MA_LSTM_Diffusion_Looping_NewVersion_Bidirectional_NoShape_3state.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='red'> RUN INITIALIZATION CELLS FIRST! (Importing and functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:47:55.190718Z",
     "start_time": "2018-07-10T08:47:55.089618Z"
    },
    "hide_output": false,
    "init_cell": true,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "data": {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw a bar that shows that the initialization is finished\n",
    "from ipywidgets import FloatProgress\n",
    "from IPython.display import display\n",
    "initialization_bar = FloatProgress(min = 0, max = 1, description='Initializing:', bar_style='success')\n",
    "initialization_bar.value = 0\n",
    "display(initialization_bar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-21T14:16:16.427339Z",
     "start_time": "2017-11-21T14:16:16.040028Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gpu_fraction = 0.4\n",
    "KTF.set_session(get_session(gpu_fraction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-20T12:07:13.479810Z",
     "start_time": "2018-06-20T12:07:13.324699Z"
    },
    "collapsed": true,
    "hide_input": false,
    "hide_output": true,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "filename = 'Tracks1000_3state_minFrames1state4_P[[0.8,0.1,0.1],[0.1,0.8,0.1],[0.1,0.1,0.8]]_D[1.0,0.1]_H0.1_scaling0.3_t0.03_pixsize0.1.p' # Datafile with tracks, end with .p\n",
    "\n",
    "# Additional features, options 'meanMSD' and 'xy'. Distance (between x0, y0 and x1, y1) is always used.\n",
    "# 'meanMSD' is the mean of MSDs up until maxOrder, with shift.\n",
    "# 'xy' are xy cordinates (x0, y0, x1, y1).\n",
    "addFeat = ['meanMSD', 'xy']\n",
    "\n",
    "# Maximum order of MSDs in 'meanMSD' (minimum order is 1)\n",
    "maxOrder = 2\n",
    "\n",
    "# Shift for calculating mean of MSDs in 'meanMSD'. \n",
    "# For odd orders, the mean of 2 * shift + 1 MSDs will be taken, for even orders, the mean of 2 * shift MSDs will be taken.\n",
    "#(In this way, the means are always taken from MSDs symmetric around middle distance.)\n",
    "shift = 2 \n",
    "\n",
    "\n",
    "windowSizeAr = [25] # Size of sliding window to get sets (number of distances)\n",
    "numHiddenAr = [200] # Number of hidden units\n",
    "\n",
    "\n",
    "kSplit = 10\n",
    "\n",
    "\n",
    "numTrain = 10000 # Number of training sets\n",
    "numVal = 5000 # Number of validation sets\n",
    "numTest = 5000 # Number of testing sets\n",
    "\n",
    "\n",
    "numEp = 1000 # Maximum number of epochs\n",
    "batchSize = 2 ** 8 # Batchsize\n",
    "\n",
    "minDel = 0\n",
    "pat = 5\n",
    "\n",
    "nClasses = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-10T02:41:37.014936Z",
     "start_time": "2018-06-09T20:06:40.635651Z"
    },
    "hide_output": false,
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data loaded\n",
      "Features extracted\n",
      "\n",
      "Split 1\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00030: early stopping\n",
      "\n",
      "Split 2\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00047: early stopping\n",
      "\n",
      "Split 3\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00042: early stopping\n",
      "\n",
      "Split 4\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00062: early stopping\n",
      "\n",
      "Split 5\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00059: early stopping\n",
      "\n",
      "Split 6\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00039: early stopping\n",
      "\n",
      "Split 7\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00041: early stopping\n",
      "\n",
      "Split 8\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00057: early stopping\n",
      "\n",
      "Split 9\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00029: early stopping\n",
      "\n",
      "Split 10\n",
      "\n",
      "Windowsize = 25 (in total 84812 sets, took 84812 sets)\n",
      "h = 200\n",
      "Epoch 00025: early stopping\n"
     ]
    }
   ],
   "source": [
    "numSets = numTrain + numTest + numVal # Total amount of sets\n",
    "vp = numVal / (numTrain + numVal) # Validationpercentage\n",
    "tp = numTest / numSets # Testpercentage\n",
    "\n",
    "earlystop = EarlyStopping(monitor = 'val_loss', min_delta = minDel, patience = pat, verbose = 1, mode = 'auto')\n",
    "\n",
    "accTrain = np.empty((len(windowSizeAr), len(numHiddenAr), kSplit))\n",
    "accTest = np.empty((len(windowSizeAr), len(numHiddenAr), kSplit))\n",
    "\n",
    "accTestGrad = [] # For gradient accuracy over window\n",
    "\n",
    "# Loading, getting all features\n",
    "x, y, s = loadData(filename)\n",
    "print('Data loaded')\n",
    "d, trs = getDistTrS(x, y, s)\n",
    "feat = getFeatVec(d, x, y, addFeat, maxOrder, shift)\n",
    "print('Features extracted')\n",
    "\n",
    "maxSets = getMaxSets(trs, windowSizeAr)\n",
    "\n",
    "for k in range(kSplit):\n",
    "    accTestGradK = []\n",
    "    print('\\nSplit ' + str(k + 1))\n",
    "    for windowSize in windowSizeAr: \n",
    "\n",
    "        # Creating sets of size windowSize (sliding window)\n",
    "        featSets, trsSets, numFeat = getFeatTrsSets(feat, trs, windowSize, addFeat, maxOrder)\n",
    "\n",
    "        # Classes 0 / 1 / 2 need to be converted into 1 0 0 / 0 1 0 / 0 0 1\n",
    "        trs2class = keras.utils.to_categorical(trsSets, num_classes = nClasses).reshape(len(trsSets), windowSize, nClasses)\n",
    "\n",
    "        # Split data into train and test set (with shuffeling)\n",
    "        featShuf, trsShuf = shuffle2zip(featSets, trs2class)\n",
    "        featMax, trsMax = featShuf[:maxSets], trsShuf[:maxSets]\n",
    "        featMaxShuf, trsMaxShuf = shuffle2zip(featMax, trsMax)\n",
    "        \n",
    "        (featTrain, trsTrain), (featTest, trsTest) = splitTrainTest(featMaxShuf[:numSets], trsMaxShuf[:numSets],\\\n",
    "                                                                    testpercentage = tp)\n",
    "\n",
    "        print('\\nWindowsize = %s (in total %s sets, took %s sets)' %(windowSize, len(featSets), maxSets))\n",
    "\n",
    "        for numHidden in numHiddenAr:\n",
    "            print('h = %s' %(numHidden))\n",
    "\n",
    "            # Creating model\n",
    "            model = Sequential()\n",
    "            model.add(Bidirectional(LSTM(numHidden, return_sequences = True), input_shape = (None, numFeat), merge_mode = 'concat'))\n",
    "            model.add(TimeDistributed(Dense((nClasses), activation = 'softmax'))) # Softmax to get 'probability distribution' \n",
    "            model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])\n",
    "            #print(model.summary())\n",
    "\n",
    "            # Training\n",
    "            history = model.fit(featTrain, trsTrain, callbacks = [earlystop], epochs = numEp,\\\n",
    "                        batch_size = batchSize, verbose = 0, validation_split = vp, shuffle = True)\n",
    "\n",
    "            # Predicting on test set and convert 1 0 0 / 0 1 0 / 0 0 1 back into 0 / 1 / 2\n",
    "            predicted = model.predict(featTest)\n",
    "            trsPred = predicted.argmax(axis = 2) # Predicted transition states\n",
    "            trsReal = trsTest.argmax(axis = 2) # Real transition states\n",
    "\n",
    "            # Get accuracy on train and test data\n",
    "            accTrain[windowSizeAr.index(windowSize), numHiddenAr.index(numHidden), k] =\\\n",
    "            np.array(history.history['acc'][-5:]).mean()\n",
    "\n",
    "            total = 0\n",
    "            accurate = 0\n",
    "            for pred, real in zip(trsPred, trsReal):\n",
    "                for p, r in zip(pred, real):\n",
    "                    total += 1\n",
    "                    if p == r:\n",
    "                        accurate += 1\n",
    "            accTest[windowSizeAr.index(windowSize), numHiddenAr.index(numHidden), k] = accurate / total\n",
    "            \n",
    "            gradList = []\n",
    "            for pred, real in zip(trsPred, trsReal):\n",
    "                grad = []\n",
    "                for p, r in zip(pred, real):\n",
    "                    if p == r:\n",
    "                        grad.append(1)\n",
    "                    else:\n",
    "                        grad.append(0)\n",
    "                gradList.append(grad)\n",
    "            accTestGradK.append(gradList)\n",
    "    accTestGrad.append(accTestGradK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-10T10:51:57.587675Z",
     "start_time": "2018-06-10T10:51:57.033097Z"
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    "collapsed": true,
    "run_control": {
     "marked": true
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   "outputs": [],
   "source": [
    "accTrainMean = accTrain.mean(axis = 2)\n",
    "accTestMean = accTest.mean(axis = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-06-10T10:51:57.698791Z",
     "start_time": "2018-06-10T10:51:57.595678Z"
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    "run_control": {
     "marked": true
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean training accuracy:\n",
      "[[ 0.93806808]]\n",
      "\n",
      "Mean testing accuracy:\n",
      "[[ 0.9240736]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#print('Training accuracy:\\n%s\\n' %(accTrain))\n",
    "print('Mean training accuracy:\\n%s\\n' %(accTrainMean))\n",
    "#print('Testing accuracy:\\n%s\\n' %(accTest))\n",
    "print('Mean testing accuracy:\\n%s\\n' %(accTestMean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-26T22:45:44.653493Z",
     "start_time": "2018-05-26T22:45:43.947844Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bidirectional_NoShape_3state_Tr100000\n"
     ]
    }
   ],
   "source": [
    "saveLabel = 'Bidirectional_NoShape_3state_Tr100000' #_Feat' + str(addFeat).replace(\"'\", \"\") + '_MaxOrder' + str(maxOrder) + '_Shift' + str(shift) + '_WS' + str(windowSize) + '_H' + str(numHidden) +\\\n",
    "# '_' + str(kSplit) + 'splits_Tr' + str(numTrain) + '_V' + str(numVal) + '_Te' + str(numTest) + '_Batch' + str(batchSize) + '_' + filename\n",
    "\n",
    "print(saveLabel)\n",
    "model.save(os.getcwd() + '/Models/20-5/Model_' + saveLabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-26T22:45:49.414182Z",
     "start_time": "2018-05-26T22:45:49.272081Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bidirectional_NoShape_Feat[meanMSD, xy]_MaxOrder2_Shift2_WS25_H200_1splits_Train100000_Val50000_Test50000_Batch256_Tracks10000_3state_minFrames1state4_P[[0.8,0.1,0.1],[0.1,0.8,0.1],[0.1,0.1,0.8]]_D[1.0,0.1]_H0.1_scaling0.3_t0.03_pixsize0.1.p\n"
     ]
    }
   ],
   "source": [
    "saveLabel = 'Bidirectional_NoShape_Feat' + str(addFeat).replace(\"'\", \"\") + '_MaxOrder' + str(maxOrder) + '_Shift' + str(shift) + '_WS' + str(windowSize) + '_H' + str(numHidden) +\\\n",
    "'_' + str(kSplit) + 'splits_Train' + str(numTrain) + '_Val' + str(numVal) + '_Test' + str(numTest) + '_Batch' + str(batchSize) + '_' + filename\n",
    "\n",
    "print(saveLabel)\n",
    "# model.save_weights(os.getcwd() + '/Models/Weights/Model_Weights_' + saveLabel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "start_time": "2018-06-07T11:48:51.115Z"
    },
    "collapsed": true,
    "run_control": {
     "marked": true
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   },
   "outputs": [],
   "source": [
    "# Plot heatmap of mean accuracies\n",
    "\n",
    "import seaborn as sns\n",
    "from matplotlib import cm\n",
    "sns.set(font_scale = 1.8)\n",
    "plt.rcParams['figure.figsize'] = (10, 4)\n",
    "plt.subplot(121)\n",
    "ax = sns.heatmap(np.flipud(accTrainMean.T), annot = True, linewidths = 3, fmt = '.2f',\\\n",
    "                 xticklabels = windowSizeAr, yticklabels = np.flipud(numHiddenAr), cmap = cm.RdYlGn, vmin = 0.75, vmax = 0.9)\n",
    "plt.xlabel('Windowsize', labelpad = 14)\n",
    "plt.ylabel('Number of hidden units', labelpad = 14)\n",
    "plt.title('Accuracy for training set', y = 1.04)\n",
    "\n",
    "plt.subplot(122)\n",
    "ax = sns.heatmap(np.flipud(accTestMean.T), annot = True, linewidths = 3, fmt = '.2f',\\\n",
    "                 xticklabels = windowSizeAr, yticklabels = np.flipud(numHiddenAr), cmap = cm.RdYlGn, vmin = 0.75, vmax = 0.9)\n",
    "plt.xlabel('WindowSize', labelpad = 14)\n",
    "plt.ylabel('Number of hidden units', labelpad = 14)\n",
    "plt.title('Accuracy for testing set', y = 1.04)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig(os.getcwd() + something + '.pdf')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T17:27:18.399428Z",
     "start_time": "2018-05-20T17:27:17.541820Z"
    }
   },
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6adbf3ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a bar graph of the accuracy gradient over window for specified windowsize and number of hidden units\n",
    "\n",
    "ws = 19 # windowsize\n",
    "h = 100 # number of hidden units\n",
    "\n",
    "\n",
    "if ws not in windowSizeAr or h not in numHiddenAr:\n",
    "    print('No experiment for this ws-h combination. ws = %s, h = %s.' %(windowSizeAr, numHiddenAr))\n",
    "    \n",
    "else:\n",
    "    plt.style.use('default')\n",
    "    plt.rcParams['figure.figsize'] = (24, 4)\n",
    "\n",
    "    totList = []    \n",
    "\n",
    "    for i in range(len(accTestGrad[0])):\n",
    "        L = []\n",
    "        for j in range(len(accTestGrad)):\n",
    "            L.append(np.array(accTestGrad[j][i]).mean(axis = 0))\n",
    "        totList.append(np.array(L).mean(axis = 0))\n",
    "\n",
    "    ind =  windowSizeAr.index(ws) * len(numHiddenAr) + numHiddenAr.index(h)\n",
    "\n",
    "    plt.bar(np.arange(ws) + 1, totList[ind])\n",
    "    plt.gca().set_xticks(np.arange(ws) + 1)\n",
    "\n",
    "    plt.xlabel('Window position')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.title('Accuracy gradient over window for windowsize %s and number of hidden units %s' %(ws, h), y = 1.04)\n",
    "    plt.gca().set_ylim([min(totList[ind] - 0.02), max(totList[ind] + 0.02)])\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T17:27:22.624430Z",
     "start_time": "2018-05-20T17:27:21.782830Z"
    },
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DE9k0rd5oPrm9NZ97RUSOq62HEv83qS9CvV2Qq67GGz+chtHI9mJbxeBENm1z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\n+XIEBgZCr9djwIABJn1s/H3v2LEDUVFRcHR0xNatW5XP3r17N0JCQuD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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6e90b3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot overall accuracy gradient for normalized windowsize between 0 and 1\n",
    "plt.style.use('default')\n",
    "plt.rcParams['figure.figsize'] = (6, 5)\n",
    "numSamp = 150\n",
    "bigList = []\n",
    "\n",
    "for split in accTestGrad:\n",
    "    for windowList in split:\n",
    "        for L in windowList:\n",
    "            M = np.interp(np.linspace(0, len(L) - 1, num = numSamp), range(len(L)), L)\n",
    "            bigList.append(M)\n",
    "        \n",
    "superList = np.array(bigList).reshape(len(windowSizeAr) * len(numHiddenAr) * numTest * kSplit, numSamp)\n",
    "superMean = superList.mean(axis = 0)\n",
    "\n",
    "plt.plot(np.linspace(0, 1, numSamp), superMean)\n",
    "plt.xlabel('Window position (normalized)')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-05-20T17:27:27.159648Z",
     "start_time": "2018-05-20T17:27:25.880739Z"
    },
    "run_control": {
     "marked": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\marloes\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\mpl_toolkits\\mplot3d\\axes3d.py:668: UserWarning: Attempting to set identical bottom==top results\n",
      "in singular transformations; automatically expanding.\n",
      "bottom=0, top=0\n",
      "  'bottom=%s, top=%s') % (bottom, top))\n"
     ]
    },
    {
     "data": {
      "image/png": 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i8gAwqwnNKkKL9nkBAICjIPgDADjljrrWczAYxOPHj+PBgwdx5cqVuHv3btRq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\n1tYWIpEIfD4fpqen4fF4znF0RHTU4eEhwuEwUqkU3n77bfT39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Xt/B1t9tpYX75DRdR2Z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vx8bG0NzcDJvNhm3btsFgMKg9JPoAiw3tWPFH8VCRbcJPLzgZz3dM4Ad/7saQewbf+t9W/O5N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klkUmrop84q0mPTpVAoIJFIYHd3F6+99prW3ous5awgEIAWMjMINA/O+CO6PFb86ecdgw0Y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\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ej4fjW4lIV4qiIBKJIJlMYnJykgcLiExCCIFvfOMbuO+++/DBD34Qb3rTm/g9hYiODQZ/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\nAABABRD+AAAAAAAAoAIIfwAAAAAAAFABh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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6adcb22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot accuracy gradient per h&windowsize combo\n",
    "\n",
    "plt.style.use('default')\n",
    "plt.rcParams['figure.figsize'] = (18, 15)\n",
    "\n",
    "totList = []    \n",
    "\n",
    "for i in range(len(accTestGrad[0])):\n",
    "    L = []\n",
    "    for j in range(len(accTestGrad)):\n",
    "        L.append(np.array(accTestGrad[j][i]).mean(axis = 0))\n",
    "    totList.append(np.array(L).mean(axis = 0))\n",
    "\n",
    "names = []\n",
    "for ws in windowSizeAr:\n",
    "    for h in numHiddenAr:\n",
    "        names.append('ws%s h%s' %(ws,h))\n",
    "\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca(projection = '3d')\n",
    "\n",
    "for i in range(len(totList)):\n",
    "    xs = np.arange(len(totList[i]))\n",
    "    ys = totList[i]\n",
    "    ax.plot(xs, ys, zs = i, zdir = 'y')\n",
    "\n",
    "ax.set_xlabel('Window point', labelpad = 20)\n",
    "ax.set_xlim3d(0, max(windowSizeAr))\n",
    "ax.set_ylabel('Experiment', labelpad = 28)\n",
    "ax.set_ylim3d(0, (len(windowSizeAr) * len(numHiddenAr)) - 1)\n",
    "ax.set_zlabel('Accuracy', labelpad = 20)\n",
    "ax.set_zlim3d(0.77, 1)\n",
    "\n",
    "plt.gca().set_xticks(np.arange(0, windowSizeAr[-1] + 1, 5)) \n",
    "plt.gca().set_zticks([0.80, 0.85, 0.90, 0.95, 1.00])\n",
    "plt.gca().set_yticks(np.arange(len(windowSizeAr) * len(numHiddenAr))) \n",
    "plt.gca().set_yticklabels(names, va = 'center', ha = 'left')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization cells"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:58.160334Z",
     "start_time": "2018-07-10T08:47:55.200698Z"
    },
    "init_cell": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# Importing and settings\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "import math\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10, 10)\n",
    "import numpy as np\n",
    "\n",
    "# Import stuff for training\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import TimeDistributed\n",
    "from keras.layers import Bidirectional\n",
    "from keras.layers.core import Dense  #,Dropout ?\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.layers.recurrent import GRU\n",
    "from keras.layers.recurrent import SimpleRNN as sRNN\n",
    "\n",
    "#from time import time\n",
    "#from keras.callbacks import TensorBoard\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "#import shutil # for deleting log folder\n",
    "import os\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:58.236361Z",
     "start_time": "2018-07-10T08:48:58.171315Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# import to limit the GPU memory\n",
    "import tensorflow as tf\n",
    "import keras.backend.tensorflow_backend as KTF\n",
    "\n",
    "def get_session(gpu_fraction):\n",
    "    '''Assume that you have 6GB of GPU memory and want to allocate ~2GB'''\n",
    "\n",
    "    num_threads = os.environ.get('OMP_NUM_THREADS')\n",
    "    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = gpu_fraction)\n",
    "\n",
    "    if num_threads:\n",
    "        return tf.Session(config = tf.ConfigProto(\n",
    "            gpu_options=gpu_options, intra_op_parallelism_threads = num_threads))\n",
    "    else:\n",
    "        return tf.Session(config = tf.ConfigProto(gpu_options = gpu_options))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:58.359447Z",
     "start_time": "2018-07-10T08:48:58.249369Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# Load data\n",
    "\n",
    "def loadData(filename):\n",
    "    xystate = pickle.load(open(os.getcwd() + '/' + filename, 'rb'))\n",
    "    x = np.array(xystate['Xcum'])\n",
    "    y = np.array(xystate['Ycum'])\n",
    "    s = np.array(xystate['StateList'])\n",
    "    \n",
    "    return x, y, s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:58.547588Z",
     "start_time": "2018-07-10T08:48:58.376461Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "def getDistTrS(x, y, s):    \n",
    "    \n",
    "    distances = []\n",
    "    trStates = []\n",
    "\n",
    "    for trackX, trackY, trackS in zip(x, y, s):\n",
    "        dist = []\n",
    "        trst = []        \n",
    "        \n",
    "        for j in range(len(trackX) - 1):            \n",
    "            dist.append(np.sqrt((trackX[j + 1] - trackX[j]) ** 2 + (trackY[j + 1] - trackY[j]) ** 2))\n",
    "            trst.append(trackS[j + 1])\n",
    "        \n",
    "        distances.append(dist)\n",
    "        trStates.append(trst)\n",
    "    \n",
    "    return distances, trStates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:59.095970Z",
     "start_time": "2018-07-10T08:48:58.560590Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "def getMeanMSDs(x, y, maxOrder, shift):\n",
    "\n",
    "    D= []\n",
    "    for trackX, trackY in zip(x, y):\n",
    "        dTrack = []\n",
    "        for order in range(1, maxOrder + 1):\n",
    "            dOrder = []\n",
    "            for j in range(len(trackX) - order):\n",
    "                dOrder.append(np.sqrt((trackX[j + order] - trackX[j]) ** 2 + (trackY[j + order] - trackY[j]) ** 2))\n",
    "            dTrack.append(dOrder)\n",
    "        D.append(dTrack)\n",
    "\n",
    "    dVec = []\n",
    "    for track in D:\n",
    "        vector = []\n",
    "        for order in range(1, maxOrder + 1):\n",
    "            v = []\n",
    "            dO = track[order - 1]\n",
    "\n",
    "            for i in range(len(track[0])):\n",
    "                if order % 2 == 0:\n",
    "                    iStart = (i - shift - ((order - 1) // 2))\n",
    "                    iEnd = iStart + (2 * shift) - 1\n",
    "                else:\n",
    "                    iStart = (i - shift - (order // 2))\n",
    "                    iEnd = iStart + (2 * shift)\n",
    "\n",
    "                if iStart < 0:\n",
    "                    iStart = 0\n",
    "\n",
    "                if iEnd > (len(track[0]) - 1):\n",
    "                    iEnd = len(track[0]) - 1\n",
    "\n",
    "                meanPoint = np.mean(dO[iStart:(iEnd + 1)])\n",
    "                v.append(meanPoint)\n",
    "            vector.append(v)\n",
    "        dVec.append(vector)\n",
    "\n",
    "    return dVec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:59.177028Z",
     "start_time": "2018-07-10T08:48:59.109979Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "def getCoord(x, y):\n",
    "    \n",
    "    coordXY = []\n",
    "    \n",
    "    for trackX, trackY, trackS in zip(x, y, s):\n",
    "        coxy = []\n",
    "        \n",
    "        for j in range(len(trackX) - 1):\n",
    "            coxy.append([trackX[j], trackY[j], trackX[j + 1], trackY[j + 1]])\n",
    "        coordXY.append(coxy)\n",
    "        \n",
    "    return coordXY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:59.437211Z",
     "start_time": "2018-07-10T08:48:59.189036Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "def getFeatVec(d, x, y, addFeat, maxOrder, shift):\n",
    "    \n",
    "    if 'meanMSD' in addFeat:\n",
    "        dMeanVec = getMeanMSDs(x, y, maxOrder, shift)\n",
    "        \n",
    "    if 'xy' in addFeat:\n",
    "        xy = getCoord(x, y)\n",
    "\n",
    "    featVec = []\n",
    "    for i in range(len(d)):\n",
    "        featTrack = []\n",
    "        for j in range(len(d[i])):\n",
    "            featPoint = []\n",
    "            featPoint.append(d[i][j])\n",
    "\n",
    "            if 'meanMSD' in addFeat:             \n",
    "                for order in range(maxOrder):\n",
    "                    featPoint.append(dMeanVec[i][order][j])\n",
    "\n",
    "            if 'xy' in addFeat:            \n",
    "                featPoint.append(xy[i][j][0])\n",
    "                featPoint.append(xy[i][j][1])\n",
    "                featPoint.append(xy[i][j][2])\n",
    "                featPoint.append(xy[i][j][3])\n",
    "\n",
    "            featTrack.append(featPoint)\n",
    "        featVec.append(featTrack)\n",
    "    \n",
    "    return featVec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:59.833492Z",
     "start_time": "2018-07-10T08:48:59.451222Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "def getFeatTrsSets(feat, trs, windowSize, addFeat, maxOrder):\n",
    "\n",
    "    featSets = []\n",
    "    trsSets = []\n",
    "\n",
    "    numFeat = 1 + ('meanMSD' in addFeat) * maxOrder + ('xy' in addFeat) * 4\n",
    "\n",
    "    for featVec, state in zip(feat, trs):\n",
    "        for j in range(len(featVec) - windowSize + 1):\n",
    "            featSets.append(featVec[j:j + windowSize])\n",
    "            trsSets.append(state[j:j + windowSize])\n",
    "            \n",
    "    featSetsReshape = np.array(featSets).reshape((len(featSets), windowSize, numFeat))\n",
    "    trsSetsReshape = np.array(trsSets).reshape((len(trsSets), windowSize, 1))\n",
    "    \n",
    "    # Normalization\n",
    "    if 'xy'in addFeat:\n",
    "        for i in range(len(featSetsReshape)):\n",
    "            for j in range(windowSize):\n",
    "                featSetsReshape[i, j, -4] = featSetsReshape[i, j, -4] - featSetsReshape[i, 0, -4]\n",
    "                featSetsReshape[i, j, -3] = featSetsReshape[i, j, -3] - featSetsReshape[i, 0, -3]\n",
    "                featSetsReshape[i, j, -2] = featSetsReshape[i, j, -2] - featSetsReshape[i, 0, -4]    \n",
    "                featSetsReshape[i, j, -1] = featSetsReshape[i, j, -1] - featSetsReshape[i, 0, -3]\n",
    "    \n",
    "        \n",
    "    return featSetsReshape, trsSetsReshape, numFeat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:59.906544Z",
     "start_time": "2018-07-10T08:48:59.844500Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# function that gets number of sets for maximum windowsize in windowSizeAr\n",
    "\n",
    "def getMaxSets(trs, windowSizeAr):\n",
    "    \n",
    "    windowMax = max(windowSizeAr)\n",
    "    maxNum = 0\n",
    "    \n",
    "    for state in trs:\n",
    "        for j in range(len(state)- windowMax + 1):\n",
    "            maxNum += 1\n",
    "            \n",
    "    return maxNum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:48:59.960581Z",
     "start_time": "2018-07-10T08:48:59.915550Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# Function that zips 2 arrays of same length and shuffles them\n",
    "\n",
    "def shuffle2zip(A, B):\n",
    "    Z = list(zip(A, B))\n",
    "    np.random.shuffle(Z)\n",
    "    Ashuf, Bshuf = zip(*Z)\n",
    "    \n",
    "    Ashuf = np.array(Ashuf)\n",
    "    Bshuf = np.array(Bshuf)\n",
    "    \n",
    "    return Ashuf, Bshuf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:49:00.028629Z",
     "start_time": "2018-07-10T08:48:59.969588Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# Function to split in training and test data\n",
    "\n",
    "def splitTrainTest(featSets, trsSets, testpercentage = 0.1):\n",
    "    numtrain = round((1 - testpercentage) * len(featSets))\n",
    "    featTrain, trsTrain = featSets[:numtrain], trsSets[:numtrain]\n",
    "    featTest, trsTest = featSets[numtrain:], trsSets[numtrain:]\n",
    "    return (featTrain, trsTrain), (featTest, trsTest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-07-10T08:49:00.081667Z",
     "start_time": "2018-07-10T08:49:00.039637Z"
    },
    "collapsed": true,
    "init_cell": true
   },
   "outputs": [],
   "source": [
    "# execution of initialization cells is done\n",
    "initialization_bar.value += 1"
   ]
  }
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