGHqz/4AkiTExpYyW1PBjaS1Bn4DBhG6Oon6RNgZ+AFSWsBmwAfVXJ/zjm3krRNqnPp+++/54QTTmDhwoU0aNCADTfckLFjxwJwwAEHcNJJJ3HNNdes9J6bbrqJU089lQ033JCmTZuuaKJdH5SbCJnZx5K2AnaIs14ws2kp3rdU0vHAU4Qm2reY2QxJo+LyMcDfgNskTScU351RgTon55yrcbbZZhtefvnlUpe1a9eOX375ZZX5LVu25MYbb8x3aDVSmqEcTgJGAg/EWf+SNNbMrsnyNgDM7HHg8Yx5YxKvPwd2q1DEzjnn6ow0xXFHAn3M7AcASf8HTATKTYScc865bNI0TBCwLDG9jNJbvjnnnHMVkrYD01ckPRin9wFuzl9Izjnn6os0DROulDQB2J6QAzrczN7Id2DOOefqvjQNE/oCM8zs9TjdQlIfM3sl79E55+qsxr2aMu/5vivNa9tvUoGicYWSpjjueqBHYvqHUuY551yNc/755+d0e2mGeckcxuGGG27giSee4KeffvKhHEqRJhGSlTzGC5jZcklp3uecc/VKacM4/PzzzwwfPpyBAwf6UA6lSNM67iNJJ0pqGP9Owns1cM65VZQ2jEP79u3ZZJNNVgzlUOLee+9l2LBhK4ZyuOCCC1YayuG3v/3tKtt/8skn6dGjB1tttRU777wzy5cvp1OnTixcuHDFOhtuuCFfffVVno80d9IkQqOA7Qhd78wB+gBH5zMo55yrjcoaxgGqPpTD3LlzGTlyJOPHj2fatGncd999FBUVsffee/Pgg6Hx8iuvvEKnTp1Ya6218neQOVZuImRmX5vZMDNb08zWMrODzOzr6gjOOedqk7KGcQCqPJTDpEmT6NevH507dwagdevWAAwdOpR77rkHCEV8Q4cOzd0BVYNyEyFJl0pqGYvinpU0T9Ih1RGcc87VNiXDOJx//vmMHj2a8ePHA7DeeuutNJTDgQceCKw8lEM2ZkYY/3Nl2267LTNnzmTu3Lk89NBD7Lfffrk/qDxKUxy3WxzfZzChOG5j4PS8RuWcc7VQtmEcoGpDOWy77bY899xzzJo1C4BvvvkGAEnsu+++nHLKKWy22Wa0adMmr8eYa2lauTWM/wcBd5vZN6Wlxs45V9OkaVKdS9mGcYCqDeXQrl07xo4dy3777cfy5ctZc801+c9//gOEIrlevXqtKPqrTdIkQo9IehdYDBwnqR2wJL9hOedc7ZNtGAeo+lAOAwcOZODAVYd469mzJ4knaWqVNN32/Cn2nP2dmS2T9COwd/5Dc67+Km/Y7Oq+w3cuX1I9dGpmCxKvfyD0muCcc85VSZqGCc4551xeeCLknHOuYCqVCEnaNNeBOOecq38qmxN6OqdROOecq5fKbJgg6R9lLQJa5Scc55zLnZl7ds3p9jZ8ZHq56zRv3pzvv/++yvsaMWIEgwcPZv/992f06NFcddVVfPjhh8ydO5e2bduusv7s2bPZbLPN2GSTTQDo27cvY8aMAWDKlCmMGDGCxYsXM2jQIK6++upSe18ohGyt4w4HTgV+KmVZxTo9cs45V2m/+c1vGDx4MAMGDMi6XpcuXZg6deoq84899ljGjh1L3759GTRoEE8++WSpzxsVQrbiuMnAW2Z2e+YfsKia4nPOuVrJzDj99NPZcsst6dq164pORpcvX85xxx3HFltsweDBgxk0aBD3339/1m1tvfXWdOrUqVJxfPHFF3z33Xdsu+22SOKwww7joYceqtS28iFbTmh/yugZwcw65ycc55yrGx544AGmTp3KtGnTmDdvHr169aJfv3689NJLzJ49m+nTp/P111+z2WabccQRR+Rkn7NmzWLrrbemZcuWXHDBBeywww589tlnK/qpA+jQoQOfffZZTvaXC9kSofvMbGdJ/2dmZ1RbRM65rA5+7X5m7rnynXOaugpXvV588UWGDx9OcXExa621Fv3792fy5Mm8+OKLHHDAARQVFbH22muz44475mR/66yzDp988glt2rRhypQp7LPPPsyYMaPU7nxqSn0QZE+E1pHUH9hL0jhCg4QVzOz1vEZWAzXu1ZR5z/ddaV7bfpMKFI1zriYrqy+3fPXx1qhRoxUjum6zzTZ06dKF999/nw4dOjBnzpwV682ZM4f27dvnJYbKyFYndA7wJ6ADcEXG3+X5D80552qvfv36cc8997Bs2TLmzp3L888/T+/evdl+++0ZP348y5cv56uvvmLChAk52d/cuXNZtmwZAB999BEffPABG2ywAeussw4tWrRg0qRJmBl33HEHe+9dc7r/LDMnZGb3A/dLOtvM/laNMTnnXE4Usphy3333ZeLEiWy11VZI4tJLL2XttddmyJAhPPvss2y55ZZsvPHG9OnTh9VXXz3rtv7xj39w6aWX8uWXX9KtWzcGDRrETTfdtNI6zz//POeccw4NGjSguLiYMWPGrBh99frrr1/RRLusnrgLJdtzQp3MbHZZCZBCoeK6ZjantOXOOVcflTwjJInLLrtslXGBioqKuPzyy2nevDnz58+nd+/edO266vNMybGBTjzxRE488cSs+x0yZAhDhgwpdVnPnj156623Kngk1SNbndBlkoqAh4EpwFygMbAhsCOwM3AuYbRV55xzKQ0ePJiFCxfy888/c/bZZ7P22msXOqSCyVYcd4CkzYGDgSOAdYAfgXeAx4ELzcwHt3POuQrKVT1QXZB1PCEzexs4q7Ibl7QHcDVQDNxkZpeUss4A4CrCMOLzzKx/ZffnnHOudkk1qF1lSCoGrgV2JRTZTZb075iwlazTCrgO2MPMPpG0Zr7icc45V/Pkczyh3sBMM/vIzH4GxrHqsOAHAQ+Y2ScAZvZ1HuNxzjlXw+QzEVoX+DQxPSfOS9oYWEPSBElTJB1W2oYkHS3pNUmvzZ07N0/hOuecq27lFsdJGg/cAjxhZssrsO3S+oXIfFS4AbANoaVdE2CipElm9v5KbzIbC4wF6NmzZ34eN3bO1TlzruiW0+11OPXNctcp1FAOtVWaOqHrCcM6/EPSfcBtZvZuivfNAdZLTHcAPi9lnXlm9gPwg6Tnga2A93HOpdJ4gFa52Ka5WLraI+1QDrVRucVxZvaMmR0M9ABmA/+R9LKkwyU1zPLWycBGkjpLWg0YBvw7Y52HgR0kNZDUFOhDaALunHO1WnUP5bBs2TJOO+00unbtSrdu3bjmmmt44oknOPDAA1esM2HCBPbcc88qH1supWodJ6kNcAhwKPAGcCewPfA7YEBp7zGzpZKOB54iNNG+xcxmSBoVl48xs3ckPQm8CSwnNOOumY/1OudcBVT3UA5jx45l1qxZvPHGGzRo0IBvvvmGli1bcswxx/DDDz/QrFkz7rnnHoYOHZqDo8udNHVCDwCbAv8E9jSzL+KieyS9lu29ZvY44cHW5LwxGdOXASv3a+Gcc7VcdQ/l8MwzzzBq1CgaNAiX9ZJ+4/bYYw8eeeQR9t9/fx577DEuvfTSnOwvV9LkhEab2X9LW2BmPXMcj3PO1QnVPZSDmZU6TtDQoUO59tprad26Nb169aJFixZ52X9lpWmivVl8qBQASWtIOi6PMTnnXK1X3UM57LbbbowZM4alS5cC8M033wAwYMAAXn/9dW688cYaVxQH6XJCI83s2pIJM1sgaSShpwPnnKuxCtlKsLqHcjjqqKN4//336datGw0bNmTkyJEcf/zxFBcXM3jwYG677TZuv/32fB5ypaRJhIokyWIeMnbHs1p+w3LOudqpUEM5NGjQgCuvvJIrr7xylWWjR49m9OjRlTia/EuTCD0F3CtpDOFh01HAk3mNyjnn6jAfyuFXaRKhM4BjgGMJvSA8DdyU9R3OOefK5EM5/KrcRCh21XN9/HPOOedyJs1zQr8BzgM6xvUFmJltkN/QnHPO1XVpiuNuBv5AGOJ7WX7Dcc45V5+kSYS+NbMn8h6Jc865eidNIvQ/SZcBDwA/lcw0s9fzFpVzzuXAvOf75nR7bftNqvB7Bg0axF133UWrVq1Wmn/eeefRvHlzTjvttFyFVyulSYT6xP/JLnoM2Cn34TjnXN1hZjz66KMUFeVz/NDaLc1QDjuW8ucJkHM1VONeTZn3fN+V/lz1mT17NpttthnHHXccPXr0oLi4mHnz5gFw4YUXsskmm7DLLrvw3nvvrXjP5MmT6datG9tuu+2K4R8gDM9w+umn06tXL7p168YNN9xQkGPKp1TJs6TfSvqjpHNK/vIdmHPO1Vbvvfcehx12GG+88QYdO3YEYMqUKYwbN4433niDBx54gMmTJ69Y//DDD2fMmDFMnDiR4uLiFfNvvvlmVl99dSZPnszkyZO58cYbmTVrVrUfTz6VmwjFnhKGAicQmmcfQGiu7ZxzrhQdO3akb9+Vc6AvvPAC++67L02bNqVly5bstddeACxcuJBFixax3XbbAXDQQQeteM/TTz/NHXfcQffu3enTpw/z58/ngw8+qL4DqQZp6oS2M7Nukt40s/MlXUFopOCcc64UzZo1K3V+aUMtZBvawcy45ppr2H333XMWW02TpjhuSfz/o6T2wC9A5/yF5JxzdU+/fv148MEHWbx4MYsWLeKRRx4BYI011qBFixZMmhRa3o0bN27Fe3bffXeuv/56fvnlFwDef/99fvjhh+oPPo/S5IQeieMJXQa8TmgZd2Neo3LOuRyoTJPqfOnRowdDhw6le/fudOzYkR122GHFsptvvpmRI0fSrFkzBgwYsGJoh6OOOorZs2fTo0cPzIx27drx0EMPFeoQ8iJrIiSpCHjWzBYC4yU9CjQ2s2+rJTrnnKtlOnXqxFtvvbVievbs2Sten3XWWZx11lmrvGeLLbbgzTfD2EeXXHIJPXuGJ2KKioq46KKLuOiii/IbdAFlTYTMbHmsA9o2Tv9E4oFV55xzVffYY49x8cUXs3TpUjp27LjSWEJ1XZriuKclDQEesHwNju6cc/XY0KFDa+TQ29UhTSJ0CtAMWCppCb/2ot0yr5E551wlmFmprdBcdoXKY6QZT6hFdQTinHNV1bhxY+bPn0+bNm08IaoAM2P+/Pk0bty42vedZjyhfqXNN7Pncx+Oc85VXocOHZgzZw5z584tdCi1TuPGjenQoUO17zdNcdzpideNgd6EsYW8/0Yy8h8AACAASURBVDjnXI3SsGFDOnf2xxhrkzTFcXsmpyWtB1yat4icc87VG2lyQpnmAFvmOhDnCm3mnl1XmbfhI9MLEIlz9UeaOqFrCL0kQOjmpzswLZ9BOeecqx/S5IReS7xeCtxtZi/lKR7nnHP1SJpE6H5giZktA5BULKmpmf2Y39Ccc87VdWl60X4WaJKYbgI8k59wnHPO1SdpEqHGZvZ9yUR83TTNxiXtIek9STMl/SnLer0kLZO0f5rtOuecqxvSJEI/SOpRMiFpG2BxeW+SVAxcCwwENgeGS9q8jPX+D3gqbdDOOefqhjR1QicD90n6PE6vQxjuuzy9gZlm9hGApHHA3sDbGeudAIwHeqWK2DnnXJ2R5mHVyZI2BTYhdF76rpn9kmLb6wKfJqbnAH2SK0haF9iX0PuCJ0LOOVfPlFscJ+n3QDMze8vMpgPNJR2XYtul9R6Y2U3rVcAZJS3vssRwtKTXJL3mfUI551zdkaY4bqSZXVsyYWYLJI0ErivnfXOA9RLTHYDPM9bpCYyLvd22BQZJWmpmK41fa2ZjgbEAPXv29DGNarE5V3RbZV6HU98sQCTOuZogTSJUJEklA9rFhgSrpXjfZGAjSZ2Bz4BhwEHJFcxsRU+Dkm4DHs1MgJxzztVdaRKhp4B7JY0hFKeNAp4s701mtlTS8fH9xcAtZjZD0qi4fEzlw3a1SbJPtsYDfIwX59yv0iRCZwDHAMcS6nmeBm5Ks3Ezexx4PGNeqYmPmY1Is03nnHN1R5rWccuB6+Ofc845lzNpetHeCLiY8MDpirFfzWyDPMblXME1HqCVGlJ4Awrnci9NcdytwLnA34EdgcMpvfm1cxXWuFdT5j3fd6V5bftNKlA0zrnqliYRamJmz8YWch8D50l6gZAwOVdrDGzdYqXpviedttL0wdUZjHMOSJcILZFUBHwQW7t9BqyZ37BcbeIXd+dcZaXpwPRkQq/ZJwLbAIcAv8tnUM455+qHVH3HxZffE+qDnHPOuZxIkxNyzjnn8iJNnZBzztVpyV49Smz4yPQCRFL/eCLknKsXkg1ovPFMzZHmYdV2wEigU3J9Mzsif2G5qvJnb5xztUGanNDDwAvAM0DWcX+cc865ikiTCDU1szPyHolzzrl6J03ruEclDcp7JM455+qdNInQSYSEaImkRfHvu3wH5pxzru5L87Bqi/LWcc455yojVRNtSXsB/eLkBDN7NH8hOeecqy/KLY6TdAmhSO7t+HdSnOecc85VSZqc0CCgexxhFUm3A28Af8pnYM45V1P4c3f5k7bHhFbAN/H16nmKxVWAdzPiXP5kjqrbuFfTAkZTt6VJhC4G3pD0P8KIqv2AP+c1Kuecc/VCmtZxd0uaAPQiJEJnmNmX+Q7MOedc3VdmwwRJm8b/PYB1gDnAp0D7OM8555yrkmw5oVOAo4ErSllmwE55icg551y9UWYiZGZHx5cDzWxJcpmkxnmNyjnnXL2QpmHCy0Bm8Vtp85yr07yZrqsJki1jGw/QKss7nPpmdYZTZWUmQpLWBtYFmkjamtAoAaAl4O0VaxhvUuqcq42y5YR2B0YAHYArE/MXAWfmMSbnnHP1RLY6oduB2yUNMbPx1RiTc865eiLNc0LjJf0W2AJonJj/13wG5pxzru4rNxGSNIZQB7QjcBOwP/BqnuNyzrl6aWDrlUfP6XvSaStNH1ydwVSDNIPabWdmhwELzOx8YFtgvfyG5Zxzrj5Ikwgtjv9/lNQe+AXonGbjkvaQ9J6kmZJW6XVb0sGS3ox/L0vaKn3ozjnnars0zwk9KqkVcBnwOqG3hJvKe5OkYuBaYFdClz+TJf3bzN5OrDYL6G9mCyQNBMYCfSp4DM4552qpNA0T/hZfjpf0KNDYzL5Nse3ewEwz+whA0jhgb8LAeCXbfjmx/iRCc3DnnHP1RJqGCb8H7jSzhWb2k6Smko4zs+vKeeu6hA5PS8whey7nSOCJMmI4mtCPHeuvv355ITvnXL3UuFfTWtezR5o6oZFmtrBkwswWACNTvG/V/iRCUd6qK0o7EhKhM0pbbmZjzaynmfVs165dil0755yrDdLUCRVJkpkZrKjrWS3F++awciu6DsDnmStJ6kaoYxpoZvNTbNc551wdkSYRegq4Nz4vZMAo4MkU75sMbCSpM/AZMAw4KLmCpPWBB4BDzez9igRe19W3ZwWcc/VTmkToDOAY4FhCEdvTpGgdZ2ZLJR1PSMSKgVvMbIakUXH5GOAcoA1wnSSApWbWszIH4pxzrvZJ0zpuOXB9/KsQM3sceDxj3pjE66OAoyq6Xeecc3VDtqEc7jWzAyVNp5QGBWbWrZS3Oeecc6llywmdHP8Pro5AnHPO1T/ZEqFHCaOnXmBmh1ZTPM455+qRbInQapJ+B2wnab/MhWb2QP7Ccs45Vx9kS4RGEVoCtwL2zFhmhKbVzjnnXKVlG1n1ReBFSa+Z2c3VGJNzzrl6IlvruJ3M7L/AAi+Oc845lw/ZiuP6A/9l1aI48OI455xzOZCtOO7c+P/w6gvHOedcfVJuL9qSTpLUUsFNkl6XtFt1BOecc65uSzOUwxFm9h2wG7AmcDhwSV6jcs45Vy+kSYRKxgUaBNxqZtMofawg55xzrkLSJEJTJD1NSISektQCWJ7fsJxzztUHaYZyOBLoDnxkZj9Kak0oknPOOeeqJE1OaFvgPTNbKOkQ4C/At/kNyznnXH2QJhG6HvhR0lbAH4GPgTvyGpVzzrl6IU0itNTMDNgbuNrMrgZalPMe55xzrlxp6oQWSfozcAjQT1Ix0DC/YTnnnKsP0uSEhgI/AUea2ZfAusBleY3KOedcvVBuTigmPFcmpj/B64Scc87lQJpue/pKmizpe0k/S1omyVvHOeecq7I0xXGjgeHAB0AT4Cjg2nwG5Zxzrn5I0zABM5spqdjMlgG3Sno5z3E555yrB9IkQj9KWg2YKulS4AugWX7Dcs45Vx+kKY47FCgGjgd+ANYDhuQzKOecc/VDmtZxH8eXi4Hz8xuOc865+qTMREjSdMIw3qUys255icg551y9kS0nNLjaonDOOVcvZUuEGgJrmdlLyZmSdgA+z2tUzjnn6oVsDROuAhaVMn9xXOacc85VSbZEqJOZvZk508xeAzrlLSLnnHP1RrZEqHGWZU3SbFzSHpLekzRT0p9KWS5J/4jL35TUI812nXPO1Q3ZEqHJkkZmzpR0JDClvA3HIR+uBQYCmwPDJW2esdpAYKP4dzRhAD3nnHP1RLaGCScDD0o6mF8TnZ7AasC+KbbdG5hpZh8BSBpHGBjv7cQ6ewN3xEHzJklqJWkdM/uigsfhnHOuFlK4/mdZQdoR2DJOzjCz/6basLQ/sIeZHRWnDwX6mNnxiXUeBS4xsxfj9LPAGbHeKbmtowk5JYBNgPfSxFCOtsC8HGynOtSWWGtLnFB7YvU4c6+2xFpT4uxoZu3ytfE0PSb8D/hfJbat0jZXiXUws7HA2ErEUCZJr5lZz1xuM19qS6y1JU6oPbF6nLlXW2KtLXFWVZq+4yprDqGfuRIdWPX5ojTrOOecq6PymQhNBjaS1Dn2wj0M+HfGOv8GDout5PoC33p9kHPO1R+pxhOqDDNbKul44ClCL9y3mNkMSaPi8jHA48AgYCbwI3B4vuIpRU6L9/KstsRaW+KE2hOrx5l7tSXW2hJnlZTbMME555zLl3wWxznnnHNZeSLknHOuYDwRcs45VzCeCNUykkp7tso552qlep0IlVzQkxf2mnyRl1QUuzhC0imSDit0TBVR085txuder38L+VDTPm9Y0adlyesWhYylomri+cyFvDXRrukkyX5tGri+pG/MbJGZWcayGsPMlgNIGgz0Av5Y2IjSS55TSbsBH5jZrELGlIjnOGBtSY2Av5nZ94WMK18kdQO2MbNb8/0dz/i8twO+Bn4ys0/ztc8UMRUDu0j6CegGLJc0xsyWFiqmtDLO50CgBTAN+MTMFhc0uCqqt4lQ4gP9PbAfMFVSMzMbVdMSIEm7Amua2Z2S2gMnAZT8oGMOaXkhYyxP4nyfTug9/ZjCRhRIOgIYChwMvE+4WF5R0KByqOTuOZ7/PYA1E9N5k/i8TwV+C7wDtJB0sZm9k899ZyGgJeHmbQ1gt/g8Y439/ZQkPskSEGAf4GXgVOAs4JkChlhl9boIQtI+wP6E3hxWB9aooUVzHwIvSdrAzD4HLgZaxws6Zra8phYnZZzP7Qg9p+9sZh9I6ippm8z1qjG2YqArMIqQMD4PXF1Tz2VFSGoIKxKDtWOvJU8QLsKZ6+bl3MfxwXY1s50ID6w3Bt6NsVS7mON5FfiZcBHfVFKTmpoARW0g3GhK2hDobWb9gC+BBcB/JTWsQdeqCqv1P7aKKOWD+gW4hJATWh84JBbH9YH83y2mEe/SPgLmAzMlnRp7Mj8V2DbeGVETf0gZRQjdCXfhRcARkv5OyHG8JGmH6jjXmZ+/mS0DvgEuJdyt7xkvVGfHHFKtJGlNQndYzSW1I3SP9QzhfB8saW9Ju0laJ1nPmIP9lnYhfF/SWYTf12FxX9tJapaLfVaEpLXM7GNgJ0KCPJiQq0DS5pLWru6YyhK7MlsT+FjSXvH3/Q0wV9JdwO7A4Dh/CDGHWxvVm+K4jAtiX+AjQldBDwFvmdlv4rKjgK0lTTezHwsdb0niYmaL4p3lU5KWmtnVkpYD50j62cxGFyrWsiTO9y7An4FdCcOCDAJGm9kfJP2FavgBZXz+/Qk3IJ8AE4AjgUMJdQT7Ey5Mw/MdUx61B54DmgJLgd8QiqL6EwaY3JJwEfscuAh4s6o7zDi/uwOvEXLwnYDtge3MbIlCt11DiRf/6qLQhdjekqYCb5rZPyU1ISSIewObAbtVZ0zlMbOvFQYRvVXS4Wb2b0k/AFsDR8eixN8BpwEvFDTYqjCzevUHnAI8DXSJ06cTimH6Ab8H3gC2LHCMSrzeh1BfsU2c7krIFR0fp7cHOhT6vGY5lsMJF8TflLLsEEJdQZdqjOdYYDpwDfAp0COe34eBBwnDlhT088/RcTYDriIkMpsl5t9DuBloArTKw35/D8wgjEEDIXH/O/BPQu59WnWfX2AE8BIhQbw3xvDHuGw74Exgi0J/Zol4WyVeFwE7At/F/2sDY4BHgFviua4xsVfqeAsdQDV/uNsTyoJXT8xrQhgwbxxwM7B5oeNMxPZ74JV44VwE7B3nbwEsB44pdIylxKyM6R7AT8BlGee8HzC1On9AwAbARKBznD6QkCPelJBraAu0LfQ5zPG5vwL4S8l5JtQnHpan/fcj3MS1i9NdCSMs944J0PHAptV8TnoSiqvWiPt/kpAjnAT8udCfWSnxFgEnAucRbo7ujfOHxIRo+zi9HbAnsH6hY67qX70pjosaAp+b2beSGpjZUgvNG8cCY/PdbLUiJPUiFFvsAhwGfAxcFVvw3SWpJCGqMTKKZLYEFpvZ6wpNg1+V9ImZXWNmiyW9RGigML864om+JvTYXiSpoZndK6kzoWjjFELxbK1lZhab7w4m1B/cBlxNyP3vE4tyXiZUzOfDl4RShj/EhhEDCXfq15tZtbc4lHQsoYjtdELVwy6Eet95kj4nFMW1NbOaMHppyfd1OfAPSV8SGnKsC2Bm4yUZ8LCkkWb2QCFjzaU62TAhVuqVdmyfA0slbZRY92BJJ5e0JiqUUirNJxMSod2AIWa2JaEI6V+S9jCzd8wsF8Oc50wiAToNuI5Qlv0PQi6jJ3Cmfm3RtyyfCVBGPOtLWt3C8z8GHMWv3/0fqWGJeWVJ2pTQZHcG4ZgmEp4nuYowYOQI4GkzeyqXrakkHSDpajN7H/gCaA3cB3QnJEyb52pfFYhpL0IJwslmNpOQCLUENpZ0KHHomBqUALUjfFYldWo3E26aLihZJyY8RxJacDYv4xpX69TVnFCzeMFB0jGED1dmdpmkdwg/1FmSvgOOI7Qy+aVQwUpqbGZL4uutgSIzm2JmX8QWMtPjqp8A44F3CxRquWIO7UBCZfgGhIdqjyVUnu4F3CvpJmBhvnKdkrYHFpnZtNh6cCQhJzaRUPT6AHCTpCWESt4R+Ygj3yS1JtQffBRzzlcAd5vZdXH5J8BdhM/iTmC+mf0EVWv5WUoO8zXgPElnmdmFifWGxH1fW9l9VUF7YJyZfRxzvV9Iegw4AegIHFtTEqCoO3CSpFcIrfd2N7OzJL0rqamZHRMT1snAxlbLH1BdSaHLA3P9R7jQ3Rxfn0yoFO8HvAXcGOcPBs4GriRRaVugeLsSLpJNCHVA78eYH4rLtyPUV91DeMahY6HPcUb8mfUQWwOvJKY7Eu6K943Tjashpj8SEuz9gBsJrcF2IDQ+OJGQC+pPaBhRbY0icnyMjQg5478RKtxbE+pjHonLi+P/28lTwxVgHaBh4nN+Dbg0Tu9EGNCya4HOz0BCM+xNEvMGE244mhT68ysj5gcJ9afbJeY1A2YTBgB9B9io0HHm/LgLHUCOP8Q2hOchtgA2Af5FKAr6U/yAHwLuSqzfoAbEPJhwZ35STGxaxfmvEO7kShKqaq/UTRF7shXfXoRmrorn/U8l5xe4DDglvi7KYzxFidfnEZoIXxmnG8UE8kHgokKfuxwdbydCsc2ZhPqD1jEhuAzYGOhDqEvMaWOb+BlvDbwYE/OShGgj4D3g/DjduoDnpiWhKOui+Bs7hJCL2LDQn1vyPGZMDyQ8s/ZqMk5gNcJD3p0LHXM+/urUyKoKHRLeB3xLeD7iTKAzcLGZbSupN+Hu6GkzG17IhghKdBWi0LXJFoRKyN9bKMNG0iRggZkNLESMaSl0fTQK2MfMPpS0M+FZoO6EByVPAPYoOa48xZBsFHEsocx/XUIucy8zm67QQ0IPQoL/BzObm6948qnkWOPzbmcD2xBuYM4hXLAeISRKDwMvmdl/crXPjHnHEi6OFxByv7/EOsD+wE6W5zq/8khaJ8a3F+GacLGZVfmZqFzI+L4OIbTM/MzMHpX0R8JzajsTcvPNzeyqwkWbX3WqTsjCA53PAucCl1soD+5MqKAF6AL8H+FZAQqVAMV9lyRAowgXxseAfYEdJP1kZp+aWV9Jz0pazwrY8WM2sS7iKGBHC62O+hJynzcQnkcpJvREkLcECFZqhHAMofJ2bzP7LFbA3y7pEDN7W9LrwJEW60Zqo5gAdQNuIlyk1iV8Bn8gfL8HEb5Pa5QkQFW94Uqc34MJOZ6vCc/+LCQ0Ab9J0rqEz36XQidAAGb2BTBG0i1xOl+tAisscT5PJiQ4zwO9JQ0HjiDcTIwjlO7U2t470qhTiVB0L/A6MFrSfELOZ2tJtxLuLHYys9kFjG+FWNH4e+C3ZvZJbCgxNCzS/8xslpntXNgoV1bKxWw2oQj0ckkLCc+EfEyom6jWCmmFJ+AHEhqe/BITpIaEfgEfkbS3mb0FLKvOuPKkNfCFhRZp70taQGh8sDYhUdgHmKzQO/z5ubjhijneQ4G7CcXdTxO6O/oJ2JbQCOWEmpbDrEmJT0kJSLw5KiK0Gj0w3jCvTbge/MHMLoitHedZzWpAkXN1LhGy0DfUx/GO7R5CE9GDCHeLf7UCDx+QoT2hNdMnCs8tPSppGeHOZ7GkT4FlhcyxJWUUIfwhzr6FUH/VG/iXmZ0s6VxCRXW1svD80eOEBzI/JTTy+JhQb/ILUGtbFCWK4JpYaBn1NvBJbM77gplNlXQPISFoFYtFexEqtiu7zxUXzPi5dwVONLNX4/IzCQ8hHwk8kGzl6Upnv/bx2JHQ88nmhEYct5rZlwrdCu0R162xrWBzqc4lQiUsPCS5P/BfwpPRYwsdUyk+JvRntYn9+sxPEeHL+T+rYeOcJBKgkwhPcB9tZt8C98e/kuKawYQHbAvhDkIrsQ/N7Jv4TMi+wMCadEdcUTEBGgwMkrSYkBv5hNAHXG9J04ABhO/6h/Gm5usq7rPkgrmRpFmEZ40GECrOAR4lDisS1/cEqAwKPcivb2bjYo7yKEIr2HnAMZLmmVlJXV5bhQ5ef6wpN6D5VGcTIQALz4n0p+beAb9EeI7id5JeBloRmhAPM7MvCxpZgqSewFpm9lgs8tqOX3NrvyPkgv5JqP/ZBzjCCjRmTLwQTlbo+v5IQjP94bU1AUrkgLYHLiQkqA8S6gz+TCh+7ElI9K8ws1dgxbAFld1n8oJ5AiGheZDQ59qJ8YJ5CyFn1FnhQeBvK3+U9cIawMWSNiO0ahxCqKP+nFAEf5ukhwhdi+1nZj8UKtDqVqcTIYBYB1Ajmdl3kq4ltOA5jtCC5ygLQzfUCApPZTcHppQ0kFB4yPNCQiX0a4TnRQaa2bmS3jSzRQUMuURjQq8BBxYqQawKSZ0ITXL/F2f1Bs4n5EYWE5qefy/pcTO7T6E7px+q2gAhKrlgbhr3tzuh546WhPq/CxQeqt4RGOoJUPniDdzPhM5cp1l4wHgOITe7FqFhwluE3M8XBQy12tWpJtq1meJAXzXpjj1xF15E+KHcA1xOGP5gO+DtWJ81hNBb9gFWg57kztEFuSAk7Qf8g5CrfDpOH0VoyntArMg+mPAg7pmE33LOuh9SGM33SmCSmY1UGPp8CLAeIZEaC3xbE1rB1SYKw0bcBowys3vivIeAf5rZ+ELGVih1ou+husDMfq6JCVCc7BDvzq4mNH8eaGZPxgToWMId+hk1KQGCmjEoYWVZ6CfsNOAKSQMI48U0IhR7LlUYkfYM4EULctr/nYWm3X8h1FkOs9CkfRwwl5DD/MYToIozs4cJLQwvlnS+pN8SBvybWtjICqfOF8e5ykk0QjgBOFChd+anCM2bj4gV33cSiuIOqI1FXjWRpGILI74S62QaErrnOZLwHNCxhHqghsBfYjFPXnJ8ZvawpKWEC2ZJPLcR+masCUWutVJsBduA0A/kfYQ6oNmFjapwPBFyZYqtC0cQEpmSDmGfJiREf5S0wMzOKWCIdUZJnY6ZLVPo2aMDMMPCCKCLCc3MjzSzYxV6BmkWm/TmtcgxJnLLCUOdLDWz+wljW7kqMLOHJO0EzLbwWEm95XVCboWM54CaEVq6NTGzmxR68v0xLmtE6A1hmtXQnhxqE0lrEMb8eZnQ6OBmQkV1D2A0oWXazsAlhG6dHi9AjLsSmr3XmEYzrm7wnJADVkmAfk8o7ikCRkgab2YL4rLDgXfN7NHCRVvniNDX4U6Eiv/DzOwlSYMIjQG+NLN7YuOVguRCLAf9zzlXGm+Y4IBV+l77HXCfmV1J6ATzFklbxOduTgEWFC7SuiX2SvANYcydrwi9X+8MEHM8k4DTJTUys3+a2QtS7gakc67QPBFyK+jXvtfOBpbElm8iPAz5Z8KY9sOsnnQnkm8x97lcvw4x/XdC8921YpNsCL0TLCLkTIHa3erPuUxeJ+RWIulowrAMcwhjw3xMKCI6H/jFCjgCbV0Unxv5G6GroZfN7AaFfvn2InTp0hoYbWYPFjBM5/LG64Rcpsy+1w4m1EvgCVBuJB4CbkqoBzod+A64Mha7/T0+ILwFodPd6bX5wVvnsvFEyK0kS99rPxY4tDojJkD9CN3hNAQmxi6cRgHXK/RGfamkzhZ7ffcEyNVVXifkypLse63G9r9XG8XeDi4ndH/Tl9Az9hpmNo0wjPswSZ2sZg074lxeeJ2QK5MXAeWewoiotwKnmNlzko4A+gGPA8/EItAW3iOBqy88J+TK5AlQXiwiNDY4HsDCkAgTgAOA3WN3SJ4AuXrDEyHn8qjkmR5JrWNT7FmEgeHaSboawMxuIwxD/7bVsIEMncs3L45zLk8SreD2Jjzk+zPwlJldHscLugH42MyOLmCYzhWU54Scy5OYAO1CeMZqGKE/uIsknRN7TT4W2FjS5t4LgquvvIm2c3kiqTXhgdOjgW0Inb7uBtwde80+Q9IesVm8c/WS54Scy4PYTf/DwKeEYZsPAS4xswnAnYQxmtb3BMjVd54Tci7HJG0KnAicVDL6qKT3gP6xf74OwP5m9kkBw3SuRvCckHM5kGgF14BQ7LYNsHVilVcJwzWcBfzLzKZUe5DO1UDeOs65Kkq0gtuV8BzQh8BBwFaEITGeSKy7hpkt8AeBnQs8J+RcFcUEaCBwDbC6mc0FxgNTgL0k7ZVYd0HJewoSrHM1jCdCzlWRpMbACcBxZvZUzOXMAR4BPgD2kdS2oEE6V0N5wwTnKiFRBLc54Xe0GvBFXNwIWAJ8S+gnrmUctM45l8FzQs5VQkyA9iQ0t54HvAxcLKm1mS2JQzU8BRSZ2ceFjNW5msxzQs5VgqTuhBFRh5nZ55LuBtYCnpJ0F3AU8OeSJtrOudJ5IuRc5fwETAUGSNoP2IXwYOpcQj3QMWb2oreCcy47b6LtXCVIag6MAIYDVwDvE8YFWmBmdxcwNOdqFU+EnKsCSauZ2c+SegK3EXpJeLbAYTlXa3jDBOeqZlkcrvta4CxPgJyrGM8JOVdFkpoBa5rZLK8Dcq5iPBFyzjlXMF4c55xzrmA8EXLOOVcwngg555wrGE+EnHPOFYwnQq7gJE2QtHvGvJMlXSepvaT7c7y/8ySdlnLd7pIGJab3kvSnXMch6a+Sdqni9lpJOi4XsTlXXTwRcjXB3cCwjHnDgLvN7HMz278AMZXoDqxIhMzs32Z2Sa53YmbnmNkzVdxMKyDviVAcPda5nPBEyNUE9wODJTUCkNQJaA+8KKmTpLfi/C0kvSppqqQ3JW2UXB7XOU3SefH1SEmTJU2TNF5S02xBSDpA0ltx/eclrQb8FRga9zlU0ghJo+P6t0m6XtL/JH0kqb+kWyS9I+m2xHa/T7zeP7ksMf82SasktpI2lPRMjOl1SV0kNZf0bJyeLmnvuPolQJcY62Xx/afHc/CmpPMT2z1b0ruS/iPp7kSOrLukSXH9ByWtEedPkHSRpOeAsyTNktQwLmspaXbJtHMV4YmQK7jY0/SrwB5x1jDgnlIe+hwFsw3NlwAAA29JREFUXG1m3YGewJxyNv2AmfUys62Ad4Ajy1n/HGD3uP5eZvZznHePmXU3s3tKec8awE7AHwiD2P0d2ALoGnvarqo7gWtjTNsRxixaAuxrZj2AHYErJAn4E/BhjPV0SbsBGwG9CTm6bST1i10MDQG2BvYjnMsSdwBnmFk3YDpwbmJZKzPrb2bnAxOA38b5w4DxZvZLDo7X1TOeCLmaIlkkNyxOZ5oInCnpDKCjmS0uZ5tbSnpB0nTgYELikM1LwG2SRgLFKeN+JCaW04GvzGy6mS0HZgCdUm6jVJJaAOua2YMAZrbEzH4EBFwk6U3gGWBdwjASmXaLf28ArwObEhKl7YGHzWyxmS0iJJ5IWp2Q0DwX3387oVPWEslE+Cbg8Pj6cMLgfc5VmCdCrqZ4CNhZUg+giZm9nrmCmd0F7AUsJozbsxOwlJW/x40Tr28DjjezrsD5GctWYWajgL8A6wFTJbVJEfdP8f/yxOuS6ZK6k2SOLmsMGVTG/IOBdsA2MVf4VRnbFXBxzBl1N7MNzezmLNstzw8lL8zsJaCTpP5AsZm9VfbbnCubJ0KuRjCz7wlFPLdQei4ISRsAH5nZP4B/A90IF+A1JbWJdUqDE29pAXwR6yoOLi8GSV3M7BUzO4cwWup6wKK4nar4Sv/f3v26RBCEYRz/PhYNBzaLwWQ4/weDUURUUCwegsG/wWI5BIPRojYFsRlUEINBUUS8oGC4pJwYxCIn1y6MYUYUf3B6ICvc84m7O7szLOy7M+/sjpSX1AaM/bRQCOEZuJc0murXnvJancBjCKEuaQDoSUU+1vUAmEnLTiCpW1IXcAIMS+pI+4bS9arAk6T+VL4AHPG9DeK9ci/ImuZZLvafbAHbfJ4p92oSmJJUBx6AYnoQF4Fz4BYovzt+Pm2vEIfLGgWTJUm9xJ7CIXAF3AFzki6BxaZaFXM1e8RF766B3C/KFoDV1MY6MEHME+1KKhEX1itDzK1JOk0TNfZTXigPnMWUETVgKoRwIWknta8ClIBqut40sJKC3Q1vQ25f2QQW+Oalwewn/ANTsxYkKRdCqKVgcwzMfjUE2uAc48BICKHwJ5W0luCekFlrWpPUR8wlrTcRgJaBQd59Q2XWDPeEzMwsM56YYGZmmXEQMjOzzDgImZlZZhyEzMwsMw5CZmaWmRcbykdnbEoZMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a rudimentary diagram\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "tick_position = np.arange(len(categories))\n",
    "plt.xticks(tick_position, categories, rotation=45)\n",
    "\n",
    "for color, classifier_name in zip(\n",
    "        ['#48110C', '#808080', '#DB4C2C', '#E38C2D', '#EBC137'],\n",
    "        sorted(classifiers)):\n",
    "    score_means = [classifiers_scores[classifier_name][category].mean()\n",
    "                   for category in categories]\n",
    "    plt.bar(tick_position, score_means, label=classifier_name, width=.11, color=color)\n",
    "    tick_position = tick_position + .09\n",
    "\n",
    "plt.ylabel('Classification accurancy (f1 score)')\n",
    "plt.xlabel('Visual stimuli category')\n",
    "plt.ylim(ymin=0)\n",
    "plt.legend(bbox_to_anchor=(1, 1))\n",
    "plt.title('Category-specific classification accuracy for different classifiers')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/nilearn/plotting/displays.py:1596: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  fraction * (x1 - x0), y1 - y0])\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/nilearn/plotting/displays.py:1596: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  fraction * (x1 - x0), y1 - y0])\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/nilearn/plotting/displays.py:1596: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  fraction * (x1 - x0), y1 - y0])\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:849: FutureWarning: The parameter 'iid' is deprecated in 0.22 and will be removed in 0.24.\n",
      "  \"removed in 0.24.\", FutureWarning\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/nilearn/plotting/displays.py:1596: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  fraction * (x1 - x0), y1 - y0])\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/nilearn/plotting/displays.py:1596: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  fraction * (x1 - x0), y1 - y0])\n",
      "/home/srastegarnia/miniconda3/lib/python3.7/site-packages/nilearn/plotting/displays.py:1596: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  fraction * (x1 - x0), y1 - y0])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 208.8x165.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 208.8x165.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 208.8x165.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 208.8x165.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 208.8x165.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 208.8x165.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the face vs house map for the different classifiers\n",
    "\n",
    "mean_epi_img = image.mean_img(func_file)\n",
    "\n",
    "# Restrict the decoding to face vs house\n",
    "condition_mask = y.isin(['face', 'house'])\n",
    "masked_timecourses = masked_timecourses[\n",
    "    condition_mask[nonrest_task_mask]]\n",
    "y_f = (y[condition_mask] == 'face')\n",
    "\n",
    "# Transform the stimuli to binary values\n",
    "y_f.astype(np.int)\n",
    "\n",
    "for classifier_name, classifier in sorted(classifiers.items()):\n",
    "    classifier.fit(masked_timecourses, y_f)\n",
    "\n",
    "    if hasattr(classifier, 'coef_'):\n",
    "        weights = classifier.coef_[0]\n",
    "    elif hasattr(classifier, 'best_estimator_'):\n",
    "        weights = classifier.best_estimator_.coef_[0]\n",
    "    else:\n",
    "        continue\n",
    "    weight_img = masker.inverse_transform(weights)\n",
    "    weight_map = get_data(weight_img)\n",
    "    threshold = np.max(np.abs(weight_map)) * 1e-3\n",
    "    plot_stat_map(weight_img, bg_img=mean_epi_img, display_mode='z', cut_coords=[-15],\n",
    "                  threshold=threshold, title='%s: face vs house' % classifier_name)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}