{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ExB_ZkOQd3gn"
   },
   "source": [
    "## PHYS-467 Machine Learning for Physicists\n",
    "\n",
    "## Week 2: Regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "lIYdn1woOS1n"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import sklearn.preprocessing as preprocessing\n",
    "from typing import Tuple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_bOi56PIZ5j1"
   },
   "source": [
    "# Exercise 1: Linear Regression in 1D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5prnd8t11mzb"
   },
   "source": [
    "**Question 1.1.a)** Write a function that creates one dimensional linear data and add a gaussian noise, i.e $y = a \\cdot x + \\varepsilon$ with $x,y,a,\\varepsilon \\in \\mathbb R$ and $\\varepsilon \\sim \\mathcal N(0,\\sigma)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "MOPFoexN1mzc"
   },
   "outputs": [],
   "source": [
    "def generate_data(n_data: int, coefficient: float, variance: float) -> Tuple[np.ndarray, np.ndarray]:\n",
    "    # The \":\" is python's notion of typing, i.e. fixing what data types a function accept, and -> says what it will return.\n",
    "    # We (sometimes) use this to help you understand what input types the function expects.\n",
    "    # In this example we have\n",
    "    # - an integer for the number of datapoints\n",
    "    # - a floating point for the coefficient w\n",
    "    # - a floating point number for the variance sigma\n",
    "    #\n",
    "    # Similarly it tells you what it expects you to return: A tuple of two numpy arrays\n",
    "    # - the x's as a numpy array, of shape (n_data, 1)\n",
    "    # - the y's as a numpy array, of shape (n_data)\n",
    "    \n",
    "    X = np.random.normal(size=(n_data,))\n",
    "    \n",
    "    y = X * coefficient + np.sqrt(variance) * np.random.normal(size=(n_data,))\n",
    "    \n",
    "    return X.reshape((-1, 1)), y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_7APlW7k1mzc",
    "outputId": "4c92cd3e-ce71-4327-a291-98c9e2e8eb32"
   },
   "outputs": [],
   "source": [
    "# here we generate some data \n",
    "coefficient = 3\n",
    "variance = 1.5\n",
    "n_data      = 30\n",
    "X, y = generate_data(n_data, coefficient, variance)\n",
    "\n",
    "# test that you ouput it in the correct shapes - if you get an error here don't continue, but make sure that you output the shape we expect you to\n",
    "# in general applications in machine learning, the last dimension is usually the feature dimension\n",
    "# since we have only one dimensional input, the number of features is 1 - but we still want to represent it in the larger dimensions, to be compatible with common libraries later on\n",
    "assert X.shape == (n_data,1,), \"Your X shape is incorrect, check that your function returns an array of size (n_data,1,)\"\n",
    "assert y.shape == (n_data,), \"Your y shape is incorrect, check that your function returns an array of size (n_data,)\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8d_8jUa01mzc"
   },
   "source": [
    "**Question 1.1.b)** Generate and plot some data (X, Y), along with the noiseless function $y = a \\cdot x$. Don't forget to label the axes and add a legend that shows that the scatterpoints are samples, and the line is the true function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "id": "Pi9TVe893Mfm",
    "outputId": "c22daf6b-7bcd-47ba-f6ea-1c8684cccca7"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the training data using matplotlib\n",
    "plt.scatter(X, y,label='samples')\n",
    "\n",
    "# label the axes\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('y');\n",
    "\n",
    "# plot the noiseless data (the ground truth)\n",
    "plt.plot(X, coefficient * X, c='k',label='ground truth')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6uHHSKOh1mze"
   },
   "source": [
    "**Question 1.2** Write a function to compute the least-square regression using the closed form you have seen in class.\n",
    "\n",
    "This function should minimize the least squares loss for the parameterized function $\\hat{y} = \\hat{a} \\cdot x$.\n",
    "Note that you can use functions from `np.linalg` to invert matrices etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "AfOq9u6u3bmj"
   },
   "outputs": [],
   "source": [
    "# Write a function to compute the linear-regression predictor in closed form\n",
    "def linear_regression(X : np.ndarray, y : np.ndarray) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    arguments:\n",
    "        - X : data matrix\n",
    "        - y : output\n",
    "    returns:\n",
    "        - a : the least square estimator\n",
    "    \"\"\"\n",
    "    a = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JwXpZg4K3TkD",
    "outputId": "5b5cc584-c946-488c-b1e5-2fae2f7aff3f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We predicted a_hat=2.72955239932338 for the coefficient, while the ground truth is a=3.\n"
     ]
    }
   ],
   "source": [
    "# Print the predicted weights (coefficient + bias)\n",
    "a_hat = linear_regression(X, y)\n",
    "assert a_hat.shape == (1,), \"You did not get a single value out to estimate a - this is wrong!\"\n",
    "print(f\"We predicted a_hat={a_hat[0]} for the coefficient, while the ground truth is a={coefficient}.\") # the notation with the f\"I will print the {variable}.\" lets you easily print text."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "drtGIrQr1mzf"
   },
   "source": [
    "**Question 1.3** Plot your training set and the ground truth function again, but this time also plot the predicted function $\\hat{y} = \\hat{a}\\cdot x$. Don't forget to add axes labels and a legend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "0pmRVbPt5iGS",
    "outputId": "7c775b4b-f3c7-4649-ea65-ce5d5b02ee3c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the training points and the predictor evaluated on a set of test points\n",
    "\n",
    "X_test = np.linspace(np.min(X), np.max(X), 10)\n",
    "\n",
    "plt.scatter(X, y,label='training data')\n",
    "plt.plot(X_test, coefficient * X_test, 'k',label='ground truth')\n",
    "plt.plot(X_test,  a_hat[0] * X_test, 'r--',label='linear regression')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('y');\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1PXbqtAx1mzg"
   },
   "source": [
    "**Question 1.4** Now estimate $a$ with the `LinearRegression` object of the module [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html). Google how to use it correctly, and check that the coefficients from your direct implementation and the modules implementation are the same.\n",
    "\n",
    "*Hint*: In the case here, we are **not** fitting the bias $b$ in the general linear regression of the form $y = ax+b$. What do you need to change when you call the sklearn function? Read the [documentation](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) carefully."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YPDYZSqV9w1S",
    "outputId": "a4f6870f-6909-44c7-a79c-9717d19f1c04"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sklearn :  2.7295523993233815\n",
      "own implementation :  2.72955239932338\n"
     ]
    }
   ],
   "source": [
    "# Repeat with Scikit-learn\n",
    "linear_regression_sk = LinearRegression(fit_intercept=False)\n",
    "linear_regression_sk.fit(X, y)\n",
    "print('sklearn : ', linear_regression_sk.coef_[0])\n",
    "print('own implementation : ', a_hat[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise 2: Fitting physical data with Linear and Polynomial Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The superconductivity dataset [1] contains 81 chemical and molecular features extracted from >20,000 superconductors along with the critical temperature (the label) in the 82nd column. \n",
    "The goal is to predict the critical temperature $T_c$ based on the 81 first features. \n",
    "If you are interested by the physical meaning of those 81 features, check out the [original paper](https://arxiv.org/pdf/1803.10260.pdf)!\n",
    "\n",
    "[1] Hamidieh, Computational Materials Science, 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, you need to download the dataset, in .csv format. You can find it here : https://archive.ics.uci.edu/dataset/464/superconductivty+data or in the zip file/github repository.\n",
    "\n",
    "Now, load the data into the notebook. Since the format is `.csv` (data table), we used the Python module called `pandas`. If you are locally running the notebook, you need to indicate the path to it, e.g. \"Downloads/train.csv\". \n",
    "On noto (the go-epfl link), you should see the train.csv already in your folder, so no need to download it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "dataset=pd.read_csv(\"train.csv\",index_col=None) # todo: optionally change \"train.csv\" to the location of the file you downloaded yourself"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "using .values() converts the table into a numpy array . Since the last column is the label $T_c$, we have for the complete dataset our X's and y's are the following."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.values[:,:-1]\n",
    "y = dataset.values[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2.1** Print the number of features and datapoints in the dataset. Verify that there are 81 features and get the exact number of datapoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 81 features and 21263 dataspoints.\n"
     ]
    }
   ],
   "source": [
    "n_samples = X.shape[0]\n",
    "n_features = X.shape[1]\n",
    "print(f\"There are {n_features} features and {n_samples} dataspoints.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2.2**  As discussed in class, when we work with applications, we usually split the data in three parts:\n",
    "- The *training* dataset: We use it to fit the model parameters.\n",
    "- The *validation* dataset: We use it to tune hyperparameters and select models.\n",
    "- The *test* dataset: We use it to evaluate the final performance on unseen data.\n",
    "  \n",
    "We need three sets because if we tuned hyperparameters directly on the test set, the model could overfit to the test set. The validation set acts as an intermediate hold-out for model selection, and the test set is kept untouched until the very end to give an unbiased estimate of the generalization performance.\n",
    "\n",
    "In the following, use the `train_test_split` function from sklearn to randomly split the complete dataset into three parts : 60% into train, 20% into validation, and 20% into test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train contains 60.0% of the data, X_validation contains 20.0%, and X_test contains 20.0%.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "rs = np.random.RandomState(23) # introducing this to make the solution give the same result every time\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=rs)\n",
    "X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=0.25, random_state=23)\n",
    "\n",
    "print(f\"X_train contains {X_train.shape[0]/n_samples*100:.1f}% of the data, \"\n",
    "      f\"X_validation contains {X_validation.shape[0]/n_samples*100:.1f}%, \"\n",
    "      f\"and X_test contains {X_test.shape[0]/n_samples*100:.1f}%.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because different features correspond to different physical quantities and have different units, thus different magnitudes of numerical values, we preprocess the array so that the entries of the vector are of the same order of magnitude.\n",
    "\n",
    "Since we are assuming that we only have the training dataset available to train the model, we compute the statistics based on the `X_train` dataset, and then use them to rescale the valiation and test data. The `StandardScaler` from sklearn uses the fit-predict framework.\n",
    "\n",
    "Once the renormalization is done we do not need to worry about it anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# record the statistics\n",
    "scaler_X = preprocessing.StandardScaler().fit(X_train)\n",
    "scaler_y = preprocessing.StandardScaler().fit(y_train.reshape(-1,1))\n",
    "\n",
    "# rescale the data\n",
    "X_test=scaler_X.transform(X_test)\n",
    "y_test = scaler_y.transform(y_test.reshape(-1,1)).reshape(-1)\n",
    "X_train=scaler_X.transform(X_train)\n",
    "y_train = scaler_y.transform(y_train.reshape(-1,1)).reshape(-1)\n",
    "X_validation=scaler_X.transform(X_validation)\n",
    "y_validation = scaler_y.transform(y_validation.reshape(-1,1)).reshape(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1f51WFYB1mzn"
   },
   "source": [
    "**Question 2.3** \n",
    "Your goal is now to fit the model using a linear regression, and evaluate its performance in predicting the critical temperature (the label y), i.e. xxx\n",
    "\n",
    "\n",
    "a) How many parameters does your model have? This time we will include the bias term (or intercept)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A linear regression model for the superconductor dataset would have 82 parameters.\n"
     ]
    }
   ],
   "source": [
    "print(f\"A linear regression model for the superconductor dataset would have {n_features+1} parameters.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) Fit the model using the LinearRegression module from sklearn, remember that this time we want the bias/intercept, and obtain a prediction for the data from the training and the validation set. Familiarize yourself with sklearns fit/predict framework."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "0W6pu3xK9cQp"
   },
   "outputs": [],
   "source": [
    "# Perform linear regression on the training set\n",
    "linear_regression_sk = LinearRegression()\n",
    "linear_regression_sk.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = linear_regression_sk.predict(X_train)\n",
    "y_val_pred = linear_regression_sk.predict(X_validation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c) Compute the mean squared error (MSE) between the label that we predict with the linear regression on the ground truth values from the validation and training set respectively. Which of the two MSE's do you expect to be better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We expect the MSE on the training data to be smaller, since this is what we directly optimized for with the linear regression. \n",
      "Indeed, we obtain a MSE of 0.27421938358793513 on the validation set, and 0.26109743102074356 on the training set.\n"
     ]
    }
   ],
   "source": [
    "print(\"We expect the MSE on the training data to be smaller, since this is what we directly optimized for with the linear regression. \")\n",
    "print(f\"Indeed, we obtain a MSE of {mean_squared_error(y_validation, y_val_pred)} on the validation set, and {mean_squared_error(y_train, y_train_pred)} on the training set.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d) Compare the predicted and actual values in the validation set with one another in a plot. This means that we want to plot points $(y,\\hat{y})$ for all items from the validation set. If a prediction is correct the point lies on the diagonal, therefore also include the diagonal plotted as a line. Would you say you obtained a good fit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.0, 4.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.linspace(np.min(y_validation),np.max(y_validation),100),np.linspace(np.min(y_validation),np.max(y_validation),100),ls=\"--\",c=\"r\", label='diagonal')\n",
    "plt.scatter(y_validation, y_val_pred, marker='.',alpha=0.3,label='validation data $(y,\\hat{y})$')\n",
    "plt.xlabel('$y$ (true $T_c$)')\n",
    "plt.grid()\n",
    "plt.ylabel('$\\hat{y}$ (predicted $T_c$)');\n",
    "plt.legend()\n",
    "plt.xlim(-1,3)\n",
    "plt.ylim(-2,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6kihBcj_1mzo"
   },
   "source": [
    "**Question 2.4**  We now consider having a only a smaller subset of the same dataset available, with only `n=400` training datapoints. We want to understand if in this case, regularization as seen in the lecture can help us to obtain a better model.\n",
    "\n",
    "We consider Ridge regression with the regularization parameter $\\lambda$, where the matrix that we want to invert is not $X^TX$ directly, but instead $X^TX + \\lambda I$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a) Replace the linear regression from 2.3b) with the Ridge regression class from the sklearn library and use  $\\lambda\\in\\{10,1\\}$. Compute the training and validation error for each. Which of the two $\\lambda$'s would you use?\n",
    "\n",
    "*Hint:* Check the documentation of the [Ridge](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) class to understand how you set $\\lambda$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For lambda=10: MSE [train=0.3148249847035344], MSE [val=0.3225242542820293].\n",
      "For lambda=1: MSE [train=0.282028916117119], MSE [val=0.3133826635701197].\n",
      "Since the validation error for lambda=1 is better, I would use this model.\n"
     ]
    }
   ],
   "source": [
    "n = 400 \n",
    "\n",
    "ridge_regression_sk = Ridge(alpha=10.0)\n",
    "ridge_regression_sk.fit(X_train[:n], y_train[:n])\n",
    "\n",
    "y_train_pred = ridge_regression_sk.predict(X_train[:n])\n",
    "mse_train = mean_squared_error(y_train[:n], y_train_pred)\n",
    "\n",
    "y_validation_pred = ridge_regression_sk.predict(X_validation)\n",
    "mse_val = mean_squared_error(y_validation, y_validation_pred)\n",
    "\n",
    "print(f\"For lambda=10: MSE [train={mse_train}], MSE [val={mse_val}].\")\n",
    "\n",
    "ridge_regression_sk = Ridge(alpha=1.0)\n",
    "ridge_regression_sk.fit(X_train[:n], y_train[:n])\n",
    "\n",
    "y_train_pred = ridge_regression_sk.predict(X_train[:n])\n",
    "mse_train = mean_squared_error(y_train[:n], y_train_pred)\n",
    "\n",
    "y_validation_pred = ridge_regression_sk.predict(X_validation)\n",
    "mse_val = mean_squared_error(y_validation, y_validation_pred)\n",
    "\n",
    "print(f\"For lambda=1: MSE [train={mse_train}], MSE [val={mse_val}].\")\n",
    "print(\"Since the validation error for lambda=1 is better, I would use this model.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) Repeat this experiment for several lambdas, `lmbdas = np.logspace(2,-5,50)`, and record the associated training and validation errors in a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "R62kcmzd_sjN",
    "outputId": "16f364b1-739e-45d2-8e5a-d90c2764f2e3"
   },
   "outputs": [],
   "source": [
    "train_errors = []\n",
    "validation_errors = []\n",
    "lmbdas = np.logspace(2,-5,50)\n",
    "\n",
    "for lmbda in lmbdas:\n",
    "    ridge_regression_sk = Ridge(alpha=lmbda)\n",
    "    ridge_regression_sk.fit(X_train[:n], y_train[:n])\n",
    "\n",
    "    y_train_pred = ridge_regression_sk.predict(X_train[:n])\n",
    "    train_errors.append(mean_squared_error(y_train[:n], y_train_pred))\n",
    "\n",
    "    y_validation_pred = ridge_regression_sk.predict(X_validation)\n",
    "    validation_errors.append(mean_squared_error(y_validation, y_validation_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c) Plot the train and validation error over the lambdas to observe that there is a minimum of the validation error in terms of lambda. Identify the best lambda for this model, and mark it on the same plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'MSE')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lmbdas,train_errors,label='training')\n",
    "plt.plot(lmbdas,validation_errors,label='validation')\n",
    "best_lmbda_idx = np.argmin(validation_errors)\n",
    "best_lmbda = lmbdas[best_lmbda_idx]\n",
    "\n",
    "plt.axvline(best_lmbda,color='red',linestyle='dashed',label=f'best $\\lambda={best_lmbda}$')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.xlabel('$\\lambda$')\n",
    "plt.title(f\"n={n} datapoints\")\n",
    "plt.ylabel('MSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "d) Print the generalization error of the model you selected. Which dataset do you need to use to estimate it and why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The finally selected model achieves an estimated generalization error via the test set as 0.3019446227645591.\n",
      "Since we used the validation set during model (hyperparameter) selection, we cannot use it to get an unbiased estiamte of the generalization error.\n"
     ]
    }
   ],
   "source": [
    "ridge_regression_sk = Ridge(alpha=best_lmbda)\n",
    "ridge_regression_sk.fit(X_train[:n], y_train[:n])\n",
    "\n",
    "y_test_pred = ridge_regression_sk.predict(X_test)\n",
    "mse_test = mean_squared_error(y_test, y_test_pred)\n",
    "\n",
    "print(f\"The finally selected model achieves an estimated generalization error via the test set as {mse_test}.\")\n",
    "print(\"Since we used the validation set during model (hyperparameter) selection, we cannot use it to get an unbiased estiamte of the generalization error.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2.5** We now want to understand more broadly how the number of samples and the selection of the best $\\lambda$ relate to one another."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a) For the different values of $\\lambda \\in \\{10000,100,1,0.0001\\}$. Plot the train and validation error over the different `n` from `trainset_sizes`, with a logscale on the x-axis.\n",
    "Is there one lambda which is optimal for all `n`?\n",
    "\n",
    "*Tip:* If you running the code takes too long, change the `100` in `np.logspace(0.5,5,100).astype(int)` to a smaller integer, then you will have less resolution on the x-axis, but you can debug your code faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No, the best value of lambda depends on n.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainset_sizes = np.logspace(0.5,5,100).astype(int)\n",
    "\n",
    "for lmbda in [10000.0,100.0,1.0,0.0001]:\n",
    "    train_errors = []\n",
    "    validation_errors = []\n",
    "    \n",
    "    for n in trainset_sizes:\n",
    "        ridge_regression_sk = Ridge(alpha=lmbda)\n",
    "        ridge_regression_sk.fit(X_train[:n], y_train[:n])\n",
    "\n",
    "        y_train_pred = ridge_regression_sk.predict(X_train[:n])\n",
    "        train_errors.append(mean_squared_error(y_train[:n], y_train_pred))\n",
    "\n",
    "        y_validation_pred = ridge_regression_sk.predict(X_validation)\n",
    "        validation_errors.append(mean_squared_error(y_validation, y_validation_pred))\n",
    "        \n",
    "    plt.plot(trainset_sizes, validation_errors, marker='.', label=lmbda)\n",
    "\n",
    "plt.xlabel('training set size $n$')\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale('log')\n",
    "plt.legend(title='lambda');\n",
    "plt.ylabel('validation MSE')\n",
    "    \n",
    "print('No, the best value of lambda depends on n.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) For every dataset size `n` find the optimal $\\lambda$ out of the `lmbdas` array that has the best validation error. \n",
    "Create two plots, with the x-axis always in log-scale:\n",
    "- Plot the best models training and validation error over for every `n`, where `n` is the x-axis. \n",
    "- Plot the optimal $\\lambda$ over `n` as the x-axis.\n",
    "What do you observe for the optimal $\\lambda$ as a function of $n$?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\lambda$')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_errors = []\n",
    "validation_errors = []\n",
    "optimal_lambda = []\n",
    "lmbdas = np.logspace(2,-5,50)\n",
    "\n",
    "for n in trainset_sizes:\n",
    "    train_errors.append(np.inf)\n",
    "    validation_errors.append(np.inf)\n",
    "    optimal_lambda.append(None)\n",
    "    for lmbda in lmbdas:\n",
    "        ridge_regression_sk = Ridge(alpha=lmbda)\n",
    "        ridge_regression_sk.fit(X_train[:n], y_train[:n])\n",
    "\n",
    "        y_train_pred = ridge_regression_sk.predict(X_train[:n])\n",
    "        mse_train = mean_squared_error(y_train[:n], y_train_pred)\n",
    "\n",
    "        y_validation_pred = ridge_regression_sk.predict(X_validation)\n",
    "        mse_val = mean_squared_error(y_validation, y_validation_pred)\n",
    "        if mse_val <= validation_errors[-1]:\n",
    "            validation_errors[-1] = mse_val\n",
    "            train_errors[-1] = mse_train\n",
    "            optimal_lambda[-1] = lmbda\n",
    "            \n",
    "plt.plot(trainset_sizes, train_errors, marker='.', label='training',c='tab:blue',linestyle='dashed')\n",
    "plt.plot(trainset_sizes, validation_errors, marker='.', label='validation',c='tab:orange',linestyle='dashed')\n",
    "plt.title('$\\lambda$ optimized for every $n$')\n",
    "plt.xlabel('training set size $n$')\n",
    "plt.xscale(\"log\")\n",
    "plt.ylabel(' MSE')\n",
    "plt.legend();\n",
    "plt.show()\n",
    "\n",
    "plt.plot(trainset_sizes,optimal_lambda,marker='.',c='red')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('training set size $n$')\n",
    "plt.ylabel('$\\lambda$')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qYZbqxtH1mzo"
   },
   "source": [
    "**Question 2.6** We now want to make the predictions better by making the model itself more powerful - the underlying relationships might not be only linear. This is why in this part of the exercise we introduce polynomial regression. (We go back to using the original train/test/val split with the full training set)\n",
    "\n",
    "a) Implement a function that generates _non-interactive_ polynomial features : for a degree $k$ and an input $x = x_1, ..., x_d$, generate $\\tilde x = x_1 / 1!, ..., x_1^k/k!, x_2/1!, ..., x_2^k/k!, ..., x_d^k/k!$.\n",
    "The factorial ensures that the magnitude of the entries of $\\tilde x$ are all of the same order, despite the power to the $k$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.special import factorial\n",
    "\n",
    "def non_interacting_polyfeat(X : np.ndarray, k : int) -> np.ndarray:\n",
    "    \"\"\"\n",
    "    arguments: \n",
    "        - X : n x d matrix, where each row is a data sample\n",
    "        - k : degree of the desired polynomial features\n",
    "    returns: \n",
    "        - X_tilde : n x (dk) matrix containing the polynomial features\n",
    "    \"\"\"\n",
    "    return np.concatenate([X**(i+1)/factorial(i+1) for i in range(k)], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) Then, fit a linear regression (no Ridge yet!) on these polynomial features with different degrees $k$. Plot the training and validation errors over the degree. Explain the phenomenon you observe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "id": "pDR76Qq6zucu",
    "outputId": "08226634-9d18-468d-d11f-854468bc0949"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We observe for some degree k we start overfitting the data: For $k$ large enough the training error is going down but validation error increases.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_errors = []\n",
    "val_errors = []\n",
    "\n",
    "degrees = range(1, 12)\n",
    "for degree in degrees:\n",
    "    linear_regression_sk = LinearRegression()\n",
    "\n",
    "    X_train_poly = non_interacting_polyfeat(X_train, degree)\n",
    "    linear_regression_sk.fit(X_train_poly, y_train)\n",
    "\n",
    "    y_train_pred_poly = linear_regression_sk.predict(X_train_poly)\n",
    "    train_errors.append(mean_squared_error(y_train, y_train_pred_poly))\n",
    "\n",
    "    y_val_pred_poly = linear_regression_sk.predict(non_interacting_polyfeat(X_validation, degree))\n",
    "    val_errors.append(mean_squared_error(y_validation, y_val_pred_poly))\n",
    "\n",
    "plt.semilogy(degrees, train_errors, label='training')\n",
    "plt.semilogy(degrees, val_errors, label='validation')\n",
    "plt.xlabel('$k$ degree of largest polynomial (model complexity)')\n",
    "plt.ylabel('MSE')\n",
    "plt.legend();\n",
    "\n",
    "print(\"We observe for some degree k we start overfitting the data: For $k$ large enough the training error is going down but validation error increases.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c) Select the best model from the above and compute its validation error, and plot the same $(y,\\hat{y})$ as in 2.3d). Is the fit better than the simple linear regression on the full dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best degree is  8\n",
      "The training error is 0.16719990909839644.\n",
      "The validation error is 0.18952253866002225.\n",
      "It is hard to tell the difference, so we have to rely on the validation MSE, which is siginificantly smaller.\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_degree = degrees[np.argmin(val_errors)]\n",
    "print('Best degree is ', best_degree)\n",
    "X_train_poly = non_interacting_polyfeat(X_train, best_degree)\n",
    "linear_regression_sk = LinearRegression() \n",
    "linear_regression_sk.fit(X_train_poly, y_train)\n",
    "\n",
    "y_val_pred_poly = linear_regression_sk.predict(non_interacting_polyfeat(X_validation, best_degree))\n",
    "y_train_pred_poly = linear_regression_sk.predict(non_interacting_polyfeat(X_train, best_degree))\n",
    "print(f\"The training error is {mean_squared_error(y_train, y_train_pred_poly)}.\")\n",
    "print(f\"The validation error is {mean_squared_error(y_validation, y_val_pred_poly)}.\")\n",
    "\n",
    "plt.plot(np.linspace(np.min(y_validation),np.max(y_validation),100),np.linspace(np.min(y_validation),np.max(y_validation),100),ls=\"--\",c=\"r\", label='diagonal')\n",
    "plt.scatter(y_validation, y_val_pred_poly, marker='.',alpha=0.3,label='validation data $(y,\\hat{y})$')\n",
    "plt.xlabel('$y$ (true $T_c$)')\n",
    "plt.grid()\n",
    "plt.ylabel('$\\hat{y}$ (predicted $T_c$)');\n",
    "plt.xlim(-1,3)\n",
    "plt.ylim(-2,4)\n",
    "plt.legend()\n",
    "print(\"It is hard to tell the difference, so we have to rely on the validation MSE, which is siginificantly smaller.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QHOHCsN61mzo"
   },
   "source": [
    "**Question 2.7** \n",
    "\n",
    "a) Pick again the best model between the different polynomial degrees and add a different regularisation strength $\\lambda$ using the Ridge regression module again. Plot the training and test errors as a function of $\\lambda$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 303
    },
    "id": "dbHzDUNSkuaB",
    "outputId": "8297eff1-7511-4956-a696-cd13a60de684"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best validation error is 0.19485388874941537\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lmbdas = np.logspace(-4, 3, 20) # regularization parameters you are supposed to use\n",
    "best_degree = 5\n",
    "train_errors = []\n",
    "val_errors_lambda = []\n",
    "\n",
    "for lmbda in lmbdas:\n",
    "\n",
    "    X_train_poly = non_interacting_polyfeat(X_train, k=best_degree)\n",
    "\n",
    "    ridge_regression = Ridge(alpha=lmbda)\n",
    "    ridge_regression.fit(X_train_poly, y_train)\n",
    "\n",
    "    y_train_pred_poly = ridge_regression.predict(X_train_poly)\n",
    "    train_errors.append(mean_squared_error(y_train, y_train_pred_poly))\n",
    "\n",
    "    y_val_pred_poly = ridge_regression.predict(non_interacting_polyfeat(X_validation, k=best_degree))\n",
    "    val_errors_lambda.append(mean_squared_error(y_validation, y_val_pred_poly))\n",
    "\n",
    "plt.plot(lmbdas, train_errors, label='training')\n",
    "plt.plot(lmbdas, val_errors_lambda, label='validation')\n",
    "best_lmbda_idx = np.argmin(val_errors_lambda)\n",
    "best_lmbda = lmbdas[best_lmbda_idx]\n",
    "plt.axvline(best_lmbda,c='red',linestyle='dashed',label=f'$\\lambda=${best_lmbda}')\n",
    "plt.xlabel('$\\lambda$')\n",
    "plt.ylabel('MSE')\n",
    "plt.xscale('log')\n",
    "plt.legend();\n",
    "print(f\"The best validation error is {val_errors_lambda[best_lmbda_idx]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) Compute the generalization error of the best ridge regression."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H668UezVHBUR",
    "outputId": "60c4ad78-d138-4826-fd3d-8ca10f1e1637"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The generalization error of the final selected model is estimated to be 0.18519597075895486.\n"
     ]
    }
   ],
   "source": [
    "ridge_regression = Ridge(alpha=best_lmbda)\n",
    "ridge_regression.fit(X_train_poly, y_train)\n",
    "\n",
    "y_test_pred_poly = ridge_regression.predict(non_interacting_polyfeat(X_test, k=best_degree))\n",
    "test_error = mean_squared_error(y_test, y_test_pred_poly)\n",
    "print(f'The generalization error of the final selected model is estimated to be {test_error}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "c) Your TA tells you they found a model that achieves a test error of 0.15 on the test dataset when they followed the train-validation-test pipeline like you did. \n",
    "- Between your model and their model, which would you choose to use in practice? \n",
    "- How can you estimate the generalization error of the model you choose? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If I want to know the generalization error, I cannot choose based on the test set, since it must remain untouched for the final evaluation. Instead, I would select the model with lower validation error, then estimate its generalization error using the held-out test set again.\n",
    "\n",
    "If I only have the test error information available from the TA I could still choose the one better one based on the test error, but then I cannot rely on the score on the \"test dataset\" as the final evaluator. I would need to obtain a new untouched dataset somewhere else to estimate an unbiased generalization error."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.16"
  },
  "vscode": {
   "interpreter": {
    "hash": "38cf3a2657c4530458a1d5b90a9ba637718c74089d900d5938397f33b4197fc5"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
