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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Seminar 01: Naive Bayes from scratch"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Today we will write Naive Bayes classifier supporting different feature probabilities.\n",
-    "\n",
-    "_Authors: [Radoslav Neychev](https://github.com/neychev), [Vladislav Goncharenko](https://github.com/v-goncharenko)_"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Loading data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:08:51.904850Z",
-     "start_time": "2020-02-11T07:08:50.413258Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from sklearn import datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "matplotlib.rcParams['font.size'] = 11"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plt.style.use('dark_background')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First to load dataset we're going to use [`sklearn`](https://scikit-learn.org/stable/) package which we will extensively use during the whole course.\n",
-    "\n",
-    "`sklearn` implement most of classical and frequently used algorithms in Machine Learning. Also it provides [User Guide](https://scikit-learn.org/stable/user_guide.html) describing principles of every bunch of algorithms implemented.\n",
-    "\n",
-    "As an entry point to main `sklearn`'s concepts we recommend [getting started tutorial](https://scikit-learn.org/stable/getting_started.html) (check it out yourself). [Further tutorials](https://scikit-learn.org/stable/tutorial/index.html) can also be handy to develop your skills."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First functionality we use is cosy loading of [common datasets](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets). All we need to do is just one function call.\n",
-    "\n",
-    "Object generated by [`load_iris`](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) is described as:\n",
-    "\n",
-    "> Dictionary-like object, the interesting attributes are:\n",
-    ">\n",
-    "> ‘data’, the data to learn,\n",
-    ">\n",
-    ">‘target’, the classification labels,\n",
-    ">\n",
-    ">‘target_names’, the meaning of the labels,\n",
-    ">\n",
-    ">‘feature_names’, the meaning of the features,\n",
-    ">\n",
-    ">‘DESCR’, the full description of the dataset,\n",
-    ">\n",
-    ">‘filename’, the physical location of iris csv dataset (added in version 0.20)\n",
-    "\n",
-    "Let's see what we have"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:08:51.918857Z",
-     "start_time": "2020-02-11T07:08:51.910566Z"
-    },
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "dataset = datasets.load_iris()\n",
-    "\n",
-    "print(dataset.DESCR)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you aren't familiar with Iris dataset - take a minute to read description above =) (as always [more info about it in Wikipedia](https://en.wikipedia.org/wiki/Iris_flower_data_set))\n",
-    "\n",
-    "__TL;DR__ 150 objects equally distributed over 3 classes each described with 4 continuous features\n",
-    "\n",
-    "Just pretty table to look at:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:08:51.940271Z",
-     "start_time": "2020-02-11T07:08:51.921326Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# for now you don't need to understand what happens in this code - just look at the table\n",
-    "ext_target = dataset.target[:, None]\n",
-    "# ext_target = np.expand_dims(dataset.target, axis=-1)\n",
-    "df = pd.DataFrame(\n",
-    "    np.concatenate((dataset.data, ext_target, dataset.target_names[ext_target]), axis=1),\n",
-    "    columns=dataset.feature_names + ['target label', 'target name'],\n",
-    ")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now give distinct names to the data we will use"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:08:52.604007Z",
-     "start_time": "2020-02-11T07:08:52.599704Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "features = dataset.data\n",
-    "target = dataset.target\n",
-    "\n",
-    "features.shape, target.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Please, remember!!!__\n",
-    "\n",
-    "Anywhere in our course we have an agreement to shape design matrix (named `features` in code above) as \n",
-    "\n",
-    "`(#number_of_items, #number_of_features)`"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualize dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Our dataset has 4 dimensions however humans are more common to 3 or even 2 dimensional data, so let's plot first 3 features colored with labels values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:09:01.519543Z",
-     "start_time": "2020-02-11T07:09:01.508622Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# projection \n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "\n",
-    "fig = plt.figure(figsize=(8, 8))\n",
-    "\n",
-    "ax = Axes3D(fig)\n",
-    "\n",
-    "ax.scatter(features[:, 0], features[:, 1], features[:, 3], c=target, marker='o')\n",
-    "ax.set_xlabel(dataset.feature_names[0])\n",
-    "ax.set_ylabel(dataset.feature_names[1])\n",
-    "ax.set_zlabel(dataset.feature_names[2])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:09:02.191167Z",
-     "start_time": "2020-02-11T07:09:01.870454Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# 3d plot\n",
-    "import plotly.express as px\n",
-    "\n",
-    "fig = plt.figure(figsize=(8, 8))\n",
-    "fig = px.scatter_3d(df, x='sepal length (cm)', y='sepal width (cm)', z='petal width (cm)',\n",
-    "                    color='target name')\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Then have a look on feature distributions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:09:03.615686Z",
-     "start_time": "2020-02-11T07:09:02.922717Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# remember this way to make subplots! It could be useful for you later in your work\n",
-    "\n",
-    "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
-    "\n",
-    "for i, axis in enumerate(axes.flat):\n",
-    "    axis.hist(features[:, i])\n",
-    "    axis.set_xlabel(dataset.feature_names[i])\n",
-    "    axis.set_ylabel('number of objects')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that every plot above have own scale"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Classifier implementation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since we aiming to implement Naive Bayes algorithm first we need some prior distribution defined.\n",
-    "\n",
-    "The most common distribution is (of course) Gaussian and it's params are mean and standard deviation. Let's implement class taking list of feature values, estimating distribution params and able to give probability density of any given feature value."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Denote the normal distribution $\\mathcal{N}(\\mu, \\sigma^2)$ PDF:\n",
-    "$$\n",
-    "f(x|\\mu, \\sigma^2) = \\frac{1}{\\sigma\\sqrt{2\\pi}}\\exp(-\\frac{(x - \\mu)^2}{2\\sigma^2})\n",
-    "$$\n",
-    "Let's implement the `GaussianDistribution` class. (Of course in practice one could always use something like `scipy.stats.norm`).\n",
-    "\n",
-    "Please note, that making computations with log probabilities is more stable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:09:05.944368Z",
-     "start_time": "2020-02-11T07:09:05.938558Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class GaussianDistribution:\n",
-    "    def __init__(self, feature):\n",
-    "        '''\n",
-    "        Args:\n",
-    "            feature: column of design matrix, represents all available values\n",
-    "                of feature to model.\n",
-    "                axis=0 stays for samples.\n",
-    "        '''\n",
-    "        self.mean = feature.mean(axis=0)\n",
-    "        self.std = feature.std(axis=0)\n",
-    "\n",
-    "    def logpdf(self, value):\n",
-    "        '''Logarithm of probability density at value'''\n",
-    "        return  # <YOUR CODE HERE>\n",
-    "    \n",
-    "    def pdf(self, value):\n",
-    "        return  # <YOUR CODE HERE>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's check the result:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy\n",
-    "_test = scipy.stats.norm(loc=features[:, :2].mean(axis=0), scale=features[:, :2].std(axis=0))\n",
-    "assert np.allclose(\n",
-    "    GaussianDistribution(features[:, :2]).logpdf(features[:5, :2]),\n",
-    "    _test.logpdf(features[:5, :2])\n",
-    ")\n",
-    "print('Seems fine!')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T00:09:57.874598Z",
-     "start_time": "2020-02-11T00:09:57.740553Z"
-    }
-   },
-   "source": [
-    "### [Probabilistic model](https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Probabilistic_model)\n",
-    "\n",
-    "Let's focus on the classification problem now. For the case of $K$ classes label $y_i \\in \\{C_1, \\ldots, C_k\\}$. Iris classification problem has 3 classes, so $K=3$. Bayes' Theorem takes the following form:\n",
-    "\n",
-    "$$\n",
-    "P(y_i = C_k|\\mathbf{x}_i) = \\frac{P(\\mathbf{x}_i|y_i = C_k) P(y_i = C_k)}{P(\\mathbf{x}_i)}\n",
-    "$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Please note, we prefer working with log probabilities here as well. So the equation above will take the following form:\n",
-    "$$\n",
-    "\\log P(y_i = C_k|\\mathbf{x}_i) = \\log P(\\mathbf{x}_i|y_i = C_k) + \\log P(y_i = C_k) - \\log P(\\mathbf{x}_i)\n",
-    "$$\n",
-    "\n",
-    "As one could mention, to find the class label with the highest probability we even do not need the last term $P(\\mathbf{x}_i)$. However, we need it to get the correct estimation of the probability $P(y_i = C_k|\\mathbf{x}_i)$. \n",
-    "\n",
-    "The $P(\\mathbf{x}_i)$ term can be computed using the following property:\n",
-    "$$\n",
-    "P(\\mathbf{x}_i) = \\sum_{k=1}^K P(y_i = C_k)  P(\\mathbf{x}_i|y_i=C_k).\n",
-    "$$\n",
-    "It can be computed from $\\log P(\\mathbf{x}_i|y_i=C_k)$ values using `logsumexp` function located in `scipy.special`.\n",
-    "\n",
-    "Now let's implement the Naive Bayes classifier itself. The class below is inherited from `sklearn` base classes and provides all the main methods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:30:45.357053Z",
-     "start_time": "2020-02-11T07:30:45.346489Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.base import BaseEstimator, ClassifierMixin\n",
-    "from scipy.special import logsumexp\n",
-    "\n",
-    "\n",
-    "class NaiveBayes(BaseEstimator, ClassifierMixin):\n",
-    "    '''\n",
-    "    Please note, using `X` and `y` for design matrix and labels in general is not a good choice,\n",
-    "    better stick to more informative naming conventions.\n",
-    "    However, to make the code consistent with sklearn implementation, we use `X` and `y` variables here.\n",
-    "    # '''\n",
-    "    def fit(self, X, y, sample_weight=None, distributions=None):\n",
-    "        '''\n",
-    "        sample_weight \n",
-    "            The argument is ignored. For comatibility only.\n",
-    "        '''\n",
-    "        assert sample_weight is None\n",
-    "        self.unique_labels = np.unique(y)\n",
-    "        \n",
-    "        # If distributions of features are not specified, they a treated Gaussian\n",
-    "        if distributions is None:\n",
-    "            distributions = [GaussianDistribution] * X.shape[1]\n",
-    "        else:\n",
-    "            # Check whether distributions are passed for all features\n",
-    "            assert len(distributions) == X.shape[1]        \n",
-    "\n",
-    "        # Here we find distribution parameters for every feature in every class subset\n",
-    "        # so P(x^i|y=C_k) will be estimated only using information from i-th feature of C_k class values\n",
-    "        self.conditional_feature_distributions = {} # label: [distibution for feature 1, ...]\n",
-    "        for label in self.unique_labels:\n",
-    "            feature_distribution = []\n",
-    "            for column_index in range(X.shape[1]):\n",
-    "                # `column_index` feature values for objects from `label` class\n",
-    "                feature_column = X[y == label, column_index]\n",
-    "                fitted_distr = distributions[column_index](feature_column)\n",
-    "                feature_distribution.append(fitted_distr)\n",
-    "            self.conditional_feature_distributions[label] = feature_distribution\n",
-    "\n",
-    "        # Prior label distributions (unconditional probability of each class)\n",
-    "        self.prior_label_distibution = {\n",
-    "            # <YOUR CODE HERE>\n",
-    "        }\n",
-    "\n",
-    "    \n",
-    "    def predict_log_proba(self, X):\n",
-    "        # Matrix of shape (n_objects : n_classes)\n",
-    "        class_log_probas = np.zeros((X.shape[0], len(self.unique_labels)), dtype=float)\n",
-    "        \n",
-    "        # Here we compute the class log probabilities for each class sequentially b\n",
-    "        for label_idx, label in enumerate(self.unique_labels):\n",
-    "            for idx in range(X.shape[1]):\n",
-    "                # All loglikelihood for every feature w.r.t. fixed label\n",
-    "\n",
-    "                class_log_probas[:, label_idx] += # <YOUR CODE HERE>\n",
-    "\n",
-    "            # Add log proba of label prior\n",
-    "            # <YOUR CODE HERE>\n",
-    "\n",
-    "        for idx in range(X.shape[1]):\n",
-    "        # If you want to get probabilities, you need to substract the log proba for every feature\n",
-    "            # <YOUR CODE HERE>\n",
-    "        return class_log_probas\n",
-    "    \n",
-    "    def predict_proba(self, X):\n",
-    "        return np.exp(self.predict_log_proba(X))\n",
-    "    \n",
-    "    def predict(self, X):\n",
-    "        log_probas = self.predict_log_proba(X)\n",
-    "        # we need to cast labels to their original form (they may start from number other than 0)\n",
-    "        return np.array([self.unique_labels[idx] for idx in log_probas.argmax(axis=1)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nb = NaiveBayes()\n",
-    "nb.fit(features, target)\n",
-    "print('log probas:\\n{}'.format(nb.predict_log_proba(features[:2])))\n",
-    "print('predicted labels:\\n{}'.format(nb.predict(features[:2])))\n",
-    "print('\\nIt`s alive! More tests coming.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's check our Naive Bayes classifier on the unseed data. To do so we will use `train_test_split` from `sklearn`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "features_train, features_test, target_train, target_test = train_test_split(features, target, test_size=0.25, random_state=24)\n",
-    "\n",
-    "print(features_train.shape, features_test.shape)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:30:45.713112Z",
-     "start_time": "2020-02-11T07:30:45.709195Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nb = NaiveBayes()\n",
-    "nb.fit(features_train, target_train, distributions=[GaussianDistribution]*4)\n",
-    "nb_test_log_proba = nb.predict_log_proba(features_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('Naive Bayes classifier accuracy on the train set: {}'.format(nb.score(features_train, target_train)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('Naive Bayes classifier accuracy on the test set: {}'.format(nb.score(features_test, target_test)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, let's comapre the Naive Bayes classifier with the `sklearn` implementations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:30:47.513267Z",
-     "start_time": "2020-02-11T07:30:47.498843Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn import naive_bayes\n",
-    "\n",
-    "sklearn_nb = naive_bayes.GaussianNB()\n",
-    "sklearn_nb.fit(features_train, target_train)\n",
-    "sklearn_nb_test_log_proba = sklearn_nb.predict_log_proba(features_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('sklearn implementation accuracy on the train set: {}'.format(sklearn_nb.score(features_train, target_train)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('sklearn implementation accuracy on the test set: {}'.format(sklearn_nb.score(features_test, target_test)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And let's even check the predictions. If you used Gaussian distribution and done everything correctly, the log probabilities should be the same."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T07:30:47.853360Z",
-     "start_time": "2020-02-11T07:30:47.841855Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "assert np.allclose(nb_test_log_proba, sklearn_nb_test_log_proba), 'log probabilities do not match'\n",
-    "print('Seems alright!')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Comparing to kNN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.neighbors import KNeighborsClassifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "knn = KNeighborsClassifier(n_neighbors=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "knn.fit(features_train, target_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "knn.score(features_train, target_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "knn.score(features_test, target_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Seem like Naive Bayes classifier performance is comparable to the kNN, while Naive Bayes does not need to store all the train data (while kNN need)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Advanced distribution for NaiveBayes\n",
-    "\n",
-    "Let's take a look at violin plots for every feature in our dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "labels = df.columns[:4]\n",
-    "\n",
-    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(15, 15), sharey=True)\n",
-    "plt.violinplot(features, showmedians=True)\n",
-    "ax.set_xticks(np.arange(1, len(labels) + 1))\n",
-    "ax.set_xticklabels(labels)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Although we do love Gaussian distribution it is still unimodal while our features are substantially multimodal (see histograms above). So we have to implement more robust distribution estimator - Kernel Density Estimator (KDE).\n",
-    "\n",
-    "Idea for this method is simple: we assign some probability density to a region around actual observation. (We will return to density estimation methods to describe them carefully later in this course).\n",
-    "\n",
-    "Fortunately `sklearn` have KDE implemented for us already. All it needs is vector of feature values."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get probability estimations using KDE one can easily access the `sklearn.neighbors` module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T00:27:57.223204Z",
-     "start_time": "2020-02-11T00:27:54.891Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.neighbors import KernelDensity\n",
-    "kde = KernelDensity(bandwidth=0.28, kernel='gaussian')\n",
-    "feature_col = features[target==2, 2]\n",
-    "kde.fit(feature_col.reshape((-1, 1)))\n",
-    "linspace = np.linspace(feature_col.min(), feature_col.max(), 1000)\n",
-    "plt.plot(linspace, np.exp(kde.score_samples(linspace.reshape((-1, 1)))))\n",
-    "plt.grid()\n",
-    "plt.xlabel('feature value')\n",
-    "plt.ylabel('probability')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To make it compatible with the Naive Bayes classifier we have implemented above, we need to create class with the same methods:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-11T00:27:57.237856Z",
-     "start_time": "2020-02-11T00:27:54.896Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class GaussianKDE:\n",
-    "    def __init__(self, feature):\n",
-    "        self.kde = KernelDensity(bandwidth=1.)\n",
-    "        self.kde.fit(feature.reshape((-1, 1)))\n",
-    "\n",
-    "    def logpdf(self, value):\n",
-    "        return self.kde.score_samples(value.reshape((-1, 1)))\n",
-    "\n",
-    "    def pdf(self, value):\n",
-    "        return np.exp(self.log_proba(value))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nb_kde = NaiveBayes()\n",
-    "nb_kde.fit(features, target, distributions=[GaussianKDE]*4)\n",
-    "print('log probas:\\n{}'.format(nb_kde.predict_log_proba(features[:2])))\n",
-    "print('predicted labels:\\n{}'.format(nb_kde.predict(features[:2])))\n",
-    "print('\\nIt`s alive!')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('KDE Naive Bayes classifier accuracy on the train set: {}'.format(nb_kde.score(features_train, target_train)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print('KDE Naive Bayes classifier accuracy on the test set: {}'.format(nb_kde.score(features_test, target_test)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Seems like the accuracy of the classifier has decreased. What is going on?\n",
-    "\n",
-    "_Hint: try varying the `bandwidth` parameter of the `KernelDensity` constructor in `GaussianKDE` class (around 0.3)._\n",
-    "\n",
-    "Let's take a closer look on the features distributions. Here comes the histogram:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
-    "\n",
-    "for ax_idx, feature_idx in enumerate([2, 3]):\n",
-    "    for label in range(3):\n",
-    "        ax = axes[ax_idx, label]\n",
-    "        feature_col = features[target==label, feature_idx]\n",
-    "        ax.hist(feature_col, bins=7)\n",
-    "        ax.grid()\n",
-    "        ax.set_title('class: {}, feature: {}'.format(\n",
-    "            dataset.target_names[label],\n",
-    "            dataset.feature_names[feature_idx]\n",
-    "        ))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We see, than the distributions within every class are unimodal. That's how KDE is approximating the PDF:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
-    "kde = KernelDensity(bandwidth=1., kernel='gaussian')\n",
-    "\n",
-    "for ax_idx, feature_idx in enumerate([2, 3]):\n",
-    "    for label in range(3):\n",
-    "        ax = axes[ax_idx, label]\n",
-    "        feature_col = features[target==label, feature_idx]\n",
-    "        kde.fit(feature_col.reshape((-1, 1)))\n",
-    "        linspace = np.linspace(\n",
-    "            0.8*feature_col.min(),\n",
-    "            1.2*feature_col.max(),\n",
-    "            1000\n",
-    "        )\n",
-    "        ax.plot(linspace, np.exp(kde.score_samples(linspace.reshape((-1, 1)))))\n",
-    "        ax.grid()\n",
-    "        ax.set_title('class: {}, feature: {}'.format(\n",
-    "            dataset.target_names[label],\n",
-    "            dataset.feature_names[feature_idx]\n",
-    "        ))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One could mention, that every feature need different `bandwidth` parameter.\n",
-    "\n",
-    "And that's how Gaussian distribution fits to the data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
-    "\n",
-    "for ax_idx, feature_idx in enumerate([2, 3]):\n",
-    "    for label in range(3):\n",
-    "        ax = axes[ax_idx, label]\n",
-    "        feature_col = features[target==label, feature_idx]\n",
-    "        gaussian_distr = GaussianDistribution(feature_col)\n",
-    "        linspace = np.linspace(\n",
-    "            feature_col.min(),\n",
-    "            feature_col.max(),\n",
-    "            1000\n",
-    "        )\n",
-    "        ax.plot(linspace, gaussian_distr.pdf(linspace.reshape((-1, 1))))\n",
-    "        ax.grid()\n",
-    "        ax.set_title('class: {}, feature: {}'.format(\n",
-    "            dataset.target_names[label],\n",
-    "            dataset.feature_names[feature_idx]\n",
-    "        ))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks a bit better. Moreover, hypothesis of the normal distribution over the features seems more promising (the features are petal length and width)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
-    "\n",
-    "for ax_idx, feature_idx in enumerate([2, 3]):\n",
-    "    for label in range(3):\n",
-    "        ax = axes[ax_idx, label]\n",
-    "        feature_col = features[target==label, feature_idx]\n",
-    "        ax.hist(feature_col, bins=7, density=True, alpha=0.4)\n",
-    "                \n",
-    "        bandwidth = abs((feature_col.min() - feature_col.max()) / 5)\n",
-    "        kde = KernelDensity(bandwidth=bandwidth, kernel='gaussian')\n",
-    "        kde.fit(feature_col.reshape((-1, 1)))\n",
-    "        linspace = np.linspace(\n",
-    "            feature_col.min(),\n",
-    "            feature_col.max(),\n",
-    "            1000\n",
-    "        )\n",
-    "        ax.plot(linspace, np.exp(kde.score_samples(linspace.reshape((-1, 1)))), c='m', label='KDE')\n",
-    "        \n",
-    "        gaussian_distr = GaussianDistribution(feature_col)\n",
-    "        linspace = np.linspace(\n",
-    "            feature_col.min(),\n",
-    "            feature_col.max(),\n",
-    "            1000\n",
-    "        )\n",
-    "        ax.plot(linspace, gaussian_distr.pdf(linspace.reshape((-1, 1))), c='g', label='Gaussian')        \n",
-    "        \n",
-    "        ax.grid()\n",
-    "        ax.set_title('class: {}, feature: {}'.format(\n",
-    "            dataset.target_names[label],\n",
-    "            dataset.feature_names[feature_idx]\n",
-    "        ))\n",
-    "\n",
-    "labels_handles = {\n",
-    "  label: handle for ax in fig.axes for handle, label in zip(*ax.get_legend_handles_labels())\n",
-    "}\n",
-    "fig.legend(\n",
-    "  labels_handles.values(),\n",
-    "  labels_handles.keys(),\n",
-    "  loc=\"lower center\",\n",
-    "  bbox_to_anchor=(0.5, 0),\n",
-    "  bbox_transform=plt.gcf().transFigure,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So, the __conclusion__: always check the distribution and the assumptions you make. They should be appropriate for the data you work with."
-   ]
-  }
- ],
- "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": "3.7.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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--- a/week0_02_linear_reg/week0_02_linear_regression_and_sgd.ipynb
+++ /dev/null
@@ -1,1467 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# ml-mipt course\n",
-    "\n",
-    "## Seminar 2\n",
-    "## Linear Regression & other stuff\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at Linear Regression and its implementations in this notebook.\n",
-    "\n",
-    "__Contents__:\n",
-    "* Linear Regression analytical solution\n",
-    "* Unstability of the solution in case of multicollinear features\n",
-    "* Gradient descent approach\n",
-    "* Stochastic gradient\n",
-    "* Instability analysis\n",
-    "* Linear Regression out of the box (sklearn, vw, etc.)\n",
-    "\n",
-    "See `week0_02_extra*` notebooks for extra (more complex or just additional) materials."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nIf you are using Google Colab, uncomment the next line to download `utils_02.py`. \\n'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'''\n",
-    "If you are using Google Colab, uncomment the next line to download `utils_02.py`. \n",
-    "'''\n",
-    "# !wget https://raw.githubusercontent.com/girafe-ai/ml-course/blob/23f_basic/week0_02_linear_reg/utils_02.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T18:43:24.015593Z",
-     "start_time": "2020-02-19T18:43:23.252434Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "random_seed = 43\n",
-    "random_seed = 45\n",
-    "\n",
-    "matplotlib.rcParams.update({'font.size': 16})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Time to generate features matrix $X$ and correct weights vector $w_{true}$. Targer vector (or matrix in general case) $Y$ is computed as  $X\\mathbf{w}_{\\text{true}}$ with gaussian noise:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_features = 2\n",
-    "n_objects = 300\n",
-    "batch_size = 10\n",
-    "num_steps = 43\n",
-    "np.random.seed(random_seed)\n",
-    "\n",
-    "# Let it be the *true* weights vector\n",
-    "w_true = np.random.normal(size=(n_features, ))\n",
-    "\n",
-    "X = np.random.uniform(-5, 5, (n_objects, n_features))\n",
-    "\n",
-    "# For different scales of features. In case of 3 features the code is equal to the commented line below\n",
-    "# X *= np.arange([1, 3, 5])[None, :]\n",
-    "X *= (np.arange(n_features) * 2 + 1)[np.newaxis, :] \n",
-    "\n",
-    "# Here comes the *true* target vector\n",
-    "Y = X.dot(w_true) + np.random.normal(0, 1, n_objects)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Recap:*\n",
-    "In case of linear model\n",
-    "$$\n",
-    "\\hat{Y} = X\\mathbf{w}\n",
-    "$$\n",
-    "and __MSE__ loss function\n",
-    "$$\n",
-    "Q(Y, X, \\mathbf{w}) = MSE(Y, X\\mathbf{w}) =  \\|Y - X\\mathbf{w}\\|^2_2 = \\sum_i (y_i - \\mathbf{x}^T_i \\mathbf{w})^2\n",
-    "$$\n",
-    "analytical solution takes simple form:\n",
-    "\n",
-    "$$\n",
-    "\\mathbf{w}^* = (X^T X)^{-1}X^T Y.\n",
-    "$$\n",
-    "\n",
-    "_To do: derive it on the practice session._\n",
-    "\n",
-    "Let's check how it works:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "w_star = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.0022709 , 0.25830368])"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_star"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.02637477, 0.2603217 ])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_true"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see, the analytical solution is quite close to the original one. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's generate the dataset with correlated features:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_features = 3\n",
-    "n_objects = 300\n",
-    "batch_size = 10\n",
-    "num_steps = 43\n",
-    "eps = 1e-3\n",
-    "\n",
-    "# Let it be the *true* weights vector\n",
-    "w_true = np.random.normal(size=(n_features, ))\n",
-    "\n",
-    "X = np.random.uniform(-5, 5, (n_objects, n_features))\n",
-    "\n",
-    "# Now we duplicate the second feature with some small noise, so featues 2 and 3 are collinear\n",
-    "X[:, -1] = X[:, -2] + np.random.uniform(-eps, eps, X[:, -2].shape)\n",
-    "\n",
-    "# Here comes the *true* target vector\n",
-    "Y = X.dot(w_true) + np.random.normal(0, 1, (n_objects))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  0.5745729 , -20.66996929,  18.40142078])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_star = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Y)\n",
-    "w_star"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.54472708, -1.64490986, -0.60649306])"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_true"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we can see, the second and third coefficents are opposite. This makes our model highly *unstable*."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "How could one actually fix it? Here comes the __regularization__.\n",
-    "\n",
-    "Let's use the L2 norm of weigths vector as a regularization term to constrain the desired solution.\n",
-    "\n",
-    "$$\n",
-    "Q_{\\text{reg}}(Y, X, \\mathbf{w}) = MSE(Y, X\\mathbf{w}) + \\lambda\\|\\mathbf{w}\\|_2^2=  \\|Y - X\\mathbf{w}\\|^2_2 + \\lambda\\|\\mathbf{w}\\|^2_2= \\sum_i (y_i - \\mathbf{x}^T_i \\mathbf{w})^2 + \\sum_p w^2_p\n",
-    "$$\n",
-    "\n",
-    "Analytical solution is available in this case as well:\n",
-    "\n",
-    "$$\n",
-    "\\mathbf{w}^*_{\\text{reg}} = (X^T X + \\lambda I_p)^{-1}X^T Y,\n",
-    "$$\n",
-    "where $I_p$ is diagonal matrix consisting of 1s (with size p).\n",
-    "\n",
-    "__Be careful with the regularization term if you have included the column of 1s into X matrix! We do not want regularize the bias (free) term in our linear model.__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.57458186, -1.15428189, -1.11399619])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_star_reg = np.linalg.inv(X.T.dot(X) + 0.05*np.eye(n_features)).dot(X.T).dot(Y)\n",
-    "w_star_reg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 0.54472708, -1.64490986, -0.60649306])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "w_true"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Gradient descent\n",
-    "\n",
-    "The analytical solution described above includes invertion of the matrix $X^T X$ (or $X^T X + \\lambda I$), which is quite expensive in terms of computation resourses. The complexity of matrix inversion can be estimated as $O(p^3 + p^2 N)$. This leads us to the iterative optimization methods, which are more efficient and are de-facto the main approach to optimization in Machine Learning.\n",
-    "\n",
-    "Gradient descent is one of the most popular optimization methods. Worth to mention the fact that the minimization (maximization) target (e.g loss function value) should be differentiable w.r.t model parameters. Using the gradient descent, the weights vector $\\mathbf{w}^{(t+1)}$ on step $t+1$ can be expressed in the following form:\n",
-    "$$\n",
-    "\\mathbf{w}^{(t+1)} = \\mathbf{w}^{(t)} - \\eta_t \\nabla Q(\\mathbf{w}^{(t)}),\n",
-    "$$\n",
-    "where $\\eta_t$ stays for the gradient step (usually referred as _learning rate_).\n",
-    "\n",
-    "The gradient in case of MSE loss function takes the following form:\n",
-    "\n",
-    "$$\n",
-    "\\nabla Q(\\mathbf{w}) = -2X^TY + 2X^TX\\mathbf{w} = 2X^T(X\\mathbf{w} - Y).\n",
-    "$$\n",
-    "\n",
-    "In this case the complexity is only $O(pN)$. To make it even more effective (and using the hypothesis of homogeneous data in the dataset) one could use _stochastic gradient descent_, which computes the gradient only over some random subset of data K points, so the final complexity decreases to $O(pK)$, where $K << N$."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Visuailizing the gradient descent trajectory\n",
-    "This part is deeply based on [Evgeny Sokolov](https://github.com/esokolov) open materials.\n",
-    "\n",
-    "Let's take a close look on the optimization path in simple two-dimentional space (where features are in different scales). We will use MSE loss function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The plots below show $\\mathbf{w}^{(t)}$ values on every step $t$. The red dot in the center stays for $\\mathbf{w}_{\\text{true}}$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_features = 2\n",
-    "n_objects = 300\n",
-    "batch_size = 10\n",
-    "num_steps = 43\n",
-    "np.random.seed(random_seed)\n",
-    "\n",
-    "# Let it be the *true* weights vector\n",
-    "w_true = np.random.normal(size=(n_features, ))\n",
-    "\n",
-    "X = np.random.uniform(-5, 5, (n_objects, n_features))\n",
-    "\n",
-    "# For different scales of features. In case of 3 features the code is equal to the commented line below\n",
-    "# X *= np.arange([1, 3, 5])[None, :]\n",
-    "X *= (np.arange(n_features) * 2 + 1)[np.newaxis, :] \n",
-    "\n",
-    "# Here comes the *true* target vector\n",
-    "Y = X.dot(w_true) + np.random.normal(0, 1, n_objects)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(random_seed)\n",
-    "w_0 = np.random.uniform(-2, 2, n_features)-0.5\n",
-    "w = w_0.copy()\n",
-    "w_list = [w.copy()]\n",
-    "step_size = 1e-2\n",
-    "\n",
-    "for i in range(num_steps):\n",
-    "    w -= # YOUR CODE HERE\n",
-    "    w_list.append(w.copy())\n",
-    "w_list = np.array(w_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 936x648 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# compute level set\n",
-    "A, B = np.meshgrid(np.linspace(-2, 2, 100), np.linspace(-2, 2, 100))\n",
-    "\n",
-    "levels = np.empty_like(A)\n",
-    "for i in range(A.shape[0]):\n",
-    "    for j in range(A.shape[1]):\n",
-    "        w_tmp = np.array([A[i, j], B[i, j]])\n",
-    "        levels[i, j] = np.mean(np.power(np.dot(X, w_tmp) - Y, 2))\n",
-    "\n",
-    "plt.figure(figsize=(13, 9))\n",
-    "plt.title('GD trajectory')\n",
-    "plt.xlabel('$w_1$')\n",
-    "plt.ylabel('$w_2$')\n",
-    "plt.xlim(w_list[:, 0].min() - 0.1, w_list[:, 0].max() + 0.1)\n",
-    "plt.ylim(w_list[:, 1].min() - 0.1, w_list[:, 1].max() + 0.1)\n",
-    "plt.gca().set_aspect('equal')\n",
-    "\n",
-    "# visualize the level set\n",
-    "CS = plt.contour(A, B, levels, levels=np.logspace(0, 2, num=15), cmap=plt.cm.rainbow_r)\n",
-    "CB = plt.colorbar(CS, shrink=0.8, extend='both')\n",
-    "\n",
-    "# visualize trajectory\n",
-    "plt.scatter(w_true[0], w_true[1], c='r')\n",
-    "plt.scatter(w_list[:, 0], w_list[:, 1])\n",
-    "plt.plot(w_list[:, 0], w_list[:, 1])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The gradient vector is orthogonal to the equipotential surface . That's the reason why the optimization path is not so smooth. Let's visualize the gradient directions to make it more clear."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 936x648 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# compute level set\n",
-    "A, B = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(-3, 3, 100))\n",
-    "A_mini, B_mini = np.meshgrid(np.linspace(-3, 3, 40), np.linspace(-3, 3, 40))\n",
-    "\n",
-    "levels = np.empty_like(A)\n",
-    "for i in range(A.shape[0]):\n",
-    "    for j in range(A.shape[1]):\n",
-    "        w_tmp = np.array([A[i, j], B[i, j]])\n",
-    "        levels[i, j] = np.mean(np.power(np.dot(X, w_tmp) - Y, 2))\n",
-    "        \n",
-    "# visualize the level set\n",
-    "plt.figure(figsize=(13, 9))\n",
-    "CS = plt.contour(A, B, levels, levels=np.logspace(-1, 1.5, num=40), cmap=plt.cm.rainbow_r)\n",
-    "CB = plt.colorbar(CS, shrink=0.8, extend='both')\n",
-    "        \n",
-    "# visualize the gradients\n",
-    "gradients = np.empty_like(A_mini)\n",
-    "for i in range(A_mini.shape[0]):\n",
-    "    for j in range(A_mini.shape[1]):\n",
-    "        w_tmp = np.array([A_mini[i, j], B_mini[i, j]])\n",
-    "        antigrad = - 2 * 1e-3 * np.dot(X.T, np.dot(X, w_tmp) - Y) / Y.shape[0]\n",
-    "        plt.arrow(A_mini[i, j], B_mini[i, j], antigrad[0], antigrad[1], head_width=0.02)\n",
-    "\n",
-    "plt.title('Antigradients demonstration')\n",
-    "plt.xlabel(r'$w_1$')\n",
-    "plt.ylabel(r'$w_2$')\n",
-    "plt.xlim((w_true[0] - 1.5, w_true[0] + 1.5))\n",
-    "plt.ylim((w_true[1] - .5, w_true[1] + .5))\n",
-    "plt.gca().set_aspect('equal')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's take a look at the _stochastic gradient descent_. Let the number of elements the loss function computed on each state (`batch_size`) be equal to $10$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(random_seed)\n",
-    "batch_size = 10\n",
-    "w = w_0.copy()\n",
-    "w_history_list = [w.copy()]\n",
-    "lr = 1e-2\n",
-    "\n",
-    "for i in range(num_steps):\n",
-    "    sample_indices = # YOUR CODE HERE\n",
-    "    w -= # YOUR CODE HERE\n",
-    "    w_history_list.append(w.copy())\n",
-    "w_history_list = np.array(w_history_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 936x648 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# compute level set\n",
-    "A, B = np.meshgrid(np.linspace(-2, 2, 100), np.linspace(-2, 2, 100))\n",
-    "\n",
-    "levels = np.empty_like(A)\n",
-    "for i in range(A.shape[0]):\n",
-    "    for j in range(A.shape[1]):\n",
-    "        w_tmp = np.array([A[i, j], B[i, j]])\n",
-    "        levels[i, j] = np.mean(np.power(np.dot(X, w_tmp) - Y, 2))\n",
-    "\n",
-    "\n",
-    "plt.figure(figsize=(13, 9))\n",
-    "plt.title('SGD trajectory')\n",
-    "plt.xlabel(r'$w_1$')\n",
-    "plt.ylabel(r'$w_2$')\n",
-    "plt.xlim((w_history_list[:, 0].min() - 0.1, w_history_list[:, 0].max() + 0.1))\n",
-    "plt.ylim((w_history_list[:, 1].min() - 0.1, w_history_list[:, 1].max() + 0.1))\n",
-    "plt.gca().set_aspect('equal')\n",
-    "\n",
-    "# visualize the level set\n",
-    "CS = plt.contour(A, B, levels, levels=np.logspace(0, 2, num=30), cmap=plt.cm.rainbow_r)\n",
-    "CB = plt.colorbar(CS, shrink=0.8, extend='both')\n",
-    "\n",
-    "# visualize trajectory\n",
-    "plt.scatter(w_true[0], w_true[1], c='r')\n",
-    "plt.scatter(w_history_list[:, 0], w_history_list[:, 1])\n",
-    "plt.plot(w_history_list[:, 0], w_history_list[:, 1])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see from the plot, SGD \"wanders\" around the optima. It is controlled by the SGD step size $\\eta_k$ and the convergence is not guaranteed in general case. For SGD method convergence given the sequence of steps $\\{\\eta_k\\}$ it is necessary that [Robbins-Monroe Conditions](https://projecteuclid.org/download/pdf_1/euclid.aoms/1177729586) are satisfied:\n",
-    "$$\n",
-    "\\sum_{k = 1}^\\infty \\eta_k = \\infty, \\qquad \\sum_{k = 1}^\\infty \\eta_k^2 < \\infty.\n",
-    "$$\n",
-    "More intuitively, those conditions may be explained as follows:\n",
-    "1. A sequence of steps $\\{\\eta_k\\}$ should diverge, so optimization method is capable or reaching any point in the given parameter space,\n",
-    "2. At the same time it should diverge \"not so fast\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's analyze SGD trajectories, which are generated by a sequence of steps, satisfying the Robbins-Monroe Conditions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.random.seed(random_seed)\n",
-    "w = w_0.copy()\n",
-    "w_list = [w.copy()]\n",
-    "lr_0 = 0.02\n",
-    "\n",
-    "for i in range(num_steps):\n",
-    "    lr = lr_0 / ((i+1) ** # What should the power be? )\n",
-    "    sample_indices = # YOUR CODE HERE\n",
-    "    w -= # YOUR CODE HERE\n",
-    "    w_list.append(w.copy())\n",
-    "w_list = np.array(w_list)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 936x648 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# compute level set\n",
-    "A, B = np.meshgrid(np.linspace(-2, 2, 100), np.linspace(-2, 2, 100))\n",
-    "\n",
-    "levels = np.empty_like(A)\n",
-    "for i in range(A.shape[0]):\n",
-    "    for j in range(A.shape[1]):\n",
-    "        w_tmp = np.array([A[i, j], B[i, j]])\n",
-    "        levels[i, j] = np.mean(np.power(np.dot(X, w_tmp) - Y, 2))\n",
-    "\n",
-    "plt.figure(figsize=(13, 9))\n",
-    "plt.title('SGD trajectory')\n",
-    "plt.xlabel(r'$w_1$')\n",
-    "plt.ylabel(r'$w_2$')\n",
-    "plt.xlim((w_list[:, 0].min() - 0.1, w_list[:, 0].max() + 0.1))\n",
-    "plt.ylim((w_list[:, 1].min() - 0.1, w_list[:, 1].max() + 0.1))\n",
-    "plt.gca().set_aspect('equal')\n",
-    "\n",
-    "# visualize the level set\n",
-    "CS = plt.contour(A, B, levels, levels=np.logspace(0, 2, num=40), cmap=plt.cm.rainbow_r)\n",
-    "CB = plt.colorbar(CS, shrink=0.8, extend='both')\n",
-    "\n",
-    "# visualize trajectory\n",
-    "plt.scatter(w_true[0], w_true[1], c='r')\n",
-    "plt.scatter(w_list[:, 0], w_list[:, 1])\n",
-    "plt.plot(w_list[:, 0], w_list[:, 1])\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Comparing the convergence speed\n",
-    "Finally, it is important to compare the convergence speed for full and stochastic GD. Let's generate a random dataset and plot the loss function value w.r.t. iteration number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# data generation\n",
-    "n_features = 50\n",
-    "n_objects = 1000\n",
-    "num_steps = 500\n",
-    "batch_size = 10\n",
-    "\n",
-    "w_true = np.random.uniform(-2, 2, n_features)\n",
-    "\n",
-    "X = np.random.uniform(-10, 10, (n_objects, n_features))\n",
-    "Y = X.dot(w_true) + np.random.normal(0, 5, n_objects)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lr_sgd = 1e-3\n",
-    "lr_gd = 1e-3\n",
-    "w_sgd = np.random.uniform(-4, 4, n_features)\n",
-    "w_gd = w_sgd.copy()\n",
-    "residuals_sgd = [np.mean(np.power(np.dot(X, w_sgd) - Y, 2))]\n",
-    "residuals_gd = [np.mean(np.power(np.dot(X, w_gd) - Y, 2))]\n",
-    "\n",
-    "for i in range(num_steps):\n",
-    "    lr = lr_sgd / ((i+1) ** 0.51)\n",
-    "    sample = np.random.randint(n_objects, size=batch_size)\n",
-    "    w_sgd -= 2 * lr * np.dot(X[sample].T, np.dot(X[sample], w_sgd) - Y[sample]) / batch_size\n",
-    "    residuals_sgd.append(np.mean(np.power(np.dot(X, w_sgd) - Y, 2)))\n",
-    "    \n",
-    "    w_gd -= 2 * lr_gd * np.dot(X.T, np.dot(X, w_gd) - Y) / Y.shape[0]\n",
-    "    residuals_gd.append(np.mean(np.power(np.dot(X, w_gd) - Y, 2)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 936x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(13, 6))\n",
-    "plt.plot(range(num_steps+1), residuals_gd, label='Full GD')\n",
-    "plt.plot(range(num_steps+1), residuals_sgd, label='SGD')\n",
-    "plt.title('Empirial risk over iterations')\n",
-    "plt.xlim((-1, num_steps+1))\n",
-    "plt.legend()\n",
-    "plt.xlabel('Iter num')\n",
-    "plt.ylabel(r'Q($w$)')\n",
-    "plt.grid()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As demonstrated, GD reaches optima within several iterations, while SGD may appear less stable and requires more time to converge. Usually larger models demonstrate larger fluctuations for loss function values during the convergence process of stochastic gradient-based methods. In practice, SGD step size may be adjusted to achieve better convergence speed and there are several methods which implement adaptive gradient descent step size: AdaGrad, Adam, RMSProp etc."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Extra: Instability analysis\n",
-    "Using the new technique, let's analyse the linear regression behavior in case of multicollinear features.\n",
-    "\n",
-    "In case of (multi-)collinear features the solution is *unstable*. Let's take a look at the *condition number* of our matrix:\n",
-    "$$\\kappa(a) = \\frac{\\sigma_\\max(A)}{\\sigma_\\min(A)}$$\n",
-    "where $\\sigma _{\\max }(A)$ and $\\sigma _{\\min }(A)$ are maximal and minimal singular values of matrix $A$ respectively. Hence "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_w_by_grad(X, Y, num_steps, w_0, lr):\n",
-    "    w = w_0.copy()\n",
-    "\n",
-    "    for i in range(num_steps):\n",
-    "        w -= 2 * lr * np.dot(X.T, np.dot(X, w) - Y) / Y.shape[0]\n",
-    "    return w\n",
-    "\n",
-    "def get_w_by_stoch_grad(X, Y, num_steps, w_0, lr_0, n_objects):\n",
-    "    w = w_0.copy()\n",
-    "    lr_0 = 0.45\n",
-    "\n",
-    "    for i in range(num_steps):\n",
-    "        lr = lr_0 / ((i+1)**0.51)\n",
-    "        sample = np.random.randint(n_objects, size=batch_size)\n",
-    "        w -= 2 * lr * np.dot(X[sample].T, np.dot(X[sample], w) - Y[sample]) / Y.shape[0]\n",
-    "    return w\n",
-    "\n",
-    "def rmse(y_true, y_pred):\n",
-    "    return np.linalg.norm(y_true-y_pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lr = 1e-3\n",
-    "sgd_lr = 0.1\n",
-    "num_steps = 250\n",
-    "noise_eps_seq = np.logspace(-2, -6, 20)\n",
-    "\n",
-    "w_0 = np.random.uniform(-2, 2, (n_features))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "condition_numbers = []\n",
-    "vector_norms_list = []\n",
-    "rmse_list = []\n",
-    "results_list = []\n",
-    "for eps in noise_eps_seq:\n",
-    "    local_condition_numbers = []\n",
-    "    local_vector_norms_list = []\n",
-    "    local_rmse_list = []\n",
-    "    for i in range(50):\n",
-    "        X[:, -1] = 2 * (X[:, -2] + np.random.uniform(-eps, eps, X[:, -2].shape))\n",
-    "\n",
-    "        a = np.linalg.eigvals(X.T.dot(X))\n",
-    "        local_condition_numbers.append(a.max() / a.min())\n",
-    "\n",
-    "        w_star = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(Y)\n",
-    "        w_star_grad = get_w_by_grad(X, Y, num_steps, w_0, lr)\n",
-    "        w_star_sgd = get_w_by_stoch_grad(X, Y, num_steps, w_0, sgd_lr, n_objects)\n",
-    "        local_vector_norms_list.append([\n",
-    "            np.linalg.norm(w_star),\n",
-    "            np.linalg.norm(w_star_grad), \n",
-    "            np.linalg.norm(w_star_sgd),\n",
-    "        ])\n",
-    "\n",
-    "        analytical_predict = X.dot(w_star)\n",
-    "        grad_predict = X.dot(w_star_grad)\n",
-    "        sgd_predict = X.dot(w_star_sgd)\n",
-    "        \n",
-    "        local_rmse_list.append([\n",
-    "            rmse(Y, analytical_predict),\n",
-    "            rmse(Y, grad_predict),\n",
-    "            rmse(Y, sgd_predict),\n",
-    "        ])\n",
-    "        \n",
-    "        results_list.append([w_star, w_star_grad, w_star_sgd])\n",
-    "\n",
-    "    condition_numbers.append([np.mean(local_condition_numbers), np.std(local_condition_numbers)])\n",
-    "    vector_norms_list.append([\n",
-    "        np.mean(np.array(local_vector_norms_list), axis=0),\n",
-    "        np.std(np.array(local_vector_norms_list), axis=0),\n",
-    "    ])\n",
-    "    rmse_list.append(np.mean(np.array(local_rmse_list), axis=0))\n",
-    "\n",
-    "condition_numbers = np.array(condition_numbers)\n",
-    "vector_norms_list = np.array(vector_norms_list)\n",
-    "rmse_list = np.array(rmse_list)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Take a close look on the collected vectors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from utils_02 import visualise"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1080x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "visualise(\n",
-    "    np.log(condition_numbers[:, 0]), \n",
-    "    np.log(condition_numbers[:, 1]),\n",
-    "    noise_eps_seq, \n",
-    "    title='Condition number in log scale by noise level',\n",
-    "    greater_than_zero=True,\n",
-    "    log_scale=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1080x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "visualise(\n",
-    "    np.log(vector_norms_list[:, 0, 0]), \n",
-    "    np.log(vector_norms_list[:, 1, 0]),\n",
-    "    noise_eps_seq, \n",
-    "    title='Vector norm in log scale for analytical solution by noise level',\n",
-    "    greater_than_zero=True,\n",
-    "    log_scale=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1080x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "visualise(\n",
-    "    vector_norms_list[:, 0, 1], \n",
-    "    vector_norms_list[:, 1, 1],\n",
-    "    noise_eps_seq, \n",
-    "    title='Vector norm in original scale for gradient solution by noise level',\n",
-    "    greater_than_zero=True,\n",
-    "    log_scale=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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aAAieaC4faSgvzwZ7srwPAICoI8BIRNnZUlER27w1JSXFworw0pD0dL8rap3wUM958xjqieQQDhW5yo3mpKZaJ2IsFRfbLlHz58f2dgEASDIEGImqooIAo6Fwl0V4lkWQuyxaIivLrvr9739+VwJEX6dOFswuXCgtW0aQgcYVFfkzq6i83AZ6Ll4c+9sGACBJEGAkqqwsuyL0ww9+V+KfeO+yaImiIgurFi70uxIgenJzpbIy+3vXrnaiuHChPb8RZKChaA/vbEpVlXVisIwTAICoIMBIZBUVyRdgOGdrn4uLE6PLoiUqK21nEnafQSJKSbFOo/oyMux9FRXSggXJ9zyHxqWn20wKv4RnFH3+uVRT418dAAAkKB/3g0TUZWbGdpCZn5yTSkulvn3tCm3QB3FGWvfu9vsGEk1VVeM7AWVk2H1/u+2sGwkIwmteSorNKOI5GQCAiCPASHQVFYl9It8wuEjEZSItkZpqB8ypqX5XAkROXp7UoUPzn5eZKW21ldSnT+yHNyJYghBgSDZouWfP2GzlCgBAEiHASHQZGf6uB46WcHCx3XbJHVzUl5lpV6OBRLClpSPNycqyIG/bbW3uDZJLTk7j3Tp+SE+XevTwZ6AoAAAJilfVZJBIXRjhGRfh4CJIB6tBUFBguzUA8a5z57YHk9nZduLYu7cN8UVyCEr3RX3Z2RaqJcprMAAAPiPASAYZGcE8sGutcHDRrRvBRVPKy5kHgPiWn28dVu2Vk2Nt/Nts4+9gR0Sfc8F93svPt8AdAAC0G7uQJIuKCmnZsvjccrCkxOontGi5bt2ktWulNWv8rgRondTUyJ/s5eZKvXpJq1bZriXsDpF4Cgtt7kRQlZTY9qoLFvhdCQAAcY0OjGSRnh6ZK5qxVFxMx0VbhafgB/mAHtiS9iwdaU5ennVj9Oxp3RlIHPEw66miIv5ehwEACBjObpJJRYW0dGnwuzCKi61WtqBrn4wM25nh88+D/zsHJJvhEovlbvn59rZihV0RX706+reJ6AmF4mdoa5cu0vr1dt8DAACtRgdGMklLa9mWhH4Jd1x07054ESl5eVJVld9VAM2LxtKR5hQU2I4lW29tO5ggPhUXx8+QTOcsWKYDCACANiHASDbl5cHb0q2oSOrTh+AiWjp2TIwhrkhsXbpYyOqHwkJ7DtpqK56D4lG8Pb+lpNguOSyNBACg1QJ2JouoC1IXRji42Gorrn5GW5cutp0fEESFhcGYYRB+TmLuTvzIzIzP57ZQyGaxMKcIAIBWIcBIRn53YRBcxB5DPdsmI8PmsSB6QqFgbTHpXN2WzV27Rm+gKCIj3rov6svIsE6MoHVFAgAQYLxqJqNQyJYVxFr9Nm2Ci9hLT7cQI17WivutpMTmI1RWEmJEU5cuwQzWnLMdI/r29Xd5C5oWhM6d9sjJsddEnpcBAGgRAoxkVVZmQ/NiobCQQXlBkZtr21SicampNo+lW7e6x0hlpXUOIbKKi4P//+qcLbvr29ceOwQZwZGXlxgdMgUFFpIBAIBmBfCyF2Ii3IWxcGH0bqOw0K5cx+P65ETWoYNtG7l0qd+VBE9uroUXWzop6tZNWreOLTcjJS0tvsK0lBR7ziwtlZYskb7/Xqqt9buq5BbPy0caKi2Vfv45uq/JAAAkADowklm0ujDqb01IeBFMXbrYyTqMc9Zl0atX41d0wzsHcAU+MoK6dKQ5KSn23Nmvn9SpU+w62fBLKSnB795prcpKCzIAAECjCDCSWWqqHYhHSkGB1Lu3neQRXASbc7bumpNxG6S3zTbWLdTcOvS0NIbuRUJJiXVoxbOUFBuI3K+fnXgSZMRWUVFiPg67dJHy8/2uAgCAwErAV3+0SseO7b8KWj+4yMmJTF2IvrQ0hnoWF1u3UGvut9nZtswEbRNvS0eak5pq4Ve/fvZnIp5UB1EiLR+pzzm6FwEAaAJHWsmuPV0Y+fkEF/EuJydYW1jGSnhQZ/fubbtyXlhoywfQevWHoyaS1FTrxOjXz/+tqhNdenpiL4ELL1fLyPC7EgAAAocjLLS+CyM/31rue/YkuEgEJSX+bKvrl9RU67po7/aL5eWJexU4WkpLE789PhSycKtvXwuHk7nDKVqKixP//zW8XC0e58QAABBFBBioW8vdnLy8uuAika9+JaOqKvv9JrrKSruqGakrm1278lhoqfR0u58li7Q0+3n79bOdfxL9hDuWkiU4zMxk5g4AAA3wqgjToUPjAx3DwUWvXpysJarwUM/GduCId+npdYM6Iym8Xp1W7+Yl6tKR5qSl2WDGvn2tA4Ugo32ys+3EPlnk5DBzBwCAeggwYLbUhZGXZ6EFwUVyCIUS82pfcbHUp0/07sPh/7dkPDlvqQ4dkqPDpynp6dax05JuNzQuWbov6isstBAMAAAQYKCe0lK7WpibWxdcJPtJR7LJyrIr5YmgvYM6WyMz0zpYsLmMjORaOtKc8nK2L24r59o/uyZedehA+AUAgAgwUF9Kig033GYbgotkVlQU/wfKOTmRGdTZGvn5ibU9aKR065Z4XT3tkZJCoNNWBQXJPdSyU6fkDXAAANiEo0r8ElcGIdmwy4ICv6tom8pKC+H8mEvRsaNdKYXp2JHlZ1tSXMz/S1tw8m6BYKLv5AMAQBMIMABszjlbehFPw/LqD+r0c1Bi586cYEh23+nUye8qgotundZJTbVZEMkuPHA5K8vvSgAA8AUBBoAtS021HTbiYThltAd1tkb4BCOewp9oYOlI07Kzk3MgZVsVF7ODS1hqqm1nnqi7RgFtlZbG6w6QBHiUA2hcZmawt/CL5aDO1khNtZ1JknW9fnm5zSFB0zp1Ctb9NsgIe34pLc1CDDoxgLpdnvr1s7eKCp5bgQRGgAGgaQUFNlciaPwY1NkaGRnWwZJsV40zM+3gEc1LS+P/qiUyMgjEtiQz054Du3RJ3rAUyS0jw7r9+va1nfScs8dCZaUFGVVVzHYDEhCveACaV1EhrV4tLV/udyWmosL/WRctkZtrV4W++srvSmIjPDuFFt6W69hRWrJEWrfO70qCi+6Lxjlng4OLiqSFC+2+5Hl+VwVEV2amdfo1tbQsNVUqK7Pn2GXLpEWLpLVrY1sngKjgKBNAy3Tv7n+7cnhQZ2Vl8MOLsJKS+N+WtqXKy222A1rOOQZ6NocAo3mhkN2Ptt2WIcJIXOFlrX362PNCS44DnLPujD59bD4Vr1FA3KMDA0DLpKTYkohPPpE2bIj97RcXW6t0PK5r7dTJrvwEpYMlGrKyWA7RVgUFdtK5cqXflQRPXh7DKlsjK8tmYyxfLn33HZ09SAzh15eiorZ/D+fs64uKpFWrpO+/5zkXiFN0YABouYwMu4IRS0Ed1Nla3bsn7pWf8NKReOmKCaLOnfn/25KgzrgJusJCabvtLDxlSRfiVXa2XTjp06d94UVDeXkW9G27bWS/L4CY4FUNQOvk59tgrFgI+qDO1gh3sCTiQLGKCv+XF8W7zEybZYA6KSmcXLSHc7asq29fluEgvuTk2E5e225rYVy0ZGfbRZnttqsbAgog8AgwALReWVn0Q4WKCpt3kZER3duJpfR0CzES6YpodnbyzPiItspKdpOor7AwvruugiItzXZq6N2b3VwQbLm51hnRu7ctrYuVzMy6bVjLyhLrNRpIQDxCAbRN167RWRIRj4M6WyMnx04mEoFz9rMk4u/JD6mpwdyy2C90DURWTo6dGHbrlpidYIhfeXlSr1722u/nENq0NOsw7dePQBkIMAIMAG0TXhIRyRf44mJb65qbG7nvGURFRYlxolpZydKRSCst5f9UslAsL8/vKhJTSYm1zJeXEz7CX+HgolevYD3eQyHrAu3Xz+YTMUgYCBQCDABtF14S0d6D4NRUuyoY74M6W6OiIr5ne+TkWKstIottVU0oxMl1NKWm2oDP7baL7owBYEvy863bImjBRUMpKVLHjjZHpls3W2oCwHf0RgFon9xcO+H65pu2fX1OjgUXiTTroqW6drVtDn/6ye9KWiclhaUj0ZSXZ106P/7odyX+oXU7NjIyLIReuVL69lvb7hmIloICC+/jbRaLc9a5VFJiWxR//338vW4DCYQjBADt16GDtHq1tHRp676uosLekvVEOLwMZ9486eef/a6m5SoruRIVbVVV0ooV0saNflcSe1lZ0qpVfleRXPLzbfnekiXSggXShg1+V4REUlhor/WJsJV4YaG91dRYkLFihd8VAUmHJSQAIqNLl5ZfVUn0QZ2tkZZm28XFy9Tz3FyWjsRCenry/j8zvNMfztW1y7OlLyKhqMiCsa23Tozwor7c3LqtXuN5OSgQh+LkiBlA4DlnBynNTbdPlkGdrZGVZXvRB1146Qhio7w8+YbHOcfJgN9CIQuk+/QJ9nwCBFf4dX6rrRJ/KHF2ti2DDQd/yX5RBogBAgwAkZOW1vhQz/DJbzIN6myNggJbNhBkVVXJOavELykpNmgxmeTns8VnUGRl2ZDFrbZKviANrReeE7HddvY6n+jBRUMZGRb89etn4TPHOUDUMAMDQGTl5Nhwyq+++uX7knVQZ2uUldkQvdbOEomFvDzayv1QXGxzCWpq/K4kNlg+EjxFRRawLlpka/6TcS4LGhcOLsrLeY2XLIDt1Mn+P5YsscdNba3fVQEJhQADQOSVlNhQz8WLGdTZWl262M4kQRpiyNIRf3XuLH3yid9VRF9qqp0oI3hSUux5vKREmj9f+uEHvyuC35yz5UZ9+9KhsyWpqRZidOwoLVtmQca6dX5XBSQElpAAiI6qKhtuxaDO1nHOWraDdCWrc2cOUP2UnS2VlvpdRfQVFcXPMNtklZ5u3XTbbJN4QxnRMs5ZN144uPh/7d1raOXpXQfw75PbJDOZzGR2MpndzF563XbdKsKioiiLl+oLabWIVqx4FwUVFEFFUVBLfVEFW1/oCsV7tZZS6g31hUN9IdJaEJXt2tqy7Wwv2+3Obp3dme5lHl88iUnmmknOOf//OefzgT+TnPwvv0ny5PzP9zwXzw03NjPTvl/TOrQGhkAPDGA4SnGDu19zc2128w9/uPvlDFdWpuPFc9/dcUdy/nz3vw/DZPjI+FheTl71qvbO8mOP6SI/DWZm2nPB6dPmqdmPrQmKT5xoS69+5jPTMzQQBkyAAdBHi4ttQtSPfCSptZsaZmfbfCZ0b36+deE/d67rSobj0CErE42bUtoL2tXV5NOfbkMGu/pbxfBs9SBYXxdcDMqxY227cKEFGU8/3XVFMFYEGAB9dfRomxPj0Ue7ub6hI/1y6lSb4PXSpa4rGTxLp46v2dk2ZPDkyRaweTE2GWZm2t+c9fXWK5DBW15uvS0vXmxzZDz5pBAQ9sBgU4A+O3my3UCO2rFjuvT3TSn9X2p3v/yujb/FxfZi7OUvbx8znmZnW2+v17ymraYhvBi+paU2Ufb997fQyFxAcEP+KgH03cZGe9d9VO9sGjrSX1tdjyfpXe7l5X5NWsvBHDvW5s55/PE2tGSS520Zd6Vsb1tDRU6das8BjN7CQuv5uLGRfOELbd6jp5/WhuAKAgyAviulzV7+yCOtq+mw3XWXsc59duZMu7mdlK7Gho9MnlJaz7ETJ5JPfaoNfZo0MzPt7+TcXNt2hgFbK2/1/TH6aWYmOX68bZcvt2XVz59PnnpKmAHZY4BRSvnpJD+cpCb5jyQ/UGu9tOPrdyd5R5K1JE8meVOt9dzm174vyS9t7vrrtdY/HFz5AFNidrZ1zX744eHO+L+66gVl3y0utndJP/vZris5uK2Z+ZlM8/OtN9faWvLJT/Z/1YWtMGIrmJif3/3xzn9182cUZma2e97Vut0zQ5jBFLtpgFFK2UjyU0nuq7VeLKW8K8kbk/zBjt3emuSPaq1/WEr5+iRvSfK9pZQTSX4lyQNp4ce/lVLeV2s9P+D/B8DkW1hoIcYjjwzn3fe5udb7gv67/fa2hOW4L195/Lju6tPg8OHk3nvbJIWPPZY899xorlvK9QOIK//d6kUBfVXK7jBjZ8+McX8ugFuw1yEkc0mWSinPJzmc5FNXfP2+JD+z+fE/JXnv5sffnOQfa61PJkkp5R+TfEuSdx6kaICpdeRIm+zr4x8f/LnvusuEbeNidraNk+5qhZpBMXnndDlxooVWn/lM60F0+fKtn2N2dm+BxPy8cIzJVUqba2ZlpT13CzOYIje9U621PlZKeWuSTyS5mOQfaq3/cMVu/57kDUl+O8m3JzlaSrktyUaST+7Y79zmY7uUUn40yY8myfr6es6ePXvr/5MeuHDhwtjWDtNiYtrp88+3bVDm5toNEOPl0qX9vQjsg1Ku+zs3Me2U66u1/Q174YWr52cwd8NY0E576vLl1q5efHFy5kpif2ZmcuGFFyaune5lCMlqktcneUmSp5L8ZSnlTbXWP9mx288m+Z1SyvcneX+Sx5LseWBWrfWhJA8lyQMPPFAffPDBvR7aK2fPns241g7TYqLa6cc+1t5xOaj5+eS++/S+GEcXLrQhRePo1Kk24/41TFQ7hQmlnfZcre054qmn2r3CIN/0YDwsLeXs449PXDvdywxE35jk47XWz9Van0/yniRfvXOHWuunaq1vqLV+eZJf3HzsqbQgY+fdyZnNxwA4qHvuaWPLD+ruu4UX42p5uU28Oo4MHwEYnlKSo0dbUPylX9rmoTl1yipjjL29BBifSPJVpZTDpZSS5BuSPLxzh1LKyVLK1rl+IW1FkiT5+ySvLaWsbvbkeO3mYwAc1MxMm9TzIDcjt93WJgRjfJ05M34rIiwtDSZ8A2Bvlpe3w4xXvaotdbyw0HVVcMtuesdTa/3XJO9O8qG0JVRnkjxUSvnVUsrrNnd7MMkjpZT/TrKe5M2bxz6Z5NeSfGBz+9WtCT0BGID5+RZi7OcF7Pz8dbvwM0YWFpLTp7uu4tZYOhWgO0eOtPD7Na8RZjB29tRnuNb6K2nLoe70yzu+/u60kONax74j2z0yABi0w4eTl7wk+Z//ubXj7rnHLP2TYn09eeKJ0S1PeVCGjwD0w5Ej24HGs8+2+TLOn0+++MWuK4NrGrM+pwBc0/HjbVnNvTp5si2/xmSYmWk3n+NgZcUYbIA+Ony43Uvcf3/y6le33n2HDnVdFexi1jaASXH6dFtW8/Ofv/F+Cwvj82KXvVtdbWOcL1zoupIb0/sCoP8OH94ONC5e3O6ZcelS15Ux5QQYAJPk7rtbt88bvYg1dGRy3Xln8vDDN9+vKzMzrbcQAONjaaltd9zRwoytpVkvXuy6MqaQISQAk6SU5GUvu36Xz7W1tqwak+nw4TY8qK9WV8dvxRQAti0tJbffntx3X/IlX9JCjaWlrqtiiriLAJg0c3NtZZIre1kcOmToyDTY2OhvDxvDRwAmx+Li7jBjY8MS2QydISQAk2hxMXnpS5OPfjSptT12zz3e/Z4Gc3PthvLcua4r2W1hQe8fgEm1uNjm4jp9uq2I9cwz29uzzyaXL3ddIRNCgAEwqVZW2pwIn/hEW2ZzebnrihiVU6fasqp9mmztxImuKwBgFBYW2ra62j6vtc2XsTPU6NPzE2NFgAEwydbW2rsea2tdV8IoldLCq498pOtKthk+AjCdStle1WTrfuSFF3YHGs88k7z4Yrd1MhYEGACTbn296wrowspKcuxY8vTTXVeSHDnSuhcDQNKGOx471rYtly7tDjQuXtweBgubBBgAMKnOnEm+8IXubwD1vgDgZhYX27b1nHH5cps/Y2eo8dxz3dZI5wQYADCpFhfbfBif/Wx3NZSyPQ4aAPZqZqbN37VzDq/nn29BxoULJgidUgIMAJhkt9+efP7zbbxxF44da12FAeCg5ueT48fblpggdAq5owCASTY7m2xsJI8+2s31DR8BYFiuNUHoiy9ePUFoVyE+AyfAAIBJd9ttyec+17rajtLWJG0AMCqzs20i65WV7ce++MXdQ09MEDq2BBgAMOm2llV95JHRXnd1tV0bALp06FDbTpxon5sgdGwJMABgGiwvtxu3J58c3TUNHwGgj240QegzzyT/+7/tX3pHgAEA02JjI3nqqdHM2L64mBw5MvzrAMAgXDlB6IULyac/3ZYjpzdmui4AABiRhYXk9OnRXGurmy4AjKPl5eQVr0juvXf3fBp0SoABANNkfb0FGcNm+AgAk0CQ0SsCDACYJjMzyZkzw73G0aOjCUkAYFQEGb0gwACAabO62kKGYdH7AoBJJcjolAADAKbRsHphzMxsT4AGAJNKkNEJAQYATKPDh5O1tcGf9/jxZHZ28OcFgD4SZIyUAAMAptUddww+bDB8BIBpJMgYCQEGAEyrubkWYgzK/Pxw59YAgL4TZAyVAAMAptnaWrK4OJhznTiRlDKYcwHAOBNkDIUAAwCmWSnJnXcO5lyGjwDAboKMgRJgAMC0W1lJjh072DkOH06WlgZTDwBMGkHGQAgwAIDWC+Mgwz/0vgCAmxNkHIgAAwBIDh1KTp3a37GlJKurg60HACaZIGNfBBgAQHP77W0lkVu1srK/4wBg2gkybokAAwBoZmeTjY1bP87wEQA4GEHGnggwAIBtt93WJuTcq9nZg08ACgA0gowbEmAAALvdddfe911dTWbcTgDAQAkyrskdBwCw25EjyYkTe9vX8BEAGB5Bxi4CDADgahsbN+9ZcehQu7ECAIZrZ5Bx9GjX1XRGgAEAXG1hITl9+sb76H0BAKO1vJy88pVTG2QIMACAa1tfb0HG9ex1mAkAMFhTGmQIMACAa5uZSc6cufbXlpfbEBIAoDtTFmQIMACA61tdvfYNkeEjANAfUxJkCDAAgBu7887dn5fSgg0AoF+2goyXvrTrSoZCgAEA3NjSUrK2tv358ePJ7Gx39QAAN7a42HUFQyHAAABu7o47tkMLw0cAgA4IMACAm5ubayHG3FyystJ1NQDAFJrrugAAYEysrbWVSUrpuhIAYArpgQEA7E0pycmTXVcBAEwpAQYAAADQewIMAAAAoPcEGAAAAEDvCTAAAACA3hNgAAAAAL0nwAAAAAB6T4ABAAAA9J4AAwAAAOg9AQYAAADQewIMAAAAoPcEGAAAAEDvCTAAAACA3hNgAAAAAL0nwAAAAAB6T4ABAAAA9J4AAwAAAOg9AQYAAADQewIMAAAAoPcEGAAAAEDvCTAAAACA3hNgAAAAAL0nwAAAAAB6T4ABAAAA9J4AAwAAAOg9AQYAAADQewIMAAAAoPcEGAAAAEDvCTAAAACA3hNgAAAAAL1Xaq1d17BLKeVzSR7d4+7Hkjy9j8vc6nF73f9kkif2Uc802O/PapS6qHGY1xzUuQ96nv0cP6w2mminN6Kdjv6a2um1aafXNg5tNNFOh3Ue7XQ8aKejv+Ygz6ud9sfdtda1qx6ttY7tluShURy31/2TfLDr70lft/3+rCa9xmFec1DnPuh59nP8sNro5r7a6ZB+1pNao3Y6mGO00+5/zpNcp3Y6mGO00+5/zpNc57CuOcjzaqf938Z9CMlfjei4/V6HbePwPeyixmFec1DnPuh59nO8NtqNcfg+aqfDOY92Oh7G5XuonQ7nPNrpeBiX7+EktdNBnlc77bneDSEZZ6WUD9ZaH+i6DuD6tFPoP+0U+k87hf6bxHY67j0w+uahrgsAbko7hf7TTqH/tFPov4lrp3pgAAAAAL2nBwYAAADQewIMAAAAoPcEGAAAAEDvCTAAAACA3hNgjEgpZaaU8uZSyttLKd/XdT3A1UopD5ZS/rmU8rullAe7rge4tlLKkVLKB0sp39p1LcDVSimv3nwufXcp5ce7rge4Winl20opv19K+YtSymu7rmevBBh7UEp5Rynl8VLKf17x+LeUUh4ppXy0lPLzNznN65OcSfJ8knPDqhWmZx5gDAAAApZJREFU1YDaaU1yIclitFMYuAG10yT5uSTvGk6VMN0G0U5rrQ/XWn8syXcm+Zph1gvTaEDt9L211h9J8mNJvmuY9Q6SZVT3oJTydWkvav6o1nr/5mOzSf47yTelvdD5QJLvTjKb5C1XnOIHN7fztdbfK6W8u9b6HaOqH6bBgNrpE7XWy6WU9SS/VWv9nlHVD9NgQO30y5LclhY0PlFr/evRVA/TYRDttNb6eCnldUl+PMkf11r/bFT1wzQYVDvdPO43k/xprfVDIyr/QOa6LmAc1FrfX0q554qHvyLJR2utH0uSUsqfJ3l9rfUtSa7q0lpKOZfkuc1PXxxetTCdBtFOdzif5NAw6oRpNqDn0weTHElyX5KLpZS/rbVeHmbdME0G9Xxaa31fkveVUv4miQADBmhAz6clyW8k+btxCS8SAcZBbCT55I7PzyX5yhvs/54kby+lfG2S9w+zMOD/3VI7LaW8Ick3Jzme5HeGWxqw6Zbaaa31F5OklPL92ew1NdTqgOTWn08fTPKGtDcD/naolQFbbvX16U8m+cYkx0opL6+1/u4wixsUAcaI1FqfTfJDXdcBXF+t9T1pYSPQc7XWP+i6BuDaaq1nk5ztuAzgBmqtb0vytq7ruFUm8dy/x5LcuePzM5uPAf2hnUL/aafQf9op9N9UtFMBxv59IMkrSikvKaUsJHljkvd1XBOwm3YK/aedQv9pp9B/U9FOBRh7UEp5Z5J/SXJvKeVcKeWHaq0vJPmJJH+f5OEk76q1/leXdcI0006h/7RT6D/tFPpvmtupZVQBAACA3tMDAwAAAOg9AQYAAADQewIMAAAAoPcEGAAAAEDvCTAAAACA3hNgAAAAAL0nwAAAAAB6T4ABAAAA9N7/ARS1AZaI5mmjAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1080x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "visualise(\n",
-    "    vector_norms_list[:, 0, 2], \n",
-    "    vector_norms_list[:, 1, 2],\n",
-    "    noise_eps_seq, \n",
-    "    title='Vector norm in original scale for sgd solution by noise level',\n",
-    "    greater_than_zero=True,\n",
-    "    log_scale=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Linear regression out of the box\n",
-    "\n",
-    "Finally, let's take a brief look at implemented versions of Linear Regression from sklearn. The main classes are:\n",
-    "\n",
-    "* [LinearRegression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) — classical linear regression (*actially, it is just `scipy.linalg.lstsq` wrapped with sklearn `Predictor` class) __analytical__ solver.\n",
-    "* [Lasso](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html) — Linear regression with L1 regularization.\n",
-    "* [Ridge](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) — Linear regression with L2 regularization.\n",
-    "\n",
-    "To minimize any other error function you are free to use [SGDRegressor](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html) (or wait for a week and we will get the great *PyTorch* automatic differentiation engine).\n",
-    "\n",
-    "Let's compare the speed of analytical and gradient solutions from the sklearn realizations.\n",
-    "\n",
-    "IPython magic `%%time` wrapper will be used.\n",
-    "\n",
-    "To measure the quality $R^2$ score will be used. It compares our model (`a`) with one always predicting mean `y`:\n",
-    "\n",
-    "$$R^2 = 1 - \\frac{\\sum_i (y_i - a(x_i))^2}{\\sum_i (y_i - \\overline{y}_i)^2}$$\n",
-    "\n",
-    "`LinearRegression` vs. `Ridge`: __Fight!__\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![](img/mortal_combat.jpg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LinearRegression, Lasso, Ridge"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n_features = 700\n",
-    "n_objects = 100000\n",
-    "num_steps = 150\n",
-    "\n",
-    "w_true = np.random.uniform(-2, 2, (n_features, 1))\n",
-    "\n",
-    "X = np.random.uniform(-100, 100, (n_objects, n_features))\n",
-    "Y = X.dot(w_true) + np.random.normal(0, 10, (n_objects, 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R2: 0.9999686217918247\n",
-      "CPU times: user 16.6 s, sys: 1.02 s, total: 17.7 s\n",
-      "Wall time: 6.59 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "lr = LinearRegression()\n",
-    "lr.fit(X, Y)\n",
-    "print(f'R2: {lr.score(X, Y)}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R2: 0.9999683179947138\n",
-      "CPU times: user 1.98 s, sys: 199 ms, total: 2.18 s\n",
-      "Wall time: 856 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "\n",
-    "lr = Ridge(alpha=0.0, solver='sparse_cg')\n",
-    "lr.fit(X, Y)\n",
-    "print(f'R2: {lr.score(X, Y)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Own neat version of Linear Regression"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's use `sklearn`'s standard interfaces to implement sealed version of our version of Linear Regression using SGD\n",
-    "\n",
-    "First we need to inherit base classes, then implement 3 main stages of regressor life as methods:\n",
-    "* hyperparameter initialization - constructor\n",
-    "* parameters training on known objects - fit method\n",
-    "* target estimation for unknown objects - predict method"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T18:48:24.750053Z",
-     "start_time": "2020-02-19T18:48:24.746577Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.base import BaseEstimator, RegressorMixin\n",
-    "# also ClassifierMixin and TransformerMixin exist"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T18:50:28.936505Z",
-     "start_time": "2020-02-19T18:50:28.927717Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class LinearRergessionSGD(BaseEstimator, RegressorMixin):\n",
-    "    '''LinearRergession with L2 regularization and SGD optimizer\n",
-    "    '''\n",
-    "    def __init__(\n",
-    "        self, C: float=1.0,\n",
-    "        batch_size: int=25,\n",
-    "        lr: float=1e-2,\n",
-    "        num_steps: int=200,\n",
-    "    ) -> None:\n",
-    "        self.C = C\n",
-    "        self.batch_size = batch_size\n",
-    "        self.lr = lr\n",
-    "        self.num_steps = num_steps\n",
-    "\n",
-    "    def fit(self, X, Y):\n",
-    "        w = np.random.randn(X.shape[1])[:, None]\n",
-    "        n_objects = len(X)\n",
-    "\n",
-    "        # this is just copied from above\n",
-    "        for i in range(self.num_steps):\n",
-    "            sample_indices = np.random.randint(n_objects, size=self.batch_size)\n",
-    "            w -= 2 * self.lr * np.dot(X[sample_indices].T, np.dot(X[sample_indices], w) - Y[sample_indices]) / self.batch_size\n",
-    "\n",
-    "        self.w = w\n",
-    "        return self\n",
-    "\n",
-    "    def predict(self, X):\n",
-    "        return X@self.w"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's generate dataset with differently scaled features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T18:50:42.390314Z",
-     "start_time": "2020-02-19T18:50:40.904780Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "n_features = 700\n",
-    "n_objects = 100000\n",
-    "num_steps = 150\n",
-    "\n",
-    "w_true = np.random.uniform(-2, 2, (n_features, 1))\n",
-    "\n",
-    "X = np.random.uniform(-100, 100, (n_objects, n_features)) * np.arange(n_features)\n",
-    "Y = X.dot(w_true) + np.random.normal(0, 10, (n_objects, 1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "and split it to train and test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T18:50:43.599348Z",
-     "start_time": "2020-02-19T18:50:43.596610Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.model_selection import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T18:55:10.577345Z",
-     "start_time": "2020-02-19T18:55:10.180458Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "x_train, x_test, y_train, y_test = train_test_split(X, Y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's test our solution"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T18:56:42.186020Z",
-     "start_time": "2020-02-19T18:56:42.118206Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-d83281f49a88>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mlrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRergessionSGD\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'R2: {lrs.score(x_test, y_test)}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/installed/miniconda3/lib/python3.7/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    422\u001b[0m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m         \u001b[0;31m# XXX: Remove the check in 0.23\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 424\u001b[0;31m         \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_reg_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    425\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'continuous-multioutput'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m             warnings.warn(\"The default value of multioutput (not exposed in \"\n",
-      "\u001b[0;32m~/installed/miniconda3/lib/python3.7/site-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m     84\u001b[0m     \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m     \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m     \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/installed/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m             _assert_all_finite(array,\n\u001b[0;32m--> 578\u001b[0;31m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[1;32m    579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/installed/miniconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m     58\u001b[0m                     \u001b[0mmsg_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m                     (type_err,\n\u001b[0;32m---> 60\u001b[0;31m                      msg_dtype if msg_dtype is not None else X.dtype)\n\u001b[0m\u001b[1;32m     61\u001b[0m             )\n\u001b[1;32m     62\u001b[0m     \u001b[0;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
-     ]
-    }
-   ],
-   "source": [
-    "own_lr = LinearRergessionSGD().fit(x_train, y_train)\n",
-    "print(f'R2: {own_lr.score(x_test, y_test)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "OOOOOOOOOPS!!!\n",
-    "\n",
-    "Something went wrong. What could it be?\n",
-    "\n",
-    "During our SGD we've encountered too big values to store in float.\n",
-    "\n",
-    "That leads us to feature normalization.\n",
-    "Lest's scale features: just subtract mean from each feature and divide by sample variation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T19:00:31.413133Z",
-     "start_time": "2020-02-19T19:00:31.410170Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.preprocessing import StandardScaler"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T19:03:03.598988Z",
-     "start_time": "2020-02-19T19:03:01.932987Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "scaler = StandardScaler()\n",
-    "scaler.fit(x_train)\n",
-    "x_scaled = scaler.transform(x_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T19:03:22.671680Z",
-     "start_time": "2020-02-19T19:03:22.643142Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "own_lr = LinearRergessionSGD().fit(x_scaled, y_train)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But for test we need to scale test features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T19:04:02.064690Z",
-     "start_time": "2020-02-19T19:04:01.902071Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "x_test_scaled = scaler.transform(x_test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T19:04:09.395759Z",
-     "start_time": "2020-02-19T19:04:09.383991Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R2: 0.9959960429469144\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'R2: {own_lr.score(x_test_scaled, y_test)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Wow! we didn't implement no `score` method. But `sklearn`'s base class provide us it aleready implemented.\n",
-    "\n",
-    "You note that scaling data before prediction is not a big pleasure. So we could get rid of this bulkiness with pipelines"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T19:08:17.547462Z",
-     "start_time": "2020-02-19T19:08:17.538028Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from sklearn.pipeline import make_pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T19:08:22.920876Z",
-     "start_time": "2020-02-19T19:08:22.917665Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "pipe = make_pipeline(\n",
-    "    StandardScaler(),\n",
-    "    LinearRergessionSGD(),\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2020-02-19T19:08:54.244228Z",
-     "start_time": "2020-02-19T19:08:52.345667Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "R2: 0.9972527090368405\n"
-     ]
-    }
-   ],
-   "source": [
-    "pipe.fit(x_train, y_train)\n",
-    "print(f'R2: {pipe.score(x_test, y_test)}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As if we don't have any complex assembly behind pipeline interface!\n",
-    "\n",
-    "And no data leak guaranteed as a gift!"
-   ]
-  }
- ],
- "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": "3.7.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
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