Machine Learning with Scikit-Learn - The Cancer Dataset - 24 - Neural Networks 6.ipynb

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    "## <center> Neural Networks in scikit-learn </center>\n",
    "\n",
    "#### Linear models:\n",
    "\n",
    "<center> ŷ = w[0] \\* x[0] + w[1] \\* x[1] + ... + w[p] \\* x[p] + b </center>"
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    "import mglearn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
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   "source": [
    "mglearn.plots.plot_two_hidden_layer_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Accuracy on the training subset: 0.906\n",
      "Accuracy on the test subset: 0.881\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, random_state=0)\n",
    "\n",
    "mlp = MLPClassifier(random_state=42)\n",
    "mlp.fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy on the training subset: {:.3f}'.format(mlp.score(X_train, y_train)))\n",
    "print('Accuracy on the test subset: {:.3f}'.format(mlp.score(X_test, y_test)))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum per each feature:\n",
      "[  2.81100000e+01   3.92800000e+01   1.88500000e+02   2.50100000e+03\n",
      "   1.63400000e-01   3.45400000e-01   4.26800000e-01   2.01200000e-01\n",
      "   3.04000000e-01   9.74400000e-02   2.87300000e+00   4.88500000e+00\n",
      "   2.19800000e+01   5.42200000e+02   3.11300000e-02   1.35400000e-01\n",
      "   3.96000000e-01   5.27900000e-02   7.89500000e-02   2.98400000e-02\n",
      "   3.60400000e+01   4.95400000e+01   2.51200000e+02   4.25400000e+03\n",
      "   2.22600000e-01   1.05800000e+00   1.25200000e+00   2.91000000e-01\n",
      "   6.63800000e-01   2.07500000e-01]\n"
     ]
    }
   ],
   "source": [
    "print('The maximum per each feature:\\n{}'.format(cancer.data.max(axis=0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the training subset: 0.995\n",
      "Accuracy on the test subset: 0.958\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit(X_train).transform(X_train)\n",
    "X_test_scaled = scaler.fit(X_test).transform(X_test)\n",
    "\n",
    "mlp = MLPClassifier(max_iter=1000, random_state=42)   \n",
    "\n",
    "mlp.fit(X_train_scaled, y_train)\n",
    "\n",
    "print('Accuracy on the training subset: {:.3f}'.format(mlp.score(X_train_scaled, y_train)))\n",
    "print('Accuracy on the test subset: {:.3f}'.format(mlp.score(X_test_scaled, y_test)))"
   ]
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   "cell_type": "code",
   "execution_count": 7,
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    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on the training subset: 0.988\n",
      "Accuracy on the test subset: 0.972\n"
     ]
    }
   ],
   "source": [
    "mlp = MLPClassifier(max_iter=1000, alpha=1, random_state=42)\n",
    "mlp.fit(X_train_scaled, y_train)\n",
    "\n",
    "print('Accuracy on the training subset: {:.3f}'.format(mlp.score(X_train_scaled, y_train)))\n",
    "print('Accuracy on the test subset: {:.3f}'.format(mlp.score(X_test_scaled, y_test)))"
   ]
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RdCjw15THzcCHyMZU+o6kNrJGoU+RNTb9Po1RJOBz3d1eCCGEEEIIIYTQEwhQ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\nKTuyimfX8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a298e71c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.imshow(mlp.coefs_[0], interpolation='None', cmap='GnBu')\n",
    "plt.yticks(range(30), cancer.feature_names)\n",
    "plt.xlabel('Columns in weight matrix')\n",
    "plt.ylabel('Input feature')\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "## <center> Advantages and Disadvantages of Neural Nets (scikit-learn) </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Stronger points:\n",
    " - can be used efficiently on large datasets\n",
    " - can build very complex models\n",
    " - many parameters for tuning\n",
    " - flexibility and rapid prototyping\n",
    " - etc.\n",
    " \n",
    "### Weaker points:\n",
    " - many parameters for tuning\n",
    " - some solvers are scale sensitive\n",
    " - data may need to be pre-processed\n",
    " - etc.\n",
    " \n",
    " \n",
    "### Alternatives:\n",
    " \n",
    " - theano\n",
    " - tensorflow\n",
    " - keras\n",
    " - lasagna\n",
    " - etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
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