{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zlju4GRaBavM"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.utils import np_utils\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.family'] = 'serif'\n",
    "plt.rcParams['font.serif'] = 'FreeSerif'\n",
    "plt.rcParams['lines.linewidth'] = 2\n",
    "plt.rcParams['lines.markersize'] = 12\n",
    "plt.rcParams['xtick.labelsize'] = 24\n",
    "plt.rcParams['ytick.labelsize'] = 24\n",
    "plt.rcParams['legend.fontsize'] = 24\n",
    "plt.rcParams['axes.titlesize'] = 36\n",
    "plt.rcParams['axes.labelsize'] = 24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ttnlbElUBavY"
   },
   "source": [
    "### Данные:\n",
    "Будем работать с датасетом [The Iris Dataset](http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YErwCKk5Bava"
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Z1gMKtTZBavi"
   },
   "outputs": [],
   "source": [
    "data = load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7vChv9xkBavp"
   },
   "outputs": [],
   "source": [
    "X, y = data['data'], data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KFqfGvwYBavt"
   },
   "outputs": [],
   "source": [
    "# encode class values as integers\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit(y)\n",
    "encoded_y = encoder.transform(y)\n",
    "# convert integers to dummy variables (i.e. one hot encoded)\n",
    "dummy_y = np_utils.to_categorical(encoded_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L75OKrNFBavy"
   },
   "source": [
    "### Задание:\n",
    "Будем решать задачу классификации на 3 класса. В качестве модели используется нейронная сеть с одним скрытым слоем. Ваша задача оценить качество решенения задачи классификации по метрике `accuracy` в зависимости от числа нейронов скрытого слоя. Также оцените дисперсию функции качества в зависимости от числа главных компонент.\n",
    "\n",
    "Нарисуйте график зависимости функции качества и ее дисперсии от числа нейронов скрытого слоя."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qQAHEhXSBav0"
   },
   "outputs": [],
   "source": [
    "def baseline_model(hidden_width=8):\n",
    "    # create model\n",
    "    model = Sequential()\n",
    "    model.add(Dense(hidden_width, input_dim=4, activation='relu'))\n",
    "    model.add(Dense(3, activation='softmax'))\n",
    "    # Compile model\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8de3nD5WBav5"
   },
   "outputs": [],
   "source": [
    "estimator = KerasClassifier(build_fn=baseline_model, epochs=200, batch_size=5, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r9YAcMlwBav-"
   },
   "outputs": [],
   "source": [
    "kfold = KFold(n_splits=10, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69709
    },
    "colab_type": "code",
    "id": "K4_tob34BawF",
    "outputId": "ad38a7c5-9f85-4801-9d46-58cfff5a9fbd",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.93333334 1.         1.         1.         1.         0.86666667\n",
      " 0.86666667 1.         1.         1.        ]\n",
      "Baseline: 96.67% (5.37%)\n"
     ]
    }
   ],
   "source": [
    "results = cross_val_score(estimator, X, dummy_y, cv=kfold)\n",
    "print(results)\n",
    "print(\"Baseline: %.2f%% (%.2f%%)\" % (results.mean()*100, results.std()*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 173
    },
    "colab_type": "code",
    "id": "tfBR_wwlBawR",
    "outputId": "0039dedd-38fe-4019-f908-c0868a39050d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 9 candidates, totalling 90 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  90 out of  90 | elapsed: 11.0min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),\n",
      "       error_score='raise',\n",
      "       estimator=<keras.wrappers.scikit_learn.KerasClassifier object at 0x7f4dda8e3be0>,\n",
      "       fit_params=None, iid=True, n_jobs=1,\n",
      "       param_grid={'hidden_width': [1, 2, 3, 4, 5, 6, 7, 8, 9]},\n",
      "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
      "       scoring=None, verbose=1)\n"
     ]
    }
   ],
   "source": [
    "param_grid={\"hidden_width\": list(range(1,10))}\n",
    "grid = GridSearchCV(estimator=estimator,param_grid=param_grid, cv=kfold, verbose=1)\n",
    "result = grid.fit(X, dummy_y)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "pgBv4yW5Bawa",
    "outputId": "3a69bd99-d51b-4099-b7fb-725dc8f11475"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.980000 using {'hidden_width': 5}\n"
     ]
    }
   ],
   "source": [
    "print(\"Best: %f using %s\" % (result.best_score_, result.best_params_))\n",
    "test_means = result.cv_results_['mean_test_score']\n",
    "test_stds = result.cv_results_['std_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "6NFtsfJHBawm",
    "outputId": "5c48df37-6718-4db9-91b8-5f01c0b12034"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,8))\n",
    "plt.plot(param_grid[\"hidden_width\"], means, label=\"Quality function\")\n",
    "plt.plot(param_grid[\"hidden_width\"], stds, label=\"Standart deviation\")\n",
    "plt.xlabel(\"Number of hidden neurons\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Homework 3\n",
    "### Dependence of the error value and its standard deviation on the sample size on training and validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sn0wbars/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n"
     ]
    }
   ],
   "source": [
    "train_std = []\n",
    "train_mean = []\n",
    "test_mean = []\n",
    "test_std = []\n",
    "data_range = np.linspace(10,len(X), 10, dtype='int')\n",
    "for k in data_range:\n",
    "    results = cross_validate(estimator, X[:k,:], dummy_y[:k,:], cv=kfold)\n",
    "    train_std.append(results['train_score'].std())\n",
    "    train_mean.append(results['train_score'].mean())\n",
    "    test_std.append(results['test_score'].std())\n",
    "    test_mean.append(results['test_score'].mean())\n",
    "    print('*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.9523258538172172, 0.9717333813808336, 0.9718044092463067, 0.9755555569021791]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(train_mean)\n",
    "plt.figure(figsize=(14,8))\n",
    "plt.plot(data_range, train_mean, label=\"train error\")\n",
    "plt.plot(data_range, train_std, label=\"train std\")\n",
    "plt.plot(data_range, test_mean, label=\"test error\")\n",
    "plt.plot(data_range, test_std, label=\"test std\")\n",
    "plt.xlabel(\"Number of samples\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "IrisClassification.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}