model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "\n",
    "with open(\"matches.out\", 'r') as data, open(\"labels.out\", 'r') as labels:\n",
    "    data = data.read().splitlines();\n",
    "    fmtD = []\n",
    "    for x in range(len(data)):\n",
    "        temp = eval(data[x])\n",
    "        fmtD.append(temp)\n",
    "    labels = labels.read().splitlines()\n",
    "    for x in range(len(labels)):\n",
    "        labels[x] = float(labels[x])\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy.random.seed(0)\n",
    "def shuffle_in_unison_scary(a, b):\n",
    "    rng_state = numpy.random.get_state()\n",
    "    numpy.random.shuffle(a)\n",
    "    numpy.random.set_state(rng_state)\n",
    "    numpy.random.shuffle(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "shuffle_in_unison_scary(fmtD, labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = fmtD[:20352]\n",
    "test_data = fmtD[20352:len(fmtD)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def vectorize_sequences(sequences, dimension=60):\n",
    "    results = numpy.zeros((len(sequences), dimension))\n",
    "    for i in range(len(sequences)):\n",
    "        for j in range(4, 34):\n",
    "            results[i, j - 4] = sequences[i][j]\n",
    "        for j in range(38, 68):\n",
    "            results[i, j - 8] = sequences[i][j]\n",
    "    return results\n",
    "\n",
    "#         results[i, sequence] = .\n",
    "#     return results\n",
    "x_train = vectorize_sequences(train_data)\n",
    "x_test = vectorize_sequences(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels = labels[:20352]\n",
    "test_labels = labels[20352:len(labels)]\n",
    "\n",
    "\n",
    "y_train = numpy.asarray(train_labels).astype('float32')\n",
    "y_test = numpy.asarray(test_labels).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = x_train.mean(axis=0)\n",
    "x_train -= mean\n",
    "std = x_train.std(axis=0)\n",
    "x_train /= std\n",
    "x_test -= mean\n",
    "x_test /= std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "def build_model():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Dense(64, activation='relu',\n",
    "    input_shape=(60,)))\n",
    "    model.add(layers.Dense(64, activation='relu'))\n",
    "    model.add(layers.Dense(1, activation=\"sigmoid\"))\n",
    "    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:74: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:4138: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "Epoch 1/20\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Victor\\Anaconda3\\envs\\keras\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
      "\n",
      "20352/20352 [==============================] - 1s 64us/step - loss: 0.0037 - mean_absolute_error: 0.0459\n",
      "Epoch 2/20\n",
      "20352/20352 [==============================] - 0s 20us/step - loss: 0.0015 - mean_absolute_error: 0.0308\n",
      "Epoch 3/20\n",
      "20352/20352 [==============================] - 0s 23us/step - loss: 0.0011 - mean_absolute_error: 0.0266\n",
      "Epoch 4/20\n",
      "20352/20352 [==============================] - 0s 21us/step - loss: 9.3497e-04 - mean_absolute_error: 0.0245\n",
      "Epoch 5/20\n",
      "20352/20352 [==============================] - 0s 21us/step - loss: 7.9702e-04 - mean_absolute_error: 0.0227\n",
      "Epoch 6/20\n",
      "20352/20352 [==============================] - 0s 17us/step - loss: 7.2965e-04 - mean_absolute_error: 0.0217\n",
      "Epoch 7/20\n",
      "20352/20352 [==============================] - 0s 15us/step - loss: 6.1385e-04 - mean_absolute_error: 0.0200\n",
      "Epoch 8/20\n",
      "20352/20352 [==============================] - 0s 19us/step - loss: 5.9116e-04 - mean_absolute_error: 0.0196\n",
      "Epoch 9/20\n",
      "20352/20352 [==============================] - 0s 18us/step - loss: 5.1949e-04 - mean_absolute_error: 0.0184\n",
      "Epoch 10/20\n",
      "20352/20352 [==============================] - 0s 16us/step - loss: 5.0316e-04 - mean_absolute_error: 0.0182\n",
      "Epoch 11/20\n",
      "20352/20352 [==============================] - 0s 16us/step - loss: 4.5252e-04 - mean_absolute_error: 0.0172\n",
      "Epoch 12/20\n",
      "20352/20352 [==============================] - 0s 16us/step - loss: 4.2933e-04 - mean_absolute_error: 0.0167\n",
      "Epoch 13/20\n",
      "20352/20352 [==============================] - 0s 16us/step - loss: 4.1236e-04 - mean_absolute_error: 0.0164\n",
      "Epoch 14/20\n",
      "20352/20352 [==============================] - 0s 18us/step - loss: 3.8199e-04 - mean_absolute_error: 0.0158\n",
      "Epoch 15/20\n",
      "20352/20352 [==============================] - 0s 16us/step - loss: 3.5736e-04 - mean_absolute_error: 0.0153\n",
      "Epoch 16/20\n",
      "20352/20352 [==============================] - 0s 13us/step - loss: 3.4214e-04 - mean_absolute_error: 0.0150\n",
      "Epoch 17/20\n",
      "20352/20352 [==============================] - 0s 20us/step - loss: 3.2141e-04 - mean_absolute_error: 0.0145\n",
      "Epoch 18/20\n",
      "20352/20352 [==============================] - 0s 17us/step - loss: 3.1944e-04 - mean_absolute_error: 0.0145\n",
      "Epoch 19/20\n",
      "20352/20352 [==============================] - 0s 17us/step - loss: 2.9772e-04 - mean_absolute_error: 0.0140\n",
      "Epoch 20/20\n",
      "20352/20352 [==============================] - 0s 13us/step - loss: 2.8657e-04 - mean_absolute_error: 0.0136\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = model.fit(x_train,\n",
    "                    y_train,\n",
    "                    epochs=20,\n",
    "                    batch_size=512,\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.69737685],\n",
       "       [0.47217613],\n",
       "       [0.7166284 ],\n",
       "       ...,\n",
       "       [0.66189826],\n",
       "       [0.64717364],\n",
       "       [0.525653  ]], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "for y in y_test:\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"model.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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