397 lines
16 KiB
Plaintext
397 lines
16 KiB
Plaintext
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{
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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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"/usr/local/lib/python3.6/dist-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.25.2) or chardet (3.0.4) doesn't match a supported version!\n",
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" RequestsDependencyWarning)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'1.13.1'"
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]
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},
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"execution_count": 1,
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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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"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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"%matplotlib inline\n",
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"import pandas as pd\n",
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"import tensorflow as tf\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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"from collections import Counter\n",
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"\n",
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"target_names = [\"Knuckle\", \"Finger\"]\n",
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"\n",
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"tf.__version__"
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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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"source": [
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"df = pd.read_pickle(\"DataStudyEvaluation/df_statistics.pkl\")\n",
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"\n",
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"lst = df.userID.unique()\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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"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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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Colocations handled automatically by placer.\n",
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"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
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"WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Use tf.cast instead.\n"
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]
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}
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],
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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 16 s, sys: 1.39 s, total: 17.4 s\n",
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"Wall time: 12.5 s\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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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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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"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": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" text-align: right;\n",
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" }\n",
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"</style>\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",
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" <tbody>\n",
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" <tr>\n",
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" <th rowspan=\"2\" valign=\"top\">0</th>\n",
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" <th>0</th>\n",
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" <td>1882</td>\n",
|
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" <td>1882</td>\n",
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" <td>1882</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>102</td>\n",
|
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" <td>102</td>\n",
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" <td>102</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th rowspan=\"2\" valign=\"top\">1</th>\n",
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" <th>0</th>\n",
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" <td>28</td>\n",
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" <td>28</td>\n",
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" <td>28</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>1</th>\n",
|
||
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" <td>2499</td>\n",
|
||
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" <td>2499</td>\n",
|
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" <td>2499</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
|
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"text/plain": [
|
||
|
|
" userID TaskID VersionID\n",
|
||
|
|
"InputMethod InputMethodPred \n",
|
||
|
|
"0 0 1882 1882 1882\n",
|
||
|
|
" 1 102 102 102\n",
|
||
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|
"1 0 28 28 28\n",
|
||
|
|
" 1 2499 2499 2499"
|
||
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]
|
||
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|
},
|
||
|
|
"execution_count": 12,
|
||
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|
"metadata": {},
|
||
|
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"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.groupby([\"InputMethod\", \"InputMethodPred\"]).count()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[[1882 102]\n",
|
||
|
|
" [ 28 2499]]\n",
|
||
|
|
"[[0.94858871 0.05141129]\n",
|
||
|
|
" [0.01108033 0.98891967]]\n",
|
||
|
|
"Accuray: 0.971\n",
|
||
|
|
"Recall: 0.969\n",
|
||
|
|
"Precision: 0.956\n",
|
||
|
|
"F1-Score: 0.971\n",
|
||
|
|
" precision recall f1-score support\n",
|
||
|
|
"\n",
|
||
|
|
" Knuckle 0.99 0.95 0.97 1984\n",
|
||
|
|
" Finger 0.96 0.99 0.97 2527\n",
|
||
|
|
"\n",
|
||
|
|
" accuracy 0.97 4511\n",
|
||
|
|
" macro avg 0.97 0.97 0.97 4511\n",
|
||
|
|
"weighted avg 0.97 0.97 0.97 4511\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(sklearn.metrics.confusion_matrix(df.InputMethod.values, df.InputMethodPred.values, labels=[0, 1]))\n",
|
||
|
|
"cm = sklearn.metrics.confusion_matrix(df.InputMethod.values, df.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.InputMethod.values, df.InputMethodPred.values))\n",
|
||
|
|
"print(\"Recall: %.3f\" % metrics.recall_score(df.InputMethod.values, df.InputMethodPred.values, average=\"macro\"))\n",
|
||
|
|
"print(\"Precision: %.3f\" % metrics.average_precision_score(df.InputMethod.values, df.InputMethodPred.values, average=\"macro\"))\n",
|
||
|
|
"print(\"F1-Score: %.3f\" % metrics.f1_score(df.InputMethod.values, df.InputMethodPred.values, average=\"macro\"))\n",
|
||
|
|
"print(sklearn.metrics.classification_report(df.InputMethod.values, df.InputMethodPred.values, target_names=target_names))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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
|
||
|
|
}
|