08. Classification.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "## 8. Classification\n",
    "\n",
    "[Data Science Playlist on YouTube](https://www.youtube.com/watch?v=VLKEj9EN2ew&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy)\n",
    "[![Python Data Science](https://apmonitor.com/che263/uploads/Begin_Python/DataScience08.png)](https://www.youtube.com/watch?v=VLKEj9EN2ew&list=PLLBUgWXdTBDg1Qgmwt4jKtVn9BWh5-zgy \"Python Data Science\")\n",
    "\n",
    "**Classification** predicts *discrete labels (outcomes)* such as `yes`/`no`, `True`/`False`, or any number of discrete levels such as a letter from text recognition, or a word from speech recognition. There are two main methods for training classifiers: unsupervised and supervised learning. The difference between the two is that unsupervised learning does not use labels while supervised learning uses labels to build the classifier. The goal of unsupervised learning is to cluster input features but without labels to guide the grouping. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![list](https://apmonitor.com/che263/uploads/Begin_Python/list.png)\n",
    "\n",
    "### Supervised Learning to Classify Numbers\n",
    "\n",
    "A dataset that is included with sklearn is a set of 1797 images of numbers that are 64 pixels (8x8) each. There are labels with each to indicate the correct answer. A Support Vector Classifier is trained on the first half of the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets, svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "\n",
    "# train classifier\n",
    "digits = datasets.load_digits()\n",
    "n_samples = len(digits.images)\n",
    "data = digits.images.reshape((n_samples, -1))\n",
    "svc = svm.SVC(gamma=0.001)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data, digits.target, test_size=0.5, shuffle=False)\n",
    "svc.fit(X_train, y_train)\n",
    "print('SVC Trained')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![expert](https://apmonitor.com/che263/uploads/Begin_Python/expert.png)\n",
    "\n",
    "### Test Number Classifier\n",
    "\n",
    "The image classification is trained on 10 randomly selected images from the other half of the data set to evaluate the training. Run the classifier test until you observe a misclassified number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "for i in range(10):\n",
    "    n = np.random.randint(int(n_samples/2),n_samples)\n",
    "    predict = svc.predict(digits.data[n:n+1])[0]\n",
    "    plt.subplot(2,5,i+1)\n",
    "    plt.imshow(digits.images[n], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.text(0,7,'Actual: ' + str(digits.target[n]),color='r')\n",
    "    plt.text(0,1,'Predict: ' + str(predict),color='b')\n",
    "    if predict==digits.target[n]:\n",
    "        plt.text(0,4,'Correct',color='g')\n",
    "    else:\n",
    "        plt.text(0,4,'Incorrect',color='orange')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![buildings](https://apmonitor.com/che263/uploads/Begin_Python/buildings.png)\n",
    "\n",
    "### Classification with Supervised Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select data set option with `moons`, `cirlces`, or `blobs`. Run the following cell to generate the data that will be used to test the classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "option = 'moons' # moons, circles, or blobs\n",
    "\n",
    "n = 2000 # number of data points\n",
    "X = np.random.random((n,2))\n",
    "mixing = 0.0 # add random mixing element to data\n",
    "xplot = np.linspace(0,1,100)\n",
    "if option=='moons':\n",
    "    X, y = datasets.make_moons(n_samples=n,noise=0.1)\n",
    "    yplot = xplot*0.0\n",
    "elif option=='circles':\n",
    "    X, y = datasets.make_circles(n_samples=n,noise=0.1,factor=0.5)\n",
    "    yplot = xplot*0.0\n",
    "elif option=='blobs':\n",
    "    X, y = datasets.make_blobs(n_samples=n,centers=[[-5,3],[5,-3]],cluster_std=2.0)\n",
    "    yplot = xplot*0.0\n",
    "# Split into train and test subsets (50% each)\n",
    "XA, XB, yA, yB = train_test_split(X, y, test_size=0.5, shuffle=False)\n",
    "# Plot regression results\n",
    "def assess(P):\n",
    "    plt.figure()\n",
    "    plt.scatter(XB[P==1,0],XB[P==1,1],marker='^',color='blue',label='True')\n",
    "    plt.scatter(XB[P==0,0],XB[P==0,1],marker='x',color='red',label='False')\n",
    "    plt.scatter(XB[P!=yB,0],XB[P!=yB,1],marker='s',color='orange',\\\n",
    "                alpha=0.5,label='Incorrect')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### S.1 Logistic Regression\n",
    "\n",
    "**Definition:** Logistic regression is a machine learning algorithm for classification. In this algorithm, the probabilities describing the possible outcomes of a single trial are modelled using a logistic function.\n",
    "\n",
    "**Advantages:** Logistic regression is designed for this purpose (classification), and is most useful for understanding the influence of several independent variables on a single outcome variable.\n",
    "\n",
    "**Disadvantages:** Works only when the predicted variable is binary, assumes all predictors are independent of each other, and assumes data is free of missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression(solver='lbfgs')\n",
    "lr.fit(XA,yA)\n",
    "yP = lr.predict(XB)\n",
    "assess(yP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### S.2 Naïve Bayes\n",
    "\n",
    "**Definition:** Naive Bayes algorithm based on Bayes’ theorem with the assumption of independence between every pair of features. Naive Bayes classifiers work well in many real-world situations such as document classification and spam filtering.\n",
    "\n",
    "**Advantages:** This algorithm requires a small amount of training data to estimate the necessary parameters. Naive Bayes classifiers are extremely fast compared to more sophisticated methods.\n",
    "\n",
    "**Disadvantages:** Naive Bayes is known to be a bad estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "nb = GaussianNB()\n",
    "nb.fit(XA,yA)\n",
    "yP = nb.predict(XB)\n",
    "assess(yP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### S.3 Stochastic Gradient Descent\n",
    "\n",
    "**Definition:** Stochastic gradient descent is a simple and very efficient approach to fit linear models. It is particularly useful when the number of samples is very large. It supports different loss functions and penalties for classification.\n",
    "\n",
    "**Advantages:** Efficiency and ease of implementation.\n",
    "\n",
    "**Disadvantages:** Requires a number of hyper-parameters and it is sensitive to feature scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "sgd = SGDClassifier(loss='modified_huber', shuffle=True,random_state=101)\n",
    "sgd.fit(XA,yA)\n",
    "yP = sgd.predict(XB)\n",
    "assess(yP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### S.4 K-Nearest Neighbours\n",
    "\n",
    "**Definition:** Neighbours based classification is a type of lazy learning as it does not attempt to construct a general internal model, but simply stores instances of the training data. Classification is computed from a simple majority vote of the k nearest neighbours of each point.\n",
    "\n",
    "**Advantages:** This algorithm is simple to implement, robust to noisy training data, and effective if training data is large.\n",
    "\n",
    "**Disadvantages:** Need to determine the value of `K` and the computation cost is high as it needs to computer the distance of each instance to all the training samples. One possible solution to determine `K` is to add a feedback loop to determine the number of neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(XA,yA)\n",
    "yP = knn.predict(XB)\n",
    "assess(yP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### S.5 Decision Tree\n",
    "\n",
    "**Definition:** Given a data of attributes together with its classes, a decision tree produces a sequence of rules that can be used to classify the data.\n",
    "\n",
    "**Advantages:** Decision Tree is simple to understand and visualise, requires little data preparation, and can handle both numerical and categorical data.\n",
    "\n",
    "**Disadvantages:** Decision tree can create complex trees that do not generalise well, and decision trees can be unstable because small variations in the data might result in a completely different tree being generated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dtree = DecisionTreeClassifier(max_depth=10,random_state=101,\\\n",
    "                               max_features=None,min_samples_leaf=5)\n",
    "dtree.fit(XA,yA)\n",
    "yP = dtree.predict(XB)\n",
    "assess(yP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### S.6 Random Forest\n",
    "\n",
    "**Definition:** Random forest classifier is a meta-estimator that fits a number of decision trees on various sub-samples of datasets and uses average to improve the predictive accuracy of the model and controls over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement.\n",
    "\n",
    "**Advantages:** Reduction in over-fitting and random forest classifier is more accurate than decision trees in most cases.\n",
    "\n",
    "**Disadvantages:** Slow real time prediction, difficult to implement, and complex algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rfm = RandomForestClassifier(n_estimators=70,oob_score=True,\\\n",
    "                             n_jobs=1,random_state=101,max_features=None,\\\n",
    "                             min_samples_leaf=3) #change min_samples_leaf from 30 to 3\n",
    "rfm.fit(XA,yA)\n",
    "yP = rfm.predict(XB)\n",
    "assess(yP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### S.7 Support Vector Classifier\n",
    "\n",
    "**Definition:** Support vector machine is a representation of the training data as points in space separated into categories by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.\n",
    "\n",
    "**Advantages:** Effective in high dimensional spaces and uses a subset of training points in the decision function so it is also memory efficient.\n",
    "\n",
    "**Disadvantages:** The algorithm does not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "svm = SVC(gamma='scale', C=1.0, random_state=101)\n",
    "svm.fit(XA,yA)\n",
    "yP = svm.predict(XB)\n",
    "assess(yP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### S.8 Neural Network\n",
    "\n",
    "The `MLPClassifier` implements a multi-layer perceptron (MLP) algorithm that trains using Backpropagation.\n",
    "\n",
    "**Definition:** A neural network is a set of neurons (activation functions) in layers that are processed sequentially to relate an input to an output.\n",
    "\n",
    "**Advantages:** Effective in nonlinear spaces where the structure of the relationship is not linear. No prior knowledge or specialized equation structure is defined although there are different network architectures that may lead to a better result.\n",
    "\n",
    "**Disadvantages:** Neural networks do not extrapolate well outside of the training domain. They may also require longer to train by adjusting the parameter weights to minimize a loss (objective) function. It is also more challenging to explain the outcome of the training and changes in initialization or number of epochs (iterations) may lead to different results. Too many epochs may lead to overfitting, especially if there are excess parameters beyond the minimum needed to capture the input to output relationship."
   ]
  },
  {
   "attachments": {
    "deep_neural_network.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![deep_neural_network.png](attachment:deep_neural_network.png)\n",
    "\n",
    "MLP trains on two arrays: array X of size (n_samples, n_features), which holds the training samples represented as floating point feature vectors; and array y of size (n_samples,), which holds the target values (class labels) for the training samples.\n",
    "MLP can fit a non-linear model to the training data. clf.coefs_ contains the weight matrices that constitute the model parameters. Currently, MLPClassifier supports only the Cross-Entropy loss function, which allows probability estimates by running the predict_proba method. MLP trains using Backpropagation. More precisely, it trains using some form of gradient descent and the gradients are calculated using Backpropagation. For classification, it minimizes the Cross-Entropy loss function, giving a vector of probability estimates. MLPClassifier supports multi-class classification by applying Softmax as the output function. Further, the model supports multi-label classification in which a sample can belong to more than one class. For each class, the raw output passes through the logistic function. Values larger or equal to 0.5 are rounded to 1, otherwise to 0. For a predicted output of a sample, the indices where the value is 1 represents the assigned classes of that sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "clf = MLPClassifier(solver='lbfgs',alpha=1e-5,max_iter=200,activation='relu',\\\n",
    "                    hidden_layer_sizes=(10,30,10), random_state=1, shuffle=True)\n",
    "clf.fit(XA,yA)\n",
    "yP = clf.predict(XB)\n",
    "assess(yP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![animal_eggs](https://apmonitor.com/che263/uploads/Begin_Python/animal_eggs.png)\n",
    "\n",
    "### Unsupervised Classification\n",
    "\n",
    "Additional examples show the potential for unsupervised learning to classify the groups. Unsupervised learning does not use the labels (`True`/`False`) so the results may need to be switched to align with the test set with `if len(XB[yP!=yB]) > n/4: yP = 1 - yP \n",
    "`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### U.1 K-Means Clustering\n",
    "\n",
    "**Definition:**  Specify how many possible clusters (or K) there are in the dataset. The algorithm then iteratively moves the K-centers and selects the datapoints that are closest to that centroid in the cluster.\n",
    "\n",
    "**Advantages:** The most common and simplest clustering algorithm.\n",
    "\n",
    "**Disadvantages:** Must specify the number of clusters although this can typically be determined by increasing the number of clusters until the objective function does not change significantly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from sklearn.cluster import KMeans\n",
    "    km = KMeans(n_clusters=2)\n",
    "    km.fit(XA)\n",
    "    yP = km.predict(XB)\n",
    "    if len(XB[yP!=yB]) > n/4: yP = 1 - yP \n",
    "    assess(yP)\n",
    "except:\n",
    "    print('K-Means failed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### U.2 Gaussian Mixture Model\n",
    "\n",
    "**Definition:** Data points that exist at the boundary of clusters may simply have similar probabilities of being on either clusters. A mixture model predicts a probability instead of a hard classification such as K-Means clustering.\n",
    "\n",
    "**Advantages:** Incorporates uncertainty into the solution.\n",
    "\n",
    "**Disadvantages:** Uncertainty may not be desirable for some applications. This method is not as common as the K-Means method for clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture\n",
    "gmm = GaussianMixture(n_components=2)\n",
    "gmm.fit(XA)\n",
    "yP = gmm.predict_proba(XB) # produces probabilities\n",
    "if len(XB[np.round(yP[:,0])!=yB]) > n/4: yP = 1 - yP \n",
    "assess(np.round(yP[:,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![idea](https://apmonitor.com/che263/uploads/Begin_Python/idea.png)\n",
    "\n",
    "### U.3 Spectral Clustering\n",
    "\n",
    "**Definition:** Spectral clustering is known as segmentation-based object categorization. It is a technique with roots in graph theory, where identify communities of nodes in a graph are based on the edges connecting them. The method is flexible and allows clustering of non graph data as well.\n",
    "It uses information from the eigenvalues of special matrices built from the graph or the data set. \n",
    "\n",
    "**Advantages:** Flexible approach for finding clusters when data doesn’t meet the requirements of other common algorithms.\n",
    "\n",
    "**Disadvantages:** For large-sized graphs, the second eigenvalue of the (normalized) graph Laplacian matrix is often ill-conditioned, leading to slow convergence of iterative eigenvalue solvers. Spectral clustering is computationally expensive unless the graph is sparse and the similarity matrix can be efficiently constructed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import SpectralClustering\n",
    "sc = SpectralClustering(n_clusters=2,eigen_solver='arpack',\\\n",
    "                        affinity='nearest_neighbors')\n",
    "yP = sc.fit_predict(XB) # No separation between fit and predict calls\n",
    "                        # need to fit and predict on same dataset\n",
    "if len(XB[yP!=yB]) > n/4: yP = 1 - yP \n",
    "assess(yP)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![expert](https://apmonitor.com/che263/uploads/Begin_Python/expert.png)\n",
    "\n",
    "### TCLab Activity\n",
    "\n",
    "Train a classifier to predict if the heater is on (100%) or off (0%). Generate data with 10 minutes of 1 second data. If you do not have a TCLab, use one of the sample data sets.\n",
    "\n",
    "- [Sample Data Set 1 (10 min)](http://apmonitor.com/do/uploads/Main/tclab_data5.txt): http://apmonitor.com/do/uploads/Main/tclab_data5.txt \n",
    "- [Sample Data Set 2 (60 min)](http://apmonitor.com/do/uploads/Main/tclab_data6.txt): http://apmonitor.com/do/uploads/Main/tclab_data6.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10 minute data collection\n",
    "import tclab, time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "with tclab.TCLab() as lab:\n",
    "    n = 600; on=100; t = np.linspace(0,n-1,n)        \n",
    "    Q1 = np.zeros(n); T1 = np.zeros(n)\n",
    "    Q2 = np.zeros(n); T2 = np.zeros(n)    \n",
    "    Q1[20:41]=on; Q1[60:91]=on; Q1[150:181]=on\n",
    "    Q1[190:206]=on; Q1[220:251]=on; Q1[260:291]=on\n",
    "    Q1[300:316]=on; Q1[340:351]=on; Q1[400:431]=on\n",
    "    Q1[500:521]=on; Q1[540:571]=on; Q1[20:41]=on\n",
    "    Q1[60:91]=on; Q1[150:181]=on; Q1[190:206]=on\n",
    "    Q1[220:251]=on; Q1[260:291]=on\n",
    "    print('Time Q1 Q2 T1   T2')\n",
    "    for i in range(n):\n",
    "        T1[i] = lab.T1; T2[i] = lab.T2\n",
    "        lab.Q1(Q1[i])\n",
    "        if i%5==0:\n",
    "            print(int(t[i]),Q1[i],Q2[i],T1[i],T2[i])\n",
    "        time.sleep(1)\n",
    "data = np.column_stack((t,Q1,Q2,T1,T2))\n",
    "data8 = pd.DataFrame(data,columns=['Time','Q1','Q2','T1','T2'])\n",
    "data8.to_csv('08-tclab.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the data file `08-tclab.csv` to train and test the classifier. Select and scale (0-1) the features of the data including `T1`, `T2`, and the 1st and 2nd derivatives of `T1`. Use the measured temperatures, derivatives, and heater value label to create a classifier that predicts when the heater is on or off. Validate the classifier with new data that was not used for training. Starting code is provided below but does not include `T2` as a feature input. **Add `T2` as an input feature to the classifer. Does it improve the classifier performance?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import gridspec\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "try:\n",
    "    data = pd.read_csv('08-tclab.csv')\n",
    "except:\n",
    "    print('Warning: Unable to load 08-tclab.csv, using online data')\n",
    "    url = 'http://apmonitor.com/do/uploads/Main/tclab_data5.txt'\n",
    "    data = pd.read_csv(url)\n",
    "    \n",
    "# Input Features: Temperature and 1st / 2nd Derivatives\n",
    "# Cubic polynomial fit of temperature using 10 data points\n",
    "data['dT1'] = np.zeros(len(data))\n",
    "data['d2T1'] = np.zeros(len(data))\n",
    "for i in range(len(data)):\n",
    "    if i<len(data)-10:\n",
    "        x = data['Time'][i:i+10]-data['Time'][i]\n",
    "        y = data['T1'][i:i+10]\n",
    "        p = np.polyfit(x,y,3)\n",
    "        # evaluate derivatives at mid-point (5 sec)\n",
    "        t = 5.0\n",
    "        data['dT1'][i] = 3.0*p[0]*t**2 + 2.0*p[1]*t+p[2]\n",
    "        data['d2T1'][i] = 6.0*p[0]*t + 2.0*p[1]\n",
    "    else:\n",
    "        data['dT1'][i] = np.nan\n",
    "        data['d2T1'][i] = np.nan\n",
    "\n",
    "# Remove last 10 values\n",
    "X = np.array(data[['T1','dT1','d2T1']][0:-10])\n",
    "y = np.array(data[['Q1']][0:-10])\n",
    "\n",
    "# Scale data\n",
    "# Input features (Temperature and 2nd derivative at 5 sec)\n",
    "s1 = MinMaxScaler(feature_range=(0,1))\n",
    "Xs = s1.fit_transform(X)\n",
    "# Output labels (heater On / Off)\n",
    "ys = [True if y[i]>50.0 else False for i in range(len(y))]\n",
    "\n",
    "# Split into train and test subsets (50% each)\n",
    "XA, XB, yA, yB = train_test_split(Xs, ys, \\\n",
    "                    test_size=0.5, shuffle=False)\n",
    "\n",
    "# Supervised Classification\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# Create supervised classification models\n",
    "lr = LogisticRegression(solver='lbfgs') # Logistic Regression\n",
    "nb = GaussianNB() # Naïve Bayes\n",
    "sgd = SGDClassifier(loss='modified_huber', shuffle=True,\\\n",
    "                    random_state=101) # Stochastic Gradient Descent\n",
    "knn = KNeighborsClassifier(n_neighbors=5) # K-Nearest Neighbors\n",
    "dtree = DecisionTreeClassifier(max_depth=10,random_state=101,\\\n",
    "          max_features=None,min_samples_leaf=5) # Decision Tree\n",
    "rfm = RandomForestClassifier(n_estimators=70,oob_score=True,n_jobs=1,\\\n",
    "          random_state=101,max_features=None,min_samples_leaf=3) # Random Forest\n",
    "svm = SVC(gamma='scale', C=1.0, random_state=101) # Support Vector Classifier\n",
    "clf = MLPClassifier(solver='lbfgs',alpha=1e-5,max_iter=200,\\\n",
    "       activation='relu',hidden_layer_sizes=(10,30,10),\\\n",
    "       random_state=1, shuffle=True) # Neural Network\n",
    "models = [lr,nb,sgd,knn,dtree,rfm,svm,clf]\n",
    "\n",
    "# Supervised learning\n",
    "yP = [None]*(len(models)+3) # 3 for unsupervised learning\n",
    "for i,m in enumerate(models):\n",
    "    m.fit(XA,yA)\n",
    "    yP[i] = m.predict(XB)\n",
    "\n",
    "# Unsupervised learning modules\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.mixture import GaussianMixture\n",
    "from sklearn.cluster import SpectralClustering\n",
    "km = KMeans(n_clusters=2)\n",
    "gmm = GaussianMixture(n_components=2)\n",
    "sc = SpectralClustering(n_clusters=2,eigen_solver='arpack',\\\n",
    "                        affinity='nearest_neighbors')\n",
    "km.fit(XA)\n",
    "yP[8] = km.predict(XB)\n",
    "gmm.fit(XA)\n",
    "yP[9] = gmm.predict_proba(XB)[:,0]\n",
    "yP[10] = sc.fit_predict(XB)\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "gs = gridspec.GridSpec(3, 1, height_ratios=[1,1,5])\n",
    "plt.subplot(gs[0])\n",
    "plt.plot(data['Time']/60,data['T1'],'r-',\\\n",
    "         label='Temperature (°C)')\n",
    "plt.ylabel('T (°C)')\n",
    "plt.legend()\n",
    "plt.subplot(gs[1])\n",
    "plt.plot(data['Time']/60,data['dT1'],'b:',\\\n",
    "         label='dT/dt (°C/sec)')    \n",
    "plt.plot(data['Time']/60,data['d2T1'],'k--',\\\n",
    "         label=r'$d^2T/dt^2$ ($°C^2/sec^2$)')\n",
    "plt.ylabel('Derivatives')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(gs[2])\n",
    "plt.plot(data['Time']/60,data['Q1']/100,'k-',\\\n",
    "         label='Heater (On=1/Off=0)')\n",
    "\n",
    "t2 = data['Time'][len(yA):-10].values\n",
    "desc = ['Logistic Regression','Naïve Bayes','Stochastic Gradient Descent',\\\n",
    "        'K-Nearest Neighbors','Decision Tree','Random Forest',\\\n",
    "        'Support Vector Classifier','Neural Network',\\\n",
    "        'K-Means Clustering','Gaussian Mixture Model','Spectral Clustering']\n",
    "for i in range(11):\n",
    "    plt.plot(t2/60,yP[i]-i-1,label=desc[i])\n",
    "\n",
    "plt.ylabel('Heater')\n",
    "plt.legend()\n",
    "\n",
    "plt.xlabel(r'Time (min)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
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