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Logistic and Linear Regression
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Logistic and Linear Regression
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic and Linear Regression\n",
"#### Project collaborators: Danissa Sandykbayeva, Karina Burunchina"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 1. Linear Regression\n",
"\n",
"Task advised by Karina mostly. Danissa improved the function 'error' and made the scatter plots of E_lin and E_pocket. \n",
"Firstly, we thought that it is possible to divide tasks. After the e-mail, we continued on our project together working on the tasks. So, 1-2 Tasks were done mostly individually, whilst 3-4 tasks - in team.\n",
"\n",
"1.0. First of all we implement all of the necessary packages and internal function"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np #some essential functions for the numerical and algebraic calculations\n",
"import math as mt #for exponential and log functions\n",
"from sklearn.model_selection import train_test_split #for the test/train split of the data\n",
"from matplotlib import pyplot as plt #for data visualization (mostly for error functions)\n",
"import seaborn as sns # importing dataset for the iris classification\n",
"iris = sns.load_dataset('iris')\n",
"import pandas as pd\n",
"from tqdm.notebook import tqdm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.1. Firstly, we define a function for creating a dataset of the desirable size and VC dimension"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [],
"source": [
"# ---------------------------\n",
"# dataset\n",
"# ---------------------------\n",
"# Function that creates a dataset of samples and labels with 'N' samples and dimension 'd'\n",
"# -> Input: 'N' - [int] number of point in the dataset to be generated\n",
"# 'd' - [int] VC dimension (length of the weights vector - 1)\n",
"# -> Output: 'x' - set of samples in the generated datasets\n",
"# Shape: (A,B) [float]: \n",
"# A = number of samples (N),\n",
"# B = dimension (d)\n",
"# 'y' - set of labels corresponding to the samples in 'c'\n",
"# Shape: (A) [int]: A = number of samples\n",
"# 'w' - weights of the hypothesis with which labels were assigned\n",
"# Shape: (A) [int]: A = dimension\n",
"# 'b' - [int] random 'b' free coefficient of the initial hypothesis \n",
"\n",
"def dataset(N, d):\n",
" w = np.random.randint(1,10, d) # weights\n",
" b = np.random.randint(1,10) # some b\n",
" x = np.random.uniform(-1,1,(N,d))*10 # our inputs\n",
" # some random line\n",
" h = x.dot(w) + b \n",
" #and labels\n",
" y = (h > 0)*1\n",
"\n",
" # adding some noise\n",
" i = 0\n",
" while i < N/10:\n",
" ind = np.random.randint(1, N)\n",
" if y [ind] == 1:\n",
" y[ind] = 0\n",
" else:\n",
" y[ind] = 1\n",
" i = i+1\n",
" return x, y, w, b\n",
"\n",
"# ---------------------------\n",
"# plt_dataset\n",
"# ---------------------------\n",
"# Additional function that plot the binary 2-D dataset with differenct color for each class and\n",
"# the original hypothesis that used for the creation of the datatset\n",
"# -> Input: 'x' - set of samples in the generated datasets\n",
"# Shape: (A,B) [float]: \n",
"# A = number of samples (N),\n",
"# B = dimension (d)\n",
"# 'y' - set of labels corresponding to the samples in 'c'\n",
"# Shape: (A) [int]: A = number of samples\n",
"# 'w' - weights of the hypothesis with which labels were assigned\n",
"# Shape: (A) [int]: A = dimension\n",
"# 'b' - [int] random 'b' free coefficient of the initial hypothesis \n",
"# -> Output: plots the dataset information \n",
"\n",
"def plt_dataset (x, w, b, y):\n",
" #plotting\n",
" plt.scatter(x[:, 0], x[:, 1], c = y)\n",
" plt.plot(x[:,0],-w[0]*x[:,0]/w[1]-b/w[1])\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.2. Generating training data sets of sizes 100 and 1000 using pre-defined functions"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#To generate 100 dataset, dimension = 2\n",
"x, y, w, b = dataset(100, 2)\n",
"plt_dataset (x, w, b, y)"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#To generate 1000 dataset, dimension = 2\n",
"N = 1000\n",
"d = 2\n",
"x, y, w, b = dataset(N, 2)\n",
"plt_dataset (x, w, b, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.3. Now we are defining the class and functions for the Pocket Learning Algorithm.The algorithm eventually performs the same process as a regular Perceptron, but with each iteration it stores the best performing weights as the 'pocket' weights and at the end returns the best weights as the solution of the learning algorithm"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {},
"outputs": [],
"source": [
"class Pocket:\n",
" # initials\n",
" def __init__(self, w, b, d):\n",
" self.w = w\n",
" self.b = b\n",
" self.d = d\n",
" self.w_pocket = np.zeros(d+1) # initial pocket\n",
" # prediction funct \n",
" def predict(self, x_data):\n",
" activ = np.dot(x_data, self.w_t[0] + self.w_t[1:])\n",
" return activ\n",
"\n",
" # fitting values\n",
" def fit(self, X, Y, size = 20):\n",
" self.w_t = np.zeros(self.d+1)\n",
" count = 0\n",
" \n",
" misClass = 1\n",
" minMisclass = 10000\n",
" \n",
" iters = 0\n",
" # iterations\n",
" while (misClass != 0 and (iters<1000)):\n",
" iters += 1\n",
" misClass = 0\n",
" \n",
" # comparing and updating\n",
" i = 0\n",
" while i < X.shape[0]:\n",
" prediction = self.predict(X[i])\n",
" training = Y[i]\n",
"\n",
" if prediction == 0:\n",
" prediction = -1\n",
" if training == 0:\n",
" training = -1\n",
"\n",
" if (prediction != training):\n",
" self.w_t[0] = self.w_t[0] + training\n",
" self.w_t[1:] = self.w_t[1:] + training*x[i]\n",
" misClass += 1\n",
" i += 1\n",
" # updating pocket \n",
" if misClass<minMisclass:\n",
" minMisclass = misClass\n",
" self.w_pocket = self.w_t\n",
" \n",
" # results\n",
" def results(self, X, Y): \n",
" #plt.colors = ['g' if l == 0 else 'b' for l in Y]\n",
" plt.scatter(X[:,0], X[:,1], c=Y)\n",
" #plt.legend()\n",
" plt.plot(X[:,0],-self.w[0]*X[:,0]/self.w[1]-self.b/self.w[1], color='r', label='Original')\n",
" plt.plot(X[:,0],-self.w_pocket[1]*X[:,0]/self.w_pocket[2]-self.w_pocket[0]/self.w_pocket[2], color='b', label='Predicted')\n",
" plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.4. Testing the pocket algorithm function by running it on the previously generated dataset of 1000 samples for T = 1000"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pocket algorithm of original and predicted values\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"p = Pocket(w, b, d)\n",
"print(\"Pocket algorithm of original and predicted values\")\n",
"p.fit(x, y)\n",
"p.results(x, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.5. Defining the class and its functions for the Linear Regression Algorithm"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [],
"source": [
"class LinearReg:\n",
" # initials\n",
" def __init__(self, w, b, d):\n",
" self.w = w\n",
" self.b = b\n",
" self.d = d\n",
"\n",
" # obtaining weights\n",
" def weights(self, X, Y, d, class_names):\n",
" self.w_lin = np.zeros((len(class_names), d+1))\n",
" X_inv = np.linalg.pinv(X) #pseudo-inverse\n",
" self.w_lin = np.dot(Y, np.transpose(X_inv))\n",
" return self.w_lin\n",
" \n",
" # results for 2D classification \n",
" def results(self, X, Y): \n",
" #plt.colors = ['g' if l == 0 else 'b' for l in Y]\n",
" plt.scatter(X[:,0], X[:,1], c=Y)\n",
" #plt.legend()\n",
" plt.plot(X[:,0],-self.w[0]*X[:,0]/self.w[1]-self.b/self.w[1], color='r', label='Original')\n",
" plt.plot(X[:,0],-self.w_lin[1]*X[:,0]/self.w_lin[2]-self.w_lin[0]/self.w_lin[2], color='b', label='Predicted')\n",
" plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.6. Testing the linear regression algorithm function by running it on the previously generated dataset of 1000 samples"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear Regression of original and predicted values\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Object that would contain the results correspoding the running Linear Regression on the\n",
"# given dataset\n",
"\n",
"l = LinearReg(w, b, d)\n",
"print(\"Linear Regression of original and predicted values\")\n",
"X0 = np.zeros((x.shape[0],1))\n",
"Xnew = np.hstack((X0,x))\n",
"l.weights(Xnew, y, d, class_names = [0])\n",
"\n",
"# Plotting the results of running Linear Regression and it's comparison to the original hypothesis\n",
"l.results(x, y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Considering two graphs above Linear Regression fits the original hypothesis better than the PLA.\n",
"\n",
"1.7. Now to see the difference in the testing Error (1/0) for both algorithms we will need to firstly define and additional function that would compute the error value for each iteration"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [],
"source": [
"# ---------------------------\n",
"# error\n",
"# ---------------------------\n",
"# functional that computes the error for the testing dataset with the pre-estimated weights\n",
"# -> Input: 'x' - set of samples in the generated datasets\n",
"# Shape: (A,B) [float]: \n",
"# A = number of samples (N),\n",
"# B = dimension (d)\n",
"# 'y' - set of labels corresponding to the samples in 'c'\n",
"# Shape: (A) [int]: A = number of samples\n",
"# 'w' - weights of the hypothesis with which labels were assigned\n",
"# Shape: (A) [int]: A = dimension\n",
"# 'N' - [int] number of points in the dataset\n",
"# -> Output:'err' - [float] error value for the testing set and given weights \n",
"\n",
"def error(x, y, w, N):\n",
" X0 = np.zeros((x.shape[0],1))\n",
" Xnew = np.hstack((X0,x))\n",
" h = np.dot(Xnew,w)\n",
" y_new = y\n",
" num = 0\n",
" for i in range(len(y)):\n",
" if y[i] == 0:\n",
" y_new[i] = -1;\n",
" num += np.sign(h[i]) != np.sign(y_new[i])\n",
" err = num/N\n",
" return err"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.8. With the above-defined function we can now go through 100 iterations of implementing both algorithms on the same testing set and observing the performance of both"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [],
"source": [
"N = 1000 # number of samples\n",
"d = 2 # dimension\n",
"E_pocket = np.zeros(100)\n",
"E_lin = np.zeros(100)\n",
"for iters in range (100):\n",
" x1, y1, w1, b1 = dataset(N, d)\n",
" \n",
" # Pocket Algorithm\n",
" p1 = Pocket(w1, b1, d)\n",
" p1.fit(x1, y1)\n",
" E_pocket[iters] = error(x1, y1, p1.w_pocket, N)\n",
" \n",
" # Linear Regression\n",
" X0 = np.zeros((x.shape[0],1))\n",
" Xnew = np.hstack((X0,x))\n",
" l1 = LinearReg(w1, b1, d)\n",
" l1.weights(Xnew, y1, d, class_names = [0])\n",
" E_lin[iters] = error(x1, y1, l1.w_lin, N)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.9. Lastly we can observe the resulting testing errors for both algorithmswith each iteration"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 800x160 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(num=None, figsize=(10, 2), dpi=80)\n",
"plt.ylim(0, 1)\n",
"plt.scatter(range(0,100),E_lin,label='$E_{lin}$')\n",
"plt.scatter(range(0,100),E_pocket,label='$E_{pocket}$')\n",
"plt.title('$E_{lin}$ and $E_{pocket}$ VS.')\n",
"plt.ylabel('t')\n",
"plt.xlabel('$E_{in}$')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is little hard to see but both errors are almost the same. To make this a little more understandable we could instead calculate the mean difference between the two:"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.005829999999999999\n"
]
}
],
"source": [
"print(np.sum(np.abs(E_lin-E_pocket))/100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Judging from the results presented above, both algorithm have very similar performance quality in terms of the error. However, when running the iterations it is very noticable how much more time-consuming Pocket Algorithm is in comparison with Linear Regression. Therefore, based on those findings we would highly recommend Linear Regression Algorithm to be used on this dataset."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 2. Logistic Regression and Gradient Descent"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Task 2 was fully designed by Danissa.\n",
"\n",
"2.1. Now we can use it for the actual calculation of the $\\nabla E^{(n)}(w)$ and defining the fundamental function for the logistic regression. $\\nabla E^{(n)}(w)$ uses the formula of the derivative of the $E^{(n)}(w)$: $\\nabla E^{(n)}(w)=\\theta(-y_nw^Tx_n)(-y_nx_n)$"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"# ---------------------------\n",
"# delta_E\n",
"# ---------------------------\n",
"# Function that calculates the value of the gradient descent (used for the weights update and\n",
"# correction in the Logistic Regression Algorithm)\n",
"# -> Input: 'data' - Single sample from the dataset\n",
"# Shape: (A) [float]: A = amount of features in the sample\n",
"# 'labels' - Label for the corresponding 'data' sample\n",
"# Shape: (1) [int {-1 or +1}]\n",
"# 'w' - Set of weights (for one OVA classifier)\n",
"# Shape: (A) [float]: A = amount of features in the sample -> weights\n",
"# 'd' - VC dimension (length of the weights vector - 1)\n",
"# -> Output: 'dE_in' - value of the gradient descent for the corresponsing sample entered\n",
"# Shape: (A) [float]: A = amount of features in the sample\n",
"\n",
"def delta_E(data,labels,w,d):\n",
" dE_in = np.zeros(d+1)\n",
" expo = np.dot(data,w)\n",
" expon = expo*labels\n",
" theta = 1.0/(1 + mt.exp(expon))\n",
" dE_in = (theta*(-labels*data))\n",
" return dE_in "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.2. The next function we will need is the function that will prepare a set of labels of -1 or +1 for each OVA binary classification task. We use the mathematical property of $-1^0=1$ and $-1^1=-1$ to make it more automatic, where power of 1 would be if the event of the current label being equal to the current ONE class in Ove-Versus-All classification, and 0 if this event is false:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"# ---------------------------\n",
"# mult_ult\n",
"# ---------------------------\n",
"# Function that transforms regular multiclass labels to a set of binary OVA classification task\n",
"# labels\n",
"# -> Input:'class_names' - set of all possible classes existing in the multiclass classifier\n",
"# Shape: (A) [strings or int]: A = amount of existing classes\n",
"# 'labels' - raw set of multiclass labels\n",
"# Shape: (A) [strings or int]: A = amount of samples/labels in the dataset\n",
"# -> Output:\n",
"# 'y_multiclass' - matrix set of the binary classification task labels for\n",
"# each OVA classifier\n",
"# Shape: (A,B) [int {-1 or +1}]: \n",
"# A = amount of classes (OVA classifiers),\n",
"# B = amount samples/labels for one class\n",
"\n",
"def mult_ult(classes_names,labels):\n",
" a = len(classes_names)\n",
" b = len(labels)\n",
" y_multiclass = []\n",
" for k in range (len(classes_names)):\n",
" y_m_class = []\n",
" for i in range (len(labels)):\n",
" y_m_class.append(pow(-1,(labels[i]!=classes_names[k])))\n",
" y_multiclass.append(y_m_class)\n",
" return y_multiclass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.3. Next we build a Logistic regression learning algorithm itself according to the previous lectures and the project manual using previous formula for $\\nabla E^{(n)}(w)$ to correct the weights vector in each iteration.\n",
"\n",
"We also changed the 'randomly pick one n from {1,2,...,N} for each iteration'(it is commented out in the code below) part because it has proven itself to be very inefficient, since the training set that is actually used in the process of learning becomes smaller that the original. Since randomly picking n might result in often iterations over the same samples and is not enough for training to be successful."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# ---------------------------\n",
"# log_reg\n",
"# ---------------------------\n",
"# Function that calculates the value of the gradient descent (used for the weights update and\n",
"# correction in the Logstic Regression Algorithm)\n",
"# -> Input: 'data' - Set of samples for training\n",
"# Shape: (A,B) [float]: A = amount of samples,\n",
"# B = amount of features in the sample\n",
"# 'labels' - matrix of multiclass labels (generated for each OVA classifier)\n",
"# Shape: (A,B) [int {-1 or +1}]: \n",
"# A = amount of classes (OVA classifiers),\n",
"# B = amount samples/labels for one class\n",
"# 'd' - [int] VC dimension (length of the weights vector - 1)\n",
"# 'T' - [int] amount of iterations in learning algorithm\n",
"# 'nu' - [float] learning rate\n",
"# 'class_names' - set of all possible classes existing in the multiclass classifier\n",
"# Shape: (A) [strings or int]: A = amount of existing classes\n",
"# -> Output: 'w' - set of weights for each OVA classifier in multiclass classification\n",
"# Shape: (A,B) [float]: A = amount of OVA classifier (amount of classes),\n",
"# B = number of weights per OVA classifier\n",
"# 'Et' - list of in sample Errors for each iteration\n",
"# Shape: (T) [strings or int]\n",
"\n",
"def log_reg(data,labels,d,T,nu,class_names):\n",
" w = np.zeros([len(class_names),d+1]) # initializing weights = [0.0,...,0.0]\n",
" Et = [] # intializing an empty list to store E_in values\n",
" for c in range (len(class_names)):\n",
" Et_n = []\n",
" for t in range (T):\n",
" err = 0.0\n",
" #n=np.random.randint(N)\n",
" #E = delta_E(data[n],labels[n],w,d)\n",
" #w = w - nu*E \n",
" for n in range (len(labels[0])):\n",
" E = delta_E(data[n],labels[c][n],w[c],d) # calculating correction vector for weights\n",
" w[c] = w[c] - nu*E # updating the weights\n",
" for n in range (len(labels[0])): # E_in error for this iteration\n",
" err = err + np.log(1 + mt.exp(-labels[c][n]*np.dot(data[n],w[c])))\n",
" Et_n.append(err/len(labels[0])) # adding error of the current iteration to the list\n",
" Et.append(Et_n)\n",
" return w, Et "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.4. The last two functions that are needed would be for working with the test data after obtaining complex weights for the OVA classification task. \n",
"\n",
"The first function prob() would be to compute probability that the sample corresponds to the a certain class. We do so by using sigmoid function on the incoming sample and weights for one binary classifier.\n",
"\n",
"The second function multiclass uses calculated weights from the pre-trained model to classify new incoming samples (testing samples) by choosing the class that the sample most probably corresponds to."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"# ---------------------------\n",
"# prob\n",
"# ---------------------------\n",
"# Function that calculates the value of the gradient descent (used for the weights update and\n",
"# correction in the Logstic Regression Algorithm)\n",
"# -> Input: 'x' - Single sample from the dataset (its feature values)\n",
"# Shape: (A) [float]: A = amount of features in the sample\n",
"# 'w' - Set of weights (for one OVA classifier)\n",
"# Shape: (A) [float]: A = amount of features in the sample -> weights\n",
"# -> Output:'p' - Probability of the sampe 'x' corresponding to the class for which weights\n",
"# are given to the function\n",
"# [float] probability value\n",
"\n",
"def prob(x,w):\n",
" p = 1/(1 + mt.exp(-np.dot(x,w)))\n",
" return p\n",
"\n",
"# ---------------------------\n",
"# multiclass\n",
"# ---------------------------\n",
"# Function that calculates the value of the gradient descent (used for the weights update and\n",
"# correction in the Logstic Regression Algorithm)\n",
"# -> Input: 'test' - Set of samples for testing\n",
"# Shape: (A,B) [float]: A = amount of samples,\n",
"# B = amount of features in the sample\n",
"# 'labels' - List of correct labels/classes for the testing set\n",
"# Shape: (A) [strings or ints, depends on classes]: \n",
"# A = amount of samples,\n",
"# 'w' - set of weights for each OVA classifier in multiclass classification\n",
"# Shape: (A,B) [float]: A = amount of OVA classifier (amount of classes),\n",
"# B = number of weights per OVA classifier\n",
"# 'class_names' - set of all possible classes existing in the multiclass classifier\n",
"# Shape: (A) [strings or int]: A = amount of existing classes\n",
"# -> Output: \n",
"# 'given_l' - list of labels predicted by the learning algorithm (using weights)\n",
"# Shape: (A) [strings or ints, depends on classes]: \n",
"# A = amount of samples,\n",
"\n",
"def multiclass(test,labels,w,classes_names):\n",
" given_l = []\n",
" probs = np.zeros(len(classes_names))\n",
" for n in range (len(labels)):\n",
" for c in range (len(classes_names)):\n",
" probs[c] = prob(test[n],w[c])\n",
" given_l.append(classes_names[np.argmax(probs)])\n",
" return given_l"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.5. Now that all of the essential functions are defined we test our Logistic Regression algorithm on the IRIS dataset:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Learning finished...\n",
"Testing accuracy [%]: 100.0\n",
"Error in testing: 0.0\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Dividing dataset into samples and labels\n",
"X = iris.iloc[:, :-1].values\n",
"y = iris.iloc[:, -1].values\n",
"\n",
"# Splitting samples and labels into training sets and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 123, stratify=y)\n",
"\n",
"# Identifying the class names for future usage and generating multiclass labels for OVA\n",
"# using 'mult_ult()'' function\n",
"classes = ['setosa', 'versicolor', 'virginica']\n",
"y_m_train = mult_ult(classes, y_train)\n",
"\n",
"# Learning from the training dataset and calculating weights for the \n",
"# final hypothesis using Logistic Regression function\n",
"weight_multi, Error_in = log_reg(X_train,y_m_train,3,2000,0.001,classes)\n",
"print(\"Learning finished...\")\n",
"\n",
"# Using learned weights to predict labels for the testing set with 'multiclass()' function\n",
"pred_y = multiclass(X_test,y_test,weight_multi, classes)\n",
"\n",
"# Printing the results of testing our final hypothesis\n",
"print(\"Testing accuracy [%]: \", end = \" \")\n",
"print(100*np.sum(pred_y==y_test)/float(len(y_test)))\n",
"E_test = 100*np.sum(pred_y!=y_test)/float(len(y_test))\n",
"print(\"Error in testing: \", end = \" \")\n",
"print(E_test)\n",
"\n",
"# Plotting In-sample and Out-of-sample Error functions depending on t (0 to T = 2000)\n",
"# in this case since out-of-sample error does not depend on T it is visualised as a \n",
"# straight line\n",
"plt.plot(range(0,2000),Error_in[0],label='$E_{in}$')\n",
"plt.plot(range(0,2000),E_test*range(0,2000),label='$E_{test}$')\n",
"plt.title('$E_{in}g^{(t)}$ and $E_{test}g^{(t)}$ VS. t for Logistic Regression')\n",
"plt.ylabel('t')\n",
"plt.xlabel('$E_{in}g^{(t)}$')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the graph above it could be seen that the in-sample error very quickly approaches smaller values, which are closer to 0, therefore taking around 500-1000 epoch should be enough for a good result in testing."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Task 3. Practical Design of Learning Algorithms\n",
"\n",
"Task 3 was firstly designed by Danissa, also Danissa integrated to the functions logistic regression model. Karina - linear regression model. Then the Validation curves were coded by Danissa, the GridSearchCV integrated and tested by Karina.\n",
"\n",
"We start by downloading the dataset that is going to be used the task"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# Loading the digits dataset from sklearn package\n",
"from sklearn import datasets\n",
"digits = datasets.load_digits()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3.1. Firstly we need to write a function that divides the given dataset consisting of data and labels into N dosjoint sets. In this case we use the ready function train_test_split() because it provides important properties like stratification already inside of it"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"# ---------------------------\n",
"# folds\n",
"# ---------------------------\n",
"# Function that divides the given dataset into N disjoint datasets\n",
"# -> Input: 'data' - samples of the dataset\n",
"# Shape: (A,B) [float]: A = amount of samples,\n",
"# B = amount of features in one sample\n",
"# 'labels' - sel of labels corresponding to the dataset\n",
"# 'N' - [int] number of sets (N-fold)\n",
"# -> Output: 'S' - matrix of all folds generated\n",
"# Shape: (A,B,C) [float]: A = amount folds,\n",
"# B = number of samples in each fold\n",
"# C = number of features in each sample\n",
"# 'y' - matrix of all labels for each corresponding fold\n",
"# Shape: (A,B) [int]: A = amount folds,\n",
"# B = number of samples/labels in each fold\n",
"\n",
"def folds(data,labels,N):\n",
" S_n = [] # final set of folds with samples\n",
" S = [] # each future fold of samples to be 'cut out'\n",
" y_n = [] # final set of folds with labels\n",
" y = [] # each future fold of labels to be 'cut out'\n",
" for n in range (N): # 'cutting out' the fold using train_test_split function\n",
" X_new, S_n, y_new, y_n = train_test_split(data, labels, test_size = 1/N, stratify=labels)\n",
" S.append(S_n) # putting newly created fold to the final set of folds\n",
" y.append(y_n)\n",
" return S,y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3.2. After that we can define the function that is going to be performing the K-folds cross validation itself"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"# Defining all existing classes\n",
"classes = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
"acc = 0.0 # accuracy value for each fold\n",
"\n",
"# Samples that are going to be used in the cross validation\n",
"# Not all samples are used for the convenience of the running time and division of the set\n",
"X = digits.data[:1000]\n",
"y = digits.target[:1000]\n",
"\n",
"# ---------------------------\n",
"# folds_train\n",
"# ---------------------------\n",
"# Function that analyzes the performance of the given K-fold validation for the algorithm and\n",
"# data provided\n",
"# -> Input:'algorithm' - [string] name of the algorithm to be tested ('log_reg' or 'lin_reg')\n",
"# 'X' - samples of the dataset\n",
"# Shape: (A,B) [float]: A = amount of samples,\n",
"# B = amount of features in one sample\n",
"# 'y' - matrix of all labels for each corresponding fold\n",
"# Shape: (A,B) [int]: A = amount folds,\n",
"# B = number of samples/labels in each fold\n",
"# 'F' - [int] number of folds\n",
"# -> Output: print testing accuracy and error for each fold, and average/generalization error\n",
"# of the validation\n",
"\n",
"def folds_train(algorithm,X,y,F):\n",
" E_testing = 0.0 # testing error\n",
" # Splitting the dataset into F amount of folds\n",
" X_folds, y_folds = folds(X,y,F)\n",
" \n",
" for i in range (F): #for each fold\n",
" print(\"Fold number\", end = \" \")\n",
" print(i+1, end = \" -----------------------\")\n",
" print(\" \")\n",
" n=0\n",
" k=0\n",
" # Assigning current fold to be the testing set\n",
" X_testing = X_folds[i]\n",
" X_training = []\n",
" y_testing = y_folds[i]\n",
" y_training = []\n",
" # Assigning all folds except for the current fold to be in the training set\n",
" while k < F:\n",
" n = k\n",
" if (n == i):\n",
" if n < F-1:\n",
" n = n + 1\n",
" k = k + 1\n",
" else:\n",
" break\n",
" X_training.append(X_folds[n])\n",
" y_training.append(y_folds[n])\n",
" k = k + 1\n",
" # Since our function do not work with 3-D matrices we need to flatten them back\n",
" X_training_flatten = []\n",
" y_training_flat = []\n",
" # Manually flattening by traversing through each element\n",
" for b in range(len(y_training)): #Traversing through the main list\n",
" for c in range (len(y_training[b])): #Traversing through each sublist\n",
" y_training_flat.append(y_training[b][c])\n",
" for g in range(len(X_training)):\n",
" for t in range(len(X_training[g])):\n",
" X_training_flatten.append(X_training[g][t])\n",
" # Now we can use the labels to generate multiclass sets of labels\n",
" y_multi_training = mult_ult(classes,y_training_flat)\n",
" # Calculating weights using chosen algorithm\n",
" if (algorithm == 'log_reg'):\n",
" weight_multi2, Error_in2 = log_reg(X_training_flatten,y_multi_training,63,100,0.01,classes)\n",
" elif (algorithm == 'lin_reg'):\n",
" lin = LinearReg(w,b,d);\n",
" weight_multi2 = lin.weights(X_training_flatten, y_multi_training, 63, classes)\n",
" # Calculating and comparing predictions, calculating accuracy and testing error\n",
" pred_y = multiclass(X_testing,y_testing,weight_multi2, classes)\n",
" acc = 100*np.sum(pred_y==y_testing)/float(len(y_testing))\n",
" E_testing = E_testing + np.sum(pred_y!=y_testing)/float(len(y_testing))\n",
" print(\"Testing accuracy [%]:\", end = \" \")\n",
" print(acc, end = \" | Error: \")\n",
" print(np.sum(pred_y!=y_testing)/float(len(y_testing)))\n",
" print(\"Average error:\", end = \" \")\n",
" print(E_testing/F) # generalization error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3.3. Now we test out K-folds CV functions on Logistic and Linear Regression algorithms"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------- 5 FOLDS -----------------------------------\n",
"Fold number 1 ----------------------- \n",
"Testing accuracy [%]: 95.0 | Error: 0.05\n",
"Fold number 2 ----------------------- \n",
"Testing accuracy [%]: 99.5 | Error: 0.005\n",
"Fold number 3 ----------------------- \n",
"Testing accuracy [%]: 89.0 | Error: 0.11\n",
"Fold number 4 ----------------------- \n",
"Testing accuracy [%]: 97.5 | Error: 0.025\n",
"Fold number 5 ----------------------- \n",
"Testing accuracy [%]: 96.5 | Error: 0.035\n",
"Average error: 0.045\n",
"----------------------------------- 10 FOLDS -----------------------------------\n",
"Fold number 1 ----------------------- \n",
"Testing accuracy [%]: 98.0 | Error: 0.02\n",
"Fold number 2 ----------------------- \n",
"Testing accuracy [%]: 97.0 | Error: 0.03\n",
"Fold number 3 ----------------------- \n",
"Testing accuracy [%]: 97.0 | Error: 0.03\n",
"Fold number 4 ----------------------- \n",
"Testing accuracy [%]: 96.0 | Error: 0.04\n",
"Fold number 5 ----------------------- \n",
"Testing accuracy [%]: 100.0 | Error: 0.0\n",
"Fold number 6 ----------------------- \n",
"Testing accuracy [%]: 97.0 | Error: 0.03\n",
"Fold number 7 ----------------------- \n",
"Testing accuracy [%]: 98.0 | Error: 0.02\n",
"Fold number 8 ----------------------- \n",
"Testing accuracy [%]: 98.0 | Error: 0.02\n",
"Fold number 9 ----------------------- \n",