deep learning in the FA and miss - fail.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "from random import shuffle\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.chdir('C:\\\\Users\\\\ning\\\\Downloads\\\\training set')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "samples=pickle.load(open('0.40-3.50_samples_with.p','rb'))\n",
    "label=pickle.load(open('0.40-3.50_labels_with.p','rb'))\n",
    "data,label = np.concatenate(samples),np.concatenate(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9060, 9012)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = StandardScaler().fit_transform(data)\n",
    "train_x,test_x, train_y, test_y = train_test_split(data,label)\n",
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(train_x.shape[1],input_dim=train_x.shape[1],activation='relu'))\n",
    "model.add(Dense(100,activation='relu'))\n",
    "model.add(Dense(10,activation='relu'))\n",
    "model.add(Dense(1,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['binary_accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9664 samples, validate on 2416 samples\n",
      "Epoch 1/5\n",
      "9664/9664 [==============================] - 365s - loss: 10.6503 - binary_accuracy: 0.3320 - val_loss: 12.7222 - val_binary_accuracy: 0.2020\n",
      "Epoch 2/5\n",
      "9664/9664 [==============================] - 346s - loss: 10.6503 - binary_accuracy: 0.3320 - val_loss: 12.7222 - val_binary_accuracy: 0.2020\n",
      "Epoch 3/5\n",
      "9664/9664 [==============================] - 342s - loss: 10.6503 - binary_accuracy: 0.3320 - val_loss: 12.7222 - val_binary_accuracy: 0.2020\n",
      "Epoch 4/5\n",
      "9664/9664 [==============================] - 336s - loss: 10.6503 - binary_accuracy: 0.3320 - val_loss: 12.7222 - val_binary_accuracy: 0.2020\n",
      "Epoch 5/5\n",
      "9664/9664 [==============================] - 334s - loss: 10.6503 - binary_accuracy: 0.3320 - val_loss: 12.7222 - val_binary_accuracy: 0.2020\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x253802fea20>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(data,label,epochs=5,batch_size=100,validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3020/3020 [==============================] - 31s    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicts = model.predict_proba(test_x,batch_size=10)\n",
    "roc_auc_score(test_y,predicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fp,tp,t = roc_curve(test_y,predicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.,  1.]), array([ 0.,  1.]))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp,tp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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