428 lines
17 KiB
Plaintext
428 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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}
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],
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"source": [
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"## USE for Multi GPU Systems\n",
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"#import os\n",
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"#os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
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"\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import math\n",
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"\n",
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"import tensorflow as tf\n",
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"\n",
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"%matplotlib inline\n",
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"\n",
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"# Importing SK-learn to calculate precision and recall\n",
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"import sklearn\n",
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"from sklearn import metrics\n",
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"\n",
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"target_names = [\"Knuckle\", \"Finger\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[ 1 2 9 6 4 14 17 16 12 3 10 18 5] [13 8 11 15 7]\n"
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]
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}
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],
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"source": [
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"df = pd.read_pickle(\"DataStudyCollection/df_statistics.pkl\")\n",
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"\n",
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"lst = df.userID.unique()\n",
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"np.random.seed(42)\n",
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"np.random.shuffle(lst)\n",
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"test_ids = lst[-5:]\n",
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"train_ids = lst[:-5]\n",
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"print(train_ids, test_ids)\n",
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"df[\"GestureId\"] = df.TaskID % 17\n",
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"\n",
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"#df_train = dfAll[dfAll.userID.isin(train_ids)]\n",
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"#df_test = dfAll[dfAll.userID.isin(test_ids)]\n",
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"\n",
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"x = np.concatenate(df.Blobs.values).reshape(-1,27,15,1)\n",
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"x = x / 255.0\n",
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"\n",
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"# convert class vectors to binary class matrices (one-hot notation)\n",
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"num_classes = len(df.TaskID.unique())\n",
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"y = tf.keras.utils.to_categorical(df.TaskID, num_classes)\n",
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"\n",
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"labels = sorted(df.TaskID.unique())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"x = np.stack(df.Blobs)\n",
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"x = x.reshape(-1, 27, 15, 1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# convert class vectors to binary class matrices (one-hot notation)\n",
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"num_classes = 2\n",
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"y = tf.keras.utils.to_categorical(df.InputMethod, num_classes)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(0.5, 1.0, 'Label for image 1 is: [1. 0.]')"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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dx7eqvoVY0gTg28AZwByqC+zn9KueFk6OiLnj6DzsNcDpQ+67EFgREbOBFWX8baOfLfFxwGMRsTYiXgNuAOb1sZ4UIuIeYOuQu+cBS8rwEuCsMS2qz/oZ4hnAUw3jGxh/v2URwB2SHpC0sN/FtDC9/DYIwNPA9H4WM9ba/Y7d29UJEbFR0juB5ZIeLS3huBURIeltdd60ny3xRuDwhvHDyn3jRkRsLLdbgFsYvz9JsHnwm+fldkuf6xlT/Qzx/cBsSUdJ2gs4G1jax3r2IGlK+d05JE0BTmP8/iTBUmBBGV4A3NrHWsZc33YnImKnpAuA24EJwOKIWN2vepqYDtxS/eQGE4HrIuK2/pYEkq4HTgIOkrQBWARcAtwo6Xyqy1zn96/CseePnS09f2Jn6TnElp5DbOk5xJaeQ2zpOcSWnkNs6f0/kZhtd/D2o3sAAAAASUVORK5CYII=\n",
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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"i = 1\n",
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"plt.imshow(x[i].reshape(27, 15)) #np.sqrt(784) = 28\n",
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"plt.title(\"Label for image %i is: %s\" % (i, y[i]))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# If GPU is not available: \n",
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"# GPU_USE = '/cpu:0'\n",
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"#config = tf.ConfigProto(device_count = {\"GPU\": 1})\n",
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"\n",
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"\n",
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"# If GPU is available: \n",
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"config = tf.ConfigProto()\n",
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"config.log_device_placement = True\n",
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"config.allow_soft_placement = True\n",
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"config.gpu_options.allow_growth=True\n",
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"config.gpu_options.allocator_type = 'BFC'\n",
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"\n",
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"# Limit the maximum memory used\n",
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"config.gpu_options.per_process_gpu_memory_fraction = 0.4\n",
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"\n",
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"# set session config\n",
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"tf.keras.backend.set_session(tf.Session(config=config))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"loadpath = \"./ModelSnapshots/CNN-33767.h5\"\n",
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"model = tf.keras.models.load_model(loadpath)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"CPU times: user 1min 45s, sys: 12 s, total: 1min 57s\n",
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"Wall time: 1min 12s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"lst = []\n",
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"batch = 100\n",
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"for i in range(0, len(x), batch):\n",
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" _x = x[i: i+batch]\n",
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" lst.extend(model.predict(_x))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"df[\"InputMethodPred\"] = lst\n",
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"df.InputMethodPred = df.InputMethodPred.apply(lambda x: np.argmax(x))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = df.groupby([\"userID\", \"TaskID\", \"VersionID\"])[[\"InputMethodPred\", \"InputMethod\"]].agg(lambda x: x.tolist()).reset_index()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"from collections import Counter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"df.InputMethod = df.InputMethod.apply(lambda x: Counter(x).most_common()[0][0])\n",
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"df.InputMethodPred = df.InputMethodPred.apply(lambda x: Counter(x).most_common()[0][0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
|
|
"text/html": [
|
|
"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
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|
"\n",
|
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" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
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" }\n",
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
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" <th></th>\n",
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" <th>userID</th>\n",
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" <th>TaskID</th>\n",
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" <th>VersionID</th>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>InputMethod</th>\n",
|
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" <th>InputMethodPred</th>\n",
|
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" <th></th>\n",
|
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" <th></th>\n",
|
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" <th></th>\n",
|
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" </tr>\n",
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|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th rowspan=\"2\" valign=\"top\">0</th>\n",
|
|
" <th>0</th>\n",
|
|
" <td>4425</td>\n",
|
|
" <td>4425</td>\n",
|
|
" <td>4425</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>124</td>\n",
|
|
" <td>124</td>\n",
|
|
" <td>124</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th rowspan=\"2\" valign=\"top\">1</th>\n",
|
|
" <th>0</th>\n",
|
|
" <td>83</td>\n",
|
|
" <td>83</td>\n",
|
|
" <td>83</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>5935</td>\n",
|
|
" <td>5935</td>\n",
|
|
" <td>5935</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" userID TaskID VersionID\n",
|
|
"InputMethod InputMethodPred \n",
|
|
"0 0 4425 4425 4425\n",
|
|
" 1 124 124 124\n",
|
|
"1 0 83 83 83\n",
|
|
" 1 5935 5935 5935"
|
|
]
|
|
},
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.groupby([\"InputMethod\", \"InputMethodPred\"]).count()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df_train = df[df.userID.isin(train_ids)]\n",
|
|
"df_test = df[df.userID.isin(test_ids)]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[3260 82]\n",
|
|
" [ 47 4274]]\n",
|
|
"[[0.97546379 0.02453621]\n",
|
|
" [0.01087711 0.98912289]]\n",
|
|
"Accuray: 0.983\n",
|
|
"Recall: 0.989\n",
|
|
"Precision: 0.977\n",
|
|
"F1-Score: 0.985\n",
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" Knuckle 0.99 0.98 0.98 3342\n",
|
|
" Finger 0.98 0.99 0.99 4321\n",
|
|
"\n",
|
|
" micro avg 0.98 0.98 0.98 7663\n",
|
|
" macro avg 0.98 0.98 0.98 7663\n",
|
|
"weighted avg 0.98 0.98 0.98 7663\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(sklearn.metrics.confusion_matrix(df_train.InputMethod.values, df_train.InputMethodPred.values, labels=[0, 1]))\n",
|
|
"cm = sklearn.metrics.confusion_matrix(df_train.InputMethod.values, df_train.InputMethodPred.values, labels=[0, 1], )\n",
|
|
"cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
|
|
"print(cm)\n",
|
|
"print(\"Accuray: %.3f\" % sklearn.metrics.accuracy_score(df_train.InputMethod.values, df_train.InputMethodPred.values))\n",
|
|
"print(\"Recall: %.3f\" % metrics.recall_score(df_train.InputMethod.values, df_train.InputMethodPred.values))\n",
|
|
"print(\"Precision: %.3f\" % metrics.average_precision_score(df_train.InputMethod.values, df_train.InputMethodPred.values))\n",
|
|
"print(\"F1-Score: %.3f\" % metrics.f1_score(df_train.InputMethod.values, df_train.InputMethodPred.values))\n",
|
|
"print(sklearn.metrics.classification_report(df_train.InputMethod.values, df_train.InputMethodPred.values, target_names=target_names))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 16,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[[1165 42]\n",
|
|
" [ 36 1661]]\n",
|
|
"[[0.96520298 0.03479702]\n",
|
|
" [0.02121391 0.97878609]]\n",
|
|
"Accuray: 0.973\n",
|
|
"Recall: 0.979\n",
|
|
"Precision: 0.967\n",
|
|
"F1-Score: 0.977\n",
|
|
" precision recall f1-score support\n",
|
|
"\n",
|
|
" Knuckle 0.97 0.97 0.97 1207\n",
|
|
" Finger 0.98 0.98 0.98 1697\n",
|
|
"\n",
|
|
" micro avg 0.97 0.97 0.97 2904\n",
|
|
" macro avg 0.97 0.97 0.97 2904\n",
|
|
"weighted avg 0.97 0.97 0.97 2904\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(sklearn.metrics.confusion_matrix(df_test.InputMethod.values, df_test.InputMethodPred.values, labels=[0, 1]))\n",
|
|
"cm = sklearn.metrics.confusion_matrix(df_test.InputMethod.values, df_test.InputMethodPred.values, labels=[0, 1], )\n",
|
|
"cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
|
|
"print(cm)\n",
|
|
"print(\"Accuray: %.3f\" % sklearn.metrics.accuracy_score(df_test.InputMethod.values, df_test.InputMethodPred.values))\n",
|
|
"print(\"Recall: %.3f\" % metrics.recall_score(df_test.InputMethod.values, df_test.InputMethodPred.values))\n",
|
|
"print(\"Precision: %.3f\" % metrics.average_precision_score(df_test.InputMethod.values, df_test.InputMethodPred.values))\n",
|
|
"print(\"F1-Score: %.3f\" % metrics.f1_score(df_test.InputMethod.values, df_test.InputMethodPred.values))\n",
|
|
"print(sklearn.metrics.classification_report(df_test.InputMethod.values, df_test.InputMethodPred.values, target_names=target_names))"
|
|
]
|
|
}
|
|
],
|
|
"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": "3.6.7"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|