linear_models_feature_contributions_(pca).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Models - Feature Contribution Analysis and Feature Annotation\n",
    "\n",
    "This notebook mainly contains:<br>\n",
    "(1) Leave one feature out predictions to assess importance of each feature<br>\n",
    "(2) Alternative random feature pertubation\n",
    "\n",
    "**Inputs**\n",
    "\n",
    "Single file containing all the 47 task contrasts for all 254 subejcts (for LH)<br>\n",
    "`/scratch/users/robert.scholz2/acc_dists/all_47_tasks_254_full_unrelated.raw.npy`\n",
    "\n",
    "Single files containing the data of all 254 subejcts per predictor modality (for LH)<br>\n",
    "given as dics, with subject numbers beeing the key, and values beeing of shape (29696, d)<br>\n",
    "e.g. for \"RS-PCA200\": `all_grads_254_full_unrelated.L.corrcoef.procrAlignGrp.npy`\n",
    "\n",
    "**Outputs**\n",
    "\n",
    "PCA per-feature pertubation results<br>\n",
    "`results/scores/pertubation_pca.yfull.from_zscored.npy`\n",
    "\n",
    "Difference in discriminability per feature and task<br>\n",
    "`results/scores/rs_pca.lin_weight_onebyone_contrib.values.diff.spatial_zscore.yfull.npy`\n",
    "\n",
    "Random 10% feature pertubations<br>\n",
    "`pertubation_pca.random.yresid.from_zscored.npy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "/usr/users/robert.scholz2/.local/lib/python3.9/site-packages/nilearn/datasets/__init__.py:93: FutureWarning: Fetchers from the nilearn.datasets module will be updated in version 0.9 to return python strings instead of bytes and Pandas dataframes instead of Numpy arrays.\n",
      "  warn(\"Fetchers from the nilearn.datasets module will be \"\n",
      "pixdim[1,2,3] should be non-zero; setting 0 dims to 1\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import scipy\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Model fitting and scoring functions\n",
    "import sklearn.linear_model\n",
    "from sklearn.utils import Bunch\n",
    "from sklearn.model_selection import train_test_split, ShuffleSplit\n",
    "\n",
    "import sys\n",
    "sys.path.append(\"/home/mpg02/MLSC/robert.scholz2/.local/lib/python3.7/site-packages\")\n",
    "\n",
    "from lib.stats import compute_all_scores, compute_scores\n",
    "from lib.stats import np_pearson_cor\n",
    "from lib.stats import comp_dice, comp_corr, r2_score, comp_tsss_similiarity, comp_cosine_similiarity\n",
    "from lib.stats import compute_batch_differentiability_score\n",
    "from lib.linear_models import predict_y_from_x, predict_from_modality_file, approximate_best_alphas, score\n",
    "\n",
    "desired_scores = {\"corr\": comp_corr, \"r2_score\": r2_score};\n",
    "\n",
    "\n",
    "# plotting libraries\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from lib.plotting import plot_corr_scores, plot_29k\n",
    "from matplotlib.colors import ListedColormap\n",
    "import os, brainspace\n",
    "import hcp_utils as hcp\n",
    "from lib.plotting import plot_gen_surf2, centered_minmax\n",
    "stub = os.path.dirname(brainspace.__file__) + \"/datasets/surfaces/\"\n",
    "\n",
    "\n",
    "from lib.plotting import plot_29k\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definition of prdedictors and targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# general data \n",
    "data_dir = \"/scratch/users/robert.scholz2/acc_dists/\"\n",
    "subjs=np.loadtxt(\"data/subjs_hcp254_full_unrelated.txt\").astype(int).astype(str);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prediction modality files\n",
    "pred_files = {\n",
    "    \"RS-ICA200\": \"all_ICAmaps_254_full_unrelated.L.c200.npy\",\n",
    "    \"RS-PCA200\" : \"all_grads_254_full_unrelated.L.corrcoef.procrAlignGrp.npy\",\n",
    "    \"Distance to Parcels (raw)\": \"all_dists_254_full_unrelated.centroid_dist.raw.npy\",\n",
    "    \"SC Blueprints\": \"all_blueprints_BPsubset247.npy\",\n",
    "    \"SEmodes\": \"all_SEmodes_254_full_unrelated.L.midthickness_MSMAll.noMW.surf.SEmodes.procAlignGrp.npy\",\n",
    "    \"Distance-PCAgra\" : \"all_dists_254_full_unrelated.29x200.grPCA_aligned.L.npy\",\n",
    "    \"Distance-PCAsal\" : \"all_dists_254_full_unrelated.29x200.aligned_to_131217.L.npy\",\n",
    "    \"FS-StructuralMaps\": \"all_fs_struct_254_full_unrelated.L.npy\",\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of subjects in task data file: 254\n",
      "First 5 subject ids: ['100206', '100610', '101006', '101309', '101915']\n",
      "Each subjects' task data has the shape: (29696, 47)\n"
     ]
    }
   ],
   "source": [
    "# prediction targets\n",
    "\n",
    "fn = \"/scratch/users/robert.scholz2/acc_dists/all_47_tasks_254_full_unrelated.raw.npy\"\n",
    "yfull_task_data =np.load(fn, allow_pickle=True).item()\n",
    "\n",
    "print(\"Number of subjects in task data file:\", len(yfull_task_data))\n",
    "print(\"First 5 subject ids:\", list(yfull_task_data.keys())[:5])\n",
    "print(\"Each subjects' task data has the shape:\", yfull_task_data[\"100206\"].shape)\n",
    "\n",
    "y_task_data = yfull_task_data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Leave one out predictions (for RS-PCA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lib.data_loading import gather_modalities, load_xy_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Concatenating predictors: 100%|██████████| 254/254 [00:02<00:00, 114.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gathered data from 254 subjects. Per subject data is of shape: (29696, 200)\n",
      "0 missing subjects:  []\n",
      "z-scoring task maps along the spatial dimension\n",
      "All 254 have task data attached to them\n",
      "z-scoring predictors along dimension 0 of size 254\n",
      "200 (254, 29696, 200) (254, 29696, 47)\n"
     ]
    }
   ],
   "source": [
    "combination = [\"RS-PCA200\"];\n",
    "comb_predictors = gather_modalities(subjs, combination, pred_files, zscore_predictors=False, modality_cache=None);\n",
    "# this additionally z-scores the y_task_data\n",
    "isubjs, xdata, ydata = load_xy_data(subjs, data_dir, comb_predictors, y_task_data, zscore_tasks=True)\n",
    "\n",
    "zdim = 0\n",
    "if not(zdim is None):\n",
    "  print(f\"z-scoring predictors along dimension {zdim} of size {xdata.shape[zdim]}\")\n",
    "  xdata = scipy.stats.zscore(xdata, axis= zdim)\n",
    "\n",
    "n_pred = xdata.shape[-1]  \n",
    "print(n_pred, xdata.shape, ydata.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(xdata, ydata, test_size=0.2, shuffle=False)\n",
    "x = x_train.reshape((-1, x_train.shape[-1]))\n",
    "y = y_train.reshape((-1, y_train.shape[-1]))\n",
    "xt = x_test.reshape((-1, x_test.shape[-1]))\n",
    "skr = sklearn.linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full feature baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(203, 29696, 200) (203, 29696, 47) (51, 29696, 200) (51, 29696, 47)\n",
      "(6028288, 200) (6028288, 47)\n",
      "[0.15772232 0.14394217 0.15459464 0.22700102 0.22848868 0.05451082\n",
      " 0.22034964 0.23481876 0.28359185 0.22120995 0.15862779 0.13049487\n",
      " 0.14907837 0.12396064 0.12640758 0.17009516 0.20462007 0.06354996\n",
      " 0.06427077 0.06500841 0.05789746 0.06738369 0.21785659 0.23464312\n",
      " 0.14163186 0.21580799 0.24087034 0.16915579 0.22017131 0.23527882\n",
      " 0.21391188 0.20930996 0.16552143 0.17255001 0.17844421 0.19010549\n",
      " 0.24682304 0.20626495 0.21074625 0.21212193 0.22874845 0.21356116\n",
      " 0.21612945 0.09938611 0.11267054 0.11837956 0.07132536]\n"
     ]
    }
   ],
   "source": [
    "# get the full-features baseline to compare against\n",
    "y_train, y_train_pred, y_test, y_test_pred, skr = predict_y_from_x(xdata, ydata, verbose=1, lmodel=\"linear\")\n",
    "scoresd, diff_scores_test = score(y_test_pred, y_test);\n",
    "baseline_diff = diff_scores_test\n",
    "baseline_corr = scoresd.corr.mean(0)\n",
    "print(baseline_diff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perturb features one by one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200/200 [2:14:12<00:00, 40.26s/it]  \n"
     ]
    }
   ],
   "source": [
    "cscores = []\n",
    "dscores = []\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(xdata, ydata, test_size=0.2, shuffle=False)\n",
    "x = x_train.reshape((-1, x_train.shape[-1]))\n",
    "y = y_train.reshape((-1, y_train.shape[-1]))\n",
    "xt = x_test.reshape((-1, x_test.shape[-1]))\n",
    "\n",
    "test_those=np.arange(x_test.shape[-1])\n",
    "    \n",
    "for i in tqdm(test_those): # [:4]):\n",
    "    \n",
    "  # perturb one feature at a time\n",
    "  x_pert = x.copy(); x_pert[:,i]=0;\n",
    "  skr.fit(x_pert, y)\n",
    "\n",
    "  xt_pert=xt.copy(); xt_pert[:,i]=0\n",
    "  y_test_pred =skr.predict(xt_pert).reshape(y_test.shape)\n",
    "\n",
    "  scoresd, diff_scores_test = score(y_test_pred, y_test);\n",
    "  cscores.append(scoresd.corr.mean(0))  \n",
    "  dscores.append(diff_scores_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the purtubation results\n",
    "\n",
    "pl = {\"dscores\":np.array(dscores), \"cscores\": np.array(cscores), \"baseline_diff\": baseline_diff, \"baseline_corr\": baseline_corr, \"skr\":skr}\n",
    "#np.save(\"results/scores/pertubation_pca.yfull.from_zscored.npy\", pl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize the pertubation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['dscores', 'cscores', 'baseline_diff', 'baseline_corr', 'skr'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = np.load(\"results/scores/pertubation_pca.yfull.from_zscored.npy\", allow_pickle=1).item()\n",
    "dscores = p[\"dscores\"]; cscores = p[\"cscores\"]; baseline_diff = p[\"baseline_diff\"]; baseline_corr =p[\"baseline_corr\"];\n",
    "p.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 47)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the score difference between full feature and perturbed models (on a per task basis)\n",
    "\n",
    "diff = (baseline_diff - np.array(dscores))\n",
    "corr = (baseline_corr - np.array(cscores))\n",
    "diff.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of the full results\n",
    "\n",
    "import cmasher as cmr\n",
    "import cmcrameri.cm as cmc\n",
    "cmap_coeffs = \"cmr.prinsenvlag_r\" #cmc.vik #\"PiYG\"\n",
    "def fxdcolorbar(mappable, *args,**kwargs): from mpl_toolkits.axes_grid1 import make_axes_locatable; last_axes = plt.gca(); ax = mappable.axes; fig = ax.figure; divider = make_axes_locatable(ax);  cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05); cbar = fig.colorbar(mappable, cax=cax, *args,**kwargs); plt.sca(last_axes); return cbar;\n",
    "unique_contrasts = np.loadtxt(\"data/task_contrasts_47_unique_names.txt\", dtype=str);\n",
    "contrast_ids_to_show = [8, 38, 16, 2, 27, 24, 5, 44, 21]\n",
    "tnames_mid = [\"Lang S-M\", \"WM2-0\", \"Motor Cue-Avg\", \"Emoti Face-Shape\", \"Social TOM-Rand\", \"Rel-Match\", \"Gamble Rew-Pun\" ,\"WM Face-Av\", \"Motor T-Avg\"]\n",
    "\n",
    "cmap_seq = \"cmr.savanna_r\"#\"cmc.batlow\"#\" #cmc.vik #\"PiYG\"\n",
    "\n",
    "v = np.absolute(diff).max()\n",
    "ax = plt.figure(figsize=(30,11)).gca()\n",
    "im = ax.matshow(diff.T, cmap =cmap_seq , vmin=0, vmax=v*0.5, aspect=2); plt.colorbar(im, ax=ax)#fxdcolorbar(im);\n",
    "plt.yticks(np.arange(len(unique_contrasts)), unique_contrasts);\n",
    "for label in np.array(ax.get_yticklabels())[contrast_ids_to_show]: label.set_color(\"red\")\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"results/plots/XXrs_pca.lin_weight_onebyone_contrib.diff.200.spatial_zscore.yfull.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same based on models predicting residualized task contrasts\n",
    "# (requires change to the target definition: \n",
    "# y_task_data =np.load(\"/scratch/users/robert.scholz2/acc_dists/all_47_tasks_254_full_unrelated.yresid.npy\", allow_pickle=True).item()\n",
    "\n",
    "cmap_seq = \"cmr.savanna_r\"\n",
    "v = np.absolute(diff).max()\n",
    "ax = plt.figure(figsize=(30,11)).gca()\n",
    "im = ax.matshow(diff.T, cmap =cmap_seq , vmin=0, vmax=v*0.5, aspect=2); plt.colorbar(im, ax=ax)#fxdcolorbar(im);\n",
    "plt.yticks(np.arange(len(unique_contrasts)), unique_contrasts);\n",
    "for label in np.array(ax.get_yticklabels())[contrast_ids_to_show]: label.set_color(\"red\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"results/plots/rs_pca.lin_weight_onebyone_contrib.diff.200.spatial_zscore.yresid.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of the main contrasts of interest\n",
    "\n",
    "coeffs = diff.T\n",
    "fig, axs = plt.subplots(1,1, figsize=(6,2.7)) # 4 plots in a row\n",
    "axs=[axs];\n",
    "\n",
    "coeffs2show = coeffs[contrast_ids_to_show,:]\n",
    "best_coefficients = np.argsort(np.absolute(coeffs2show), axis=1)[:,::-1]\n",
    "\n",
    "n_comps = 30;\n",
    "data = coeffs2show[:,:n_comps]\n",
    "\n",
    "aspr=(data.shape[1]/data.shape[0])/2.4\n",
    "#im = axs[0].matshow(data, cmap = cmap_seq, vmin=-v, vmax=v, aspect=aspr); \n",
    "im = axs[0].matshow(data, cmap = cmap_seq, vmin=0, vmax=v*0.5, aspect=aspr); \n",
    "#for (i, j), z in np.ndenumerate(coeffs2show): \n",
    "#  if j in best_coefficients[i,:3]: plt.text(j, i, '{:.2f}'.format(z), ha='center', va='center', size=5)\n",
    "\n",
    "plt.colorbar(im,fraction=0.020, pad=0.02, ax=axs[0]);#fxdcolorbar(im, ax=axs[0]);#plt.colorbar(im, ax=axs[0]);\n",
    "axs[0].set_yticks(np.arange(len(tnames_mid))); axs[0].set_yticklabels( tnames_mid);\n",
    "axs[0].set_xlabel(f\"One by one contributions to differentiability by RS-PCA components (first {n_comps} of 200)\"); axs[0].xaxis.set_label_position('top') \n",
    "plt.savefig(\"results/plots/rs_pca.lin_weight_onebyone_contrib.diff.30.spatial_zscore.yresid.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn = \"results/scores/rs_pca.lin_weight_onebyone_contrib.values.diff.spatial_zscore.yfull.npy\"\n",
    "data = dict(contr=diff.T, unique_contrasts=unique_contrasts, contrast_ids_to_show = contrast_ids_to_show, best = best_coefficients_full);\n",
    "np.save(fn, data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listing of list highest coefficients per component:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lang S-M         [1, 0, 8, 6, 10, 2, 23, 9, 3, 30]       [0.0976, 0.0142, 0.0132, 0.0108, 0.0082, 0.0058, 0.0047, 0.0045, 0.0038, 0.003]\n",
      "WM2-0            [1, 8, 6, 19, 18, 10, 21, 4, 23, 17]    [0.0185, 0.0116, 0.0084, 0.0066, 0.0058, 0.0052, 0.0045, 0.004, 0.0036, 0.0034]\n",
      "Motor Cue-Avg    [0, 12, 1, 23, 13, 10, 29, 17, 3, 18]   [0.0306, 0.0249, 0.0179, 0.0046, 0.0038, 0.0035, 0.0021, 0.0015, 0.0014, 0.0012]\n",
      "Emoti Face-Shape [7, 12, 5, 23, 1, 16, 4, 2, 28, 21]     [0.0166, 0.0081, 0.0071, 0.0056, 0.0046, 0.0044, 0.0041, 0.003, 0.0026, 0.0024]\n",
      "Social TOM-Rand  [6, 5, 11, 4, 16, 27, 0, 10, 14, 1]     [0.0317, 0.0115, 0.0102, 0.0041, 0.0034, 0.0022, 0.0021, 0.0019, 0.0018, 0.0017]\n",
      "Rel-Match        [0, 6, 5, 1, 7, 4, 16, 18, 10, 14]      [0.0128, 0.0127, 0.0114, 0.0046, 0.0033, 0.0031, 0.003, 0.0019, 0.0016, 0.0011]\n",
      "Gamble Rew-Pun   [2, 1, 10, 3, 7, 5, 6, 9, 15, 0]        [0.0045, -0.0036, 0.0023, 0.0022, 0.0014, -0.0013, 0.0008, 0.0008, 0.0008, 0.0008]\n",
      "WM Face-Av       [1, 0, 21, 2, 15, 4, 12, 23, 19, 5]     [0.0113, 0.0072, 0.0067, 0.0049, 0.0044, 0.0038, 0.0035, 0.0024, 0.0023, 0.0016]\n",
      "Motor T-Avg      [13, 7, 14, 4, 19, 11, 0, 22, 21, 15]   [0.0198, 0.0041, 0.0035, 0.0026, 0.0019, 0.0018, -0.0017, 0.0017, 0.0016, 0.0015]\n"
     ]
    }
   ],
   "source": [
    "# list 10 best coefficients per component:\n",
    "for cn in range(best_coefficients.shape[0]):\n",
    "  cname = tnames_mid[cn]; \n",
    "  p1 = cname +  (17-len(cname))*\" \" + str( list(best_coefficients[cn, :10])); \n",
    "  print(p1, (55-len(p1))*\" \" , list(coeffs2show[cn,  best_coefficients[cn, :10]].round(4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EMOTION_FACES    [5, 3, 16, 19, 10, 23, 21, 24, 7, 17]   [0.0183, 0.0093, 0.0053, 0.0033, 0.0032, 0.0031, 0.0024, 0.0018, 0.0016, 0.0016]\n",
      "EMOTION_SHAPES   [5, 3, 0, 4, 17, 24, 12, 7, 19, 1]      [0.0102, 0.0088, 0.0063, 0.0052, 0.0045, 0.0033, 0.003, -0.0024, 0.0024, 0.0023]\n",
      "EMOTION_FACES-SHAPES[7, 12, 5, 23, 1, 16, 4, 2, 28, 21]  [0.0166, 0.0081, 0.0071, 0.0056, 0.0046, 0.0044, 0.0041, 0.003, 0.0026, 0.0024]\n",
      "GAMBLING_PUNISH  [1, 5, 6, 23, 9, 19, 0, 27, 8, 3]       [0.0151, 0.014, 0.0137, 0.009, 0.0089, 0.0083, 0.0083, 0.0063, 0.0044, 0.0042]\n",
      "GAMBLING_REWARD  [1, 6, 5, 9, 23, 19, 3, 0, 27, 24]      [0.0171, 0.0167, 0.0141, 0.0108, 0.0077, 0.0071, 0.0066, 0.0066, 0.0053, 0.0035]\n",
      "GAMBLING_REWARD-PUNISH[2, 1, 10, 3, 7, 5, 6, 9, 15, 0]   [0.0045, -0.0036, 0.0023, 0.0022, 0.0014, -0.0013, 0.0008, 0.0008, 0.0008, 0.0008]\n",
      "LANGUAGE_MATH    [8, 4, 5, 16, 6, 3, 25, 23, 17, 29]     [0.0255, 0.0161, 0.0107, 0.0046, 0.0038, 0.0036, 0.0034, 0.0022, 0.0022, 0.0021]\n",
      "LANGUAGE_STORY   [1, 5, 3, 4, 0, 8, 2, 16, 17, 19]       [0.0272, 0.0134, 0.011, 0.0098, 0.0073, 0.0042, 0.0039, 0.0035, 0.0012, 0.0012]\n",
      "LANGUAGE_STORY-MATH[1, 0, 8, 6, 10, 2, 23, 9, 3, 30]     [0.0976, 0.0142, 0.0132, 0.0108, 0.0082, 0.0058, 0.0047, 0.0045, 0.0038, 0.003]\n",
      "MOTOR_CUE        [0, 1, 12, 13, 23, 2, 24, 4, 8, 15]     [0.04, 0.0177, 0.0139, 0.0068, 0.0045, 0.0035, 0.0022, 0.0022, 0.002, 0.0018]\n",
      "MOTOR_LF         [0, 15, 2, 27, 43, 19, 14, 22, 3, 13]   [0.0102, 0.0098, 0.0087, 0.0086, 0.0043, 0.0032, 0.0031, 0.0027, 0.0026, 0.0026]\n",
      "MOTOR_LH         [27, 3, 2, 12, 4, 14, 16, 43, 22, 19]   [0.0069, 0.0056, 0.0048, 0.0031, 0.0029, 0.0028, 0.0026, 0.0023, 0.0022, 0.0021]\n",
      "MOTOR_RF         [27, 0, 15, 2, 14, 43, 4, 19, 12, 22]   [0.0092, 0.0088, 0.0071, 0.0053, 0.0051, 0.0051, 0.0048, 0.0047, 0.0031, 0.0029]\n",
      "MOTOR_RH         [4, 27, 3, 16, 2, 43, 0, 15, 7, 9]      [0.0096, 0.0071, 0.005, 0.0044, 0.0034, 0.0033, 0.0031, 0.0019, 0.0017, 0.0017]\n",
      "MOTOR_T          [13, 27, 4, 3, 22, 2, 0, 15, 12, 41]    [0.0115, 0.008, 0.0072, 0.0049, 0.0046, 0.0041, 0.0034, 0.002, 0.0018, 0.0013]\n",
      "MOTOR_AVG        [27, 0, 2, 4, 15, 3, 43, 22, 12, 13]    [0.0107, 0.0076, 0.0072, 0.0069, 0.0056, 0.005, 0.0043, 0.004, 0.0026, 0.0023]\n",
      "MOTOR_CUE-AVG    [0, 12, 1, 23, 13, 10, 29, 17, 3, 18]   [0.0306, 0.0249, 0.0179, 0.0046, 0.0038, 0.0035, 0.0021, 0.0015, 0.0014, 0.0012]\n",
      "MOTOR_LF-AVG     [0, 2, 15, 14, 17, 19, 4, 43, 6, 11]    [0.0065, 0.0053, 0.0049, 0.0042, 0.0029, 0.0023, -0.0022, 0.0015, 0.0015, 0.0012]\n",
      "MOTOR_LH-AVG     [0, 4, 27, 5, 1, 2, 15, 16, 25, 10]     [0.006, 0.0034, 0.0018, 0.0014, -0.0014, 0.0013, 0.0012, 0.0012, 0.0008, -0.0008]\n",
      "MOTOR_RF-AVG     [14, 19, 21, 16, 5, 15, 0, 43, 3, 27]   [0.0066, 0.0047, 0.0029, 0.0029, 0.0029, 0.0029, 0.0026, 0.0017, 0.0017, 0.0012]\n",
      "MOTOR_RH-AVG     [13, 19, 4, 16, 12, 0, 7, 14, 22, 3]    [0.0188, 0.004, -0.0033, 0.0029, 0.0029, 0.0024, 0.0024, 0.0019, 0.0017, -0.0016]\n",
      "MOTOR_T-AVG      [13, 7, 14, 4, 19, 11, 0, 22, 21, 15]   [0.0198, 0.0041, 0.0035, 0.0026, 0.0019, 0.0018, -0.0017, 0.0017, 0.0016, 0.0015]\n",
      "RELATIONAL_MATCH [1, 3, 5, 6, 23, 8, 12, 14, 0, 13]      [0.0262, 0.0202, 0.0159, 0.0132, 0.0056, 0.0056, 0.0048, 0.0046, 0.0038, 0.0034]\n",
      "RELATIONAL_REL   [5, 1, 6, 3, 8, 14, 23, 12, 18, 13]     [0.0283, 0.0277, 0.0226, 0.0205, 0.0059, 0.0053, 0.0053, 0.005, 0.003, 0.0022]\n",
      "RELATIONAL_REL-MATCH[0, 6, 5, 1, 7, 4, 16, 18, 10, 14]   [0.0128, 0.0127, 0.0114, 0.0046, 0.0033, 0.0031, 0.003, 0.0019, 0.0016, 0.0011]\n",
      "SOCIAL_RANDOM    [3, 0, 5, 1, 22, 17, 13, 30, 11, 12]    [0.0261, 0.0085, 0.0071, 0.0066, 0.0058, 0.0057, 0.004, 0.004, 0.0039, 0.0027]\n",
      "SOCIAL_TOM       [3, 5, 11, 6, 16, 4, 1, 22, 17, 0]      [0.0292, 0.0226, 0.0143, 0.0073, 0.0049, 0.0045, 0.0036, 0.0035, 0.0033, 0.0032]\n",
      "SOCIAL_TOM-RANDOM[6, 5, 11, 4, 16, 27, 0, 10, 14, 1]     [0.0317, 0.0115, 0.0102, 0.0041, 0.0034, 0.0022, 0.0021, 0.0019, 0.0018, 0.0017]\n",
      "WM_2BK_BODY      [1, 5, 6, 3, 19, 23, 8, 9, 4, 27]       [0.0185, 0.0109, 0.0103, 0.0089, 0.0081, 0.008, 0.0077, 0.0048, 0.0032, 0.003]\n",
      "WM_2BK_FACE      [5, 8, 6, 19, 23, 1, 3, 21, 4, 27]      [0.0135, 0.0112, 0.0111, 0.0097, 0.0073, 0.0057, 0.0054, 0.005, 0.0041, 0.004]\n",
      "WM_2BK_PLACE     [5, 1, 6, 8, 18, 19, 3, 2, 27, 9]       [0.0159, 0.0107, 0.0099, 0.0097, 0.0065, 0.0051, 0.0049, 0.0046, 0.0046, 0.0037]\n",
      "WM_2BK_TOOL      [8, 5, 6, 1, 19, 3, 23, 9, 27, 18]      [0.0132, 0.0119, 0.0108, 0.0091, 0.0071, 0.006, 0.0052, 0.0052, 0.0033, 0.0027]\n",
      "WM_0BK_BODY      [5, 3, 23, 9, 6, 19, 1, 16, 14, 27]     [0.0105, 0.0104, 0.0047, 0.0044, 0.004, 0.0039, 0.0036, 0.0031, 0.003, 0.0026]\n",
      "WM_0BK_FACE      [3, 19, 9, 6, 23, 5, 12, 8, 16, 27]     [0.0122, 0.0057, 0.0052, 0.0052, 0.0047, 0.0044, 0.0032, 0.0031, 0.0026, 0.0025]\n",
      "WM_0BK_PLACE     [3, 5, 16, 8, 6, 1, 9, 18, 19, 7]       [0.0165, 0.0161, 0.0076, 0.0074, 0.0067, 0.0065, 0.0037, 0.003, 0.003, 0.003]\n",
      "WM_0BK_TOOL      [3, 5, 6, 9, 1, 19, 8, 23, 16, 27]      [0.0172, 0.0147, 0.0086, 0.0075, 0.0038, 0.0035, 0.0033, 0.003, 0.0028, 0.0022]\n",
      "WM_2BK           [5, 1, 6, 8, 19, 3, 23, 9, 27, 18]      [0.016, 0.0136, 0.0127, 0.0125, 0.0095, 0.0072, 0.0067, 0.0054, 0.0046, 0.0045]\n",
      "WM_0BK           [3, 5, 6, 9, 16, 19, 8, 23, 1, 27]      [0.0193, 0.015, 0.008, 0.0073, 0.0052, 0.0051, 0.005, 0.0047, 0.0044, 0.0034]\n",
      "WM_2BK-0BK       [1, 8, 6, 19, 18, 10, 21, 4, 23, 17]    [0.0185, 0.0116, 0.0084, 0.0066, 0.0058, 0.0052, 0.0045, 0.004, 0.0036, 0.0034]\n",
      "WM_BODY          [1, 5, 3, 6, 23, 19, 8, 9, 27, 17]      [0.0131, 0.013, 0.0105, 0.0083, 0.0077, 0.0072, 0.0059, 0.0056, 0.0034, 0.0023]\n",
      "WM_FACE          [5, 3, 6, 19, 8, 23, 9, 21, 27, 4]      [0.0114, 0.01, 0.0098, 0.0093, 0.0085, 0.0074, 0.0053, 0.0046, 0.004, 0.0036]\n",
      "WM_PLACE         [5, 3, 1, 8, 6, 18, 16, 19, 9, 27]      [0.0184, 0.0111, 0.0106, 0.0099, 0.0093, 0.0057, 0.0057, 0.0047, 0.0044, 0.0043]\n",
      "WM_TOOL          [5, 3, 6, 8, 9, 1, 19, 23, 27, 14]      [0.0159, 0.0129, 0.0117, 0.0091, 0.0076, 0.0073, 0.0065, 0.0049, 0.0033, 0.0024]\n",
      "WM_BODY-AVG      [7, 8, 18, 5, 16, 15, 6, 1, 2, 30]      [0.0108, 0.006, 0.0031, 0.0029, 0.002, 0.0019, 0.0018, 0.0015, 0.0015, 0.0015]\n",
      "WM_FACE-AVG      [1, 0, 21, 2, 15, 4, 12, 23, 19, 5]     [0.0113, 0.0072, 0.0067, 0.0049, 0.0044, 0.0038, 0.0035, 0.0024, 0.0023, 0.0016]\n",
      "WM_PLACE-AVG     [7, 2, 18, 16, 5, 4, 8, 23, 12, 17]     [0.0125, 0.0081, 0.0058, 0.0043, 0.0037, 0.0027, 0.0027, 0.0026, 0.0023, 0.0023]\n",
      "WM_TOOL-AVG      [3, 6, 31, 4, 30, 5, 9, 7, 21, 0]       [0.0049, 0.0045, 0.0031, 0.0026, 0.0025, -0.0019, 0.0016, 0.0016, 0.0014, -0.0013]\n"
     ]
    }
   ],
   "source": [
    "# list 10 best coefficients per component:\n",
    "\n",
    "best_coefficients_full = np.argsort(np.absolute(coeffs), axis=1)[:,::-1]\n",
    "for cn in range(best_coefficients_full.shape[0]):\n",
    "  cname = unique_contrasts[cn]; \n",
    "  p1 = cname +  (17-len(cname))*\" \" + str( list(best_coefficients_full[cn, :10])); \n",
    "  print(p1, (55-len(p1))*\" \" , list(coeffs[cn,  best_coefficients_full[cn, :10]].round(4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Alternative: Random Pertubation of 10% of features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "n_pertubations = int(n_pred*0.1); #20; try to perturb always around 10% of the features\n",
    "n_perm = 1000; #int(5*n_pred); \n",
    "random_feature_subsets = np.zeros((n_perm, n_pertubations)).astype(int);\n",
    "\n",
    "np.random.seed(110)\n",
    "for i in range(n_perm):\n",
    "  random_feature_subsets[i,:] = np.random.permutation(n_pred)[:n_pertubations]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cscores = []\n",
    "dscores = []\n",
    "    \n",
    "for feat_set in tqdm(random_feature_subsets[:4]):\n",
    "  x_pert = x.copy(); x_pert[:,feat_set]=0;\n",
    "  skr.fit(x_pert, y)\n",
    "  xt_pert=xt.copy(); xt_pert[:,feat_set]=0\n",
    "  y_test_pred =skr.predict(xt_pert).reshape(y_test.shape)\n",
    "  scoresd, diff_scores_test = score(y_test_pred, y_test);\n",
    "  cscores.append(scoresd.corr.mean(0))  \n",
    "  dscores.append(diff_scores_test)\n",
    "\n",
    "print(len(dscores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"pertubation_pca.random.yresid.from_zscored.npy\", {\"dscores\":np.array(dscores), \"cscores\": np.array(cscores), \"baseline\": baseline_diffscore})"
   ]
  }
 ],
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