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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DREAMER Dominance EMI-GRU 48_16"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adapted from Microsoft's notebooks, available at https://github.com/microsoft/EdgeML authored by Dennis et al."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from tabulate import tabulate\n",
"import os\n",
"import datetime as datetime\n",
"import pickle as pkl\n",
"import pathlib"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:17:51.796585Z",
"start_time": "2018-12-14T14:17:49.648375Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import os\n",
"import sys\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"# Making sure edgeml is part of python path\n",
"sys.path.insert(0, '../../')\n",
"#For processing on CPU.\n",
"os.environ['CUDA_VISIBLE_DEVICES'] ='0'\n",
"\n",
"np.random.seed(42)\n",
"tf.set_random_seed(42)\n",
"\n",
"# MI-RNN and EMI-RNN imports\n",
"from edgeml.graph.rnn import EMI_DataPipeline\n",
"from edgeml.graph.rnn import EMI_GRU\n",
"from edgeml.trainer.emirnnTrainer import EMI_Trainer, EMI_Driver\n",
"import edgeml.utils\n",
"\n",
"import keras.backend as K\n",
"cfg = K.tf.ConfigProto()\n",
"cfg.gpu_options.allow_growth = True\n",
"K.set_session(K.tf.Session(config=cfg))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:17:51.803381Z",
"start_time": "2018-12-14T14:17:51.798799Z"
}
},
"outputs": [],
"source": [
"# Network parameters for our LSTM + FC Layer\n",
"NUM_HIDDEN = 128\n",
"NUM_TIMESTEPS = 48\n",
"ORIGINAL_NUM_TIMESTEPS = 128\n",
"NUM_FEATS = 16\n",
"FORGET_BIAS = 1.0\n",
"NUM_OUTPUT = 5\n",
"USE_DROPOUT = True\n",
"KEEP_PROB = 0.75\n",
"\n",
"# For dataset API\n",
"PREFETCH_NUM = 5\n",
"BATCH_SIZE = 32\n",
"\n",
"# Number of epochs in *one iteration*\n",
"NUM_EPOCHS = 2\n",
"# Number of iterations in *one round*. After each iteration,\n",
"# the model is dumped to disk. At the end of the current\n",
"# round, the best model among all the dumped models in the\n",
"# current round is picked up..\n",
"NUM_ITER = 4\n",
"# A round consists of multiple training iterations and a belief\n",
"# update step using the best model from all of these iterations\n",
"NUM_ROUNDS = 10\n",
"LEARNING_RATE=0.001\n",
"\n",
"# A staging direcory to store models\n",
"MODEL_PREFIX = '/home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru'"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"# Loading Data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:17:52.040352Z",
"start_time": "2018-12-14T14:17:51.805319Z"
},
"hidden": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_train shape is: (61735, 6, 48, 16)\n",
"y_train shape is: (61735, 6, 5)\n",
"x_test shape is: (6860, 6, 48, 16)\n",
"y_test shape is: (6860, 6, 5)\n"
]
}
],
"source": [
"# Loading the data\n",
"path='/home/sf/data/DREAMER/Dominance/Fast_GRNN/48_16/'\n",
"x_train, y_train = np.load(path + 'x_train.npy'), np.load(path + 'y_train.npy')\n",
"x_test, y_test = np.load(path + 'x_test.npy'), np.load(path + 'y_test.npy')\n",
"x_val, y_val = np.load(path + 'x_val.npy'), np.load(path + 'y_val.npy')\n",
"\n",
"# BAG_TEST, BAG_TRAIN, BAG_VAL represent bag_level labels. These are used for the label update\n",
"# step of EMI/MI RNN\n",
"BAG_TEST = np.argmax(y_test[:, 0, :], axis=1)\n",
"BAG_TRAIN = np.argmax(y_train[:, 0, :], axis=1)\n",
"BAG_VAL = np.argmax(y_val[:, 0, :], axis=1)\n",
"NUM_SUBINSTANCE = x_train.shape[1]\n",
"print(\"x_train shape is:\", x_train.shape)\n",
"print(\"y_train shape is:\", y_train.shape)\n",
"print(\"x_test shape is:\", x_val.shape)\n",
"print(\"y_test shape is:\", y_val.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Computation Graph"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:17:52.053161Z",
"start_time": "2018-12-14T14:17:52.042928Z"
}
},
"outputs": [],
"source": [
"# Define the linear secondary classifier\n",
"def createExtendedGraph(self, baseOutput, *args, **kwargs):\n",
" W1 = tf.Variable(np.random.normal(size=[NUM_HIDDEN, NUM_OUTPUT]).astype('float32'), name='W1')\n",
" B1 = tf.Variable(np.random.normal(size=[NUM_OUTPUT]).astype('float32'), name='B1')\n",
" y_cap = tf.add(tf.tensordot(baseOutput, W1, axes=1), B1, name='y_cap_tata')\n",
" self.output = y_cap\n",
" self.graphCreated = True\n",
"\n",
"def restoreExtendedGraph(self, graph, *args, **kwargs):\n",
" y_cap = graph.get_tensor_by_name('y_cap_tata:0')\n",
" self.output = y_cap\n",
" self.graphCreated = True\n",
" \n",
"def feedDictFunc(self, keep_prob=None, inference=False, **kwargs):\n",
" if inference is False:\n",
" feedDict = {self._emiGraph.keep_prob: keep_prob}\n",
" else:\n",
" feedDict = {self._emiGraph.keep_prob: 1.0}\n",
" return feedDict\n",
" \n",
"EMI_GRU._createExtendedGraph = createExtendedGraph\n",
"EMI_GRU._restoreExtendedGraph = restoreExtendedGraph\n",
"\n",
"if USE_DROPOUT is True:\n",
" EMI_Driver.feedDictFunc = feedDictFunc"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:17:52.335299Z",
"start_time": "2018-12-14T14:17:52.055483Z"
}
},
"outputs": [],
"source": [
"inputPipeline = EMI_DataPipeline(NUM_SUBINSTANCE, NUM_TIMESTEPS, NUM_FEATS, NUM_OUTPUT)\n",
"emiGRU = EMI_GRU(NUM_SUBINSTANCE, NUM_HIDDEN, NUM_TIMESTEPS, NUM_FEATS,\n",
" useDropout=USE_DROPOUT)\n",
"emiTrainer = EMI_Trainer(NUM_TIMESTEPS, NUM_OUTPUT, lossType='xentropy',\n",
" stepSize=LEARNING_RATE)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:18:05.031382Z",
"start_time": "2018-12-14T14:17:52.338750Z"
}
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"g1 = tf.Graph() \n",
"with g1.as_default():\n",
" # Obtain the iterators to each batch of the data\n",
" x_batch, y_batch = inputPipeline()\n",
" # Create the forward computation graph based on the iterators\n",
" y_cap = emiGRU(x_batch)\n",
" # Create loss graphs and training routines\n",
" emiTrainer(y_cap, y_batch)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EMI Driver"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:35:15.209910Z",
"start_time": "2018-12-14T14:18:05.034359Z"
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Update policy: top-k\n",
"Training with MI-RNN loss for 5 rounds\n",
"Round: 0\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.03100 Acc 0.36979 | Val acc 0.38717 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1000\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.02785 Acc 0.40104 | Val acc 0.41647 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1001\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.02730 Acc 0.39062 | Val acc 0.45000 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1002\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.02400 Acc 0.46354 | Val acc 0.48513 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1003\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1003\n",
"Round: 1\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.02273 Acc 0.52604 | Val acc 0.51706 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1004\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.02227 Acc 0.55729 | Val acc 0.55423 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1005\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.02006 Acc 0.60417 | Val acc 0.58017 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1006\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01900 Acc 0.66667 | Val acc 0.59985 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1007\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1007\n",
"Round: 2\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01803 Acc 0.67708 | Val acc 0.61268 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1008\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01809 Acc 0.63021 | Val acc 0.62828 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1009\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01681 Acc 0.65625 | Val acc 0.63499 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1010\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01673 Acc 0.70312 | Val acc 0.64227 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1011\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1011\n",
"Round: 3\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01625 Acc 0.66667 | Val acc 0.65262 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1012\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01641 Acc 0.68750 | Val acc 0.66122 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1013\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01519 Acc 0.70833 | Val acc 0.65583 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1014\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01486 Acc 0.70312 | Val acc 0.66268 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1015\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1015\n",
"Round: 4\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01496 Acc 0.69792 | Val acc 0.67128 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1016\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01475 Acc 0.72917 | Val acc 0.67303 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1017\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01540 Acc 0.70833 | Val acc 0.66764 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1018\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.01428 Acc 0.75000 | Val acc 0.67609 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1019\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1019\n",
"Round: 5\n",
"Switching to EMI-Loss function\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.90647 Acc 0.74479 | Val acc 0.65758 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1020\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.91761 Acc 0.64583 | Val acc 0.65933 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1021\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.88657 Acc 0.70833 | Val acc 0.65991 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1022\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.89605 Acc 0.68229 | Val acc 0.66749 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1023\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1023\n",
"Round: 6\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.88819 Acc 0.69792 | Val acc 0.66910 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1024\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.85815 Acc 0.71875 | Val acc 0.66910 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1025\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.87857 Acc 0.71354 | Val acc 0.67128 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1026\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.87222 Acc 0.72396 | Val acc 0.66574 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1027\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1026\n",
"Round: 7\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.86202 Acc 0.70312 | Val acc 0.66720 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1028\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.88255 Acc 0.68229 | Val acc 0.66545 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1029\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.89641 Acc 0.64583 | Val acc 0.66647 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1030\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.87491 Acc 0.67708 | Val acc 0.66297 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1031\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1028\n",
"Round: 8\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.88003 Acc 0.67708 | Val acc 0.66429 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1032\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.87404 Acc 0.69271 | Val acc 0.65904 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1033\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.87308 Acc 0.72396 | Val acc 0.66603 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1034\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.86821 Acc 0.67708 | Val acc 0.66822 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1035\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1035\n",
"Round: 9\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.90025 Acc 0.69792 | Val acc 0.66020 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1036\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.89532 Acc 0.66146 | Val acc 0.66676 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1037\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.87133 Acc 0.69271 | Val acc 0.66706 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1038\n",
"Epoch 1 Batch 1925 ( 3855) Loss 0.86914 Acc 0.68750 | Val acc 0.66837 | Model saved to /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru, global_step 1039\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1039\n"
]
}
],
"source": [
"with g1.as_default():\n",
" emiDriver = EMI_Driver(inputPipeline, emiGRU, emiTrainer)\n",
"\n",
"emiDriver.initializeSession(g1)\n",
"y_updated, modelStats = emiDriver.run(numClasses=NUM_OUTPUT, x_train=x_train,\n",
" y_train=y_train, bag_train=BAG_TRAIN,\n",
" x_val=x_val, y_val=y_val, bag_val=BAG_VAL,\n",
" numIter=NUM_ITER, keep_prob=KEEP_PROB,\n",
" numRounds=NUM_ROUNDS, batchSize=BATCH_SIZE,\n",
" numEpochs=NUM_EPOCHS, modelPrefix=MODEL_PREFIX,\n",
" fracEMI=0.5, updatePolicy='top-k', k=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluating the trained model"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:35:15.218040Z",
"start_time": "2018-12-14T14:35:15.211771Z"
}
},
"outputs": [],
"source": [
"# Early Prediction Policy: We make an early prediction based on the predicted classes\n",
"# probability. If the predicted class probability > minProb at some step, we make\n",
"# a prediction at that step.\n",
"def earlyPolicy_minProb(instanceOut, minProb, **kwargs):\n",
" assert instanceOut.ndim == 2\n",
" classes = np.argmax(instanceOut, axis=1)\n",
" prob = np.max(instanceOut, axis=1)\n",
" index = np.where(prob >= minProb)[0]\n",
" if len(index) == 0:\n",
" assert (len(instanceOut) - 1) == (len(classes) - 1)\n",
" return classes[-1], len(instanceOut) - 1\n",
" index = index[0]\n",
" return classes[index], index\n",
"\n",
"def getEarlySaving(predictionStep, numTimeSteps, returnTotal=False):\n",
" predictionStep = predictionStep + 1\n",
" predictionStep = np.reshape(predictionStep, -1)\n",
" totalSteps = np.sum(predictionStep)\n",
" maxSteps = len(predictionStep) * numTimeSteps\n",
" savings = 1.0 - (totalSteps / maxSteps)\n",
" if returnTotal:\n",
" return savings, totalSteps\n",
" return savings"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:35:16.257489Z",
"start_time": "2018-12-14T14:35:15.221029Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy at k = 2: 0.669893\n",
"Savings due to MI-RNN : 0.625000\n",
"Savings due to Early prediction: 0.133547\n",
"Total Savings: 0.675080\n"
]
}
],
"source": [
"k = 2\n",
"predictions, predictionStep = emiDriver.getInstancePredictions(x_test, y_test, earlyPolicy_minProb,\n",
" minProb=0.99, keep_prob=1.0)\n",
"bagPredictions = emiDriver.getBagPredictions(predictions, minSubsequenceLen=k, numClass=NUM_OUTPUT)\n",
"print('Accuracy at k = %d: %f' % (k, np.mean((bagPredictions == BAG_TEST).astype(int))))\n",
"mi_savings = (1 - NUM_TIMESTEPS / ORIGINAL_NUM_TIMESTEPS)\n",
"emi_savings = getEarlySaving(predictionStep, NUM_TIMESTEPS)\n",
"total_savings = mi_savings + (1 - mi_savings) * emi_savings\n",
"print('Savings due to MI-RNN : %f' % mi_savings)\n",
"print('Savings due to Early prediction: %f' % emi_savings)\n",
"print('Total Savings: %f' % (total_savings))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:35:17.044115Z",
"start_time": "2018-12-14T14:35:16.259280Z"
},
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" len acc macro-fsc macro-pre macro-rec micro-fsc micro-pre \\\n",
"0 1 0.670185 0.635187 0.713956 0.607175 0.670185 0.670185 \n",
"1 2 0.669893 0.639764 0.639024 0.641768 0.669893 0.669893 \n",
"2 3 0.631232 0.574154 0.594423 0.641324 0.631232 0.631232 \n",
"3 4 0.541956 0.520735 0.628534 0.588688 0.541956 0.541956 \n",
"4 5 0.470873 0.480797 0.664928 0.542186 0.470873 0.470873 \n",
"5 6 0.420783 0.450270 0.700213 0.506986 0.420783 0.420783 \n",
"\n",
" micro-rec \n",
"0 0.670185 \n",
"1 0.669893 \n",
"2 0.631232 \n",
"3 0.541956 \n",
"4 0.470873 \n",
"5 0.420783 \n",
"Max accuracy 0.670185 at subsequencelength 1\n",
"Max micro-f 0.670185 at subsequencelength 1\n",
"Micro-precision 0.670185 at subsequencelength 1\n",
"Micro-recall 0.670185 at subsequencelength 1\n",
"Max macro-f 0.639764 at subsequencelength 2\n",
"macro-precision 0.639024 at subsequencelength 2\n",
"macro-recall 0.641768 at subsequencelength 2\n"
]
}
],
"source": [
"# A slightly more detailed analysis method is provided. \n",
"df = emiDriver.analyseModel(predictions, BAG_TEST, NUM_SUBINSTANCE, NUM_OUTPUT)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Picking the best model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2018-12-14T14:35:54.899340Z",
"start_time": "2018-12-14T14:35:17.047464Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1003\n",
"Round: 0, Validation accuracy: 0.4851, Test Accuracy (k = 2): 0.486151, Total Savings: 0.628213\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1007\n",
"Round: 1, Validation accuracy: 0.5999, Test Accuracy (k = 2): 0.601143, Total Savings: 0.633056\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1011\n",
"Round: 2, Validation accuracy: 0.6423, Test Accuracy (k = 2): 0.638405, Total Savings: 0.635002\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1015\n",
"Round: 3, Validation accuracy: 0.6627, Test Accuracy (k = 2): 0.657881, Total Savings: 0.638982\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1019\n",
"Round: 4, Validation accuracy: 0.6761, Test Accuracy (k = 2): 0.674033, Total Savings: 0.641478\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1023\n",
"Round: 5, Validation accuracy: 0.6675, Test Accuracy (k = 2): 0.658114, Total Savings: 0.659983\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1026\n",
"Round: 6, Validation accuracy: 0.6713, Test Accuracy (k = 2): 0.665053, Total Savings: 0.664241\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1028\n",
"Round: 7, Validation accuracy: 0.6672, Test Accuracy (k = 2): 0.662837, Total Savings: 0.664690\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1035\n",
"Round: 8, Validation accuracy: 0.6682, Test Accuracy (k = 2): 0.665578, Total Savings: 0.670677\n",
"INFO:tensorflow:Restoring parameters from /home/sf/data/DREAMER/Dominance/48_16/models/GRU/model-gru-1039\n"
]
}
],
"source": [
"devnull = open(os.devnull, 'r')\n",
"for val in modelStats:\n",
" round_, acc, modelPrefix, globalStep = val\n",
" emiDriver.loadSavedGraphToNewSession(modelPrefix, globalStep, redirFile=devnull)\n",
" predictions, predictionStep = emiDriver.getInstancePredictions(x_test, y_test, earlyPolicy_minProb,\n",
" minProb=0.99, keep_prob=1.0)\n",
"\n",
" bagPredictions = emiDriver.getBagPredictions(predictions, minSubsequenceLen=k, numClass=NUM_OUTPUT)\n",
" print(\"Round: %2d, Validation accuracy: %.4f\" % (round_, acc), end='')\n",
" print(', Test Accuracy (k = %d): %f, ' % (k, np.mean((bagPredictions == BAG_TEST).astype(int))), end='')\n",
" mi_savings = (1 - NUM_TIMESTEPS / ORIGINAL_NUM_TIMESTEPS)\n",
" emi_savings = getEarlySaving(predictionStep, NUM_TIMESTEPS)\n",
" total_savings = mi_savings + (1 - mi_savings) * emi_savings\n",
" print(\"Total Savings: %f\" % total_savings)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"params = {\n",
" \"NUM_HIDDEN\" : 128,\n",
" \"NUM_TIMESTEPS\" : 48, #subinstance length.\n",
" \"ORIGINAL_NUM_TIMESTEPS\" : 128,\n",
" \"NUM_FEATS\" : 16,\n",
" \"FORGET_BIAS\" : 1.0,\n",
" \"NUM_OUTPUT\" : 5,\n",
" \"USE_DROPOUT\" : 1, # '1' -> True. '0' -> False\n",
" \"KEEP_PROB\" : 0.75,\n",
" \"PREFETCH_NUM\" : 5,\n",
" \"BATCH_SIZE\" : 32,\n",
" \"NUM_EPOCHS\" : 2,\n",
" \"NUM_ITER\" : 4,\n",
" \"NUM_ROUNDS\" : 10,\n",
" \"LEARNING_RATE\" : 0.001,\n",
" \"MODEL_PREFIX\" : '/home/sf/data/DREAMER/Dominance/model-gru'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"gru_dict = {**params}\n",
"gru_dict[\"k\"] = k\n",
"gru_dict[\"accuracy\"] = np.mean((bagPredictions == BAG_TEST).astype(int))\n",
"gru_dict[\"total_savings\"] = total_savings\n",
"gru_dict[\"y_test\"] = BAG_TEST\n",
"gru_dict[\"y_pred\"] = bagPredictions\n",
"\n",
"# A slightly more detailed analysis method is provided. \n",
"df = emiDriver.analyseModel(predictions, BAG_TEST, NUM_SUBINSTANCE, NUM_OUTPUT)\n",
"print (tabulate(df, headers=list(df.columns), tablefmt='grid'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dirname = \"home/sf/data/DREAMER/Dominance/GRU/\"\n",
"pathlib.Path(dirname).mkdir(parents=True, exist_ok=True)\n",
"print (\"Results for this run have been saved at\" , dirname, \".\")\n",
"\n",
"now = datetime.datetime.now()\n",
"filename = list((str(now.year),\"-\",str(now.month),\"-\",str(now.day),\"|\",str(now.hour),\"-\",str(now.minute)))\n",
"filename = ''.join(filename)\n",
"\n",
"#Save the dictionary containing the params and the results.\n",
"pkl.dump(gru_dict,open(dirname + filename + \".pkl\",mode='wb'))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'home/sf/data/DREAMER/Dominance/GRU/2019-8-11|2-30.pkl'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dirname+filename+'.pkl'"
]
}
],
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"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
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"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
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